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Outflow Kinematics Manifested by the Hα Line: Gas Outflows in Type 2 AGNs. IV.
Kang, Daeun,Woo, Jong-Hak,Bae, Hyun-Jin American Astronomical Society 2017 The Astrophysical Journal Vol.845 No.2
<P>Energetic ionized gas outflows driven by active galactic nuclei (AGNs) have been studied as a key phenomenon related to AGN feedback. To probe the kinematics of the gas in the narrow-line region, [O III] lambda 5007 has been utilized in a number of studies showing nonvirial kinematic properties due to AGN outflows. In this paper, we statistically investigate whether the H alpha emission line is influenced by AGN-driven outflows by measuring the kinematic properties based on the H alpha line profile and comparing them with those of [O III]. Using the spatially integrated spectra of similar to 37,000 Type 2 AGNs at z < 0.3 selected from the Sloan Digital Sky Survey DR7, we find a nonlinear correlation between H alpha velocity dispersion and stellar velocity dispersion that reveals the presence of the nongravitational component, especially for AGNs with a wing component in H alpha. The large H alpha velocity dispersion and velocity shift of luminous AGNs are clear evidence of AGN outflow impacts on hydrogen gas, while relatively smaller kinematic properties compared to those of [O III] imply that the observed outflow effect on the H alpha line is weaker than the case of [O III].</P>
( Daeun Kang ),( In Beom Jung ),( Su Yel Lee ),( Se Jin Park ),( Wan Jin Hwang ),( Minhyeok Lee ),( Sun Jung Kwon ),( Dong Ho Park ),( Ji Woong Son ) 대한결핵 및 호흡기학회 2020 대한결핵 및 호흡기학회 추계학술대회 초록집 Vol.128 No.-
Particulate matter (PM) has various systemic effects, such as respiratory, cardiovascular, endocrine, as well as having effects on the nervous systems. So far, there have been many epidemiologic studies, but studies related to the biological mechanisms are insufficient. We researched the effects of PM on lung epithelial cells with Next Generation Sequencing (NGS) and validated this with quantitative real-time polymerase chain reaction (qRT-PCR). We cultured the group treated with PM10 at a concentration of 50μg/mL and the untreated group for seven days in five lung cell lines: NCI-H358, HCC-827, A549, NCI-H292, BEAS-2B. Then, we extracted the RNA from the sample and performed NGS. As a result of NGS, various gene expressions were upregulated or downregulated. Among them, we selected the gene whose mean fold change was more than doubled and changed in the same direction in all five cell lines. Based on these genes, we selected the top 10 genes, either upregulated or downregulated, to validate with the qRT-PCR. There were the four genes that matched the NGS and qRT-PCR Results, all of which were upregulated genes(Table 1). The four genes are CYP1A1, CYP1B1, LINC01816, and BPIFA2. All four genes that matched the two Results were up-regulated genes and none of the down-regulated genes matched. CYP1A1 and CYP1B1 are known to cause lung cancer by metabolizing polycyclic aromatic hydrocarbons, and long non-coding RNA is also known to play an important role in lung cancer. Considering this, we thought that PM10 might be associated with lung cancer by activating CYP1A1, CYP1B1, and LINC01816.
코퍼스 – 실험 – 딥러닝 연구방법론 비교분석: ‘-도록’ 통제 구문을 중심으로
강다은(Daeun Kang),송상헌(Sanghoun Song) 한국중원언어학회 2022 언어학연구 Vol.- No.62
This study aims to examine how convergent results are showing on specific language phenomenon, by using methodological pluralism. Focusing on the ‘-tolok’ control construction, we compared the results of three research methodologies: corpus, experiment, deep learning. Previous studies used corpus exploration and language experiment separately or deep learning based on English data. However, it was not sufficiently implemented that comprehensively examining the three methodologies and deep learning analysis using large amount of data based on specific Korean language phenomenon. Accordingly, we demonstrated whether the results of quantitative analysis agree with each other for the ‘-tolok’ control construction using methodological pluralism. Furthermore, the types of Korean ‘control verb’ are classified into two types. This study is significant in showing that different types of methodology can be complement to each other by adding deep learning to the corpus and experimental methods. Additionally, we empirically revealed the necessity of revisiting the using ‘seltukha-’ as a control verb in Korean and presented four verbs that require further study to be classified as control verb, including ‘seltukha-’.
Toward Natural and Intelligible Speech Synthesis : An Empirical Study on Transfer Learning
Chaewon Kang,Jeewoo Yoon,Daeun Lee,Migyeong Kang,Seohyun Lim,Juho Jung,Sejung Son,Jinyoung Han 한국방송·미디어공학회 2023 한국방송공학회 학술발표대회 논문집 Vol.2023 No.6
To synthesize natural and intelligible speech with a small amount of data, transfer learning with well-maintained and pre-trained data has been known to be useful. However, little attention has been paid to answer the following research questions with empirically-grounded evidence, How much pre-trained (source) speech data (e.g., 10 K utterances or 10 hours) used in transfer learning is enough for generating natural and intelligible speech? and For generating natural and intelligible speech, how much (target) speech data should at least be provided?, which are essential for the quality of speech synthesis. To answer these questions, this paper conducts extensive experiments on speech synthesis with multiple source and target data with different lengths, speakers, and languages. We show that intelligible and natural speech can be synthesized with only 500 utterances of target data using transfer learning. Our work also reveals that at least 5000 utterances of source pre-trained data are required to synthesize decent speech.
gsGator: an integrated web platform for cross-species gene set analysis
Kang, Hyunjung,Choi, Ikjung,Cho, Sooyoung,Ryu, Daeun,Lee, Sanghyuk,Kim, Wankyu BioMed Central 2014 BMC bioinformatics Vol.15 No.-
<P><B>Background</B></P><P>Gene set analysis (GSA) is useful in deducing biological significance of gene lists using a priori defined gene sets such as gene ontology (GO) or pathways. Phenotypic annotation is sparse for human genes, but is far more abundant for other model organisms such as mouse, fly, and worm. Often, GSA needs to be done highly interactively by combining or modifying gene lists or inspecting gene-gene interactions in a molecular network.</P><P><B>Description</B></P><P>We developed <I>gsGator</I>, a web-based platform for functional interpretation of gene sets with useful features such as cross-species GSA, simultaneous analysis of multiple gene sets, and a fully integrated network viewer for visualizing both GSA results and molecular networks. An extensive set of gene annotation information is amassed including GO & pathways, genomic annotations, protein-protein interaction, transcription factor-target (TF-target), miRNA targeting, and phenotype information for various model organisms. By combining the functionalities of <I>Set Creator, Set Operator and Network Navigator</I>, user can perform highly flexible and interactive GSA by creating a new gene list by any combination of existing gene sets (intersection, union and difference) or expanding genes interactively along the molecular networks such as protein-protein interaction and TF-target. We also demonstrate the utility of our interactive and cross-species GSA implemented in gsGator by several usage examples for interpreting genome-wide association study (GWAS) results. gsGator is freely available at http://gsGator.ewha.ac.kr.</P><P><B>Conclusions</B></P><P>Interactive and cross-species GSA in gsGator greatly extends the scope and utility of GSA, leading to novel insights via conserved functional gene modules across different species.</P>