Ontologiebasierte Werkzeuge zur Unterstützung von Organisationen bei der Einführung und Durchführung von Wissensmanagement

Im Rahmen dieser Arbeit wurden zwei Softwarewerkzeuge für die technische Unterstützung von Wissensmanagement (WM) konzipiert und technisch umgesetzt, die zum einen den Reifegrad von Unternehmen unter Verwendung von beliebigen Reifegradmodellen für WM erfassen und darauf basierend Handlungsempfehlungen zur Verfügung stellen, zum anderen auf das Anforderungsprofil eines Unternehmens zugeschnittene Best Practices für WM identifizieren und auf die neue Unternehmenssituation übertragen

Take Part Prototype: Creating New Ways of Participation Through Augmented and Virtual Reality [in press]

Famous examples like the Amazon headquarter in New York City or the Stuttgart 21 train station demonstrate that construction projects are often subjects of common interest and can therefore produce protests if citizens feel unheard in urban planning. In this manuscript, we would therefore like to investigate whether e-participation can be used as a tool to foster citizen involvement in construction projects that are of public interest. To this end, we present a prototype that combines participation with augmented and virtual reality. While offering a source for a better understanding of construction processes, our prototype allows users to bring in their own design suggestions and discuss these with others. With this prototype paper, we thus want to demonstrate how augmented and virtual reality can lay the ground for innovative ways of political participation that would offer great potential for project initiators and citizens

A rapid method for detection of five known mutations associated with aminoglycoside-induced deafness

<p>Abstract</p> <p>Background</p> <p>South Africa has one of the highest incidences of multidrug-resistant tuberculosis (MDR-TB) in the world. Concomitantly, aminoglycosides are commonly used in this country as a treatment against MDR-TB. To date, at least five mutations are known to confer susceptibility to aminoglycoside-induced hearing loss. The aim of the present study was to develop a rapid screening method to determine whether these mutations are present in the South African population.</p> <p>Methods</p> <p>A multiplex method using the SNaPshot technique was used to screen for five mutations in the <it>MT-RNR1 </it>gene: A1555G, C1494T, T1095C, 961delT+C(n) and A827G. A total of 204 South African control samples, comprising 98 Mixed ancestry and 106 Black individuals were screened for the presence of the five mutations.</p> <p>Results</p> <p>A robust, cost-effective method was developed that detected the presence of all five sequence variants simultaneously. In this pilot study, the A1555G mutation was identified at a frequency of 0.9% in the Black control samples. The 961delT+C(n) variant was present in 6.6% of the Black controls and 2% of the Mixed ancestry controls. The T1095C, C1494T and A827G variants were not identified in any of the study participants.</p> <p>Conclusion</p> <p>The frequency of 0.9% for the A1555G mutation in the Black population in South Africa is of concern given the high incidence of MDR-TB in this particular ethnic group. Future larger studies are warranted to determine the true frequencies of the aminoglycoside deafness mutations in the general South African population. The high frequencies of the 961delT+C(n) variant observed in the controls suggest that this change is a common non-pathogenic polymorphism. This genetic method facilitates the identification of individuals at high risk of developing hearing loss prior to the start of aminoglycoside therapy. This is important in a low-resource country like South Africa where, despite their adverse side-effects, aminoglycosides will continue to be used routinely and are accompanied with very limited or no audiological monitoring.</p

GlobalFiler Express DNA amplification kit in South Africa: Extracting the past from the present

