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    A Study on the Determinants of Development of e-Business Industry in China

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    20์„ธ๊ธฐ ํ›„๋ฐ˜๋ถ€ํ„ฐ ์ปดํ“จํ„ฐ, ์ธํ„ฐ๋„ท๊ณผ ์ •๋ณดํ†ต์‹ ์ด ๊ฒฐํ•ฉ๋˜๋ฉด์„œ ์ƒˆ๋กœ์šด ์‚ฐ์—…๋“ค์ด ๋“ฑ์žฅํ•˜๋ฉด์„œ ์‚ฐ์—…๊ตฌ์กฐ, ์†Œ๋น„๊ตฌ์กฐ๋ฅผ ๋ณ€ํ™”์‹œํ‚ค๋ฉด์„œ ์ „๋ฐ˜์ ์ธ ๊ฒฝ์ œ๊ตฌ์กฐ๋ฅผ ๋ณ€ํ™”์‹œํ‚ค๊ณ  ์žˆ๋‹ค. ํŠนํžˆ ์ „์„ธ๊ณ„์˜ ๊ฐ€๊ณ„, ๊ธฐ์—…๊ณผ ์ •๋ถ€ ๋“ฑ ๋ชจ๋“  ๊ฒฝ์ œ์ฃผ์ฒด๋“ค๊ณผ ์—ฐ๊ฒฐํ•  ์ˆ˜ ์žˆ๋Š” ์ธํ„ฐ๋„ท ํ”Œ๋žซํผ์ด๋ผ๋Š” ๊ฐ€์ƒ๊ณต๊ฐ„์—์„œ ์ด๋ฃจ์–ด์ง„ ์ „์ž์ƒ๊ฑฐ๋ž˜๋ฐฉ์‹์€ ์ „ํ†ต์ ์ธ ๊ฑฐ๋ž˜๋ฐฉ์‹์„ ๋น ๋ฅธ ์†๋„๋กœ ๋Œ€์ฒดํ•˜๊ณ  ์žˆ๋‹ค. ์ „์ž์ƒ๊ฑฐ๋ž˜๋ฐฉ์‹์„ ํ†ตํ•ด ๋ฐœ์ƒํ•˜๋Š” ํ˜„์ƒ์€ ๊ธฐ์กด ๊ฒฝ์ œ์ด๋ก ์œผ๋กœ ์„ค๋ช…ํ•  ์ˆ˜ ์—†๊ฒŒ ๋˜์—ˆ๋‹ค. ์ด์— ๋”ฐ๋ผ ์™ธ๋ถ€ ๊ทœ๋ชจ๊ฒฝ์ œ์™€ ๋„คํŠธ์›Œํฌ ํšจ๊ณผ๋ฅผ ํฌํ•จํ•˜๋Š” ์™ธ๋ถ€ํšจ๊ณผ์™€ ๊ฑฐ๋ž˜๋น„์šฉ์ด๋ก  ๋“ฑ ์ƒˆ๋กœ์šด ๊ฐœ๋…์„ ๋ฐ”ํƒ•์œผ๋กœ ์ด๋Ÿฐ ํ˜„์ƒ์„ ์„ค๋ช…ํ•˜์˜€๋‹ค. 2000๋…„์— ๋“ค์–ด์„œ๋ฉด์„œ ์ค‘๊ตญ์€ ๊ฑฐ๋Œ€ํ•œ ๋‚ด๋ถ€์‹œ์žฅ๊ณผ ์ •๋ถ€์˜ ์ ๊ทน์ ์ธ ์ง€์›์ •์ฑ…์— ํž˜์ž…์–ด ์œ ๋ก€๊ฐ€ ์—†์„ ์ •๋„๋กœ ๋น ๋ฅธ ์†๋„๋กœ ์„ฑ์žฅํ•˜์˜€๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ค‘๊ตญ ์ „์ž์ƒ๊ฑฐ๋ž˜ ์‚ฐ์—…์— ๋ฏธ์น˜๋Š” ์š”์ธ์„ ์™ธ๋ถ€ ๊ทœ๋ชจ๊ฒฝ์ œ์™€ ๋„คํŠธ์›Œํฌ ํšจ๊ณผ์™€ ๊ฑฐ๋ž˜๋น„์šฉ์ด๋ก ์„ ๋ฐ”ํƒ•์œผ๋กœ ์‹ค์ฆ๋ถ„์„์„ ํ•˜์˜€๋‹ค. ๋˜ํ•œ ์ค‘๊ตญ์˜ ์ „์ž์ƒ๊ฑฐ๋ž˜๊ฐ€ ๋ฌด์—ญ์— ์‹ค์งˆ์ ์œผ๋กœ ์˜ํ–ฅ์„ ๋ฏธ์ณค๋Š”๊ฐ€๋ฅผ ์‹ค์ฆ๋ถ„์„์„ ํ•˜์˜€๋‹ค. ์ถ”์ •๋ชจํ˜• I์€ ์‚ฐ์—…๋‚ด๋ถ€์ ์ธ ์š”์ธ์— ์ดˆ์ ์„ ๋งž์ถ”์–ด ์ƒ์‚ฐํ•จ์ˆ˜๋ฅผ ์„ค์ •ํ•œ๋‹ค. ์ „์ž์ƒ๊ฑฐ๋ž˜ ์„œ๋น„์Šค๋Š” ์ƒ์‚ฐ๊ณผ์ •์— ์ž๋ณธ(K)๊ณผ ๋…ธ๋™(L)์ด ํˆฌ์ž…๋˜๋ฉฐ ์ค‘๋ฆฝ์  ๊ธฐ์ˆ ์ง„๋ณด๋ฅผ ์ƒ์ •ํ•œ๋‹ค. ์ด๋Ÿฐ ์ƒ์‚ฐํ•จ์ˆ˜๋ฅผ ์ถ”์ •๋ชจํ˜•์œผ๋กœ ์ „ํ™˜ํ•˜๊ธฐ ์œ„ํ•ด Cobb-Douglas์ƒ์‚ฐํ•จ์ˆ˜๋ฅผ ๊ฐ€์ •ํ•œ๋‹ค. Q_t=A_0 e^ฮณt L_t^ฮฑ K_t^ฮฒ (1) ์—ฌ๊ธฐ์„œ Q_t๋Š” ์ „์ž์ƒ๊ฑฐ๋ž˜๋Ÿ‰, L_t๋Š” ๋…ธ๋™๋Ÿ‰, K_t๋Š” ์ž๋ณธ๋Ÿ‰์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์ด๋•Œ ์„ ํ˜•ํ•จ์ˆ˜ ํ˜•ํƒœ๋กœ ์ „ํ™˜ํ•˜๊ธฐ ์œ„ํ•ด ์–‘์ชฝ์— ์ž์—ฐ๋กœ๊ทธ๋ฅผ ์ทจํ•˜๋ฉด ์‹(2)๊ฐ€ ๋œ๋‹ค. ใ€–lnQใ€—_t=c+ฮฑlnL_t+ฮฒlnK_t+ฮณt+ฯต_t (2) ์—ฌ๊ธฐ์„œ c=ใ€–lnAใ€—_0์œผ๋กœ ์ƒ์ˆ˜ํ•ญ์„, ฯต_t๋Š” ๊ต๋ž€ํ•ญ(disturbance term)์„ ๋‚˜ํƒ€๋‚ธ๋‹ค. ๊ต๋ž€ํ•ญ์€ ฯต_t~N(0,ฯƒ^2)์œผ๋กœ ์ •๊ทœ๋ถ„ํฌ ํ•œ๋‹ค. ์—ฌ๊ธฐ์„œ ์ถ”์ •์น˜ ์ค‘์—์„œ ฮฑ+ฮฒ>1์ด๋ฉด ๊ธฐ์ˆ ์ง„๋ณด๋ฅผ ํ†ต์ œํ•˜๊ณ  ๋‚œ ์ดํ›„ ์ „์ž์ƒ๊ฑฐ๋ž˜ ์‚ฐ์—…์—์„œ ๊ทœ๋ชจ์˜ ๊ฒฝ์ œ๊ฐ€ ์ž‘์šฉํ•˜๊ณ  ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ๋ณด์—ฌ์ค€๋‹ค. ์ถ”์ •๋ชจํ˜• II๋Š” ์™ธ๋ถ€ํšจ๊ณผ๋ฅผ ๋ฐ˜์˜ํ•˜๊ธฐ ์œ„ํ•œ ์ถ”์ •๋ชจํ˜•์ด๋‹ค. ์‹(3)์€ ์™ธ์ƒ๋ณ€์ˆ˜๋ฅผ ํฌํ•จํ•œ ๊ฒƒ์ด๋‹ค. ใ€–lnQใ€—_t=ฮฒ_0+ฮฒ_1 lnL_t+ฮฒ_2 lnK_t+ฮฒ_3 lnใ€–GPใ€—_t+ฮฒ_4 lnใ€–IDใ€—_t+ฮฒ_5 lnใ€–GLใ€—_t+ฮฒ_6 lnใ€–LOใ€—_t+ฮฒ_7 t+ฯต_t (3) ์—ฌ๊ธฐ์„œ ใ€–GPใ€—_t๋Š” ๊ฐ€๊ณ„์˜ ์†Œ๋น„์ˆ˜์ค€์„ ํ†ตํ•ด ๋„คํŠธ์›Œํฌ ํšจ๊ณผ์˜ ์ง€ํ‘œ๋กœ 1์ธ๋‹น GDP, ใ€–IDใ€—_t๋Š” ์™ธ๋ถ€๊ทœ๋ชจ๊ฒฝ์ œํšจ๊ณผ๋ฅผ ์ธก์ •ํ•˜๊ธฐ ์œ„ํ•œ ์ง€ํ‘œ๋กœ ์ธํ„ฐ๋„ท ๋ณด๊ธ‰๋ฅ , ใ€–GLใ€—_t๋Š” ์„ธ๊ณ„ํ™”์ง€ํ‘œ๋กœ์„œ GDP๋Œ€๋น„ ๋ฌด์—ญ์ด์•ก, ใ€–LOใ€—_t๋Š” ๋ฌผ๋ฅ˜์ง€ํ‘œ๋กœ์„œ ๋ฌผ๋ฅ˜๊ฒฝ๊ธฐ์ง€์ˆ˜(LPI)๋ฅผ ๋‚˜ํƒ€๋‚ธ๋‹ค. ์ถ”์ •๋ชจํ˜• III์€ Clarke and Wallsten(2004)์˜ ์ถ”์ •๋ชจํ˜•์„ ๋ฐ”ํƒ•์œผ๋กœ ์ „์ž์ƒ๊ฑฐ๋ž˜๋ฐฉ์‹์ด ์ค‘๊ตญ์˜ ๋ฌด์—ญํ™•๋Œ€์— ๊ธฐ์—ฌํ–ˆ๋Š”๊ฐ€๋ฅผ ์ถ”์ •ํ•˜๊ธฐ ์œ„ํ•ด ์„ค์ •ํ•œ ๊ฒƒ์ด๋‹ค. ์ข…์†๋ณ€์ˆ˜๋Š” ์ˆ˜์ถœ๋ณ€์ˆ˜๋กœ ํ•˜๊ณ  ์„ค๋ช…๋ณ€์ˆ˜๋กœ ์ธํ„ฐ๋„ท์‚ฌ์šฉ์ž์ˆ˜์™€ ํ†ต์ œ๋ณ€์ˆ˜(์ธ๊ตฌ์ˆ˜, 1์ธ๋‹น GDP, ๋”๋ฏธ๋ณ€์ˆ˜)๋ฅผ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ด๊ฒƒ์„ ์ˆ˜์‹์„ ๋‚˜ํƒ€๋‚ด๋ฉด ๋‹ค์Œ ์‹(4)์™€ ๊ฐ™๋‹ค. ใ€–lnXใ€—_t=ฮฒ_0+ฮฒ_1 lnใ€–IUใ€—_t+ฮฒ_2 lnใ€–GPใ€—_t+ฮฒ_3 lnใ€–POใ€—_t+ฮฒ_4 D_t+ฮฒ_5 t+ฯต_t (4) ์—ฌ๊ธฐ์„œ ์ข…์†๋ณ€์ˆ˜ X_t๋Š” ์ˆ˜์ถœ๋ณ€์ˆ˜๋กœ์„œ ์ˆ˜์ถœ์ด์•ก/GDP ๋น„์œจ์„ ๋‚˜ํƒ€๋‚ธ ๊ฒƒ์ด๋‹ค. ใ€–IUใ€—_t๋Š” ์ „์ž์ƒ๊ฑฐ๋ž˜ ๋ฐฉ์‹ ์‚ฌ์šฉ์ง€ํ‘œ๋กœ์„œ ์ธํ„ฐ๋„ท ์‚ฌ์šฉ์ž์ˆ˜๊ฐ€ ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. ํ†ต์ œ๋ณ€์ˆ˜๋กœ ์ธ๊ตฌ์ˆ˜(ใ€–POใ€—_t), 1์ธ๋‹น GDP(ใ€–GPใ€—_t)์ด ์‚ฌ์šฉ๋˜์—ˆ๋‹ค. D_t๋Š” ๋”๋ฏธ๋ณ€์ˆ˜๋กœ์„œ 2007๋…„๋ฅผ ๊ธฐ์ ์œผ๋กœ ์ด์ „์€ 0, ์ดํ›„๋Š” 1๋กœ ๋‚˜ํƒ€๋‚ธ๋‹ค. 2007๋…„ ์ธํ„ฐ๋„ท ์‚ฌ์šฉ์ž์ˆ˜์—์„œ ๊ตฌ์กฐ์  ์ „ํ™˜์ด ๋ฐœ์ƒํ–ˆ๋Š”๊ฐ€๋ฅผ ์—ฌ๋ถ€๋ฅผ ํ™•์ธํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋Ÿฐ ์ถ”์ •๋ชจํ˜•์„ 2000-2019๋…„ ๊ฐ„ 20๋…„ ์‹œ๊ณ„์—ด์ž๋ฃŒ๋ฅผ ๊ฐ€์ง€๊ณ  ์™„์ „์ˆ˜์ •์ตœ์†Œ์ž์Šน๋ฒ•(fully-modified ordinary least square)์œผ๋กœ ์ถ”์ •ํ•˜์˜€๋‹ค. ์ถ”์ •๊ฒฐ๊ณผ๋ฅผ ์ •๋ฆฌํ•˜๋ฉด ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ์ฒซ์งธ, ์ „์ž์ƒ๊ฑฐ๋ž˜์˜ ๊ทœ๋ชจ์˜ ๊ฒฝ์ œ๋Š” ๋‚ด๋ถ€์š”์ธ๋ณด๋‹ค๋Š” ์™ธ๋ถ€๊ฒฝ์ œํšจ๊ณผ์— ์˜ํ•ด์„œ ์ด๋ฃจ์–ด์ง€๊ณ  ์žˆ๋‹ค. ๋‘˜์งธ, ์ „์ž์ƒ๊ฑฐ๋ž˜ ์‚ฐ์—…์— ๋„คํŠธ์›Œํฌ ์™ธ๋ถ€ํšจ๊ณผ๊ฐ€ ์‹ค์งˆ์ ์œผ๋กœ ์กด์žฌํ•œ๋‹ค. ์…‹์งธ, ์„ธ๊ณ„ํ™”์˜ ์˜ํ–ฅ์€ ์กด์žฌํ•˜์ง€๋งŒ ๋‹ค๋ฅธ ์š”์ธ์— ๋น„ํ•ด ํฌ์ง€ ์•Š์•˜๋‹ค. ๋„ท์งธ ๋ฌผ๋ฅ˜๊ด€๋ จ์š”์ธ์€ ๊ฑฐ์˜ ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€ ๋ชปํ–ˆ๋‹ค. ๋‹ค์„ฏ์งธ, ์ „์ž์ƒ๊ฑฐ๋ž˜ ์‚ฐ์—…์˜ ๋ฐœ์ „์€ ์ค‘๊ตญ์˜ ์ˆ˜์ถœ(๋ฌด์—ญ)์— ์‹ค์งˆ์ ์ธ ์˜ํ–ฅ์„ ๋ฏธ์นœ๋‹ค. ์—ฌ์„ฏ์งธ ๋„คํŠธ์›Œํฌ ํšจ๊ณผ๊ฐ€ ์‹ค์งˆ์ ์œผ๋กœ ์ˆ˜์ถœ์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๊ณ  ์žˆ๋‹ค. ์ผ๊ณฑ์งธ, ์ธ๊ตฌ์ˆ˜๋„ ์ˆ˜์ถœ๋ณ€์ˆ˜์— ์˜ํ–ฅ์„ ๋ฏธ์น˜์ง€๋งŒ ๊ทธ ์˜ํ–ฅ๋ ฅ์€ ๊ทธ๋‹ค์ง€ ํฌ์ง€ ์•Š์•˜๋‹ค. ์ด๋Ÿฐ ์ถ”์ •๊ฒฐ๊ณผ๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์ค‘๊ตญ์˜ ์ „์ž์ƒ๊ฑฐ๋ž˜ ์‚ฐ์—…์—์„œ ์ค‘๊ตญ์ •๋ถ€์˜ ์ง€์›์ •์ฑ…์œผ๋กœ ์™ธ๋ถ€ ๊ทœ๋ชจ์˜ ๊ฒฝ์ œ์™€ ๊ฒฝ์ œ์„ฑ์žฅ์œผ๋กœ ๋„คํŠธ์›Œํฌ ํšจ๊ณผ๊ฐ€ ์‹ค์งˆ์ ์œผ๋กœ ์ž‘์šฉํ•˜๊ณ  ์žˆ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋…ธ๋™๊ณผ ์ž๋ณธ์˜ ์ƒ์‚ฐ์„ฑ์€ ๋†’์ง€ ์•Š์•„ ๊ธฐ์—… ๋‚ด๋ถ€ ๊ทœ๋ชจ์˜ ๊ฒฝ์ œ๋Š” ๋ฐœ์ƒํ•˜์ง€ ์•Š์•˜๊ณ  ๋ฌผ๋ฅ˜ ์ฒด๊ณ„๋Š” ํšจ์œจ์ ์ด ๋ชปํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋”ฐ๋ผ์„œ ์ค‘๊ตญ ์ •๋ถ€์ •์ฑ…์€ ํ•œํŽธ์œผ๋กœ ๊ธฐ์—… ๋‚ด๋ถ€์˜ ํšจ์œจ์„ฑ์„ ์ œ๊ณ ์‹œํ‚ค๋Š” ๋ฐฉํ–ฅ์œผ๋กœ, ๋‹ค๋ฅธ ํ•œํŽธ์œผ๋กœ ๋ฌผ๋ฅ˜ ์ฒด๊ณ„์˜ ํšจ์œจ์„ฑ์„ ํ–ฅ์ƒํ•  ์ˆ˜ ์žˆ๋Š” ๋ฐฉํ–ฅ์œผ๋กœ ์ด๋ฃจ์–ด์ ธ์•ผ ํ•œ๋‹ค๋Š” ์ •์ฑ…์  ํ•จ์˜๋ฅผ ๊ฐ–๋Š”๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ณธ ๋…ผ๋ฌธ์€ ์ค‘๊ตญ ์ „์ž์ƒ๊ฑฐ๋ž˜ ์‚ฐ์—…์ด 2000๋…„๋ถ€ํ„ฐ ๋ณธ๊ฒฉ์ ์œผ๋กœ ์„ฑ์žฅํ•จ์— ๋”ฐ๋ผ ์‹œ๊ณ„์—ด์ž๋ฃŒ๊ฐ€ ์งง๋‹ค. ๊ทธ๋Ÿฌ๋ฉด ์ถ”์ •๊ฒฐ๊ณผ์—์„œ ์ž์œ ๋„๊ฐ€ ๋†’์ง€ ์•Š์•„ ํ•ด์„์˜ ํ•œ๊ณ„์„ฑ์„ ๊ฐ–๋Š”๋‹ค. ์ด๋Ÿฐ ์ ์„ ๋ณด๊ฐ•ํ•˜๊ธฐ ์œ„ํ•ด ๋‹ค๋ฅธ ์ง€์—ญ๊ณผ ๊ตญ๊ฐ€๋ฅผ ํฌํ•จํ•œ ํŒจ๋„ ์ž๋ฃŒ๋ฅผ ํ™œ์šฉํ•˜์—ฌ ์‹ค์ฆ๋ถ„์„์„ ํ•  ํ•„์š”๊ฐ€ ์žˆ๋‹ค. ๊ทธ๋ž˜์„œ ํ–ฅํ›„ ์—ฐ๊ตฌ๊ณผ์ œ๋กœ ์•„์‹œ์•„์ง€์—ญ, ๋ถ๋ฏธ์ง€์—ญ, ์œ ๋Ÿฝ์ง€์—ญ, ๋‚จ๋ฏธ์ง€์—ญ, ์•„ํ”„๋ฆฌ์นด์ง€์—ญ ๋“ฑ์œผ๋กœ ๋‚˜๋ˆ„์–ด ์•ž์—์„œ ์‚ฌ์šฉํ•œ ์š”์ธ๋ฟ๋งŒ ์•„๋‹ˆ๋ผ ์ด๋Ÿฐ ์ง€์—ญ๋ณ€์ˆ˜๋„ ์ค‘๊ตญ ์ „์ž์ƒ๊ฑฐ๋ž˜ ์‚ฐ์—…์— ์–ด๋–ค ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š”๊ฐ€๋ฅผ ์‚ดํŽด๋ณผ ๊ฒƒ์ด๋‹ค.โ…  ์„œ๋ก  1 1. ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 2. ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• 3 3. ์—ฐ๊ตฌ์˜ ๊ตฌ์„ฑ 4 โ…ก ์ „์ž์ƒ๊ฑฐ๋ž˜์˜ ์ด๋ก ์  ๋ฐฐ๊ฒฝ 7 1. ์ธํ„ฐ๋„ท์˜ ์™ธ๋ถ€ํšจ๊ณผ 7 2. ๊ฑฐ๋ž˜๋น„์šฉ์ด๋ก  11 3. ๋ฌด์—ญํ™•๋Œ€๋ก  13 4. ๊ธฐ์กด ๋ฌธํ—Œ์—ฐ๊ตฌ 15 โ…ข ์ค‘๊ตญ ์ „์ž์ƒ๊ฑฐ๋ž˜์˜ ๋ฐœ์ „ ํ˜„ํ™ฉ 23 1. ์ „์ž์ƒ๊ฑฐ๋ž˜์˜ ํŠน์„ฑ 23 2. ์ค‘๊ตญ ์ „์ž์ƒ๊ฑฐ๋ž˜์˜ ๋ฐœ์ „ํ˜„ํ™ฉ 41 โ…ฃ ์‹ค์ฆ๋ถ„์„ ๋ชจํ˜• ์„ค์ • 59 1. ์ถ”์ •๋ชจํ˜• ์„ค์ • 59 2. ํ†ต๊ณ„์ž๋ฃŒ 61 3. ๋ณ€์ˆ˜๊ด€๋ จ ์„ค๋ช… 63 4. ํ†ต๊ณ„๋ถ„์„ ๋ฐฉ๋ฒ• 65 โ…ค ์ถ”์ •๊ฒฐ๊ณผ ๋ฐ ํ•ด์„ 68 1. ์ถ”์ •๊ฒฐ๊ณผ: ๋‹จ์œ„๊ทผ ๊ฒ€์ •๊ณผ ๊ณต์ ๋ถ„ ๊ฒ€์ • 68 2. ์ถ”์ •๋ชจํ˜•์˜ ์ถ”์ •๊ฒฐ๊ณผ ๋ฐ ํ•ด์„ 71 โ…ฆ ๊ฒฐ๋ก  78 ์ฐธ๊ณ ๋ฌธํ—Œ 80 Acknowledgements 85Docto

    Attenuation of the influenza virus by microRNA response element in vivo and protective efficacy against 2009 pandemic H1N1 virus in mice

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    SummaryBackgroundThe 2009 influenza pandemics underscored the need for effective vaccines to block the spread of influenza virus infection. Most live attenuated vaccines utilize cold-adapted, temperature-sensitive virus. An alternative to live attenuated virus is presented here, based on microRNA-induced gene silencing.MethodsIn this study, miR-let-7b target sequences were inserted into the H1N1 genome to engineer a recombinant virus โ€“ miRT-H1N1. Female BALB/c mice were