Metabolic and Lipidomic Assessment of Kidney Cells Exposed to Nephrotoxic Vancomycin Dosages

Vancomycin is a glycopeptide antibiotic used against multi-drug resistant gram-positive bacteria such as Staphylococcus aureus (MRSA). Although invaluable against resistant bacteria, vancomycin harbors adverse drug reactions including cytopenia, ototoxicity, as well as nephrotoxicity. Since nephrotoxicity is a rarely occurring side effect, its mechanism is incompletely understood. Only recently, the actual clinically relevant concentration the in kidneys of patients receiving vancomycin was investigated and were found to exceed plasma concentrations by far. We applied these clinically relevant vancomycin concentrations to murine and canine renal epithelial cell lines and assessed metabolic and lipidomic alterations by untargeted and targeted gas chromatography-mass spectrometry and liquid chromatography-mass spectrometry analyses. Despite marked differences in the lipidome, both cell lines increased anabolic glucose reactions, resulting in higher sorbitol and lactate levels. To the best of our knowledge, this is the first endometabolic profiling of kidney cells exposed to clinically relevant vancomycin concentrations. The presented study will provide a valuable dataset to nephrotoxicity researchers and might add to unveiling the nephrotoxic mechanism of vancomycin.     

Prediction of the strength distribution for unidirectional fibre reinforced composites

A statistical model is proposed which predicts the strength distribution of composites. It accounts for the following parameters: fibre strength distribution, fibre spatial distribution, fibre-matrix interface properties, matrix plasticity, load transfer at broken fibres and load profile of the specimen (bending or tension). The model is solved via Monte Carlo simulation for small specimens and via an analytical approach for large parts. Both techniques predict well the strength distribution of the composite. Aluminium matrix composites reinforced with unidirectional ceramic fibres show a large scatter in three point bending strength. This scatter is caused by the probability that a cluster of broken fibres of a critical size occurs at a given stress level. When the limiting stress is reached the critical cluster grows in an unstable way leading to catastrophic failure of the specimen. The results obtained by this model agree favourably with the experimental findings and indicate that strength prediction using other concepts, like fracture mechanics, global load sharing, a simple rule of mixtures or Weibull statistics, is unreliable for predicting the strength of large composites.     

Topology analysis of electric vehicles,with a focus on the traction battery

Over recent years, the number of battery electric vehicles (BEVs) has drastically increased due to new European Union (EU) regulations. These regulations force vehicle manufacturers to adjust their product range in order to fulfill the imposed carbon dioxide limits. Such an adjustment enforces the usage of battery electric vehicles. However, research into the optimal BEV architectures and topologies is still in progress. Therefore, the aim of this paper is an analysis of all the current electric vehicle topologies. From this analysis, the authors identify different basic battery shapes. Subsequently, these shapes are used to describe the impact of the battery on the passenger compartment. As an initial result of this analysis, the authors create a new denomination method, via which it is possible to cluster the battery topologies. In a second step, the collected data is clustered using the novel denomination method. Finally, this paper presents the benchmark topologies for the analyzed segments.

     

Impacts of electrification & automation of public bus transportation on sustainability—A case study in Singapore

Forschung im Ingenieurwesen - Electrification and automation are attracting interest from the public-transportation sector for their potential to improve energy efficiency, cost efficiency, and...     

TUM autonomous motorsport: An autonomous racing software for the Indy Autonomous Challenge

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 $270\text{km h}{}^{-1}$ and $28ms{}^{-2}$. 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.