The mechanisms which underlie defects in learning and memory are a major area of focus with the increasing incidence of Alzheimer's disease in the aging population. The complex genetically-controlled, age-, and environmentally-dependent onset and progression of the cognitive deficits and neuronal pathology call for better understanding of the fundamental biology of the nervous system function. In this study, we focus on nuclear receptor binding factor-2 (NRBF2) which modulates the transcriptional activities of retinoic acid receptor α and retinoid X receptor α, and the autophagic activities of the BECN1-VPS34 complex. Since both transcriptional regulation and autophagic function are important in supporting neuronal function, we hypothesized that NRBF2 deficiency may lead to cognitive deficits. To test this, we developed a new mouse model with nervous system-specific knockout of Nrbf2. In a series of behavioral assessment, we demonstrate that NRBF2 knockout in the nervous system results in profound learning and memory deficits. Interestingly, we did not find deficits in autophagic flux in primary neurons and the autophagy deficits were minimal in the brain. In contrast, RNAseq analyses have identified altered expression of genes that have been shown to impact neuronal function. The observation that NRBF2 is involved in learning and memory suggests a new mechanism regulating cognition involving the role of this protein in regulating networks related to the function of retinoic acid receptors, protein folding, and quality control.
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http://dx.doi.org/10.1038/s41374-020-0433-4 | DOI Listing |
Curr Nutr Rep
January 2025
School of Medical, Indigenous and Health Sciences, Faculty of Science, Medicine and Health, University of Wollongong NSW, Wollongong, 2522, Australia.
Purpose Of The Review: Clinical trials suggest that dietary anthocyanins may enhance cognitive function. This systematic literature review and meta-analysis aimed to identify the effect of anthocyanin on cognition and mood in adults.
Recent Findings: Using a random-effects model, Hedge's g scores were calculated to estimate the effect size.
Geroscience
January 2025
Department of Surgery, Immanuel Clinic Rüdersdorf, University Clinic of Brandenburg Medical School, Berlin, Germany.
Aging is a multi-organ disease, yet the traditional approach has been to study each organ in isolation. Such organ-specific studies have provided invaluable information regarding its pathomechanisms. However, an overall picture of the whole-body network (WBN) during aging is still incomplete.
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January 2025
Department of Mathematics, School of Advanced Sciences, VIT-AP University, Besides AP Secretariate, Amaravati, Andhra Pradesh, 522237, India.
Heavy hexagonal coding is a type of quantum error-correcting coding in which the edges and vertices of a low-degree graph are assigned auxiliary and physical qubits. While many topological code decoders have been presented, it is still difficult to construct the optimal decoder due to leakage errors and qubit collision. Therefore, this research proposes a Re-locative Guided Search optimized self-sparse attention-enabled convolutional Neural Network with Long Short-Term Memory (RlGS2-DCNTM) for performing effective error correction in quantum codes.
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January 2025
Department of Chemical Engineering, Al-Amarah University, Maysan, Iraq.
In this paper, the usage of a predictive surrogate model for the estimate of flow variables in the transient phase of coolant injection from the nose cone by combining the Long Short-Term Memory (LSTM) and Proper Orthogonal Decomposition (POD) technique. The velocity, pressure, and mass fraction of the counterflow jet is evaluated via this hybrid technique and the source of discrepancy of a predictive surrogate model with Full order model is explained in this study. The POD modes for the efficient prediction of the different flow variables are defined.
View Article and Find Full Text PDFCommun Biol
January 2025
Institute of Automation, Chinese Academy of Sciences, Beijing, China.
Whether working memory (WM) is encoded by persistent activity using attractors or by dynamic activity using transient trajectories has been debated for decades in both experimental and modeling studies, and a consensus has not been reached. Even though many recurrent neural networks (RNNs) have been proposed to simulate WM, most networks are designed to match respective experimental observations and show either transient or persistent activities. Those few which consider networks with both activity patterns have not attempted to directly compare their memory capabilities.
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