53 research outputs found

    A Solution Methodology for DistributedInformation System Configuration Problem Simultaneously Considering File Allocation and Computer Location Assignment

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    We have undertaken to develop an efficient solution methodology to adress distributed information system configuration problems through mathematical programming. We have simultaneously considered file allocation and computer location assignment problems which are two aspects of the design tighly coupled in a distributed computer system. A model for solving the problem is shown to be a class of nonlinearinteger programming problems and procedures are developed for computing its lower bound. A heuristic algorithm is also developed and some results are obtained. Numerical results yield practical low cost solutions with substantial savings in computer processing time

    ์ค‘์„ธ๊ตญ์–ด '-แ„ฏแ†ž๋…€' ๊ตฌ๋ฌธ์˜ ๊ตฌ์กฐ์™€ ์„ฑ๊ฒฉ

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    Risk factors and prognostic factors for branch retinal vein occlusion

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    ์˜ํ•™๊ณผ/์„์‚ฌ[ํ•œ๊ธ€] ๋ง๋ง‰๋ถ„์ง€์ •๋งฅํ์‡„(branch retinal vein occlusion: BRVO)๋Š” ๋‹น๋‡จ๋ณ‘์„ฑ๋ง๋ง‰์ฆ ๋‹ค์Œ์œผ๋กœ ํ”ํ•œ ๋ง๋ง‰ํ˜ˆ๊ด€ ์žฅ์• ๋กœ์„œ, ๋ง๋ง‰ ๋‚ด์˜ ๋™๋งฅ๊ณผ ์ •๋งฅ์ด ๊ต์ฐจํ•˜๋Š” ๋ถ€์œ„์—์„œ ์ •๋งฅ์ด ํ์‡„๋˜์–ด ์ƒ๊ธฐ๋Š” ์งˆํ™˜์ด๋‹ค. ์ด ์งˆํ™˜์„ ์ผ์œผํ‚ค๋Š” ์œ„ํ—˜์ธ์ž๋Š”, ๊ตญ์†Œ์ธ์ž๋กœ์„œ ๋…น๋‚ด์žฅ, ์งง์€ ์•ˆ์ถ•์žฅ๊ธธ์ด ๋“ฑ์ด, ์ „์‹ ์ธ์ž๋กœ๋Š” ๊ณ ํ˜ˆ์••, ๋‹น๋‡จ, ์‹ฌํ˜ˆ๊ด€ ์งˆํ™˜ ๋“ฑ์ด ๋ณด๊ณ ๋œ ๊ฒฝ์šฐ๊ฐ€ ์žˆ์œผ๋‚˜, ์ตœ๊ทผ์—๋„ ์•„์ง ์ด๊ฒฌ์ด ๋งŽ๊ณ , ๊ตญ๋‚ด์—์„œ๋Š” ๊ณ ํ˜ˆ์••๊ณผ ์•ˆ์ถ•์žฅ๊ธธ์ด์— ๊ด€ํ•œ ๋ณด๊ณ ๊ฐ€ ์žˆ์„ ๋ฟ์ด๋‹ค. ์ด ๋ฐ–์— ์—ฐ๋ น, ์„ฑ๋ณ„ ๋“ฑ์ด ๊ด€๋ จ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋ณด๊ณ ๋˜์–ด ์žˆ๋‹ค. ์ด ์งˆํ™˜์˜ ์˜ˆํ›„๋Š” ๊ณ ํ˜ˆ์••์˜ ์œ ๋ฌด, ์ดˆ๊ธฐ ๋ชจ์„ธํ˜ˆ๊ด€ ๋น„๊ด€๋ฅ˜์˜ ์ •๋„์— ๋”ฐ๋ผ ๋‹ค๋ฅด๋‹ค๊ณ  ๋ณด๊ณ ๋œ ๊ฒฝ์šฐ๋„ ์žˆ์œผ๋‚˜, ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์—†๋‹ค๋Š” ๋ณด๊ณ ๋„ ์žˆ์œผ๋ฉฐ ์ดˆ๊ธฐ์‹œ๋ ฅ์ด ์˜ˆํ›„์™€ ๊ด€๋ จ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋ณด๊ณ ๋œ ๊ฒฝ์šฐ๋„ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์„ฑ๋ณ„, ๋‚˜์ด, ๊ณ ํ˜ˆ์••, ๊ณ ์ง€ํ˜ˆ์ฆ, ๋…น๋‚ด์žฅ, ์งง์€ ์•ˆ์ถ•์žฅ๊ธธ์ด, ๋‹น๋‡จ, ํก์—ฐ, ์Œ์ฃผ๋ฅผ BRVO์˜ ๊ฐ€๋Šฅํ•œ ์œ„ํ—˜์ธ์ž๋กœ ๊ฐ€์ •ํ•˜๊ณ , BRVO๊ฐ€ ๋ฐœ์ƒํ•œ ํ™˜์ž๊ตฐ๊ณผ ๋Œ€์กฐ๊ตฐ์—์„œ ์ƒ๊ธฐ ์ธ์ž๋ฅผ ๋น„๊ตํ•จ์œผ๋กœ์จ ํ†ต๊ณ„์  ์œ ์˜์„ฑ์„ ์•Œ์•„๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ๋˜, ํ™˜์ž๊ตฐ์„ ์ „ํ–ฅ์  ๋˜๋Š” ํ›„ํ–ฅ์ ์œผ๋กœ ๊ฒฝ๊ณผ๊ด€์ฐฐํ•จ์œผ๋กœ์จ ์œ„ํ—˜์ธ์ž๋“ค์ด ์‹œ๋ ฅ์˜ˆํ›„์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ณ , ์ดˆ๊ธฐ์‹œ๋ ฅ, ์ดˆ๊ธฐ ๋ชจ์„ธํ˜ˆ๊ด€ ๋น„๊ด€๋ฅ˜ ์ •๋„ ๋“ฑ ์˜ˆํ›„์ธ์ž๋กœ์„œ ์•„์ง ์ฐฌ๋ฐ˜์ด ๊ฑฐ๋“ญ๋˜๊ณ  ์žˆ๋Š” ์š”์†Œ๋“ค์— ๋Œ€ํ•ด ํ†ต๊ณ„์  ๊ฒ€์ •์„ ํ†ตํ•ด ๊ทธ ์˜ํ–ฅ์„ ์•Œ์•„๋ณด๊ณ ์ž ํ•˜์˜€์œผ๋ฉฐ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๊ณผ๋ฅผ ์–ป์—ˆ๋‹ค. 1. ๋Œ€์ƒ๊ตฐ์€ BRVO๋กœ ์ง„๋‹จ๋œ 82๋ช…, 82์•ˆ์ด์—ˆ์œผ๋ฉฐ, ๋‚จ์ž 24๋ช…, ์—ฌ์ž 58๋ช…์ด์—ˆ๋‹ค. ํ™˜์ž์˜ ํ‰๊ท  ์—ฐ๋ น์€ 58.8ยฑ11.5์„ธ ์˜€์œผ๋ฉฐ, ๋‚จ์ž๋Š” 57.1ยฑ12.8์„ธ(๋ฒ”์œ„29-77์„ธ)์˜€๊ณ , ์—ฌ์ž๋Š” 59.5ยฑ11.0์„ธ(๋ฒ”์œ„ 33-84์„ธ)๋กœ ๋‚จ๋…€๊ฐ„ ์—ฐ๋ น์˜ ํ†ต๊ณ„ํ•™์  ์ฐจ์ด๋Š” ์—†์—ˆ๋‹ค. 2. ๋Œ€์กฐ๊ตฐ์€ sex-, age-matched group์œผ๋กœ ๊ฐ™์€ ๊ธฐ๊ฐ„๋™์•ˆ ๋ฐฑ๋‚ด์žฅ ์ˆ˜์ˆ ์„ ๋ฐ›์€ ํ™˜์ž 82๋ช…์˜ ์ˆ˜์ˆ ๋ฐ›์ง€ ์•Š์€ ๋ฐ˜๋Œ€ํŽธ ์ •์ƒ์•ˆ 82์•ˆ์œผ๋กœ ํ•˜์˜€๋‹ค. 3. ๊ณ ํ˜ˆ์••(p=0,001), ๋†’์€ ์•ˆ์••(p=0.031), ์งง์€ ์•ˆ์ถ•์žฅ๊ธธ์ด(p=0.001)๊ฐ€ BRVO์˜ ์œ„ํ—˜์ธ์ž๋กœ ํ†ต๊ณ„์  ์˜๋ฏธ๊ฐ€ ์žˆ์—ˆ๋‹ค. 4. ์œ„ํ—˜์ธ์ž ๊ฐ„์˜ ์˜ํ–ฅ์„ ํ†ต์ œํ•œ ์ƒํƒœ์—์„œ๋Š” ๊ณ ํ˜ˆ์••๋งŒ์ด ์œ„ํ—˜์ธ์ž๋กœ์„œ ์œ ์˜์„ฑ์„ ๊ฐ€์ง€๋ฉฐ(p=0.0001), ๊ณ ํ˜ˆ์••์ด ์žˆ์„ ๊ฒฝ์šฐ BRVO์— ์ดํ™˜๋  ํ™•๋ฅ ์ด 7.9๋ฐฐ ์ฆ๊ฐ€ํ•œ๋‹ค(95% ์‹ ๋ขฐ๊ตฌ๊ฐ„์—์„œ odds ratio=7.90). 