• Journal of Microbiology and Biotechnology
• /
• v.27 no.4
• /
• pp.709-717
• /
• 2017

Mucosal tissues are the initial site through which most pathogens invade. As such, vaccines and adjuvants that modulate mucosal immune functions have emerged as important agents for disease prevention. Herein, we investigated the immunomodulatory mechanisms of the B subunit of Escherichia coli heat-labile enterotoxin type IIa ($LT-IIa-B_5$), a potent non-toxic mucosal adjuvant. Alternations in gene expression in response to $LT-IIa-B_5$ were identified using a genome-wide transcriptional microarray that focused on dendritic cells (DC), a type of cell that broadly orchestrates adaptive and innate immune responses. We found that $LT-IIa-B_5$ enhanced the homing capacity of DC into the lymph nodes and selectively regulated transcription of pro-inflammatory cytokines, chemokines, and cytokine receptors. These data are consistent with a model in which directional activation and differentiation of immune cells by $LT-IIa-B_5$ serve as a critical mechanism whereby this potent adjuvant amplifies mucosal immunity to co-administered antigens.

• Journal of Life Science
• /
• v.9 no.1
• /
• pp.58-63
• /
• 1999

Highly metastatic mouse T-lymphoma CS21 cells can grow in vitro when cocultured with CA12 lymph node stromal cells, but they undergo apoptotic cell death when separated from CA12 stromal cells. It has been found that cysteine and interleukin-7(IL-7) as antiapoptotic soluble factors that produced by CA12 stromal cells. In this study, we report that an ICE family protease is activated in CS21 cells when separated from CA12 stromal cells and cultured alone. Enzyme purification using an avidin affinity column revealed that the involved cysteine protease possessed caspase3-like death protease activity. In addition, when IL-7 was added to CS21 cell culture, the protease activity could not be detected during partial purification of the enzyme. Taken together, these results strongly suggest that the caspase3-like protease activation is suppressed by IL-7 as an antiapoptotic factor that leads to abrogation of apoptosis execution.

• Journal of The Korean Society of Agricultural Engineers
• /
• v.54 no.4
• /
• pp.65-71
• /
• 2012

The objective of this study was to estimate surface runoff from rice paddy plots using an artificial neural network (ANN). A field experiment with three treatment levels was conducted in the NICS saemangum experimental field located in Iksan, Korea. The ANN model with the optimal network architectures, named Paddy1901 with 19 input nodes, 1 hidden layer with 16 neurons nodes, and 1 output node, was adopted to predict surface runoff from the plots. The model consisted of 7 parameters of precipitation, irrigation rate, ponding depth, average temperature, relative humidity, wind speed, and solar radiation on the daily basis. Daily runoff, as the target simulation value, was computed using a water balance equation. The field data collected in 2011 were used for training and validation of the model. The model was trained based on the error back propagation algorithm with sigmoid activation function. Simulation results for the independent training and testing data series showed that the model can perform well in simulating surface runoff from the study plots. The developed model has a main advantage that there is no requirement for any prior assumptions regarding the processes involved. ANN model thus can be a good tool to predict surface runoff from rice paddy fields.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.16 no.2
• /
• pp.378-383
• /
• 2012

Nowadays, the study is going on develop USN(Ubiquitous Sensor Network) with diffusion of the internet and development of computer network technology. USN sensor nodes equipped with various types of sensors provide sensor information to each individual sensors. To do this there needs standardized data description language for allowing many people to use based on XML in web services environment. In this paper, USN sensor information required for application services in a standardized form to describe the sensor data representation language was designed. USN-based technology utilized in the field, and will be utilized for service activation.

• Journal of the Institute of Electronics Engineers of Korea SD
• /
• v.39 no.4
• /
• pp.1-9
• /
• 2002

The distribution characteristics of data retention time for DRAM was studied in connection with the probability distribution of the cell parameters. Using the cell parameters and the transient characteristics of cell node voltage, data retention time was investigated. The activation energy for dielectric layer growth on cell capacitance, the recombination trap energy for leakage current in the junction depletion region, and the sensitivity characteristics of sense amplifier were used as the random variables to perform the Monte Carlo simulation, and the probability distributions of cell parameters and distribution characteristics of cumulative failure bit on data retention time in DRAM cells were calculated. we found that the sensitivity characteristics of sense amplifier strongly affected on the tail bit distribution of data retention time.

