• BMB Reports
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• v.42 no.8
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• pp.506-510
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• 2009

The Toll signalling pathway in invertebrates is responsible for defense against Gram-positive bacteria and fungi, leading to the expression of antimicrobial peptides via NF-$\kappa$B-like transcription factors. Gram-negative binding protein 3 (GNBP3) detects beta-1,3-glucan, a fungal cell wall component, and activates a three step serine protease cascade for activation of the Toll signalling pathway. Here, we showed that the recombinant N-terminal domain of Tenebrio molitor GNBP3 bound to beta-1,3-glucan, but did not activate down-stream serine protease cascade in vitro. Reversely, the N-terminal domain blocked GNBP3-mediated serine protease cascade activation in vitro and also inhibited beta-1,3-glucan-mediated antimicrobial peptide induction in Tenebrio molitor larvae. These results suggest that the N-terminal GNBP homology domain of GNBP3 functions as a beta-1,3-glucan binding domain and the C-terminal domain of GNBP3 may be required for the recruitment of immediate down-stream serine protease zymogen during Toll signalling pathway activation.

• International Journal of Internet, Broadcasting and Communication
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• v.11 no.4
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• pp.1-10
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• 2019

This Dietary cholesterol augments lipid profile and primes production and activation of platelets, leading to development of atherosclerosis which produce several detrimental effects on cardiovascular health. Ethnomedicine and Mediterranean diet are natural sources and cost effective modes against several ailments including cardiovascular diseases while fermented foods have gained interest due to their increased nutrient profile, enhanced bioavailability and efficacy. Garlic has been known to reduce cholesterol and inhibit platelet activation. We examined whether fermented garlic ameliorates effects of hypercholesterolemia and platelet functions in rats. Methodology: Male SD rats were fed with hypercholesterolemia diet and treated with spirulina, fermented and non-fermented preparations of garlic for one month. Platelet aggregation and granule secretion were assessed to evaluate platelet activation. Liver and kidney weights, lipid and enzymatic profile of serum and whole blood analysis was performed. Expressions of SREBP, ACAT-2 and HMG-CoA were assessed using RT-PCR while liver and adipose tissues were analyzed for histological changes. Both fermented and non-fermented garlic inhibited platelet aggregation and granule secretion while fermented garlic showed greater inhibitor tendency. Fermented garlic significantly reduced liver weight and triglycerides concentrations than non-fermented garlic. Similarly, fermented garlic greatly abrogated the detrimental effects of steatosis on liver and adipose tissues. Fermented garlic significantly improved lipid profile and modulated platelet functions, thereby inhibiting atherosclerosis and platelet related cardiovascular disorders.

• Journal of Korea Water Resources Association
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• v.54 no.spc1
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• pp.1143-1154
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• 2021

The selection of activation function has a great influence on the groundwater level prediction performance of artificial neural network (ANN) model. In this study, five activation functions were applied to ANN model for two groundwater level observation wells in the middle mountainous area of the Pyoseon watershed in Jeju Island. The results of the prediction of the groundwater level were compared and analyzed, and the optimal activation function was derived. In addition, the results of LSTM model, which is a widely used recurrent neural network model, were compared and analyzed with the results of the ANN models with each activation function. As a result, ELU and Leaky ReLU functions were derived as the optimal activation functions for the prediction of the groundwater level for observation well with relatively large fluctuations in groundwater level and for observation well with relatively small fluctuations, respectively. On the other hand, sigmoid function had the lowest predictive performance among the five activation functions for training period, and produced inappropriate results in peak and lowest groundwater level prediction. The ANN-ELU and ANN-Leaky ReLU models showed groundwater level prediction performance comparable to that of the LSTM model, and thus had sufficient potential for application. The methods and results of this study can be usefully used in other studies.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.3
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• pp.1724-1733
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• 2014

This paper presents a pattern recognition analysis of two spirals problem and optimization of Cascade Correlation learning algorithm using in combination with a non-monotone function as CosExp(cosine-modulated symmetric exponential function) and a monotone function as sigmoid function. In addition, the algorithm's optimization is attempted. By using genetic algorithms the optimization of the algorithm will attempt. In the first experiment, by using CosExp activation function for candidate neurons of the learning algorithm is analyzed the recognized pattern in input space of the two spirals problem. In the second experiment, CosExp function for output neurons is used. In the third experiment, the sigmoid activation functions with various parameters for candidate neurons in 8 pools and CosExp function for output neurons are used. In the fourth experiment, the parameters are composed of 8 pools and displacement of the sigmoid function to determine the value of the three parameters is obtained using genetic algorithms. The parameter values applied to the sigmoid activation functions for candidate neurons are used. To evaluate the performance of these algorithms, each step of the training input pattern classification shows the shape of the two spirals. In the optimizing process, the number of hidden neurons was reduced from 28 to15, and finally the learning algorithm with 12 hidden neurons was optimized.