vaccinated intranasally with the miRT-H1N1 and challenged with a lethal dose of homologous virus.ResultsThis miRT-H1N1 virus was attenuated in mice, while it exhibited wild-type characteristics in chicken embryos. Mice vaccinated intranasally with the miRT-H1N1 responded with robust immunity that protected the vaccinated mice from a lethal challenge with the wild-type 2009 pandemic H1N1 virus.ConclusionsThese results indicate that the influenza virus containing microRNA response elements (MREs) is attenuated in vivo and can be used to design a live attenuated vaccine

    S3IM: Stochastic Structural SIMilarity and Its Unreasonable Effectiveness for Neural Fields

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    Recently, Neural Radiance Field (NeRF) has shown great success in rendering novel-view images of a given scene by learning an implicit representation with only posed RGB images. NeRF and relevant neural field methods (e.g., neural surface representation) typically optimize a point-wise loss and make point-wise predictions, where one data point corresponds to one pixel. Unfortunately, this line of research failed to use the collective supervision of distant pixels, although it is known that pixels in an image or scene can provide rich structural information. To the best of our knowledge, we are the first to design a nonlocal multiplex training paradigm for NeRF and relevant neural field methods via a novel Stochastic Structural SIMilarity (S3IM) loss that processes multiple data points as a whole set instead of process multiple inputs independently. Our extensive experiments demonstrate the unreasonable effectiveness of S3IM in improving NeRF and neural surface representation for nearly free. The improvements of quality metrics can be particularly significant for those relatively difficult tasks: e.g., the test MSE loss unexpectedly drops by more than 90% for TensoRF and