• Journal of Ecology and Environment
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• 제31권2호
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• pp.89-95
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• 2008

Sensing the approach of a predator is critical to the survival of prey, especially when the prey has no choice but to escape at a precisely timed moment. Escape behavior has been approached from both proximate and ultimate perspectives. On the proximate level, empirical research about electrophysiological mechanisms for detecting predators has focused on vision, an important modality that helps prey to sense approaching danger. Studies of looming-sensitive neurons in locusts are a good example of how the selective sensitivity of nervous systems towards specific targets, especially approaching objects, has been understood and realistically modeled in software and robotic systems. On the ultimate level, general optimality models have provided an evolutionary framework by considering costs and benefits of visually elicited escape responses. A recent paper showed how neural network models can be used to understand the evolution of visually mediated antipredatory behaviors. We discuss this new trend towards integration of these relatively disparate approaches, the proximate and the ultimate perspectives, for understanding of the evolution of behavior of predators and prey. Focusing on one of the best-studied escape pathway models, the Orthopteran LGMD/DCMD pathway, we discuss how ultimate-level optimality modeling can be integrated with proximate-level studies of escape behaviors in animals.

• 대한의용생체공학회:학술대회논문집
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• 대한의용생체공학회 1996년도 추계학술대회
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• pp.297-300
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• 1996

In male reproducible health and fertility and IVF(in-vitro fertilization), semen analysis has been most important. But the traditional tools for semen analysis are subjective, imprecise, inaccurate, difficult to standardize, and difficult to reproduce mainly due to their manually oriented operations. The purpose of a morphometric analysis of sperm is to microscopically type-classify spermatozoa cytologically according to their morphology of heads. Until now, the strict criteria method has long been used in clinic to discriminate normal spermatozoa from abnormal ones. This method cannot classify the diverse groups of abnormal spermatozoa in detail and shows variations in inter-operators and intra-operator In this paper, we developed a new method of a sperm morphometric analysis using artificial neural networks which are widely used in pattern recognition and image processing.

• Journal of information and communication convergence engineering
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• 제1권3호
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• pp.104-108
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• 2003

A biologically inspired fuzzy multilayer perceptron is proposed in this paper. The proposed algorithm is established under consideration of biological neuronal structure as well as fuzzy logic operation. We applied this suggested learning algorithm to benchmark problem in neural network such as exclusive OR and 3-bit parity, and to digit image recognition problems. For the comparison between the existing and proposed neural networks, the convergence speed is measured. The result of our simulation indicates that the convergence speed of the proposed learning algorithm is much faster than that of conventional backpropagation algorithm. Furthermore, in the image recognition task, the recognition rate of our learning algorithm is higher than of conventional backpropagation algorithm.

• Molecules and Cells
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• 제24권3호
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• pp.307-315
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• 2007

Gene regulation is a fundamental process in biological systems, where transcription factors (TFs) play crucial roles. Inferring transcriptional interactions between TFs and their target genes has utmost importance for understanding the complex regulatory mechanisms in cellular systems. On one hand, with the rapid progress of various high-throughput experiment techniques, more and more biological data become available, which makes it possible to quantitatively study gene regulation in a systematic manner. On the other hand, transcription regulation is a complex biological process mediated by many events such as post-translational modifications, degradation, and competitive binding of multiple TFs. In this review, with a particular emphasis on computational methods, we report the recent advances of the research topics related to transcriptional regulatory networks, including how to infer transcriptional interactions, reveal combinatorial regulation mechanisms, and reconstruct TF activity profiles.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2003년도 ICCAS
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• pp.845-848
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• 2003

There are many researches on using human neuro-biological signals for various problems such as controlling a mechanical object and/or interfacing human with the computer. It is one of very interesting topics that human can use various instruments without learning specific knowledge if the instruments can be controlled as human intends. In this paper, we proposed an intelligent motion planner for a redundant manipulator, which is controlled by humans neuro-biological signals, especially, EOG (Electrooculogram). We found the optimal motion planner for the redundant manipulator that can move to the desired point. We used neural networks to find the inverse kinematics solution of the manipulator. We also showed the performance of the proposed motion planner with several simulations.

