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    ํ”„๋กœ์ฝœ๋ผ๊ฒ IIIํ˜• N-๋ง๋‹จ ํŽฉํƒ€์ด๋“œ์˜ ์‹ ์†ํ•œ ๋ถ„์„์„ ์œ„ํ•œ ํ˜•๊ด‘ ์„ผ์„œ โ€œํ€œ์น˜๋ฐ”๋””โ€ ๊ฐœ๋ฐœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๋ฐ”์ด์˜ค์—”์ง€๋‹ˆ์–ด๋ง์ „๊ณต, 2021.8. ์‹ ์Šนํ˜‘.Procollagen type III N-terminal peptide (PIIINP) is a major biomarker of growth hormone which is on the prohibited substances list by the World Anti-Doping Agency (WADA). The recently developed and conducted PIIINP analysis methods are radio immunoassay (RIA) or fluorescence-based sandwich enzyme-linked immunosorbent assay (ELISA), and these have issues with radiation safety, time-consuming, and expensive equipment. Therefore, the development of an analysis method that can overcome these shortcomings is required. To this end, we tried to develop a high-throughput doping analysis method using a fluorescence-based antibody sensor called โ€œQuenchbody. The quenchbody consists of a single chain variable fragment (scFv) and a fluorophore which emits fluorescence depending on the presence or absence of an antigen The sequence of anti-PIIINP scFv was obtained from a hybridoma cell and cloned scFv was expressed in E. coli. Inclusion body refolding was performed to obtain more scFv, and fluorescence was conjugated to native and refolded anti-PIIINP scFv that were confirmed to have antigen binding affinity against PIIINP. Finally, dose-dependent fluorescence signal were confirmed with a fluorescence spectrophotometer. The best limit of detection (LOD) and limit of quantitation (LOQ) were calculated as 1.64 nM and 3.89 nM for TAMRA-labeled quenchbody with native anti-PIIINP scFv, according to the five-point logistic curve regression. Furthermore, with 2 nM of quenchbody, the analysis could be performed within 30 minutes from the experimental preparations to validations. Thus, we confirmed the high-throughput and high sensitivity capabilities of quenchbody required for a new doping analysis method.ํ”„๋กœ์ฝœ๋ผ๊ฒ IIIํ˜• N-๋ง๋‹จ ํŽฉํƒ€์ด๋“œ๋Š” ์„ฑ์žฅ ํ˜ธ๋ฅด๋ชฌ์˜ ์ฃผ์š”ํ•œ ๋ฐ”์ด์˜ค ๋งˆ์ปค๋กœ์„œ ์„ฑ์žฅํ˜ธ๋ฅด๋ชฌ์€ ์„ธ๊ณ„๋ฐ˜๋„ํ•‘๊ธฐ๊ตฌ์˜ ๊ธˆ์ง€์•ฝ๋ฌผ๋ชฉ๋ก์— ์˜ฌ๋ผ์™€ ์žˆ๋‹ค. ์ด์— ๋”ฐ๋ผ, ๊ทผ๋ž˜์— ๊ฐœ๋ฐœ๋œ ๋ฐฉ์‚ฌ๋ฉด์—ญ์ธก์ •๋ฒ•์ด๋‚˜ ํ˜•๊ด‘๊ธฐ๋ฐ˜์˜ ์ƒŒ๋“œ์œ„์น˜ ํšจ์†Œ๊ฒฐํ•ฉ ๋ฉด์—ญํก์ฐฉ๊ฒ€์‚ฌ์™€ ๊ฐ™์€ ๋ฐฉ๋ฒ•์œผ๋กœ ํ”„๋กœ์ฝœ๋ผ๊ฒ III N-๋ง๋‹จ ํŽฉํƒ€์ด๋“œ๋ฅผ ๊ฒ€์ถœํ•˜๊ณ  ์žˆ์œผ๋‚˜, ์ด๋“ค ๋ฐฉ๋ฒ•์€ ๋ฐฉ์‚ฌ๋Šฅ ์•ˆ์ „์„ฑ, ๊ธด ๋ถ„์„ ์†Œ์š” ์‹œ๊ฐ„, ๊ฐ’๋น„์‹ผ ์žฅ๋น„์˜ ์‚ฌ์šฉ๊ณผ ๊ฐ™์€ ๋ฌธ์ œ๊ฐ€ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋ฏ€๋กœ ๊ธฐ์กด ๋ถ„์„๋ฒ•์˜ ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ์ƒˆ๋กœ์šด ๋ฐฉ๋ฒ•์˜ ๊ฐœ๋ฐœ์ด ์š”๊ตฌ๋˜๊ณ  ์žˆ๋Š” ์ƒํ™ฉ์ด๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ˜•๊ด‘๊ธฐ๋ฐ˜์˜ ํ•ญ์ฒด ์„ผ์„œ์ธ ํ€œ์น˜๋ฐ”๋””๋ฅผ ๋งŒ๋“ค์–ด ๊ณ ์† ๋„ํ•‘ ๋ถ„์„๋ฒ•์„ ๊ฐœ๋ฐœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ํ€œ์น˜๋ฐ”๋””๋Š” ๋‹จ์ผ ์‡„ ๊ฐ€๋ณ€ ๋‹จํŽธ ํ˜•ํƒœ๋กœ ๋งŒ๋“ค์–ด์ง„ ํ•ญ์ฒด์™€, ํ•ญ์›์˜ ์œ ๋ฌด์— ๋”ฐ๋ผ ํ˜•๊ด‘์„ ๋ฐฉ์ถœํ•˜๋Š” ํ˜•๊ด‘ ๋ถ„์ž๋กœ ์ด๋ฃจ์–ด์ง„๋‹ค. ํ•ญํ”„๋กœ์ฝœ๋ผ๊ฒ IIIํ˜• N-๋ง๋‹จ ํŽฉ๋‹ค์ด๋“œ์˜ ๋‹จ์ผ ์‡„ ๊ฐ€๋ณ€ ๋‹จํŽธ์˜ ์„œ์—ด์€ ํ•˜์ด๋ธŒ๋ฆฌ๋„๋งˆ ์„ธํฌ๋กœ๋ถ€ํ„ฐ ์–ป์–ด์กŒ์œผ๋ฉฐ ์žฌ์กฐํ•ฉ๋œ ๋‹จ๋ฐฑ์งˆ์€ ๋Œ€์žฅ๊ท ์—์„œ ๋ฐœํ˜„๋˜์—ˆ๋‹ค. ๋งŽ์€ ์–‘์˜ ๋‹จ์ผ ์‡„ ๊ฐ€๋ณ€ ๋‹จํŽธ์„ ์–ป๊ธฐ ์œ„ํ•ด ๋ด‰์ž…์ฒด ์žฌ์ ‘ํž˜์ด ์‹œ๋„๋˜์—ˆ์œผ๋ฉฐ, ํ•ญ์› ๊ฒฐํ•ฉ์„ฑ์ด