• 한국식품과학회지
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• 제23권6호
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• pp.759-763
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• 1991

게살과 구조적으로 유사한 대두단백 섬유는 dope를 방사하여 얻은 것으로, 대두단백의 dispersion이나 dope의 성질에 의존한다. 대두단백섬유의 출발물질인 대두단백의 dispersion과 dope의 동적 물성을 측정함으로서 그들의 점탄성을 조사하였다. 단백질 농도가 증가할때 dispersion이나 dope모두 단백질간의 상호반응이 일어났다. Dispersion에 알칼리 용액을 넣은 dope는 고분자 중합물질에 상호연결된 특성을 나타내었다. 반면에 dispersion에서는 무정형 중합물질의 특성이 나타났다. Dispersion에 대한 화학변형이 동적물성에 미치는 영향도 관찰하였다.

• 한국전기전자재료학회논문지
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• 제19권2호
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• pp.116-125
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• 2006

We have presented a nanoelectromechanical (NEM) model based on atomistic simulations. Our models were applied to a NEM device as called a nanotube random access memory (NRAM) operated by an atomistic capacitive model including a tunneling current model. We have performed both static and dynamic analyses of a NRAM device. The turn-on voltage obtained from molecular dynamics simulations was less than the half of the turn-on voltage obtained from the static simulation. Since the suspended carbon nanotube (CNT) oscillated with the amplitude for the oscillation center under an externally applied force, the quantity of the CNT-gold interaction in the static analysis was different from that in the dynamic analysis. When the gate bias was applied, the oscillation centers obtained from the static analysis were different from those obtained from the dynamics analysis. Therefore, for the range of the potential difference that the CNT-gold interaction effects in the static analysis were negligible, the vibrations of the CNT in the dynamics analysis significantly affected the CNT-gold interaction energy and the turn-on voltage. The turn-on voltage and the tunneling resistance obtained from our tunneling current model were in good agreement with previous experimental and theoretical works.

• 한국소음진동공학회:학술대회논문집
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• 한국소음진동공학회 2008년도 춘계학술대회논문집
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• pp.476-479
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• 2008

Understanding the dynamics of proteins is essential to gain insight into biological functions of proteins. The protein dynamics is delineated by conformational fluctuation (i.e. thermal vibration), and thus, thermal vibration of proteins has to be understood. In this paper, a simple mechanical model was considered for understanding protein's dynamics. Specifically, a mechanical vibration model was developed for understanding the large protein dynamics related to biological functions. The mechanical model for large proteins was constructed based on simple elastic model (i.e. Tirion's elastic model) and model reduction methods (dynamic model condensation). The large protein structure was described by minimal degrees of freedom on the basis of model reduction method that allows one to transform the refined structure into the coarse-grained structure. In this model, it is shown that a simple reduced model is able to reproduce the thermal fluctuation behavior of proteins qualitatively comparable to original molecular model. Moreover, the protein's dynamic behavior such as collective dynamics is well depicted by a simple reduced mechanical model. This sheds light on that the model reduction may provide the information about large protein dynamics, and consequently, the biological functions of large proteins.

• Food Science and Biotechnology
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• 제15권2호
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• pp.264-269
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• 2006

Basic rheological data of dandelion leaf concentrates were determined to predict processing aptitude and usefulness of dandelion leaf concentrates as functional food materials. Hot water and 70% ethanol extracts of dandelion leaves were concentrated at 5, 20, and 50 Brix, and their static and dynamic viscosities, and Arrhenius plots were investigated. Most concentrated dandelion leaves extracted with hot water and 70% ethanol showed flow behaviors close to Newtonian fluid based on power law model evaluation. Apparent viscosity of concentrated dandelion leaves extracted with hot water and 70% ethanol decreased with increasing temperature. Yield stresses of concentrated dandelion leaves extracted with hot water and 70% ethanol by Herschel-Bulkley model application were 0.020-0.641 and 0.017-0.079 Pa, respectively. Activation energies of concentrated dandelion leaves extracted with hot water and 70% ethanol were $2.102-32.669{\times}10^3$ and $1.657-5.382{\times}10^3\;J/mol{\cdot}kg$ with increasing concentration, respectively. Loss modulus (G") predominated over storage modulus (G') at all applied frequencies, showing typical flow behavior of low molecular solution. G' and G" of concentrated dandelion leaves extracted with hot water slowly increased with increasing frequency compared to those of concentrated dandelion leaves extracted with 70% ethanol.

• 대한화학회지
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• 제30권2호
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• pp.172-180
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• 1986

치환-벤조산, -베젠 및 -나프탈렌의 세 계열에 대해 LCAO MO법으로 Hammett치환기 상수를 해석하였다. Hammett ${\sigma}$값은 이론적으로 유도효과와 공명효과의 독립적인 기여의 합으로 취급됨을 알았다. 즉, 유도효과를 정적유도효과(static inductive effect, Is)와 동적유도효과 (dynamic inductive effect, Id)로 나누어 각각 net charge 및 근사자기편극률 지수로 취하고 공명효과로서 치환 분자와 비치환분자의 HOMO에너지 차$({\Delta}E^{HOMO})$로 취하였으며 계산결과 Hammett ${\sigma}$값은 정적유도효과, 동적유도효과 및 ${\Delta}E^{HOMO}$의 합에 의존됨을 알았다.

