• Microbiology and Biotechnology Letters
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• v.46 no.3
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• pp.194-200
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• 2018

Glycogen plays important roles in bacteria. Its structure and storage capability have received more attention recently because of the potential correlations with environmental durability and pathogenicity. However, the low level of intracellular glycogen makes extraction and structure characterization difficult, inhibiting functional studies. Bacteria grown in regular media such as lysogeny broth and tryptic soy broth do no accumulate large amounts of glycogen. Comparative analyses of bacterial media reported in literature for glycogen-related studies revealed that there was no consistency in the recipes reported. Escherichia coli $DH5{\alpha}$ is a convenient model organism for gene manipulation studies with respect to glycogen. Additionally, M9 minimal salts medium is widely used to improve glycogen accumulation, although its composition varies. In this study, we optimized the M9 medium by adjusting the concentrations of itrogen source, tryptone, carbon source, and glucose, in order to achieve a balance between the growth rate and glycogen accumulation. Our result showed that $1{\times}M9$ minimal salts medium containing 0.4% tryptone and 0.8% glucose was a well-balanced nutrient source for enhancing the growth and glycogen storage in bacteria. This result will help future investigations related to bacterial physiology in terms of glycogen function.

• Journal of KIISE:Software and Applications
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• v.37 no.2
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• pp.120-128
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• 2010

A hypernetwork is a generalized hypo-graph and a probabilistic graphical model based on evolutionary learning. Hypernetwork models have been applied to various domains including pattern recognition and bioinformatics. Nevertheless, conventional hypernetwork models have the limitation that they can manage data with categorical or discrete attibutes only since the learning method of hypernetworks is based on equality comparison of hyperedges with learned data. Therefore, real-valued data need to be discretized by preprocessing before learning with hypernetworks. However, discretization causes inevitable information loss and possible decrease of accuracy in pattern classification. To overcome this weakness, we propose a novel feature-wise L1-distance based method for real-valued attributes in learning hypernetwork models in this study. We show that the proposed model improves the classification accuracy compared with conventional hypernetworks and it shows competitive performance over other machine learning methods.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.10
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• pp.1674-1682
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• 2020

Objective: This study aimed to elucidate the effect of miR-140 on the proliferation of porcine fetal fibroblasts (PFFs) and identify the target genes of miR-140 in PFFs. Methods: In this study, bioinformatics software was used to predict and verify target genes of miR-140. Quantitative polymerase chain reaction and western blot were used to detect the relationship between miR-140 and its target genes in PFFs. Dual luciferase reporter gene assays were performed to assess the interactions among miR-140, type 1 insulin-like growth factor receptor (IGF1R), and SRY-box 4 (SOX4). The effect of miR-140 on the proliferation of PFFs was measured by CCK-8 when PFFs were transfected with a miR-140 mimic or inhibitor. The transcription factor SOX4 binding to promoter of IGF1R was detected by chromatin immunoprecipitation assay (ChIP). Results: miR-140 directly targeted IGF1R and inhibited proliferation of PFFs. Meanwhile, miR-140 targeted transcription factor SOX4 that binds to promoter of porcine IGF1R to indirectly inhibit the expression of IGF1R. In addition, miR-140 inhibitor promoted PFFs proliferation, which is abrogated by SOX4 or IGF1R knockdown. Conclusion: miR-140 inhibited PFFs proliferation by directly targeting IGF1R and indirectly inhibiting IGF1R expression via SOX4, which play an important role in the development of porcine fetal.