• 한국고분자학회:학술대회논문집
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• 한국고분자학회 2006년도 IUPAC International Symposium on Advanced Polymers for Emerging Technologies
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• pp.295-295
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• 2006

In present study, poly(acrylic acid) (PAA) and hydrophobically modified poly(ethylene oxide) (HM-PEO) multilayers based on the hydrogen bonding between the component polymer pair have been prepared by the LbL deposition method. Dip assembled HM-PEO/PAA multilayers yield unique film morphologies in comparison with PEO/PAA multilayers due to the micellar formation of HM-PEO owing to the hydrophobic attraction between alkyl chains end-capped with the PEO chains. Individual HM-PEO micelles were connected through the bridging PEO chains to form temporary networks on multilayer surface and induced peculiar surface morphology on HM-PEO/PAA multilayers above the critical number of bilayers.

• BMB Reports
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• 제50권1호
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• pp.12-19
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• 2017

Traditionally, biologists have devoted their careers to studying individual biological entities of their own interest, partly due to lack of available data regarding that entity. Large, high-throughput data, too complex for conventional processing methods (i.e., "big data"), has accumulated in cancer biology, which is freely available in public data repositories. Such challenges urge biologists to inspect their biological entities of interest using novel approaches, firstly including repository data retrieval. Essentially, these revolutionary changes demand new interpretations of huge datasets at a systems-level, by so called "systems biology". One of the representative applications of systems biology is to generate a biological network from high-throughput big data, providing a global map of molecular events associated with specific phenotype changes. In this review, we introduce the repositories of cancer big data and cutting-edge systems biology tools for network generation, and improved identification of therapeutic targets.

• 대한약학회:학술대회논문집
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• 대한약학회 2003년도 Proceedings of the Convention of the Pharmaceutical Society of Korea Vol.2-1
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• pp.97-99
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• 2003

With advances in determining the entire DNA sequence of the human genome, it is now critical to systematically identify the function of a number of genes in the human genome. These biological problems, especially those in human diseases including cancer, should be addressed in human cells in which genetic approaches have been extremely difficult to implement. To overcome this, my efforts have focused on the development of a novel “chemical genetic/genomic approach” that uses small molecules to “probe and identify” the function of genes in specific biological process or pathway in human cells. (omitted)

• Molecules and Cells
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• 제41권11호
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• pp.953-963
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• 2018

The stepwise development of T cells from a multipotent precursor is guided by diverse mechanisms, including interactions among lineage-specific transcription factors (TFs) and epigenetic changes, such as DNA methylation and hydroxymethylation, which play crucial roles in mammalian development and lineage commitment. To elucidate the transcriptional networks and epigenetic mechanisms underlying T-cell lineage commitment, we investigated genome-wide changes in gene expression, DNA methylation and hydroxymethylation among populations representing five successive stages of T-cell development (DN3, DN4, DP, $CD4^+$, and $CD8^+$) by performing RNA-seq, MBD-seq and hMeDIP-seq, respectively. The most significant changes in the transcriptomes and epigenomes occurred during the DN4 to DP transition. During the DP stage, many genes involved in chromatin modification were up-regulated and exhibited dramatic changes in DNA hydroxymethylation. We also observed 436 alternative splicing events, and approximately 57% (252) of these events occurred during the DP stage. Many stage-specific, differentially methylated regions were observed near the stage-specific, differentially expressed genes. The dynamic changes in DNA methylation and hydroxymethylation were associated with the recruitment of stage-specific TFs. We elucidated interactive networks comprising TFs, chromatin modifiers, and DNA methylation and hope that this study provides a framework for the understanding of the molecular networks underlying T-cell lineage commitment.

• Journal of Communications and Networks
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• 제17권5호
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• pp.506-516
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• 2015

Wireless sensor networks (WSNs) are generally comprised of densely deployed sensor nodes, which results in highly redundant sensor data transmissions and energy waste. Since the sensor nodes depend on batteries for energy, previous studies have focused on designing energy-efficient medium access control (MAC) protocols to extend the network lifetime. However, the energy-efficient protocols induce an extra end-to-end delay, and therefore recent increase in focus on WSNs has led to timely and reliable communication protocols for mission-critical applications. In this paper, we propose an energy efficient and delay guaranteeing node scheduling scheme inspired by biological systems, which have gained considerable attention as a computing and problem solving technique.With the identification of analogies between cellular signaling systems and WSN systems, we formulate a new mathematical model that considers the networking challenges of WSNs. The proposed bio-inspired algorithm determines the state of the sensor node, as required by each application and as determined by the local environmental conditions and the states of the adjacent nodes. A control analysis shows that the proposed bio-inspired scheme guarantees the system stability by controlling the parameters of each node. Simulation results also indicate that the proposed scheme provides significant energy savings, as well as reliable delay guarantees by controlling the states of the sensor nodes.