DVGO over eight novel view synthesis tasks; a 198% F-score gain and a 64% Chamfer L1L_{1} distance reduction for NeuS over eight surface reconstruction tasks. Moreover, S3IM is consistently robust even with sparse inputs, corrupted images, and dynamic scenes.Comment: ICCV 2023 main conference. Code: https://github.com/Madaoer/S3IM. 14 pages, 5 figures, 17 table

    Hierarchical Amortized Training for Memory-efficient High Resolution 3D GAN

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    Generative Adversarial Networks (GAN) have many potential medical imaging applications, including data augmentation, domain adaptation, and model explanation. Due to the limited embedded memory of Graphical Processing Units (GPUs), most current 3D GAN models are trained on low-resolution medical images. In this work, we propose a novel end-to-end GAN architecture that can generate high-resolution 3D images. We achieve this goal by separating training and inference. During training, we adopt a hierarchical structure that simultaneously generates a low-resolution version of the image and a randomly selected sub-volume of the high-resolution image. The hierarchical design has two advantages: First, the memory demand for training on high-resolution images is amortized among subvolumes. Furthermore, anchoring the high-resolution subvolumes to a single low-resolution image ensures anatomical consistency between subvolumes. During inference, our model can directly generate full high-resolution images. We also incorporate an encoder with a similar hierarchical structure into the model to extract features from the images. Experiments on 3D thorax CT and brain MRI demonstrate that our approach outperforms state of the art in image generation and clinical-relevant feature extraction.Comment: 12 pages, 9 figures. Under revie

    Anti-tumor activity of polysaccharides extracted from Senecio scandens Buch, -Ham root on hepatocellular carcinoma

