Weak hydrogen bonding motifs of ethylamino neurotransmitter radical cations in a hydrophobic environment: infrared spectra of tryptamine(+)-(N-2)(n) clusters (n <= 6)

Dieser Beitrag ist mit Zustimmung des Rechteinhabers aufgrund einer (DFG geförderten) Allianz- bzw. Nationallizenz frei zugänglich.This publication is with permission of the rights owner freely accessible due to an Alliance licence and a national licence (funded by the DFG, German Research Foundation) respectively.Size-selected clusters of the tryptamine cation with N2 ligands, TRA+–(N2)n with n = 1–6, are investigated by infrared photodissociation (IRPD) spectroscopy in the hydride stretch range and quantum chemical calculations at the ωB97X-D/cc-pVTZ level to characterize the microsolvation of this prototypical aromatic ethylamino neurotransmitter radical cation in a nonpolar solvent. Two types of structural isomers exhibiting different interaction motifs are identified for the TRA+–N2 dimer, namely the TRA+–N2(H) global minimum, in which N2 forms a linear hydrogen bond (H-bond) to the indolic NH group, and the less stable TRA+–N2(π) local minima, in which N2 binds to the aromatic π electron system of the indolic pyrrole ring. The IRPD spectrum of TRA+–(N2)2 is consistent with contributions from two structural H-bound isomers with similar calculated stabilization energies. The first isomer, denoted as TRA+–(N2)2(2H), exhibits an asymmetric bifurcated planar H-bonding motif, in which both N2 ligands are attached to the indolic NH group in the aromatic plane via H-bonding and charge–quadrupole interactions. The second isomer, denoted as TRA+–(N2)2(H/π), has a single and nearly linear H-bond of the first N2 ligand to the indolic NH group, whereas the second ligand is π-bonded to the pyrrole ring. The natural bond orbital analysis of TRA+–(N2)2 reveals that the total stability of these types of clusters is not only controlled by the local H-bond strengths between the indolic NH group and the N2 ligands but also by a subtle balance between various contributing intermolecular interactions, including local H-bonds, charge–quadrupole and induction interactions, dispersion, and exchange repulsion. The systematic spectral shifts as a function of cluster size suggest that the larger TRA+–(N2)n clusters with n = 3–6 are composed of the strongly bound TRA+–(N2)2(2H) core ion to which further N2 ligands are weakly attached to either the π electron system or the indolic NH proton by stacking and charge–quadrupole forces

Membrane Cholesterol Strongly Influences Confined Diffusion of Prestin

Prestin is the membrane motor protein that drives outer hair cell (OHC) electromotility, a process that is essential for mammalian hearing. Prestin function is sensitive to membrane cholesterol levels, and numerous studies have suggested that prestin localizes in cholesterol-rich membrane microdomains. Previously, fluorescence recovery after photobleaching experiments were performed in HEK cells expressing prestin-GFP after cholesterol manipulations, and revealed evidence of transient confinement. To further characterize this apparent confined diffusion of prestin, we conjugated prestin to a photostable fluorophore (tetramethylrhodamine) and performed single-molecule fluorescence microscopy. Using single-particle tracking, we determined the microscopic diffusion coefficient from the full time course of the mean-squared deviation. Our results indicate that prestin undergoes diffusion in confinement regions, and that depletion of membrane cholesterol increases confinement size and decreases confinement strength. By interpreting the data in terms of a mathematical model of hop-diffusion, we quantified these cholesterol-induced changes in membrane organization. A complementary analysis of the distribution of squared displacements confirmed that cholesterol depletion reduces prestin confinement. These findings support the hypothesis that prestin function is intimately linked to membrane organization, and further promote a regulatory role for cholesterol in OHC and auditory function

Modeling of GERDA Phase II data

The GERmanium Detector Array (GERDA) experiment at the Gran Sasso underground laboratory (LNGS) of INFN is searching for neutrinoless double-beta ($0\nu\beta\beta$) decay of $^{76}$Ge. The technological challenge of GERDA is to operate in a "background-free" regime in the region of interest (ROI) after analysis cuts for the full 100$\,$kg$\cdot$yr target exposure of the experiment. A careful modeling and decomposition of the full-range energy spectrum is essential to predict the shape and composition of events in the ROI around $Q_{\beta\beta}$ for the $0\nu\beta\beta$ search, to extract a precise measurement of the half-life of the double-beta decay mode with neutrinos ($2\nu\beta\beta$) and in order to identify the location of residual impurities. The latter will permit future experiments to build strategies in order to further lower the background and achieve even better sensitivities. In this article the background decomposition prior to analysis cuts is presented for GERDA Phase II. The background model fit yields a flat spectrum in the ROI with a background index (BI) of $16.04^{+0.78}_{-0.85} \cdot 10^{-3}\,$cts/(kg$\cdot$keV$\cdot$yr) for the enriched BEGe data set and $14.68^{+0.47}_{-0.52} \cdot 10^{-3}\,$cts/(kg$\cdot$keV$\cdot$yr) for the enriched coaxial data set. These values are similar to the one of Gerda Phase I despite a much larger number of detectors and hence radioactive hardware components

