Traction force microscopy with optimized regularization and automated Bayesian parameter selection for comparing cells

Adherent cells exert traction forces on to their environment, which allows them to migrate, to maintain tissue integrity, and to form complex multicellular structures. This traction can be measured in a perturbation-free manner with traction force microscopy (TFM). In TFM, traction is usually calculated via the solution of a linear system, which is complicated by undersampled input data, acquisition noise, and large condition numbers for some methods. Therefore, standard TFM algorithms either employ data filtering or regularization. However, these approaches require a manual selection of filter- or regularization parameters and consequently exhibit a substantial degree of subjectiveness. This shortcoming is particularly serious when cells in different conditions are to be compared because optimal noise suppression needs to be adapted for every situation, which invariably results in systematic errors. Here, we systematically test the performance of new methods from computer vision and Bayesian inference for solving the inverse problem in TFM. We compare two classical schemes, L1- and L2-regularization, with three previously untested schemes, namely Elastic Net regularization, Proximal Gradient Lasso, and Proximal Gradient Elastic Net. Overall, we find that Elastic Net regularization, which combines L1 and L2 regularization, outperforms all other methods with regard to accuracy of traction reconstruction. Next, we develop two methods, Bayesian L2 regularization and Advanced Bayesian L2 regularization, for automatic, optimal L2 regularization. Using artificial data and experimental data, we show that these methods enable robust reconstruction of traction without requiring a difficult selection of regularization parameters specifically for each data set. Thus, Bayesian methods can mitigate the considerable uncertainty inherent in comparing cellular traction forces

First steps towards a modelling toolbox suitable for evaluating resilience of German inland waterways in context of climate change

modelling approach to study climate change impact and adaptation strategies on German inland waterways is presented. In the German federal research program “Network of Experts” a modelling chain that consists of an ensemble of global and regional climate and hydrological models is used to generate hydrological data for a range of climate scenarios. This data serves as input in a novel approach to interlink river engineering related models. As shown for the case study Lower Rhine in Germany, nested one-dimensional, long-term morphodynamic numerical modelling on a large spatial and temporal scale and two-dimensional hydrodynamic-numerical modelling on a more detailed level will be carried out. The River Navigation Assessment Tool (RiNA) connects and weighs model outputs and limitless further information for conducting an integrated waterway assessment. It allows an efficient use of modelling resources and could serve as a decision-making tool for the German Federal Waterways and Shipping Administration to cope with the impacts of climate change

Long-term results in malignant pleural mesothelioma treated with neoadjuvant chemotherapy, extrapleural pneumonectomy and intensity-modulated radiotherapy

Introduction: We investigated the clinical outcome and the toxicity of trimodal therapy of malignant pleural mesothelioma (MPM) treated with neoadjuvant chemotherapy, extrapleural pneumonectomy (EPP) and adjuvant intensity-modulated radiotherapy (IMRT). Methods: Chemotherapy regimens included Cisplatin/Pemetrexed, Carboplatin/Pemetrexed and Cisplatin/Gemcitabine, followed by EPP. 62 patients completed the adjuvant radiotherapy. IMRT was carried out in two techniques, either step&shoot or helical tomotherapy. Median target dose was 48 Gy to 54 Gy. Toxicity was scored with the Common Terminology Criteria (CTC) for Adverse Events. We used Kaplan-Meier method to estimate actuarial rate of locoregional control (LRC),distant control (DC) and overall survival (OS),measured from the date of surgery. Rates were compared using the logrank test. For multivariate analysis the Cox proportional hazard model was used. Results: The median OS, LRC and DC times were 20.4, 31.4 and 21.4 months. The 1-,2-,3-year OS rates were 63, 42, 28 %,the LRC rates were 81, 60, 40 %,and the DC rates were 62, 48, 41 %. We observed no CTC grade 4 or grade 5 toxicity. Step&shoot and helical tomotherapy were equivalent both in dosimetric characteristics and clinical outcome. Biphasic tumor histology was associated with worse clinical outcome compared to epitheloid histology. Conclusions: Mature clinical results of trimodal treatment for MPM were presented. They indicate that hemithoracic radiotherapy after EPP can be safely administered by either step&shoot IMRT and tomotherapy. However, the optimal prospective patient selection for this aggressive trimodal therapy approach remains unclear. This study can serve as a benchmark for current and future therapy concepts for MPM