ํ™•์ธ๋œ ์ฒœ์—ฐ ๋ฐ ์žฌ์ ‘ํž˜ ๋‹จ์ผ ์‡„ ๊ฐ€๋ณ€ ๋‹จํŽธ์— ํ˜•๊ด‘์„ ๋ถ€์ฐฉํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ํ˜•๊ด‘๊ด‘๋„๊ณ„๋ฅผ ์ด์šฉํ•˜์—ฌ ํ€œ์น˜๋ฐ”๋””์˜ ํ•ญ์› ๋†๋„์— ๋”ฐ๋ฅธ ํ˜•๊ด‘ ์„ธ๊ธฐ์˜ ์ฆ๊ฐ€๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ๋กœ 5๊ฐœ ๋ณ€์ˆ˜ ๋กœ์‹œ์Šคํ‹ฑ ๊ณก์„  ํšŒ๊ท€ ๋ถ„์„์— ๋”ฐ๋ผ TAMRA-๋ผ๋ฒจ๋œ ์ฒœ์—ฐ ๋‹จ์ผ ์‡„ ๊ฐ€๋ณ€ ๋‹จํŽธ์„ ์‚ฌ์šฉํ•œ ํ€œ์น˜๋ฐ”๋””๊ฐ€ ๊ฐ๊ฐ 1.64 nM๊ณผ 3.89 nM์˜ ๊ฒ€์ถœํ•œ๊ณ„์™€ ์ •๋Ÿ‰ํ•œ๊ณ„๋ฅผ ๊ฐ€์ ธ ๊ฐ€์žฅ ์šฐ์ˆ˜ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ, ์ด๋Š” 2 nM์˜ ํ€œ์น˜๋ฐ”๋””๋ฅผ ์ด์šฉํ•ด ์‹คํ—˜ ์ค€๋น„๋ถ€ํ„ฐ ๊ฒฐ๊ณผ ๊ฒ€์ฆ๊นŒ์ง€ 30๋ถ„ ์ด๋‚ด์— ๋ถ„์„์„ ์ง„ํ–‰ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์šฐ๋ฆฌ๋Š” ์ƒˆ๋กœ์šด ๋„ํ•‘ ๋ถ„์„๋ฒ•์— ํ•„์š”ํ•œ ์‹ ์†์„ฑ๊ณผ ๊ณ ๊ฐ๋„์„ฑ์„ ํ€œ์น˜๋ฐ”๋””์—์„œ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค.Abstract 1 Contents 3 List of figures 5 List of tables 7 1. Introduction 8 2. Materials and methods 12 2.1. Chemicals and materials 12 2.2. Strains and vectors 13 2.3. DNA construction 16 2.3.1. ELISA of anti-PIIINP IgG from hybridoma 35J22 16 2.3.2. cDNA extraction of anti-PIIINP IgG 16 2.3.3. Anti-PIIINP IgG4 expression in HEK293-F 18 2.3.4. ELSIA of anti-PIIINP IgG4 from HEK293-F 19 2.3.5. Anti-PIIINP scFv construction 19 2.4. Production of anti-PIIINP scFv 20 2.4.1. Expression optimization 20 2.4.2. MBP fusion anti-PIIINP scFv expression 21 2.4.3. Molecular chaperone co-expression of anti-PIIINP scFv 21 2.4.4. Native anti-PIIINP scFv purification 22 2.4.5. MBP cleavage and purification 22 2.5. Refolding of anti-PIIINP scFv 23 2.5.1. Inclusion body isolation 23 2.5.2. Optimization of disulfide bond formation 24 2.5.3. Optimization of refolding additives 25 2.5.4. Refolding scale-up: step-wise dialysis refolding 26 2.6. ELISA of native and refolded anti-PIIINP scFv 27 2.7. Fluorescence labeling and purification 28 2.8. ELISA of quenchbody 29 2.9. Fluorescence measurement 29 3. Results 31 3.1. ELISA of anti-PIIINP IgG from hybridoma 35J22 31 3.2. ELISA of anti-PIIINP IgG4 from HEK293-F 34 3.3. Production of anti-PIIINP scFv 36 3.3.1. Native anti-PIIINP scFv 36 3.3.2. MBP fusion anti-PIIINP scFv 39 3.3.3. Molecular chaperone co-expression 41 3.4. Refolding of anti-PIIINP scFv 43 3.4.1. Inclusion body isolation 43 3.4.2. Optimization of disulfide bond formation 43 3.4.3. Optimization of refolding additives 44 3.4.4. Refolding scale-up: step-wise dialysis refolding 45 3.5. ELISA of native and refolded anti-PIIINP scFv 49 3.6. Fluorescence labeling and purification 51 3.7. ELISA of quenchbody 56 3.8. Doseโ€“dependent fluorescent response 58 4. Discussion and conclusion 63 5. References 66์„

    ๋”ฅ๋Ÿฌ๋‹์„ ํ™œ์šฉํ•œ ์Šคํƒ€์ผ ์ ์‘ํ˜• ์Œ์„ฑ ํ•ฉ์„ฑ ๊ธฐ๋ฒ•

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2020. 8. ๊น€๋‚จ์ˆ˜.The neural network-based speech synthesis techniques have been developed over the years. Although neural speech synthesis has shown remarkable generated speech quality, there are still remaining problems such as modeling power in a neural statistical parametric speech synthesis system, style expressiveness, and robust attention model in the end-to-end speech synthesis system. In this thesis, novel alternatives are proposed to resolve these drawbacks of the conventional neural speech synthesis