• 제목/요약/키워드: biological network

검색결과 763건 처리시간 0.031초

Brain Activation Pattern and Functional Connectivity Network during Experimental Design on the Biological Phenomena

  • Lee, Il-Sun;Lee, Jun-Ki;Kwon, Yong-Ju
    • 한국과학교육학회지
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    • 제29권3호
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    • pp.348-358
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    • 2009
  • The purpose of this study was to investigate brain activation pattern and functional connectivity network during experimental design on the biological phenomena. Twenty six right-handed healthy science teachers volunteered to be in the present study. To investigate participants' brain activities during the tasks, 3.0T fMRI system with the block experimental-design was used to measure BOLD signals of their brain and SPM2 software package was applied to analyze the acquired initial image data from the fMRI system. According to the analyzed data, superior, middle and inferior frontal gyrus, superior and inferior parietal lobule, fusiform gyrus, lingual gyrus, and bilateral cerebellum were significantly activated during participants' carrying-out experimental design. The network model was consisting of six nodes (ROIs) and its six connections. These results suggested the notion that the activation and connections of these regions mean that experimental design process couldn't succeed just a memory retrieval process. These results enable the scientific experimental design process to be examined from the cognitive neuroscience perspective, and may be used as a basis for developing a teaching-learning program for scientific experimental design such as brain-based science education curriculum.

Laminin-1 Phosphorylation by Protein Kinase A: Effect on self assembly and heparin binding

  • Koliakos, George;Kouzi-Koliakos, Kokkona;Triantos, Athanasios;Trachana, Varvara;Kavoukopoulos, Evaggelos;Gaitatzi, Mary;Dimitriadou, Aphrodite
    • BMB Reports
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    • 제33권5호
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    • pp.370-378
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    • 2000
  • Incubation of purified laminin1-nidogen1 complexes with $[{\gamma}-^{32}P]-ATP$ in the presence of the catalytic subunit of the protein kinase A (cAMP-dependent protein kinase) resulted in the phosphorylation of the alpha chain of laminin-1 and of the nidogen-1 molecule. Aminoacid electrophoresis indicated that phosphate was incorporated on serine residues. The phosphorylation effect of laminin-1 on the process of self assembly was studied by turbidometry. In these experiments, the phosphorylated laminin-1 showed a reduced maximal aggregation capacity in comparison to the non-phosphorylated molecule. Examination of the laminin-1 network under the electron microscope showed that the phosphorylated sample formed mainly linear extended oligomers, in contrast to controls that formed large and dense multimeric aggregates. Heparin binding on phosphorylated laminin-1 in comparison to controls was also tested using solid-phase binding assays. The results indicated an enhanced heparin binding to the phosphorylated protein. The results of this study indicate that laminin1-nidogen1 is a substrate for protein kinase A in vitro. This phosphorylation had an obvious influence on the lamininl-nidogen1 network formation and the heparin binding capacity of this molecule. However, further studies are needed to investigate whether or not this phenomenon could play a role in the formation of the structure of basement membranes in vivo.

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Microbiota, co-metabolites, and network pharmacology reveal the alteration of the ginsenoside fraction on inflammatory bowel disease

  • Dandan Wang;Mingkun Guo;Xiangyan Li;Daqing Zhao;Mingxing Wang
    • Journal of Ginseng Research
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    • 제47권1호
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    • pp.54-64
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    • 2023
  • Background: Panax ginseng Meyer (P. ginseng) is a traditional natural/herbal medicine. The amelioration on inflammatory bowel disease (IBD) activity rely mainly on its main active ingredients that are referred to as ginsenosides. However, the current literature on gut microbiota, gut microbiota-host co-metabolites, and systems pharmacology has no studies investigating the effects of ginsenoside on IBD. Methods: The present study was aimed to investigate the role of ginsenosides and the possible underlying mechanisms in the treatment of IBD in an acetic acid-induced rat model by integrating metagenomics, metabolomics, and complex biological networks analysis. In the study ten ginsenosides in the ginsenoside fraction (GS) were identified using Q-Orbitrap LC-MS. Results: The results demonstrated the improvement effect of GS on IBD and the regulation effect of ginsenosides on gut microbiota and its co-metabolites. It was revealed that 7 endogenous metabolites, including acetic acid, butyric acid, citric acid, tryptophan, histidine, alanine, and glutathione, could be utilized as significant biomarkers of GS in the treatment of IBD. Furthermore, the biological network studies revealed EGFR, STAT3, and AKT1, which belong mainly to the glycolysis and pentose phosphate pathways, as the potential targets for GS for intervening in IBD. Conclusion: These findings indicated that the combination of genomics, metabolomics, and biological network analysis could assist in elucidating the possible mechanism underlying the role of ginsenosides in alleviating inflammatory bowel disease and thereby reveal the pathological process of ginsenosides in IBD treatment through the regulation of the disordered host-flora co-metabolism pathway.

Reverse Engineering of a Gene Regulatory Network from Time-Series Data Using Mutual Information

  • Barman, Shohag;Kwon, Yung-Keun
    • 한국정보처리학회:학술대회논문집
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    • 한국정보처리학회 2014년도 추계학술발표대회
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    • pp.849-852
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    • 2014
  • Reverse engineering of gene regulatory network is a challenging task in computational biology. To detect a regulatory relationship among genes from time series data is called reverse engineering. Reverse engineering helps to discover the architecture of the underlying gene regulatory network. Besides, it insights into the disease process, biological process and drug discovery. There are many statistical approaches available for reverse engineering of gene regulatory network. In our paper, we propose pairwise mutual information for the reverse engineering of a gene regulatory network from time series data. Firstly, we create random boolean networks by the well-known $Erd{\ddot{o}}s-R{\acute{e}}nyi$ model. Secondly, we generate artificial time series data from that network. Then, we calculate pairwise mutual information for predicting the network. We implement of our system on java platform. To visualize the random boolean network graphically we use cytoscape plugins 2.8.0.

