We developed a novel method for assessing spatial learning that is compatible with the requirements of electrophysiological recording of multiple single neurons. The behavioral task utilized a rectangular track with 8 reward boxes of which a subset contained available food (bait). Errors were scored whenever the rat investigated a non-baited box location (commission), failed to investigate a baited box location (omission), or hesitated in front of a non-baited box location (hesitation). Several controls encouraged the animal to solve the task through allocentric cues rather than through procedural strategies or simple local cue pairing. The learning curve for this task (3-5 d to criterion) was comparable to that of other spatial learning tasks when adequately motivated. The types of errors varied as the animal learned the task. Unlike other spatial learning tasks, the multi-box track allows many repeated samples of the same spatial coordinates within a short period of time to allow, for example, reliable determination of place fields while recording from hippocampal cells. Multiple trials per session also allow for high intensity training important for many learning assessments such as the timing and type of sleep involved in learning and memory.
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http://dx.doi.org/10.1016/s0165-0270(02)00233-9 | DOI Listing |
Front Neurosci
January 2025
HealthPartners Institute, Neuroscience Research, HealthPartners Neuroscience Center, Saint Paul, MN, United States.
Introduction: Intranasal (IN) deferoxamine (DFO) has emerged over the past decade as a promising therapeutic in preclinical experiments across neurodegenerative and neurovascular diseases. As an antioxidant iron chelator, its mechanisms are multimodal, involving the binding of brain iron and the consequent engagement of several pathways to counter pathogenesis across multiple diseases. We and other research groups have shown that IN DFO rescues cognitive impairment in several rodent models of Alzheimer Disease (AD).
View Article and Find Full Text PDFBioData Min
January 2025
School of Mathematics, Foshan University, Foshan, 528000, China.
Background: The accurate identification of molecular subtypes in digestive tract cancer (DTC) is crucial for making informed treatment decisions and selecting potential biomarkers. With the rapid advancement of artificial intelligence, various machine learning algorithms have been successfully applied in this field. However, the complexity and high dimensionality of the data features may lead to overlapping and ambiguous subtypes during clustering.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
Centre for Big Data Research in Health, University of New South Wales, Sydney, Australia.
Background: Due to the complexity and cost of preparing histopathological slides, deep learning-based methods have been developed to generate high-quality histological images. However, existing approaches primarily focus on spatial domain information, neglecting the periodic information in the frequency domain and the complementary relationship between the two domains. In this paper, we proposed a generative adversarial network that employs a cross-attention mechanism to extract and fuse features across spatial and frequency domains.
View Article and Find Full Text PDFNat Commun
January 2025
Department of Computational Biology and Medical Science, Graduate School of Frontier Sciences, the University of Tokyo, Tokyo, Japan.
Spatial transcriptomics is an essential application for investigating cellular structures and interactions and requires multimodal information to precisely study spatial domains. Here, we propose STAIG, a deep-learning model that integrates gene expression, spatial coordinates, and histological images using graph-contrastive learning coupled with high-performance feature extraction. STAIG can integrate tissue slices without prealignment and remove batch effects.
View Article and Find Full Text PDFPLoS One
January 2025
Department of Electrical Engineering, College of Engineering, Taif University, Taif, Saudi Arabia.
Modernizing power systems into smart grids has introduced numerous benefits, including enhanced efficiency, reliability, and integration of renewable energy sources. However, this advancement has also increased vulnerability to cyber threats, particularly False Data Injection Attacks (FDIAs). Traditional Intrusion Detection Systems (IDS) often fall short in identifying sophisticated FDIAs due to their reliance on predefined rules and signatures.
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