We have shown that ginsenoside Rf (Rf) regulates voltagedependent Ca 2ϩ channels through pertussis toxin (PTX)-sensitive G proteins in rat sensory neurons. These results suggest that Rf can act through a novel G protein-linked receptor in the nervous system. In the present study, we further examined the effect of Rf on G protein-coupled inwardly rectifying K ϩ (GIRK) channels after coexpression with size-fractionated rat brain mRNA and GIRK1 and GIRK4 (GIRK1/4) channel cRNAs in Xenopus laevis oocytes using two-electrode voltage-clamp techniques. We found that Rf activated GIRK channel in a dose-dependent and reversible manner after coexpression with subfractions of rat brain mRNA and GIRK1/4 channel cRNAs. This Rf-evoked current was blocked by Ba 2ϩ , a potassium channel blocker. The size of rat brain mRNA responding to Rf was about 6 to 7 kilobases. However, Rf did not evoke GIRK current after injection with this subfraction of rat brain mRNA or GIRK1/4 channel cRNAs alone. Other ginsenosides, such as Rb 1 and Rg 1 , evoked only slight induction of GIRK currents after coexpression with the subfraction of rat brain mRNA and GIRK1/4 channel cRNAs. Acetylcholine and serotonin almost did not induce GIRK currents after coexpression with the subfraction of rat brain mRNA and GIRK1/4 channel cRNAs. Rf-evoked GIRK currents were not altered by PTX pretreatment but were suppressed by intracellularly injected guanosine-5Ј-(2-O-thio) diphosphate, a nonhydrolyzable GDP analog. These results indicate that Rf activates GIRK channel through an unidentified G protein-coupled receptor in rat brain and that this receptor can be cloned by the expression method demonstrated here.
Using traditional computational fluid dynamics and aeroacoustics methods, the accurate simulation of aeroacoustic sources requires high compute resources to resolve all necessary physical phenomena. In contrast, once trained, artificial neural networks such as deep encoder-decoder convolutional networks allow to predict aeroacoustics at lower cost and, depending on the quality of the employed network, also at high accuracy. The architecture for such a neural network is developed to predict the sound pressure level in a 2D square domain. It is trained by numerical results from up to 20,000 GPU-based lattice-Boltzmann simulations that include randomly distributed rectangular and circular objects, and monopole sources. Types of boundary conditions, the monopole locations, and cell distances for objects and monopoles serve as input to the network. Parameters are studied to tune the predictions and to increase their accuracy. The complexity of the setup is successively increased along three cases and the impact of the number of feature maps, the type of loss function, and the number of training data on the prediction accuracy is investigated. An optimal choice of the parameters leads to network-predicted results that are in good agreement with the simulated findings. This is corroborated by negligible differences of the sound pressure level between the simulated and the network-predicted results along characteristic lines and by small mean errors.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.