Modeling of GERDA Phase II data

The GERmanium Detector Array (Gerda) experiment at the Gran Sasso underground laboratory (LNGS) of INFN is searching for neutrinoless double-beta (0νββ) decay of 76Ge. The technological challenge of Gerda is to operate in a “background-free” regime in the region of interest (ROI) after analysis cuts for the full 100 kg·yr target exposure of the experiment. A careful modeling and decomposition of the full-range energy spectrum is essential to predict the shape and composition of events in the ROI around Qββ for the 0νββ search, to extract a precise measurement of the half-life of the double-beta decay mode with neutrinos (2νββ) and in order to identify the location of residual impurities. The latter will permit future experiments to build strategies in order to further lower the background and achieve even better sensitivities. In this article the background decomposition prior to analysis cuts is presented for Gerda Phase II. The background model fit yields a flat spectrum in the ROI with a background index (BI) of 16.04+0.78−0.85⋅10−3 cts/(keV·kg·yr) for the enriched BEGe data set and 14.68+0.47−0.52⋅10−3 cts/(keV·kg·yr) for the enriched coaxial data set. These values are similar to the one of Phase I despite a much larger number of detectors and hence radioactive hardware components

Potential of measured relative shifts in collision cross section values for biotransformation studies.

Ion mobility spectrometry-mass spectrometry (IMS-MS) separates gas phase ions due to differences in drift time from which reproducible and analyte-specific collision cross section (CCS) values can be derived. Internally conducted in vitro and in vivo metabolism (biotransformation) studies indicated repetitive shifts in measured CCS values (CCSmeas) between parent drugs and their metabolites. Hence, the purpose of the present article was (i) to investigate if such relative shifts in CCSmeas were biotransformation-specific and (ii) to highlight their potential benefits for biotransformation studies. First, mean CCSmeas values of 165 compounds were determined (up to n = 3) using a travelling wave IMS-MS device with nitrogen as drift gas (TWCCSN2, meas). Further comparison with their predicted values (TWCCSN2, pred, Waters CCSonDemand) resulted in a mean absolute error of 5.1%. Second, a reduced data set (n = 139) was utilized to create compound pairs (n = 86) covering eight common types of phase I and II biotransformations. Constant, discriminative, and almost non-overlapping relative shifts in mean TWCCSN2, meas were obtained for demethylation (- 6.5 ± 2.1 Å2), oxygenation (hydroxylation + 3.8 ± 1.4 Å2, N-oxidation + 3.4 ± 3.3 Å2), acetylation (+ 13.5 ± 1.9 Å2), sulfation (+ 17.9 ± 4.4 Å2), glucuronidation (N-linked: + 41.7 ± 7.5 Å2, O-linked: + 38.1 ± 8.9 Å2), and glutathione conjugation (+ 49.2 ± 13.2 Å2). Consequently, we propose to consider such relative shifts in TWCCSN2, meas (rather than absolute values) as well for metabolite assignment/confirmation complementing the conventional approach to associate changes in mass-to-charge (m/z) values between a parent drug and its metabolite(s). Moreover, the comparison of relative shifts in TWCCSN2, meas significantly simplifies the mapping of metabolites into metabolic pathways as demonstrated

A Study on the Human and the Automation in Automated Driving: Getting to Know Each Other

In recent years, advanced driver assistant systems (ADAS) and solutions for automated driving have been introduced by several automotive original equipment manufacturers (OEMs) and suppliers. Currently, these types of automation are designed for partially automated driving, but the step towards higher levels of automation can be expected to be made soon. One of the most commonly addressed use cases is driving on a highway such as the German Autobahn. In this paper, we propose an approach for adapting the automation’s behavior to the human’s driving preferences, providing a cognitive automation system with a machine-learning algorithm. This system has been implemented in a simulator for automated driving and has been used in a study addressing conditional automation. Within the presented experiment, typical situations for automated driving under varying conditions have been tested in the driving simulator. During cooperative human-machine driving, the automation can learn the human’s preferences regarding relevant driving states