• BMB Reports
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• v.48 no.12
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• pp.677-684
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• 2015

Cardiovascular function is regulated by the rhythmicity of circadian, infradian and ultradian clocks. Specific time scales of different cell types drive their functions: circadian gene regulation at hours scale, activation-inactivation cycles of ion channels at millisecond scales, the heart's beating rate at hundreds of millisecond scales, and low frequency autonomic signaling at cycles of tens of seconds. Heart rate and rhythm are modulated by a hierarchical clock system: autonomic signaling from the brain releases neurotransmitters from the vagus and sympathetic nerves to the heart's pacemaker cells and activate receptors on the cell. These receptors activating ultradian clock functions embedded within pacemaker cells include sarcoplasmic reticulum rhythmic spontaneous Ca²⁺ cycling, rhythmic ion channel current activation and inactivation, and rhythmic oscillatory mitochondria ATP production. Here we summarize the evidence that intrinsic pacemaker cell mechanisms are the end effector of the hierarchical brain-heart circadian clock system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.43 no.6
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• pp.991-1000
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• 1994

To control nonlinear systems adaptively, we improve learning speed of neural networks and present a novel control algorithm characterized by compensation of control inputs. In an error-backpropagation algorithm for tranining multilayer neural networks(MLNN's) the effect of the slope of activation functions on learning performance is investigated and the learning speed of neural networks is improved by auto-adjusting the slope of activation functions. The control system is composed of two MLNN's, one for control and the other for identification, with the weights initialized by off-line training. The control algoritm is modified by a control strategy which compensates the control error induced by the indentification error. Computer simulations show that the proposed control algorithm is efficient in controlling a nonlinear system with abruptly changing parameters.

• Journal of Life Science
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• v.18 no.6
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• pp.891-896
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• 2008

The function of mast cells as effector cells in allergy has been extensively studied. Mast cells activated through high affinity IgE-receptor ($Fc{\varepsilon}RI$) release diverse mediators, and lead to smooth muscle constriction, vasodilation, increase of vascular permeability, leukocyte recruitment and activation, mucus secretion, and tissue proliferation and remodeling. However, various other immunological and non-immunological signals can lead to the activation of mast cells. In resent years, mast cells have been identified to be involved in a complex range of immune functions. Mast cells can be important as key players in the regulation of innate as well as adapted immune responses, and may influence the development of allergy, autoimmune disorder and peripheral tolerance. This review summarizes the recent advances in the understanding of effector functions of mast cells in immune responses.

• Steel and Composite Structures
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• v.42 no.4
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• pp.459-475
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• 2022

In this paper a model for the prediction of the ultimate axial compressive capacity of square and rectangular Concrete Filled Steel Tubes, based on an Artificial Neural Network modeling procedure is presented. The model is trained and tested using an experimental database, compiled for this reason from the literature that amounts to 1193 specimens, including long, thin-walled and high-strength ones. The proposed model was selected as the optimum from a plethora of alternatives, employing different activation functions in the context of Artificial Neural Network technique. The performance of the developed model was compared against existing methodologies from design codes and from proposals in the literature, employing several performance indices. It was found that the proposed model achieves remarkably improved predictions of the ultimate axial load.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2022.11a
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• pp.121-122
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• 2022

In this study, compared and analyzed various Activation Functions to present a methodology for developing a natural intelligence-based prediction system. As a result of the analysis, ELU was the best with RMSE: 62.87, R²: 0.96, and the error rate was 4%. However, it is considered desirable to construct a prediction system by combining each algorithm model for optimization.

• Journal of Sensor Science and Technology
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• v.8 no.1
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• pp.45-52
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• 1999

In this raper, an enhanced learning method is proposed for improving the learning speed of the error back propagation learning algorithm. In order to cope with the premature saturation phenomenon at the initial learning stage, a variation scheme of active functions is introduced by using higher order functions, which does not need much increase of computation load. It naturally changes the learning rate of inter-connection weights to a large value as the derivative of sigmoid function abnormally decrease to a small value during the learning epoch. Also, we suggest the hybrid learning method incorporated the proposed method with the momentum training algorithm. Computer simulation results show that the proposed learning algorithm outperforms the conventional methods such as momentum and delta-bar-delta algorithms.