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Novel functional roles of caspase-related genes in the regulation of apoptosis and autophagy
신주현,민상현 대한약리학회 2016 The Korean Journal of Physiology & Pharmacology Vol.20 No.6
Caspases, a family of cysteine proteases, cleave substrates and play significant roles in apoptosis, autophagy, and development. Recently, our group identified 72 genes that interact with Death Caspase-1 (DCP-1) proteins in Drosophila by genetic screening of 15,000 EP lines. However, the cellular functions and molecular mechanisms of the screened genes, such as their involvement in apoptosis and autophagy, are poorly understood in mammalian cells. In order to study the functional characterizations of the genes in human cells, we investigated 16 fulllength human genes in mammalian expression vectors and tested their effects on apoptosis and autophagy in human cell lines. Our studies revealed that ALFY, BIRC4, and TAK1 induced autophagy, while SEC61A2, N-PAC, BIRC4, WIPI1, and FALZ increased apoptotic cell death. BIRC4 was involved in both autophagy and apoptosis. Western blot analysis and luciferase reporter activity indicated that ALFY, BIRC4, PDGFA, and TAK1 act in a p53-dependent manner, whereas CPSF1, SEC61A2, N-PAC, and WIPI1 appear to be p53-independent. Overexpression of BIRC4 and TAK1 caused upregulation of p53 and accumulation of its target proteins as well as an increase in p53 mRNA levels, suggesting that these genes are involved in p53 transcription and expression of its target genes followed by p53 protein accumulation. In conclusion, apoptosis and/or autophagy mediated by BIRC4 and TAK1 may be regulated by p53 and caspase activity. These novel findings may provide valuable information that will aid in a better understanding of the roles of caspase-related genes in human cell lines and be useful for the process of drug discovery.
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明末 ‘補儒易佛論’의 成立과 進化 ― 徐光啟의 『闢釋氏諸妄』을 중심으로 ―
신주현 명청사학회 2019 명청사연구 Vol.0 No.51
In the history of Early-Modern China, it is noteworthy that European religious thinking, namely Catholicism, was introduced to its intellectual milieu. As is well known, Matteo Ricci initiated and developed the adaptive mission policy called accommodationism in China, suggesting the thesis that “Catholicism complements Confucianism and replaces Buddhism”, buru yifo. The innovative mission policy enabled Chinese literati society to accept the European religious thoughts. This study aims to provide implications on the foundation of buru yifo in the context of intellectual history which has not been thoroughly investigated in existing scholarly works. From the perspective of the formulation and evolution of the Jesuit accommodationism in China, of buru yifo, it examines a hybridized form of anti-Buddhist discourse by the comparison between Matteo Ricci and Xu Guangqi. Xu successively shared the legacy of Ricci in his anti-Buddhist discussions, but beyond that, he further hybridized Neo-Confucian way of anti-Buddhist polemics as one of Chinese intellectuals in late Ming period. In his Pishishi zhuwang, Xu continued a Christian critique of Buddhism shown in Tianzhu shiyi particularly sharing Thomas Aquinas’ theory on souls and the presupposition of Omnipotent God. Furthermore, beyond Ricci, Xu criticized various other points of Buddhist doctrines specifically centering on their bringing about socio-moral harms. Such characteristic of Xu’s work demonstrates that the first-generation Chinese Catholic did not merely accept Jesuit accommodationism in one-sided manner, but enjoyed an agency of evolving the Jesuit discourse into a further hybridized form for his sake.
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신주현,김은희,임명진 (사)한국스마트미디어학회 2022 스마트미디어저널 Vol.11 No.3
In the document extraction summary study, various methods for selecting important sentences based on the relationship between sentences were proposed. In the Korean document summary using the summation similarity of sentences, the summation similarity of the sentences was regarded as the amount of sentence information, and the summary sentences were extracted by selecting important sentences based on this. However, the problem is that it does not take into account the various importance that each sentence contributes to the entire document. Therefore, in this study, we propose a document extraction summary method that provides a summary by selecting important sentences based on the amount of quantitative and semantic information in the sentence. As a result, the extracted sentence agreement was 58.56% and the ROUGE-L score was 34, which was superior to the method using only the combined similarity. Compared to the deep learning-based method, the extraction method is lighter, but the performance is similar. Through this, it was confirmed that the method of compressing information based on semantic similarity between sentences is an important approach in document extraction summary. In addition, based on the quickly extracted summary, the document generation summary step can be effectively performed.
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신주현,임명진,이명호 (사)한국스마트미디어학회 2022 스마트미디어저널 Vol.11 No.8
Due to COVID-19, the importance of non-face-to-face counseling is increasing as the face-to-face counseling method has progressed to non-face-to-face counseling. The advantage of non-face-to-face counseling is that it can be consulted online anytime, anywhere and is safe from COVID-19. However, it is difficult to understand the client's mind because it is difficult to communicate with non-verbal expressions. Therefore, it is important to recognize emotions by accurately analyzing text and voice in order to understand the client's mind well during non-face-to-face counseling. Therefore, in this paper, text data is vectorized using FastText after separating consonants, and voice data is vectorized by extracting features using Log Mel Spectrogram and MFCC respectively. We propose a multi-emotion recognition model that recognizes five emotions using vectorized data using an LSTM model. Multi-emotion recognition is calculated using RMSE. As a result of the experiment, the RMSE of the proposed model was 0.2174, which was the lowest error compared to the model using text and voice data, respectively. COVID-19로 인해 대면으로 이루어지던 상담 방식이 비대면으로 진행되면서 비대면 상담의 중요성이 높아지고 있다. 비대면 상담은 온라인으로 언제 어디서든 상담할 수 있고, COVID-19에 안전하다는 장점이 있다. 그러나 비언어적 표현의 소통이 어려워 내담자의 마음을 이해하기 어렵다. 이에 비대면 상담 시 내담자의 마음을 잘 알기 위해서는 텍스트와 음성을 정확하게 분석하여 감정을 인식하는 것이 중요하다. 따라서 본 논문에서는 텍스트 데이터는 자음을 분리한 후 FastText를 사용하여 벡터화하고, 음성 데이터는 Log Mel Spectrogram과 MFCC를 사용하여 각각 특징을 추출하여 벡터화한다. 벡터화된 데이터를 LSTM 모델을 활용하여 5가지 감정을 인식하는 다중 감정인식 모델을 제안한다. 다중 감정인식은 RMSE을 활용하여 계산한다. 실험 결과 텍스트와 음성 데이터를 각각 사용한 모델보다 제안한 모델의 RMSE가 0.2174로 가장 낮은 오차를 확인하였다.
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