Mechanisms of hippocampus-related memory formation are time-of-day-dependent. While the circadian system and clock genes are related to timing of hippocampal mnemonic processes (acquisition, consolidation, and retrieval of long-term memory [LTM]) and long-term potentiation (LTP), little is known about temporal gating mechanisms. Here, the role of the neurohormone melatonin as a circadian time cue for hippocampal signaling and memory formation was investigated in C3H/He wildtype (WT) and melatonin receptor-knockout ( ) mice. Immunohistochemical and immunoblot analyses revealed the presence of melatonin receptors on mouse hippocampal neurons. Temporal patterns of time-of-day-dependent clock gene protein levels were profoundly altered in mice compared to WT animals. On the behavioral level, WT mice displayed better spatial learning efficiency during daytime as compared to nighttime. In contrast, high error scores were observed in mice during both, daytime and nighttime acquisition. Day-night difference in LTP, as observed in WT mice, was absent in mice and in WT animals, in which the sympathetic innervation of the pineal gland was surgically removed to erase rhythmic melatonin synthesis. In addition, treatment of melatonin-deficient C57BL/6 mice with melatonin at nighttime significantly improved their working memory performance at daytime. These results illustrate that melatonin shapes time-of-day-dependent learning efficiency in parallel to consolidating expression patterns of clock genes in the mouse hippocampus. Our data suggest that melatonin imprints a time cue on mouse hippocampal signaling and gene expression to foster better learning during daytime.
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http://dx.doi.org/10.1111/jpi.12553 | DOI Listing |
Front Plant Sci
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Information and Communication Engineering, Yeungnam University, Gyeongsan, Republic of Korea.
Smart farming is a hot research area for experts globally to fulfill the soaring demand for food. Automated approaches, based on convolutional neural networks (CNN), for crop disease identification, weed classification, and monitoring have substantially helped increase crop yields. Plant diseases and pests are posing a significant danger to the health of plants, thus causing a reduction in crop production.
View Article and Find Full Text PDFAdv Model Simul Eng Sci
January 2025
Department of Mechanical and Process Engineering, Institute for Mechanical Systems, ETH Zürich, Zürich, 8092 Switzerland.
We extend (EUCLID Efficient Unsupervised Constitutive Law Identification and Discovery)-a data-driven framework for automated material model discovery-to pressure-sensitive plasticity models, encompassing arbitrarily shaped yield surfaces with convexity constraints and non-associated flow rules. The method only requires full-field displacement and boundary force data from one single experiment and delivers constitutive laws as interpretable mathematical expressions. We construct a material model library for pressure-sensitive plasticity models with non-associated flow rules in four steps: (1) a Fourier series describes an arbitrary yield surface shape in the deviatoric stress plane; (2) a pressure-sensitive term in the yield function defines the shape of the shear failure surface and determines plastic deformation under tension; (3) a compression cap term determines plastic deformation under compression; (4) a non-associated flow rule may be adopted to avoid the excessive dilatancy induced by plastic deformations.
View Article and Find Full Text PDFFront Neurorobot
January 2025
Software Engineering College, Zhengzhou University of Light Industry, Zhengzhou, China.
Introduction: Tracking the hidden states of dynamic systems is a fundamental task in signal processing. Recursive Kalman Filters (KF) are widely regarded as an efficient solution for linear and Gaussian systems, offering low computational complexity. However, real-world applications often involve non-linear dynamics, making it challenging for traditional Kalman Filters to achieve accurate state estimation.
View Article and Find Full Text PDFNPJ Comput Mater
January 2025
School of Mechanical, Aerospace, and Manufacturing Engineering, University of Connecticut, Storrs, CT USA.
Machine learning has advanced the rapid prediction of inorganic materials properties, yet data scarcity for specific properties and capturing thermodynamic stability remains challenging. We propose a framework utilizing a Graph Neural Network with composition-based and crystal structure-based architectures, combined with a transfer learning scheme. This approach accurately predicts energy-related properties (e.
View Article and Find Full Text PDFDigit Health
January 2025
School of Computer Science, The University of Sydney, Sydney, NSW, Australia.
Objective: Machine learning (ML) has enabled healthcare discoveries by facilitating efficient modeling, such as for cancer screening. Unlike clinical trials, real-world data used in ML are often gathered for multiple purposes, leading to bias and missing information for a specific classification task. This challenge is especially pronounced in healthcare because of stringent ethical considerations and resource constraints.
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