In this study, the GlobalFiler Express amplification kit was evaluated for forensic use in 541 South African individuals belonging to the Afrikaaner, amaXhosa,1 amaZulu,1 Asian Indian and Coloured population groups. Allelic frequencies, genetic diversity parameters and forensic informative metrics were calculated for each population. A total of 301 alleles were observed ranging between 5 and 44.2 repeat units, 43 were rarely observed partial repeats and seven were novel. The combined match probability (CMP) ranged from 2.21x10 (Coloured) to 5.21x10 (AmaZulu), and the combined power of exclusion (CPE) 0.9999999978 (Afrikaaner) to 0.99999999979 (AmaZulu) respectively. No significant departures from Hardy-Weinberg equilibrium (HWE) were observed after Bonferroni correction. Strong evidence of genetic structure was detected using the coancestry coefficient? Analysis of Molecular Variance (AMOVA) and an unsupervised Bayesian clustering method (STRUCTURE). The efficiency of assignment of individuals to population groups was evaluated by applying likelihood ratios with WHICHRUN, and the individual ancestral membership probabilities inferred by STRUCTURE. Likelihood ratios performed the best in the assignment of individuals to population groups. Signs of positive selection were detected for TH01 and D13S317 and purifying/balancing selection for locus SE33. These three loci also displayed the largest informativeness for assignment (In) values. The results of this study supports the use of the GlobalFiler STR profiling kit for forensic applications in South Africa with the additional capability to predict ethnicity or continental origin of a random sample.IS

A framework for the successful introduction of km using cbr and semantic web technologies

This document describes our current work on developing a framework which supports organizations in the successful implementation of Knowledge Management (KM). It follows the holistic approach of a KM introduction by considering technological, organizational and human aspects, as well as the organizational culture in equal measure. The framework provides recommendations based on Case-Based Reasoning (CBR) techniques and Semantic Web technologies. It supports the four processes of Aamodt & Plaza's CBR-cycle. The best practice cases for a successful KM implementation are structured by the use of an ontology

Using fingerprints and machine learning tools for the prediction of novel dual active compounds for leukotriene A4 hydrolase and soluble epoxide hydrolase

