• The Korean Journal of Physiology and Pharmacology
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• v.21 no.3
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• pp.353-360
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• 2017

Several human diseases have been associated with mitochondrial voltage-dependent anion channel-1 (VDAC1) due to its role in calcium ion transportation and apoptosis. Recent studies suggest that VDAC1 may interact with endothelium-dependent nitric oxide synthase (eNOS). Decreased VDAC1 expression may limit the physical interaction between VDAC1 and eNOS and thus impair nitric oxide production, leading to cardiovascular diseases, including pulmonary arterial hypertension (PAH). In this report, we conducted meta-analysis of genome-wide expression data to identify VDAC1 influenced genes implicated in PAH pathobiology. First, we identified the genes differentially expressed between wild-type and Vdac1 knockout mouse embryonic fibroblasts in hypoxic conditions. These genes were deemed to be influenced by VDAC1 deficiency. Gene ontology analysis indicates that the VDAC1 influenced genes are significantly associated with PAH pathobiology. Second, a molecular signature derived from the VDAC1 influenced genes was developed. We suggest that, VDAC1 has a protective role in PAH and the gene expression signature of VDAC1 influenced genes can be used to i) predict severity of pulmonary hypertension secondary to pulmonary diseases, ii) differentiate idiopathic pulmonary artery hypertension (IPAH) patients from controls, and iii) differentiate IPAH from connective tissue disease associated PAH.

• KIEAE Journal
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• v.17 no.5
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• pp.69-76
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• 2017

Purpose: This study aimed at developing an Artificial Neural Network (ANN) model for predicting the amount of cooling energy consumption of the variable refrigerant flow (VRF) cooling system by the different set-points of the control variables, such as supply air temperature of air handling unit (AHU), condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. Applying the predicted results for the different set-points, the control algorithm, which embedded the ANN model, will determine the most energy efficient control strategy. Method: The ANN model was developed and tested its prediction accuracy by using matrix laboratory (MATLAB) and its neural network toolbox. The field data sets were collected for the model training and performance evaluation. For completing the prediction model, three major steps were conducted - i) initial model development including input variable selection, ii) model optimization, and iii) performance evaluation. Result: Eight meaningful input variables were selected in the initial model development such as outdoor temperature, outdoor humidity, indoor temperature, cooling load of the previous cycle, supply air temperature of AHU, condenser fluid temperature, condenser fluid pressure, and refrigerant evaporation temperature. The initial model was optimized to have 2 hidden layers with 15 hidden neurons each, 0.3 learning rate, and 0.3 momentum. The optimized model proved its prediction accuracy with stable prediction results.

• Journal of Veterinary Science
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• v.24 no.1
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• pp.13.1-13.11
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• 2023

Background: Highly pathogenic avian influenza viruses (HPAIVs) is an extremely contagious and high mortality rates in chickens resulting in substantial economic impact on the poultry sector. Therefore, it is necessary to elucidate the pathogenic mechanism of HPAIV for infection control. Objective: Gene set enrichment analysis (GSEA) can effectively avoid the limitations of subjective screening for differential gene expression. Therefore, we performed GSEA to compare HPAI-infected resistant and susceptible Ri chicken lines. Methods: The Ri chickens Mx(A)/BF2(B21) were chosen as resistant, and the chickens Mx(G)/BF2(B13) were selected as susceptible by genotyping the Mx and BF2 genes. The tracheal tissues of HPAIV H5N1 infected chickens were collected for RNA sequencing followed by GSEA analysis to define gene subsets to elucidate the sequencing results. Results: We identified four differentially expressed pathways, which were immune-related pathways with a total of 78 genes. The expression levels of cytokines (IL-1β, IL-6, IL-12), chemokines (CCL4 and CCL5), type interferons and their receptors (IFN-β, IFNAR1, IFNAR2, and IFNGR1), Jak-STAT signaling pathway genes (STAT1, STAT2, and JAK1), MHC class I and II and their co-stimulatory molecules (CD80, CD86, CD40, DMB2, BLB2, and B2M), and interferon stimulated genes (EIF2AK2 and EIF2AK1) in resistant chickens were higher than those in susceptible chickens. Conclusions: Resistant Ri chickens exhibit a stronger antiviral response to HPAIV H5N1 compared with susceptible chickens. Our findings provide insights into the immune responses of genetically disparate chickens against HPAIV.

• IEIE Transactions on Smart Processing and Computing
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• v.1 no.2
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• pp.78-87
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• 2012

In this paper, we address the tracking problem caused by camera motion and rolling shutter effects associated with CMOS sensors in consumer handheld cameras, such as mobile cameras, digital cameras, and digital camcorders. A modified particle filtering method is proposed for simultaneously tracking objects and compensating for the effects of camera motion. The proposed method uses an elastic registration algorithm (ER) that considers the global affine motion as well as the brightness and contrast between images, assuming that camera motion results in an affine transform of the image between two successive frames. By assuming that the camera motion is modeled globally by an affine transform, only the global affine model instead of the local model was considered. Only the brightness parameter was used in intensity variation. The contrast parameters used in the original ER algorithm were ignored because the change in illumination is small enough between temporally adjacent frames. The proposed particle filtering consists of the following four steps: (i) prediction step, (ii) compensating prediction state error based on camera motion estimation, (iii) update step and (iv) re-sampling step. A larger number of particles are needed when camera motion generates a prediction state error of an object at the prediction step. The proposed method robustly tracks the object of interest by compensating for the prediction state error using the affine motion model estimated from ER. Experimental results show that the proposed method outperforms the conventional particle filter, and can track moving objects robustly in consumer handheld imaging devices.

