• The Korean Journal of Physiology and Pharmacology
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• v.16 no.3
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• pp.199-204
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• 2012

We evaluated the role of Tat-mediated p66shc transduction on the activation of endothelial nitric oxide synthase in cultured mouse endothelial cells. To construct the Tat-p66shc fusion protein, human full length p66shc cDNA was fused with the Tat-protein transduction domain. Transduction of TAT-p66shc showed a concentration- and time-dependent manner in endothelial cells. Tat-mediated p66shc transduction showed increased hydrogen peroxide and superoxide production, compared with Tat-p66shc (S/A), serine 36 residue mutant of p66shc. Tat-mediated p66shc transduction decreased endothelial nitric oxide synthase phosphorylation in endothelial cells. Furthermore, Tat-mediated p66shc transduction augmented TNF-${\alpha}$-induced p38 MAPK phosphorylation in endothelial cells. These results suggest that Tat-mediated p66shc transduction efficiently inhibited endothelial nitric oxide synthase phosphorylation in endothelial cells.

• Archives of Pharmacal Research
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• v.19 no.1
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• pp.18-22
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• 1996

Acrylate-terminated poly (ethylene glycol) (PEG) macromer was prepared by the reaction of PEG with acryloyl chloride. Photopolymerization of PEG macromer resulted in the formation of cross-linked PEG network. Interpenetrating polymer networks (IPNs) based on PEG and poly(acrylic acid) (PAA) was obtained via template polymerization of AA to the PEG network by UV curing. The swelling degree of the IPNs hydrogel increased with an increase of pH value due to the association-dissociation between carboxylic acid of PAA and either of PEG through hydrogen bounding. The swelling-deswelling behavior proceeded reversibly for the IPNs upon changing pH. Release of indomethacin from the IPNs demonstrated "on-off" regulation with pH fluctuation.

• International Journal of Computer Science & Network Security
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• v.22 no.3
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• pp.89-94
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• 2022

The Saudi Vision 2030 defined the directions of the national economy and market towards diversifying sources of income, and developing energy to become less dependent on oil. The study sought through a theoretical review to identify the reality of the energy sector and the areas of investment available in the field of renewable energy. Findings showed that investment in the renewable energy sector is a promising source according to solar, wind, hydrogen, geothermal energy and burning waste than landfill to extract biogas for less emission. The renewable energy sector faces challenges related to technology, production cost, price, quantity of production and consumption, and markets. The study revealed some recommendations providing and suggested electronic marketing system to provide investors and consumers with energy available from renewable sources.

• International Journal of Advanced Culture Technology
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• v.11 no.1
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• pp.325-331
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• 2023

In this paper, we propose a mixed neural network structure of CNN and LSTM that can be used to detect or predict odor occurrence, which is most required in manufacturing industry or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data such as hydrogen sulfide, ammonia, benzene, and toluene in real time, and applies this data to an inference model to detect and predict odor conditions. The proposed model evaluated the prediction accuracy of the learning model through performance indicators according to accuracy, and the evaluation result showed an average performance of 94% or more.

• International Journal of Advanced Culture Technology
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• v.11 no.4
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• pp.464-469
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• 2023

As modern society develops, the number of diseases caused by bad smells is increasing. As it can harm people's health, it is important to predict in advance the extent to which bad smells may occur, inform the public about this, and take preventive measures. In this paper, we propose a hybrid neural network structure of CNN and LSTM that can be used to detect or predict the occurrence of odors, which are most required in manufacturing or real life, using odor complex sensors. In addition, the proposed learning model uses a complex odor sensor to receive four types of data, including hydrogen sulfide, ammonia, benzene, and toluene, in real time, and applies this data to the inference model to detect and predict the odor state. The proposed model evaluated the prediction accuracy of the training model through performance indicators based on accuracy, and the evaluation results showed an average performance of more than 94%.

• Transactions of the Korean hydrogen and new energy society
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• v.23 no.6
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• pp.678-687
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• 2012

Concentration fields of solid powder in a liquid fuel were quantitatively measured by a visualization technique. The measurement system consists of a camcoder and three LCD monitors. The solid powder (glass powder) were filled in a head tank which was installed over a main mixing tank ($D{\times}H$, $310{\times}370mm$). The main mixing tank was filled with JetA1 fuel oil. With a sudden opening of the upper tank by pressurized nitrogen gas with 1.9 bar, the solid powder were poured into the JetA1 oil. An impeller type agitator was being rotated in the mixing with 700 rpm for the enhancements of mixing. Uniform visualization for the mixing flow field was made by the light from the three LCD monitors, and the visualized images were captured by the camcoder. The color images captured by the camcoder The color information of the captured images was decoded into three principle colors R, G, and B to get quantitattive relations between the concentrations of the solid powder and the colors. To get better fitting for the strong non-linearity between the concentration and the color, a neural network which has strong fitting performances was used. Analyses on the transient mixing of the solid powders were quantitatively made.