• BMB Reports
• /
• v.49 no.5
• /
• pp.293-296
• /
• 2016

The immunoregulatory cytokine Interleukin 10 (IL-10) protein is produced by various cells during the course of inflammatory disorders. Mainly, it downregulates pro-inflammatory cytokines, antigen presentation, and helper T cell activation. In this study, we show that the ratio of IL-10-producing cells was significantly increased in lineage negative (i.e., not T, B, or leukocyte cell lineages) cells than in lineage positive cells in lymphoid and peripheral tissues. We further observed that IL-10-producing innate lymphoid cells (ILCs), here called firstly ILC10, were increased in number in oxazolone-induced contact hypersensitivity (CHS) mice. In detail, IL-10-producing lineage negative cells were elevated in the axillary, inguinal lymph node, and ear tissues of CHS mice. Notably, the cells expressed classical ILC marker proteins such as CD45, CD127, and Sca-1. Altogether, our findings suggest for the first time that ILC10s are present in various physiological settings and could be involved in numerous immune responses as regulatory cells.

• Proceedings of the KIEE Conference
• /
• 1998.07b
• /
• pp.740-743
• /
• 1998

This paper presents an optimization algorithm for a stable Dynamic Neural Network (DNN) using genetic algorithm. Optimized DNN is applied to a problem of controlling nonlinear dynamical systems. DNN is dynamic mapping and is better suited for dynamical systems than static forward neural network. The real time implementation is very important, and thus the neuro controller also needs to be designed such that it converges with a relatively small number of training cycles. SDNN has considerably fewer weights than DNN. The object of proposed algorithm is to the number of self dynamic neuron node and the gradient of activation functions are simultaneously optimized by genetic algorithms. To guarantee convergence, an analytic method based on the Lyapunov function is used to find a stable learning for the SDNN. The ability and effectiveness of identifying and controlling, a nonlinear dynamic system using the proposed optimized SDNN considering stability' is demonstrated by case studies.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
• /
• v.26 no.10
• /
• pp.89-94
• /
• 2012

This research is to apply the control of neuron networks for the real-time walking control of Multi-articulated robot. Multi-articulated robot is expressed with a complicated mathematical model on account of the mechanic, electric non-linearity which each articulation of mechanism has, and includes an unstable factor in time of walking control. If such a complex expression is included in control operation, it leads to the disadvantage that operation time is lengthened. Thus, if the rapid change of the load or the disturbance is given, it is difficult to fulfill the control of desired performance. This paper proposes a new mode to implement a neural network controller by installing a real object for controlling and an algorithm for this, which can replace the existing method of implementing a neural network controller by utilizing activation function at the output node. The proposed control algorithm generated control signs corresponding to the non-linearity of Multi-articulated robot, which could generate desired motion in real time.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
• /
• 2011.10a
• /
• pp.815-816
• /
• 2011

Nowadays, the Study is going on develop USN (Ubiquitous Sensor Network) with Diffusion of the Internet and development of computer network technology. USN sensor nodes equipped with various types of sensors provide sensor information to each individual sensors. To do this, there needs standardized data description language for allowing many people to use based on XML in web-service enviroment. In this paper USN sensor information required for application services in a standardized form to describe the sensor data representation language was designed. USN-based technology utilized in the field, and will be utilized for service activation.

• Journal of Ocean Engineering and Technology
• /
• v.35 no.5
• /
• pp.336-346
• /
• 2021

The prediction of wave conditions is crucial in the field of marine and ocean engineering. Hence, this study aims to predict the significant wave height through machine learning (ML), a soft computing method. The adopted metocean data, collected from 2012 to 2020, were obtained from the Korea Institute of Ocean Science and Technology. We adopted the feedforward neural network (FNN) and long-short term memory (LSTM) models to predict significant wave height. Input parameters for the input layer were selected by Pearson correlation coefficients. To obtain the optimized hyperparameter, we conducted a sensitivity study on the window size, node, layer, and activation function. Finally, the significant wave height was predicted using the FNN and LSTM models, by varying the three input parameters and three window sizes. Accordingly, FNN (W48) (i.e., FNN with window size 48) and LSTM (W48) (i.e., LSTM with window size 48) were superior outcomes. The most suitable model for predicting the significant wave height was FNN(W48) owing to its accuracy and calculation time. If the metocean data were further accumulated, the accuracy of the ML model would have improved, and it will be beneficial to predict added resistance by waves when conducting a sea trial test.