@article{MatternRungeIdleretal.1996,
  author    = {Mattern, Petra-Corinna and Runge, Heike and Idler, Frank and Schade, Werner and Annem{\"u}ller, Gerolf},
  title     = {Charakterisierung einiger Bacteriocine im Hinblick auf m{\"o}gliche Anwendungen im Lebensmittelbereich : Stammauswahl},
  year      = {1996},
  language  = {de}
}
@article{RungeDeraMatternetal.1996,
  author    = {Runge, Heike and Dera, Yolande and Mattern, Petra-Corinna and Idler, Frank and Schwabe, Werner and Annem{\"u}ller, Gerolf},
  title     = {Screening of lactic acid bacteria for biological acidification of tropical fruit juices},
  year      = {1996},
  language  = {en}
}
@article{RungeScherbaumCurtisetal.2013,
  author    = {Runge, Antonia K. and Scherbaum, Frank and Curtis, Andrew and Riggelsen, Carsten},
  title     = {An interactive tool for the elicitation of subjective probabilities in probabilistic seismic-hazard analysis},
  series = {Bulletin of the Seismological Society of America},
  volume    = {103},
  journal   = {Bulletin of the Seismological Society of America},
  number    = {5},
  publisher = {Seismological Society of America},
  address   = {Albany},
  issn      = {0037-1106},
  doi       = {10.1785/0120130026},
  pages     = {2862 -- 2874},
  year      = {2013},
  abstract  = {In probabilistic seismic-hazard analysis, epistemic uncertainties are commonly treated within a logic-tree framework in which the branch weights express the degree of belief of an expert in a set of models. For the calculation of the distribution of hazard curves, these branch weights represent subjective probabilities. A major challenge for experts is to provide logically consistent weight estimates (in the sense of Kolmogorovs axioms), to be aware of the multitude of heuristics, and to minimize the biases which affect human judgment under uncertainty. We introduce a platform-independent, interactive program enabling us to quantify, elicit, and transfer expert knowledge into a set of subjective probabilities by applying experimental design theory, following the approach of Curtis and Wood (2004). Instead of determining the set of probabilities for all models in a single step, the computer-driven elicitation process is performed as a sequence of evaluations of relative weights for small subsets of models. From these, the probabilities for the whole model set are determined as a solution of an optimization problem. The result of this process is a set of logically consistent probabilities together with a measure of confidence determined from the amount of conflicting information which is provided by the expert during the relative weighting process. We experiment with different scenarios simulating likely expert behaviors in the context of knowledge elicitation and show the impact this has on the results. The overall aim is to provide a smart elicitation technique, and our findings serve as a guide for practical applications.},
  language  = {en}
}
@article{ChaykovskavonWebskyRahnenfuehreretal.2011,
  author    = {Chaykovska, Lyubov and von Websky, Karoline and Rahnenf{\"u}hrer, Jan and Alter, Markus L. and Heiden, Susi and Fuchs, Holger and Runge, Frank and Klein, Thomas and Hocher, Berthold},
  title     = {Effects of DPP-4 Inhibitors on the Heart in a Rat Model of Uremic Cardiomyopathy},
  series = {PLoS one},
  volume    = {6},
  journal   = {PLoS one},
  number    = {11},
  publisher = {PLoS},
  address   = {San Fransisco},
  issn      = {1932-6203},
  doi       = {10.1371/journal.pone.0027861},
  pages     = {9},
  year      = {2011},
  abstract  = {Background: Uremic cardiomyopathy contributes substantially to mortality in chronic kidney disease (CKD) patients. Glucagon-like peptide-1 (GLP-1) may improve cardiac function, but is mainly degraded by dipeptidyl peptidase-4 (DPP-4). Methodology/Principal Findings: In a rat model of chronic renal failure, 5/6-nephrectomized [5/6N] rats were treated orally with DPP-4 inhibitors (linagliptin, sitagliptin, alogliptin) or placebo once daily for 4 days from 8 weeks after surgery, to identify the most appropriate treatment for cardiac dysfunction associated with CKD. Linagliptin showed no significant change in blood level AUC(0-infinity) in 5/6N rats, but sitagliptin and alogliptin had significantly higher AUC(0-infinity) values; 41\% and 28\% (p=0.0001 and p=0.0324), respectively. No correlation of markers of renal tubular and glomerular function with AUC was observed for linagliptin, which required no dose adjustment in uremic rats. Linagliptin 7 mu mol/kg caused a 2-fold increase in GLP-1 (AUC 201.0 ng/l*h) in 5/6N rats compared with sham-treated rats (AUC 108.6 ng/l*h) (p=0.01). The mRNA levels of heart tissue fibrosis markers were all significantly increased in 5/6N vs control rats and reduced/normalized by linagliptin. Conclusions/Significance: DPP-4 inhibition increases plasma GLP-1 levels, particularly in uremia, and reduces expression of cardiac mRNA levels of matrix proteins and B-type natriuretic peptides (BNP). Linagliptin may offer a unique approach for treating uremic cardiomyopathy in CKD patients, with no need for dose-adjustment.},
  language  = {en}
}