Search

CD38 is an enzyme that catalyzes the formation of cyclic ADP ribose and nicotinic acid adenine dinucleotide phosphate, both of which are involved in the mobilization of Ca(2+) from intracellular stores. Recently, CD38 has been shown to regulate oxytocin release from hypothalamic neurons. Importantly, CD38 mutations are associated with autism spectrum disorders (ASD) and CD38 knockout (CD38(-/-)) mice display ASD-like behavioral phenotypes including deficient parental behavior and poor social recognition memory. Although ASD and learning deficits commonly co-occur, the role of CD38 in learning and memory has not been investigated. We report that CD38(-/-) mice show deficits in various learning and memory tasks such as the Morris water maze, contextual fear conditioning, and the object recognition test. However, either long-term potentiation or long-term depression is not impaired in the hippocampus of CD38(-/-) mice. Our results provide convincing evidence that CD38(-/-) mice show deficits in various learning and memory tasks including spatial and non-spatial memory tasks. Our data demonstrate that CD38 is critical for regulating hippocampus-dependent learning and memory without modulating synaptic plasticity.

Background: The aim of the study was to examine the differences of boredom, pain, and surprise. In addition to that, it was conducted to propose approaches for emotion recognition based on physiological signals., Methods: Three emotions, boredom, pain, and surprise, are induced through the presentation of emotional stimuli and electrocardiography (ECG), electrodermal activity (EDA), skin temperature (SKT), and photoplethysmography (PPG) as physiological signals are measured to collect a dataset from 217 participants when experiencing the emotions. Twenty-seven physiological features are extracted from the signals to classify the three emotions. The discriminant function analysis (DFA) as a statistical method, and five machine learning algorithms (linear discriminant analysis (LDA), classification and regression trees (CART), self-organizing map (SOM), Naïve Bayes algorithm, and support vector machine (SVM)) are used for classifying the emotions., Results: The result shows that the difference of physiological responses among emotions is significant in heart rate (HR), skin conductance level (SCL), skin conductance response (SCR), mean skin temperature (meanSKT), blood volume pulse (BVP), and pulse transit time (PTT), and the highest recognition accuracy of 84.7% is obtained by using DFA., Conclusions: This study demonstrates the differences of boredom, pain, and surprise and the best emotion recognizer for the classification of the three emotions by using physiological signals.