• Journal of Sensor Science and Technology
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• v.21 no.1
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• pp.46-52
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• 2012

The rapid water pollution in stream, river, lake and sea in recent years raises an urgent need for continuous monitoring and policymaking to conserve the global clean environment. In particular, the increasing water pollution in coastal marine areas adds to the importance of the environmental monitoring systems. In this paper, the mobile server is designed to gathers information of the water quality at coastal areas. The obtained data by the server is transmitted from field servers to the base station via multi-hop communication in wireless sensor network. The information collected includes dissolved oxygen(DO), hydrogen ion exponent(pH), temperature, etc. By the information provided the real-time monitoring of water quality at the coastal marine area. In addition, wireless sensor network-based flooding routing protocol was designed and used to transfer the measured water quality information efficiently. Telosb sensor node is programmed using nesC language in TinyOS platform for small scale wireless sensor network monitoring from a remote server.

• Bulletin of the Korean Chemical Society
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• v.28 no.9
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• pp.1472-1476
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• 2007

Artificial neural networks (ANNs) are successfully developed for the modeling and prediction of normalized polarity parameter (ETN) of 216 various solvents with diverse chemical structures using a quantitative-structure property relationship. ANN with architecture 5-9-1 is generated using five molecular descriptors appearing in the multi-parameter linear regression (MLR) model. The most positive charge of a hydrogen atom (q+), total charge in molecule (qt), molecular volume of solvent (Vm), dipole moment (μ) and polarizability term (πI) are input descriptors and its output is ETN. It is found that properly selected and trained neural network with 192 solvents could fairly represent the dependence of normalized polarity parameter on molecular descriptors. For evaluation of the predictive power of the generated ANN, an optimized network is applied for prediction of the ETN values of 24 solvents in the prediction set, which are not used in the optimization procedure. Correlation coefficient (R) and root mean square error (RMSE) of 0.903 and 0.0887 for prediction set by MLR model should be compared with the values of 0.985 and 0.0375 by ANN model. These improvements are due to the fact that the ETN of solvents shows non-linear correlations with the molecular descriptors.

• Molecules and Cells
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• v.25 no.3
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• pp.352-357
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• 2008

Osteoprotegerin (OPG) is a soluble decoy receptor that inhibits osteoclastogenesis and is closely associated with bone resorption processes. We have designed and determined the solution structures of potent OPG analogue peptides, derived from sequences of the cysteine-rich domain of OPG. The inhibitory effects of the peptides on osteoclastogenesis are dose-dependent ($10^{-6}M-10^{-4}M$), and the activity of the linear peptide at $10^{-4}M$ is ten-fold higher than that of the cyclic OPG peptide. Both linear and cyclic peptides have a ${\beta}$-turn-like conformation and the cyclic peptide has a rigid conformation, suggesting that structural flexibility is an important factor for receptor binding. Based on structural and biochemical information about RANKL and the OPG peptides, we suggest that complex formation between the peptide and RANKL is mediated by both hydrophobic and hydrogen bonding interactions. These results provide structural insights that should aid in the design of peptidyl-mimetic inhibitors for treating metabolic bone diseases caused by abnormal osteoclast recruitment.

• International Journal of Control, Automation, and Systems
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• v.4 no.3
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• pp.382-393
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• 2006

Mechanical system dynamics plays an important role in the area of computational structural biology. Elastic network models (ENMs) for macromolecules (e.g., polymers, proteins, and nucleic acids such as DNA and RNA) have been developed to understand the relationship between their structure and biological function. For example. a protein, which is basically a folded polypeptide chain, can be simply modeled as a mass-spring system from the mechanical viewpoint. Since the conformational flexibility of a protein is dominantly subject to its chemical bond interactions (e.g., covalent bonds, salt bridges, and hydrogen bonds), these constraints can be modeled as linear spring connections between spatially proximal representatives in a variety of coarse-grained ENMs. Coarse-graining approaches enable one to simulate harmonic and anharmonic motions of large macromolecules in a PC, while all-atom based molecular dynamics (MD) simulation has been conventionally performed with an aid of supercomputer. A harmonic analysis of a macroscopic mechanical system, called normal mode analysis, has been adopted to analyze thermal fluctuations of a microscopic biological system around its equilibrium state. Furthermore, a structure-based system optimization, called elastic network interpolation, has been developed to predict nonlinear transition (or folding) pathways between two different functional states of a same macromolecule. The good agreement of simulation and experiment allows the employment of coarse-grained ENMs as a versatile tool for the study of macromolecular dynamics.