system. In the first approach, we propose an adversarially trained variational recurrent neural network (AdVRNN), which applies a variational recurrent neural network (VRNN) to represent the variability of natural speech for acoustic modeling in neural statistical parametric speech synthesis. Also, we apply an adversarial learning scheme in training AdVRNN to overcome the oversmoothing problem. From the experimental results, we have found that the proposed AdVRNN based method outperforms the conventional RNN-based techniques. In the second approach, we propose a novel style modeling method employing mutual information neural estimator (MINE) in a style-adaptive end-to-end speech synthesis system. MINE is applied to increase target-style information and suppress text information in style embedding by applying MINE loss term in the loss function. The experimental results show that the MINE-based method has shown promising performance in both speech quality and style similarity for the global style token-Tacotron. In the third approach, we propose a novel attention method called memory attention for end-to-end speech synthesis, which is inspired by the gating mechanism of long-short term memory (LSTM). Leveraging the gating technique's sequence modeling power in LSTM, memory attention obtains the stable alignment from the content-based and location-based features. We evaluate the memory attention and compare its performance with various conventional attention techniques in single speaker and emotional speech synthesis scenarios. From the results, we conclude that memory attention can generate speech with large variability robustly. In the last approach, we propose selective multi-attention for style-adaptive end-to-end speech synthesis systems. The conventional single attention model may limit the expressivity representing numerous alignment paths depending on style. To achieve a variation in attention alignment, we propose using a multi-attention model with a selection network. The multi-attention plays a role in generating candidates for the target style, and the selection network choose the most proper attention among the multi-attention. The experimental results show that selective multi-attention outperforms the conventional single attention techniques in multi-speaker speech synthesis and emotional speech synthesis.๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์Œ์„ฑ ํ•ฉ์„ฑ ๊ธฐ์ˆ ์€ ์ง€๋‚œ ๋ช‡ ๋…„๊ฐ„ ํš”๋ฐœํ•˜๊ฒŒ ๊ฐœ๋ฐœ๋˜๊ณ  ์žˆ๋‹ค. ๋”ฅ๋Ÿฌ๋‹์˜ ๋‹ค์–‘ํ•œ ๊ธฐ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ์Œ์„ฑ ํ•ฉ์„ฑ ํ’ˆ์งˆ์€ ๋น„์•ฝ์ ์œผ๋กœ ๋ฐœ์ „ํ–ˆ์ง€๋งŒ, ์•„์ง ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ์Œ์„ฑ ํ•ฉ์„ฑ์—๋Š” ์—ฌ๋Ÿฌ ๋ฌธ์ œ๊ฐ€ ์กด์žฌํ•œ๋‹ค. ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜์˜ ํ†ต๊ณ„์  ํŒŒ๋ผ๋ฏธํ„ฐ ๊ธฐ๋ฒ•์˜ ๊ฒฝ์šฐ ์Œํ–ฅ ๋ชจ๋ธ์˜ deterministicํ•œ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์—ฌ ๋ชจ๋ธ๋ง ๋Šฅ๋ ฅ์˜ ํ•œ๊ณ„๊ฐ€ ์žˆ์œผ๋ฉฐ, ์ข…๋‹จํ˜• ๋ชจ๋ธ์˜ ๊ฒฝ์šฐ ์Šคํƒ€์ผ์„ ํ‘œํ˜„ํ•˜๋Š” ๋Šฅ๋ ฅ๊ณผ ๊ฐ•์ธํ•œ ์–ดํ…์…˜(attention)์— ๋Œ€ํ•œ ์ด์Šˆ๊ฐ€ ๋Š์ž„์—†์ด ์žฌ๊ธฐ๋˜๊ณ  ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ด๋Ÿฌํ•œ ๊ธฐ์กด์˜ ๋”ฅ๋Ÿฌ๋‹ ๊ธฐ๋ฐ˜ ์Œ์„ฑ ํ•ฉ์„ฑ ์‹œ์Šคํ…œ์˜ ๋‹จ์ ์„ ํ•ด๊ฒฐํ•  ์ƒˆ๋กœ์šด ๋Œ€์•ˆ์„ ์ œ์•ˆํ•œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์ ‘๊ทผ๋ฒ•์œผ๋กœ์„œ, ๋‰ด๋Ÿด ํ†ต๊ณ„์  ํŒŒ๋ผ๋ฏธํ„ฐ ๋ฐฉ์‹์˜ ์Œํ–ฅ ๋ชจ๋ธ๋ง์„ ๊ณ ๋„ํ™”ํ•˜๊ธฐ ์œ„ํ•œ adversarially trained variational recurrent neural network (AdVRNN) ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. AdVRNN ๊ธฐ๋ฒ•์€ VRNN์„ ์Œ์„ฑ ํ•ฉ์„ฑ์— ์ ์šฉํ•˜์—ฌ ์Œ์„ฑ์˜ ๋ณ€ํ™”๋ฅผ stochastic ํ•˜๊ณ  ์ž์„ธํ•˜๊ฒŒ ๋ชจ๋ธ๋งํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ์ ๋Œ€์  ํ•™์Šต์ (adversarial learning) ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์—ฌ oversmoothing ๋ฌธ์ œ๋ฅผ ์ตœ์†Œํ™” ์‹œํ‚ค๋„๋ก ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ์ œ์•ˆ๋œ ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ๊ธฐ์กด์˜ ์ˆœํ™˜ ์‹ ๊ฒฝ๋ง ๊ธฐ๋ฐ˜์˜ ์Œํ–ฅ ๋ชจ๋ธ๊ณผ ๋น„๊ตํ•˜์—ฌ ์„ฑ๋Šฅ์ด ํ–ฅ์ƒ๋จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ ์ ‘๊ทผ๋ฒ•์œผ๋กœ์„œ, ์Šคํƒ€์ผ ์ ์‘ํ˜• ์ข…๋‹จํ˜• ์Œ์„ฑ ํ•ฉ์„ฑ ๊ธฐ๋ฒ•์„ ์œ„ํ•œ ์ƒํ˜ธ ์ •๋ณด๋Ÿ‰ ๊ธฐ๋ฐ˜์˜ ์ƒˆ๋กœ์šด ํ•™์Šต ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๊ธฐ์กด์˜ global style token(GST) ๊ธฐ๋ฐ˜์˜ ์Šคํƒ€์ผ ์Œ์„ฑ ํ•ฉ์„ฑ ๊ธฐ๋ฒ•์˜ ๊ฒฝ์šฐ, ๋น„์ง€๋„ ํ•™์Šต์„ ์‚ฌ์šฉํ•˜๋ฏ€๋กœ ์›ํ•˜๋Š” ๋ชฉํ‘œ ์Šคํƒ€์ผ์ด ์žˆ์–ด๋„ ์ด๋ฅผ ์ค‘์ ์ ์œผ๋กœ ํ•™์Šต์‹œํ‚ค๊ธฐ ์–ด๋ ค์› ๋‹ค. ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ธฐ ์œ„ํ•ด GST์˜ ์ถœ๋ ฅ๊ณผ ๋ชฉํ‘œ ์Šคํƒ€์ผ ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ์˜ ์ƒํ˜ธ ์ •๋ณด๋Ÿ‰์„ ์ตœ๋Œ€ํ™” ํ•˜๋„๋ก ํ•™์Šต ์‹œํ‚ค๋Š” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•˜์˜€๋‹ค. ์ƒํ˜ธ ์ •๋ณด๋Ÿ‰์„ ์ข…๋‹จํ˜• ๋ชจ๋ธ์˜ ์†์‹คํ•จ์ˆ˜์— ์ ์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ mutual information neural estimator(MINE) ๊ธฐ๋ฒ•์„ ๋„์ž…ํ•˜์˜€๊ณ  ๋‹คํ™”์ž ๋ชจ๋ธ์„ ํ†ตํ•ด ๊ธฐ์กด์˜ GST ๊ธฐ๋ฒ•์— ๋น„ํ•ด ๋ชฉํ‘œ ์Šคํƒ€์ผ์„ ๋ณด๋‹ค ์ค‘์ ์ ์œผ๋กœ ํ•™์Šต์‹œํ‚ฌ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์„ธ๋ฒˆ์งธ ์ ‘๊ทผ๋ฒ•์œผ๋กœ์„œ, ๊ฐ•์ธํ•œ ์ข…๋‹จํ˜• ์Œ์„ฑ ํ•ฉ์„ฑ์˜ ์–ดํ…์…˜์ธ memory attention์„ ์ œ์•ˆํ•œ๋‹ค. Long-short term memory(LSTM)์˜ gating ๊ธฐ์ˆ ์€ sequence๋ฅผ ๋ชจ๋ธ๋งํ•˜๋Š”๋ฐ ๋†’์€ ์„ฑ๋Šฅ์„ ๋ณด์—ฌ์™”๋‹ค. ์ด๋Ÿฌํ•œ ๊ธฐ์ˆ ์„ ์–ดํ…์…˜์— ์ ์šฉํ•˜์—ฌ ๋‹ค์–‘ํ•œ ์Šคํƒ€์ผ์„ ๊ฐ€์ง„ ์Œ์„ฑ์—์„œ๋„ ์–ดํ…์…˜์˜ ๋Š๊น€, ๋ฐ˜๋ณต ๋“ฑ์„ ์ตœ์†Œํ™”ํ•  ์ˆ˜ ์žˆ๋Š” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๋‹จ์ผ ํ™”์ž์™€ ๊ฐ์ • ์Œ์„ฑ ํ•ฉ์„ฑ ๊ธฐ๋ฒ•์„ ํ† ๋Œ€๋กœ memory attention์˜ ์„ฑ๋Šฅ์„ ํ™•์ธํ•˜์˜€์œผ๋ฉฐ ๊ธฐ์กด ๊ธฐ๋ฒ• ๋Œ€๋น„ ๋ณด๋‹ค ์•ˆ์ •์ ์ธ ์–ดํ…์…˜ ๊ณก์„ ์„ ์–ป์„ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋งˆ์ง€๋ง‰ ์ ‘๊ทผ๋ฒ•์œผ๋กœ์„œ, selective multi-attention (SMA)์„ ํ™œ์šฉํ•œ ์Šคํƒ€์ผ ์ ์‘ํ˜• ์ข…๋‹จํ˜• ์Œ์„ฑ ํ•ฉ์„ฑ ์–ดํ…์…˜ ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ๊ธฐ์กด์˜ ์Šคํƒ€์ผ ์ ์‘ํ˜• ์ข…๋‹จํ˜• ์Œ์„ฑ ํ•ฉ์„ฑ์˜ ์—ฐ๊ตฌ์—์„œ๋Š” ๋‚ญ๋…์ฒด ๋‹จ์ผํ™”์ž์˜ ๊ฒฝ์šฐ์™€ ๊ฐ™์€ ๋‹จ์ผ ์–ดํ…์…˜์„ ์‚ฌ์šฉํ•˜์—ฌ ์™”๋‹ค. ํ•˜์ง€๋งŒ ์Šคํƒ€์ผ ์Œ์„ฑ์˜ ๊ฒฝ์šฐ ๋ณด๋‹ค ๋‹ค์–‘ํ•œ ์–ดํ…์…˜ ํ‘œํ˜„์„ ์š”๊ตฌํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋‹ค์ค‘ ์–ดํ…์…˜์„ ํ™œ์šฉํ•˜์—ฌ ํ›„๋ณด๋“ค์„ ์ƒ์„ฑํ•˜๊ณ  ์ด๋ฅผ ์„ ํƒ ๋„คํŠธ์›Œํฌ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์ตœ์ ์˜ ์–ดํ…์…˜์„ ์„ ํƒํ•˜๋Š” ๊ธฐ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. SMA ๊ธฐ๋ฒ•์€ ๊ธฐ์กด์˜ ์–ดํ…์…˜๊ณผ์˜ ๋น„๊ต ์‹คํ—˜์„ ํ†ตํ•˜์—ฌ ๋ณด๋‹ค ๋งŽ์€ ์Šคํƒ€์ผ์„ ์•ˆ์ •์ ์œผ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.1 Introduction 1 1.1 Background 1 1.2 Scope of thesis 3 2 Neural Speech Synthesis System 7 2.1 Overview of a Neural