Best Selling Books by Daniel Kraus

Abstract: Rationale: To establish a spatially exact co-registration procedure between in vivo multiparametric magnetic resonance imaging (mpMRI) and (immuno)histopathology of soft tissue sarcomas (STS) to identify imaging parameters that reflect radiation therapy response of STS. Methods: The mpMRI-Protocol included diffusion-weighted (DWI), intravoxel-incoherent motion (IVIM), and dynamic contrast-enhancing (DCE) imaging. The resection specimen was embedded in 6.5% agarose after initial fixation in formalin. To ensure identical alignment of histopathological sectioning and in vivo imaging, an ex vivo MRI scan of the specimen was rigidly co-registered with the in vivo mpMRI. The deviating angulation of the specimen to the in vivo location of the tumor was determined. The agarose block was trimmed accordingly. A second ex vivo MRI in a dedicated localizer with a 4 mm grid was performed, which was matched to a custom-built sectioning machine. Microtomy sections were stained with hematoxylin and eosin. Immunohistochemical staining was performed with anti-ALDH1A1 antibodies as a radioresistance and anti-MIB1 antibodies as a proliferation marker. Fusion of the digitized microtomy sections with the in vivo mpMRI was accomplished through nonrigid co-registration to the in vivo mpMRI. Co-registration accuracy was qualitatively assessed by visual assessment and quantitatively evaluated by computing target registration errors (TRE). Results: The study sample comprised nine tumor sections from three STS patients. Visual assessment after nonrigid co-registration showed a strong morphological correlation of the histopathological specimens with ex vivo MRI and in vivo mpMRI after neoadjuvant radiation therapy. Quantitative assessment of the co-registration procedure using TRE analysis of different pairs of pathology and MRI sections revealed highly accurate structural alignment, with a total median TRE of 2.25 mm (histology - ex vivo MRI), 2.22 mm (histology - in vivo mpMRI), and 2.02 mm (ex vivo MRI - in vivo mpMRI). There was no significant difference between TREs of the different pairs of sections or caudal, middle, and cranial tumor parts, respectively. Conclusion: Our initial results show a promising approach to obtaining accurate co-registration between histopathology and in vivo MRI for STS. In a larger cohort of patients, the method established here will enable the prospective identification and validation of in vivo imaging biomarkers for radiation therapy response prediction and monitoring in STS patients via precise molecular and cellular correlation

In this thesis, an introduction to Nonlinear Model Predictive Control (NMPC) combining both theory and application is presented. The basis of the NMPC is described to give a general idea how this process control strategy works. For this purpose, two test casestudies are implemented. The first one is a simple two tanks in series to get acquainted with open-source platform the JModelica.org and the Optimica extension.Finally, a more complex example, which consists of solving a start-up problem of a steam boiler using the JModelica framework, is provided. The results demonstrate the effort saved in solving an optimal control problem when using the JModelica framework in comparison to individual, case-specifically arranged solutions.

Many loan commitment contracts contain a material adverse change clause which allows banks to renege or step back from their commitment based on rather subjective claims regarding the borrowers'' prospective financial situation. While this sounds like an attractive option for banks, empirical evidence shows that, despite its frequent appearance in contracts, the clause is rarely invoked (cf. Sufi, 2009; Ivashina and Scharfstein, 2010). In the present paper, we argue that this is due to the fact that, in combination with appropriate pricing on the spot loan market, the clause is essentially an effective means to screen the borrowers'' riskiness. In particular, it renders loan commitment contracts comparably more expensive for high-risk borrowers, thereby directing them to the spot loan market. Low-risk borrowers, in turn, are still attracted to loan commitment contracts as for them the implicit extra cost is lower. Thus, in equilibrium, there is no need for banks to step back from earlier promises. Moreover, we show that the presence of the clause increases welfare as it decreases credit rationing for low-risk entrepreneurs and reduces total loan costs.

Bioparticles surround us in many areas of daily life. They are used, for example, in plant cultivation, the food industry or for environmental applications. The characterization of these particles is a challenge due to their small size. The present work therefore deals with the development and investigation of novel multispectral imaging techniques for the characterization and sorting of bioparticles in microfluidic flow. For this purpose, innovative microfluidic structures were tested to place particles in the focal plane of the optical system by controlled fluid rotation. These structures build the basis for optical characterization and image-based sorting at the single particle level. Microalgae of the type Haematococcus pluvialis (HP) were used as model organism for the investigations as well as for the subsequent image-based sorting. These microalgae are cultivated in a two-step process, where they change their morphological as well as their spectral properties if they are exposed to external stress factors. Such characteristics make them interesting for multispectral imaging analysis. During the development and optimization of biotechnological cultivation processes, continuous monitoring of the spectral and morphological properties of the cells is an important quality pa-rameter. Due to the large number of cells analyzed at the single cell level per measurement, pre-cise information about the physiological state of the cells as well as the composition of the entire population is possible. With the novel universally applicable multispectral imaging platform, several thousand particles per minute could be captured and classified. By implementing real-time classification, the entire analysis process could be significantly accelerated and automated. The measured values of the multispectral imaging correlate with the measured values of the chemically extracted dyes in the particles.