Supramolecular Liquid Crystals Containing Hydrogen Bond between Carboxylic Acid and Pyridyl Moieties and their Thermotropic Mesomorphism

  • Lee, Seung-Jun;You, Mi-Kyoung;Lee, Ji-Won;Lee, Shin-Woo;Jho, Jae-Young
    • 한국고분자학회:학술대회논문집
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    • 한국고분자학회 2006년도 IUPAC International Symposium on Advanced Polymers for Emerging Technologies
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    • pp.297-297
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    • 2006
  • Recently columnar liquid crystals have been studied due to their possible application to organic conducting materials. Supramolecular columnar liquid crystals consist of mesogenic unit which can aggregate into discs that will make up the columns which associate to form a two-dimensional network. In this study, we prepared supramolecular columnar liquid crystals containing hydrogen bonding between carboxylic acid and, pyridine moieties. Thermal and structural properties of prepared complexe were investigated, and it exhibited hexagonal columnar structure ($Col_{h}$) at room temperature.

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역전달 신경회로망을 이용한 심전도 파형의 부정맥 분류 (Classification of ECG Arrhythmia Signals Using Back-Propagation Network)

  • 권오철;최진영
    • 대한의용생체공학회:의공학회지
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    • 제10권3호
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    • pp.343-350
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    • 1989
  • A new algorithm classifying ECG Arrhythmia signals using Back-propagation network is proposed. The base-line of ECG signal is detected by high pass filter and probability density function then input data are normalized for learning and classifying. In addition, ECG data are scanned to classify Arrhythmia signal which is hard to find R-wave. A two-layer perceptron with one hidden layer along with error back-propagation learning rule is utilized as an artificial neural network. The proposed algorithm shows outstanding performance under circumstances of amplitude variation, baseline wander and noise contamination.

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Initiative of the Asian Network for Sustainable Organic Farming Technology

  • Lee, Y.;Choi, H.S.;Lee, S.M.;Lee, H.J.
    • 한국유기농업학회지
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    • 제19권spc호
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    • pp.111-114
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    • 2011
  • Construction of the Asian Network for Sustainable Organic Farming Technology (ANSOFT) will be cooperatively administered by the public researchers in 12 Asian member countries from 2010 to 2012. ANSOFT will bring forward multiple reports, which will be constantly renewed by the member countries, regarding environmental issues, plant and landscape protection techniques, regulations and policies of each country's government on an organic agriculture, and natural resources such as organic seeds and biological agents.

Study of Collective Synchronous Dynamics in a Neural Network Model

  • Cho, Myoung Won
    • Journal of the Korean Physical Society
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    • 제73권9호
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    • pp.1385-1392
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    • 2018
  • A network with coupled biological neurons provides various forms of collective synchronous dynamics. Such phase-locking dynamics states resemble eigenvectors in a linear coupling system in that the forms are determined by the symmetry of the coupling strengths. However, the states behave as attractors in a nonlinear dynamics system. We here study the collective synchronous dynamics in a neural system by using a novel theory. We exhibit how the period and the stability of individual phase-locking dynamics states are determined by the characteristics of synaptic couplings. We find that, contrary to common sense, the firing rate of a synchronized state decreases with increasing synaptic coupling strength.

MMPI 분석도구로서 인공신경망 분석과 로지스틱 회귀분석의 비교 (Comparison between Logistic Regression and Artificial Neural Networks as MMPI Discriminator)

  • 이재원;정범석;김미숙;최지욱;안병은
    • 생물정신의학
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    • 제12권2호
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    • pp.165-172
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    • 2005
  • Objectives:The purpose of this study is to 1) conduct a discrimination analysis of schizophrenia and bipolar affective disorder using MMPI profile through artificial neural network analysis and logistic regression analysis, 2) to make a comparison between advantages and disadvantages of the two methods, and 3) to demonstrate the usefulness of artificial neural network analysis of psychiatric data. Procedure:The MMPI profiles for 181 schizophrenia and bipolar affective disorder patients were selected. Of these profiles, 50 were randomly placed in the learning group and the remaining 131 were placed in the validation group. The artificial neural network was trained using the profiles of the learning group and the 131 profiles of the validation group were analyzed. A logistic regression analysis was then conducted in a similar manner. The results of the two analyses were compared and contrasted using sensitivity, specificity, ROC curves, and kappa index. Results:Logistic regression analysis and artificial neural network analysis both exhibited satisfactory discriminating ability at Kappa index of greater than 0.4. The comparison of the two methods revealed artificial neural network analysis is superior to logistic regression analysis in its discriminating capacity, displaying higher values of Kappa index, specificity, and AUC(Area Under the Curve) of ROC curve than those of logistic regression analysis. Conclusion:Artificial neural network analysis is a new tool whose frequency of use has been increasing for its superiority in nonlinear applications. However, it does possess insufficiencies such as difficulties in understanding the relationship between dependent and independent variables. Nevertheless, when used in conjunction with other analysis tools which supplement it, such as the logistic regression analysis, it may serve as a powerful tool for psychiatric data analysis.

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