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Sci Rep
Department of Psychology, School of Social Sciences, Heriot-Watt University, Edinburgh, EH14 4AS, United Kingdom.
Published: May 2018
Newly encoded memories are labile and consolidate over time. The importance of sleep in memory consolidation has been well known for almost a decade. However, recent research has shown that awake quiescence, too, can support consolidation: people remember more new memories if they quietly rest after encoding than if they engage in a task. It is not yet known how exactly this rest-related consolidation benefits new memories, and whether it affects the fine detail of new memories. Using a sensitive picture recognition task, we show that awake quiescence aids the fine detail of new memories. Young adults were significantly better at discriminating recently encoded target pictures from similar lure pictures when the initial encoding of target pictures had been followed immediately by 10 minutes of awake quiescence than an unrelated perceptual task. This novel finding indicates that, in addition to influencing how much we remember, our behavioural state during wakeful consolidation determines, at least in part, the level of fine detail of our new memories. Thus, our results suggest that rest-related consolidation protects the fine detail of new memories, allowing us to retain detailed memories.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5931514 | PMC |
http://dx.doi.org/10.1038/s41598-018-25313-y | DOI Listing |
Comput Med Imaging Graph
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Academy for Engineering and Technology, Fudan University, Shanghai, 200433, China; Shanghai Key Laboratory of Medical Image Computing and Computer Assisted Intervention, Shanghai, 200032, China. Electronic address:
Artificial intelligence (AI) has shown great promise in analyzing nasal endoscopic images for disease detection. However, current AI systems require extensive expert-labeled data for each specific medical condition, limiting their applications. In this work, the challenge is addressed through two key innovations, the creation of the first large-scale pre-training dataset of nasal endoscopic images, and the development of a novel self-learning AI system specifically designed for nasal endoscopy, named NaMA-Mamba.
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Professor, Department of Pathology, Hinduhrudaysamrat Balasaheb Thackarey Medical College and Dr Rustom Narsi Cooper Municipal General Hospital, Mumbai, Maharashtra, India.
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View Article and Find Full Text PDFNat Protoc
March 2025
Interdisciplinary Institute for Neuroscience, University of Bordeaux, CNRS UMR 5297, Bordeaux, France.
In the mammalian brain, a large network of excitable and modulatory cells efficiently processes, analyzes and stores vast amounts of information. The brain's anatomy influences the flow of neural information between neurons and glia, from which all thought, emotion and action arises. Consequently, one of the grand challenges in neuroscience is to uncover the finest structural details of the brain in the context of its overall architecture.
View Article and Find Full Text PDFPlant Biotechnol (Tokyo)
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Research Institute for Bioscience Products & Fine Chemicals, Ajinomoto Co., Inc., 1-1, Suzuki-cho, Kawasaki-ku, Kawasaki, Kanagawa 210-8681, Japan.
Coronaridine, a monoterpenoid indole alkaloid, is present in and the related species . Recent exhaustive analysis revealed its presence in , though specific details remain unknown. We conducted a detailed analysis of coronaridine in , detecting it in seedlings post-germination up to 8 weeks after sowing, with peak abundance at 3-4 weeks.
View Article and Find Full Text PDFSci Rep
March 2025
School of Computer Science, Hubei University of Technology, Wuhan, 430068, China.
Transformer architectures have demonstrated remarkable performance in image super-resolution (SR). However, existing Transformer-based models generally suffer from insufficient local feature modeling, weak feature representation capabilities, and unreasonable loss function design, especially when reconstructing high-resolution (HR) images, where the restoration of fine details is poor. To address these issues, we propose a novel SR model, Parallel Attention Recursive Generalization Transformer (PARGT) in this study, which can effectively capture the fine-grained interactions between local features of the image and other regions, resulting in clearer and more coherent generated details.
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