@article{ChewleEmmerlingWeber, author = {Chewle, Surahit and Emmerling, Franziska and Weber, Marcus}, title = {Effect of choice of solvent on crystallization pathway of Paracetamol: An experimental and theoretical case study}, series = {Crystals}, volume = {10}, journal = {Crystals}, number = {12}, doi = {10.3390/cryst10121107}, pages = {1107}, abstract = {The choice of solvents influences crystalline solid formed during the crystallization of active pharmaceutical ingredients (API). The underlying effects are not always well understood because of the complexity of the systems. Theoretical models are often insufficient to describe this phenomenon. In this study, the crystallization behavior of the model drug paracetamol in different solvents was studied based on experimental and molecular dynamics data. The crystallization process was followed in situ using time-resolved Raman spectroscopy. Molecular dynamics with simulated annealing algorithm was used for an atomistic understanding of the underlying processes. The experimental and theoretical data indicate that paracetamol molecules adopt a particular geometry in a given solvent predefining the crystallization of certain polymorphs.}, language = {en} } @article{RaySunkaraSchuetteetal.2020, author = {Ray, Sourav and Sunkara, Vikram and Sch{\"u}tte, Christof and Weber, Marcus}, title = {How to calculate pH-dependent binding rates for receptor-ligand systems based on thermodynamic simulations with different binding motifs}, series = {Molecular Simulation}, volume = {46}, journal = {Molecular Simulation}, number = {18}, publisher = {Taylor and Francis}, doi = {10.1080/08927022.2020.1839660}, pages = {1443 -- 1452}, year = {2020}, abstract = {Molecular simulations of ligand-receptor interactions are a computational challenge, especially when their association- ('on'-rate) and dissociation- ('off'-rate) mechanisms are working on vastly differing timescales. One way of tackling this multiscale problem is to compute the free-energy landscapes, where molecular dynamics (MD) trajectories are used to only produce certain statistical ensembles. The approach allows for deriving the transition rates between energy states as a function of the height of the activation-energy barriers. In this article, we derive the association rates of the opioids fentanyl and N-(3-fluoro-1-phenethylpiperidin-4-yl)-N-phenyl propionamide (NFEPP) in a μ-opioid receptor by combining the free-energy landscape approach with the square-root-approximation method (SQRA), which is a particularly robust version of Markov modelling. The novelty of this work is that we derive the association rates as a function of the pH level using only an ensemble of MD simulations. We also verify our MD-derived insights by reproducing the in vitro study performed by the Stein Lab.}, language = {en} } @article{SahuSzengelMukhopadhyayetal.2020, author = {Sahu, Manish and Szengel, Angelika and Mukhopadhyay, Anirban and Zachow, Stefan}, title = {Surgical phase recognition by learning phase transitions}, series = {Current Directions in Biomedical Engineering (CDBME)}, volume = {6}, journal = {Current Directions in Biomedical Engineering (CDBME)}, number = {1}, publisher = {De Gruyter}, doi = {https://doi.org/10.1515/cdbme-2020-0037}, pages = {20200037}, year = {2020}, abstract = {Automatic recognition of surgical phases is an important component for developing an intra-operative context-aware system. Prior work in this area focuses on recognizing short-term tool usage patterns within surgical phases. However, the difference between intra- and inter-phase tool usage patterns has not been investigated for automatic phase recognition. We developed a Recurrent Neural Network (RNN), in particular a state-preserving Long Short Term Memory (LSTM) architecture to utilize the long-term evolution of tool usage within complete surgical procedures. For fully automatic tool presence detection from surgical video frames, a Convolutional Neural Network (CNN) based architecture namely ZIBNet is employed. Our proposed approach outperformed EndoNet by 8.1\% on overall precision for phase detection tasks and 12.5\% on meanAP for tool recognition tasks.}, language = {en} } @article{ShiEvlyukhinReinhardtetal., author = {Shi, Liping and Evlyukhin, Andrey B. and Reinhardt, Carsten and Babushkin, Ihar and Zenin, Vladimir A. and Burger, Sven and Malureanu, Radu and Chichkov, Boris N. and Morgner, Uwe and Kovacev, Milutin}, title = {Progressive Self-Boosting Anapole-Enhanced Deep-Ultraviolet Third Harmonic During Few-Cycle Laser Radiation}, series = {ACS Photonics}, volume = {7}, journal = {ACS Photonics}, doi = {10.1021/acsphotonics.0c00753}, pages = {1655}, language = {en} }