In this study, we compared the effects of MK-801 and hippocampal lesions on re-training of Morris water maze place task in familiar and novel environments in rats. In Experiment 1, rats were pre-trained with the place task. After acquiring the task, rats were re-trained with the same task in a familiar environment following MK-801 injection, and were then trained with the same task in a novel environment following MK-801 injection. In the familiar environment, MK-801 had no effect, but in the novel environment performance was impaired. In Experiment 2, after the place task training, the hippocampus was lesioned, and rats were re-trained with the same task in the familiar environment then retrained again in the novel environment. Rats showed severe impairment in both environments. These two experiments suggest different functions for NMDA receptors and the hippocampus. The results of Experiment 1 showed that NMDA receptors are not required for utilizing spatial representations but they play an important role in the construction of spatial representations. The results of Experiment 2 show that the hippocampus is necessary for both the utilization of spatial representations already formed and the formation of new spatial representations.
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http://dx.doi.org/10.1515/revneuro.2006.17.1-2.163 | DOI Listing |
Neural Netw
January 2025
School of Computer Science and Technology, Huazhong University of Science and Technology, Wuhan, 430070, Hubei, China.
In the Imbalanced Multivariate Time Series Classification (ImMTSC) task, minority-class instances typically correspond to critical events, such as system faults in power grids or abnormal health occurrences in medical monitoring. Despite being rare and random, these events are highly significant. The dynamic spatial-temporal relationships between minority-class instances and other instances make them more prone to interference from neighboring instances during classification.
View Article and Find Full Text PDFEnviron Monit Assess
January 2025
Department of Computer Science and Engineering, Sathyabama Institute of Science and Technology, Shollinganallur, Chennai, India.
Municipal waste classification is significant for effective recycling and waste management processes that involve the classification of diverse municipal waste materials such as paper, glass, plastic, and organic matter using diverse techniques. Yet, this municipal waste classification process faces several challenges, such as high computational complexity, more time consumption, and high variability in the appearance of waste caused by variations in color, type, and degradation level, which makes an inaccurate waste classification process. To overcome these challenges, this research proposes a novel Channel and Spatial Attention-Based Multiblock Convolutional Network for accurately classifying municipal waste that utilizes a unique attention mechanism for enhancing feature learning and waste classification accuracy.
View Article and Find Full Text PDFMol Divers
January 2025
Key Laboratory for Macromolecular Science of Shaanxi Province, School of Chemistry and Chemical Engineering, Shaanxi Normal University, Xi'an, 710119, People's Republic of China.
Molecular Property Prediction (MPP) is a fundamental task in important research fields such as chemistry, materials, biology, and medicine, where traditional computational chemistry methods based on quantum mechanics often consume substantial time and computing power. In recent years, machine learning has been increasingly used in computational chemistry, in which graph neural networks have shown good performance in molecular property prediction tasks, but they have some limitations in terms of generalizability, interpretability, and certainty. In order to address the above challenges, a Multiscale Molecular Structural Neural Network (MMSNet) is proposed in this paper, which obtains rich multiscale molecular representations through the information fusion between bonded and non-bonded "message passing" structures at the atomic scale and spatial feature information "encoder-decoder" structures at the molecular scale; a multi-level attention mechanism is introduced on the basis of theoretical analysis of molecular mechanics in order to enhance the model's interpretability; the prediction results of MMSNet are used as label values and clustered in the molecular library by the K-NN (K-Nearest Neighbors) algorithm to reverse match the spatial structure of the molecules, and the certainty of the model is quantified by comparing virtual screening results across different K-values.
View Article and Find Full Text PDFJ Clin Med
January 2025
Department of Psychology, Università degli Studi della Campania "L. Vanvitelli", 81100 Caserta, Italy.
Mental representation of spatial information relies on egocentric (body-based) and allocentric (environment-based) frames of reference. Research showed that spatial memory deteriorates as Alzheimer's disease (AD) progresses and that allocentric spatial memory is among the earliest impaired areas. Most studies have been conducted in static situations despite the dynamic nature of real-world spatial processing.
View Article and Find Full Text PDFBiomolecules
January 2025
Institute of Chemical Biology, National Hellenic Research Foundation, 11635 Athens, Greece.
Protein-Protein Interaction (PPI) prediction plays a pivotal role in understanding cellular processes and uncovering molecular mechanisms underlying health and disease. Structure-based PPI prediction has emerged as a robust alternative to sequence-based methods, offering greater biological accuracy by integrating three-dimensional spatial and biochemical features. This work summarizes the recent advances in computational approaches leveraging protein structure information for PPI prediction, focusing on machine learning (ML) and deep learning (DL) techniques.
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