The effect of rasagiline on learning and memory in Lister-Hooded rats was investigated in this study. Two cognitive tests were used: a 24-h temporal deficit novel object recognition test and a modified water maze task. Rasagiline (0.3 and 1 mg/kg) was administered subcutaneously 15 min before the cognitive tests. In a novel object recognition test, rasagiline treatment enhanced object recognition memory. A small effect was observed with 0.3 mg/kg rasagiline; at 1 mg/kg, rasagiline-treated animals spent twice as much time exploring the novel object. On the water maze test, the use of an on-demand platform allowed adjustment of the difficulty of this spatial learning task. This enabled the detection of a small positive effect of rasagiline (1 mg/kg) on spatial learning, which was not observed in earlier reports. For the first time, our study has showed the procognitive effect of rasagiline in young healthy rats. On the basis of these findings, a monoamine oxidase-B inhibitor would seem to be a potential symptomatic treatment for cognitive impairments affecting patients with neurodegenerative disorders.
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http://dx.doi.org/10.1097/FBP.0b013e32833aec02 | DOI Listing |
Bioinformatics
January 2025
School of Artificial Intelligence, University of Chinese Academy of Sciences, Beijing, 100049, China.
Motivation: Ensuring connectivity and preventing fractures in tubular object segmentation are critical for downstream analyses. Despite advancements in deep neural networks (DNNs) that have significantly improved tubular object segmentation, existing methods still face limitations. They often rely heavily on precise annotations, hindering their scalability to large-scale unlabeled image datasets.
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January 2025
NUS-ISS, National University of Singapore, Singapore 119615, Singapore.
Recognizing the action of plastic bag taking from CCTV video footage represents a highly specialized and niche challenge within the broader domain of action video classification. To address this challenge, our paper introduces a novel benchmark video dataset specifically curated for the task of identifying the action of grabbing a plastic bag. Additionally, we propose and evaluate three distinct baseline approaches.
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January 2025
School of Mechanical and Electrical Engineering, China University of Mining and Technology (Beijing), Beijing 100083, China.
Unsupervised Domain Adaptation for Object Detection (UDA-OD) aims to adapt a model trained on a labeled source domain to an unlabeled target domain, addressing challenges posed by domain shifts. However, existing methods often face significant challenges, particularly in detecting small objects and over-relying on classification confidence for pseudo-label selection, which often leads to inaccurate bounding box localization. To address these issues, we propose a novel UDA-OD framework that leverages scale consistency (SC) and Temporal Ensemble Pseudo-Label Selection (TEPLS) to enhance cross-domain robustness and detection performance.
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December 2024
Department of Computer Science & Artificial Intelligence, Jeonbuk National University, Jeonju-si 54896, Republic of Korea.
Recently, computer vision methods have been widely applied to agricultural tasks, such as robotic harvesting. In particular, fruit harvesting robots often rely on object detection or segmentation to identify and localize target fruits. During the model selection process for object detection, the average precision (AP) score typically provides the de facto standard.
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December 2024
School of Coal Engineering, Shanxi Datong University, Datong 037000, China.
In the complex environment of fully mechanized mining faces, the current object detection algorithms face significant challenges in achieving optimal accuracy and real-time detection of mine personnel and safety helmets. This difficulty arises from factors such as uneven lighting conditions and equipment obstructions, which often lead to missed detections. Consequently, these limitations pose a considerable challenge to effective mine safety management.
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