The present study established an effective procedure for studying spatial conditional discrimination learning in juvenile rats using a T-maze. Wire mesh located on the floor of the maze as well as a second, identical T-maze apparatus served as conditional cues which signaled whether a left or a right response would be rewarded. In Experiment 1, conditional discrimination was evident on Postnatal Day (PND) 30 when mesh+maze or maze-alone were the conditional cues, but not when mesh-alone was the cue. Experiment 2 confirmed that mesh-alone was sufficiently salient to support learning of a simple (nonconditional) discrimination. Its failure to serve as a conditional cue in Experiment 1 does not reflect its general ineffectiveness as a stimulus. Experiment 3 confirmed that the learning shown in Experiment 1 was indeed conditional in nature by comparing performance on conditional versus nonconditional versions of the task. Experiment 4 showed that PND19 and PND23 pups also were capable of performing the task when maze+mesh was the cue; however, the findings indicate that PND19 subjects do not use a conditional strategy to learn this task. The findings suggest postnatal ontogeny of conditional discrimination learning and underscore the importance of conditional cue salience, and of identifying task strategies, in developmental studies of conditional discrimination learning.
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Cogn Neurodyn
December 2025
Department of Electronics and Communication Engineering, Karpagam College of Engineering, Coimbatore, Tamil Nadu 641032 India.
Cross subject Electroencephalogram (EEG) emotion recognition refers to the process of utilizing electroencephalogram signals to recognize and classify emotions across different individuals. It tracks neural electrical patterns, and by analyzing these signals, it's possible to infer a person's emotional state. The objective of cross-subject recognition is to create models or algorithms that can reliably detect emotions in both the same person and several other people.
View Article and Find Full Text PDFNeural Netw
January 2025
School of Computer Engineering and Science, Shanghai University, Shanghai, 200444, China. Electronic address:
Conditional adversarial domain adaptation (CADA) is one of the most commonly used unsupervised domain adaptation (UDA) methods. CADA introduces multimodal information to the adversarial learning process to align the distributions of the labeled source domain and unlabeled target domain with mode match. However, CADA provides wrong multimodal information for challenging target features due to utilizing classifier predictions as the multimodal information, leading to distribution mismatch and less robust domain-invariant features.
View Article and Find Full Text PDFInterdiscip Sci
January 2025
College of Science, Dalian Jiaotong University, Dalian, 116028, China.
Accurate prediction of drug-drug interaction (DDI) is essential to improve clinical efficacy, avoid adverse effects of drug combination therapy, and enhance drug safety. Recently researchers have developed several computer-aided methods for DDI prediction. However, these methods lack the substructural features that are critical to drug interactions and are not effective in generalizing across domains and different distribution data.
View Article and Find Full Text PDFDev Med Child Neurol
December 2024
Department of Psychiatry and Behavioral Sciences, Stanford University, Stanford, CA, USA.
Aim: To replicate and extend previous psychometric findings for the Autism Symptom Dimensions Questionnaire (ASDQ).
Method: Using a cross-sectional design in two samples, including a total of 3366 children and adolescents (aged 2-17 years; 1399 with autism spectrum disorder) and a small case series, factor structure, measurement invariance, reliability, construct validity, screening and diagnostic efficiency, and detection of reliable change were examined for the ASDQ.
Results: Strong psychometric properties were observed, including replicable factor structure, strong measurement invariance, adequate-to-excellent scale and conditional reliability, strong convergent and discriminant validity, and good screening efficiency.
Heliyon
October 2024
School of Artificial Intelligence, Wenzhou Polytechnic, Wenzhou City, 325035, Zhejiang Province, China.
Generating high quality histopathology images like immunohistochemistry (IHC) stained images is essential for precise diagnosis and the advancement of computer-aided diagnostic (CAD) systems. Producing IHC images in laboratory is quite expensive and time consuming. Recently, some attempts have been made based on artificial intelligence techniques (particularly, deep learning) to generate IHC images.
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