Age-related hearing loss is linked to cognitive impairment, but the mechanisms that relate to these conditions remain unclear. Evidence shows that the activation of medial olivocochlear (MOC) neurons delays cochlear aging and hearing loss. Consequently, the loss of MOC function may be related to cognitive impairment. The α9/α10 nicotinic receptor is the main target of cholinergic synapses between the MOC neurons and cochlear outer hair cells. Here, we explored spatial learning and memory performance in middle-aged wild-type (WT) and α9-nAChR subunit knock-out (KO) mice using the Barnes maze and measured auditory brainstem response (ABR) thresholds and the number of cochlear hair cells as a proxy of cochlear aging. Our results show non-significant spatial learning differences between WT and KO mice, but KO mice had a trend of increased latency to enter the escape box and freezing time. To test a possible reactivity to the escape box, we evaluated the novelty-induced behavior using an open field and found a tendency towards more freezing time in KO mice. There were no differences in memory, ABR threshold, or the number of cochlear hair cells. We suggest that the lack of α9-nAChR subunit alters novelty-induced behavior, but not spatial learning in middle-aged mice, by a non-cochlear mechanism.
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http://dx.doi.org/10.3390/brainsci13050794 | DOI Listing |
PLoS One
January 2025
Department of Computer Science, University of Jaén, Jaén, Spain.
In the production sector, the usefulness of predictive systems as a tool for management and decision-making is well known. In the agricultural sector, a correct economic balance of the farm depends on making the right decisions. For this purpose, having information in advance on crop yields is an extraordinary help.
View Article and Find Full Text PDFPLoS One
January 2025
Division of Biological Sciences, US Fish and Wildlife Southwest Regional Office, Albuquerque, New Mexico, United States of America.
There is growing interest in using deep learning models to automate wildlife detection in aerial imaging surveys to increase efficiency, but human-generated annotations remain necessary for model training. However, even skilled observers may diverge in interpreting aerial imagery of complex environments, which may result in downstream instability of models. In this study, we present a framework for assessing annotation reliability by calculating agreement metrics for individual observers against an aggregated set of annotations generated by clustering multiple observers' observations and selecting the mode classification.
View Article and Find Full Text PDFMetab Brain Dis
January 2025
School of Natural Product Studies, Department of Pharmaceutical Technology, Jadavpur University, Kolkata, 700 032, India.
Alzheimer's disease is a complex neurodegenerative disease characterized by progressive decline in cognitive function and behaviour. Ginger is the rhizome of the plant Zingiber officinale Roscoe, has been an important ingredient of many Ayurveda formulations to treat neurological disorders. The present study aims to estimate the variation of 6-gingerol content in nine different ginger samples collected from Manipur, India, investigate the neuroprotective potential of the most potent ginger sample against scopolamine-induced cognitively impaired mice, and validate the therapeutic claim by molecular docking analysis.
View Article and Find Full Text PDFActa Neurochir (Wien)
January 2025
Department of Neurosurgery, College of Medicine, University of Michigan, Ann Arbor, MI, USA.
Background: Wall shear stress (WSS) plays a crucial role in the natural history of intracranial aneurysms (IA). However, spatial variations among WSS have rarely been utilized to correlate with IAs' natural history. This study aims to establish the feasibility of using spatial patterns of WSS data to predict IAs' rupture status (i.
View Article and Find Full Text PDFChaos
January 2025
Complex Systems Group, Department of Mathematics and Statistics, The University of Western Australia, Crawley, Western Australia 6009, Australia.
We propose a universal method based on deep reinforcement learning (specifically, soft actor-critic) to control the chimera state in the coupled oscillators. The policy for control is learned by maximizing the expectation of the cumulative reward in the reinforcement learning framework. With the aid of the local order parameter, we design a class of reward functions for controlling the chimera state, specifically confining the spatial position of coherent and incoherent domains to any desired lateral position of oscillators.
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