• Asian-Australasian Journal of Animal Sciences
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• 제32권5호
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• pp.607-613
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• 2019

Objective: Intramuscular fat (IMF) content plays an important role in meat quality. Identification of single nucleotide polymorphisms (SNPs) and genes related to pig IMF, especially using pig populations with high IMF content variation, can help to establish novel molecular breeding tools for optimizing IMF in pork and unveil the mechanisms that underlie fat metabolism. Methods: We collected muscle samples of 453 Chinese Lulai black pigs, measured IMF content by Soxhlet petroleum-ether extraction method, and genotyped genome-wide SNPs using GeneSeek Genomic Profiler Porcine HD BeadChip. Then a genome-wide association study was performed using a linear mixed model implemented in the GEMMA software. Results: A total of 43 SNPs were identified to be significantly associated with IMF content by the cutoff p<0.001. Among these significant SNPs, the greatest number of SNPs (n = 19) were detected on Chr.9, and two linkage disequilibrium blocks were formed among them. Additionally, 17 significant SNPs are mapped to previously reported quantitative trait loci (QTLs) of IMF and confirmed previous QTLs studies. Forty-two annotated genes centering these significant SNPs were obtained from Ensembl database. Overrepresentation test of pathways and gene ontology (GO) terms revealed some enriched reactome pathways and GO terms, which mainly involved regulation of basic material transport, energy metabolic process and signaling pathway. Conclusion: These findings improve our understanding of the genetic architecture of IMF content in pork and facilitate the follow-up study of fine-mapping genes that influence fat deposition in muscle.

• Journal of Ginseng Research
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• 제45권1호
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• pp.58-65
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• 2021

Background: Panax ginseng, as one of the most widely used herbal medicines worldwide, has been studied comprehensively in terms of the chemical components and pharmacology. The proteins from ginseng are also of great importance for both nutrition value and the mechanism of secondary metabolites. However, the proteomic studies are less reported in the absence of the genome information. With the completion of ginseng genome sequencing, the proteome profiling has become available for the functional study of ginseng protein components. Methods: We optimized the protein extraction process systematically by using SDS-PAGE and one-dimensional liquid chromatography mass spectrometry. The extracted proteins were then analyzed by two-dimensional chromatography separation and cutting-edge mass spectrometry technique. Results: A total of 2,732 and 3,608 proteins were identified from ginseng root and cauline leaf, respectively, which was the largest data set reported so far. Only around 50% protein overlapped between the cauline leaf and root tissue parts because of the function assignment for plant growing. Further gene ontology and KEGG pathway revealed the distinguish difference between ginseng root and leaf, which accounts for the photosynthesis and metabolic process. With in-deep analysis of functional proteins related to ginsenoside synthesis, we interestingly found the cytochrome P450 and UDP-glycosyltransferase expression extensively in cauline leaf but not in the root, indicating that the post glucoside synthesis of ginsenosides might be carried out when growing and then transported to the root at withering. Conclusion: The systematically proteome analysis of Panax ginseng will provide us comprehensive understanding of ginsenoside synthesis and guidance for artificial cultivation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제15권6호
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• pp.2049-2068
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• 2021

AI combined with NLP techniques has promoted the use of Virtual Assistants and have made people rely on them for many diverse uses. Conversational Agents are the most promising technique that assists computer users through their operation. An important challenge in developing Conversational Agents globally is transferring the groundbreaking expertise obtained in English to other languages. AI is making it possible to transfer this learning. There is a dire need to develop systems that understand secular languages. One such difficult language is Hindi, which is the fourth most spoken language in the world. Semantic similarity is an important part of Natural Language Processing, which involves applications such as ontology learning and information extraction, for developing conversational agents. Most of the research is concentrated on English and other European languages. This paper presents a Corpus-based word semantic similarity measure for Hindi. An experiment involving the translation of the English benchmark dataset to Hindi is performed, investigating the incorporation of the corpus, with human and machine similarity ratings. A significant correlation to the human intuition and the algorithm ratings has been calculated for analyzing the accuracy of the proposed similarity measures. The method can be adapted in various applications of word semantic similarity or module for any other language.

