• Animal Systematics, Evolution and Diversity
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• v.40 no.4
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• pp.308-313
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• 2024

In this study, three species of the subfamily Olethreutinae; Matsumuraeses ussuriensis (Caradja, 1916), Pseudacroclita luteispecula (Kuznetsov, 1979) and Zeiraphera hiroshii Kawabe, 1980 are reported for the first time from Korea. All species descriptions in this study are based on data obtained through the collection and rearing of larvae. This research is expected to make a significant contribution to the understanding of the ecological characteristics of this family. Descriptions, biological information, host plants, high-resolution photographs of larvae, adults, and genitalia are provided for each species. Additionally, host plants for some species were reported for the first time in this study.

• Biotechnology and Bioprocess Engineering:BBE
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• v.10 no.5
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• pp.418-424
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• 2005

The complexity of the biological system/biological systems has been fascinating and challenging for a long time. With the advent of mathematical tools with various omics technology, systems biology was born and is already ubiquitous in every area of biology and biotechnology. Microalgal biotechnology is no exception in this new trend. As tens of microalgal genomes become publicly available on the Internet, vast amounts of data from genomics, transcriptomics, and proteomics are reported everyday. Though there has not yet been enough data gathered on microalgal metabolomics, the in silica models for relatively simple cyanobacteria or for organelles, such as chloroplasts, will appear presently. With the help of systems biology, a more in-depth understanding of microalgae will be possible. Consequently, most industrially-interested microalgae can be metabolically redesigned/reconfigured as cell factories. Microalgae will be served as the hosts in white biotechnology.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.60-62
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• 1994

In this paper, we present a signal analysis workstation in which the user can scrutinize and quantify biological signals, observe the effects of various signal processing algorithms on them, and eventually get some interpretation of clinical use. Within the system, the user can also access all the information in the central data base, such as patient personal information, biological signal information, and insert his interpretation results obtained into the data base after his careful observation. The software system is designed in an object-oriented paradigm, and written in C++ as a window-based application program.

• Smart Structures and Systems
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• v.8 no.1
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• pp.107-117
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• 2011

Biological sensory systems continuously monitor and analyze changes in real-world environments that are relevant to an animal's specific behavioral needs and goals. Understanding the sensory mechanisms and information processing principles that biological systems utilize for efficient sensory data acquisition may provide useful guidance for the design of smart-sensing systems in engineering applications. Weakly electric fish, which use self-generated electrical energy to actively sense their environment, provide an excellent model system for studying biological principles of sensory data acquisition. The electrosensory system enables these fish to hunt and navigate at night without the use of visual cues. To achieve reliable, real-time task performance, the electrosensory system implements a number of smart sensing strategies, including efficient stimulus encoding, multi-scale virtual sensor arrays, task-dependent filtering and online subtraction of sensory expectation.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.533-536
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• 2007

The LiDAR data structure has the potential for modeling in three dimensions because the LiDAR data can represent voxels with z value under certain defined conditions. Therefore, it is possible to classify the physical damaged degree of vegetation by forest fire as using the LiDAR data because the physical loss of canopy height and width by forest fire can be relative to an amount of points reached to the ground through the canopy of damaged forest. On the other hand, biological damage of vegetation by forest fire can be explained using the NDVI (Normalized Difference Vegetation Index) which show vegetation vitality. In this study, we graded the damaged degree of vegetation by forest fire in Yangyang-Gun of South Korea using the LiDAR data for physical grading and digital aerial photograph including Red, Green, Blue and Near Infra-Red bands for biological grading. The LiDAR data was classified into 2 classes, of which one was Serious Physical Damaged (SPD) and the other was Light Physical Damaged (LPD) area. The NDVI was also classified into 2 classes which are Serious Biological Damaged (SBD) and Light Biological Damaged (LBD) area respectively. With each 2 classes ofthe LiDAR data and NDVI, the damaged area by forest fire was graded into 4 degrees like damaged class 1,2,3 and 4 grade. As a result of this study, 1 graded area was the broadest and next was the 3 grade. With this result, we could know that the burned area by forest fire in Yangyang-Gun was damaged rather biologically because the NDVI in 1 and 3 grade appeared low value whereas the LiDAR data in 1 and 3 grade included light physical damage like the LPD.

• BMB Reports
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• v.41 no.3
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• pp.184-193
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• 2008

Living organisms are comprised of various systems at different levels, i.e., organs, tissues, and cells. Each system carries out its diverse functions in response to environmental and genetic perturbations, by utilizing biological networks, in which nodal components, such as, DNA, mRNAs, proteins, and metabolites, closely interact with each other. Systems biology investigates such systems by producing comprehensive global data that represent different levels of biological information, i.e., at the DNA, mRNA, protein, or metabolite levels, and by integrating this data into network models that generate coherent hypotheses for given biological situations. This review presents a systems biology framework, called the 'Integrative Proteomics Data Analysis Pipeline' (IPDAP), which generates mechanistic hypotheses from network models reconstructed by integrating diverse types of proteomic data generated by mass spectrometry-based proteomic analyses. The devised framework includes a serial set of computational and network analysis tools. Here, we demonstrate its functionalities by applying these tools to several conceptual examples.

• Proceedings of the Zoological Society Korea Conference
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• 1999.10b
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• pp.72.2-72
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• 1999

No Abstract, See Full Text

• Journal of Species Research
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• v.5 no.3
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• pp.261-263
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• 2016

In 2012, the NIBR started publishing the Journal of Species Research (JSR) as an international specialized journal of biological taxonomy focusing on taxonomic research. JSR Volume 5 Number 3, to be published in October 2016, has been planned as a 'Special Edition on New and Unrecorded Species of Invertebrates in Korea', and so it consists of the reports of 149 new and unrecorded invertebrate species (including protozoa) discovered in Korea. In future, the JSR should further accelerate the use of such methods to generate valid data for new species and effectively support the compilation of 'National List of Species of Korea'. In this way, it will contribute significantly to enrich for biodiversity in Korea.

• Proceedings of the KOSOMBE Conference
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• v.1994 no.05
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• pp.49-52
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• 1994

We have developed a biological signal measurement, archiving, and communication system (SiMACS). The front end of the system is the intelligent data processing unit (IDPU) which includes ECG, EEG, EMG, blood pressure, respiration, temperature measurement modules, module control and data acquisition unit, real-time display and signal processing unit. IDPUS are connected to central data base unit through LAN(Ethernet). Workstations which receive signals from central DB and provide various signal analysis tools are also connected to the network. The developed PC-based SiMACS is described.

• Proceedings of the KOSOMBE Conference
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• v.1997 no.05
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• pp.199-202
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• 1997

We are developing computer integrated polysomnography system. This system integrates conventional polysomnography with computer for data management, automatic analysis, scoring, and data transmission. In the first stage, we have developed the signal interface and user interface for the manual scoring and data management. For the automatic scoring of sleep stage, we have developed the protocol and have applied the analytic method in its primitive form. In the second stage we will develope a partially automatic scoring system, and finalize the fully automatic system in the final third stage.