• Asian-Australasian Journal of Animal Sciences
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• v.33 no.9
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• pp.1378-1386
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• 2020

Objective: Chinese indigenous sheep breeds can be classified into the following three categories by their tail morphology: fat-tailed, fat-rumped and thin-tailed sheep. The typical sheep breeds corresponding to fat-tailed, fat-rumped, and thin-tailed sheep are large-tailed Han, Altay, and Tibetan sheep, respectively. Detection of copy number variation (CNV) and selection signatures provides information on the genetic mechanisms underlying the phenotypic differences of the different sheep types. Methods: In this study, PennCNV software and F-statistics (F_ST) were implemented to detect CNV and selection signatures, respectively, on the X chromosome in three Chinese indigenous sheep breeds using ovine high-density 600K single nucleotide polymorphism arrays. Results: In large-tailed Han, Altay, and Tibetan sheep, respectively, a total of six, four and 22 CNV regions (CNVRs) with lengths of 1.23, 0.93, and 7.02 Mb were identified on the X chromosome. In addition, 49, 34, and 55 candidate selection regions with respective lengths of 27.49, 16.47, and 25.42 Mb were identified in large-tailed Han, Altay, and Tibetan sheep, respectively. The bioinformatics analysis results indicated several genes in these regions were associated with fat, including dehydrogenase/reductase X-linked, calcium voltage-gated channel subunit alpha1 F, and patatin like phospholipase domain containing 4. In addition, three other genes were identified from this analysis: the family with sequence similarity 58 member A gene was associated with energy metabolism, the serine/arginine-rich protein specific kinase 3 gene was associated with skeletal muscle development, and the interleukin 2 receptor subunit gamma gene was associated with the immune system. Conclusion: The results of this study indicated CNVRs and selection regions on the X chromosome of Chinese indigenous sheep contained several genes associated with various heritable traits.

• Korean Journal of Environmental Biology
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• v.30 no.2
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• pp.77-89
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• 2012

With the advent of the genomics era powered by DNA sequencing technologies, life science is being transformed significantly and biological research and development have been accelerated. Environmental biology concerns the relationships among living organisms and their natural environment, which constitute the global biogeochemical cycle. As sustainability of the ecosystems depends on biodiversity, examining the structure and dynamics of the biotic constituents and fully grasping their genetic and metabolic capabilities are pivotal. The high-speed high-throughput next-generation sequencing can be applied to barcoding organisms either thriving or endangered and to decoding the whole genome information. Furthermore, diversity and the full gene complement of a microbial community can be elucidated and monitored through metagenomic approaches. With regard to human welfare, microbiomes of various human habitats such as gut, skin, mouth, stomach, and vagina, have been and are being scrutinized. To keep pace with the rapid increase of the sequencing capacity, various bioinformatic algorithms and software tools that even utilize supercomputers and cloud computing are being developed for processing and storage of massive data sets. Environmental genomics will be the major force in understanding the structure and function of ecosystems in nature as well as preserving, remediating, and bioprospecting them.

• The Journal of the Korea Contents Association
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• v.11 no.5
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• pp.32-40
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• 2011

The tracking and analysis of cell activities in time-lapse sequences plays an important role in understanding complex biological processes such as the spread of the tumor, an invasion of the virus, the wound recovery and the cell division. For automatic tracking of cells, the tasks such as the cell detection at each frame, the investigation of the correspondence between cells in previous and current frames, the identification of the cell division and the recognition of new cells must be performed. This paper proposes an automatic cell tracking algorithm. In the first frame, the marker of each cell is extracted using the feature vector obtained by the analysis of cellular regions, and then the watershed algorithm is applied using the extracted markers to produce the cell segmentation. In subsequent frames, the segmentation results of the previous frame are incorporated in the segmentation process for the current frame. A combined criterion of geometric and intensity property of each cell region is used for the proper association between previous and current cells to obtain correct cell tracking. Simulation results show that the proposed method improves the tracking performance compared to the tracking method in Cellprofiler (the software package for automatic analysis of bioimages).

