Bachelor Thesis
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Institute
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 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.
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.
DropConnect (the generalization of Dropout) is a very simple regularization technique that was introduced a few years ago and has become extremely popular because of its simplicity and effectiveness. In this thesis, a suitable architecture for applying DropConnect to Learning Vector Quantization networks is proposed along with a reference implementation and experimental results. Inmany classification tasks, the uncertainty of themodel is a vital piece of information for experts. Methods to extract the uncertainty and stability using DropConnect are also proposed and the corresponding experimental results are documented.
In this work, we discuss the key role that “conflict minerals” (Gold, Coltan, Cobalt, Tin, Tungsten) play in global supply chains and high-technology industries, and the issues surrounding their extraction and trade in origin
countries, particularly in the African Congo Basin and the Great Lakes Region. We discuss ongoing international efforts to combat violence, child labour and human rights violations at mineral extraction areas, particularly in the Democratic Republic of the Congo (DRC), where very large mineral reserves have been discovered. We present the OECD Due Diligence Guidance for Responsible Supply Chains of Minerals from Conflict-Affected and High-Risk Areas, and the
GOTS MineralTrace mineral proof-of-origin and trade chain certification solution developed by ibes AG in Germany, which automates and simplifies the implementation of the OECD Guidance. We discuss a pilot project in DRC involving the GOTS GoldTrace application, based on the MineralTrace platform. We point out MineralTrace’s benefits and its limitations. We analyse possible solutions to said limitations, including an analysis of blockchain-based transactional information exchange and record keeping systems, and finally we propose a new MineralTrace Application Programming Interface (API) that solves current limitations, introduces configuration flexibility for client applications, introduces workflow flexibility to adapt MineralTrace to any country or region, and simplifies data export functionality.
Object detection and classification is active field of research inmachine learning and computervision. Depending on the application there are different limitations to adjust to, but also possibilities to take advantage of. In my thesis, We focus on classification and detection of video sequence during night-time and the proposed method is robust since it does use image thresholding [8] which is commonly use in other methods and the thesis uses histograms of oriented gradients (HOG) [37] as features and support vector machine (SVM) [74] as classifier. It is of great importance that the extracted features from the images should be robust and distinct enough to help the classifier distinguish between high-beam and a low-beam. The classifier is part of the object detection which predicts whether or not a testing image matches one group or the other. In our case that is predicting whether or not an image belongs to high or low-beam sequence.
This bachelor thesis examines two main topics: Corporate Social Responsibility and Corporate Philanthropy as an integral part of it. It was written in order to prove the high importance of business philanthropy in today’s global market and to encourage companies to strengthen their CSR policy so as to contribute to the resolution of social problems. This paper reviews the theoretical framework of CSR, its evolution, types and theories relating to Corporate Philanthropy. Also it represents a comparative analysis of successful practices of corporate philanthropy in pharmaceutical and other global industries predominantly in Europe and USA. This work underlines competitive advantages and important socio-economic impact of CP and suggest recommendations for companies in developing their CSR activities. The subsequent paper is based on internet research using articles, presentations, reports and studies, websites and official legal documents.
The subject of the following paper is the mental well-being of employees at their work and how the leader can improve this well-being using positive psychology. The paper is compilatory in nature because it uses research and literature of experts to analyse how employee mental well-being can be further stimulated. The expert literature is used to present tools, but also to demonstrate the effectiveness of these tools through real-life case studies and evidence. The paper wishes to inform persons, leaders, and entire organizations how positive psychology can be beneficial to organizational members’ well-being in the long term. Using a compilation of positive psychology literature and reallife case studies’ analysis, the informative purpose of the thesis can be achieved.