Statistical Parametric Speech Synthesis System 7 2.2 Overview of End-to-end Speech Synthesis System 9 2.3 Tacotron2 10 2.4 Attention Mechanism 12 2.4.1 Location Sensitive Attention 12 2.4.2 Forward Attention 13 2.4.3 Dynamic Convolution Attention 14 3 Neural Statistical Parametric Speech Synthesis using AdVRNN 17 3.1 Introduction 17 3.2 Background 19 3.2.1 Variational Autoencoder 19 3.2.2 Variational Recurrent Neural Network 20 3.3 Speech Synthesis Using AdVRNN 22 3.3.1 AdVRNN based Acoustic Modeling 23 3.3.2 Training Procedure 24 3.4 Experiments 25 3.4.1 Objective performance evaluation 28 3.4.2 Subjective performance evaluation 29 3.5 Summary 29 4 Speech Style Modeling Method using Mutual Information for End-to-End Speech Synthesis 31 4.1 Introduction 31 4.2 Background 33 4.2.1 Mutual Information 33 4.2.2 Mutual Information Neural Estimator 34 4.2.3 Global Style Token 34 4.3 Style Token end-to-end speech synthesis using MINE 35 4.4 Experiments 36 4.5 Summary 38 5 Memory Attention: Robust Alignment using Gating Mechanism for End-to-End Speech Synthesis 45 5.1 Introduction 45 5.2 BACKGROUND 48 5.3 Memory Attention 49 5.4 Experiments 52 5.4.1 Experiments on Single Speaker Speech Synthesis 53 5.4.2 Experiments on Emotional Speech Synthesis 56 5.5 Summary 59 6 Selective Multi-attention for style-adaptive end-to-End Speech Syn-thesis 63 6.1 Introduction 63 6.2 BACKGROUND 65 6.3 Selective multi-attention model 66 6.4 EXPERIMENTS 67 6.4.1 Multi-speaker speech synthesis experiments 68 6.4.2 Experiments on Emotional Speech Synthesis 73 6.5 Summary 77 7 Conclusions 79 Bibliography 83 ์š”์•ฝ 93 ๊ฐ์‚ฌ์˜ ๊ธ€ 95Docto

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    Evaluation of Chinese Corporate Reform Based on the Profitability of Listed Companies

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    ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ์ค‘๊ตญ์˜ 589๊ฐœ ์ƒ์žฅ๊ธฐ์—…์˜ 1998-2002๋…„ ๊ธฐ๊ฐ„ ๋™์•ˆ์˜ ๊ฒฝ์˜์„ฑ๊ณผ์˜ ๋ถ„์„์„ ํ†ตํ•˜์—ฌ 1990๋…„๋Œ€ ํ›„๋ฐ˜ ์ดํ›„ ๊ฐ•๋„ ๋†’๊ฒŒ ์ง„ํ–‰๋œ ๊ตญ์œ ๊ธฐ์—…๊ฐœํ˜์˜ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์˜ ๋ถ„์„๊ฒฐ๊ณผ์— ์˜ํ•˜๋ฉด ๊ตญ์œ ๊ธฐ์—…์˜์ˆ˜์ต์„ฑ์€ 1990๋…„๋Œ€ ํ›„๋ฐ˜ ์ดํ›„ ์ƒ๋Œ€์ ์œผ๋กœ ๊ฐœ์„ ๋˜์—ˆ์œผ๋‚˜ ๋น„๊ตญ์œ ๊ธฐ์—…์˜ ์ˆ˜์ต์„ฑ์„ ๋Šฅ๊ฐ€ํ•  ์ •๋„๋กœ ๊ฒฝ์˜์„ฑ๊ณผ๊ฐ€ ๊ฐœ์„ ๋˜์ง€๋Š” ์•Š์€ ๊ฒƒ์œผ๋กœ ํŒŒ์•…๋œ๋‹ค. ๋˜ํ•œ ๊ณ„๋Ÿ‰๊ฒ€์ฆ๊ฒฐ๊ณผ์— ์˜ํ•˜๋ฉด ์ƒ์žฅ๊ธฐ์—…์˜ ์†Œ์œ ์ œํ˜•ํƒœ, ์†Œ์œ ๊ถŒ์ง‘์ค‘๋„, ์ง€์—ญ๊ตฌ๋ถ„ ๋“ฑ์ด ๊ธฐ์—…์ˆ˜์ต์„ฑ์„ ๊ฒฐ์ •ํ•˜๋Š” ์ค‘์š”ํ•œ ๋ณ€์ˆ˜๋กœ์„œ ๊ตญ์œ ๊ธฐ์—…์ด๊ณ , ์†Œ์œ ๊ถŒ์ด ๋ถ„์‚ฐ๋˜์–ด ์žˆ์„์ˆ˜๋ก, ์ค‘์„œ๋ถ€ ์ง€์—ญ์ผ์ˆ˜๋ก ์ˆ˜์ต์„ฑ์ด ๋–จ์–ด์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚œ๋‹ค. ํŠนํžˆ ์†Œ์œ ์ œ ํ˜•ํƒœ์™€ ๊ด€๋ จํ•˜์—ฌ ๊ตญ์œ ๊ธฐ์—…์„ ๋‹ค์‹œ ์ง€๋ฐฉ์ •๋ถ€ ์†Œ์œ ์˜ ๊ตญ์œ ๊ธฐ์—…๊ณผ ์ค‘์•™์ •๋ถ€ ํ˜น์€ ๊ตญ์œ  ๋Œ€ํ˜•๊ธฐ์—… ์†Œ์œ ์˜ ๊ตญ์œ ๊ธฐ์—…์œผ๋กœ ์„ธ๋ถ„ํ™”ํ•˜์—ฌ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ ์ผ๋ฐ˜์ ์œผ๋กœ ์ง€๋ฐฉ์ •๋ถ€ ์†Œ์œ ์˜ ๊ตญ์œ ๊ธฐ์—…์˜ ์ˆ˜์ต์„ฑ์ด ๋–จ์–ด์ง€๋Š” ํ˜„์ƒ์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•˜์—ฌ ์ง€๋ฐฉ์ •๋ถ€์— ์˜ํ•œ ์ •์ฑ…๊ฐ„์„ญ์„ ์ค„์ด๋Š” ๋ฐฉํ–ฅ์œผ๋กœ์˜ ๊ตญ์œ ๊ธฐ์—… ๊ฐœํ˜์ด ์ด๋ฃจ์–ด์ ธ์•ผ ํ•œ๋‹ค๋Š” ์ •์ฑ…์  ํ•จ์˜๋ฅผ ๋„์ถœํ•  ์ˆ˜ ์žˆ๋‹ค.This paper studies the performance of the Chinese Corporations listed in the Shanghai and Shenzhen Stock Exchanges. We find that the latest