Goecke, F. R., Labes, A. ., Wiese, J. ., & Imhoff, J. F. (2013). Phylogenetic analysis and antibiotic activity of bacteria isolated from the surface of two co-occurring macroalgae from the Baltic Sea. European Journal of Phycology, 48, 47–60. Abgerufen von http://oceanrep.geomar.de/19592/
Abstract
Bacteria associated with Fucus vesiculosus and Delesseria sanguinea, two macroalgae from the Kiel Fjord were investigated seasonally over two years by cultivation-based methods. A total of 166 bacterial strains were isolated from the macroalgae, affiliated to seven classes of bacteria (Actinobacteria, Bacilli, Alphaproteobacteria, Betaproteobacteria, Gammaproteobacteria, Cytophagia and Flavobacteria). According to 16S rRNA gene sequence similarities they were arranged in 82 phylotypes of\ensuremath>99.0\% sequence identity. Assuming that chemical factors rule the bacteriamacroalga and bacteriabacteria interactions on algal surfaces, we tested the antibiotic activity of the bacterial isolates not only against a panel of four standard test organisms (Bacillus subtilis, Candida glabrata, Escherichia coli and Staphylococcus lentus) but also four macroalga-associated microorganisms: Algicola bacteriolytica and Pseudoalteromonas elyakovii (macroalgal pathogens), and Bacillus algicola and Formosa algae (strains associated with algal surfaces). Organic extracts of more than 51\% of the isolates from the two macroalgae inhibited the growth of at least one of the tested microorganisms. As much as 46\% and 45\% of the isolates derived from F. vesiculosus and D. sanguinea, respectively, showed antimicrobial activity against the set of macroalga-associated bacteria, compared with 13 and 19\% against a standard set of microorganisms. High antibacterial activity against macroalgal pathogens and bacterial competitors support the assumption that complex chemical interactions shape the relationships of bacteria associated with macroalgae and suggest that these bacteria are a rich source of antimicrobial metabolites.
Labes, A. . (2013). Early drug discovery: Models for Entering Pharmaceutical Pipelines. In 2. Deutsch-Russisches Forum Biotechnologie. Abgerufen von http://oceanrep.geomar.de/23001/
Gencel, C. ., Petersen, K. ., Mughal, A. A., & Iqbal, M. I. (2013). A decision support framework for metrics selection in goal-based measurement programs: GQM-DSFMS. Journal of Systems and Software, 86, 3091–3108.
Petersen, K. ., & Gencel, C. . (2013). Worldviews, Research Methods, and their Relationship to Validity in Empirical Software Engineering Research. In The Joint Conference of the 23nd International Workshop on Software Measurement (IWSM) and the 8th International Conference on Software Process and Product Measurement (Mensura).
Uzunkol, O. . (2013). Generalized class invariants with "Thetanullwerte". Turkish Journal of Mathematics, 37, 165–181. http://doi.org/10.3906/mat-1106-4
Brandenburg, M. . (2013). Quantitative Models for Value-Based Supply Chain Management. Lecture Notes in Economics and Mathematical Systems (S. 219). Berlin, Heidelberg: Springer. http://doi.org/10.1007/978-3-642-31304-2
Abstract
Supply chain management (SCM) strives for creating competitive advantage and value for customers by integrating business processes from end users through original suppliers. However, the question of how SCM influences the value of a firm is not fully answered. Various conceptual frameworks that explain the coherence of SCM and company value, comprehended as value-based SCM, are well accepted in scientific research, but quantitative approaches to value-based SCM are found rather seldom. The book contributes to this research gap by proposing quantitative models that allow for assessing influences of SCM on the value of a firm. Opposed to existing models that limit the observation to chosen facets of SCM or selected value drivers, this holistic approach is adequate to • reflect configurational and operational aspects of SCM, • cover all phases of the product life cycle, • financially compare value impacts of profitability-related and asset-related value drivers, and • assess influences of dynamics and uncertainties on company value.
Labes, A. . (2013). Genome based methods for the exploration of natural products from marine fungi for the treatment of cancer. In Tagung der Vereinigung für allgemeine und angewandte Mikrobiologie. Abgerufen von http://oceanrep.geomar.de/23000/
Feyh, M. ., & Petersen, K. . (2013). Lean software development measures and indicators-a systematic mapping study. In International Conference on Lean Enterprise Software and Systems (S. 32–47).