• Proceedings of the KSAR Conference
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• 2001.03a
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• pp.29-29
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• 2001

This study was carried out to examine, by the reverse transcription chain reaction(RT-PCR)and Immunostain assays, epidermal growth factor mRNA expression in bovine ova during oocyte maturation in vitro(0-2lh)and after fertilization in vitro(6-144hr: zygotes to blastocysts). In this study, the transcripts of EGF was detected in oocytes using primers for EGF. Transcripts for EGF mRNA was not detected in oocytes through in vitro maturation. But EGF mRNA were present after fertilization up to the 2-cell stage and the blastocyst stage. The highest mRNA levels in 4-cell stage embryos were decreased at 8cell stage and then reincreased upto morulae and blastocysts. The results of this study showed EGF mRNA are present in embryo after fertilization and this factors are involved in the regulation of bovine embryo development.

• IEIE Transactions on Smart Processing and Computing
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• v.5 no.6
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• pp.383-389
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• 2016

This paper presents a novel automatic white balance (AWB) algorithm for consumer imaging devices. While existing AWB methods require reference white patches to correct color, the proposed method performs the AWB function using only an input image in two steps: i) white point detection, and ii) color constancy gain computation. Based on the dark channel prior assumption, a white point or region can be accurately extracted, because the intensity of a sufficiently bright achromatic region is higher than that of other regions in all color channels. In order to finally correct the color, the proposed method computes color constancy gain values based on the Y component in the XYZ color space. Experimental results show that the proposed method gives better color-corrected images than recent existing methods. Moreover, the proposed method is suitable for real-time implementation, since it does not need a frame memory for iterative optimization. As a result, it can be applied to various consumer imaging devices, including mobile phone cameras, compact digital cameras, and computational cameras with coded color.

• KIEAE Journal
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• v.17 no.4
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• pp.83-88
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• 2017

Purpose: The objective of this study is to develop a predictive model for calculating the amount of cooling load for the different setback temperatures during the setback period. An artificial neural network (ANN) is applied as a predictive model. The predictive model is designed to be employed in the control algorithm, in which the amount of cooling load for the different setback temperature is compared and works as a determinant for finding the most energy-efficient optimal setback temperature. Method: Three major steps were conducted for proposing the ANN-based predictive model - i) initial model development, ii) model optimization, and iii) performance evaluation. Result:The proposed model proved its prediction accuracy with the lower coefficient of variation of the root mean square errors (CVRMSEs) of the simulated results (Mi) and the predicted results (Si) under generally accepted levels. In conclusion, the ANN model presented its applicability to the thermal control algorithm for setting up the most energy-efficient setback temperature.

• IEIE Transactions on Smart Processing and Computing
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• v.6 no.4
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• pp.262-268
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• 2017

Intelligent video surveillance systems have been developed to monitor global areas and find specific target objects using a large-scale database. However, person re-identification presents some challenges, such as pose change and occlusions. To solve the problems, this paper presents an improved person re-identification method using sparse representation and saliency-based dictionary construction. The proposed method consists of three parts: i) feature description based on salient colors and textures for dictionary elements, ii) orthogonal atom selection using cosine similarity to deal with pose and viewpoint change, and iii) measurement of reconstruction error to rank the gallery corresponding a probe object. The proposed method provides good performance, since robust descriptors used as a dictionary atom are generated by weighting some salient features, and dictionary atoms are selected by reducing excessive redundancy causing low accuracy. Therefore, the proposed method can be applied in a large scale-database surveillance system to search for a specific object.

• YAKHAK HOEJI
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• v.40 no.3
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• pp.262-272
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• 1996

The constituents of Sanguisorba hakusanensis leaves (Rosaceae), of which the roots have been used as an astringent, hemostatics and antiphlogistics, were studied phytoche mically. From water fraction of the MeOH extract, gallic acid 3-O-${\beta}$-D-(6'-O-galloyl)-glucopyranoside(I), quercetin-3-O-${\beta$-D-galactopyranoside(II), quercetin-3-O-${\alpha}$-L-arabinopyranoside(III) and $2{\alpha},\;3{\beta},\;19{\alpha}$, 23-tetrahydroxyurs-12-en-28-oic acid 28-O-${\beta}$-D-glucopyranoside(IV) were isolated by column chromatographic separation using Amberlite XAD-2, ODS-gel and Sephadex LH-20. The structure of these compounds were elucidated by spectroscopic parameters of $^1H$-NMR, $^{13}C$-NMR, $^1H-^1H$ COSY, $^{13}C-^1H$ COSY, EI-Mass, FAB-Mass, IR, UV and by comparison with authentic samples.

• Journal of Dairy Science and Biotechnology
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• v.38 no.3
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• pp.142-145
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• 2020

Enterococcus faecalis CAUM157, isolated from raw cow's milk, is a Gram-positive, facultatively anaerobic, and non-spore-forming bacterium capable of inhabiting a wide range of environmental niches. E. faecalis CAUM157 was observed to produce a two-peptide bacteriocin that had a wide range of activity against several pathogens, including Listeria monocytogenes, Staphylococcus aureus, and periodontitis-causing bacteria. The whole genome of E. faecalis CAUM157 was sequenced using the PacBio RS II platform, revealing a genome size of 2,972,812 bp with a G+C ratio of 37.44%, assembled into two contigs. Annotation analysis revealed 2,830 coding sequences, 12 rRNAs, and 61 tRNAs. Further, in silico analysis of the genome identified a single bacteriocin gene cluster.