5. ์ดˆ๊ธฐ์‹œ๋ ฅ์ด ์ข‹์„์ˆ˜๋ก(p=0.01), ๋ชจ์„ธํ˜ˆ๊ด€ ๋น„๊ด€๋ฅ˜์˜์—ญ์˜ ํฌ๊ธฐ๊ฐ€ ์ž‘์„์ˆ˜๋ก(p=0.01) ํ†ต๊ณ„ํ•™์ ์œผ๋กœ ์˜๋ฏธ์žˆ๊ฒŒ ์˜ˆํ›„๊ฐ€ ์ข‹์•˜๋‹ค. 6. ์˜ˆํ›„์ธ์ž ๊ฐ„์˜ ์˜ํ–ฅ์„ ํ†ต์ œํ•œ ์ƒํƒœ์—์„œ๋Š” ์•ˆ์••(p=0.03)๊ณผ ๋ชจ์„ธํ˜ˆ๊ด€ ๋น„๊ด€๋ฅ˜์˜์—ญ์˜ ํฌ๊ธฐ(p=0.0002)๊ฐ€ ์˜ˆํ›„์ธ์ž๋กœ์„œ ์˜๋ฏธ๊ฐ€ ์žˆ์—ˆ๋‹ค. 7.BRVO์˜ ํ์‡„๋ถ€์œ„๋Š” ์ƒ์ด์ธก์ด 54์•ˆ(65,9%)๋กœ ๊ฐ€์žฅ ๋งŽ์•˜๊ณ , ํ•˜์ด์ธก 23์•ˆ(28.0%), ์ƒ๋น„์ธก 3์•ˆ(3.7%), ํ•˜๋น„์ธก 2์•ˆ(2.4%)์˜ ์ˆœ์ด์—ˆ๋‹ค. 8. ํ‰๊ท  ์ถ”์ ๊ด€์ฐฐ ๊ธฐ๊ฐ„์€ 21.9๊ฐœ์›”ยฑ7.8๊ฐœ์›”(๋ฒ”์œ„ 6๊ฐœ์›”-45๊ฐœ์›”)์ด์—ˆ์œผ๋ฉฐ, ํ•ฉ๋ณ‘์ฆ์œผ๋กœ ์ง€์†์  ํ™ฉ๋ฐ˜๋ถ€์ข… 38์•ˆ(46.3%), ๋ง๋ง‰์‹ ์ƒํ˜ˆ๊ด€ 38์•ˆ(46.3%), ์œ ๋ฆฌ์ฒด์ถœํ˜ˆ 18์•ˆ(22.0%)์ด์—ˆ๊ณ  ์‹ ์ƒํ˜ˆ๊ด€์„ฑ ๋…น๋‚ด์žฅ์ด๋‚˜ ๋ง๋ง‰๋ฐ•๋ฆฌ๋ฅผ ๋ณด์ธ ์˜ˆ๋Š” ์—†์—ˆ์œผ๋ฉฐ, 1์•ˆ์—์„œ ๋ฐ˜๋Œ€ํŽธ ๋ˆˆ์— BRVO๊ฐ€ ๋ฐœ์ƒํ•˜์˜€๋‹ค. [์˜๋ฌธ] Branch retinal vein occlusion(BRVO) is the second most common cause of visual loss after diabetic retinopathy, and it invariably occurs at the site of arteriovenous crossing. Several systemic and local factors, such as hypertension, diabetes, cardiovascular disease, glaucoma, and short axial length, have been reported in association with BRVO, and there have been various reports about visual prognosis. However, there is no firm evidence that any factor plays a pathogenic or prognostic role. In this study, it is assumed that sex, age, hypertension, hyperlpidemia, glaucoma, short axial length, diabetes, smoking or alcohol may be a risk factor for BRVO, and the study was conducted to determine whether there is any statistically significant difference between BRVO and control group. And the study was designed to identify the effect of each risk factor, initial visual acuity, or the size of capillary nonperfusion area, on the visual prognosis of BRVO. Following results were obtained. 