• Asian-Australasian Journal of Animal Sciences
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• v.21 no.11
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• pp.1551-1558
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• 2008

The mammalian cellular retinoic acid-binding proteins, CRABP-I and CRABP-II, bind retinoic acid which acts as an inducer of differentiation in several biological systems. To investigate a possible role for CRABP-II in bovine adipogenesis, we have cloned bovine CRABP-II cDNA and the coding region for CRABP-I. The predicted amino acid sequences of CRABP-II were highly conserved among several animal species (human, mouse, and rat at 97%, 93%, and 93%, respectively). The expression pattern of bovine CRABP-II was examined in greater details by applying RT-PCR to various bovine tissues. CRABP-II mRNA was expressed in most adipose-containing tissues. Moreover, the expression of CRABP-I and -II mRNA dramatically increased during the differentiation of adipocytes from bovine intramuscular fibroblast-like cells. The effects of retinoic acid on adipocyte differentiation of bovine intramuscular fibroblast-like cells were concentration-dependent. Retinoic acid activated the formation of lipid droplets at a level of 1 nM, whereas inhibition was observed at a level of $1{\mu}M$. CRABP-I gene was up-regulated and CRABP-II gene down-regulated by retinoic acid during adipocyte differentiation. These results suggest that CRABPs may play an important role in the regulation of intracellular retinoic acid concentrations during adipogenesis.

• Journal of Biosystems Engineering
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• v.17 no.1
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• pp.65-78
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• 1992

When using a computer vision system for a measurement, the geometrically distorted input image usually restricts the site and size of the measuring window. A geometrically distorted image caused by the image sensing and processing hardware degrades the accuracy of the visual measurement and prohibits the arbitrary selection of the measuring scope. Therefore, an image calibration is inevitable to improve the measuring accuracy. A calibration process is usually done via four steps such as measurement, modeling, parameter estimation, and compensation. In this paper, the efficient error calibration technique of a geometrically distorted input image was developed using a neural network. After calibrating a unit pixel, the distorted image was compensated by training CMLAN(Cerebellar Model Linear Associator Network) without modeling the behavior of any system element. The input/output training pairs for the network was obtained by processing the image of the devised sampled pattern. The generalization property of the network successfully compensates the distortion errors of the untrained arbitrary pixel points on the image space. The error convergence of the trained network with respect to the network control parameters were also presented. The compensated image through the network was then post processed using a simple DDA(Digital Differential Analyzer) to avoid the pixel disconnectivity. The compensation effect was verified using known sized geometric primitives. A way to extract directly a real scaled geometric quantity of the object from the 8-directional chain coding was also devised and coded. Since the developed calibration algorithm does not require any knowledge of modeling system elements and estimating parameters, it can be applied simply to any image processing system. Furthermore, it efficiently enhances the measurement accuracy and allows the arbitrary sizing and locating of the measuring window. The applied and developed algorithms were coded as a menu driven way using MS-C language Ver. 6.0, PC VISION PLUS library functions, and VGA graphic functions.

• Food Science of Animal Resources
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• v.35 no.5
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• pp.692-702
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• 2015

Lactobacillus mucosae is a natural resident of the gastrointestinal tract of humans and animals and a potential probiotic bacterium. To understand the global protein expression profile and metabolic features of L. mucosae LM1 in the early stationary phase, the QExactive^TM Hybrid Quadrupole-Orbitrap Mass Spectrometer was used. Characterization of the intracellular proteome identified 842 proteins, accounting for approximately 35% of the 2,404 protein-coding sequences in the complete genome of L. mucosae LM1. Proteome quantification using QExactive^TM Orbitrap MS detected 19 highly abundant proteins (> 1.0% of the intracellular proteome), including CysK (cysteine synthase, 5.41%) and EF-Tu (elongation factor Tu, 4.91%), which are involved in cell survival against environmental stresses. Metabolic pathway annotation of LM1 proteome using the Kyoto Encyclopedia of Genes and Genomes (KEGG) database showed that half of the proteins expressed are important for basic metabolic and biosynthetic processes, and the other half might be structurally important or involved in basic cellular processes. In addition, glycogen biosynthesis was activated in the early stationary phase, which is important for energy storage and maintenance. The proteogenomic data presented in this study provide a suitable reference to understand the protein expression pattern of lactobacilli in standard conditions