state-owned enterprises reform (SOEs) has not been so successful in the sense that in comparison with non-SOEs, the profitability of SOEs was lower in every period. However, in the period of macroeconomic downturn, SOEs' profitability drop was less severe than other forms of company ownership. Therefore, we also can suggest that the latest reform measures had some positive effects on the profitability of SOEs. The empirical results show that ownership forms, region, debt ratios, and ownership concentrations are the determinant factors of profitability of Chinese-listed firms. The higher the debt ratio, the more equally distributed the ownership, SOEs located in the middle-west region, their corporate profitability could be more severely deteriorated. Especially on the profitability among different ownership forms of SOEs, we find that in comparison with the SOEs managed by local governments, the SOEs managed by central government or larger enterprises performs well. From this fact, we can have a policy implication that the separation of local government politics from SOEs can be an effective measure of SOEs reform

    A Study on internet service encounter satisfaction

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ฒฝ์˜ํ•™๊ณผ ๊ฒฝ์˜ํ•™์ „๊ณต,2000.ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)-

    An Analysis of Business Failure in the Chinese Listed Companies

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    ๋ณธ ๋…ผ๋ฌธ์€ 1998-2004๋…„ ์ƒ์žฅ๊ธฐ์—… ์ž๋ฃŒ๋ฅผ ์‚ฌ์šฉ, ST, PT ๋“ฑ ๊ด€๋ฆฌ๋Œ€์ƒ๊ธฐ์—…์„ ๋ถ€์‹ค๊ธฐ์—…์œผ๋กœ ์ •์˜ํ•˜๊ณ  ๋กœ์ง“๋ถ„์„์„ ์›์šฉํ•˜์—ฌ ์ค‘๊ตญ์˜ ๊ธฐ์—…๋ถ€์‹ค ์š”์ธ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ด๋Ÿฌํ•œ ๋ถ„์„๊ฒฐ๊ณผ๋ฅผ ์š”์•ฝํ•˜๋ฉด ์ฒซ์งธ, ์ง€๋ฐฉ์ •๋ถ€ ์†Œ์œ  ๊ตญ์œ ๊ธฐ์—…์˜ ๊ฒฝ์šฐ ๋ถ€์‹ค๊ฐ€๋Šฅ์„ฑ์ด ๋†’๊ฒŒ ๋‚˜ํƒ€๋‚œ๋‹ค. ์ด๋Š” ๊ธฐ์กด์˜ ์—ฐ๊ตฌ์—์„œ ์ง€๋ฐฉ์ •๋ถ€์— ์˜ํ•œ ๊ธฐ์—…์ˆ˜์ต์„ฑ ๊ด€๋ฆฌ์˜ ๊ฐ€๋Šฅ์„ฑ์ด ๋†’๋‹ค๋Š” ์—ฐ๊ตฌ๊ฒฐ๊ณผ์™€ ๋ถ€ํ•ฉํ•œ๋‹ค. ๋‘˜์งธ, 1์ธ๋‹น ๋งค์ถœ, ์ž์‚ฐ๊ทœ๋ชจ๋Š” ๊ธฐ์—…๋ถ€์‹ค ๊ฐ€๋Šฅ์„ฑ์„ ๋‚ฎ์ถ”๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, ๋ณด๋ฅ˜ ์˜๊ฒฌ ์ •๋„์˜ ์•ฝํ•œ ํšŒ๊ณ„๊ฐ์‚ฌ์˜๊ฒฌ ๋˜ํ•œ ๊ธฐ์—…๋ถ€์‹ค ๊ฐ€๋Šฅ์„ฑ์„ ์˜ˆ์ธกํ•˜๋Š”๋ฐ ์žˆ์–ด์„œ ๋งค์šฐ ์œ ์šฉํ•œ ๋ณ€์ˆ˜์ด๋ฉฐ ์ด๋Š” ์ค‘๊ตญ๊ธฐ์—…์˜ ๊ฒฝ์šฐ ์‚ฌ์†Œํ•œ ํšŒ๊ณ„์žฅ๋ถ€์˜ ์˜ค๋ฅ˜๋ฅผ ์ง€์ ํ•˜๋Š” ํšŒ๊ณ„์˜๊ฒฌ ๋˜ํ•œ ์‹ฌ๊ฐํ•œ ๊ฒฝ์˜๋ถ€์‹ค์„ ๋‚ดํฌํ•˜๊ณ  ์žˆ์Œ์„ ์˜๋ฏธํ•œ๋‹ค. This paper studies main factors of business failures in the Chinese listed companies. Our methodology is focused on the logit analysis of ST and PT companies. We find that the ownership forms have significant effects on the probability of business failure, in which the local government-controlled SOEs face significantly higher risks in their business failures. Secondly, the asset sizes and total sales to employees also matter in explaining business failure in the Chinese listed companies. Thirdly, weak accounting reports also have their explanatory powers in business failures this means that the minor errors on the financial reports could have involved serious business risks in the Chinese listed firms

    Pitting Corrosion Behavior of Aluminum under Residual Stress

