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Phillipp Richter Gregor Schubert Anja Maria Schaible Prof. Dr. Levent Cavas Prof. Dr. Oliver Werz Prof. Dr. Georg Pohnert 《ChemMedChem》2014,9(8):1655-1659
Caulerpenyne (CYN) is a sesquiterpene from green algae with known inhibitory properties against soybean lipoxygenase. Here we introduce a detailed structure–activity study elucidating the inhibitory effects of CYN and a library of six synthetic CYN analogues on isolated human 5‐lipoxygenase (5‐LO) and cellular 5‐LO in polymorphonuclear leukocytes. Essential structural elements are identified and a structurally simplified inhibitor is introduced. The modes of 5‐LO inhibition by CYN and the synthetic inhibitors cannot be assigned to any of the known categories of lipoxygenase inhibitors. These compounds clearly interfere directly with 5‐LO and represent rather small and flexible molecules, with unique structures among 5‐LO inhibitors identified thus far. 相似文献
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Betül Şen Buse Demirkan Menderes Levent Aysun Şavk Fatih Şen 《International Journal of Hydrogen Energy》2021,46(51):26234-26235
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Johannes Betz Tobias Betz Felix Fent Maximilian Geisslinger Alexander Heilmeier Leonhard Hermansdorfer Thomas Herrmann Sebastian Huch Phillip Karle Markus Lienkamp Boris Lohmann Felix Nobis Levent Ögretmen Matthias Rowold Florian Sauerbeck Tim Stahl Rainer Trauth Frederik Werner Alexander Wischnewski 《野外机器人技术杂志》2023,40(4):783-809
For decades, motorsport has been an incubator for innovations in the automotive sector and brought forth systems, like, disk brakes or rearview mirrors. Autonomous racing series such as Roborace, F1Tenth, or the Indy Autonomous Challenge (IAC) are envisioned as playing a similar role within the autonomous vehicle sector, serving as a proving ground for new technology at the limits of the autonomous systems capabilities. This paper outlines the software stack and approach of the TUM Autonomous Motorsport team for their participation in the IAC, which holds two competitions: A single-vehicle competition on the Indianapolis Motor Speedway and a passing competition at the Las Vegas Motor Speedway. Nine university teams used an identical vehicle platform: A modified Indy Lights chassis equipped with sensors, a computing platform, and actuators. All the teams developed different algorithms for object detection, localization, planning, prediction, and control of the race cars. The team from Technical University of Munich (TUM) placed first in Indianapolis and secured second place in Las Vegas. During the final of the passing competition, the TUM team reached speeds and accelerations close to the limit of the vehicle, peaking at around and . This paper will present details of the vehicle hardware platform, the developed algorithms, and the workflow to test and enhance the software applied during the 2-year project. We derive deep insights into the autonomous vehicle's behavior at high speed and high acceleration by providing a detailed competition analysis. On the basis of this, we deduce a list of lessons learned and provide insights on promising areas of future work based on the real-world evaluation of the displayed concepts. 相似文献
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