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    ๋ถ„์ž๋Ÿ‰์ด ๋‹จ์ผํ•˜๊ณ  ์„œ์—ด์ด ์ •์˜๋œ ํด๋ฆฌ์—์Šคํ„ฐ์˜ ํ•ฉ์„ฑ๊ณผ ์ •๋ณด ์ €์žฅ ๋งค์ฒด๋กœ์˜ ํ™œ์šฉ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ™”ํ•™๋ถ€, 2022. 8. ๊น€๊ฒฝํƒ.๋ถ„์ž๋Ÿ‰, ๋‹จ๋Ÿ‰์ฒด ์„œ์—ด ๋ฐ ์ž…์ฒด๋ฐฐ์—ด์ด ์™„๋ฒฝํ•˜๊ฒŒ ํ†ต์ œ๋œ ์„œ์—ด ํŠน์ด์  ๊ณ ๋ถ„์ž ํ•ฉ์„ฑ์€ ๊ณ ๋ถ„์ž ํ™”ํ•™ ๋ถ„์•ผ์˜ ์ค‘์š”ํ•œ ๊ณผ์ œ์ด๋‹ค. ์„œ์—ด์ด ์ •์˜๋œ ๊ณ ๋ถ„์ž๋Š” ํด๋Œ€๋จธ, ์ด‰๋งค ๋ฐ ํ•ญ๊ท  ๋ฌผ์งˆ ๋“ฑ ๊ด‘๋ฒ”์œ„ํ•œ ์˜์—ญ์— ํ™œ์šฉ๋  ์ž ์žฌ์„ฑ์„ ๊ฐ€์ง€๊ณ  ์žˆ๋‹ค. ํŠนํžˆ, ์ด๋Š” ๋‹จ๋Ÿ‰์ฒด ์„œ์—ด์„ ๋””์ง€ํ„ธ ์ •๋ณด๋กœ ๋ณ€ํ™˜ํ•จ์œผ๋กœ์จ ์ •๋ณด ์ €์žฅ ๋งค์ฒด๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋‹จ๊ณ„์  ๋ฐ˜๋ณต ํ•ฉ์„ฑ๋ฒ•๊ณผ ๋ฐ˜๋ณต ์ง€์ˆ˜ ์„ฑ์žฅ๋ฒ• ๋“ฑ ๊ท ์ผํ•œ ๊ฑฐ๋Œ€๋ถ„์ž๋ฅผ ๋งŒ๋“ค๊ธฐ ์œ„ํ•œ ์—ฌ๋Ÿฌ๊ฐ€์ง€ ์ „๋žต๋“ค์ด ๊ฐœ๋ฐœ๋˜์—ˆ์ง€๋งŒ ๊ทœ๋ชจ ํ™•์žฅ์„ฑ, ๊ณ ๋ถ„์ž ๊ธธ์ด ๋˜๋Š” ๋ฐ˜๋ณต๋˜๋Š” ์„œ์—ด๊ณผ ๊ฐ™์€ ํ•œ๊ณ„๊ฐ€ ์žˆ์—ˆ๋‹ค. ํ•„์ž๋Š” ๊ต์ฐจ ์ˆ˜๋ ด๋ฒ•์„ ํ†ตํ•˜์—ฌ ์„œ์—ด์ด ์ •์˜๋œ ํด๋ฆฌ์—์Šคํ„ฐ๋ฅผ ํ•ฉ์„ฑํ•˜๊ณ  ์ƒ์„ฑ๋œ ๊ณ ๋ถ„์ž์˜ ๋น„์ฃผ๊ธฐ์  ์„œ์—ด์„ ์ด์šฉํ•˜์—ฌ ์ •๋ณด๋ฅผ ์ €์žฅํ•˜๋Š” ์—ฐ๊ตฌ๋“ค์„ ํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํฌ๊ธฐ ๋ฐฐ์ œ ํฌ๋กœ๋งˆํ† ๊ทธ๋ž˜ํ”ผ (prep-SEC) ๋ฅผ ์ด์šฉํ•œ ์ •์ œ ๋ฐฉ๋ฒ•๊ณผ ํ•จ๊ป˜ ๊ต์ฐจ ์ˆ˜๋ ด๋ฒ•์„ ํ†ตํ•˜์—ฌ ํฐ ๋ถ„์ž๋Ÿ‰์„ ๊ฐ–๋Š” ์„œ์—ด ํŠน์ด์  ํด๋ฆฌํŽ˜๋‹๋ฝํƒ€์ด๋“œ-๋ฝํƒ€์ด๋“œ ๊ณต์ค‘ํ•ฉ์ฒด (PcLs) ๋ฅผ ํ•ฉ์„ฑํ•˜์˜€๋‹ค. ํ•ด๋‹น ๋ฐฉ๋ฒ•์€ ์ตœ์†Œํ•œ์˜ ํ™”ํ•™ ๋ฐ˜์‘์œผ๋กœ ์ด์ง„๋ฒ•์œผ๋กœ ์ธ์ฝ”๋”ฉ๋œ PcL์˜ ํ™•์žฅ ๊ฐ€๋Šฅํ•œ ํ•ฉ์„ฑ์„ ์šฉ์ดํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. 64๋น„ํŠธ PcL์— ์ €์žฅ๋œ ์ •๋ณด๋Š” MALDI-TOF ํ…๋ค ์งˆ๋Ÿ‰ ๋ถ„์„๊ธฐ์˜ ๋‹จ์ผ ์ธก์ •์œผ๋กœ ๋””์ฝ”๋”ฉํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋˜ํ•œ, ๋ถ„ํ•ด์„ฑ ์‹œํ€€์‹ฑ ๋ฐฉ๋ฒ•์€ ํฐ ๋ถ„์ž๋Ÿ‰์˜ 128-๋น„ํŠธ PcL์˜ ๋‹จ๋Ÿ‰์ฒด ์„œ์—ด ๋ถ„์„์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ๊ต์ฐจ ์ˆ˜๋ ด ๋ฐฉ๋ฒ•์„ ํ†ตํ•œ ์„œ์—ด ํŠน์ด์  ๊ณ ๋ถ„์ž์˜ ํ•ฉ์„ฑ์€ ๋‹จ๊ณ„์  ๊ฒฝ์ œ์  ํ•ฉ์„ฑ์ด์ง€๋งŒ ๋Œ€์šฉ๋Ÿ‰ ์ •๋ณด ์ €์žฅ์„ ์œ„ํ•ด์„œ๋Š” ๋Œ€๊ทœ๋ชจ ํ™”ํ•™ ๋ฐ˜์‘์„ ์ˆ˜๋ฐ˜ํ•˜๋Š” ๋‹ค์ˆ˜์˜ ์„œ์—ด ํŠน์ด์  ๊ณ ๋ถ„์ž์˜ ํ•ฉ์„ฑ์ด ์š”๊ตฌ๋œ๋‹ค. ํ•ด๋‹น ๋ฌธ์ œ๋Š” ์œ ๋™ ํ™”ํ•™์„ ์ด์šฉํ•œ ์„œ์—ด ํŠน์ด์  ํด๋ฆฌ๋ฝํƒ€์ด๋“œ-๊ธ€๋ผ์ด์ฝœ๋ผ์ด๋“œ ๊ณต์ค‘ํ•ฉ์ฒด (PLGAs) ์˜ ๋ฐ˜์ž๋™์  ํ•ฉ์„ฑ๋ฒ• ๊ฐœ๋ฐœ์„ ํ†ตํ•˜์—ฌ ํ•ด๊ฒฐํ•˜์˜€๋‹ค. ํ•ด๋‹น ๊ฐ€์†ํ™”๋œ ํ•ฉ์„ฑ๋ฒ•์€ ๋ฐฐ์น˜ ๋ฐ˜์‘๊ณผ ๋น„๊ตํ•˜์—ฌ ํ›จ์”ฌ ๋” ์ ์€ ์‹œ๊ฐ„ ๋‚ด์— 896-๋น„ํŠธ ํฌ๊ธฐ์˜ ๋น„ํŠธ๋งต ์ด๋ฏธ์ง€๋ฅผ 14๊ฐœ์˜ PLGA ๊ณ ๋ถ„์ž ์‚ฌ์Šฌ์— ์ธ์ฝ”๋”ฉํ•  ์ˆ˜ ์žˆ๊ฒŒ ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๊ณ ๋ถ„์ž ์‚ฌ์Šฌ์˜ ์‹๋ณ„์ž๋กœ ์ž‘๋™ํ•˜๋Š” 8-๋น„ํŠธ์˜ ์ฃผ์†Œ ์ฝ”๋“œ์˜ ๋„์ž…์€ ํ˜ผํ•ฉ๋ฌผ ์ƒํƒœ์ธ ์—ฌ๋Ÿฌ PLGA ๊ณ ๋ถ„์ž ์‚ฌ์Šฌ์˜ ํ…๋ค ์งˆ๋Ÿ‰ ์‹œํ€€์‹ฑ์„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ์„œ์—ด์ด ์ •์˜๋œ ๊ณ ๋ถ„์ž์˜ ์„œ์—ด์„ ๋ถ„์„ํ•˜๋Š” ๋ฐฉ๋ฒ•์˜ ๋Œ€๋ถ€๋ถ„์€ ํ…๋ค ์งˆ๋Ÿ‰ ๋ถ„์„๊ณผ ์ž๊ธฐํฌ์ƒ ์‹œํ€€์‹ฑ ๋“ฑ์˜ ํŒŒ๊ดด์  ์‹œํ€€์‹ฑ ๋ฐฉ๋ฒ•์ธ๋ฐ ์ด๋Š” ํ•„์—ฐ์ ์œผ๋กœ ๋ถ„์„ ๋•Œ๋งˆ๋‹ค ๊ณ ๋ถ„์ž๋ฅผ ์†Œ๋น„ํ•ด์•ผํ•œ๋‹ค. ๋”ฐ๋ผ์„œ, ํƒ„์†Œ ๋™์œ„์›์†Œ ํ•ต์ž๊ธฐ ๊ณต๋ช… ๋ถ„๊ด‘๋ฒ• (13C NMR spectroscopy) ๋ฅผ ํ†ตํ•˜์—ฌ ์ˆœ์ˆ˜ ๊ฑฐ์šธ์ƒ ์ด์„ฑ์งˆ์ฒด์ธ ์˜ฌ๋ฆฌ๊ณ ๋ฝํƒ€์ดํŠธ-๊ธ€๋ผ์ด์ฝœ๋ผ์ด๋“œ ๊ณต์ค‘ํ•ฉ์ฒด (oLGs) ์™€ ์˜ฌ๋ฆฌ๊ณ ๋งŒ๋ธ๋ผ์ด๋“œ-ํŽ˜๋‹๋ฝํƒ€์ด๋“œ ๊ณต์ค‘ํ•ฉ์ฒด (oMPs) ์˜ ์„œ์—ด์„ ๋ถ„์„ํ•˜๋Š” ๋น„ํŒŒ๊ดด์ ์ธ ์‹œํ€€์‹ฑ ๋ฐฉ๋ฒ•์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. oMP์™€ oLG ํ˜ผํ•ฉ๋ฌผ์˜ ์„œ์—ด์€ ์„œ์—ด์„ ๋‚˜ํƒ€๋‚ด๋Š” ํ”ผํฌ์˜ ๊ฒน์น˜์ง€ ์•Š๋Š” ํ™”ํ•™์  ์ด๋™ ์˜์—ญ์œผ๋กœ ์ธํ•ด ๋‹จ์ผ 13C NMR ์ธก์ •์œผ๋กœ ํ•ด๋…ํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์œ ๋™ ํ™”ํ•™ ํ•ฉ์„ฑ์„ ํ†ตํ•˜์—ฌ 192-๋น„ํŠธ์˜ ๋น„ํŠธ๋งต์„ ์ˆœ์ˆ˜ ๊ฑฐ์šธ์ƒ ์ด์„ฑ์งˆ์ฒด์ธ oMP์™€ oLG์— ์ธ์ฝ”๋”ฉํ•˜์˜€๊ณ  12๊ฐœ์˜ oMP์™€ oLG ๋“ฑ๋ชฐ ํ˜ผํ•ฉ๋ฌผ์„ ์˜จ์ „ํ•˜๊ฒŒ ๋””์ฝ”๋”ฉํ•˜์˜€๋‹ค. ์ƒ๋ถ„ํ•ด์„ฑ ํด๋ฆฌํ•˜์ด๋“œ๋ก์‹œ์•Œ์นด๋…ธ์—์ดํŠธ (PHAs)๋Š” ํƒ„ํ™”์ˆ˜์†Œ ๊ธฐ๋ฐ˜์˜ ํ”Œ๋ผ์Šคํ‹ฑ์˜ ๋Œ€์ฒด์ œ๋กœ ๋งŽ์€ ๊ด€์‹ฌ์„ ๋ฐ›๊ณ  ์žˆ์ง€๋งŒ ์ƒ๋ฌผํ•™์ ยทํ™”ํ•™์  ๋ฐฉ๋ฒ•์„ ํ†ตํ•ด ์ƒ์„ฑ๋˜๋Š” PHAs์˜ ์ œํ•œ์ ์ธ ํ™”ํ•™ ๊ตฌ์กฐ๋กœ ์ธํ•ด ์‘์šฉ ๋ฒ”์œ„๊ฐ€ ์ œํ•œ์ ์ด๋‹ค. ๋”ฐ๋ผ์„œ, ํ•„์ž๋Š” ์›ํ•˜๋Š” ์›์ž ๊ตฌ์„ฑ, ์ž…์ฒดํ™”ํ•™์  ํ˜•ํƒœ, ๊ด€๋Šฅ๊ธฐ๋ฅผ ์ง€๋‹ˆ๋Š” ํ•˜์ด๋“œ๋ก์‹œ์•Œ์นด๋…ธ์—์ดํŠธ (HAs) ๋‹จ๋Ÿ‰์ฒด๋ฅผ ๊ตฌํ•˜๊ธฐ ์‰ฌ์šด ๋ง๋‹จ ์—ํญ์‚ฌ์ด๋“œ ๋ถ„์ž ๋ฐ ๋ง๋‹จ ์•Œํ‚จ ๋ถ„์ž๋กœ๋ถ€ํ„ฐ ์ƒ์‚ฐํ•˜๋Š” ํ•ฉ์„ฑ ๊ณผ์ •์„ ํ™•๋ฆฝํ•˜์˜€๋‹ค. ํ•ด๋‹น ๋ฐฉ๋ฒ•์œผ๋กœ ์ƒ์„ฑ๋œ HAs ๋‹จ๋Ÿ‰์ฒด๋Š” ์„œ์—ด ํŠน์ด์  PHAs๋ฅผ ์ƒ์„ฑํ•˜๋Š” ๋นŒ๋”ฉ ๋ธ”๋ก์œผ๋กœ ์‚ฌ์šฉ๋˜์–ด ํ•ด๋‹น ๊ณ ๋ถ„์ž์˜ ๋ถ„์ž๋Ÿ‰, ๋‹จ๋Ÿ‰์ฒด ์„œ์—ด, ์ž…์ฒด ๋ฐฐ์—ด ๋ฐ ๊ธฐ๋Šฅ์  ๋ถ€๋ถ„์„ ์™„์ „ํžˆ ํ†ต์ œํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ถ”๊ฐ€์ ์œผ๋กœ, ์„œ์—ด ์ œํ•œ์  PHAs์˜ ๊ฑฐ๋Œ€ ๋ถ„์ž ๊ณตํ•™์„ ํ†ตํ•ด ๊ณ ๋ถ„์ž์˜ ๊ฒฐ์ •์„ฑ๊ณผ ์—ด์  ํŠน์„ฑ์„ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์—ฐ๊ตฌ๋“ค์„ ํ†ตํ•ด ํ•„์ž๋Š” ๋Œ€๋Ÿ‰์œผ๋กœ ํฐ ๋ถ„์ž๋Ÿ‰์˜ ์„œ์—ด์ด ์ •์˜๋œ ํด๋ฆฌ์—์Šคํ„ฐ๋ฅผ ํ•ฉ์„ฑํ•˜๋Š” ๋ฐฉ๋ฒ•์„ ํ™•๋ฆฝํ•˜๊ณ  ์œ ๋™ ํ™”ํ•™์„ ๋„์ž…ํ•˜์—ฌ ํšจ์œจ์„ฑ์„ ๋†’์ผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์„œ์—ด ํŠน์ด์  ๊ณ ๋ถ„์ž๋Š” ์ •๋ณด ์ €์žฅ ๋งค์ฒด๋กœ์„œ ํ™œ์šฉํ•˜์˜€๊ณ  ์ธ์ฝ”๋”ฉ๊ณผ ๋””์ฝ”๋”ฉ ์ „๋žต์„ ๋ฐœ์ „์‹œ์ผฐ๋‹ค. ๋˜ํ•œ, ์„œ์—ด ์ œํ•œ์  ๊ณ ๋ถ„์ž๋ฅผ ํ†ตํ•œ ๊ฑฐ๋Œ€ ๋ถ„์ž ๊ณตํ•™์˜ ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ํ•„์ž๋Š” ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋“ค์ด ๊ณ ๋ถ„์ž์˜ ๊ตฌ์กฐ-๊ธฐ๋Šฅ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„๊ณผ ๊ฐ™์€ ๊ณ ๋ถ„์ž ํ™”ํ•™์˜ ๊ทผ๋ณธ์ ์ธ ๋ฌธ์ œ๋“ค์„ ํ•ด๊ฒฐํ•˜๊ณ  ๊ด‘๋ฒ”์œ„ํ•œ ์—ฐ๊ตฌ ๋ถ„์•ผ์— ์‚ฌ์šฉ๋˜๋Š” ํ˜์‹ ์ ์ธ ์žฌ๋ฃŒ ๊ฐœ๋ฐœ์— ๊ธฐ์—ฌํ•  ๊ฒƒ์ด๋ผ ๊ธฐ๋Œ€ํ•œ๋‹ค.The synthesis of sequence-defined polymers with perfect control over the molecular weight, monomer sequence, and stereoconfiguration remains a challenge in polymer chemistry. Sequence-defined polymers can be used in extensive applications, such as foldamers, catalysis, and antimicrobials. Particularly, these polymers can serve as information storage media by converting their monomer sequences into digital information such as binary code. Several strategies to synthesize uniform macromolecules, such as stepwise iterative synthesis and iterative exponential growth, have been developed, but they have limitations in terms of scalability, polymer length, and periodic sequence. In this study, the synthesis of sequence-defined polyesters using a cross-convergent method and information storage using the aperiodic sequence of the polymers are demonstrated. In this dissertation, high molecular weight sequence-defined poly(phenyllactic-co-lactic acid)s (PcLs) were synthesized using a cross-convergent method combined with preparative size exclusion chromatography (prep-SEC) for purification. This method facilitates scalable synthesis of binary-encoded PcLs with minimal chemical reactions. The stored information in a 64-bit PcL could be decoded via a single measurement using MALDI-TOF tandem mass spectrometry. Furthermore, a degradative sequencing method was used for the analysis of the monomer sequence of a high molecular weight 128-bit PcL. Despite the step-economical synthesis of sequence-defined polymers via the cross-convergent method, large information storage requires the synthesis of a multitude of sequence-defined polymers accompanied by massive chemical reactions. This challenge could be overcome by the semiautomated synthesis of sequence-defined poly(L-lactic-co-glycolic acid)s (PLGAs) using continuous flow chemistry. This accelerated synthesis allowed the encoding of a bitmap image (896-bit) in 14 PLGAs incurring only a fraction of time compared to batch reactions. Moreover, introducing an 8-bit address code as the PLGA chain identifier enabled direct tandem mass sequencing of the mixture of several PLGA chains. Most sequencing methods for sequence-defined polymers, such as tandem mass and self-immolative sequencing, which inevitably consume polymers in each analysis, are destructive. Therefore, a nondestructive sequencing method for sequence-defined enantiopure oligoesters, oligo(L-lactic-co-glycolic