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.47 no.3
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• pp.22-28
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• 2010

Adaboost is well known for a representative learner as one of the kernel methods. Adaboost which is based on the statistical learning theory shows good generalization performance and has been applied to various pattern recognition problems. However, Adaboost is basically to deal with a two-class classification problem, so we cannot solve directly a multi-class problem with Adaboost. One-Vs-All and Pair-Wise have been applied to solve the multi-class classification problem, which is one of the multi-class problems. The two methods above are ones of the output coding methods, a general approach for solving multi-class problem with multiple binary classifiers, which decomposes a complex multi-class problem into a set of binary problems and then reconstructs the outputs of binary classifiers for each binary problem. However, two methods cannot show good performance. In this paper, we propose the method to solve a multi-target classification problem by using radial basis function of Adaboost weak classifier.

• The KIPS Transactions:PartB
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• v.15B no.3
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• pp.245-252
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• 2008

We present a classification algorithm based on ant colony system(ACS) for classifying digital images. The ACS has been recently emerged as a useful tool for the pattern recognition, image extraction, and edge detection. The classification algorithm of digital images is very important in the application areas of digital image coding, image analysis, and image recognition because it significantly influences the quality of images. The conventional procedures usually classify digital images with the fixed value for the associated parameters and it requires postprocessing. However, the proposed algorithm utilizing randomness of ants yields the stable and enhanced images even for processing the rapidly changing images. It is also expected that, due to this stability and flexibility of the present procedure, the digital images are stably classified for processing images with various noises and error signals arising from processing of the drastically fast moving images could be automatically compensated and minimized.

• Journal of Broadcast Engineering
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• v.23 no.6
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• pp.886-895
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• 2018

In this paper, we introduce a technique to detect the video forgery by using the regularity that occurs in the video compression process. The proposed method uses the hierarchical regularity lost by the video double compression and the frame deletion. In order to extract such irregularities, the depth information of CU and TU, which are basic units of HEVC, is used. For improving performance, we make a depth map of CU and TU using local information, and then create input data by grouping them in GoP units. We made a decision whether or not the video is double-compressed and forged by using a general three-dimensional convolutional neural network. Experimental results show that it is more effective to detect whether or not the video is forged compared with the results using the existing machine learning algorithm.

• Journal of Plant Biotechnology
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• v.46 no.2
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• pp.97-105
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• 2019

Dehydration Responsive Element Binding (DREB) gene is one of the essential transcription factors plants use for responding to stress conditions including salinity, drought, and cold stress. The purpose of this study was to isolate the full length and characterize the DREB gene from three different genotypes of sugarcane, wild, commercial cultivar, and interspecific hybrid sugarcane. The length of the gene, designated ScDREB was 789 bp, and coding for a putative polypeptide of 262 amino acid residues. Sequences of the gene were submitted to the GenBank database with accession numbers of KX280722.1, KX280721.1, and KX280719.1 for wild sugarcane, commercial cultivar (KPS94-13), and interspecific hybrid (Biotec2), respectively. In silico characterization indicated that the deduced polypeptide contains a putative nuclear localization signal (NLS) sequence, and a conserved AP2/ERF domain of the DREB family, at 82-140 amino residues. Based on multiple sequence alignment, sequences of the gene from the three sugarcane genotypes were classified in the DREB2 group. Gene expression analysis indicated, that ScDREB2 expression pattern in tested sugarcane was up-regulated by salt stress. When the plants were under 100 mM NaCl stress, relative expressions of the gene in leaves was higher than those in roots. In contrast, under 200 mM NaCl stress, relative expressions of the gene in roots was higher than those in leaves. This is the first report on cloning the full length and characterization, of ScDREB2 gene of sugarcane. Results indicate that ScDREB2 is highly responsive to salt stress.

