FURTHERING PHARMACOLOGICAL AND PHYSIOLOGICAL ASSESSMENT OF THE GLUTAMATERGIC RECEPTORS AT THE DROSOPHILA NEUROMUSCULAR JUNCTION

Drosophila larval neuromuscular junctions (NMJs) serve as a model for synaptic physiology. The molecular sequence of the postsynaptic glutamate receptors has been described; however, the pharmacological profile has not been fully elucidated. Despite the postsynaptic molecular sequence used to classify the receptors as a kainate subtype, they do not respond pharmacologically as such. Kainate does not depolarize the muscle, but dampens evoked EPSP amplitudes. Quantal responses show a decreased amplitude and area under the voltage curve indicative of reduced postsynaptic receptor sensitivity to glutamate transmission. ATPA, a kainate receptor agonist, did not mimic kainate’s action. The metabotropic glutamate receptor agonist t-ACPD had no effect. Domoic acid, a quisqualate receptor antagonist, blocks the postsynaptic receptors without depolarizing the muscle, which supports the presence of quisqualate subtype receptors. The results suggest a direct postsynaptic action of kainate due to partial antagonist action on the quisqualate receptors. There does not appear to be presynaptic auto-regulation via a kainate receptor subtype or a metabotropic auto-receptor. A complete pharmacological profiling of the known receptor subtypes at this NMJ has not yet occurred; however, this study aids in furthering the ongoing investigations to provide a clearer picture of pharmokinetic profile and specificity of the receptor subtypes

Numerical Simulations for Ground Motion Amplifications on the Gentle Hill during the 2017 ML5.4 Pohang Earthquake

Department of Urban and Environmental Engineering (Urban Infrastructure Engineering)The November, 15, 2017, Pohang, South Korea, earthquake with a local magnitude (ML) of 5.4 caused diverse damage to buildings near the epicenter. This study focuses on the correlation between ground motion amplifications by topographic effects and damage pattern at the town of Gokgang-ri. Severe damage such as cracks and collapses occurred in a northern part of the town located on slopes, plateaus, or ridges facing the epicenter, whereas only minor damage occurred to buildings located on the opposite side of the slope. Northern part and southern part of the town have similar geological and soil condition. Two aftershocks were recorded at temporary seismic stations. A series of numerical simulations were conducted using the recorded ground motions and soil properties were measured in Gokgang-ri. It turned out that there are large ground motion amplifications at the slopes and ridges facing the epicenter. The ground motion amplificationss are influenced by incidence angle. In this study, amplifications for input motions with incidence angles of 10?? and 15?? are relatively lager than those for input motions with 0?? and 20??.clos

Dynamic Anchor Selection and Real-Time Pose Prediction for Ultra-wideband Tagless Gate

Ultra-wideband (UWB) is emerging as a promising solution that can realize proximity services, such as UWB tagless gate (UTG), thanks to centimeter-level localization accuracy based on two different ranging methods such as downlink time-difference of arrival (DL-TDoA) and double-sided two-way ranging (DS-TWR). The UTG is a UWB-based proximity service that provides a seamless gate pass system without requiring real-time mobile device (MD) tapping. The location of MD is calculated using DL-TDoA, and the MD communicates with the nearest UTG using DS-TWR to open the gate. Therefore, the knowledge about the exact location of MD is the main challenge of UTG, and hence we provide the solutions for both DL-TDoA and DS-TWR. In this paper, we propose dynamic anchor selection for extremely accurate DL-TDoA localization and pose prediction for DS-TWR, called DynaPose. The pose is defined as the actual location of MD on the human body, which affects the localization accuracy. DynaPose is based on line-of-sight (LOS) and non-LOS (NLOS) classification using deep learning for anchor selection and pose prediction. Deep learning models use the UWB channel impulse response and the inertial measurement unit embedded in the smartphone. DynaPose is implemented on Samsung Galaxy Note20 Ultra and Qorvo UWB board to show the feasibility and applicability. DynaPose achieves a LOS/NLOS classification accuracy of 0.984, 62% higher DL-TDoA localization accuracy, and ultimately detects four different poses with an accuracy of 0.961 in real-time.Comment: arXiv admin note: substantial text overlap with arXiv:2402.0839

Power-Efficient Indoor Localization Using Adaptive Channel-aware Ultra-wideband DL-TDOA

Among the various Ultra-wideband (UWB) ranging methods, the absence of uplink communication or centralized computation makes downlink time-difference-of-arrival (DL-TDOA) localization the most suitable for large-scale industrial deployments. However, temporary or permanent obstacles in the deployment region often lead to non-line-of-sight (NLOS) channel path and signal outage effects, which result in localization errors. Prior research has addressed this problem by increasing the ranging frequency, which leads to a heavy increase in the user device power consumption. It also does not contribute to any increase in localization accuracy under line-of-sight (LOS) conditions. In this paper, we propose and implement a novel low-power channel-aware dynamic frequency DL-TDOA ranging algorithm. It comprises NLOS probability predictor based on a convolutional neural network (CNN), a dynamic ranging frequency control module, and an IMU sensor-based ranging filter. Based on the conducted experiments, we show that the proposed algorithm achieves 50% higher accuracy in NLOS conditions while having 46% lower power consumption in LOS conditions compared to baseline methods from prior research

MIDMs: Matching Interleaved Diffusion Models for Exemplar-based Image Translation

We present a novel method for exemplar-based image translation, called matching interleaved diffusion models (MIDMs). Most existing methods for this task were formulated as GAN-based matching-then-generation framework. However, in this framework, matching errors induced by the difficulty of semantic matching across cross-domain, e.g., sketch and photo, can be easily propagated to the generation step, which in turn leads to degenerated results. Motivated by the recent success of diffusion models overcoming the shortcomings of GANs, we incorporate the diffusion models to overcome these limitations. Specifically, we formulate a diffusion-based matching-and-generation framework that interleaves cross-domain matching and diffusion steps in the latent space by iteratively feeding the intermediate warp into the noising process and denoising it to generate a translated image. In addition, to improve the reliability of the diffusion process, we design a confidence-aware process using cycle-consistency to consider only confident regions during translation. Experimental results show that our MIDMs generate more plausible images than state-of-the-art methods