acid)s (oLGs) and oligo(L-mandelic-co-D-phenyllactic acid)s (oMPs), was developed using 13C NMR spectroscopy. The sequence of a mixture of oLG and oMP was deciphered via a single 13C NMR measurement because of the non-overlapping chemical shift region of the sequence-indicating peaks. A bitmap image (192-bit) was encoded in enantiopure octameric oLG and oMP through semi-automated flow synthesis, and 12 equimolar mixtures of oLG and oMP were decoded completely. Although biodegradable polyhydroxyalkanoates (PHAs) are an attractive alternative to hydrocarbon-based plastics, their application is inhibited by the limited chemical structure of PHAs derived by biological and chemical synthesis. Therefore, a synthetic procedure for a library of hydroxyalkanoates (HAs) with desired atomic compositions, stereochemical configurations, and substituent chemistry from accessible terminal epoxides was established. The defined HAs served as building blocks for sequence-defined PHAs with controlled molecular weight, monomer sequence, stereoconfiguration, and functional moieties. Furthermore, macromolecular engineering of sequence-regulated PHAs allowed for controlling crystallinity and thermal properties of the polymers. From the above experiments, a new method for the scalable synthesis of high molecular weight sequence-defined polyesters was established, and their efficiency was improved by introducing flow chemistry. Furthermore, it was demonstrated that the sequence-defined polymers could serve as information storage media, and encoding and decoding strategies were developed. Finally, the potential of macromolecular engineering using sequence-regulated polymers was suggested. These results will contribute to the unlimited diversity of polymers in the discovery and understanding of structure-function relationships. Furthermore, molecular engineering of synthetic polymers will also contribute to the development of molecular media, paving way for a broad range of potential applications.Chapter 1. Introduction 1.1 Overview 1 1.2 Stepwise iterative synthesis 3 1.3 Iterative exponential growth 6 1.4 Digital information storage in synthetic macromolecules 10 1.5 Decoding methods of sequence-defined polymers 14 1.6 Summary of thesis 18 1.7 References 19 Chapter 2. High-Density Information Storage in an Absolutely Defined Aperiodic Sequence of Monodisperse Copolyester 2.1 Abstract 28 2.2 Introduction 28 2.3 Results and discussion 30 2.4 Conclusion 47 2.5 Experimental 48 2.6 References 51 Chapter 3. Semiautomated Synthesis of Sequence-Defined Polymers for Information Storage 3.1 Abstract 58 3.2 Introduction 58 3.3 Results and discussion 60 3.4 Conclusion 74 3.5 Experimental 74 3.6 References 76 Chapter 4. Nondestructive Sequencing of Enantiopure Oligoesters by Nuclear Magnetic Resonance Spectroscopy 4.1 Abstract 81 4.2 Introduction 81 4.3 Results and discussion 84 4.4 Conclusion 100 4.5 Experimental 101 4.6 References 105 Chapter 5. Synthesis of Enantiomeric ฯ‰-Substituted Hydroxyalkanoates from Terminal Epoxides: Building Blocks for Sequence-defined polyesters and Macromolecular Engineering 5.1 Abstract 111 5.2 Introduction 111 5.3 Results and discussion 113 5.4 Conclusion 126 5.5 Experimental 126 5.6 References 130 Appendix A.2.1 Characteriztion of PcLs 135 A.2.2 MALDI-TOF/TOF tandem mass sequencing results of PcLs 143 A.2.3 Degradative sequencing result of 128-bit PcL 157 A.3.1 Characteriztion of PLGAs 162 A.3.2 MALDI-TOF/TOF tandem mass sequencing results of PLGAs 168 A.3.3 MALDI-TOF/TOF tandem mass sequencing of the mixture of the fourteen sequence-defined PLGAs 196 A.4.1 1H and 13C NMR spectra of sequence-defined enantiopure oLGs 206 A.4.2 13C NMR sequencing results of octameric sequence-defined oLGs 223 A.5.1 Characteriztion of HAs 227 Abstract (in Korean) 235 Acknowledgement (in Korean) 238๋ฐ•

    ๊ธ‰์„ฑ ๋‹ด๋‚ญ์—ผ ํ™˜์ž์—์„œ ๋‹ด์ฆ™ ๋ฐฐ์–‘์˜ ๋ฏธ์ƒ๋ฌผํ•™ ๋ฐ ํ•ญ์ƒ์ œ ๊ฐ์ˆ˜์„ฑ์— ๋”ฐ๋ฅธ ๊ฒฝํ—˜์  ํ•ญ์ƒ์ œ ์„ ํƒ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์˜๊ณผ๋Œ€ํ•™ ์˜ํ•™๊ณผ, 2023. 2. ์žฅ์ง„์˜.Background: Bacterial infection is common in acute cholecystitis (AC). To identify appropriate empirical antibiotics, we investigated AC-associated microorganisms and their susceptibilities to antibiotics. We also compared preoperative clinical findings of patients grouped according to specific microorganisms. Methods: Patients who underwent laparoscopic cholecystectomy for AC between 2018 and 2019 were enrolled. Bile cultures and antibiotic susceptibility tests were performed, and clinical findings of patients were noted. Results: A total of 282 patients were enrolled (147 culture-positive and 135 culture-negative). The most frequent microorganisms were Escherichia (n=53, 32.7%), Enterococcus (n=37, 22.8%), Klebsiella (n=28, 17.3%), and Enterobacter (n=18, 11.1%). For Gram-negative microorganisms, second-generation cephalosporin (cefotetan: 96.2%) was more effective than third-generation cephalosporin (cefotaxime: 69.8%). Vancomycin and teicoplanin (83.8%) were the most effective antibiotics for Enterococcus. Patients with Enterococcus had higher rates of CBD stones (51.4%, p=0.001) and biliary drainage (81.1%, p=0.002), as well as higher levels of liver enzymes (p=0.001), than patients with other microorganisms. Patients with ESBL-producing bacteria had higher rates of CBD stones (36.0% vs. 6.8%, p=0.001) and biliary drainage (64.0% vs. 32.4%, p=0.005) than those without. Conclusion: Preoperative clinical findings of AC are related to the genus of microorganisms in bile samples. Periodic antibiotic susceptibility tests should be conducted to select appropriate empirical antibiotics.๋ฐฐ๊ฒฝ: ๊ธ‰์„ฑ ๋‹ด๋‚ญ์—ผ ํ™˜์ž์—์„œ ์„ธ๊ท  ๊ฐ์—ผ์€ ํ”ํ•˜๊ฒŒ ๋ฐœ์ƒํ•˜๋ฉฐ, ๊ฒฝํ—˜์  ํ•ญ์ƒ์ œ ์„ ํƒ์€ ์ค‘์š”ํ•˜๋‹ค. ๋”ฐ๋ผ์„œ ๊ธ‰์„ฑ ๋‹ด๋‚ญ์—ผ ํ™˜์ž์—์„œ ํ™•์ธ๋œ ๋ฏธ์ƒ๋ฌผ ๋ฐ ํ•ญ์ƒ์ œ ๊ฐ์ˆ˜์„ฑ์„ ํ† ๋Œ€๋กœ ํ™˜์ž์˜ ์ˆ˜์ˆ  ์ „ ์ž„์ƒ ์–‘์ƒ์„ ๋น„๊ตํ•˜์—ฌ ๋” ์ ์ ˆํ•œ ๊ฒฝํ—˜์  ํ•ญ์ƒ์ œ๋ฅผ ์ œ์‹œํ•˜๊ณ ์ž ๋ณธ ์—ฐ๊ตฌ๋ฅผ ์‹œํ–‰ํ•˜์˜€๋‹ค. ๋ฐฉ๋ฒ•: ๋ณธ ์—ฐ๊ตฌ๋Š” 2018๋…„๋ถ€ํ„ฐ 2019๋…„๊นŒ์ง€ ์„œ์šธ๋Œ€ํ•™๊ต ๋ณด๋ผ๋งค๋ณ‘์›์—์„œ ๊ธ‰์„ฑ ๋‹ด๋‚ญ์—ผ์œผ๋กœ ์ˆ˜์ˆ ์„ ๋ฐ›์€ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์˜๋ฌด๊ธฐ๋ก ๊ฒ€ํ† ๋ฅผ ํ†ตํ•ด ์ง„ํ–‰๋˜์—ˆ๋‹ค. ๋‹ด์ฆ™ ๋ฐฐ์–‘ ๋ฐ ํ•ญ์ƒ์ œ ๊ฐ์ˆ˜์„ฑ ๊ฒ€์‚ฌ๊ฐ€ ์‹œํ–‰๋˜์—ˆ๊ณ , ํ™˜์ž์— ๋”ฐ๋ฅธ ์ž„์ƒ ์–‘์ƒ์„ ์กฐ์‚ฌํ•˜์˜€๋‹ค. ๊ฒฐ๊ณผ: ๋ถ„์„์— ํฌํ•จ๋œ 282๋ช…์˜ ํ™˜์ž ์ค‘ ๋ฐฐ์–‘-์–‘์„ฑ์€ 147๋ช…, ๋ฐฐ์–‘-์Œ์„ฑ์€ 135๋ช…์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๊ฐ€์žฅ ํ”ํ•˜๊ฒŒ ๋ฐฐ์–‘๋œ ๊ท ์ฃผ๋Š” Escherichia (n=53, 32.7%), Enterococcus (n=37, 22.8%), Klebsiella (n=28, 17.3%), ๊ทธ๋ฆฌ๊ณ  Enterobacter (n=18, 11.1%) ์ˆœ์ด์—ˆ๋‹ค. ๊ทธ๋žŒ ์Œ์„ฑ๊ท ์˜ ๊ฒฝ์šฐ, 2์„ธ๋Œ€ ์„ธํŒ”๋กœ์Šคํฌ๋ฆฐ๊ณ„ ํ•ญ์ƒ์ œ(cefotetan: 96.2%)๊ฐ€ 3์„ธ๋Œ€ ์„ธํŒ”๋กœ์Šคํฌ๋ฆฐ๊ณ„ ํ•ญ์ƒ์ œ(cefotaxime: 69.8%)๋ณด๋‹ค ๋” ํšจ๊ณผ์ ์ธ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. Enterococcus ๊ท ์ฃผ์˜ ๊ฒฝ์šฐ, ๋ฐ˜์ฝ”๋งˆ์ด์‹ ๊ณผ ํ…Œ์ด์ฝ”ํ”Œ๋ผ๋‹Œ(83.8%)์ด ๊ฐ€์žฅ ํšจ๊ณผ์ ์ธ ํ•ญ์ƒ์ œ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋˜ํ•œ, ๋‹ค๋ฅธ ๊ท ์ฃผ์— ๊ฐ์—ผ๋œ ํ™˜์ž์— ๋น„ํ•ด Enterococcus์— ๊ฐ์—ผ๋œ ํ™˜์ž์—์„œ ์ด๋‹ด๊ด€๊ฒฐ์„์ด ์žˆ๊ฑฐ๋‚˜ (51.4%, p=0.001), ๋‹ด๋„๋ฐฐ์•ก์ˆ ์„ ์‹œํ–‰๋ฐ›์€ ๊ฒฝ์šฐ๊ฐ€ ๋” ๋งŽ์•˜๊ณ  (81.1%, p=0.002), ๋†’์€ ๊ฐ„ํšจ์†Œ ์ˆ˜์น˜๋ฅผ ๋ณด์˜€๋‹ค (p=0.001). ESBL ์ƒ์‚ฐ ๊ท ์ฃผ๋ฅผ ๊ฐ€์ง„ ํ™˜์ž์˜ ๊ฒฝ์šฐ, ๋‹ค๋ฅธ ๊ท ์ฃผ์— ๊ฐ์—ผ๋œ ํ™˜์ž์— ๋น„ํ•ด ์ด๋‹ด๊ด€๊ฒฐ์„์ด ์žˆ๊ฑฐ๋‚˜ (36.0% vs. 6.8%, p=0.001), ๋‹ด๋„๋ฐฐ์•ก์ˆ ์„ ์‹œํ–‰๋ฐ›์€ ๊ฒฝ์šฐ๊ฐ€ ๋” ๋งŽ์•˜๋‹ค (64.0% vs. 32.4%, p=0.005). ๊ฒฐ๋ก : ๊ธ‰์„ฑ ๋‹ด๋‚ญ์—ผ ํ™˜์ž์—์„œ ์ˆ˜์ˆ  ์ „ ์ž„์ƒ ์–‘์ƒ์€ ๊ฐ์—ผ๋œ ์„ธ๊ท ์˜ ์ข…๋ฅ˜์™€ ๊ด€๋ จ์ด ์žˆ๋‹ค. ๋˜ํ•œ, ์ ์ ˆํ•œ ๊ฒฝํ—˜์  ํ•ญ์ƒ์ œ๋ฅผ ์„ ํƒํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ง€์—ญ ์‚ฌํšŒ์˜ ํ™˜์ž๋ฅผ ๋Œ€์ƒ์œผ๋กœ ์ฃผ๊ธฐ์ ์ธ ํ•ญ์ƒ์ œ ๊ฐ์ˆ˜์„ฑ ๊ฒ€์‚ฌ๋ฅผ ์‹œํ–‰ํ•ด์•ผ ํ•œ๋‹ค.1. Introduction 0 2. Methods 1 3. Results 4 4. Discussion 10 5. References 16 Tables Table 1 21 Table 2 23 Table 3 24 Table 4 25 Table 5 26 Table 6 27 Supplementary table 1 28 Figures Figure 1 29 Figure 2 30 Figure 3 31์„

