• Journal of Biomedical Engineering Research
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• v.31 no.3
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• pp.177-186
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• 2010

In 3D ultrasound color Doppler imaging (CDI), 8-16 pulse transmissions (ensembles) per each scanline are used for effective clutter rejection and flow estimation, but it yields a low volume acquisition rate. In this paper, we have evaluated three flow estimation methods: autoregression (AR), eigendecomposition (ED), and autocorrelation combined with adaptive clutter rejection (AC-ACR) for a small ensemble size (E=4). The performance of AR, ED and AC-ACR methods was compared using 2D and 3D in vivo data acquired under different clutter conditions (common carotid artery, kidney and liver). To evaluate the effectiveness of three methods, receiver operating characteristic (ROC) curves were generated. For 2D kidney in vivo data, the AC-ACR method outperforms the AR and ED methods in terms of the area under the ROC curve (AUC) (0.852 vs. 0.793 and 0.813, respectively). Similarly, the AC-ACR method shows higher AUC values for 2D liver in vivo data compared to the AR and ED methods (0.855 vs. 0.807 and 0.823, respectively). For the common carotid artery data, the AR provides higher AUC values, but it suffers from biased estimates. For 3D in vivo data acquired from a kidney transplant patient, the AC-ACR with E=4 provides an AUC value of 0.799. These in vivo experiment results indicate that the AC-ACR method can provide more robust flow estimates compared to the AR and ED methods with a small ensemble size.

• Journal of the Korean Institute of Intelligent Systems
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• v.13 no.1
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• pp.119-124
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• 2003

In this paper, we modeled positive selection and negative selection that is developing process of cytotoxic T-cell that plays important role in biological immune system. Also, we developed change detection algorithm, which is very Important part in detecting data change by intrusion and data infection by computer virus. Proposed method is the algorithm that produces MHC receptor lot recognizing self and antigen detector for recognizing non-self. Therefore, proposed method detects self and intruder by two type of detectors like real immune system. We show the effectiveness and characteristics of proposed change detection algorithm by simulation about point and block change of self file.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.41 no.4
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• pp.51-58
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• 2004

This paper proposes a new artificial immune approach to on-line hardware test which is the most indispensable technique for fault tolerant hardware. A novel algorithm of generating tolerance conditions is suggested based on the principle of the antibody diversity. Tolerance conditions in artificial immune system correspond to the antibody in biological immune system. In addition, antigen presenting cell (APC) is realized by Quine-McCluskey method in this algorithm and tolerance conditions are generated through GA (Genetic Algorithm). The suggested method is applied to the on-line monitoring of a typical FSM (a decade counter) and its effectiveness is demonstrated by the computer simulation.

• The Plant Pathology Journal
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• v.24 no.3
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• pp.283-288
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• 2008

Plant leaf surface is an important niche for diverse epiphytic microbes, including bacteria and fungi. Plant leaf surface plays a critical frontline defense against pathogen infections. The objective of our study was to evaluate the effectiveness of a starch-based super-absorbent polymer(SAP) combination, which enhances water potential and nutrient availability to plant leaves. We evaluated the effect of SAP on the maintenance of bacterial populations. In order to monitor bacterial populations in situ, a SAP mixture containing Pseudomonas syringae pv. tabaci that expressed recombinant green fluorescent protein(GFPuv) was spray-challenged onto whole leaves of Nicotiana benthamiana. The SAP combination treatment enhanced bacterial robustness, as indicated by disease severity and incidence. Unexpectedly, bacterial numbers were not significantly different between leaves treated with the SAP combination and those treated with water alone. Furthermore, young leaves treated with the SAP combination had more severe symptoms and a greater number of bacterial spots caused by primary and secondary infections compared to young leaves treated with the water control. In contrast, bacterial cell numbers did not statistically differ between the two groups, which indicated that measurement of viable GFP-based bacterial spots may provide a more sensitive methodology for assessing virulence of bacterial pathogens than methods that require dilution plating following maceration of bacterial-inoculated leaf tissue. Our study suggests that the SAP combination successfully increased bacterial aggressiveness, which could either be used to promote the ability of biological agents to control weedy plants or increase the robustness of saprophytic epiphytes against competition from potentially harmful microbes.

• International Journal of Contents
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• v.8 no.1
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• pp.23-29
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• 2012

Multiple expression levels of genes obtained using time series microarray experiments have been exploited effectively to enhance understanding of a wide range of biological phenomena. However, the unique nature of microarray data is usually in the form of large matrices of expression genes with high dimensions. Among the huge number of genes presented in microarrays, only a small number of genes are expected to be effective for performing a certain task. Hence, discounting the majority of unaffected genes is the crucial goal of gene selection to improve accuracy for disease diagnosis. In this paper, a non-Gaussian weight matrix obtained from an incremental model is proposed to extract useful features of multivariate time series microarrays. The proposed method can automatically identify a small number of significant features via discovering hidden variables from a huge number of features. An unsupervised hierarchical clustering representative is then taken to evaluate the effectiveness of the proposed methodology. The proposed method achieves promising results based on predictive accuracy of clustering compared to existing methods of analysis. Furthermore, the proposed method offers a robust approach with low memory and computation costs.