• Journal of IKEEE
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• v.23 no.3
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• pp.910-916
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• 2019

Non-local similarity of natural images is one of highly exploited features in various applications dealing with images. Unique edges, texture, and pattern of the images are frequently repeated over the entire image. Once the similar image blocks are classified into a cluster, representative features of the image blocks can be extracted from the cluster. The bigger the size of the cluster is the better the additive white noise can be separated. Denoising is one of major research topics in the image processing field suppressing the additive noise. In this paper, a denoising algorithm is proposed which first clusters the noisy image blocks based on similarity, extracts the feature of the cluster, and finally recovers the original image. Performance experiments with several images under various noise strengths show that the proposed algorithm recovers the details of the image such as edges, texture, and patterns while outperforming the previous methods in terms of PSNR in removing the additive Gaussian noise.

• Microbiology and Biotechnology Letters
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• v.47 no.1
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• pp.25-33
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• 2019

Lactobacillus rhamnosus BFE5264, isolated from a Maasai fermented milk product ("kule naoto"), was previously shown to exhibit bile acid resistance, cholesterol assimilation, and adhesion to HT29-MTX cells in vitro. In this study, we re-annotated and analyzed the previously reported complete genome sequence of strain BFE5264. The genome consists of a circular chromosome of 3,086,152 bp and a putative plasmid, which is the largest one identified among L. rhamnosus strains. Among the 2,883 predicted protein-coding genes, those with carbohydrate-related functions were the most abundant. Genome analysis of strain BFE5264 revealed two consecutive CRISPR regions and no known virulence factors or antimicrobial resistance genes. In addition, previously known highly variable regions in the genomes of L. rhamnosus strains were also evident in strain BFE5264. Pairwise comparison with the most studied probiotic strain L. rhamnosus GG revealed strain BFE5264-specific deletions, probably due to insertion sequence-mediated recombination. The latter was associated with loss of the spaCBA pilin gene cluster and exopolysaccharide biosynthetic genes. Comparative genomic analysis of the sequences from all available L. rhamnosus strains revealed that they were clustered into two groups, being within the same species boundary based on the average nucleotide identities. Strain BFE5264 had a sister group relationship with the group that contained strain GG, but neither ANI-based hierarchical clustering nor core-gene-based phylogenetic tree construction showed a clear distinctive pattern associated with the isolation source, implying that the genotype alone cannot account for their ecological niches. These results provide insights into the probiotic mechanisms of strain BFE5264 at the genomic level.

• Korean Journal of Agricultural Science
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• v.48 no.3
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• pp.643-654
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• 2021

Phytophthora capsici is one of the most destructive hemibiotrophic pathogens; it can cause blight in chili peppers, and secrete various effector proteins to infect the plants. These effectors contain an N-terminal conserved RXLR motif. Here, we generated full-length RXLR effector coding genes using primer pairs, and cloned them into the pGR106 vector for in planta expression. Two of these genes, PcREK6 and PcREK41 (P. capsici RXLR effector from the Korea isolate), were further characterized. PcREK6 and PcREK41 genes showed that they encode effector proteins with a general modular structure, including the N-terminal conserved RXLR-DEER motif and signal peptide sequences. PcREK6 and PcREK41 expressions were strongly induced when the chili pepper plants (Capsicum annuum) were challenged with P. capsici. These results provide molecular evidence to elucidate the virulence or avirulence factors in chili pepper. Our results also showed that two effectors induce hypersensitive response (HR) cell death when expressed in chili leaves. Cell death suppression assays in Nicotiana benthamiana revealed that most effectors could not suppress programmed cell death (PCD) triggered by Bcl-associated X (BAX) or Phytophthora infestans elicitin (INF1). However, PcREK6 fully suppressed PCD triggered by BAX, while PcREK41 partially suppressed PCD triggered by INF1 elicitin. These results suggest that PcREK effectors from P. capsici interact with putative resistance (R) proteins in planta, and different effectors may target different pathways in a plant cell to suppress pattern-triggered immunity (PTI) or effector-triggered immunity (ETI).