Automatisierte Anbindung von Simulations- und Optimierungssoftware zur parallelen Lösung inverser Problemklassen

In dieser Arbeit wird die in Python geschriebene Software “Environment for Combining Optimization and Simulation Software” (EFCOSS) zur Verbindung von Optimierungsalgorithmen mit Simulationsroutinen beschrieben, welche für verschiedene Problemstellungen aus der Geothermie und Materialwissenschaft angewendet wird. Zur Lösung werden inverse Probleme für Parameterbestimmung, Space-Mapping, Optimal Experimental Design (OED) und Modellidentifikation aufgestellt und diese mit Hilfe von EFCOSS gelöst. Dies wird nur möglich, da die interne Struktur von EFCOSS grundlegend umgearbeitet und dabei eine neue Softwarearchitektur unter Verwendung von Standard Python Paketen geschaffen wurde. Die Software ist gezielt darauf ausgelegt, mehrere Optimierungsprobleme, Zielfunktionen und Simulationsroutinen auszuwerten und damit ein breites Anwendungsspektrum effektiv zu lösen. Beispielhaft werden Simulationsmodelle für die geologischen Gegebenheiten in der Region um Perth in Australien, sowie in der Toskana in Italien, betrachtet, um neue Explorationsbohrlochpositionen mit Hilfe von OED zu finden, die niedrige Unsicherheiten in das Modell einbringen. Darüber hinaus werden Parameterschätzprobleme mit grob und fein aufgelösten Modellen der Regionen mit Hilfe des Space-Mapping Algorithmus optimiert und hierbei eine hohe Genauigkeit erzielt. Mit Hilfe der Modellidentifikation werden Modelle der Metallplastizität miteinander verglichen und Aussagen über deren Güte getroffen. Durch den automatisierten Einsatz von automatischem Differenzieren, sowohl im Vorwärts-, wie auch im Rückwärtsmodus, Parallelisierung und Wiederverwendung von bereits berechneten Ergebnissen, werden die seriellen Ausführungszeiten zur Lösung der gegebenen Probleme von mehreren Tagen beziehungsweise Wochen auf wenige Minuten gesenkt.This work introduces a novel extension of the Python software "Environment for Combining Optimization and Simulation Software'' (EFCOSS). The extension addresses the solution of optimization problems of different types. Various problem instances that demonstrate the feasibility of this approach in new practical application scenarios include geothermal engineering and material science. In a more general context, EFCOSS enables to investigate the questions of which parameter values best fit a given computer model to measurements from real-world experiments, how should such experiments be designed with minimal uncertainty, which computer models should be used for a specific task, and how can the efficiency of such investigations be improved by using simpler models. These questions are addressed by employing techniques from parameter estimation, space mapping, optimal experimental design, and model identification that are implemented and brought together in EFCOSS. To this end, the internal structure of EFCOSS had to be redesigned completely to introduce a new software architecture based on standard Python packages. This new architecture allows for multiple optimization problems, objective functions, and simulation routines to be used within a single application. Simulation models of geothermal reservoirs in the regions of Perth in Australia and Tuscany in Italy are used as illustrating examples of optimal experimental design to find the location of new borehole sites that introduce low uncertainty in the parameter estimation. Furthermore, new parameter estimation problems are solved using space-mapping algorithms. Model identification is applied to metal-plasticity models to investigate different kinds of models. By combining automatic differentiation, parallelization, and reuse of intermediate results, the serial runtimes of the described problems are reduced from several weeks to minutes

Efficient Implementation of Parallel Path Planning Algorithms on GPUs

In robot systems several computationally intensivetasks can be found, with path planning being one of them.Especially in dynamically changing environments, it is difficult tomeet real-time constraints with a serial processing approach. Forthose systems using standard computers, a promising option is toemploy a GPGPU as a coprocessor in order to offload those taskswhich can be efficiently parallelized. We implemented selectedparallel path planning algorithms on NVIDIA's CUDA platformand were able to accelerate all of these algorithms efficientlycompared to a multi-core implementation. We present the resultsand more detailed information about the implementation of thesealgorithms

Functional Contribution of Elevated Circulating and Hepatic Non-Classical CD14+CD16+ Monocytes to Inflammation and Human Liver Fibrosis

