The influence of estrogens on modifying cognition has been extensively studied, revealing that a wide array of factors can significantly impact cognition, including, but not limited to, subject age, estrogen exposure duration, administration mode, estrogen formulation, stress history, and progestogen presence. Less known is whether long-term, extended exposure to estrogens would benefit or otherwise impact cognition. The present study examined the effects of 17β-estradiol (E2) exposure for seven months, beginning in late adulthood and continuing into middle age, using a regimen of cyclic exposure (bi-monthly subcutaneous injection of 10 μg E2), or Cyclic+Tonic exposure (bi-monthly subcutaneous injection of 10 μg E2 + Silastic capsules of E2) in ovariectomized female Fischer-344-CDF rats. Subjects were tested on a battery of learning and memory tasks. All groups learned the water radial-arm maze (WRAM) and Morris water maze tasks in a similar fashion, regardless of hormone treatment regimen. In the asymptotic phase of the WRAM, rats administered a Cyclic+Tonic E2 regimen showed enhanced performance when working memory was taxed compared to Vehicle and Cyclic E2 groups. Assessment of spatial memory on object placement and object recognition was not possible due to insufficient exploration of objects; however, the Cyclic+Tonic group showed increased total time spent exploring all objects compared to Vehicle-treated animals. Overall, these data demonstrate that long-term Cyclic+Tonic E2 exposure can result in some long-term cognitive benefits, at least in the spatial working memory domain, in a surgically menopausal rat model.
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http://dx.doi.org/10.1016/j.yhbeh.2019.104656 | DOI Listing |
Sci Rep
January 2025
North Carolina School of Science and Mathematics, Durham, NC, 27705, USA.
Mobile Ad Hoc Networks (MANETs) are increasingly replacing conventional communication systems due to their decentralized and dynamic nature. However, their wireless architecture makes them highly vulnerable to flooding attacks, which can disrupt communication, deplete energy resources, and degrade network performance. This study presents a novel hybrid deep learning approach integrating Convolutional Neural Networks (CNN) with Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU) architectures to effectively detect and mitigate flooding attacks in MANETs.
View Article and Find Full Text PDFMed Phys
January 2025
National Cancer Center/National Clinical Research Center for Cancer/Cancer Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China.
Background: Respiratory motion during radiotherapy (RT) may reduce the therapeutic effect and increase the dose received by organs at risk. This can be addressed by real-time tracking, where respiration motion prediction is currently required to compensate for system latency in RT systems. Notably, for the prediction of future images in image-guided adaptive RT systems, the use of deep learning has been considered.
View Article and Find Full Text PDFSeizure
December 2024
University College Hospital, London, UK; UCL Queen Square Institute of Neurology: Department of Clinical and Experimental Epilepsy, London WC1N 3BG, UK. Electronic address:
Objective: Professional bodies recommend the use of performance validity tests (PVTs) to aid the interpretation of scores obtained in neuropsychological assessments, but base rates of failure differ according to neurological diagnosis and the associated impairments. This review summarises the PVT literature in people with epilepsy with the aim of establishing base rates of PVT failure and the factors associated with PVT performance in this population.
Methods: Ovid and PubMed databases were searched for studies reporting PVT test performance in people with epilepsy.
Comput Biol Med
January 2025
LMA Laboratory, University of Bejaia, Bejaia 06000, Algeria. Electronic address:
Social networks are increasingly taking over daily life, creating a volume of unsecured data and making it very difficult to capture safe data, especially in times of crisis. This study aims to use a Convolutional Neural Network (CNN)-Long Short-Term Memory (LSTM)-based hybrid model for health monitoring and health crisis forecasting. It consists of efficiently retrieving safe content from multiple social media sources.
View Article and Find Full Text PDFInt J Eat Disord
January 2025
School of Psychological Sciences, University of Haifa, Haifa, Israel.
Objective: Difficulty updating information in working memory has been proposed to underlie ruminative thinking in individuals with anorexia nervosa (AN). However, evidence regarding updating difficulties in AN remains inconclusive, particularly among adolescents. It has been proposed that exposure to negative emotion and disorder-salient stimuli may uniquely influence updating in AN.
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