Rhetorische Sprache - ein Kriterium zur Differenzierung von politischen und sozialen/soziologischen Institutionenbegriffen ; e. method. Überlegung mit zwei Beisp

Rhetorische Sprache - ein Kriterium zur Differenzierung von politischen und sozialen/soziologischen Institutionenbegriffen ; e. method. Überlegung mit zwei Beisp. - In: Die Eigenart der Institutionen / Gerhard Göhler (Hrsg.). - Baden- Baden : Nomos Verl.-Ges., 1994. - S. 221-24

Die Perspektive und die Zeichen

Die Perspektive und die Zeichen : hermet. Verschlüsselungen bei Giovanni Pico della Mirandola. - München : Fink, 1996. - 511 S. - (Die Geistesgeschichte und ihre Methoden ; 18) (Münchner Universitätsschriften). - Zugl.: Augsburg, Univ., Diss., 199

Die Managerin und der Mönch. Über die Zukunft unserer Bildungsanstalten

Erhart W. Die Managerin und der Mönch. Über die Zukunft unserer Bildungsanstalten. In: Kimmich D, Thumfart A, eds. Universität ohne Zukunft?. Frankfurt a. M.: Suhrkamp; 2004: 108-125

Traffic Sign Detection and Classification on the Austrian Highway Traffic Sign Data Set

Advanced Driver Assistance Systems rely on automated traffic sign recognition. Today, Deep Learning methods outperform other approaches in terms of accuracy and processing time; however, they require vast and well-curated data sets for training. In this paper, we present the Austrian Highway Traffic Sign Data Set (ATSD), a comprehensive annotated data set of images of almost all traffic signs on Austrian highways in 2014, and corresponding images of full traffic scenes they are contained in. Altogether, the data set consists of almost 7500 scene images with more than 28,000 detailed annotations of more than 100 distinct traffic sign classes. It covers diverse environments, ranging from urban to rural and mountainous areas, and includes many images recorded in tunnels. We further evaluate state-of-the-art traffic sign detectors and classifiers on ATSD to establish baselines for future experiments. The data set and our baseline models are freely available online

Fuehrungsgruppen und die politische Integration Ostdeutschlands

Im vorliegenden Arbeitspapier werden die funktionalen Eliten und das Elitehandeln im Prozess der deutsch-deutschen Transformation aus einer politologischen Perspektive betrachtet. In einem ersten Schritt wird das heuristische Raster skizziert, mit dessen Hilfe die Transitionsforschung und auch Teile der Eliteforschung innerhalb der Transitionsforschung Fuehrungsgruppen und deren Handeln situieren und analysieren. In einem zweiten Schritt werden einige empirische Ergebnisse von Elitehandeln in ausgewaehlten Segmenten des politischen Systems im Prozess der deutsch-deutschen Einigung vorgestellt. Dabei wird die These verfolgt, dass in den unterschiedlichen Segmenten des politischen Systems und gemaess den unterschiedlichen strukturellen wie kulturellen Handlungslogiken verschiedene Integrations- und Responsivitaetsverhaeltnisse vorliegen. (ICI2)SIGLEAvailable from http://www.soziologie.uni-halle.de/publikationen/pdf/0203.pdf / FIZ - Fachinformationszzentrum Karlsruhe / TIB - Technische InformationsbibliothekDEGerman

Complement activation in children with Streptococcus pneumoniae associated hemolytic uremic syndrome

Background Hemolytic uremic syndrome caused by invasive pneumococcal disease (P-HUS) is rare in children and adolescents, but accompanied by high mortality in the acute phase and complicated by long-term renal sequelae. Abnormalities in the alternative complement pathway may additionally be contributing to the course of the disease but also to putative treatment options. Methods Retrospective study to assess clinical course and laboratory data of the acute phase and outcome of children with P-HUS. Results We report on seven children (median age 12 months, range 3-28 months) diagnosed with P-HUS. Primary organ manifestation was meningitis in four and pneumonia in three patients. All patients required dialysis which could be discontinued in five of them after a median of 25 days. In two patients, broad functional and genetic complement analysis was performed and revealed alternative pathway activation and risk haplotypes in both. Three patients were treated with the complement C5 inhibitor eculizumab. During a median follow-up time of 11.3 years, one patient died due to infectious complications after transplantation. Two patients showed no signs of renal sequelae. Conclusions Although pathophysiology in P-HUS remains as yet incompletely understood, disordered complement regulation seems to provide a clue to additional insights for pathology, diagnosis, and even targeted treatment

Lifting Hospital Electronic Health Record Data Treasures: Challenges and Opportunities