1. The BRVO group consisted of 24 male and 58 female, aged of 57.1 ยฑ12.8(29-77 years) in male and 59.5ยฑ 11,0(33-84 years) in female. 2. The control group consisted of sex-, age-matched patients who underwent cataract surgery at the same period. 3. Hypertension(p=0.001), increased intraocular pressure(p=0.031), and short axial length(p=0,001) were statistically significant as a risk factor for BRVO. 4. When the influence of confounding variables was controlled, hypertension was statistically significant as a risk factor for BRVO(p=0.0001). 5. Good initial visual acuity(p=0.01) and small capillary nonpefusion area(p=0,01) were statistically correlated to good visual prognosis. 6, When the influence of confounding variables was controlled, lower intraocular pressure(p=0.03) and small capillary nonperfusion area(p=0.0002)were statistically correlated to good visual prognosis. 7. The frequency of occlusion site was superotemporal 65.9%, inferotemporal 28.0%, superonasal 3.7%, and inferonasal 2.4% respectively. 8. The complications were persistent macular edema(46.3%), retina neovascularization (46.3%), vitreous hemorrhage(22.0%), and neither neovascular glaucoma nor retinal detachment. The prognosis of BRVO is highly unpredictable because nonischemic types may convert into ischemic types within the first several months. Since persistent macular edema is the most common cause of poor visual recovery, further evaluation of the degree of initial macular edema or the status of perifoveal capillary ring is needed.restrictio

