The study of learning and memory using the chicken model has relied on three learning paradigms, passive avoidance learning, imprinting and the pebble floor task. Passive avoidance learning and imprinting have been used predominantly in very young chickens and cannot be used to access learning and memory in older chickens. We have established a new behavioural learning paradigm, Discriminative Taste Aversion Learning (DTAL), that can be used with both young and older animals. The task requires chickens to discriminate between food crumbs dyed either red or yellow with one colour being associated with the aversive tasting substance, methylanthranilate. Learning can be tested at various times after the training session by presenting chickens with the coloured food crumbs without an aversive taste. Both chickens tested at 5 and 15 days post-hatch learned to avoid the aversive crumbs. Furthermore, the protein synthesis inhibitor anisomycin (30 mM; 10 microl per hemisphere) injected into the intermediate medial hyperstriatum ventrale 15 min pre-training or 45 min post-training blocked long-term memory for the DTAL task when tested 24 h later. Memory for the task was unaffected by anisomycin injection 120 min post-training or in control animals injected with saline at similar times. The timing of the cellular processes of protein synthesis needed for consolidation of the DTAL appears to be similar to those described for the other behavioural paradigms in young chickens.
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http://dx.doi.org/10.1016/s1074-7427(02)00011-4 | DOI Listing |
J Med Internet Res
December 2024
Joint Laboratory of Artificial Intelligence for Critical Care Medicine, Department of Critical Care Medicine and Institutes for Systems Genetics, Frontiers Science Center for Disease-related Molecular Network, West China Hospital, Sichuan University, Chengdu, China.
Background: Large language models (LLMs) are increasingly integrated into medical education, with transformative potential for learning and assessment. However, their performance across diverse medical exams globally has remained underexplored.
Objective: This study aims to introduce MedExamLLM, a comprehensive platform designed to systematically evaluate the performance of LLMs on medical exams worldwide.
Chem Rev
December 2024
Ming Hsieh Department of Electrical and Computer Engineering, University of Southern California, Los Angeles, California 90089, United States.
Conventional artificial intelligence (AI) systems are facing bottlenecks due to the fundamental mismatches between AI models, which rely on parallel, in-memory, and dynamic computation, and traditional transistors, which have been designed and optimized for sequential logic operations. This calls for the development of novel computing units beyond transistors. Inspired by the high efficiency and adaptability of biological neural networks, computing systems mimicking the capabilities of biological structures are gaining more attention.
View Article and Find Full Text PDFAnal Chem
December 2024
Department of Chemistry, Institutes of Biomedical Sciences, Zhongshan Hospital, Fudan University, Shanghai 200433, China.
With the aging global population, the incidence of osteoporosis (OP) is increasing, putting more individuals at risk. Since postmenopausal osteoporosis (PMOP) often remains asymptomatic until a fracture occurs, making the early clinical diagnosis of PMOP particularly challenging. In this work, the AuNPs-anchored hierarchical porous ZrO microspheres (Au/HPZOMs) is designed to assist laser desorption/ionization mass spectrometry (LDI-MS) for the requirement of serum metabolic fingerprints of PMOP, postmenopausal osteopenia (PMON), and healthy controls (HC) and realize the early diagnosis and surveillance of PMOP.
View Article and Find Full Text PDFJ Comput Biol
December 2024
Electrical, Computer and Biomedical Engineering, Toronto Metropolitan University, Toronto, Canada.
Image-to-image translation has gained popularity in the medical field to transform images from one domain to another. Medical image synthesis via domain transformation is advantageous in its ability to augment an image dataset where images for a given class are limited. From the learning perspective, this process contributes to the data-oriented robustness of the model by inherently broadening the model's exposure to more diverse visual data and enabling it to learn more generalized features.
View Article and Find Full Text PDFMethods Mol Biol
January 2025
School of Biological Sciences, Victoria University of Wellington, Wellington, New Zealand.
The detached leaf assay is a valuable method for studying plant-pathogen interactions, enabling the assessment of pathogenicity, plant resistance, and treatment effects. In this protocol, we outline how to set up a Phytophthora detached leaf assay and use non-expert machine learning tools to increase the reliability and throughput of the image analysis. Utilizing ilastik for pixel classification and Python scripts for segmentation, manual correction, and temporal linking, the pipeline provides objective and quantitative data over time.
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