Prior to puberty, male rats, but not female rats, prefer a striatum-based stimulus-response learning strategy rather than a hippocampus-based place strategy on a water maze task that can be solved using either strategy. Neurochemically, learning strategy preference has been linked to the ratio of cholinergic muscarinic receptor binding in the hippocampus relative to the striatum, with lower ratios displayed by males compared to females and by stimulus-response learners compared to place learners. Sex differences in a variety of different behaviors are established by the organizational influence of testosterone on brain development. Therefore, the current study investigated the potential organizational effects of neonatal testosterone on learning strategy preference and the hippocampus:striatum ratio of muscarinic receptor binding in prepubertal male and female rats. Similar to vehicle-treated control males, prepubertal females treated with testosterone propionate on the first two days of life preferred a stimulus-response strategy on a dual-solution water maze task. Conversely, vehicle-treated prepubertal females were more likely to use a place strategy. Consistent with previous findings, the hippocampus:striatum ratio of muscarinic receptor binding was lower in rats preferring a stimulus-response strategy compared to those using a place strategy and lower in control males compared to control females. However, the hippocampus:striatum ratio was not reversed by neonatal testosterone treatment of females as predicted. The current study is the first to show that sex differences in how a navigational task is learned prior to puberty is impacted by the presence of testosterone during vulnerable periods in brain development.
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http://dx.doi.org/10.1016/j.yhbeh.2019.02.005 | DOI Listing |
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December 2024
The School of Nursing, Fujian Medical University, No. 1 Xuefu North Road, Fuzhou, 350122, Fujian, China.
Diabetes Mellitus combined with Mild Cognitive Impairment (DM-MCI) is a high incidence disease among the elderly. Patients with DM-MCI have considerably higher risk of dementia, whose daily self-care and life management (i.e.
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December 2024
Department of Computer Science, Birzeit University, P.O. Box 14, Birzeit, West Bank, Palestine.
Accurate classification of logos is a challenging task in image recognition due to variations in logo size, orientation, and background complexity. Deep learning models, such as VGG16, have demonstrated promising results in handling such tasks. However, their performance is highly dependent on optimal hyperparameter settings, whose fine-tuning is both labor-intensive and time-consuming.
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December 2024
Department of Applied Mathematics, Faculty of Mathematical Science, Ferdowsi University of Mashhad, Mashhad, Iran.
This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML) techniques. The dataset, collected from 699 participants at the Gol-Gohar mine in Iran between 2016 and 2020, includes demographic, occupational, lifestyle, and medical information. After preprocessing and feature engineering, the Random Forest algorithm was identified as the best-performing model, achieving 99% accuracy for HTN prediction and 97% for CVD, outperforming other algorithms such as Logistic Regression and Support Vector Machines.
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December 2024
School of Artificial Intelligence and Computer Science, Jiangnan University, Wuxi, 214122, China.
The unknown boundary issue, between superior computational capability of deep neural networks (DNNs) and human cognitive ability, has becoming crucial and foundational theoretical problem in AI evolution. Undoubtedly, DNN-empowered AI capability is increasingly surpassing human intelligence in handling general intelligent tasks. However, the absence of DNN's interpretability and recurrent erratic behavior remain incontrovertible facts.
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December 2024
Oncology Bioinformatics, Genentech, South San Francisco, CA, USA.
Based on the success of cancer immunotherapy, personalized cancer vaccines have emerged as a leading oncology treatment. Antigen presentation on MHC class I (MHC-I) is crucial for the adaptive immune response to cancer cells, necessitating highly predictive computational methods to model this phenomenon. Here, we introduce HLApollo, a transformer-based model for peptide-MHC-I (pMHC-I) presentation prediction, leveraging the language of peptides, MHC, and source proteins.
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