The aim of this work was to establish a new way of predicting novel dual active compounds by combining classical fingerprint representation with state-of-the-art machine learning algorithms. Advantages and disadvantages of the applied 2D- and 3D-fingerprints were investigated. Further, the impact of various machine learning algorithms was analyzed. The new method developed in this work was used to predict compounds, which inhibit two different targets (LTA4H and sEH) involved in the same disease pattern (inflammation). The development of multitarget drugs has become more important in recent years. Many widespread diseases like metabolic syndrome, or cancer are of a multifactorial nature, which makes them hard to be treated effectively with a single drug. The new in silico method presented in this work can help to accelerate the design and development of multitarget drugs, saving time and efforts. The nowadays readily available access to a large number of 3D-structures of biological targets and published activity data of millions of synthesized compounds enabled this study and was used as a starting point for this work. Four different data sets were compiled (crystalized ligands from the PDB, active and inactive compounds from ChEMBL23, newly designed compounds using a combinatorial library). Those data sets were collected and processed using an automated KNIME workflow. This automation has the advantage of allowing easy change and update of compound sources and adapted processing ways. In a next step, the compounds from the compiled data sets were represented using a variety of well-established 2D- and 3D-fingerprints (PLIF, AtomPair, Morgan, FeatMorgan, MACCS). All those fingerprints share the same underlying bit string scheme but vary in the way they describe the molecular structure. Especially the difference between 2D- and 3D-fingerprints was investigated. 2D-fingerprints are solely based on ligand information. 3D-fingerprints, on the other hand, are based on X-ray structure information of protein-ligand complexes. One major difference between 2D- and 3D-fingerprints usage is the need for a 3D-conformation (pose) of the compound in the targets of interest when using 3D-fingerprints. This additional step is time-consuming and brings further uncertainties to the method. Based on the calculated fingerprints state-of-the-art machine learning algorithms (SVC, RF, XGB and ADA) were used to predict novel dual active compounds. The models were evaluated by 10-fold cross validation and accuracy as the primary measure of model performance was maximized. Second, individual parameters of the four machine learning algorithms were optimized in a grid search to achieve maximal accuracy using the optimized partitioning scheme. Overall accuracies, regardless of fingerprint and machine learning algorithm, are slightly better for LTA4H than for sEH. The goal to predict dual active compounds was realized by comparing the set of predicted to be active compounds for LTA4H and sEH. For the 3D-fingerprint PLIF the machine learning algorithm Random Forest was chosen, from which compounds for synthesis and testing were selected. Of 115 predicted to be active compounds, six compounds were cherry picked. Two compounds showed very good/moderate dual inhibitory activity. Of the 2D-fingerprints, the AtomPair fingerprint in combination with the machine learning algorithm Random Forest was chosen from which compounds were selected for synthesis and testing. 116 compounds were predicted to be dual active against LTA4H and sEH. One of those compounds showed good dual inhibitory activity. In this work it was possible to show advantages and disadvantages of using 2D- and 3D-fingerprints in combination with machine learning algorithms. Both strategies (2D: ligand-based, 3D: structure-based) lead to the prediction of novel dual active compounds with moderate to very good inhibitory activity. The method developed in this work is able to predict dual active compounds with very good inhibitory activity and novel (previously unknown) scaffolds inhibiting the targets LTA4H and sEH. This contribution to in silico drug design is promising and can be used for the prediction of novel dual active compounds. Those compounds can further be optimized regarding binding affinity, solubility and further pharmacological and physicochemical properties.Ziel dieser Arbeit ist es neuartige Verbindungen vorherzusagen, die nicht nur ein Einzelnes, sondern zugleich zwei unterschiedliche Proteine inhibieren. Die Zielproteine dieser Arbeit (Leukotrien A4 Hydrolase (LTA4H) und lösliche Epoxid Hydrolase (sEH)) befinden sich in der Arachidonsäure (AA) Kaskade und werden mit verschiedenen inflammatorischen Erkrankungen in Verbindung gebracht (z.B. Asthma, Rheumatoide Arthritis, Dermatitis und Atherosklerose). Die AA Kaskade zeigt eine intensive Kommunikation zwischen den einzelnen Metabolisierungswegen. Die Inhibition von nur einem Metabolisierungsweg lässt den metabolischen Abbau von AA über die anderen beiden Metabolisierungswege zu. Dadurch werden positive Auswirkungen von verabreichten Wirkstoffen verringert. Werden jedoch zwei verschiedene Metabolisierungswege gleichzeitig von einem Wirkstoff inhibiert kann dieses Phänomen überwunden werden. Dies kann über die Gabe von mehreren Wirkstoffen oder einen Wirkstoff, der mehrere Proteine inhibiert erreicht werden (dualer Wirkstoff). Ein dualer Wirkstoff minimiert die Gefahr unvorhersehbarer Wirkstoffinteraktionen, die durch die Gabe von zwei verschiedenen Wirkstoffen hervorgerufen werden können..

Effekt der Therapie mit einem dualen Endothelinrezeptorantagonisten bei Patienten mit pulmonaler Hypertonie in Folge terminaler Linksherzinsuffizienz

PH bei Linksherzinsuffizienz hat unbehandelt eine schlechte Prognose. Welche Effekte ein ERA im Hinblick auf die Hämodynamik und das klinische Outcome bei diesem Patientenkollektiv erzielt, wird in dieser Studie untersucht. Die Einschlusskriterien entsprachen den Kriterien der PH bei Linksherzinsuffizienz. 54 prä-HTx-Patienten erhielten zweimal täglich Bosentan. 28 Patienten bekamen die leitliniengerechte Herzinsuffizienztherapie ohne Bosentan und dienten als Referenzgruppe. Die hämodynamischen Parameter, echokardiographischen Werte, HTx-Rate, Mortalität, Anzahl der klinischen Aufenthalte sowie Laborparameter wurden analysiert. Die ERA-Therapie geht bei dieser Patientengruppe sowohl mit positiven hämodynamischen Effekten als auch mit einer Verbesserung des klinischen Outcomes einher