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    Although crystallographic orientation-dependent corrosion of aluminum is dominated by its crystallographic orientation, surface crystallinity under the deformations caused by residual stresses significantly varies with the corrosion stage. Therefore, analysis of the influences of both crystallographic orientation of aluminum and residual stresses on the corrosion resistance of aluminum at all corrosion stages is necessary. Accordingly, herein, the role of residual stress in the crystallographic orientation-dependent corrosion behavior of aluminum at the pit initiation and propagation stages was investigated. A clear correlation between step dissolution of aluminum, crystallographic orientation of aluminum, and residual stress was experimentally, theoretically, and computationally demonstrated based on work function and first-principles calculations. Aluminum atoms exhibit sequential dissolution behaviors, resulting in step configuration of the surface. Residual stress can slightly restrain or promote pit nucleation at the pit initiation stage depending on its sign. However, on the step surface, residual stress is a primary factor determining the dissolution rates of atoms at the pit propagation stage regardless of the crystallographic orientation. Neighboring coordination numbers and distance between atoms were measured to elucidate the effects of crystallographic orientation and residual stress on the corrosion behavior of aluminum. Results of the theoretical and first-principles calculations were adequately consistent with each other and supported the experimentally determined pit density and depth behaviors. This study clarify the role of residual stress in pitting corrosion of aluminum and further expected to broaden the options to improve the corrosion resistance of parts in various industry.1. Introduction 01 1.1 Residual Stress 01 1.2 Residual Stress developed in Industries 03 1.2.1 Mechanisms of the generation of residual stress 04 1.2.2 Research trends of residual stress 07 1.3 Aluminum in Industries 10 1.4 Objectives of Research 13 2. Theoretical Background 16 2.1 Residual Stress on Electrochemical Properties 16 2.1.1 Galvanic corrosion 17 2.1.2 Pitting corrosion 21 2.1.3 Stress corrosion 25 3. Experimental methods 28 3.1 Materials 28 3.2 Residual stress 29 3.3 Corrosion test 32 4. Computational methods 33 4.1 Surface modeling 33 4.2 First-principles calculation 35 5. Results and Discussion 36 5.1 Residual stress distribution 36 5.2 Dissolution of aluminum without residual stress 38 5.3 Dissolution of aluminum with residual stress 42 5.4 The work function calculation 47 5.5 The first-principles approach 53 6. Conclusions 59 References 60Maste

    Discontinuations of treatment with oral or long-acting injectable paliperidone in outpatients with schizophrenia: An analysis from HIRAโ€“NPS (National Patients Sample) data of South Korea, 2017-2018

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    ์กฐํ˜„๋ณ‘ ์น˜๋ฃŒ์— ์ˆœ์‘ํ•˜์ง€ ์•Š๋Š” ํ™˜์ž๋Š” ๋†’์€ ์žฌ๋ฐœ๋ฅ ์„ ๋ณด์—ฌ์ค€๋‹ค. ์žฌ๋ฐœ ๋ฐฉ์ง€๋ฅผ ์œ„ํ•ด ๊ฐ€์žฅ ์ค‘์š”ํ•œ ๊ฒƒ์€ ์ง€์†์ ์ธ ์•ฝ๋ฌผ์น˜๋ฃŒ๋‹ค. ์žฅ๊ธฐ์ง€์†ํ˜• ์น˜๋ฃŒ์ œ์˜ ์‚ฌ์šฉ์€ ํ™˜์ž๋“ค์˜ ์žฌ๋ฐœ์„ ๋ฐฉ์ง€ํ•ด ์ค„ ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ, ์ฃผ๊ธฐ์ ์œผ๋กœ ์น˜๋ฃŒ ์ƒํ™ฉ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์–ด ํ™˜์ž์˜ ์น˜๋ฃŒ ์ˆœ์‘๋„ ํ‰๊ฐ€๋„ ๊ฐ€๋Šฅํ•˜๋‹ค. ์žฅ๊ธฐ์ง€์†ํ˜• ์น˜๋ฃŒ์ œ์˜ ์‚ฌ์šฉ์ด ์ ์ฐจ ์ฆ๊ฐ€ํ•˜๋Š” ์‹œ์ ์—, ๊ฒฝ๊ตฌํ˜• ์น˜๋ฃŒ์ œ์™€ ์žฅ๊ธฐ์ง€์†ํ˜• ์น˜๋ฃŒ์ œ๊ฐ€ ์กฐํ˜„๋ณ‘ ํ™˜์ž์˜ ์•ฝ๋ฌผ์น˜๋ฃŒ ์ค‘๋‹จ์œ„ํ—˜์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์‚ดํŽด๋ณด์•˜๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ฑด๊ฐ•๋ณดํ—˜์‹ฌ์‚ฌํ‰๊ฐ€์›์—์„œ ์ œ๊ณตํ•˜๋Š” ์ „์ฒดํ™˜์ž๋ฐ์ดํ„ฐ์…‹(HIRA-NPS)์„ ํ™œ์šฉํ•˜์—ฌ Paliperidone ์„ฑ๋ถ„ ํ•ญ์ •์‹ ๋ณ‘์ œ์ œ์˜ ๊ฒฝ๊ตฌํ˜• ์น˜๋ฃŒ์ œ์™€ ์žฅ๊ธฐ์ง€์†ํ˜• ์น˜๋ฃŒ์ œ์˜ ์•ฝ๋ฌผ์น˜๋ฃŒ ์ค‘๋‹จ์œ„ํ—˜์„ ์กฐ์‚ฌํ•˜๊ธฐ ์œ„ํ•ด ์ฝ•์Šค๋น„๋ก€์œ„ํ—˜ ๋ชจ๋ธ์„ ์‚ฌ์šฉํ•˜์—ฌ ์ƒ์กด๋ถ„์„์„ ์‹ค์‹œํ–ˆ๋‹ค. ์—ฐ๊ตฌ๊ธฐ๊ฐ„ ๋™์•ˆ ์ด 522๋ช…์˜ ์กฐํ˜„๋ณ‘ ํ™˜์ž์ค‘ 164๋ช…(31.4%)์˜ ํ™˜์ž๊ฐ€ ์•ฝ๋ฌผ์น˜๋ฃŒ๋ฅผ ์ค‘๋‹จํ–ˆ๋‹ค. ๊ฒฝ๊ตฌ์šฉ ์น˜๋ฃŒ์ œ๋ฅผ ์‚ฌ์šฉํ•œ ํ™˜์ž ์ด 349๋ช…์ค‘ 38.1%์ธ 133๋ช…์ด 1๋…„ ์ด๋‚ด์— ์•ฝ๋ฌผ์น˜๋ฃŒ๋ฅผ ์ค‘๋‹จํ•˜์˜€๊ณ , ์žฅ๊ธฐ์ง€์†ํ˜• ์น˜๋ฃŒ์ œ๋ฅผ ์‚ฌ์šฉํ•œ ํ™˜์ž ์ด 173๋ช…์ค‘ 17.9%์ธ 31๋ช…์ด 1๋…„ ์ด๋‚ด์— ์•ฝ๋ฌผ์น˜๋ฃŒ๋ฅผ ์ค‘๋‹จํ•˜์—ฌ, ์žฅ๊ธฐ์ง€์†ํ˜• ์น˜๋ฃŒ์ œ์˜ ์•ฝ๋ฌผ์น˜๋ฃŒ ์ค‘๋‹จ์œ„ํ—˜์ด ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜๊ฒŒ ๋‚ฎ์•˜๋‹ค(P<.0001). ๊ฒฝ๊ตฌ์šฉ ์น˜๋ฃŒ์ œ๋กœ ์น˜๋ฃŒํ•œ ํ™˜์ž๋“ค์ด ์žฅ๊ธฐ์ง€์†ํ˜• ์น˜๋ฃŒ์ œ๋กœ ์น˜๋ฃŒ๋ฐ›์€ ํ™˜์ž๋“ค์— ๋น„ํ•ด 2.36๋ฐฐ ์•ฝ๋ฌผ์น˜๋ฃŒ ์ค‘๋‹จ์œ„ํ—˜์ด ๋” ๋†’์•˜๋‹ค(P<.0001). ์ด ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์กฐํ˜„๋ณ‘ ์น˜๋ฃŒ์— ์žˆ์–ด ์žฅ๊ธฐ์ง€์†ํ˜• ์น˜๋ฃŒ์ œ๊ฐ€ ์•ฝ๋ฌผ์น˜๋ฃŒ ์ค‘๋‹จ์œ„ํ—˜์— ์žˆ์–ด ์œ ๋ฆฌํ•˜๋‹ค๋Š” ๊ฒƒ์ด ๋‹ค์‹œ ํ•œ๋ฒˆ ๋ฐํ˜€์กŒ๋‹ค. ์šฐ๋ฆฌ๋‚˜๋ผ ์กฐํ˜„๋ณ‘ ํ™˜์ž๋“ค์˜ ํšจ๊ณผ์ ์ธ ์ •์‹ ์งˆํ™˜ ๊ด€๋ฆฌ๋ฅผ ์œ„ํ•ด ์•ฝ๋ฌผ์น˜๋ฃŒ ์ค‘๋‹จ์œ„ํ—˜์ด ๋‚ฎ์€ ์ œํ˜•์˜ ์‚ฌ์šฉ์„ ์ ๊ทน์ ์œผ๋กœ ๊ฒ€ํ† ํ•˜๋Š” ์ •์ฑ…์ ์ธ ์ ‘๊ทผ์ด ํ•„์š”ํ•˜๋‹ค. Background: Treatment compliant patients with schizophrenia had a lower relapse rate. Continuous medication is important for them. Long-acting antipsychotics makes it possible not only symptom relapse prevention but also treatment compliance monitoring in patients with schizophrenia. As the utilization of long-action antipsychotics has been increased, we analyzed the treatment discontinuation hazard ratio of oral or long-acting antipsychotics(paliperidone). Method: The data from the Health Insurance Review and Assessment Service โ€“ 2017 and 2018 National Patient Sample (HIRA-NPS) includes 2,955,004 patients. 522 patients with schizophrenia treated by paliperidone were analyzed. We performed survival analysis using a Cox proportional hazards model was used to examine the relationship between discontinuation hazard ratio and type of medication. Results: There was a significant difference in treatment discontinuation rate (38.1% Oral treatment and 17.9% Long-Acting Treatment, p<.0001) The Oral Treatment were associated with the risk of discontinuation (Oral antipsychotics = Adjusted Hazard Ratio: 2.36, 95% Confidence interval: 1.56-3.57; Long-acting injection=ref). Conclusions: The study found the advantages of Long-Acting Treatment over oral antipsychotics again. South Korea needs a policy approach to consider of using long-acting antipsychotics for effective mental health management.open์„

    ์„ ์ง„๋ณตํ•ฉ์žฌ๋ฃŒ์—์˜ ์‘์šฉ์„ ์œ„ํ•œ ๋ฐฉํ–ฅ์กฑ ์•ก์ •์—์Šคํ…Œ๋ฅด ์—ํญ์‹œ ์ˆ˜์ง€์˜ ํ•ฉ์„ฑ ๋ฐ ๋ฌผ์„ฑํ–ฅ์ƒ

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    Thesis (doctoral)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ณต์—…ํ™”ํ•™๊ณผ,1998.Docto
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