    ์ž๋…€์˜ ์–ด๋ฆฐ์ด์ง‘ ๋ฐ ์œ ์น˜์› ์ด์šฉ์ด ์—ฌ์„ฑ์˜ ๋…ธ๋™๊ณต๊ธ‰์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์ œํ•™๋ถ€, 2014. 2. ๋ฅ˜๊ทผ๊ด€.๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ž๋…€์˜ ์–ด๋ฆฐ์ด์ง‘ ๋ฐ ์œ ์น˜์›์ด ์—ฌ์„ฑ์˜ ๋…ธ๋™๊ณต๊ธ‰์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์‹ค์ฆ๋ถ„์„ํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ณผ์ •์—์„œ ์ž๋…€์˜ ๋ณด์œก์‹œ์„ค ์ด์šฉ๊ณผ ์—ฌ์„ฑ์˜ ์ทจ์—… ๊ฒฐ์ • ์‚ฌ์ด์— ์—ญ ์ธ๊ณผ๊ด€๊ณ„(reverse causality)๊ฐ€ ์กด์žฌํ•˜๊ณ  ์ด๋กœ ์ธํ•ด ๋‚ด์ƒ์„ฑ ๋ฌธ์ œ๊ฐ€ ๋ฐœ์ƒํ•  ์ˆ˜ ์žˆ์Œ์„ ๊ณ ๋ คํ•˜์—ฌ ์ด๋ฅผ ํ•ด๊ฒฐํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ ๋ณด์œก์‹œ์„ค์„ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋Š” ์•„๋™์˜ ์—ฐ๋ น์— ์ œํ•œ์ด ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์— ๊ทผ๊ฑฐํ•˜์—ฌ, Gelbach(2002)์˜ ์—ฐ๊ตฌ์™€ ๊ฐ™์ด ์ž๋…€์˜ ์—ฐ๋ น ๋ฐ ์ถœ์ƒ ๋ถ„๊ธฐ๋ฅผ ์–ด๋ฆฐ์ด์ง‘ ๋ฐ ์œ ์น˜์› ์ด์šฉ๋ฅ ์— ๋Œ€ํ•œ ๋„๊ตฌ๋ณ€์ˆ˜๋กœ ์‚ฌ์šฉํ•˜์—ฌ ๋‚ด์ƒ์„ฑ์„ ํ†ต์ œํ•˜์˜€๋‹ค. ์‹ค์ฆ๋ถ„์„์„ ์œ„ํ•ด 7์ฐจ๋ถ€ํ„ฐ 12์ฐจ๊นŒ์ง€ ์ด 6๊ฐœ๋…„ ํ•œ๊ตญ๋…ธ๋™ํŒจ๋„ ์ž๋ฃŒ๋ฅผ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ ๋งŒ 2-3์„ธ ์•„๋™์„ ๋‘” ์—ฌ์„ฑ์„ ๋Œ€์ƒ์œผ๋กœ ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•˜์˜€๋‹ค. ์ž๋…€์˜ ์—ฐ๋ น ๋ฐ ์ถœ์ƒ ๋ถ„๊ธฐ๋ฅผ ๋„๊ตฌ๋ณ€์ˆ˜๋กœ ์‚ฌ์šฉํ•˜์—ฌ ์ž๋…€์˜ ๋ณด์œก์‹œ์„ค ์ด์šฉ์ด ์—ฌ์„ฑ์˜ ์ทจ์—…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ์ž๋…€์˜ ์–ด๋ฆฐ์ด์ง‘ ๋ฐ ์œ ์น˜์› ์ด์šฉ์ด ์œ ์˜ํ•˜์ง€๋Š” ์•Š์œผ๋‚˜ ์—ฌ์„ฑ์˜ ๋…ธ๋™๊ณต๊ธ‰์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ๋„๊ตฌ๋ณ€์ˆ˜๋ฅผ ์‚ฌ์šฉํ•˜์ง€ ์•Š์€ ๊ฒฐ๊ณผ์— ๋น„ํ•ด ์ถ”์ •๊ณ„์ˆ˜๊ฐ€ ์ž‘๊ฒŒ ๋‚˜ํƒ€๋‚˜, ์—ญ ์ธ๊ณผ๊ด€๊ณ„์—์„œ ๋น„๋กฏ๋˜๋Š” ๋‚ด์ƒ์„ฑ์„ ํ†ต์ œํ•˜์ง€ ์•Š์„ ๊ฒฝ์šฐ ์ž๋…€์˜ ์–ด๋ฆฐ์ด์ง‘ ๋ฐ ์œ ์น˜์› ์ด์šฉ์ด ์—ฌ์„ฑ์˜ ์ทจ์—…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์ด ๊ณผ๋Œ€ํ‰๊ฐ€๋  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํ•œํŽธ ๋งŒ 2-3์„ธ์˜ ์ž๋…€๋ฅผ ๋‘” ์—ฌ์„ฑ์˜ ๋…ธ๋™๊ณต๊ธ‰ ๊ฒฐ์ •์— ์žˆ์–ด์„œ ์ž๋…€ ๋ณด์œก๊ณผ ๊ด€๋ จ๋œ ์š”์ธ์ด ์ค‘์š”ํ•จ์—๋„ ๋ถˆ๊ตฌํ•˜๊ณ , ์ž๋…€์˜ ์–ด๋ฆฐ์ด์ง‘ ๋ฐ ์œ ์น˜์› ์ด์šฉ์ด ๋ฏธ์น˜๋Š” ํšจ๊ณผ๊ฐ€ ์œ ์˜ํ•˜์ง€ ์•Š์€ ์ด์œ ๋Š” ๋ณด์œก์‹œ์„ค์—์„œ ์ œ๊ณต๋˜๋Š” ๋ณด์œก์„œ๋น„์Šค์˜ ์งˆ์ด๋‚˜ ์‹œ๊ฐ„์ด ์—ฌ์„ฑ์˜ ์ง์ ‘์ ์ธ ๋ณด์œก์„ ์™„์ „ํžˆ ๋Œ€์ฒดํ•˜๊ธฐ์— ๋ถ€์กฑํ•˜๊ธฐ ๋•Œ๋ฌธ์ธ ๊ฒƒ์œผ๋กœ ํŒ๋‹จ๋œ๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  ยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยท 1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยท 1 ์ œ 2 ์ ˆ ๋ณธ ๋…ผ๋ฌธ์˜ ๊ธฐ์—ฌ์™€ ๊ตฌ์„ฑ ยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยท 8 ์ œ 2 ์žฅ ๋ณด์œก๋ณด์กฐ์— ๊ด€ํ•œ ์„ ํ–‰์—ฐ๊ตฌ ยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยท 10 ์ œ 3 ์žฅ ์‹ค์ฆ๋ถ„์„ ยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยท 14 ์ œ 1 ์ ˆ ์ž๋ฃŒ ๋ฐ ๋ถ„์„๋ฐฉ๋ฒ• ยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยท 14 ์ œ 2 ์ ˆ ์‹ค์ฆ๋ถ„์„๊ฒฐ๊ณผ ยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยท 21 ์ œ 4 ์žฅ ๊ฒฐ๋ก  ยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยท 33 ์ฐธ๊ณ ๋ฌธํ—Œ ยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยท 35 Abstract ยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยทยท 36Maste

    ์ปดํ“จํ„ฐ๋น„์ „์„ ์œ„ํ•œ ๊ทธ๋ž˜ํ”„์ •ํ•ฉ๊ณผ ๊ณ ์ฐจ๊ทธ๋ž˜ํ”„์ •ํ•ฉ: ์ƒˆ๋กœ์šด ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๋ถ„์„์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์ „๊ธฐยท์ปดํ“จํ„ฐ๊ณตํ•™๋ถ€, 2016. 8. ์ด๊ฒฝ๋ฌด.Establishing image feature correspondences is fundamental problem in computer vision and machine learning research fields. Myriad of graph matching algorithms have been proposed to tackle this problem by regarding correspondence problem as a graph matching problem. However, the graph matching problem is challenging since there are various types of noises in real world scenarioe.g., non-rigid motion, view-point change, and background clutter. The objective of this dissertation is to propose robust graph matching algorithms for feature correspondence task in computer vision and to investigate an effective graph matching strategy. For the purpose, at first, two robust simulation based graph matching algorithms are introduced: the one is based on Random Walks simulation and the other is based on Markov Chain Monte Carlo sampling simulation. Secondly, two different graph matching formulations and their transformal relation are studied since equivalence between two formulations are not well studied in graph matching fields. It is demonstrated that conventional graph matching algorithms can solve both types of formulations by proposing conversion principle between two formulations. Finally, these whole statements are extended into hypergraph matching problem by introducing two robust hypergraph matching algorithms which are based on Random Walks and Markov Chain Monte Carlo, by relating two different hypergraph matching formulations, and by reinterpreting previous hypergraph matching algorithms into their counterpart formulations. Throughout chapters in this dissertation, comparative and extensive experiments verify characteristics of formulations, transformal relations, and algorithms. Synthetic graph matching problems as well as real image feature correspondence problems are performed in various and severe noise conditions.Chapter 1 Introduction 1 1.1 Graph Matching Problem 1 1.1.1 Graph Matching for Computer Vision 1 1.1.2 Graph Matching Formulation 2 1.1.3 Extension to Hypergraph Matching 5 1.2 Outline of Dissertation 6 Chapter 2 Graph Matching via Random Walks 9 2.1 Introduction 9 2.1.1 Related Works 10 2.2 Problem Formulation 12 2.2.1 Graph Matching Formulation 12 2.2.2 Hypergraphs Matching Formulation 13 2.3 Graph Matching via Random Walks 16 2.3.1 Random Walks for Graph Matching 16 2.3.2 Reweighting Jumps for Graph Matching 19 2.4 Hypergraph Matching via Random Walks 22 2.4.1 Hypergraph Random Walks 22 2.4.2 Reweighting Jumps for Hypergraph Matching 23 2.5 Experiments 26 2.5.1 Random Graph Matching 27 2.5.2 Synthetic Point Matching 34 2.5.3 Image Sequence Matching 37 2.5.4 Image Feature Matching 39 2.6 Conclusion 44 Chapter 3 Graph Matching via Markov Chain Monte Carlo 45 3.1 Introduction 45 3.2 Graph Matching Formulation 47 3.3 Algorithm 49 3.3.1 State Transition 49 3.3.2 Energy Formulation 49 3.3.3 Data-Driven Proposal 51 3.4 Hypergraph Extension 53 3.4.1 Hypergraph Matching Problem 53 3.4.2 Energy Formulation & Data-Driven Proposal 54 3.5 Experiment 54 3.5.1 Random Graph Matching Problem 54 3.5.2 Random Hypergraph Matching Problem 58 3.6 Conclusion 59 Chapter 4 Graph and Hypergraph Matching Revisited 63 4.1 Introduction 63 4.2 Related Works 65 4.3 Two Types of Formulations 66 4.3.1 Adjacency-based Formulation 67 4.3.2 Affinity-based Formulation 69 4.3.3 Relation between Two Formulations 70 4.4 Affinity Measures 72 4.5 Existing Methods & Re-interpretations 74 4.5.1 Spectral Matching 74 4.5.2 Integer Projected Fixed Point 75 4.5.3 Reweighted Random Walks Matching 76 4.5.4 Factorized Graph Matching 77 4.6 High-order Methods & Reinterpretations 78 4.6.1 Hypergraph Matching by Zass and Shashua 81 4.6.2 SVD-based Hypergraph Matching 82 4.6.3 Tensor Power Iteration based Hypergraph Matching 82 4.6.4 Reweighted Random Walks for Hypergraph Matching 83 4.6.5 Discrete Hypergraph Matching 85 4.7 Experiments & Comparison 85 4.8 Conclusion 102 Chapter 5 Conclusion 105 5.1 Summary and Contribution of Dissertation 105 5.2 Future Works 107 Bibliography 109 ๊ตญ๋ฌธ ์ดˆ๋ก 117Docto

    Preparation and Characterization of Polysaccharide Superabsorbent Polymers Crosslinked with Starch Aldehydes and Carboxymethylcellulose

