08 Fakultät Mathematik und Physik

Permanent URI for this collectionhttps://elib.uni-stuttgart.de/handle/11682/9

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    Optimizing NV magnetometry for magnetoneurography and magnetomyography applications
    (2023) Zhang, Chen; Zhang, Jixing; Widmann, Matthias; Benke, Magnus; Kübler, Michael; Dasari, Durga; Klotz, Thomas; Gizzi, Leonardo; Röhrle, Oliver; Brenner, Philipp; Wrachtrup, Jörg
    Magnetometers based on color centers in diamond are setting new frontiers for sensing capabilities due to their combined extraordinary performances in sensitivity, bandwidth, dynamic range, and spatial resolution, with stable operability in a wide range of conditions ranging from room to low temperatures. This has allowed for its wide range of applications, from biology and chemical studies to industrial applications. Among the many, sensing of bio-magnetic fields from muscular and neurophysiology has been one of the most attractive applications for NV magnetometry due to its compact and proximal sensing capability. Although SQUID magnetometers and optically pumped magnetometers (OPM) have made huge progress in Magnetomyography (MMG) and Magnetoneurography (MNG), exploring the same with NV magnetometry is scant at best. Given the room temperature operability and gradiometric applications of the NV magnetometer, it could be highly sensitive in the pT/Hz-range even without magnetic shielding, bringing it close to industrial applications. The presented work here elaborates on the performance metrics of these magnetometers to the state-of-the-art techniques by analyzing the sensitivity, dynamic range, and bandwidth, and discusses the potential benefits of using NV magnetometers for MMG and MNG applications.
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    Solubilization of inclusion bodies : insights from explainable machine learning approaches
    (2023) Walther, Cornelia; Martinetz, Michael C.; Friedrich, Anja; Tscheließnig, Anne-Luise; Voigtmann, Martin; Jung, Alexander; Brocard, Cécile; Bluhmki, Erich; Smiatek, Jens
    We present explainable machine learning approaches for gaining deeper insights into the solubilization processes of inclusion bodies. The machine learning model with the highest prediction accuracy for the protein yield is further evaluated with regard to Shapley additive explanation (SHAP) values in terms of feature importance studies. Our results highlight an inverse fractional relationship between the protein yield and total protein concentration. Further correlations can also be observed for the dominant influences of the urea concentration and the underlying pH values. All findings are used to develop an analytical expression that is in reasonable agreement with experimental data. The resulting master curve highlights the benefits of explainable machine learning approaches for the detailed understanding of certain biopharmaceutical manufacturing steps.
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    Influence of bacterial swimming and hydrodynamics on attachment of phages
    (2024) Lohrmann, Christoph; Holm, Christian; Datta, Sujit S.
    Bacteriophages (“phages”) are viruses that infect bacteria. Since they do not actively self-propel, phages rely on thermal diffusion to find target cells - but can also be advected by fluid flows, such as those generated by motile bacteria themselves in bulk fluids. How does the flow field generated by a swimming bacterium influence how it encounters phages? Here, we address this question using coupled molecular dynamics and lattice Boltzmann simulations of flagellated bacteria swimming through a bulk fluid containing uniformly-dispersed phages. We find that while swimming increases the rate at which phages attach to both the cell body and flagellar propeller, hydrodynamic interactions strongly suppress this increase at the cell body, but conversely enhance this increase at the flagellar bundle. Our results highlight the pivotal influence of hydrodynamics on the interactions between bacteria and phages, as well as other diffusible species, in microbial environments.