    ๊ธˆ์†ํ‘œ๋ฉด์—์„œ์˜ ํ™”ํ•™๋ฐ˜์‘์— ๋Œ€ํ•œ ์ด๋ก ์  ์—ฐ๊ตฌ

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    Thesis (doctoral)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :ํ™”ํ•™๊ณผ ๋ฌผ๋ฆฌํ™”ํ•™์ „๊ณต,1995.Docto

    ๊ทผ๋Œ€์–ด ์ž๋ฃŒ๋กœ์„œ์˜ ใ€Ž์ฆ์ˆ˜๋ฌด์›๋ก์–ธํ•ดใ€

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    1796๋…„(์ •์กฐ 20)์— ์ฒ˜์Œ์œผ๋กœ ๊ฐ„ํ–‰๋œ ใ€Žๅขžไฟฎ็„กๅฏƒ้Œ„่ซบ่งฃใ€์—๋Š” ๋‹น์‹œ์˜ ๋‹ค๋ฅธ ๋ฌธํ—Œ๋“ค์—์„œ ๋ฐœ๊ฒฌ๋˜๋Š” ๊ตญ์–ด ์‚ฌ์‹ค์€ ๋ฌผ๋ก , ๋‹ค๋ฅธ ๊ณณ์—์„œ๋Š” ์ž˜ ๋ฐœ๊ฒฌ๋˜์ง€ ์•Š๊ฑฐ๋‚˜ ์ด์ฑ…์—์„œ๋งŒ ๋‚˜๋‹ค๋‚˜๋Š” ๋…ํŠนํ•œ ๊ตญ์–ด ์‚ฌ์‚ด๋„ ์ฐพ์„ ์ˆ˜ ์žˆ๋‹ค. ํ‘œ๊ธฐยท์Œ์šด๋ก ์  ์ธก๋ฉด์—์„œ ๋ณผ ๋•Œ ์ด ์ฑ…์—๋Š” ๋‹น์‹œ์˜ ๊ตญ์–ด ์‚ฌ์‹ค์ด ์ž˜ ๋ฐ˜์˜๋˜์–ด ์žˆ๋‹ค๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๋ฌธ๋ฒ•๋ก ์  ์ธก๋ฉด์—์„œ ๋ณผ ๋•Œ, ์ด ์ฑ…์—๋Š” 15์„ธ๊ธฐ ๊ตญ์–ด์—์„œ์™€ ๊ฐ™์€ ํ˜•ํƒœ๋‚˜ ์šฉ๋ฒ•์„ ์ง€๋‹Œ ์š”์†Œ์™€ ๋‹น์‹œ์˜ ๊ตญ์–ด ํ˜„์‹ค์„ ๋ฐ˜์˜ํ•˜๊ณ  ์žˆ๋Š” ์š”์†Œ ๋ชจ๋‘ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋‹ค. ์ „์ž๋Š” ํŠนํžˆ ๋ฌธ์žฅ์ด๋‚˜ ๊ตฌ๋ฌธ์˜ ์ฐจ์›์—์„œ ๊ทธ๋Ÿฌํ–ˆ์œผ๋ฉฐ, ํ›„์ž๋Š” ํŒŒ์ƒ ์ ‘๋ฏธ์‚ฌ๋“ฑ ๋‹จ์–ด ํ˜•์„ฑ์˜ ์ฐจ์›์—์„œ ๊ทธ๋Ÿฌํ–ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๋…ํŠนํ•œ ํ˜„์ƒ์€ ใ€Žๅขžไฟฎ็„กๅฏƒ้Œ„่ซบ่งฃใ€๊ฐ€ ๊ฐ„ํ–‰๋˜๊ธฐ ์ด์ „์— ใ€Ž์‹ ์ฃผ๋ฌด์›๋กใ€, ใ€Ž์ฆ์ˆ˜๋ฌด์›๋กใ€๋“ฑ์˜ ํ•œ๋ฌธ๋ณธ์— ๊ตฌ๊ฒฐ์„ ๋‹ฌ์•„์„œ ์ด๋ฅผ ๊ต์žฌ๋กœ ๊ฒ€์‹œ ๋‹ด๋‹น ์‹ค๋ฌด ๊ณ„์ธต์„ ์ง€์†์ ์œผ๋กœ ๊ต์œก์‹œํ‚ค๊ณ  ํ•™์Šตํ•˜๋„๋กํ–ˆ๊ธฐ ๋•Œ๋ฌธ์— ๋‚˜ํƒ€๋‚œ ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์–ดํœ˜๋ก ์  ์ธก๋ฉด์—์„œ๋Š” ๋ช‡ ๋‹จ์–ด๊ฐ€ 15์„ธ๊ธฐ์ ์ธ ์šฉ๋ฒ•์œผ๋กœ ์‚ฌ์šฉ๋œ ๊ฒฝ์šฐ๊ฐ€ ์žˆ์—ˆ๋‹ค. ๋”ฐ๋ผ์„œ ์ด ์ฑ…์„ ๊ทผ๋Œ€์–ด ์ž๋ฃŒ๋กœ ์ด์šฉํ•˜๊ธฐ ์œ„ํ•ด์„œ๋Š” ์ด ์ฑ…์— ๋‚˜ํƒ€๋‚œ ํ˜„์ƒ๋“ค์ด ๋‹น์‹œ์˜ ๊ตญ์–ด๋ฅผ ๋ฐ˜์˜ํ•˜๊ณ  ์žˆ๋Š” ๊ฒƒ์ธ์ง€ ๊ทธ๋ ‡์ง€ ์•Š์€ ๊ฒƒ์ธ์ง€๋ฅผ ์ •ํ™•ํžˆ ๊ตฌ๋ถ„ํ•  ์ˆ˜ ์žˆ์–ด์•ผ ํ•œ๋‹ค