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋ฐ”์ด์˜ค์‹œ์Šคํ…œ.์†Œ์žฌํ•™๋ถ€(๋ฐ”์ด์˜ค์†Œ์žฌ๊ณตํ•™์ „๊ณต), 2019. 2. ๋ฐ•์ข…์‹ .๋‹ค๋‹น๋ฅ˜ ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€๋ฅผ ์ œ์กฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์™€ ์นด๋ฅด๋ณต์‹œ๋ฉ”ํ‹ธ์…€๋ฃฐ๋กœ์˜ค์Šค๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ํŠนํžˆ ๋‹ค๋ฅธ ๊ฐ€๊ต์ œ ์—†์ด ์šฉ์•ก ๊ณต์ •์œผ๋กœ ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€๋ฅผ ์ œ์กฐํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ๋ฅผ ๋„์ž…ํ•˜์˜€๋‹ค. ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ๋Š” ์‚ฐ ์กฐ๊ฑด์—์„œ ์นด๋ฅด๋ณต์‹œ๋ฉ”ํ‹ธ์…€๋ฃฐ๋กœ์˜ค์Šค์˜ ์ˆ˜์‚ฐ๊ธฐ์™€ ๋ฐ˜์‘์„ ์‹œํ‚ด์œผ๋กœ์จ ์•„์„ธํƒˆ ๊ฐ€๊ต๋ฅผ ํ˜•์„ฑํ•˜์˜€๋‹ค. ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€์˜ ์ œ์กฐ์— ์•ž์„œ, ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ๋Š” ์ „๋ถ„์˜ ์ž…์ž ํ˜•ํƒœ๊ฐ€ ์œ ์ง€๋˜๋Š” ๋น„๊ท ์งˆ ๋ฐ˜์‘๊ณผ ํ˜ธํ™”๋ฅผ ํ†ตํ•ด ์ž…์ž ํ˜•ํƒœ๋ฅผ ๊นจ๋œจ๋ฆฌ๋Š” ๊ท ์งˆ ๋ฐ˜์‘์œผ๋กœ ๊ฐ๊ฐ ์ œ์กฐํ•˜์˜€๋‹ค. ์‚ฐํ™”๋ฅผ ํ†ตํ•ด ์ œ์กฐ๋œ ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ๋Š” ์ ์™ธ์„  ๋ถ„๊ด‘๋ถ„์„, X์„  ๊ด‘์ „์ž ๋ถ„๊ด‘๋ถ„์„, 13C ๊ณ ์ฒด์ƒ ํ•ต์ž๊ธฐ๊ณต๋ช… ๋ถ„๊ด‘๋ถ„์„์œผ๋กœ ๊ตฌ์กฐ ๋ถ„์„์„ ํ•˜์˜€์œผ๋ฉฐ, ์ œ์กฐ ๊ณผ์ •์—์„œ ๊ธ€๋ฆฌ์ฝ”์‚ฌ์ด๋“œ ๊ฒฐํ•ฉ๊ณผ ๋ฌด์ˆ˜๊ธ€๋ฃจ์ฝ”์˜ค์Šค ๊ณ ๋ฆฌ์˜ C-2, C-3 ๊ฒฐํ•ฉ์ด ๋™์‹œ์— ๋Š์–ด์ง€๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ฃผ์‚ฌ์ „์žํ˜„๋ฏธ๊ฒฝ ๋ถ„์„์€ ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์˜ ํ‘œ๋ฉด์ด ์ฐŒ๊ทธ๋Ÿฌ์ง„ ์ž…์ž ๋˜๋Š” ๋งค์šฐ ์ฃผ๋ฆ„์ง„ ํ‰๋ฉด์œผ๋กœ ์กด์žฌํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์‚ฐํ™”๋œ ์ „๋ถ„์˜ ์•Œ๋ฐํ•˜์ด๋“œ๊ธฐ ์น˜ํ™˜๋„์™€ ์ ๋„๋ฅผ ์ธก์ •ํ•˜์˜€์„ ๋•Œ, ์‚ฐํ™”์ œ์˜ ์–‘์ด ์ฆ๊ฐ€ํ•จ์— ๋”ฐ๋ผ ์น˜ํ™˜๋„๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ  ์ ๋„๊ฐ€ ๊ฐ์†Œํ•˜๋Š” ๊ฒฝํ–ฅ์„ ๋ณด์˜€๋‹ค. ๋น„๊ท ์งˆ ๋ฐ˜์‘์˜ ๊ฒฐ๊ณผ์—์„œ๋Š” ๋ฐ€ ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ๊ฐ€ ๋†’์€ ์•„๋ฐ€๋กœํŽ™ํ‹ด ํ•จ๋Ÿ‰ ๋ฐ ์ž‘์€ ์ž…์ž ํฌ๊ธฐ๋กœ ์ธํ•ด ์น˜ํ™˜๋„๊ฐ€ ๋†’์•˜๋‹ค. ๊ท ์งˆ ๋ฐ˜์‘์˜ ๊ฒฐ๊ณผ์—์„œ๋Š” ์˜ฅ์ˆ˜์ˆ˜ ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ๊ฐ€ ๋‚ฎ์€ ์ ๋„์™€ ๋†’์€ ์น˜ํ™˜๋„๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ๋น„๊ท ์งˆ ๋ฐ ๊ท ์งˆ ๋ฐ˜์‘์˜ ์ƒ์„ฑ๋ฌผ์„ ๋น„๊ตํ•˜์˜€์„ ๋•Œ, ์˜ฅ์ˆ˜์ˆ˜ ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ๋Š” ๊ท ์งˆ ๋ฐ˜์‘์˜ ์ƒ์„ฑ๋ฌผ์ด, ๋‚˜๋จธ์ง€ ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ๋Š” ๋น„๊ท ์งˆ ๋ฐ˜์‘์˜ ์ƒ์„ฑ๋ฌผ์ด ๋” ํฐ ์น˜ํ™˜๋„๋ฅผ ๋ณด์˜€๋‹ค. ๋‹ค๋‹น๋ฅ˜ ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€๋Š” ์ ์™ธ์„  ๋ถ„๊ด‘๋ถ„์„, X์„  ๊ด‘์ „์ž ๋ถ„๊ด‘๋ถ„์„, 13C ๊ณ ์ฒด์ƒ ํ•ต์ž๊ธฐ๊ณต๋ช… ๋ถ„๊ด‘๋ถ„์„, ์—ด ์ค‘๋Ÿ‰ ๋ถ„์„์„ ํ†ตํ•ด ์•„์„ธํƒˆ ๊ฐ€๊ต๊ฐ€ ํ˜•์„ฑ๋˜์—ˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํก์ˆ˜๊ฐ€ ๋ฐœ์ƒํ•˜๋ฉด, ๋ฌผ ํ™•์‚ฐ์— ์˜ํ•ด ๋‹ค๋‹น๋ฅ˜ ๊ฐ„์˜ ์ˆ˜์†Œ ๊ฒฐํ•ฉ์ด ๊นจ์ง€๊ณ  CMC์— ์กด์žฌํ•˜๋Š” ์นด๋ฅด๋ณต์‹ค๊ธฐ๋“ค์˜ ๋ฐ˜๋ฐœ๋ ฅ์ด ์ƒ๊ธฐ๋ฉด์„œ, ์•„์„ธํƒˆ ๊ฐ€๊ต์˜ ํ•œ๊ณ„์ ๊นŒ์ง€ ํŒฝ์œค์ด ์ผ์–ด๋‚ฌ๋‹ค. ์ž์œ ํก์ˆ˜๋Šฅ์„ ์ธก์ •ํ•˜์˜€์„ ๋•Œ, ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์˜ ์น˜ํ™˜๋„์™€ ์นด๋ฅด๋ณต์‹œ๋ฉ”ํ‹ธ์…€๋ฃฐ๋กœ์˜ค์Šค์˜ ํ•จ๋Ÿ‰์ด ์ฆ๊ฐ€ํ• ์ˆ˜๋ก, ์ž์œ ํก์ˆ˜๋Šฅ์€ ์ƒ์Šนํ•˜์˜€๋‹ค. ์ตœ๋Œ€ ์ž์œ ํก์ˆ˜๋Šฅ์€ ๋น„๊ท ์งˆ ๋ฐ˜์‘์˜ ์ƒ์„ฑ๋ฌผ ์ค‘์—์„œ ๋ฐ€ ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ๋ฅผ ์‚ฌ์šฉํ–ˆ์„ ๋•Œ 251.2 g/g, ๊ท ์งˆ ๋ฐ˜์‘์˜ ์ƒ์„ฑ๋ฌผ ์ค‘์—์„œ ์˜ฅ์ˆ˜์ˆ˜ ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ๋ฅผ ์‚ฌ์šฉํ–ˆ์„ ๋•Œ, 228.5 g/g์ด์—ˆ๋‹ค. ๊ฒ” ๋ถ„์œจ์€ ์ „๋ฐ˜์ ์œผ๋กœ ์ž์œ ํก์ˆ˜๋Šฅ์— ๋ฐ˜๋น„๋ก€ํ•˜์˜€๋‹ค. ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€์˜ ๋Œ€ํ‘œ๊ตฐ์„ ์„ ์ •ํ•˜์—ฌ ํก์ˆ˜ ๊ฑฐ๋™์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ „์ฒด ํก์ˆ˜ ๊ณผ์ •์€ pseudo-second-order swelling kinetic model์„ ๋”ฐ๋ž๋‹ค. ํก์ˆ˜์™€ ๊ณ ๋ถ„์ž ์‚ฌ์Šฌ์˜ ๊ด€๊ณ„๋ฅผ ๋ถ„์„ํ•œ Fickian diffusion model์—์„œ๋Š” ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ๋ฅผ ์‚ฌ์šฉํ•œ ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€๊ฐ€ ๊ณ ๋ถ„์ž ์‚ฌ์Šฌ์˜ ์œ ๋™์„ฑ ์ฆ๊ฐ€๋กœ ์ž์œ ํก์ˆ˜๋Šฅ์ด ํ–ฅ์ƒ๋˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ฃผ์‚ฌ์ „์žํ˜„๋ฏธ๊ฒฝ ๋ถ„์„์—์„œ๋Š” ๋†’์€ ์ž์œ ํก์ˆ˜๋Šฅ์„ ๋ณด์˜€๋˜ ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€์ผ์ˆ˜๋ก, ๋‹ค๊ณต์„ฑ ๊ตฌ์กฐ๋ฅผ ๋ณด์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์•„์„ธํƒˆ ๊ฐ€๊ต์— ๊ธฐ๋ฐ˜ํ•˜์—ฌ ์นœํ™˜๊ฒฝ์ ์ธ ๋‹ค๋‹น๋ฅ˜ ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€๋ฅผ ์ œ์กฐํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‹ค๋‹น๋ฅ˜ ๊ณ ์œ ์˜ ์ƒ๋ถ„ํ•ด์„ฑ๊ณผ ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ตฌํ˜„ํ•œ ๊ณ ํก์ˆ˜์„ฑ์„ ๋™์‹œ์— ์ ‘๋ชฉํ•œ๋‹ค๋ฉด, ๋‹ค๋‹น๋ฅ˜ ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€์˜ ํ™œ์šฉ ๋ฒ”์œ„๋Š” ํฌ๊ฒŒ ๋„“์–ด์งˆ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.The starch aldehydes and carboxymethylcellulose were used to prepare polysaccharide superabsorbent polymers. Particularly, the starch aldehydes were introduced to prepare the superabsorbent polymers by a solution process without any other crosslinking agent. The starch aldehydes were reacted with hydroxyl groups of carboxymethylcellulose under acid conditions to form acetal bridges. Prior to the preparation of the superabsorbent polymers, the starch aldehydes were prepared as a heterogeneous reaction in which the particle form of the starch was maintained and a homogeneous reaction in which the particle form was broken through gelatinization. The starch aldehydes prepared by oxidation were characterized by FT-IR, XPS, and 13C solid NMR. As a result, it was confirmed that the glycoside bonds and the C-2 and C-3 bonds of the anhydroglucose ring were broken at the same time in the oxidation. FE-SEM confirmed that the surface of starch aldehydes were present as crushed particles or very corrugated planes. When the degree of substitution(DS) and viscosity for starch aldehydes were measured, the DS increased and viscosity decreased with increasing amount of oxidizing agent. As a result of the heterogeneous reaction, wheat starch aldehydes showed high DS due to high amylopectin content and small particle size. As a result of the homogeneous reaction, corn starch aldehydes showed low viscosity and high DS. When the products of heterogeneous and homogeneous reaction were compared, corn starch aldehydes exhibited a higher DS at homogeneous reaction and the other starch aldehydes had a higher DS under heterogeneous reaction. The acetal crosslinking of polysaccharide superabsorbent polymers was confirmed by FT-IR, XPS, 13C solid NMR, and TGA. When the absorption occurred, the hydrogen bonds between the polysaccharides were broken by the water diffusion, and the repulsive forces of the carboxyl groups in CMC were generated. As a result, the swelling occurred to the limit of acetal crosslinking. When the water absorbency was measured, the water absorbency increased as the DS of starch aldehydes and the content of carboxymethylcellulose increased. The equilibrium water absorbency was 251.2 g/g when using wheat starch aldehydes by heterogeneous reaction and 228.5 g/g when using corn starch aldehydes by homogeneous reaction, respectively. The gel fraction was inversely proportional to the water absorbency as a whole. Representative group of superabsorbent polymers was selected and the absorption behavior was analyzed. The entire absorption behavior followed the pseudo-second-order swelling kinetic model. In the Fickian diffusion model analyzing the relationship between absorption and polymer chains, it was confirmed that the superabsorbent polymers using starch aldehydes improved the water absorbency by increasing the fluidity of the polymer chains. In the FE-SEM, the superabsorbent polymers with high water absorption showed a porous structure. Through this study, it was