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    Purpose: To optimize the extraction conditions of polysaccharides from the root of Senecio scandens Buch,-Ham. (PRS) and evaluate its anti-tumor effect on hepatocellular carcinoma.Methods: Response surface methodology (RSM) applied with a Box-Behnken design (BBD, three levels and three factors) was employed to determine the effect of extraction time, number of extraction and ratio of water to raw material on the yield of PRS. The anti-tumor effect of PRS on A549, HL60, S180 and H22 cell lines was evaluated in vitro by 3-(4,5-dimethylthiazol-2-yl) -2,5-diphenyltetrazolium bromide (MTT) assay, while in vivo anti-tumor effect was evaluated in H22 tumor transplanted mice. Furthermore, expressions of proteins including caspase-3, caspase-9, Bcl-2 and Bax were determined by western blotting assay.Results: The established BBD model was highly significant and the optimal conditions were: extraction time, 3.06 h; number of extractions, 2; and ratio of water to raw material, 16.17 mL/g. PRS showed significant inhibitory effect on H22 cells (IC50 = 42.4 ฮผg/mL), and significantly inhibited the growth of transplanted H22 tumors in mice at the doses of 20, 40 and 80 mg/kg (p < 0.05, p < 0.05 and p < 0.01, respectively). Treatment with PRS (20, 40 and 80 ฮผg/mL) significantly up-regulated the expressions of Bax, caspase-3 and caspase-9 in H22 cells, whereas Bcl-2 protein was significantly down-regulated.Conclusion: The results suggest that PRS possesses significant anti-tumor activity on H22 cell line in vitro and in vivo, and the mechanism may be closely related to the induction of mitochondria-mediated apoptosis.Keywords: Senecio scandens, Polysaccharides, Hepatocellular carcinoma, Response surface methodology, Anti-tumor activity, Apoptosi
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