• Journal of Microbiology and Biotechnology
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• 제32권3호
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• pp.365-377
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• 2022

Mammalian target of rapamycin (mTOR) is a serine-threonine kinase member of the cellular phosphatidylinositol 3-kinase (PI3K) pathway, which is involved in multiple biological functions by transcriptional and translational control. mTOR is a downstream mediator in the PI3K/Akt signaling pathway and plays a critical role in cell survival. In cancer, this pathway can be activated by membrane receptors, including the HER (or ErbB) family of growth factor receptors, the insulin-like growth factor receptor, and the estrogen receptor. In the present work, we congregated an electronic network of mTORC1 built on an assembly of data using natural language processing, consisting of 470 edges (activations/interactions and/or inhibitions) and 206 nodes representing genes/proteins, using the Cytoscape 3.6.0 editor and its plugins for analysis. The experimental design included the extraction of gene expression data related to five distinct types of cancers, namely, pancreatic ductal adenocarcinoma, hepatic cirrhosis, cervical cancer, glioblastoma, and anaplastic thyroid cancer from Gene Expression Omnibus (NCBI GEO) followed by pre-processing and normalization of the data using R & Bioconductor. ExprEssence plugin was used for network condensation to identify differentially expressed genes across the gene expression samples. Gene Ontology (GO) analysis was performed to find out the over-represented GO terms in the network. In addition, pathway enrichment and functional module analysis of the protein-protein interaction (PPI) network were also conducted. Our results indicated NOTCH1, NOTCH3, FLCN, SOD1, SOD2, NF1, and TLR4 as upregulated proteins in different cancer types highlighting their role in cancer progression. The MCODE analysis identified gene clusters for each cancer type with MYC, PCNA, PARP1, IDH1, FGF10, PTEN, and CCND1 as hub genes with high connectivity. MYC for cervical cancer, IDH1 for hepatic cirrhosis, MGMT for glioblastoma and CCND1 for anaplastic thyroid cancer were identified as genes with prognostic importance using survival analysis.

• Journal of Ginseng Research
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• 제47권1호
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• pp.159-165
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• 2023

Background: Red ginseng marc, the residue of red ginseng left after water extraction, is rich in dietary fiber. Dietary fiber derived from fruits or vegetables can promote the proliferation of probiotics, and it is a key technology in the food industry to increase the productivity of probiotics by adding growth-enhancing substances such as dietary fiber. In this study, the effect of red ginseng dietary fiber (RGDF) on the growth of probiotic bacterial strains was investigated at the phenotypic and genetic levels. Methods: We performed transcriptome profiling of Lactiplantibacillus plantarum IDCC3501 in two phases of culture (logarithmic (L)-phase and stationary (S)-phase) in two culture conditions (with or without RGDF) using RNA-seq. Differentially expressed genes (DEGs) were identified and classified according to Gene Ontology terms. Results: The growth of L.plantarum IDCC3501 was enhanced in medium supplemented with RGDF up to 2%. As a result of DEG analysis, 29 genes were upregulated and 30 were downregulated in the RGDF-treated group in the L-phase. In the S-phase, 57 genes were upregulated and 126 were downregulated in the RGDF-treated group. Among the upregulated genes, 5 were upregulated only in the L-phase, 10 were upregulated only in the S-phase, and 3 were upregulated in both the L- and S-phases. Conclusions: Transcriptome analysis could be a valuable tool for elucidating the molecular mechanisms by which RGDF promotes the proliferation of L.plantarum IDCC3501. This growth-promoting effect of RGDF is important, since RGDF could be used as a prebiotic source without additional chemical or enzymatic processing.

• 지능정보연구
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• 제17권2호
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• pp.39-60
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• 2011

웹 2.0 기술이 소개된 이후 소셜 네트워크 서비스는 미래 정보기술의 기초로서 중요하게 인식되고 있다. 이에, 웹2.0 환경에서 소셜 네트워크를 구축하기 위하여 온톨로지 기반의 사용자 프로필 기술 도구인 FOAF를 활용하기 위한 다양한 연구가 이뤄지고 있다. 그러나 FOAF를 이용하여 소셜 네트워크를 생성 및 관리하는 대부분의 방법은 시간의 흐름에 따라 변화하는 사용자의 소셜 네트워크를 자동적으로 반영하기 어려운 단점이 있으며 다양한 소셜 미디어 서비스가 제공되는 환경에서는 FOAF를 동적으로 관리하기가 쉽지 않다. 따라서 본 논문에서는 기존 FOAF를 이용한 소셜 네트워크 추출방법의 한계를 극복하기 위하여 사용자 프로파일 기술 언어인 FOAF와 웹 저작물 출판 매커니즘인 RSS를 OLAP 시스템에 적용시켜 동적으로 FOAF를 갱신하고 관리하기 위한 방법을 제안한다. 본 논문에서 제안하는 방법은 수집한 FOAF와 RSS 파일들을 스타스키마로 설계된 데이터베이스에 넣어 OLAP 큐브를 생성한다. 그리고 OLAP 연산을 이용하여 사용자의 연결관계를 분석하고 FOAF에 그 결과를 반영한다. 본 논문에서 제안하는 방법은 이기종 분산처리 환경 하에서 데이터의 상호호환성을 보장할 뿐만 아니라 시간의 흐름에 따른 사용자의 관심 및 이슈 등의 변화를 효과적으로 반영한다.