possible to prepare environmental-friendly polysaccharide superabsorbent polymers based on acetal crosslinking. It is expected that the application range of polysaccharide superabsorbent polymers will be broadened if the inherent biodegradability of polysaccharides and high absorption property realized in this study are combined.1. ์„œ ๋ก  1 2. ๋ฌธํ—Œ์—ฐ๊ตฌ 5 2.1 ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€์˜ ์›๋ฆฌ 6 2.2 ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€์˜ ์ œ์กฐ ๊ณต์ • 8 2.2.1 ์ค‘ํ•ฉ ๋ฐ ๊ฐ€๊ต์— ์˜ํ•œ ๋ง์ƒ ๊ตฌ์กฐ ํ˜•์„ฑ 8 2.2.1.1 ๋ผ๋””์นผ ์ค‘ํ•ฉ์— ์˜ํ•œ ๊ณ ๋ถ„์ž ์ค‘ํ•ฉ ๋ฐ ๊ฐ€๊ต 8 2.2.1.2 ๋‹ค๋ฅธ ๋ฐฉ๋ฒ•์— ์˜ํ•œ ๊ณ ๋ถ„์ž ์ค‘ํ•ฉ ๋ฐ ๊ฐ€๊ต 13 2.2.2 ์„ฑ๋Šฅ ๊ฐ•ํ™”๋ฅผ ์œ„ํ•œ ์—ฌ๋Ÿฌ ๊ณต์ •๋“ค 15 2.2.2.1 ๋‚˜๋…ธ ๋ณตํ•ฉํ™” 15 2.2.2.2 ํ‘œ๋ฉด ๊ฐ€๊ต 16 2.2.2.3 Interpenetrating polymer network(IPN) ๊ตฌ์กฐ 19 2.3 ๋‹ค๋‹น๋ฅ˜ ๊ธฐ๋ฐ˜ ๊ทธ๋ผํ”„ํŠธ ๊ณต์ค‘ํ•ฉ์ฒด์˜ ์ œ์กฐ 21 2.3.1 ์ „๋ถ„ 21 2.3.2 CMC 25 2.3.3 ์•Œ๊ธด์‚ฐ 26 2.3.4 ํ‚คํ† ์‚ฐ 30 2.3.5 ๊ธฐํƒ€ ๋‹ค๋‹น๋ฅ˜ 32 2.4 ๋‹ค๋‹น๋ฅ˜๋กœ๋งŒ ๊ตฌ์„ฑ๋œ ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€ 33 2.4.1 ๊ธฐ์กด ๊ณต์ •์„ ์‘์šฉํ•œ ๊ฐ€๊ต 33 2.4.2 ๋‹ค๋‹น๋ฅ˜ ์•Œ๋ฐํ•˜์ด๋“œ๋ฅผ ์ด์šฉํ•œ ๊ฐ€๊ต 35 2.4.2.1 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์˜ ํŠน์ง•๊ณผ ํ™œ์šฉ 36 2.4.2.2 ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€๋กœ์„œ์˜ ํ™œ์šฉ 38 3. ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 41 3.1 ์žฌ๋ฃŒ 41 3.2 ์‹คํ—˜๋ฐฉ๋ฒ• 42 3.2.1 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์˜ ์ œ์กฐ 42 3.2.2 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ/CMC ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€์˜ ์ œ์กฐ 44 3.2.3 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์™€ ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€์˜ ๊ตฌ์กฐ ๋ถ„์„ 45 3.2.3.1 ์ ์™ธ์„  ๋ถ„๊ด‘๋ถ„์„ 45 3.2.3.2 X์„  ๊ด‘์ „์ž ๋ถ„๊ด‘๋ถ„์„ 45 3.2.3.3 ํ•ต์ž๊ธฐ๊ณต๋ช… ๋ถ„๊ด‘๋ถ„์„ 47 3.2.3.4 ์ฃผ์‚ฌ์ „์žํ˜„๋ฏธ๊ฒฝ ๋ถ„์„ 47 3.2.3.5 ์—ด ์ค‘๋Ÿ‰ ๋ถ„์„ 47 3.2.4 ์ „๋ถ„, ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ, ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€์˜ ์„ฑ์งˆ ์ธก์ • 48 3.2.4.1 ์ „๋ถ„์˜ ์•„๋ฐ€๋กœ์˜ค์Šค ํ•จ๋Ÿ‰ ๊ณ„์‚ฐ 48 3.2.4.2 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์˜ ์น˜ํ™˜๋„ ์ •๋Ÿ‰ 48 3.2.4.3 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์˜ ์ ๋„ ์ธก์ • 49 3.2.4.4 ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€์˜ ์ž์œ ํก์ˆ˜๋Šฅ ์ธก์ • 50 4. ๊ฒฐ๊ณผ ๋ฐ ๊ณ ์ฐฐ 52 4.1 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์˜ ์ œ์กฐ ๊ณต์ • ๋ฐ ์ˆ˜์œจ 52 4.2 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์˜ ๊ตฌ์กฐ ๋ฐ ์„ฑ์งˆ 56 4.2.1 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์˜ ๊ตฌ์กฐ ํŠน์„ฑ 56 4.2.1.1 ์ ์™ธ์„  ๋ถ„๊ด‘๋ถ„์„ 56 4.2.1.2 X์„  ๊ด‘์ „์ž ๋ถ„๊ด‘๋ถ„์„ 62 4.2.1.3 ํ•ต์ž๊ธฐ๊ณต๋ช… ๋ถ„๊ด‘๋ถ„์„ 65 4.2.1.4 ์ฃผ์‚ฌ์ „์žํ˜„๋ฏธ๊ฒฝ ๋ถ„์„ 68 4.2.2 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์˜ ์น˜ํ™˜๋„์™€ ์ ๋„์˜ ๋ถ„์„ 76 4.2.2.1 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ์˜ ์น˜ํ™˜๋„ ๊ณ„์‚ฐ 76 4.2.2.2 ์ ๋„ ์ธก์ • ๋ฐ ์น˜ํ™˜๋„์™€์˜ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ 80 4.3 ์ „๋ถ„ ์•Œ๋ฐํ•˜์ด๋“œ/CMC ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€์˜ ๊ตฌ์กฐ 90 4.3.1 ์ ์™ธ์„  ๋ถ„๊ด‘๋ถ„์„ 90 4.3.2 X์„  ๊ด‘์ „์ž ๋ถ„๊ด‘๋ถ„์„ 95 4.3.3 ํ•ต์ž๊ธฐ๊ณต๋ช… ๋ถ„๊ด‘๋ถ„์„ 99 4.3.4 ์—ด ์ค‘๋Ÿ‰ ๋ถ„์„ 102 4.4 ์ „๋ถ„์•Œ๋ฐํ•˜์ด๋“œ/CMC ๊ณ ํก์ˆ˜์„ฑ์ˆ˜์ง€์˜ ํก์ˆ˜๋Šฅ 107 4.4.1 ๊ณ ํก์ˆ˜์„ฑ ์ˆ˜์ง€ ์ œ์กฐ ๊ณต์ •์˜ ์ตœ์ ํ™” 107 4.4.2 ์ž์œ ํก์ˆ˜๋Šฅ ๋ถ„์„ 109 4.4.3 ๊ฒ” ๋ถ„์œจ ๋ถ„์„ 116 4.4.4 ํก์ˆ˜ ๊ฑฐ๋™์˜ ๋ถ„์„ 120 4.4.5 ํ˜•ํƒœํ•™์  ํŠน์„ฑ 135 5. ๊ฒฐ ๋ก  142 ์ฐธ๊ณ ๋ฌธํ—Œ 146 Abstract 168Docto

    KTX์™€ SRT ๋™์ผ ์‹œ์ข…์ฐฉ์—ญ ์šดํ–‰์˜ ๊ฒฝ์šฐ๋ฅผ ์ค‘์‹ฌ์œผ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ํ–‰์ •๋Œ€ํ•™์› ๊ณต๊ธฐ์—…์ •์ฑ…ํ•™๊ณผ, 2020. 8. ํ™์ค€ํ˜•.2004๋…„ 4์›”๋ถ€ํ„ฐ KTX๋ผ๋Š” ๊ณ ์†์ฒ ๋„๊ฐ€ ๊ฐœํ†ต๋˜์–ด ํ˜„์žฌ๊นŒ์ง€ ์šดํ–‰ ์ค‘์— ์žˆ๊ณ  2016๋…„ 12์›”๋ถ€ํ„ฐ๋Š” SRT๋ผ๋Š” ๊ณ ์†์ฒ ๋„๊ฐ€ ์šดํ–‰์„ ์‹œ์ž‘ํ•˜์˜€๋‹ค. ๊ตญ๋ฏผ์€ ๊ณ ์†์ฒ ๋„ ์ด์šฉ์— ์žˆ์–ด์„œ๋Š” ๋‘ ๊ฐ€์ง€์˜ ์„ ํƒ์ง€๋ฅผ ๊ฐ€์ง€๊ฒŒ ๋˜์—ˆ๋‹ค. KTX์™€ SRT์˜ ๊ฐ€์žฅ ํฐ ์ฐจ์ด๋Š” SRT๊ฐ€ KTX๋ณด๋‹ค ๋™์ผ ๊ตฌ๊ฐ„์—์„œ ์š”๊ธˆ์ด 10% ์ €๋ ดํ•˜๋Š” ๊ฒƒ์ด๋‹ค. ๋˜ ํ•˜๋‚˜ ์ค‘์š”ํ•˜๊ฒŒ ๋‹ค๋ฅธ ์ ์€ ์ˆ˜๋„๊ถŒ ๊ณ ๊ฐ์€ ์„œ์šธ์—ญ, ์šฉ์‚ฐ์—ญ, ๊ด‘๋ช…์—ญ์—์„œ๋Š” KTX๋งŒ ์ด์šฉํ•  ์ˆ˜ ์žˆ๊ณ , ์ˆ˜์„œ์—ญ, ๋™ํƒ„์—ญ, ์ง€์ œ์—ญ์—์„œ๋Š” SRT๋งŒ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด์ฒ˜๋Ÿผ ์ˆ˜๋„๊ถŒ์„ ์ œ์™ธํ•˜๋ฉด KTX์™€ SRT๋Š” ๋™์ผ๊ตฌ๊ฐ„(์—ญ)์„ ์šดํ–‰ํ•˜๋ฏ€๋กœ ๊ณ ์†์ฒ ๋„ ์ด์šฉ ๊ณ ๊ฐ์€ ์ˆ˜๋„๊ถŒ ์ด์™ธ์˜ ์—ญ์—์„œ๋Š” ๋‘˜ ์ค‘ ํ•˜๋‚˜๋ฅผ ์„ ํƒํ•˜์—ฌ ์ด์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด ๊ฐ™์€ ์กฐ๊ฑด ์†์—์„œ ๋งŒ์ผ ๊ณ ๊ฐ์ด ๋Œ€์ „โ†”๋ถ€์‚ฐ๊ณผ ๊ฐ™์ด KTX์™€ SRT๊ฐ€ ๋ชจ๋‘ ์ •์ฐจํ•˜๋Š” ์—ญ์—์„œ ๊ณ ์†์ฒ ๋„๋ฅผ ์ด์šฉํ•˜๋Š” ๊ฒฝ์šฐ ์–ด๋– ํ•œ ์š”์ธ์— ์˜ํ•ด ๋‘ ์—ด์ฐจ ์ค‘ ํ•˜๋‚˜๋ฅผ ์„ ํƒํ•  ๊ฒƒ์ธ์ง€์— ๋Œ€ํ•ด์„œ ์˜๋ฌธ์ด ์ƒ๊ฒจ ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์˜จ๋ผ์ธ ์„ค๋ฌธ์กฐ์‚ฌ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ๊ณ ๊ฐ์ด ๊ณ ์†์—ด์ฐจ๋ฅผ ์„ ํƒํ•˜๋Š” ๋ฐ ์žˆ์–ด์„œ ๊ฐ€๊ฒฉ, ์‹œ๊ฐ„, ๋ธŒ๋žœ๋“œ ์ธ์ง€๋„ ๋“ฑ ๊ฐœ๋ณ„ ์š”์ธ๋“ค์ด ์—ด์ฐจ๋ฅผ ์„ ํƒํ•˜๋Š” ๋ฐ์— ์–ด๋– ํ•œ ์˜ํ–ฅ์„ ์ฃผ๊ณ  ์žˆ๋Š”์ง€์— ๋Œ€ํ•ด์„œ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ณ ๊ฐ์ด ๊ฐ€๊ฒฉ์ด 10% ์ €๋ ดํ•œ SRT๋ฅผ ์ด์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ ๋Œ€๊ธฐํ•˜๋Š” ์‹œ๊ฐ„์ด ๋ธŒ๋žœ๋“œ ์ธ์ง€๋„, ์„œ๋น„์Šค์˜ ์งˆ, ๊ณ ๊ฐ์˜ ์—ฐ๋ น๋Œ€, ์—ด์ฐจ ์ด์šฉ๋ชฉ์  ๋“ฑ์— ๋”ฐ๋ผ ์ฐจ์ด๊ฐ€ ์กด์žฌํ•  ๊ฒƒ์ด๋ผ๋Š” ๊ฐ€์„ค์„ ์„ธ์› ๋‹ค. ๊ทธ๋ฆฌ๊ณ  ๋ธŒ๋žœ๋“œ ์ธ์ง€๋„์™€ ์„œ๋น„์Šค ์งˆ์ด ๋Œ€๊ธฐ์‹œ๊ฐ„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์€ ์†Œ๋“์— ๋”ฐ๋ผ ์ฐจ์ด๊ฐ€ ์žˆ์„ ๊ฒƒ์ด๋ผ๋Š” ๊ฐ€์„ค์„ ์„ธ์› ๋‹ค. ์„œ๋น„์Šค์˜ ์งˆ๊ณผ ์ด์šฉ๋ชฉ์ ์€ ๋Œ€๊ธฐ์‹œ๊ฐ„๊ณผ ์œ ์˜๋ฏธํ•œ ๊ด€๊ณ„๊ฐ€ ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๊ณ , ์„œ๋น„์Šค์˜ ์งˆ์€ ์†Œ๋“์ด๋ผ๋Š” ์กฐ์ ˆ๋ณ€์ˆ˜์— ์˜ํ–ฅ์„ ๋ฐ›๊ณ  ์žˆ์Œ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ฆ‰, ๊ณ ๊ฐ๊ณผ ์—ญ์‚ฌ์™€์˜ ์ ‘๊ทผ์„ฑ์„ ๋ฐฐ์ œํ•œ ์ƒํ™ฉ์—์„œ ๋™์ผ ์‹œ์ข…์ฐฉ์—ญ์„ ์ด์šฉํ•˜๋Š” ๊ณ ๊ฐ์€ ๋‘ ์—ด์ฐจ ์ค‘์— ๋จผ์ € ์ถœ๋ฐœํ•˜๋Š” ์—ด์ฐจ๋ฅผ ์ด์šฉํ•˜์ง€๋Š” ์•Š๋Š”๋‹ค๋Š” ์œ ์˜๋ฏธํ•œ ๊ฒฐ๊ณผ๋ฅผ ์–ป์–ด๋ƒˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ์‹ค์งˆ์ ์œผ๋กœ KTX์™€ SRT์˜ ๊ฒฝ์Ÿ์ด ๋ฐœ์ƒํ•˜๋Š” ๋™์ผ ์‹œ์ข…์ฐฉ ์šดํ–‰๊ตฌ๊ฐ„์—์„œ ๊ฒฝ์Ÿ์šฐ์œ„๋ฅผ ์ ํ•˜๊ธฐ ์œ„ํ•ด์„œ ์–‘์‚ฌ๊ฐ€ ์–ด๋– ํ•œ ๋ถ€๋ถ„์— ์ดˆ์ ์„ ๋งž์ถ”๊ณ  ์˜์—… ์ „๋žต์„ ๊ตฌ์‚ฌํ•ด์•ผ ํ•˜๋Š”์ง€์— ๋Œ€ํ•ด ๊ณ ๋ฏผํ•˜๊ณ  ์ด๋ฅผ ๋ณด์™„ํ•˜๋„๋ก ํ•˜๋Š” ๋ฐ์— ์˜์˜๊ฐ€ ์žˆ๋‹ค.KTX was opened in April 2004 and has been in operation until now. In December 2016, SRT, a high-speed railway, started operation. The people have two options when it comes to using the high-speed railway. The biggest difference between KTX and SRT is that the SRT is 10 percent cheaper in the same section than KTX. Another is that customers in the Seoul metropolitan area can only use KTX at Seoul Station, Yongsan Station and Gwangmyeong Station, and only SRT at Suseo Station, Dongtan Station and Jije Station. Excluding the metropolitan area, KTX and SRT operate the same section, so customers who use the high-speed railway can choose between the two at stations other than the metropolitan area. Under these conditions, if a customer uses a high-speed train at a station where both KTX and SRT stop, such as Daejeon and Busan, the question has arisen over what factors would make the customer choose between the two trains. It hypothesized that the time that customers wait to use SRT, which is 10% cheaper, will vary depending on brand recognition, quality of service, customer age range, and purpose of train use. And hypothesized that the impact of brand awareness and quality of service on waiting time will vary depending on income. The results of the online survey were analyzed through multiple regression analyses. The analysis showed that the quality of the service and the purpose of use were significantly related to waiting time, and that the quality of the service was affected by the adjustment variable called income. In other words, customers who use the same stop-and-run station in the absence of access to history with customers have significant results that they do not use the train that departs first between the two trains. This study is actually meaningful in making the two companies consider what part of the same stop-and-run operation where competition between KTX and SRT takes place and make up for it.