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Immunomodulatory functional foods and their molecular mechanisms
Kim Jae Hwan,Kim Da Hyun,Jo Seongin,Cho Min Je,Cho Ye Ryeong,Lee Yong Joon,Byun Sanguine 생화학분자생물학회 2022 Experimental and molecular medicine Vol.54 No.-
The immune system comprises a complex group of processes that provide defense against diverse pathogens. These defenses can be divided into innate and adaptive immunity, in which specific immune components converge to limit infections. In addition to genetic factors, aging, lifestyle, and environmental factors can influence immune function, potentially affecting the susceptibility of the host to disease-causing agents. Chemical compounds in certain foods have been shown to regulate signal transduction and cell phenotypes, ultimately impacting pathophysiology. Research has shown that the consumption of specific functional foods can stimulate the activity of immune cells, providing protection against cancer, viruses, and bacteria. Here, we review a number of functional foods reported to strengthen immunity, including ginseng, mushrooms, chlorella, and probiotics (Lactobacillus plantarum). We also discuss the molecular mechanisms involved in regulating the activity of various types of immune cells. Identifying immune-enhancing functional foods and understanding their mechanisms of action will support new approaches to maintain proper health and combat immunological diseases.
Multilayer Perceptron Model to Estimate Solar Radiation with a Solar Module
Kim, Joonyong,Rhee, Joongyong,Yang, Seunghwan,Lee, Chungu,Cho, Seongin,Kim, Youngjoo Korean Society for Agricultural Machinery 2018 바이오시스템공학 Vol.43 No.4
Purpose: The objective of this study was to develop a multilayer perceptron (MLP) model to estimate solar radiation using a solar module. Methods: Data for the short-circuit current of a solar module and other environmental parameters were collected for a year. For MLP learning, 14,400 combinations of input variables, learning rates, activation functions, numbers of layers, and numbers of neurons were trained. The best MLP model employed the batch backpropagation algorithm with all input variables and two hidden layers. Results: The root-mean-squared error (RMSE) of each learning cycle and its average over three repetitions were calculated. The average RMSE of the best artificial neural network model was $48.13W{\cdot}m^{-2}$. This result was better than that obtained for the regression model, for which the RMSE was $66.67W{\cdot}m^{-2}$. Conclusions: It is possible to utilize a solar module as a power source and a sensor to measure solar radiation for an agricultural sensor node.
Multilayer Perceptron Model to Estimate Solar Radiation with a Solar Module
( Joonyong Kim ),( Joongyong Rhee ),( Seunghwan Yang ),( Chungu Lee ),( Seongin Cho ),( Youngjoo Kim ) 한국농업기계학회 2018 바이오시스템공학 Vol.43 No.4
Purpose: The objective of this study was to develop a multilayer perceptron (MLP) model to estimate solar radiation using a solar module. Methods: Data for the short-circuit current of a solar module and other environmental parameters were collected for a year. For MLP learning, 14,400 combinations of input variables, learning rates, activation functions, numbers of layers, and numbers of neurons were trained. The best MLP model employed the batch backpropagation algorithm with all input variables and two hidden layers. Results: The root-mean-squared error (RMSE) of each learning cycle and its average over three repetitions were calculated. The average RMSE of the best artificial neural network model was 48.13 W·m-2. This result was better than that obtained for the regression model, for which the RMSE was 66.67 W·m-2. Conclusions: It is possible to utilize a solar module as a power source and a sensor to measure solar radiation for an agricultural sensor node.