Medial entorhinal grid cells display strikingly symmetric spatial firing patterns. The clarity of these patterns motivated the use of specific activity pattern shapes to classify entorhinal cell types. While this approach successfully revealed cells that encode boundaries, head direction, and running speed, it left a majority of cells unclassified, and its pre-defined nature may have missed unconventional, yet important coding properties. Here, we apply an unbiased statistical approach to search for cells that encode navigationally relevant variables. This approach successfully classifies the majority of entorhinal cells and reveals unsuspected entorhinal coding principles. First, we find a high degree of mixed selectivity and heterogeneity in superficial entorhinal neurons. Second, we discover a dynamic and remarkably adaptive code for space that enables entorhinal cells to rapidly encode navigational information accurately at high running speeds. Combined, these observations advance our current understanding of the mechanistic origins and functional implications of the entorhinal code for navigation. VIDEO ABSTRACT.
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http://dx.doi.org/10.1016/j.neuron.2017.03.025 | DOI Listing |
Medicine (Baltimore)
January 2025
Centro Universitario de Enfermería Cruz Roja, University of Seville, Seville, Spain.
Background: There is an increased prevalence of mental health problems in various population groups as a result of the COVID-19 pandemic and its consequences, especially regarding anxiety, stress, depression, fear, and sleep disturbances, require to be investigated longitudinally.
Objective: This study aimed to determine the impact that the COVID-19 pandemic had on the mental health of Nursing students, as well as to examine other associated factors such as anxiety, fear, sleep disturbances, and coping strategies.
Method: This systematic review and meta-analysis were designed following the PRISMA guidelines and were registered in PROSPERO with code CRD42024541904.
Sports Med
January 2025
Medical Services, Real Madrid, Madrid, Spain.
Background: Aging is associated with sustained low-grade inflammation, which has been linked to age-related diseases and mortality. Long-term exercise programs have been shown to be effective to for attenuating this process; however, subsequent detraining might negate some of these benefits. Master athletes, as a model of lifelong consistent exercise practice, have been suggested to present similar inflammatory profiles to untrained young adults.
View Article and Find Full Text PDFBrief Bioinform
November 2024
Agricultural Genomics Institute at Shenzhen, Chinese Academy of Agricultural Sciences, No. 97 Buxin Road, Dapeng New District, Shenzhen 518124, China.
Identifying the regulatory effects of noncoding variants presents a significant challenge. Recently, the accumulation of epigenomic profiling data in wheat has provided an opportunity to model the functional impacts of these variants. In this study, we introduce Language of Genome for Wheat (LOGOWheat), a deep learning-based tool designed to predict the regulatory effects of noncoding variants in wheat.
View Article and Find Full Text PDFPlant Genome
March 2025
School of Biological and Behavioural Sciences, Queen Mary University of London, London, E1 4NS, UK.
Repetitive DNA contributes significantly to plant genome size, adaptation, and evolution. However, little is understood about the transcription of repeats. This is addressed here in the plant green foxtail millet (Setaria viridis).
View Article and Find Full Text PDFPlant Methods
January 2025
School of Electronic and Information Engineering, Liaoning Technical University, Huludao, 125105, China.
Apricot trees, serving as critical agricultural resources, hold a significant role within the agricultural domain. Conventional methods for detecting pests and diseases in these trees are notably labor-intensive. Many conditions affecting apricot trees manifest distinct visual symptoms that are ideally suited for precise identification and classification via deep learning techniques.
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