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Background: Few studies have explored the relationship between macronutrient intake and sleep outcomes using daily data from mobile apps.

Objective: This cross-sectional study aimed to examine the associations between macronutrients, dietary components, and sleep parameters, considering their interdependencies.

Methods: We analyzed data from 4825 users of the Pokémon Sleep and Asken smartphone apps, each used for at least 7 days to record objective sleep parameters and dietary components, respectively.

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The hippocampal dentate gyrus (DG) is thought to orthogonalize inputs from the entorhinal cortex (pattern separation) and relay this information to the CA3 region. In turn, attractor dynamics in CA3 perform a pattern completion or error correction operation before sending its output to CA1. In a mouse model of congenital hypoplasia of the DG, a deficiency in the (Wls) gene, specifically in cells expressing , which targets neuronal progenitors, led to an almost total absence of dentate granule cells and modestly impaired performance in spatial tasks.

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The time course and organization of hippocampal replay.

Science

January 2025

Department of Neuroscience, University of California, Berkeley, Berkeley, CA, USA.

The mechanisms by which the brain replays neural activity sequences remain unknown. Recording from large ensembles of hippocampal place cells in freely behaving rats, we observed that replay content is strictly organized over multiple timescales and governed by self-avoidance. After movement cessation, replays avoided the animal's previous path for 3 seconds.

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Social animals live in groups and interact volitionally in complex ways. However, little is known about neural responses under such natural conditions. Here, we investigated hippocampal CA1 neurons in a mixed-sex group of five to 10 freely behaving wild Egyptian fruit bats that lived continuously in a laboratory-based cave and formed a stable social network.

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Selection and optimization of drive nodes in drive-response networks.

Chaos

January 2025

College of Computer Science and Software Engineering, Shenzhen University, Guangdong 518060, China.

This paper considers the selection and optimization of drive nodes based on the controllability of multilayer networks. The intra-layer network topologies are arbitrary, and the node dynamics are linear time-invariant dynamical systems. The study focuses on the number and selection of drive nodes in a special class of drive-response networks.

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