• Journal of Computing Science and Engineering
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• v.5 no.3
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• pp.257-268
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• 2011

Machine learning and data mining have found many applications in biological domains, where we look to build predictive models based on labeled training data. However, in practice, high quality labeled data is scarce, and to label new data incurs high costs. Transfer and multitask learning offer an attractive alternative, by allowing useful knowledge to be extracted and transferred from data in auxiliary domains helps counter the lack of data problem in the target domain. In this article, we survey recent advances in transfer and multitask learning for bioinformatics applications. In particular, we survey several key bioinformatics application areas, including sequence classification, gene expression data analysis, biological network reconstruction and biomedical applications.

• Journal of Marine Bioscience and Biotechnology
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• v.5 no.4
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• pp.26-32
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• 2011

Green fluorescent protein (GFP), a very important biological agent that involves shifting the color of bioluminescence from blue to green in luminous coelenterates and to increase the quantum yield of light emission. GFP discovered in medusa, Aequorea victoria is a key factor of various biotechnological and cell biological applications. Beside these applications, GFP of A. victoria is generally stable, which does not require co-factors for activity and can be functionally expressed in different bacterial species. This property of GFPs from A. victoria permits them to be a unique tool to monitor gene expression and protein localization in different organisms. The present review brings out the past milestones and future perspectives on GFPs, with an elaborative reviewing on its applications.

• Journal of Microbiology and Biotechnology
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• v.29 no.2
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• pp.179-190
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• 2019

Bacillus subtilis spore surface display (BSSD) technology is considered to be one of the most promising approaches for expressing heterologous proteins with high activity and stability. Currently, this technology is used for various purposes, such as the production of enzymes, oral vaccines, drugs and multimeric proteins, and the control of environmental pollution. This paper presents an overview of the latest developments in BSSD technology and its application in protein engineering. Finally, the major limitations of this technology and future directions for its research are discussed.

• 한국생물공학회:학술대회논문집
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• 2005.04a
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• pp.93-94
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• 2005

• 한국생물공학회:학술대회논문집
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• 2005.04a
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• pp.469-469
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• 2005

• 한국생물공학회:학술대회논문집
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• 2005.10a
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• pp.771-772
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• 2005

• Proceedings of the Korean Society for Applied Microbiology Conference
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• 2005.06a
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• pp.298-302
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• 2005

• Journal of Microbiology and Biotechnology
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• v.26 no.9
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• pp.1505-1516
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• 2016

The detection of food pathogens is an important aspect of food safety. A range of detection systems and new analytical materials have been developed to achieve fast, sensitive, and accurate monitoring of target pathogens. In this review, we summarize the characteristics of selected nanomaterials and their applications in food, and place focus on the monitoring of biological and chemical contaminants in food. The unique optical and electrical properties of nanomaterials, such as gold nanoparticles, nanorods, quantum dots, carbon nanotubes, graphenes, nanopores, and polydiacetylene nanovesicles, are closely associated with their dimensions, which are comparable in scale to those of targeted biomolecules. Furthermore, their optical and electrical properties are highly dependent on local environments, which make them promising materials for sensor development. The specificity and selectivity of analytical nanomaterials for target contaminants can be achieved by combining them with various biological entities, such as antibodies, oligonucleotides, aptamers, membrane proteins, and biological ligands. Examples of nanomaterial-based analytical systems are presented together with their limitations and associated developmental issues.

• Proceedings of the Korean Vacuum Society Conference
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• 2016.02a
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• pp.208.2-208.2
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• 2016

Many researchers have paid attention to the studies on the interaction between non-thermal plasma and aqueous solutions for biomedical applications. The gas composition in the plasma is very important. Oxygen and nitrogen are the main gases of interest in biological applications. Especially, we focus on the oxygen concentration. In this experiment, we studied the role of oxygen concentration in plasma induced chemical reactions in solution. At first, the amount of ions are measured according to changing the oxygen concentration. And we checked the relationship between these ions and pH value. Secondly, when the oxygen concentration is changed, it identified the type and amount of radical generated by the plasma. In order to confirm the effect of these chemical property change to biological material, hemoglobin and RBCs are chosen. RBCs are one of the common basic biological cells. Thirdly, when plasma treated according to oxygen concentration in nitrogen feeding gas, oxidation of hemoglobin and RBC is checked. Finally, membrane oxidation of RBC is measured to examine the relation between hemoglobin oxidation and membrane damage through relative hemolysis and Young's modulus. Our results suggest that reactive species generated by the plasma differsdepending on the oxygen concentration changes. The pH values are decreased when oxygen concentration increased. OH decrease and NO increase are also observed. These reactive species makes change of chemical properties of solution. We also able to confirm that the difference in these reactive species to affect the oxidation of the Hb and RBCs. The Hb and RBCs are more oxidized with the high oxygen concentration conditions. But membrane is damaged more by plasma treatment with only nitrogen gas. It is shown that red blood cells membrane damage and oxidation of hemoglobin are not directly related.

• Korean Journal of Biological Psychiatry
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• v.4 no.2
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• pp.194-197
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• 1997

Neural network models, also known as connectionist models or PDP models, simulate some functions of the brain and may promise to give insight in understanding the cognitive brain functions. The models composed of neuron-like elements that are linked into circuits can learn and adapt to its environment in a trial and error fashion. In this article, the history and principles of the neural network modeling are briefly reviewed, and its applications to psychiatry are discussed.