• Journal of KIISE:Software and Applications
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• v.32 no.6
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• pp.461-472
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• 2005

In this paper, we design and implement a graphical bio-ontology management system based on OWL(Web Ontology Language). It allows domain experts to easily manage sophisticated bio-ontologies in which biological knowledge is encoded. The knowledge can be seamlessly modeled into the ontology by well defined graphical notations, which capture most of subtle semantics inherently existing between biological terms. Our system provides a new construction mechanism, which can determine a considerable part of relationships between terms by their inheritance and inverse-inheritance. For keeping their semantics to be consistent, the mechanism supplies domain experts with information available from relationships being constructed or already constructed. The constructed ontology is basically formatted by OWL, which may benefit from its powerful semantic expressiveness. Additionally, it can be automatically translated into other standard languages without semantic loss, such as RDF/RDFS, DAML+OIL and so on. The main characteristics of our system is that it enables domain experts to delicately model the bio-ontology by the visualized construction mechanisms adopting well-defined graphical notations based on OWL.

• Journal of KIISE:Software and Applications
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• v.32 no.2
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• pp.99-107
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• 2005

In this paper, wc propose a now missing imputation method for minimizing loss of information linkage disequilibrium-based and haplotype-based imputation method, which estimate missing values of the data based on the specificity of Single Nucleotide Polymorphism(SNP) genotype data. Method for imputing data is needed to minimize the loss of information caused by experimental missing data. In general, missing imputation of biological data has used major allele imputation method. but this approach is not optima]. 1'his method has high error rates of missing values estimation since the characteristics of the genotype data are not considered not take into consideration the specific structure of the data. In this paper, we show the results of the comparative evaluation of our model methods and major imputation method for the estimation of missing values.

• KIPS Transactions on Software and Data Engineering
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• v.2 no.2
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• pp.123-130
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• 2013

As size of genomic data is increasing rapidly, the needs for high-performance computing system to process and store genomic data is also increasing. In this paper, we captured I/O trace of a system which analyzed 500 million sequence reads data in Genome analysis pipeline for 86 hours. The workload created 630 file with size of 1031.7 Gbyte and deleted 535 file with size of 91.4 GByte. What is interesting in this workload is that 80% of all accesses are from only two files among 654 files in the system. Size of read and write request in the workload was larger than 512 KByte and 1 Mbyte, respectively. Majority of read write operations show random and sequential patterns, respectively. Throughput and bandwidth observed in each processing phase was different from each other.

• Animal Bioscience
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• v.35 no.4
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• pp.517-526
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• 2022

Objective: The growth differentiation factor 8 (GDF8) gene plays a key role in bone formation, resorption, and skeletal muscle development in mammals. Here, we studied the genetic variants of GDF8 and their contribution to body conformation traits in Chinese Dabieshan cattle. Methods: Single nucleotide polymorphisms (SNPs) were identified in the bovine GDF8 gene by DNA sequencing. Phylogenetic analysis, motif analysis, and genetic diversity analysis were conducted using bioinformatics software. Association analysis between five SNPs, haplotype combinations, and body conformation traits was conducted in 380 individuals. Results: The GDF8 was highly conserved in seven species, and the GDF8 sequence of cattle was most similar to the sequences of sheep and goat based on the phylogenetic analysis. The motif analysis showed that there were 12 significant motifs in GDF8. Genetic diversity analysis indicated that the polymorphism information content of the five studied SNPs was within 0.25 to 0.5. Haplotype analysis revealed a total of 12 different haplotypes and those with a frequency of <0.05 were excluded. Linkage disequilibrium analysis showed a strong linkage (r²>0.330) between the following SNPs: g.5070C>A, g.5076T>C, and g.5148A>C. Association analysis indicated these five SNPs were associated with some of the body conformation traits (p<0.05), and the animals with haplotype combination H1H1 (-GGGG CCTTAA-) had greater wither height, hip height, heart girth, abdominal girth, and pin bone width than the other (p<0.05) Dabieshan cattle. Conclusion: Overall, our results indicate that the genetic variants of GDF8 affected the body conformation traits of Chinese Dabieshan cattle, and the GDF8 gene could make a strong candidate gene in Dabieshan cattle breeding programs.