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Aneurysmal subarachnoid hemorrhage (aSAH) is associated with early and delayed brain injury due to several underlying and interrelated processes, which include inflammation, oxidative stress, endothelial, and neuronal apoptosis. Treatment with melatonin, a cytoprotective neurohormone with anti-inflammatory, anti-oxidant and anti-apoptotic effects, has been shown to attenuate early brain injury (EBI) and to prevent delayed cerebral vasospasm in experimental aSAH models. Less is known about the role of endogenous melatonin for aSAH outcome and how its production is altered by the pathophysiological cascades initiated during EBI. In the present observational study, we analyzed changes in melatonin levels during the first three weeks after aSAH.
The integration of frequently changing, volatile product data from different manufacturers into a single catalog is a significant challenge for small and medium-sized e-commerce companies. They rely on timely integrating product data to present them aggregated in an online shop without knowing format specifications, concept understanding of manufacturers, and data quality. Furthermore, format, concepts, and data quality may change at any time. Consequently, integrating product catalogs into a single standardized catalog is often a laborious manual task. Current strategies to streamline or automate catalog integration use techniques based on machine learning, word vectorization, or semantic similarity. However, most approaches struggle with low-quality or real-world data. We propose Attribute Label Ranking (ALR) as a recommendation engine to simplify the integration process of previously unknown, proprietary tabular format into a standardized catalog for practitioners. We evaluate ALR by focusing on the impact of different neural network architectures, language features, and semantic similarity. Additionally, we consider metrics for industrial application and present the impact of ALR in production and its limitations.
Plant virus-like particles, and in particular, tobacco mosaic virus (TMV) particles, are increasingly being used in nano- and biotechnology as well as for biochemical sensing purposes as nanoscaffolds for the high-density immobilization of receptor molecules. The sensitive parameters of TMV-assisted biosensors depend, among others, on the density of adsorbed TMV particles on the sensor surface, which is affected by both the adsorption conditions and surface properties of the sensor. In this work, Ta₂O₅-gate field-effect capacitive sensors have been applied for the label-free electrical detection of TMV adsorption. The impact of the TMV concentration on both the sensor signal and the density of TMV particles adsorbed onto the Ta₂O₅-gate surface has been studied systematically by means of field-effect and scanning electron microscopy methods. In addition, the surface density of TMV particles loaded under different incubation times has been investigated. Finally, the field-effect sensor also demonstrates the label-free detection of penicillinase immobilization as model bioreceptor on TMV particles.
This book provides a compact introduction to the bootstrap method. In addition to classical results on point estimation and test theory, multivariate linear regression models and generalized linear models are covered in detail. Special attention is given to the use of bootstrap procedures to perform goodness-of-fit tests to validate model or distributional assumptions. In some cases, new methods are presented here for the first time.
The text is motivated by practical examples and the implementations of the corresponding algorithms are always given directly in R in a comprehensible form. Overall, R is given great importance throughout. Each chapter includes a section of exercises and, for the more mathematically inclined readers, concludes with rigorous proofs. The intended audience is graduate students who already have a prior knowledge of probability theory and mathematical statistics.