• BMB Reports
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• v.37 no.6
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• pp.648-656
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• 2004

This paper deals with the development of a predictive probabilistic model, a composite dependency-reflecting model (CDRM), which was designed to detect core promoter regions and transcription start sites (TSS) in vertebrate genomic DNA sequences, an issue of some importance for genome annotation. The model actually represents a combination of first-, second-, third- and much higher order or long-range dependencies obtained using the expanded maximal dependency decomposition (EMDD) procedure, which iteratively decomposes data sets into subsets on the basis of dependency degree and patterns inherent in the target promoter region to be modeled. In addition, decomposed subsets are modeled by using a first-order Markov model, allowing the predictive model to reflect dependency between adjacent positions explicitly. In this way, the CDRM allows for potentially complex dependencies between positions in the core promoter region. Such complex dependencies may be closely related to the biological and structural contexts since promoter elements are present in various combinations separated by various distances in the sequence. Thus, CDRM may be appropriate for recognizing core promoter regions and TSSs in vertebrate genomic contig. To demonstrate the effectiveness of our algorithm, we tested it using standardized data and real core promoters, and compared it with some current representative promoter-finding algorithms. The developed algorithm showed better accuracy in terms of specificity and sensitivity than the promoter-finding ones used in performance comparison.

• Journal of the Korean Institute of Intelligent Systems
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• v.10 no.4
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• pp.315-323
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• 2000

In this paper, we propose a method of constructing neural networks using bio-inpired emergent and evolutionary concepts. This method is algorithm that is based on the characteristics of the biological DNA and growth of plants, Here is, we propose a constructing method to make a DNA coding method for production rule of L-system. L-system is based on so-called the parallel rewriting nechanism. The DNA coding method has no limitation in expressing the produlation the rule of L-system. Evolutionary algotithms motivated by Darwinaian natural selection are population based searching methods and the high performance of which is highly dependent on the representation of solution space. In order to verify the effectiveness of our scheme, we apply it one step ahead prediction of Mackey-Glass time series, Sunspot data and KOSPI data.

• Journal of Biomedical Engineering Research
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• v.32 no.4
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• pp.295-304
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• 2011

In this study, we evaluated the EEG based BCI algorithm using common spatial pattern to find realistic applicability using neurofeedback EEG based BCI algorithm - EEG mode, feature vector calculation, the number of selected channels, 3 types of classifier, window size is evaluated for 10 subjects. The experimental results have been evaluated depending on conditioned experiment whether neurofeedback is used or not In case of using neurofeedback, a few subjects presented exceptional but general tendency presented the performance improvement Through this study, we found a motivation of development for the specific classifier based BCI system and the assessment evaluation system. We proposed a need for an optimized algorithm applicable to the robust motor imagery evaluation system with more useful functionalities.

• Research in Plant Disease
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• v.24 no.2
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• pp.99-112
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• 2018

Certain agrochemicals may be tuned for increased effectiveness when downsized to nanoparticles (NPs), where one dimension is less than 100 nm. The NPs may function as fertilizers, pesticides and products to improve plant health through seed priming, growth promotion, and induction of systemic tolerance to stress. Formulations will allow targeted applications with timed release, reducing waste and pollution when compared to treatments with bulk-size products. The NPs may be a single component, such as nano-ZnO as a fertilizer, or be composites of compatible materials, for example where N, P, and K plus micronutrients are available. The active materials could be loaded into porous carriers or tethered to base nanostructures. Coatings could include such natural products alginate, chitosan, zein, or silica. Certain NPs are taken up and transported in the plant's phloem and xylem so systemic effects are feasible. Timed and targeted release of the active product could be achieved in response to changes in pH or availability of ligands within the plant or the rhizosphere. Global research has revealed the many potentials offered by NP formulations to aid sustainability in agriculture. Current work will provide information needed by regulatory agencies to assess their safety in the agricultural setting.

• Journal of Plant Biotechnology
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• v.34 no.4
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• pp.313-322
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• 2007

Extract of Japanese red pine needles has been used in Asia pacific regions since long periods believing its valuable properties as tonic and ability of curing diseases of unidentified symptoms. Some selective compounds present in the extract and their effects were analyzed. Carbohydrates and vitamin c were identified using HPLC; terpenoid compounds by GC-MS; anti-bacterial analysis by paper discs, plates count and gastrointestinal motility by whole cell patch clamp. The extract is a mixture of compounds therefore its diverse effect was expected. Self-fermentation in extract proceeds after spontaneous appearance of yeast strains without inoculation. Effects and composition of the extract vary with varying period of self-fermentation. Extract inhibits the growth of bacteria dose dependently exhibiting its antibacterial properties however effectiveness increases with increase in fermentation period. The extract also can modulate gastrointestinal motility in murine small intestine by modulating pace maker currents in ICC mediated through ATP sensitive potassium channel.