Electronic health records (EHRs) have been successfully used in data science and machine learning projects. However, most of these data are collected for clinical use rather than for retrospective analysis. This means that researchers typically face many different issues when attempting to access and prepare the data for secondary use. We aimed to investigate how raw EHRs can be accessed and prepared in retrospective data science projects in a disciplined, effective, and efficient way. We report our experience and findings from a large-scale data science project analyzing routinely acquired retrospective data from the Kepler University Hospital in Linz, Austria. The project involved data collection from more than 150,000 patients over a period of 10 years. It included diverse data modalities, such as static demographic data, irregularly acquired laboratory test results, regularly sampled vital signs, and high-frequency physiological waveform signals. Raw medical data can be corrupted in many unexpected ways that demand thorough manual inspection and highly individualized data cleaning solutions. We present a general data preparation workflow, which was shaped in the course of our project and consists of the following 7 steps: obtain a rough overview of the available EHR data, define clinically meaningful labels for supervised learning, extract relevant data from the hospital’s data warehouses, match data extracted from different sources, deidentify them, detect errors and inconsistencies therein through a careful exploratory analysis, and implement a suitable data processing pipeline in actual code. Only few of the data preparation issues encountered in our project were addressed by generic medical data preprocessing tools that have been proposed recently. Instead, highly individualized solutions for the specific data used in one’s own research seem inevitable. We believe that the proposed workflow can serve as a guidance for practitioners, helping them to identify and address potential problems early and avoid some common pitfalls

Genetic variants of methionine metabolism and DNA methylation

AIM: Altered DNA methylation is associated with important and common pathologies such as cancer. The origin of altered DNA methylation is unknown. The methyl groups for DNA methylation are provided by methionine metabolism. This metabolism is characterized by a high interindividual variability, which is in part explained by genetic variants. METHODS: In a cohort of 313 individuals derived from a family-based study with index cases of cerebrovascular disease, we analyzed whether global methylation of leukocyte DNA was associated with age, gender, homocysteine plasma levels or functionally relevant genetic variants. RESULTS: We observed an association of the G-allele of the methionine synthase variant c.2756A>G (D919G) with global methylation (% methylation ± 1 SD, AA: 41.3 ± 14.9; AG: 36.4 ± 18.2; GG: 30.8 ± 16.9; F = 4.799; p = 0.009). The methionine synthase variant c.2756A>G is associated with various types of cancer. CONCLUSION: Our data suggest that an impact on DNA methylation may contribute to the clinical relevance of the methionine synthase variant

Machine learning based outcome prediction of microsurgically treated unruptured intracranial aneurysms

Abstract Machine learning (ML) has revolutionized data processing in recent years. This study presents the results of the first prediction models based on a long-term monocentric data registry of patients with microsurgically treated unruptured intracranial aneurysms (UIAs) using a temporal train-test split. Temporal train-test splits allow to simulate prospective validation, and therefore provide more accurate estimations of a model’s predictive quality when applied to future patients. ML models for the prediction of the Glasgow outcome scale, modified Rankin Scale (mRS), and new transient or permanent neurological deficits (output variables) were created from all UIA patients that underwent microsurgery at the Kepler University Hospital Linz (Austria) between 2002 and 2020 (n = 466), based on 18 patient- and 10 aneurysm-specific preoperative parameters (input variables). Train-test splitting was performed with a temporal split for outcome prediction in microsurgical therapy of UIA. Moreover, an external validation was conducted on an independent external data set (n = 256) of the Department of Neurosurgery, University Medical Centre Hamburg-Eppendorf. In total, 722 aneurysms were included in this study. A postoperative mRS > 2 was best predicted by a quadratic discriminant analysis (QDA) estimator in the internal test set, with an area under the receiver operating characteristic curve (ROC-AUC) of 0.87 ± 0.03 and a sensitivity and specificity of 0.83 ± 0.08 and 0.71 ± 0.07, respectively. A Multilayer Perceptron predicted the post- to preoperative mRS difference > 1 with a ROC-AUC of 0.70 ± 0.02 and a sensitivity and specificity of 0.74 ± 0.07 and 0.50 ± 0.04, respectively. The QDA was the best model for predicting a permanent new neurological deficit with a ROC-AUC of 0.71 ± 0.04 and a sensitivity and specificity of 0.65 ± 0.24 and 0.60 ± 0.12, respectively. Furthermore, these models performed significantly better than the classic logistic regression models (p  2, a pre- and postoperative difference in mRS > 1 point and a GOS < 5. Therefore, generalizability of the models could not be demonstrated in the external validation. A SHapley Additive exPlanations (SHAP) analysis revealed that this is due to the most important features being distributed quite differently in the internal and external data sets. The implementation of newly available data and the merging of larger databases to form more broad-based predictive models is imperative in the future