• Biomolecules & Therapeutics
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• 제26권1호
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• pp.10-18
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• 2018

Tumors are dynamic metabolic systems which highly augmented metabolic fluxes and nutrient needs to support cellular proliferation and physiological function. For many years, a central hallmark of tumor metabolism has emphasized a uniformly elevated aerobic glycolysis as a critical feature of tumorigenecity. This led to extensive efforts of targeting glycolysis in human cancers. However, clinical attempts to target glycolysis and glucose metabolism have proven to be challenging. Recent advancements revealing a high degree of metabolic heterogeneity and plasticity embedded among various human cancers may paint a new picture of metabolic targeting for cancer therapies with a renewed interest in glucose metabolism. In this review, we will discuss diverse oncogenic and molecular alterations that drive distinct and heterogeneous glucose metabolism in cancers. We will also discuss a new perspective on how aberrantly altered glycolysis in response to oncogenic signaling is further influenced and remodeled by dynamic metabolic interaction with surrounding tumor-associated stromal cells.

• 한국재료학회지
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• 제17권11호
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• pp.569-573
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• 2007

The static and dynamic hydrophobicities of the water droplets placed on a hydrophobic surface coated using a fluoroalkylsilanes monolayer with different molecular chain lengths were investigated through direct observation of the actual droplet motion during the sliding process. The surface roughness of both was found to be less than 1 nm. The static contact angles of the coated FAS-3 and FAS-17 were respectively $80^{\circ}$ and $108^{\circ}$ at $150^{\circ}C$, 1 h. The slope of sliding acceleration against the water droplet mass exhibited an inflection point, thus suggesting the switching of the dominant sliding mode from slipping to rolling. While their sliding angles were similar in value, notable differences were exhibited in terms of their sliding behavior. This can be understood as being due to the contribution of the shear stress difference at the interface between the solid surface and water during the sliding process. These results show that the sliding acceleration of the water droplets depends strongly on the balance between gravitational and retentive forces on the hydrophobic surface.

• 한국자기공명학회논문지
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• 제23권3호
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• pp.61-66
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• 2019

RNAs exhibit distinct structural and dynamic features required for proper function. The hydrogen-bonded imino protons of RNAs are a probe of the conformational transition and dynamic feature. MicroRNAs originate from primary transcripts containing hairpin structures. The levels of mature miR156 influence the flowering time of plants. To understand the molecular mechanism of biological function of $miR156:miR156^*$ duplex, we performed hydrogen exchange study on the model RNAs mimicking two phenotypes of $miR156:miR156^*$, $miR156:miR156^*$ (m-miR156a) and $miR156:miR156^*$ (m-miR156g) duplexes. This study found that the internal bulge of m-miR156a destabilized the neighboring base-pairs, whereas the bulge structure of m-miR156g did not affect the thermal stabilities of the neighboring base-pairs.

• 한국자기공명학회논문지
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• 제23권1호
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• pp.26-32
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• 2019

${\alpha}B$-crystallin (${\alpha}BC$) is a member of a small heat-shock protein (sHSP) superfamily and plays a predominant role in cellular protein homeostasis network by rescuing misfolded proteins from irreversible aggregation. ${\alpha}BC$ assembles into dynamic and polydisperse high molecular weight complexes containing 12 to 48 monomers; this variable stereochemistry of ${\alpha}BC$ has been linked to quaternary subunit exchange and its chaperone activity. The chaperone activity of ${\alpha}BC$ poses great potential as therapeutic agents for various neurodegenerative diseases. In this mini-review, we briefly outline the recent advancement in structural characterization of ${\alpha}BCs$ and its potential role to inhibit protein misfolding and aggregation in various human diseases. In particular, nuclear magnetic resonance (NMR) spectroscopy and its complimentary techniques have contributed much to elucidate highly-dynamic nature of ${\alpha}BCs$, among which notable advancements are discussed in detail. We highlight the importance of resolving the structural details of various ${\alpha}BC$ oligomers, their quaternary dynamics, and structural heterogeneity.

• 대한의용생체공학회:의공학회지
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• 제42권5호
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• pp.232-240
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• 2021

Heterogeneity in cancer is the major obstacle for precision medicine and has become a critical issue in the field of a cancer diagnosis. Many attempts were made to disentangle the complexity by molecular classification. However, multi-dimensional information from dynamic responses of cancer poses fundamental limitations on biomolecular marker-based conventional approaches. Cell morphology, which reflects the physiological state of the cell, can be used to track the temporal behavior of cancer cells conveniently. Here, we first present a hybrid learning-based platform that extracts cell morphology in a time-dependent manner using a deep convolutional neural network to incorporate multivariate data. Feature selection from more than 200 morphological features is conducted, which filters out less significant variables to enhance interpretation. Our platform then performs unsupervised clustering to unveil dynamic behavior patterns hidden from a high-dimensional dataset. As a result, we visualize morphology state-space by two-dimensional embedding as well as representative morphology clusters and trajectories. This cell morphology profiling strategy by hybrid learning enables simplification of the heterogeneous population of cancer.