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Aminoacyl-tRNA synthetases (aaRSs) are key enzymes in the process of protein biosynthesis, charging tRNA molecules with their corresponding amino acid. Whereas adenosine phosphate fixation is common to all aaRSs, recognition of the respective amino acid to ensure correct translation poses a complex task, which is still not understood to its full extent. Using all aaRS structures in the Protein Data Bank (PDB), this thesis reveals further details about the specificitydetermining interactions of each aaRS. Moreover, inspection of the similarities between these enzymes using the structure-derived interaction data reinforces the sequence-based evolutionary trace of aaRSs to a certain degree: The concurrent development of two distinct Classes of aaRS is apparent at functional level, and previously determined evolutionary subclasses coincide altogether with specific aminoacyl recognition in each aaRS Type. Still, discrimination of amino acids in aaRSs involves a multitude of further relevant mechanisms. Eventually, analysis of specificity-relevant binding site interactions sheds light on how aaRS evolved to distinguish different amino acids.
In this master thesis, we define a new bivariate polynomial which we call the defensive alliance polynomial and denote it by da(G; x; y). It is a generalization of the alliance polynomial and the strong alliance polynomial. We show the relation between da(G; x; y) and the alliance, the strong alliance, the induced connected subgraph polynomials as well as the cut vertex sets polynomial. We investigate information encoded about G in da(G; x; y). We discuss the defensive alliance polynomial for the path graphs, the cycle graphs, the star graphs, the double star graphs, the complete graphs, the complete bipartite graphs, the regular graphs, the wheel graphs, the open wheel graphs, the friendship graphs, the triangular book graphs and the quadrilateral book graphs. Also, we prove that the above classes of graphs are characterized by its defensive alliance polynomial. We present the defensive alliance polynomial of the graph formed of attaching a vertex to a complete graph. We show two pairs of graphs which are not characterized by the alliance polynomial but characterized by the defensive alliance polynomial.
Also, we present three notes on results in the literature. The first one is improving a bound and the other two are counterexamples.
This study presents an analysis of the coverage made by the journals El País (Spain), Folha de S. Paulo (Brazil) and Süddeutsche Zeitung (Germany) about the protests in Brazil against the 2013 Confederations Cup and the 2014 FIFA World Cup to establish a comparison between them and see which topics were emphasized by the newspapers and which tone they use in their reporting. Based on the research questions, four categories were developed for the analysis of the journals: article structure; topic of the article; actors/group of persons and tone of the reporting, all of them composed by several subcategories. It was concluded that the themes highlighted by the European newspapers were different from those stressed on the Brazilian diary. Nonetheless, all the reviewed newspapers made a neutral coverage of the protests.
kein Abstract vorhanden
Path decomposition of a graph has received an important amount of interest over the past decades because of its applications in algorithmic graph theory and in real life problems. For the computation of a path decomposition of small width, we use different heuritics approaches. One of the most useful method is by Bodlaender and Kloks. In this thesis, we focus on the computation, applications, transformation and approximation of a path decomposition of small width.
It is easy to convert a path decomposition in to nice path decomposition with same width, which is more convinent to use to find the graph parameters like independent sets, chromatic polynomials etc. Inspired by [28], we find an algorithm to compute the chromatic polynomial of a graph via nice path decomposition with small width.
In this work, the task is to cluster microarray gene expression data of the cyanobacterium Nostoc PCC 7120 for detection of messenger RNA (mRNA) degradation patterns. Searched are characteristic patterns of degradation which are caused by specific enzymes (ribonucleases) allowing a further biological investigation regarding biochemical mechanisms. The mRNA degradation is part of the regulation of gene expression because it regulates the amount and longevity of mRNA, which is available for translation into proteins. A particular class of RNA degrading enzymes are exoribonucleases which degrade the molecule from its ends, whereby a degradation from the 5’ end, the 3’ end or from both ends is theoretically possible.