BACKGROUND: Monocyte-derived macrophages critically perpetuate inflammatory responses after liver injury as a prerequisite for organ fibrosis. Experimental murine models identified an essential role for the CCR2-dependent infiltration of classical Gr1/Ly6C(+) monocytes in hepatic fibrosis. Moreover, the monocyte-related chemokine receptors CCR1 and CCR5 were recently recognized as important fibrosis modulators in mice. In humans, monocytes consist of classical CD14(+)CD16(-) and non-classical CD14(+)CD16(+) cells. We aimed at investigating the relevance of monocyte subpopulations for human liver fibrosis, and hypothesized that 'non-classical' monocytes critically exert inflammatory as well as profibrogenic functions in patients during liver disease progression. METHODOLOGY/PRINCIPAL FINDINGS: We analyzed circulating monocyte subsets from freshly drawn blood samples of 226 patients with chronic liver disease (CLD) and 184 healthy controls by FACS analysis. Circulating monocytes were significantly expanded in CLD-patients compared to controls with a marked increase of the non-classical CD14(+)CD16(+) subset that showed an activated phenotype in patients and correlated with proinflammatory cytokines and clinical progression. Correspondingly, CD14(+)CD16(+) macrophages massively accumulated in fibrotic/cirrhotic livers, as evidenced by immunofluorescence and FACS. Ligands of monocyte-related chemokine receptors CCR2, CCR1 and CCR5 were expressed at higher levels in fibrotic and cirrhotic livers, while CCL3 and CCL4 were also systemically elevated in CLD-patients. Isolated monocyte/macrophage subpopulations were functionally characterized regarding cytokine/chemokine expression and interactions with primary human hepatic stellate cells (HSC) in vitro. CD14(+)CD16(+) monocytes released abundant proinflammatory cytokines. Furthermore, CD14(+)CD16(+), but not CD14(+)CD16(-) monocytes could directly activate collagen-producing HSC. CONCLUSIONS/SIGNIFICANCE: Our data demonstrate the expansion of CD14(+)CD16(+) monocytes in the circulation and liver of CLD-patients upon disease progression and suggest their functional contribution to the perpetuation of intrahepatic inflammation and profibrogenic HSC activation in liver cirrhosis. The modulation of monocyte-subset recruitment into the liver via chemokines/chemokine receptors and their subsequent differentiation may represent promising approaches for therapeutic interventions in human liver fibrosis

Identification of regulatory variants associated with genetic susceptibility to meningococcal disease.

Non-coding genetic variants play an important role in driving susceptibility to complex diseases but their characterization remains challenging. Here, we employed a novel approach to interrogate the genetic risk of such polymorphisms in a more systematic way by targeting specific regulatory regions relevant for the phenotype studied. We applied this method to meningococcal disease susceptibility, using the DNA binding pattern of RELA - a NF-kB subunit, master regulator of the response to infection - under bacterial stimuli in nasopharyngeal epithelial cells. We designed a custom panel to cover these RELA binding sites and used it for targeted sequencing in cases and controls. Variant calling and association analysis were performed followed by validation of candidate polymorphisms by genotyping in three independent cohorts. We identified two new polymorphisms, rs4823231 and rs11913168, showing signs of association with meningococcal disease susceptibility. In addition, using our genomic data as well as publicly available resources, we found evidences for these SNPs to have potential regulatory effects on ATXN10 and LIF genes respectively. The variants and related candidate genes are relevant for infectious diseases and may have important contribution for meningococcal disease pathology. Finally, we described a novel genetic association approach that could be applied to other phenotypes

Association between diesel exposure at work and prostate cancer

Objectives: The possible etiologic relevance of occupational factors such as cadmium, cutting oils, diesel fuel and fumes, herbicides, polycyclic aromatic hydrocarbons (PAH), polychlorinated biphenyls, soot, tar, mineral oil, and solvents to prostate cancer was studied. Methods: A case-referent study design was used to recruit 192 subjects with histologically confirmed prostate cancer and 210 referents who had prostate cancer histologically excluded either in one of two urologic practices (Hamburg and Frankfurt) or in the urological policlinic of the Frankfurt University. Data were gathered with a self-administered questionnaire and analyzed using logistic regression to control for age, region, and cigarette smoking. A job-exposure matrix was used for assigning exposure. For the calculation of dose-years, the duration of contact with specific substances was weighted by the intensity and probability of exposure according to a job-exposure matrix. Results: The analysis of dose-years yielded a statistically significant association between occupational exposure to diesel fuel or fumes and prostate cancer (odds ratio 3.7, 95% confidence interval 1.4-9.8, for subjects exposed to more than 25 dose-years in a comparison with subjects never exposed). For the other substances, no statistically significant differences in exposure were found between the cases and referents. When only jobs with a high exposure probability were used to classify the participants as exposed, only exposure to PAH was significantly associated with prostate cancer. Conclusion: In keeping with results from other studies, this study provides further evidence that exposure to diesel fuel or fumes - possibly mediated through PAH - may be associated with the development of prostate cancer