Background: The opioid antagonists naloxone/naltrexone are involved in improving learning and memory, but their cellular and molecular mechanisms remain unknown. We investigated the effect of naloxone/naltrexone on hippocampal α-amino-3-hydroxy-5-methyl-4-isoxazole propionic acid receptor (AMPAR) trafficking, a molecular substrate of learning and memory, as a probable mechanism for the antagonists activity.
Methods: To measure naloxone/naltrexone-regulated AMPAR trafficking, pHluorin-GluA1 imaging and biochemical analyses were performed on primary hippocampal neurons. To establish the in vivo role of GluA1-Serine 845 (S845) phosphorylation on the behavioral effect induced by inhibition of the endogenous μ-opioid receptor (MOR) by naltrexone, MOR knockout, and GluA1-S845A mutant (in which Ser(845) was mutated to Ala) mice were tested in a water maze after chronic naltrexone administration. Behavioral responses and GluA1 levels in the hippocampal postsynaptic density in wild-type and GluA1-S845A mutant mice were compared using western blot analysis.
Results: In vitro prolonged naloxone/naltrexone exposure significantly increased synaptic and extrasynaptic GluA1 membrane expression as well as GluA1-S845 phosphorylation. In the MOR knockout and GluA1-S845A mutant mice, naltrexone did not improve learning, which suggests that naltrexone acts via inhibition of endogenous MOR action and alteration of GluA1 phosphorylation. Naltrexone-treated wild-type mice had significantly increased phosphorylated GluA1-S845 and GluA1 levels in their hippocampal postsynaptic density on the third day of acquisition, which is the time when naltrexone significantly improved learning.
Conclusions: The beneficial effect of naltrexone on spatial learning and memory under normal conditions appears to be the result of increasing GluA1-S845 phosphorylation-dependent AMPAR trafficking. These results can be further explored in a mouse model of memory loss.
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http://dx.doi.org/10.1016/j.biopsych.2015.04.019 | DOI Listing |
Sensors (Basel)
December 2024
Department of Semiconductor Systems Engineering, Sejong University, Seoul 05006, Republic of Korea.
Recently, AI systems such as autonomous driving and smart homes have become integral to daily life. Intelligent multi-sensors, once limited to single data types, now process complex text and image data, demanding faster and more accurate processing. While integrating NPUs and sensors has improved processing speed and accuracy, challenges like low resource utilization and long memory latency remain.
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December 2024
ENSTA Bretagne, Lab-STICC, UMR CNRS 6285, 29806 Brest, France.
Satellite SAR (synthetic aperture radar) imagery offers global coverage and all-weather recording capabilities, making it valuable for applications like remote sensing and maritime surveillance. However, its use in machine learning-based automatic target classification faces challenges, including the limited availability of SAR target training samples and the inherent constraints of SAR images, which provide less detailed features compared to natural images. These issues hinder the effective training of convolutional neural networks (CNNs) and complicate the transfer learning process due to the distinct imaging mechanisms of SAR and natural images.
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December 2024
Instituto Universitario de Automática e Informática Industrial, Universitat Politècnica de València, C/Camino de Vera, s/n, 46022 Valencia, Spain.
A Mixed Reality (MR) application using an optical see-through headset was developed to assess short-term spatial memory. A study with 29 participants was conducted. Data from this study were compared to two previous studies using mobile Augmented Reality (AR) and Virtual Reality (VR) with headsets.
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December 2024
Department of Electrical and Information Engineering, Polytechnic University of Bari, 70126 Bari, Italy.
Intrusion Detection Systems (IDSs) are a crucial component of modern corporate firewalls. The ability of IDS to identify malicious traffic is a powerful tool to prevent potential attacks and keep a corporate network secure. In this context, Machine Learning (ML)-based methods have proven to be very effective for attack identification.
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December 2024
B-DAT and CICAEET, Nanjing University of Information Science and Technology, Nanjing 210044, China.
Existing learning-based remote sensing change detection (RSCD) commonly uses semantic-agnostic binary masks as supervision, which hinders their ability to distinguish between different semantic types of changes, resulting in a noisy change mask prediction. To address this issue, this paper presents a Language-guided semantic clustering framework that can effectively transfer the rich semantic information from the contrastive language-image pretraining (CLIP) model for RSCD, dubbed LSC-CD. The LSC-CD considers the strong zero-shot generalization of the CLIP, which makes it easy to transfer the semantic knowledge from the CLIP into the CD model under semantic-agnostic binary mask supervision.
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