The Transmembrane Domain C of AMPA Receptors is Critically Involved in Receptor Function and Modulation

Ionotropic glutamate receptors are major players in synaptic transmission and are critically involved in many cognitive events. Although receptors of different subfamilies serve different functions, they all show a conserved domain topology. For most of these domains, structure–function relationships have been established and are well understood. However, up to date the role of the transmembrane domain C in receptor function has been investigated only poorly. We have constructed a series of receptor chimeras and point mutants designed to shed light on the structural and/or functional importance of this domain. We here present evidence that the role of transmembrane domain C exceeds that of a mere scaffolding domain and that several amino acid residues located within the domain are crucial for receptor gating and desensitization. Furthermore, our data suggest that the domain may be involved in receptor interaction with transmembrane AMPA receptor regulatory proteins

Optical control of NMDA-receptors with a diffusible photoswitch

N-methyl-D-aspartate receptors (NMDARs) play a central role in synaptic plasticity, learning and memory, and are implicated in various neuronal disorders. We synthesized a diffusible photochromic glutamate analogue, azobenzene-triazole-glutamate (ATG), which is specific for NMDARs and functions as a photoswitchable agonist. ATG is inactive in its dark-adapted trans-isoform, but can be converted into its active cis-isoform using one-photon (near UV) or two-photon (740 nm) excitation. Irradiation with violet light photo-inactivates ATG within milliseconds, allowing agonist removal on the timescale of NMDAR deactivation. ATG is compatible with Ca2+ imaging and can be used to optically mimic synaptic coincidence detection protocols. Thus, ATG can be used like traditional caged glutamate compounds, but with the added advantages of NMDAR specificity, low antagonism of GABAR-mediated currents, and precise temporal control of agonist delivery

Automated Cardiac Realtime MRI Evaluation

We introduce our workflow to tackle automated evaluation of cardiac realtime MRI. The key approach is inspired by Active Learning and consists of N steps. First a limited amount of Training Data is annotated by staff with expert knowledge in the domain of pediatric cardiology. With this data we train a UNet using nnU-Net (Isensee, et. al). We then predict semantic labels with the trained model and use various techniques to judge the quality of each prediction. With that we are able to label each predicted segmentation with high or low quality. Predictions judged as low quality ones, are then presented to the domain experts and are manually corrected by them. Then, we can add those high quality labels to the training data set and start a new iteration by training the model. When the quality of predictions of an entire data set to be analyzed is high enough, we go on to synchronize the data set by assembling volumes of specific cardiac-respiration combinations based on the semantic segmentations. Finally, we are able to compute the stroke volume at different respiratory phases and compare them. The workflow explained above is deployed as a Plugin for the Software "3D Slicer"

Q/R site interactions with the M3 helix in GluK2 kainate receptor channels revealed by thermodynamic mutant cycles

RNA editing at the Q/R site near the apex of the pore loop of AMPA and kainate receptors controls a diverse array of channel properties, including ion selectivity and unitary conductance and susceptibility to inhibition by polyamines and cis-unsaturated fatty acids, as well as subunit assembly into tetramers and regulation by auxiliary subunits. How these different aspects of channel function are all determined by a single amino acid substitution remains poorly understood; however, several lines of evidence suggest that interaction between the pore helix (M2) and adjacent segments of the transmembrane inner (M3) and outer (M1) helices may be involved. In the present study, we have used double mutant cycle analysis to test for energetic coupling between the Q/R site residue and amino acid side chains along the M3 helix. Our results demonstrate interaction with several M3 locations and particularly strong coupling to substitution for L614 at the level of the central cavity. In this location, replacement with smaller side chains completely and selectively reverses the effect of fatty acids on gating of edited channels, converting strong inhibition of wild-type GluK2(R) to nearly 10-fold potentiation of GluK2(R) L614A

Expression and characterization of the bacterial mechanosensitive channel MscS in Xenopus laevis oocytes

We have successfully expressed and characterized mechanosensitive channel of small conductance (MscS) from Escherichia coli in oocytes of the African clawed frog, Xenopus laevis. MscS expressed in oocytes has the same single-channel conductance and voltage dependence as the channel in its native environment. Two hallmarks of MscS activity, the presence of conducting substates at high potentials and reversible adaptation to a sustained stimulus, are also exhibited by oocyte-expressed MscS. In addition to its ease of use, the oocyte system allows the user to work with relatively large patches, which could be an advantage for the visualization of membrane deformation. Furthermore, MscS can now be compared directly to its eukaryotic homologues or to other mechanosensitive channels that are not easily studied in E. coli

Reinforcement Learning zur Erstellung zustandsorientierter Putzstrategien in konzentrierenden solarthermischen Kraftwerken

Der Hauptteil dieser Arbeit beschäftigt sich zunächst mit den theoretischen Grundlagen von Reinforcement Learning und künstlichen neuronalen Netzen. Dabei wird der Schwerpunkt auf die im weiteren Verlauf genutzten Methoden gelegt. Im Anschluss wird die praktische Anwendung der Reinforcement Learning Methoden auf das Problem der Putzsteuerung in CSP Kraftwerken beschrieben. Dafür wird zuerst das Problem genauer spezifiziert und beschrieben wie der Algorithmus auf das Problem angewandt wird. Daraufhin wird die Implementierung dargestellt, um schließlich den Algorithmus in zwei Schritten auszuwerten. Im ersten Schritt wird der Algorithmus untersucht: Hier wird zum einen betrachtet wie robust der Algorithmus gegenüber der zufälligen Initialisierung ist, zum anderen wird untersucht wie gut die gefundene Lösung ist, indem die Ergebnisse in einem leichten Fall mit einer optimalen Lösung verglichen und in einem komplexeren Fall die Putzentscheidungen beobachtet und einer Plausibilitätsprüfung unterzogen werden. Im zweiten Abschnitt der Auswertung wird mit Hilfe des Algorithmus das zugrundeliegende Problem der Putzstrategien-Findung analysiert. Dafür wird zum einen untersucht, welchen Nutzen die Verfügbarkeit verschiedener Vorhersagehorizonte der Verschmutzungsraten für die Putzstrategie hat, zum anderen wird die Umwelt verändert: Es werden mehrere starke Verschmutzungsereignisse hinzugefügt oder der Wasserpreis erhöht. Hierbei soll beobachtet werden, wie die Strategie auf diese Veränderung angepasst wird und welche Schlüsse sich daraus für andere Standorte ergeben. Abschließend sollen die Schwierigkeiten der vorgestellten Methode diskutiert werden