์ œ 1 ์žฅ ์„œ ๋ก  1 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„ ๋ฐ ๋ฐฉ๋ฒ• 2 ์ œ 2 ์žฅ ์ด๋ก ์  ๋…ผ์˜์™€ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  6 ์ œ 1 ์ ˆ ์ด๋ก ์  ๋…ผ์˜ 6 1. ํ†ตํ–‰ ์‹œ๊ฐ„๊ฐ€์น˜์™€ ๊ฐ€๊ฒฉ 6 2. ํ–‰๋™๊ฒฝ์ œํ•™๊ณผ ๊ตํ†ต์ˆ˜๋‹จ ์„ ํƒ 8 3. ํƒ„๋ ฅ์„ฑ์˜ ์ •์˜์™€ ์ข…๋ฅ˜ 10 ์ œ 2 ์ ˆ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  13 1. ๊ณ ์†์ฒ ๋„ ๊ฒฝ์Ÿ์ฒด์ œ ๋„์ž… 13 2. ๊ตํ†ต์ˆ˜๋‹จ ์„ ํƒ ์š”์ธ 16 3. ๊ตํ†ต์ˆ˜๋‹จ ์„ ํƒ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ 18 ์ œ 3 ์ ˆ ์„ ํ–‰์—ฐ๊ตฌ์™€์˜ ์ฐจ๋ณ„์„ฑ 20 ์ œ 3 ์žฅ ์—ฐ๊ตฌ์˜ ์„ค๊ณ„ 21 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋ชจํ˜• ๋ฐ ๊ฐ€์„ค 21 1. ์—ฐ๊ตฌ๊ณผ์ œ 21 2. ์—ฐ๊ตฌ๋ชจํ˜• ๋ฐ ๊ฐ€์„ค 24 ์ œ 4 ์žฅ ๊ฒฐ๊ณผ๋ถ„์„ 29 ์ œ 1 ์ ˆ ์กฐ์‚ฌ ๋ฐ ๋ถ„์„๋ฐฉ๋ฒ• 29 1. ์„ ํ˜ธ์˜์‹(Stated Preference ์ดํ•˜ SP)์กฐ์‚ฌ 29 2. ์„ค๋ฌธ๋Œ€์ƒ ๋ฐ ์กฐ์‚ฌ๋ฐฉ๋ฒ• 31 ์ œ 2 ์ ˆ ๋ถ„์„์ž๋ฃŒ์˜ ๊ด€๊ณ„ ๋ถ„์„ 32 1. ๋ณ€์ˆ˜๊ฐ„์˜ ์ƒ๊ด€๊ด€๊ณ„ ๋ถ„์„ 32 ์ œ 3 ์ ˆ ์กฐ์‚ฌ์ž๋ฃŒ์˜ ์ผ๋ฐ˜ํ˜„ํ™ฉ 41 1. ์กฐ์‚ฌ๋Œ€์ƒ์˜ ์ธ๊ตฌ ํ†ต๊ณ„ํ•™์  ํŠน์„ฑ 41 2. ์ฃผ์š” ๋ณ€์ˆ˜๋“ค์˜ ๊ธฐ์ˆ ํ†ต๊ณ„ ๋ถ„์„ 43 ์ œ 4 ์ ˆ ๊ฐ€์„ค์˜ ๊ฒ€์ • ๋ฐ ํ•ด์„ 49 1. ๋ธŒ๋žœ๋“œ์ธ์ง€๋„, ์„œ๋น„์Šค์˜ ์งˆ, ์—ฐ๋ น๋Œ€, ์ด์šฉ๋ชฉ์ ์ด ํ›„์†์—ด์ฐจ ์ด์šฉ์„ ์œ„ํ•œ ๋Œ€๊ธฐ์‹œ๊ฐ„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ ๋ถ„์„ 49 2. ๋ธŒ๋žœ๋“œ์ธ์ง€๋„์™€ ์„œ๋น„์Šค์˜ ์งˆ์ด ๋Œ€๊ธฐ์‹œ๊ฐ„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ์†Œ๋“์˜ ์กฐ์ ˆํšจ๊ณผ ๋ถ„์„ 53 3. ๊ฐ€์„ค๊ฒ€์ • ๋ฐ ๋ถ„์„์š”์•ฝ 55 ์ œ 5 ์žฅ ๊ฒฐ๋ก  57 ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ์š”์•ฝ 57 1. ๊ฐ€์„ค๊ฒ€์ • ๋ฐ ํ•ด์„๊ฒฐ๊ณผ 58 ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ์˜์˜ ๋ฐ ์‹œ์‚ฌ์  61 ์ œ 3 ์ ˆ ํ–ฅํ›„ ์—ฐ๊ตฌ๊ณผ์ œ 62 ์ฐธ๊ณ ๋ฌธํ—Œ 64Maste

    The Effects of Innovation Strategy and Policy on Product Innovation Performance and Technology Entrepreneurship

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ˜‘๋™๊ณผ์ • ๊ธฐ์ˆ ๊ฒฝ์˜ยท๊ฒฝ์ œยท์ •์ฑ…์ „๊ณต, 2018. 2. ๊น€์—ฐ๋ฐฐ.This dissertation consists of two essays on the effects of innovation strategy and policy on technology commercialization performance. Innovation is regarded as one of the important determinants for national economic growth. Stimulating innovation performance requires not only the implementation of an innovation policy at national level but also an innovation strategy at the firm level. The first essay assessed the effect of the simultaneous implementation of different intellectual property protection methods (IPPMs) on a firms product innovation performance. Seven widely used intellectual property protection methods in the manufacturing industries are grouped into two categories โ€“ formal and informal. Complementary effect is then tested within and between the two groups. An additional analysis on the moderating role of the industrial complexity in terms of technology on the complementary effect of intellectual property protection methods on product innovation performance is also conducted. The data of 1297 manufacturing firms analyzed in the first essay is from Korean Innovation Survey (KIS) 2010. Throughout the cross-sectional tobit-regression, within group complementary effects are revealed in both formal and informal groups of IPPMs. On the other hand, the result of testing between groups effects of IPPMs differs by the level of industrial technological complexity. Implementing IPPMs from two different groups are in a relation of complementarity when industrial technology is complex and of substitution when industrial technology is discrete (or simple). The second essay investigates the relationship among market failure, national culture, government intervention, and technology entrepreneurship. Market failure in entrepreneurship market has been regarded as a justification for government intervention in the shape of policy. It has been also emphasized that the accordance with not only economic aspects, but also national context is important in framing the technology entrepreneurship policy. To find out the effectiveness of the government intervention, direct effect of market failure, national culture, and government intervention on technology entrepreneurial activity is analyzed by using panel data of 49 countries from 2007 to 2013. Indirect effect of government intervention is then tested to reveal the moderating role of the policy on the effect of market failure and national culture on technology entrepreneurship. By conducting random effect model analysis on panel data of 199 observations, three main findings are obtained. First, information asymmetry affects technology entrepreneurship and its effect varies by the novelty of the technology involved. Second, individualism has a negative effect on technology entrepreneurial activity level. Last, government intervention has both direct and indirect effect on technology entrepreneurship, but the effect varies according to the characteristic of government intervention.Introduction 1 Essay I The Complementary effects of intellectual property protection methods on product innovation performance and the moderating role of industrial technological complexity 5 1. Introduction 6 2. Theoretical background 10 2.1. Single use of intellectual property protection method 10 2.2. Multi-use of intellectual property protection methods 12 2.3. Determinants of intellectual property protection methods 14 2.4. Industrial technological complexity 16 3. Research framework 18 3.1. Complementarity of intellectual property protection mechanisms 18 3.2. Inter-industrial difference in technological complexity 20 4. Method 23 4.1. Data 23 4.2. Empirical model 25 4.3. Variables 26 4.3.1. Intellectual property protection methods and product innovation performance 26 4.3.2. Triples as a proxy for technological complexity of industry 27 4.3.3. Control variables 31 4.4. Estimation equations 35 5. Results and discussion 38 5.1. Complementarity of intellectual property protection methods 38 5.2. Moderating effect of technological complexity of industry 41 5.3. Additional analysis 48 6. Conclusion 52 7. Limitation 56 Essay II The effects of market failure, national culture, and government intervention on technology entrepreneurship 59 1. Introduction 60 2. Theoretical background 64 2.1. Technology entrepreneurship 64 2.2. Market failure 67 2.3. Government intervention 69 2.4. National culture 71 2.5. Market failure, technology entrepreneurship and government intervention 73 2.6. National Culture, technology entrepreneurship and government intervention 76 3. Research framework 79 3.1. Market failure, technology entrepreneurship, and government intervention 79 3.2. National culture, technology entrepreneurship, and government intervention 82 4. Method 85 4.1. Data 85 4.2. Variables 86 4.2.1. Measuring information asymmetry 86 4.2.2. National culture 87 4.2.3. Control variables 89 4.3. Empirical model 94 4.4. Estimation equations 95 4.4.1. Direct effects of market failure, national culture, and government intervention on technology entrepreneurship 95 4.4.2. Moderating effects of government intervention 97 5. Results 101 5.1. Direct effects of market failure, national culture, and government intervention on technology entrepreneurship 101 5.2. Moderating effects of government intervention 105 6. Discussion 110 6.1. Information asymmetry and technology entrepreneurship 110 6.2. National culture and technology entrepreneurship 111 6.3. Market failure, national culture and government intervention 113 6.4. Additional analysis 122 7. Conclusion 125 8. Limitations 128 Conclusion 129 Appendix 132 Bibliography 145 -Essay I- 145 -Essay II- 150Docto

