Functional consequences of hypothyroidism include impaired learning and memory and inability to produce long-term potentiation (LTP) in hippocampus. Olibanum has been used for variety of therapeutic purposes. In traditional medicine, oilbanum is used to enhance learning and memory. In the present study the effect of olibanum on memory deficit in hypothyroid rats was investigated. Male wistar rats were divided into four groups and treated for 180 days. Group 1 received tap drinking water while in group 2, 0.03% methimazol was added to drinking water. Group 3 and 4 were treated with 0.03% methimazole as well as 100 and 500 mg/kg olibanum respectively. The animals were tested in Morris water maze. The swimming speed was significantly lower and the distance and time latency were higher in group 2 compared with group 1. In groups 3 and 4 the swimming speed was significantly higher while, the length of the swim path and time latency were significantly lower in comparison with group 2. It is concluded that methimazole-induced hypothyroidism impairs learning and memory in adult rats which could be prevented by using olibanum.
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http://dx.doi.org/10.1007/s12272-010-0317-z | 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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