In this investigation, the information about exoribonucleolytic degradation is given in a microarray data set containing gene expression values of 1,251 genes. The data set provides gene expression vectors containing the expression values of up to ten short distinct sections of a gene ordered from the genes 5’ end to its 3’ end. For each gene, expression vectors are available for both nitrogen fixing and non-nitrogen fixing conditions, which have to be considered separately due to biological reasons. Accordingly, after filtering and preprocessing, two datasets for clustering are obtained consisting of 133 ten-dimensional expression vectors. The similarity of the expression vectors is judged by a newly correlation based similarity measure and compared with the results obtained by use of the Euclidean distance. A non-linear transformation of the correlations was applied to obtain a dissimilarity measure. By choice of parameters within this transformation a user specific differentiation between negative and positive correlated gene expression vectors and an adequate adjustment regarding the noise level of gene expression values is possible.
Clustering was performed using Affinity Propagation (AP). The number of clusters obtained by AP depends on the so-called self-similarity for the data vectors. This dependence was used to identify stable cluster solutions by self-similarity control. To evaluate the clustering results, Median Fuzzy c-Means (M-FCM) was used. Further, several cluster validity measures are applied and visual inspections by t-distributed Stochastic Neighbor Embedding (t-SNE) as well as cluster visualization are provided for mathematical interpretation analysis of clusters.
To validate the clustering results biologically, the found data structure is checked for biological adequacy. A deeper investigation into the mechanisms behind mRNA-degradation was achieved by use of a RNA-Seq data set. Contained 40 (base pair) bp long reads for non-nitrogen fixing and nitrogen fixing conditions were assembled using bacteria-specific ab-initio assembly of Rockhopper. Thus, mRNA (transcript)-sequences of the clustered genes are obtained. A further investigation of the untranslated regions (UTRs) is performed here due to the assumption that exoribonucleases recognize specific transcript-sequences outside of the annotated gene regions as their binding sites. These UTRs need to be analyzed regarding sequence similarity using motif-finding algorithms.
Community acquired pneumonia (CAP) is a very common, yet infectious and sometimes lethal disease. Therefor, this disease is connected to high costs of diagnosis and treatment. To actually reduce the costs for health care in this matter, diagnosis and treatment must get cheaper to conduct with no loss in predictive accuracy. One effective way in doing so would be the identification of easy detectable and highly specific transcriptomic markers, which would reduce the amount of work required for laboratory tests by possibly enhanced diagnosis capability.
Transcriptomic whole blood data, derived from the PROGRESS study was combined with several documented features like age, smoking status or the SOFA score. The analysis pipeline included processing by self organizing maps for dimensionality and noise reduction, as well as diffusion pseudotime (DPT). Pseudotime enabled modelling a disease run of CAP, where each sample represented a state/time in the modelled run. Both methods combined resulted in a proposed disease run of CAP, described by 1476 marker genes. The additional conduction of a geneset analysis also provided information about the immune related functions of these marker genes.
In the following study we evaluated capabilities of how a simple autoencoder can be used to trainGeneralized Learning Vector Quantization classifier. Specifically, we proved that the bottlenecks of an autoencoder serve as an "information filter" which tries to best represent the desired output in that particular layer in the statistical sense of mutual information.
Autoencoder model was trained for purely unsupervised task and leveraged the advantages by learning feature representations. As a result, the model got the significant value of the accuracy. Implementation and tuning of the model was carried out using Tensor Flow [1].
An extra study has been dedicated to improve traditional GLVQ algorithm taken from sklearn-lvg [2] using the bottleneck from an autoencoder.
The study has revealed potential of bottlenecks of an autoencoder as pre-processing tool in improving the accuracy of GLVQ. Specifically, the model was capable to identify 75% improvements of accuracy in GLVQ comparing to original one, which has about 62%. Consequently, the research exposed the need for further improvement of the model in the present problem case.
In the context of globalization and the internationalization of international markets, mergers and acquisitions are becoming increasingly important for transnational corporations and national economies of countries as a form of internationalization, integration and the way to attract foreign investment. In the framework of this paper, the theoretical aspects of mergers and acquisitions have been analyzed, and the experience of Germany, China and Russia in attracting investments through mergers and acquisitions has been examined, and the success of this method for each country has been assessed.