    Role of Nrf2-mediated energy metabolism in the inhibition of liver injury and PPIX-inhibition of cancer progression

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    ํ•™์œ„๋…ผ๋ฌธ (๋ฐ•์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์•ฝํ•™๊ณผ ์•ฝ๋ฌผํ•™ ์ „๊ณต, 2017. 2. ๊น€์ƒ๊ฑด.๊ฐ„์„ธํฌ์•”์€ ์•” ๊ด€๋ จ ์‚ฌ๋ง์›์ธ ์ค‘ ํ•˜๋‚˜๋กœ ์˜ˆํ›„๊ฐ€ ๋‚˜์˜๋‹ค. ์ค‘๊ฐ„์—ฝ์„ฑ ๊ฐ„์„ธํฌ๋Š” ์ข…์–‘ ๋ฏธ์„ธํ™˜๊ฒฝ์— ์ ์‘ํ•˜์—ฌ ์„ธํฌ์˜ ์„ฑ์žฅ๊ณผ ์ „์ด๋ฅผ ๋•๊ณ , ๋งˆ์ดํฌ๋กœRNA์˜ ์ด์ƒ ์กฐ์ ˆ์— ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ๋‹ค์–‘ํ•œ ๋ณ‘๋ฆฌ์  ์ƒํ™ฉ์—์„œ ์†Œํฌ์ฒด๊ฐ€ ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ๋ฐ›๊ฒŒ ๋˜๋ฉด ์ดˆ๊ธฐ์—๋Š” ์†Œํฌ์ฒด์˜ ๊ธฐ๋Šฅ์  ์šฉ๋Ÿ‰์„ ๋Š˜๋ ค ์„ธํฌ์˜ ์ƒ์กด์„ ์œ ์ง€ํ•˜์ง€๋งŒ ์ง€์†์ ์ธ ์ŠคํŠธ๋ ˆ์Šค๋Š” ์„ธํฌ ์‚ฌ๋ฉธ์„ ์ผ์œผํ‚จ๋‹ค. ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค๊ฐ€ ๋ฐœ์ƒํ–ˆ์„ ๋•Œ ์„ธํฌ์˜ ์ƒ์กด๊ณผ ์‚ฌ๋ฉธ์„ ๊ฒฐ์ •ํ•˜๋Š” ์Šค์œ„์น˜ ๋ถ„์ž์— ๋Œ€ํ•œ ์ •๋ณด๋Š” ๊ฑฐ์˜ ์—†๋‹ค. ์„ธํฌ์˜ ์—๋„ˆ์ง€ ๋Œ€์‚ฌ ๊ณผ์ •์— ์žˆ์–ด์„œ ์‚ฐ์†Œ ์†Œ๋ชจ์™€ ํ™œ์„ฑ์‚ฐ์†Œ์˜ ์ฆ๊ฐ€๋Š” ์„ธํฌ์˜ ๊ธฐ๋Šฅ ์กฐ์ ˆ ๋ฐ ํ™œ์„ฑ์‚ฐ์†Œ ์ œ๊ฑฐ๋ฅผ ์œ„ํ•œ ๊ธฐ์ „์— ์˜ํ–ฅ์„ ์ค€๋‹ค. Nrf2 (Nuclear Factor, Erythroid 2 Like 2)๋Š” ํ•ญ์‚ฐํ™” ๋ฐ ํ•ด๋… ํšจ์†Œ์˜ ๋ฐœํ˜„์„ ์กฐ์ ˆํ•˜๋ฉฐ ์‚ฐํ™”์  ์ŠคํŠธ๋ ˆ์Šค์™€ ๋ฐœ์•” ๊ณผ์ •์œผ๋กœ๋ถ€ํ„ฐ ์„ธํฌ๋ฅผ ๋ณดํ˜ธํ•˜๋Š” ์ง„ํ•ต์„ธํฌ์˜ ํ•ต์‹ฌ ์ „์‚ฌ์ธ์ž์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” Nrf2๊ฐ€ ๊ฐ„์„ธํฌ์•”์˜ ์ง„ํ–‰์„ ์–ต์ œํ•˜๋ฉฐ ์„ธํฌ ์—๋„ˆ์ง€ ๋Œ€์‚ฌ ์กฐ์ ˆ์„ ํ†ตํ•˜์—ฌ ์„ธํฌ ๋ณดํ˜ธ์ž‘์šฉ์„ ํ•œ๋‹ค๋Š” ๊ฐ€์„ค์„ ์ œ๊ธฐํ•˜์˜€๋‹ค. ์œ„ ๊ฐ€์„ค์„ ๊ฒ€์ฆํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ๋‹ค์Œ ์„ธ ๊ฐ€์ง€ ์—ฐ๊ตฌ๋ชฉํ‘œ๋ฅผ ์„ค์ •ํ•˜์˜€๋‹ค. ์ฒซ์งธ, Nrf2 ํ™œ์„ฑํ™” ์ž‘์šฉ์ด ์žˆ๋Š” ๋Œ€ํ‘œ์  ํ›„๋ณด๋ฌผ์งˆ๋กœ์„œ ํ”„๋กœํ† ํฌ๋ฅดํ”ผ๋ฆฐ์— ์˜ํ•œ ๋งŒ์„ฑ ๊ฐ„์งˆํ™˜ (๊ฐ„์„ธํฌ์•”) ์ œ์–ด ํšจ๋Šฅ ๋ฐ ๊ด€๋ จ๋œ ๊ธฐ์ „์„ ์—ฐ๊ตฌํ•˜์˜€๊ณ , ๋‘˜์งธ, Nrf2๊ฐ€ Sirt3์˜ ๋ฐœํ˜„ ์ฆ๊ฐ€๋ฅผ ํ†ตํ•˜์—ฌ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ์—๋„ˆ์ง€ ๋Œ€์‚ฌ๋ฅผ ์กฐ์ ˆํ•˜๋Š” ๊ฐ€๋Šฅ์„ฑ์„ ๊ทœ๋ช…ํ•˜๊ณ ์ž ํ–ˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, Nrf2๋ฅผ ํ™œ์„ฑํ™”ํ•˜๋Š” ๋ฐœ๊ตดํ•œ ํƒ€๊ฒŸ์„ ํ†ตํ•˜์—ฌ ๊ฐ„์†์ƒ์— ์ˆ˜๋ฐ˜๋˜๋Š” ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ๋ฐฉ์–ดํ•˜๋Š”์ง€ ์—ฌ๋ถ€๋ฅผ ๊ด€์ฐฐํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. Nrf2 ํ™œ์„ฑ์„ ํ†ตํ•œ ๊ฐ„์„ธํฌ์•”์˜ ์–ต์ œ ํšจ๊ณผ๋ฅผ ์ฆ๋ช…ํ•˜๊ธฐ ์œ„ํ•˜์—ฌ ํ™œ์šฉ๋œ ์—ฐ๊ตฌ๋ฐฉ๋ฒ•์€ Nrf2 ํ™œ์„ฑ ํ›„๋ณด๋ฌผ์งˆ์„ ์ข…์–‘์ด์‹๋™๋ฌผ๊ณผ ์ค‘๊ฐ„์—ฝ์„ฑ ๊ฐ„์•” ์„ธํฌ์ฃผ์— ์ฒ˜๋ฆฌํ•˜์—ฌ ๊ทธ ํšจ๋Šฅ ๋ฐ ๋ณ€ํ™”์— ๊ด€๋ จ ๊ธฐ์ „์„ ํƒ๊ตฌํ•˜์˜€๋‹ค. ํ•ญ์•”์—ฐ๊ตฌ ์ธก๋ฉด์—์„œ ๋ณผ ๋•Œ ํ”„๋กœํ† ํฌ๋ฅดํ”ผ๋ฆฐ์€ miR-199a-5p๋ฅผ ์ฆ๊ฐ€์‹œ์ผœ HIF-1ฮฑ์™€ E2F3๋ฅผ ์–ต์ œํ•˜์˜€์œผ๋ฉฐ ์ด๋ฅผ ํ†ตํ•œ ํ•ญ์•”์ œ ์ €ํ•ญ์„ฑ์—๋„ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋‹ค. ๋˜ํ•œ, ๊ฐ„์„ธํฌ์•” ํ™˜์ž์˜ ์‹œ๋ฃŒ์—์„œ๋„ miR-199a-5p์˜ ๋ฐœํ˜„ ์ €ํ•ด ๋ฐ E2F3์™€์˜ ๊ด€๋ จ์„ฑ์„ ๋ณผ ์ˆ˜ ์žˆ์—ˆ์œผ๋ฉฐ, ๊ฐ„์„ธํฌ์•”์˜ ์ฆ์‹๊ณผ ์นจ์Šต ๋ฐ ์ด๋™์„ฑ์„ ์–ต์ œํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ฐ„์„ธํฌ ์†์ƒ ๋ฐฉ์–ด ์—ฐ๊ตฌ๋Š”, Nrf2 ๊ฒฐ์† ์ฅ ๋ชจ๋ธ๊ณผ Nrf2 ๊ณผ๋ฐœํ˜„ ์„ธํฌ ๋ชจ๋ธ์„ ํ™œ์šฉํ•˜์˜€์œผ๋ฉฐ ์œ ์ฒด์—ญํ•™์„ ํ†ตํ•œ Sirt3 ์œ ์ „์ž ๋„์ž…์„ ํ†ตํ•˜์—ฌ ๋ณ€ํ™”๋ฅผ ํ™•์ธํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ์— ๋”ฐ๋ฅด๋ฉด, ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„ ํ™˜์ž ์‹œ๋ฃŒ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ๋น„๋งŒ ์ฅ ๋ชจ๋ธ์—์„œ๋„ ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค ๋งˆ์ปค์™€ ๊ฐ„ ์†์ƒ ์ง€ํ‘œ๊ฐ€ ์–‘์˜ ์ƒ๊ด€๊ด€๊ณ„๋ฅผ ๋‚˜ํƒ€๋ƒˆ๋‹ค. ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค ์œ ๋„ ๋ฌผ์งˆ์— ์˜ํ•ด ์ผ์–ด๋‚œ ์†์ƒ ๊ฐ„์„ธํฌ์—์„œ๋Š” Nrf2๊ฐ€ ์–ต์ œ๋˜์—ˆ์œผ๋ฉฐ, Nrf2 ๊ฒฐ์† ์ฅ ๋ชจ๋ธ์—์„œ๋Š” ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค๊ฐ€ ์ฆ๊ฐ€ํ•˜์˜€๋‹ค. Nrf2๋Š” CPT1๊ณผ PGC-1ฮฑ์˜ ๋ฐœํ˜„๊ณผ ์‚ฐ์†Œ์†Œ๋ชจ๋Ÿ‰์„ ์ฆ๊ฐ€์‹œ์ผœ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ๊ธฐ๋Šฅ์„ ์กฐ์ ˆํ•˜๋ฉฐ, ์ „์‚ฌ์ธ์ž๋กœ์จ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ํŠน์ด์  ์œ ์ „์ž์ธ Sirt3๋ฅผ ์กฐ์ ˆํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํ•ด์„๋œ๋‹ค. ๋น„์•Œ์ฝœ์„ฑ ์ง€๋ฐฉ๊ฐ„ ํ™˜์ž ์‹œ๋ฃŒ์™€, ๊ณ ์ง€๋ฐฉ ์‹์ด๋ฅผ ๋จน์ธ ์ฅ์—์„œ Nrf2์™€ Sirt3๊ฐ€ ๊ณตํ†ต์ ์œผ๋กœ ๊ฐ์†Œํ–ˆ์œผ๋ฉฐ, ์ด๋Š” ์†Œํฌ์ œ ์ŠคํŠธ๋ ˆ์Šค๋ฅผ ์ฆ๊ฐ€ ์‹œํ‚ค๋Š” ์—ญํ• ์„ ํ•จ์„ ์„ธํฌ์ฃผ ๋ชจ๋ธ์—์„œ ํ™•์ธํ•˜์˜€๋‹ค. ์œ ์ฒด์—ญํ•™์„ ์ด์šฉํ•˜์—ฌ Sirt3 ์œ ์ „์ž๋ฅผ ์ฅ์— ์ฃผ์ž… (Hydrodynamic gene delivery)ํ•œ ๊ฒฐ๊ณผ์—์„œ๋„, Nrf2์˜ ๊ฒฐ์†์€ ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค์˜ ์ฆ๊ฐ€๋ฅผ ์œ ๋ฐœํ•˜์˜€์œผ๋ฉฐ Sirt3๋ฅผ ๋„์ž…ํ•  ๋•Œ ์ด๋Ÿฌํ•œ ๋ณ€ํ™”๋Š” ํ•ด์†Œ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” 1) ๊ฐ„์„ธํฌ์•”์—์„œ Nrf2 ํ™œ์„ฑ ํ›„๋ณด๋ฌผ์งˆ์ธ ํ”„๋กœํ† ํฌ๋ฅดํ”ผ๋ฆฐ์€ miR-199a-5p๋ฅผ ์ฆ๊ฐ€์‹œํ‚ค๋ฉฐ ์—๋„ˆ์ง€ ๋Œ€์‚ฌ ๋ฐ ํ˜ˆ๊ด€์‹ ์ƒ์— ๊ด€๋ จ๋œ ์œ ์ „์ž๋ฅผ ํ™œ์„ฑํ™” ์‹œํ‚ค๋Š” HIF-1์™€ ์„ธํฌ ์ฃผ๊ธฐ์— ๊ธฐ์—ฌํ•˜๋Š” E2F3๋ฅผ ์–ต์ œํ•˜์—ฌ ๊ฐ„์งˆํ™˜์˜ ๋งŒ์„ฑํ™”์— ์˜ํ•œ ๊ฐ„์„ธํฌ์•”์˜ ๋ฐœ์ „์„ ์–ต์ œํ•˜๋Š” ํšจ๋Šฅ์„ ๋ณด์ด๋ฉฐ, 2) ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค์— ์˜ํ•œ ๊ฐ„์„ธํฌ ์†์ƒ์—์„œ๋Š” ํ•ญ์‚ฐํ™” ์ธ์ž์ธ Nrf2๊ฐ€ ๊ฐ์†Œํ•˜๋Š”๋ฐ, ์ด๋Š” ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ํŠน์ด์  Sirt3 ์กฐ์ ˆ์„ ํ†ตํ•œ ์—๋„ˆ์ง€ ๋Œ€์‚ฌ์˜ ๋ณ€ํ™”๋กœ ํ•ด์„๋œ๋‹ค. ์ข…ํ•ฉํ•˜๋ฉด Nrf2์˜ ํ™œ์„ฑํ™”์ œ ์ค‘์—๋Š” ์ข…์–‘ ์•…์„ฑํ™”๋ฅผ ์–ต์ œํ•˜๋Š” ํ›„๋ณด ๋ฌผ์งˆ์ด ์žˆ์œผ๋ฉฐ, Nrf2์˜ ํ™œ์„ฑํ™”๋Š” ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„์˜ ํ™œ์„ฑ์„ ์ฆ์ง„์‹œํ‚ค๋Š” ํšจ๊ณผ๊ฐ€ ์žˆ๋Š”๋ฐ ์ด๋Š” Sirt3์˜ ์œ ๋„ ๋ฐœํ˜„์„ ๊ฒฝ์œ ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ํ•ด์„๋œ๋‹ค. ์ด๋Ÿฌํ•œ ๊ฒฐ๊ณผ๋Š” Nrf2 ํ™œ์„ฑํ™” ํšจ๊ณผ์˜ ์ƒˆ๋กœ์šด ์‘์šฉ ๊ฐ€๋Šฅ์„ฑ์„ ์ œ์‹œํ•œ๋‹ค.I. ์„œ๋ก  1 II. ์—ฐ๊ตฌ์žฌ๋ฃŒ ๋ฐ ๋ฐฉ๋ฒ• 9 II-1. ์‹œ์•ฝ ๋ฐ ์žฌ๋ฃŒ 9 II-2. ์„ธํฌ ๋ฐฐ์–‘ 9 II-3. ์ธ์ฒด ์กฐ์ง ์‹œ๋ฃŒ 10 II-4. ๋ฉด์—ญํ™”ํ•™์  ๋ถ„์„ (Immunoblotting) 10 II-5. ๋ฉด์—ญ์นจ๊ฐ•๋ฒ• (Immunoprecipitation) 11 II-6. ํ˜•์งˆ๋„์ž… (Transfection) 11 II-7. ๋ฆฌํฌํ„ฐ ์œ ์ „์ž ๋ถ„์„ (Reporter gene assay) 11 II-8. ์ƒ๋ฌผ์ •๋ณดํ•™์  ๋ถ„์„ 12 II-9. ๋งˆ์ดํฌ๋กœRNA์˜ Realtime RT-PCR 12 II-10. cDNA์˜ Realtime RT-PCR 13 II-11. ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„DNA ์ธก์ • 14 II-12. siRNA knockdown ์‹คํ—˜ 15 II-13. ํฌ๋กœ๋งˆํ‹ด ๋ฉด์—ญ์นจ๊ฐ•๋ฒ• (Chromatin Immunoprecipitation, ChIP) 15 II-14. ๋งˆ์ดํฌ๋กœRNA ์œ ์‚ฌ์ฒด ๋ฐ ์ €ํ•ด์ œ ์ผ๊ณผ์„ฑ ๋„์ž… 16 II-15. ์ฒด์™ธ ์„ธํฌ์˜ ์ด๋™์„ฑ/์นจ์Šต์„ฑ ๋ถ„์„๋ฒ• 16 II-16. ์„ธํฌ ๋…์„ฑ ๋ถ„์„ (MTT assay) 17 II-17. ์ด์ข…์ด์‹ 17 II-18. ์œ ์ฒด์—ญํ•™์„ ์ด์šฉํ•œ ์œ ์ „์ž ์ฃผ์ž… (Hydrodynamic gene delivery) 18 II-19. ํ˜ˆ์•ก์˜ ํ™”ํ•™์  ๋ถ„์„ 18 II-20. ๋ฉด์—ญ์กฐ์งํ™”ํ•™์  ์—ผ์ƒ‰ 18 II-21. ํ†ต๊ณ„๋ถ„์„ 19 III. ์—ฐ๊ตฌ๊ฒฐ๊ณผ 20 III-I. Nrf2 ์œ ๋„ ๋ฌผ์งˆ์— ์˜ํ•œ ๊ฐ„์„ธํฌ์•”์˜ ์•…์„ฑํ™” ์–ต์ œ ํšจ๋Šฅ 20 III-I-1. ํ”„๋กœํ† ํฌ๋ฅดํ”ผ๋ฆฐ์˜ HIF-1ฮฑ์™€ ์ƒํ”ผ-์ค‘๊ฐ„์—ฝ์„ฑ ์ดํ–‰ ๋ถ„์ž ๋งˆ์ปค ์–ต์ œ 20 III-I-2. ํ”„๋กœํ† ํฌ๋ฅดํ”ผ๋ฆฐ์— ์˜ํ•œ ๋งˆ์ดํฌ๋กœRNA ์กฐ์ ˆ 24 III-I-3. miR-199a-5p ์˜ ์ƒˆ๋กœ์šด ํƒ€๊ฒŸ ๋„์ถœ 27 III-I-4. ์ธ๊ฐ„ ๊ฐ„์„ธํฌ์•” ๋ฐ ์ข…์–‘ ์ด์‹ ๋™๋ฌผ ๋ชจ๋ธ์—์„œ์˜ E2F3 ์ฆ๊ฐ€ 30 III-I-5. ํ”„๋กœํ† ํฌ๋ฅดํ”ผ๋ฆฐ์— ์˜ํ•œ ์•”์„ธํฌ ์ฆ์‹ ๋ฐ ์นจ์Šต์„ฑ/์ด๋™์„ฑ ์–ต์ œ 33 III-I-6. ์ข…์–‘ ์ด์‹ ๋™๋ฌผ์‹คํ—˜ 36 III-II. Nrf2 ํ™œ์„ฑ์— ์˜ํ•œ ๊ฐ„์„ธํฌ ์†์ƒ ์–ต์ œ 39 III-II-1. Nrf2์— ์˜ํ•œ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ํŠน์ด์  Sirt3 ์œ ์ „์ž ๋ฐœํ˜„ ์กฐ์ ˆ 39 III-II-2. Nrf2์— ์˜ํ•œ Sirt3์˜ ์ „์‚ฌ์กฐ์ ˆ 42 III-II-3. Nrf2์— ์˜ํ•œ ๋ฏธํ† ์ฝ˜๋“œ๋ฆฌ์•„ ๊ธฐ๋Šฅ ์กฐ์ ˆ 45 III-II-4. ์ธ๊ฐ„ ๋ฐ ๋™๋ฌผ ์ง€๋ฐฉ๊ฐ„ ๋ชจ๋ธ์—์„œ์˜ Nrf2 ๋ฐ Sirt3 ๊ฐ์†Œ 48 III-II-5. ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค์— ์˜ํ•œ ๊ฐ„์„ธํฌ ์†์ƒ์—์„œ์˜ ํ•ญ์‚ฐํ™” ์ธ์ž Nrf2์˜ ์—ญํ•  51 III-II-6. Nrf2 ๊ฒฐ์†๊ณผ ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค ์œ ๋„์ œ์— ์˜ํ•œ ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค ์ฆ๊ฐ€ 54 III-II-7. ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค์—์„œ Sirt3 ์œ ์ „์ž์˜ ์—ญํ•  57 III-II-8. Sirt3 ์œ ์ „์ž ์ „๋‹ฌ์— ์˜ํ•œ ์†Œํฌ์ฒด ์ŠคํŠธ๋ ˆ์Šค ๋ฐ ๊ฐ„ ์†์ƒ ์–ต์ œ 60 III-II-9. Nrf2 ํ™œ์„ฑ ํ›„๋ณด๋ฌผ์งˆ์— ์˜ํ•œ Sirt3 ์ฆ๊ฐ€ 63 IV. ๊ณ ์ฐฐ 65 V. ์ฐธ๊ณ ๋ฌธํ—Œ 72 VI. ์˜๋ฌธ์š”์•ฝ 83Docto

