Mineralogische und geochemische Untersuchungen an tektonischen Störungszonen im Grundgebirge des Schwarzwaldes

[GE] In Gneisen und Anatexiten des Schwarzwaldes treten verbreitet Ruscheizonen mit grauen bis roten Kluftletten auf. Die Fraktionen <C 10 /um dieser Letten wurden geochemisch und auf ihren Mineralbestand untersucht und mit ihrem Nebengestein verglichen. Die roten Kluftletten unterscheiden sich von den grauen durch den höheren Oxydationsgrad des Eisens (als Hämatit) und durch geringere Gehalte an CaO und MgO. Die Veränderungen wurden vermutlich durch oxydierende, evtl. C02-haltige deszendente Wässer bewirkt. Sehr oft besteht ein Zusammenhang zwischen dem Vorkommen roter Ruscheizonen mit roten, anscheinend fossilen Böden. Hämatitisierung durch aszendente hydrothermale Lösungen ist in den hier untersuchten Fällen weniger wahrscheinlich[EN] Shear zones with grey to red fault gouges are frequent in the gneisses and anatexites of the Black Forest (Germany). The fractions <C 10 of those gouges were studied mineralogically and geochemically and compared with their country rocks. The red fault gouges are distinguished from the grey ones by a higher degree of oxidation of Fe and lesser contents of Mg and Ca. It is assumed, that these changes were brought about by oxidizing, CC>2- bearing waters of meteoric origin. A connection of the occurence of red fault gouges with fossil red soils is offen observed. Hydrothermal hematitization is less likely in the cases considered.Die Verfasser danken der Deutschen Forschungsgemeinschaft und dem Consejo Superior de Investigaciones Scientificas (Spanien) für die Förderung dieser Arbeit. Dank gilt ferner den Herren Dr. P. B u c k und Dr. V. K rä m er im Kristallographischen Institut der Universität Freiburg i. Br. für die Anfertigung von elektronenmikroskopischen Aufnahmen und thermogravimetrischen Diagrammen sowie den Kollegen am Mineralogischen Institut der Universität Freiburg i. Br. für mannigfache Hilfe bei der Durchführung der Analysen

Lightning-induced shock lamellae in quartz

Using transmission electron microscopy we show that planar deformation lamellae occur within quartz in the substrate of a rock fulgurite, i.e., a lightning-derived glass. These lamellae exist only in a narrow zone adjacent to the quartz/fulgurite boundary and are comparable to planar deformation features (“shock lamellae”) caused by hypervelocity impacts of extra-terrestrial objects. Our observations strongly suggest that the lamellae described here have been formed as a result of the fulgurite-producing lightning strike. This event must have generated a transient pressure pulse, whose magnitude, however, is uncertain at this stage. © 2015 GeoScience Worl

Application of transfer learning to predict drug-induced human in vivo gene expression changes using rat in vitro and in vivo data.

The liver is the primary site for the metabolism and detoxification of many compounds, including pharmaceuticals. Consequently, it is also the primary location for many adverse reactions. As the liver is not readily accessible for sampling in humans; rodent or cell line models are often used to evaluate potential toxic effects of a novel compound or candidate drug. However, relating the results of animal and in vitro studies to relevant clinical outcomes for the human in vivo situation still proves challenging. In this study, we incorporate principles of transfer learning within a deep artificial neural network allowing us to leverage the relative abundance of rat in vitro and in vivo exposure data from the Open TG-GATEs data set to train a model to predict the expected pattern of human in vivo gene expression following an exposure given measured human in vitro gene expression. We show that domain adaptation has been successfully achieved, with the rat and human in vitro data no longer being separable in the common latent space generated by the network. The network produces physiologically plausible predictions of human in vivo gene expression pattern following an exposure to a previously unseen compound. Moreover, we show the integration of the human in vitro data in the training of the domain adaptation network significantly improves the temporal accuracy of the predicted rat in vivo gene expression pattern following an exposure to a previously unseen compound. In this way, we demonstrate the improvements in prediction accuracy that can be achieved by combining data from distinct domains