    ํ†ตํ•ฉ๊ณต๊ณต์กฐ์ง์˜ ๊ต์ฐจ์ธ์‚ฌ์— ๋”ฐ๋ผ ๋ฆฌ๋”-๊ตฌ์„ฑ์› ๊ตํ˜ธ๊ด€๊ณ„(LMX ์งˆ)๊ฐ€ ์กฐ์ง๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๊ด€ํ•œ ์—ฐ๊ตฌ

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ํ–‰์ •๋Œ€ํ•™์› : ๊ณต๊ธฐ์—…์ •์ฑ…ํ•™๊ณผ, 2013. 8. ์ด์Šน์ข….๊ตญ๊ฐ€๊ฒฝ์Ÿ๋ ฅ์„ ์œ„ํ•œ ํ–‰์ •๊ฐœํ˜์˜ ์š”๊ตฌ๋Š” ๊ณต๊ณต์กฐ์ง์˜ ๊ฐœํŽธ ๋ฐ ํ†ตํํ•ฉ์ด๋ผ๋Š” ๋ฐฉ๋ฒ•์œผ๋กœ ๋ชจ์ƒ‰๋˜์–ด ์™”๋‹ค. ์ง€๋‚œ ์ •๋ถ€์—์„œ๋„ ์ •๋ถ€๋ถ€์ฒ˜ ๋ฐ ์ค€์ •๋ถ€๊ธฐ๊ด€์—์„œ ์ƒ๋‹นํ•œ ๊ทœ๋ชจ์˜ ํ†ตํ•ฉ์ด ์žˆ์—ˆ์œผ๋ฉฐ, ์‹ค์งˆ์ ์ธ ์กฐ์งํ†ตํ•ฉ ๋‹ฌ์„ฑ์„ ์œ„ํ•ด ์กฐ์ง์œตํ•ฉ๊ด€๋ฆฌ(PMI)๋ฅผ ๋„์ž…ํ•ด ๋ฌผ๋ฆฌ์  ํ†ตํ•ฉ ์ดํ›„ ๋ฌธํ™”โ€ค์ธ์‚ฌโ€ค์กฐ์ง๊ธฐ๋Šฅ ์ธก๋ฉด์—์„œ ํ™”ํ•™์ ์ธ ์œตํ•ฉ์„ ํ†ตํ•œ ์‹œ๋„ˆ์ง€ ํšจ๊ณผ๋ฅผ ์ฐฝ์ถœํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ํ–‰์ •์•ˆ์ „๋ถ€๊ฐ€ ์ง„ํ–‰ํ•œ ์กฐ์ง์œตํ•ฉ๊ด€๋ฆฌ๋Š” ํ†ตํ•ฉ์กฐ์ง์˜ ํŠน์„ฑ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š์€ ํš์ผ์ ์ธ ์ ์šฉ์ด๋ฉฐ ํ†ตํ•ฉ์กฐ์ง์˜ ๋‚ดยท์™ธ๋ถ€์  ์ƒํ™ฉ์„ ๊ณ ๋ คํ•˜์ง€ ์•Š์€ ๊ฒƒ์œผ๋กœ, ์‚ฌํ›„๊ด€๋ฆฌ์˜ ํšจ๊ณผ๋ฅผ ๊ทน๋Œ€ํ™”์‹œํ‚ค๋Š” ๋ฐ์—๋Š” ํ•œ๊ณ„๊ฐ€ ์žˆ๋‹ค๊ณ  ํ•˜๊ฒ ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ํ†ตํ•ฉ ๊ณต๊ณต๊ธฐ๊ด€์— ์‹œํ–‰๋œ PMI ์ค‘ ๊ต์ฐจ์ธ์‚ฌ ํ”„๋กœ๊ทธ๋žจ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด, ํ†ตํ•ฉ ์งํ›„ ๊ตฌ์„ฑ์›์˜ ์—ญํ•  ๋ฐ ๊ทผ๋ฌด์—ฌ๊ฑด์ด ๋ณ€ํ™”๋˜๋Š” ์ƒํ™ฉ์—์„œ ๊ต์ฐจ์ธ์‚ฌ์˜ ํš์ผ์  ์ „๋ฉด ์ ์šฉ์€ ๋ฆฌ๋”์‹ญ์˜ ๋ฐœํ˜„์„ ์•ฝํ™”์‹œํ‚ค๊ณ  ๋ฆฌ๋”์™€ ๊ตฌ์„ฑ์› ๊ฐ„์˜ ๊ตํ˜ธ๊ด€๊ณ„๋ฅผ ์ €ํ•˜์‹œํ‚ด์œผ๋กœ์„œ ์˜คํžˆ๋ ค ์กฐ์งํ†ตํ•ฉ์„ ์ €ํ•ดํ•˜๋Š” ์ธก๋ฉด์ด ์žˆ๋‹ค๋Š” ์ ์— ์ฃผ๋ชฉํ•˜์˜€๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ์—ฐ๊ตฌ๋Š” ์ง€๋‚œ ์ •๋ถ€ ๊ฐ€์žฅ ๋Œ€๊ทœ๋ชจ์˜ ์กฐ์งํ†ตํ•ฉ์ด ์ด๋ฃจ์–ด์ง„ ํ•œ๊ตญํ† ์ง€์ฃผํƒ๊ณต์‚ฌ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ๊ต์ฐจ์ธ์‚ฌ์— ๋”ฐ๋ผ ๋ฆฌ๋”-๊ตฌ์„ฑ์› ๊ตํ˜ธ๊ด€๊ณ„(LMX ์งˆ)๊ฐ€ ์กฐ์ง๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ฒ€์ฆํ•จ์œผ๋กœ์จ ํ•ด๋‹น ๊ธฐ๊ด€์€ ๋ฌผ๋ก  ํ–ฅํ›„ ๊ณต๊ณต์กฐ์ง ๊ฐœํŽธ ๋ฐ ํ†ตํ•ฉ์— ์žˆ์–ด ์„ฑ๊ณต์ ์ธ ์กฐ์ง์œตํ•ฉ ์ธ์ ๊ด€๋ฆฌ ๊ฐœ์„ ๋ฐฉํ–ฅ์„ ์ œ์‹œํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๋ณธ ์—ฐ๊ตฌ์˜ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ํ†ตํ•ฉ์กฐ์ง ๊ตฌ์„ฑ์›๋“ค์˜ ๊ต์ฐจ์ธ์‚ฌ ์—ฌ๋ถ€์— ๋”ฐ๋ผ LMX ์งˆ์ด ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ๋Š”์ง€ ๊ฒ€์ฆํ•œ ๊ฒฐ๊ณผ, ๋น„๊ต์ฐจ์ธ์‚ฌ ์ง‘๋‹จ์ด ๊ต์ฐจ์ธ์‚ฌ ์ง‘๋‹จ์— ๋น„ํ•ด ์ •์„œ์  ์• ์ฐฉ, ์ถฉ์„ฑ์‹ฌ, ์ „๋ฌธ์  ์กด๊ฒฝ์ด ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘˜์งธ, LMX ์งˆ์ด ์กฐ์ง๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์•Œ์•„๋ณธ ๊ฒฐ๊ณผ, ๊ณตํ—Œ์˜์š•์€ ์ •์„œ์  ๋ชฐ์ž…, ์ง€์†์  ๋ชฐ์ž…์— ์ •(+)์˜ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋ฉฐ ์ „๋ฌธ์  ์กด๊ฒฝ์€ ๊ทœ๋ฒ”์  ๋ชฐ์ž…์— ์ •(+)์˜ ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ์ด๋Ÿฌํ•œ ํ•˜๋ถ€์š”์ธ ๊ฐ„ ์˜ํ–ฅ๊ด€๊ณ„ ๊ฐ€์„ค๋“ค์„ ํ†ตํ•ด ์„ค๊ณ„ํ•œ ์—ฐ๊ตฌ๋ชจํ˜•์€ ๊ตฌ์กฐ๋ฐฉ์ •์‹ ๋ชจํ˜•๋ถ„์„์— ์˜ํ•ด ์ ํ•ฉํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์…‹์งธ, LMX์งˆ์ด ์กฐ์ง๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ๋Œ€ํ•œ ๊ต์ฐจ์ธ์‚ฌ์˜ ์กฐ์ ˆํšจ๊ณผ๋ฅผ ๊ฒ€์ฆํ•œ ๊ฒฐ๊ณผ LMX ์งˆ์ด ์กฐ์ง๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” ์ •(+)์˜ ๊ด€๊ณ„๊ฐ•๋„๋Š” ๋น„๊ต์ฐจ์ธ์‚ฌ ์ง‘๋‹จ์—์„œ ๊ต์ฐจ์ธ์‚ฌ ์ง‘๋‹จ์— ๋น„ํ•ด ๊ทธ ์˜ํ–ฅ๋ ฅ์ด ํฐ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์ข…ํ•ฉ์ ์œผ๋กœ ๋ณผ ๋•Œ, ๊ต์ฐจ์ธ์‚ฌ๋Š” LMX ์งˆ์„ ์ €ํ•˜์‹œํ‚ด์„ ํ™•์ธ ํ•  ์ˆ˜ ์žˆ์—ˆ๊ณ , ๋˜ํ•œ LMX ์งˆ์ด ์กฐ์ง๋ชฐ์ž…์— ๊ธ์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นจ์„ ๊ฒ€์ฆํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ๊ต์ฐจ์ธ์‚ฌ๋กœ ์ธํ•ด ์ €ํ•˜๋œ LMX ์งˆ๋กœ ์ธํ•˜์—ฌ ์กฐ์ง๋ชฐ์ž…์— ๋ถ€์ •์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นจ์„ ์œ ์ถ”ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋˜ํ•œ ๋น„๊ต์ฐจ์ธ์‚ฌ ์ง‘๋‹จ์—์„œ LMX ์งˆ์ด ๋ฏธ์น˜๋Š” ์กฐ์ง๋ชฐ์ž…์— ๋Œ€ํ•œ ๊ธ์ •์ ์ธ ์˜ํ–ฅ๋ ฅ์ด ๋” ๋†’์€ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜, ๊ต์ฐจ์ธ์‚ฌ๋ฐฐ์น˜ ๋œ ๊ฒฝ์šฐ ์กฐ์ง๋ชฐ์ž…์— ๋ฏธ์น˜๋Š” LMX์˜ ๊ธ์ •์ ์ธ ์˜ํ–ฅ๋ ฅ์ด ์ €ํ•˜๋  ๊ฒƒ์ด๋ผ๊ณ  ์ถ”๋ก ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๊ฐ€ ๊ฐ€์ง€๋Š” ์‹œ์‚ฌ์ ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ๊ต์ฐจ์ธ์‚ฌ๋ฅผ ์‹œํ–‰ํ•œ ์ง‘๋‹จ์— ๋Œ€ํ•ด ๋ฆฌ๋”์™€ ๊ตฌ์„ฑ์› ๊ฐ„ LMX ์งˆ ํ–ฅ์ƒ์„ ์œ„ํ•œ ์กฐ์ง์ฐจ์›์—์„œ์˜ ๋ณ„๋„์˜ ๋…ธ๋ ฅ์ด ํ•„์š”ํ•˜๋‹ค. ๋‘˜์งธ, ํ†ตํ•ฉ์กฐ์ง์—์„œ ์‹ค์งˆ์  ํ†ตํ•ฉ์„ ๋‹ฌ์„ฑํ•˜๊ณ  ์กฐ์ง๋ชฐ์ž…์„ ์ œ๊ณ ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์žฅ๊ธฐ์ ์ธ ๊ด€์ ์—์„œ ๋ฆฌ๋”์™€ ๊ตฌ์„ฑ์› ๊ฐ„ LMX ์งˆ์„ ์ „๋žต์ ์œผ๋กœ ๊ด€๋ฆฌํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ ๊ต์ฐจ์ธ์‚ฌ๋Š” ์ธ์‚ฌ์œตํ•ฉ๊ด€๋ฆฌ ์ „๋žต์œผ๋กœ ์šฉ์ดํ•˜๊ณ  ํšจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ•์ž„์—๋Š” ๋ถ„๋ช…ํ•˜๋‚˜, ๋ฆฌ๋”์‹ญ์„ ์•ฝํ™”์‹œํ‚ค๊ณ  ๋‚ด์ง‘๋‹จ ๊ฐ•ํ™”ํ˜„์ƒ์„ ๊ฐ€์ ธ์˜ค๋Š” ๋“ฑ์˜ ์กฐ์งํ†ตํ•ฉ ์ €ํ•ด์š”์ธ์œผ๋กœ ์ž‘์šฉํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐ”, ๋„์ž… ์‹œ๊ธฐ ๋ฐ ๋„์ž… ๋ฐฉ๋ฒ•, ๋„์ž… ํ›„ ๊ด€๋ฆฌ ๋“ฑ์— ์žˆ์–ด ๋ฉด๋ฐ€ํ•œ ๊ฒ€ํ† ๊ฐ€ ํ•„์š”ํ•˜๋‹ค.์ œ 1 ์žฅ ์„œ๋ก  ์ œ 1 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•„์š”์„ฑ ๋ฐ ๋ชฉ์  ์ œ 2 ์ ˆ ์—ฐ๊ตฌ๋ฌธ์ œ ์ œ 3 ์ ˆ ์—ฐ๊ตฌ์˜ ๋Œ€์ƒ๊ณผ ๋ฒ”์œ„ ์ œ 4 ์ ˆ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• ์ œ 2 ์žฅ ์ด๋ก ์  ๋ฐฐ๊ฒฝ ๋ฐ ์„ ํ–‰์—ฐ๊ตฌ ๊ฒ€ํ†  ์ œ 1 ์ ˆ ๋ฆฌ๋”-๊ตฌ์„ฑ์› ๊ตํ˜ธ๊ด€๊ณ„(LMX) 1. LMX ์ด๋ก  2. LMX์˜ ์งˆ์˜ ๋ถ„์„ ๋ฐ ์ธก์ • ์ œ 2 ์ ˆ ์กฐ์ง๋ชฐ์ž… 1. ์กฐ์ง๋ชฐ์ž…์˜ ๊ฐœ๋…๊ณผ ์—ฐ๊ตฌ 2. ์กฐ์ง๋ชฐ์ž…์˜ ๋ถ„์„ ๋ฐ ์ธก์ • ์ œ 3 ์žฅ ์—ฐ๊ตฌ์˜ ์„ค๊ณ„ ์ œ 1 ์ ˆ ์—ฐ๊ตฌ๋ชจํ˜• ๋ฐ ๊ฐ€์„ค์„ค์ • 1. ์—ฐ๊ตฌ๋ชจํ˜• 2. ๊ฐ€์„ค์„ค์ • ์ œ 2 ์ ˆ ๋ณ€์ˆ˜์˜ ์กฐ์ž‘์  ์ •์˜ ๋ฐ ์ธก์ •๋„๊ตฌ 1. ๋ณ€์ˆ˜์˜ ์กฐ์ž‘์  ์ •์˜ 2. ์ธก์ •๋„๊ตฌ ๊ตฌ์„ฑ ์ œ 3 ์ ˆ ์ž๋ฃŒ์˜ ์ˆ˜์ง‘ ๋ฐ ๋ถ„์„๋ฐฉ๋ฒ• 1. ์ž๋ฃŒ์ˆ˜์ง‘ 2. ๋ถ„์„๋ฐฉ๋ฒ• ์ œ 4 ์žฅ ๊ฐ€์„ค์˜ ๊ฒ€์ฆ ๋ฐ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ๋ถ„์„ ์ œ 1 ์ ˆ ๊ธฐ์ดˆํ†ต๊ณ„๋Ÿ‰ ๋ถ„์„ 1. ํ‘œ๋ณธ์˜ ์ธ๊ตฌํ†ต๊ณ„์  ํŠน์„ฑ 2. ์ฃผ์š” ๋ณ€์ˆ˜๋ณ„ ๊ธฐ์ดˆํ†ต๊ณ„๋Ÿ‰ ์ œ 2 ์ ˆ ํƒ€๋‹น์„ฑ ๋ฐ ์‹ ๋ขฐ์„ฑ ๊ฒ€์ฆ 1. ํƒ€๋‹น์„ฑ ๊ฒ€์ฆ 2. ์‹ ๋ขฐ์„ฑ ๊ฒ€์ฆ ์ œ 3 ์ ˆ ์ธ๊ตฌํ†ต๊ณ„์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ์ฃผ์š” ๋ณ€์ˆ˜์˜ ์ฐจ์ด๊ฒ€์ฆ 1. ์ธ๊ตฌํ†ต๊ณ„์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ LMX ์งˆ ์ฐจ์ด 2. ์ธ๊ตฌํ†ต๊ณ„์  ํŠน์„ฑ์— ๋”ฐ๋ฅธ ์กฐ์ง๋ชฐ์ž… ์ฐจ์ด ์ œ 4 ์ ˆ ๊ฐ€์„ค์˜ ๊ฒ€์ฆ ๋ฐ ์—ฐ๊ตฌ๊ฒฐ๊ณผ์˜ ๋ถ„์„ 1. ๊ต์ฐจ์ธ์‚ฌ ์—ฌ๋ถ€์— ๋”ฐ๋ฅธ LMX ์งˆ์˜ ์ฐจ์ด์— ๋Œ€ํ•œ ๊ฐ€์„ค๊ฒ€์ฆ 2. LMX ์งˆ๊ณผ ์กฐ์ง๋ชฐ์ž…์— ๋Œ€ํ•œ ๊ฐ€์„ค๊ฒ€์ฆ 3. LMX ์งˆ๊ณผ ์กฐ์ง๋ชฐ์ž…์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋ชจํ˜• ๊ฒ€์ฆ 4. LMX ์งˆ๊ณผ ์กฐ์ง๋ชฐ์ž…์— ๋Œ€ํ•œ ๊ต์ฐจ์ธ์‚ฌ์˜ ์กฐ์ ˆํšจ๊ณผ ๊ฒ€์ฆ ์ œ 5 ์žฅ ๊ฒฐ๋ก  ๋ฐ ์‹œ์‚ฌ์  ์ œ 1 ์ ˆ ๋ถ„์„๊ฒฐ๊ณผ์˜ ์š”์•ฝ ์ œ 2 ์ ˆ ์—ฐ๊ตฌ์˜ ์‹œ์‚ฌ์  ์ œ 3 ์ ˆ ์—ฐ๊ตฌ์˜ ํ•œ๊ณ„ ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ๋ฐฉํ–ฅ ์ฐธ๊ณ ๋ฌธํ—Œ ๋ถ€๋ก AbstractMaste

    ์–ธ์–ดํ•™์— ์žˆ์–ด์„œ์˜ ์ด๋ก ๊ณผ ์ž๋ฃŒ

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    ์˜ค๋Š˜ ํ† ๋ก ํšŒ์˜ ์ฃผ์ œ๋Š” ใ€Œ์–ธ์–ดํ•™์— ์žˆ์–ด์„œ์˜ ์ด๋ก ๊ณผ ์ž๋ฃŒใ€์—…๋‹ˆ๋‹ค. ์šฐ๋ฆฌ๊ฐ€ ์ด๋Ÿฐ ์ œ๋ชฉ์„ ์žก๊ฒŒ ๋œ ๊ฒƒ์€ ์–ธ์–ดํ•™์ด๋ผ๊ณ  ํ•˜๋Š” ๊ฒƒ์ด ์ˆ˜ํ•™์ด๋‚˜ ๋…ผ๋ฆฌํ•™๊ณผ ๊ฐ™์€ ๋ถ„์•ผ์™€๋Š” ๋‹ฌ๋ฆฌ ์–ธ์–ด๋ผ๊ณ  ํ•˜๋Š” ๊ฒฝํ—˜์ ์ธ ์‚ฌ์‹ค๏ผŒ ์ž๋ฃŒ๋ฅผ ๋Œ€์ƒ์œผ๋กœ ํ•˜๋Š” ๊ฒฝํ—˜๊ณผํ•™์ด๋ผ๊ณ  ํ•˜๋Š” ์ฒจ์—์„œ, ์ด๋ก ๊ณผ ์ž๋ฃŒ๊ฐ„์— ์ƒํ˜ธ์ž‘์šฉ๋ฌธ์ œ๊ฐ€ ๋…ผ์˜๋ผ์•ผ ๋˜๊ฒ ๊ณ  ๋˜ํ•œ ๊ทธ ์„ค๋ช…์˜ ๋ฌธ์ œ์™€ ์ž๋ฃŒ์˜ ๊ธฐ์ˆ  ๋ฌธ์ œ ์‚ฌ์ด์— ์–ด๋– ํ•œ ์šด์ œ๊ฐ€ ์ œ๊ธฐ๋  ์ˆ˜ ์žˆ๊ฒ ๋Š”๊ฐ€ ํ•˜๋Š” ๊ฒƒ์„ ํ•œ ๋ฒˆ ์งš๊ณ  ๋„˜์–ด๊ฐ€์•ผ๊ฒ ๋‹ค๋Š” ์ƒ๊ฐ์—์„œ์˜€์Šต๋‹ˆ๋‹ค
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