    Graph representation learning for deviation-aware trace clustering

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    MasterThe goal of process mining is to extract meaningful information from the event log for three main goals: process discovery, conformance checking, and process model enhancement. However, the high complexity of large event log leads traditional process mining algorithms to discover low-quality and complex process models (i.e., spaghetti models). Therefore, it is necessary to cluster event logs into smaller clusters to discover high-quality process models. In this paper, we suggest a novel deviation information aware trace representation method for trace clustering. Further, we suggest a representation learning method using graph neural networks to enrich trace representations with deviation information and behavioral information, which leads to the improvement of trace clustering performance. We evaluate the goodness of the proposed method with three real-life event logs and proved that the deviation information and graph representation learning method improves the quality of process models compared to other trace clustering methods.ํ”„๋กœ์„ธ์Šค ๋งˆ์ด๋‹์—์„œ ํŠธ๋ ˆ์ด์Šค๋ฅผ ์ ์ ˆํ•˜๊ฒŒ ํด๋Ÿฌ์Šคํ„ฐ๋ง ํ•˜๋Š” ๊ฒƒ์€ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ํŠธ๋ ˆ์ด์Šค์˜ ํด๋Ÿฌ์Šคํ„ฐ๋ง์„ ํ†ตํ•ด ์šฐ๋ฆฌ๋Š” ๋” ๊ฐ„๋‹จํ•˜๊ณ  ์ด๋ฒคํŠธ ๋กœ๊ทธ์˜ ์ •๋ณด๋ฅผ ์ž˜ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋Š” ํ”„๋กœ์„ธ์Šค ๋ชจ๋ธ์„ ๋„์ถœํ•  ์ˆ˜ ์žˆ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ์šฐ๋ฆฌ๋Š” ๊ฐ ํŠธ๋ ˆ์ด์Šค๋ฅผ ํ‘œํ˜„ํ•˜๋Š” ๋ฒกํ„ฐ๊ฐ€ ํŠธ๋ ˆ์ด์Šค์˜ ํŠน์„ฑ ์ •๋ณด๋ฅผ ์ž˜ ํฌํ•จํ•  ์ˆ˜ ์žˆ๋„๋ก ํ•ด์•ผ ํ•œ๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์ด๋ฅผ ์œ„ํ•ด ์ด์ƒ ํฌ์ธํŠธ ์ •๋ณด๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜์—ฌ ํŠธ๋ ˆ์ด์Šค๋ฅผ ํ‘œํ˜„ํ•˜๋Š” ๋ฒกํ„ฐ๋ฅผ ๋„์ถœํ•˜๋Š” ๊ฒƒ์„ ์ œ์•ˆํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ๋ฏธ๋ฆฌ ๋„์ถœ๋œ ํ”„๋กœ์„ธ์Šค ๋ชจ๋ธ๊ณผ ๊ฐ ํŠธ๋ ˆ์ด์Šค๋ฅผ ๋น„๊ตํ•จ์œผ๋กœ์จ ๋„์ถœํ•œ ์ด์ƒ ํฌ์ธํŠธ ์ •๋ณด๋ฅผ ํŠธ๋ ˆ์ด์Šค ํ‘œํ˜„ ๋ฒกํ„ฐ๋กœ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ๋˜ํ•œ ์ธ์Šคํ„ด์Šค ๊ทธ๋ž˜ํ”„๋ฅผ ํ™œ์šฉํ•œ ๊ทธ๋ž˜ํ”„ ๋ ˆํ”„๋ ˆ์  ํ…Œ์ด์…˜ ํ•™์Šต์„ ํ†ตํ•ด ํŠธ๋ ˆ์ด์Šค์˜ ์ด์ƒ ํฌ์ธํŠธ ์ •๋ณด์™€ ํ–‰๋™ ์ •๋ณด๋ฅผ ํ•จ๊ป˜ ํ•™์Šตํ•œ ๋ฒกํ„ฐ๋ฅผ ํŠธ๋ ˆ์ด์Šค ํ‘œํ˜„ ๋ฒกํ„ฐ๋กœ ํ™œ์šฉํ•˜๋Š” ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ์‹ค์ œ ๋ฐ์ดํ„ฐ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์‹คํ—˜์„ ํ†ตํ•ด ์ด์ƒ ํฌ์ธํŠธ ์ •๋ณด๋ฅผ ํ™œ์šฉํ•œ ํŠธ๋ ˆ์ด์Šค์˜ ํ‘œํ˜„ ๋ฒกํ„ฐ๊ฐ€ ํด๋Ÿฌ์Šคํ„ฐ๋ง ์ดํ›„์— ๋” ๊ฐ„๋‹จํ•˜๊ณ , ์ด๋ฒคํŠธ ๋กœ๊ทธ ๋ฐ์ดํ„ฐ์— ๋ถ€ํ•ฉํ•˜๋Š” ํ”„๋กœ์„ธ์Šค ๋ชจ๋ธ์„ ๋„์ถœํ•จ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ๊ทธ๋ž˜ํ”„ ๋ ˆํ”„๋ ˆ์  ํ…Œ์ด์…˜ ํ•™์Šต์„ ํ†ตํ•ด ๋„์ถœ๋œ ํŠธ๋ ˆ์ด์Šค์˜ ํ‘œํ˜„ ๋ฒกํ„ฐ๋„ ๋” ์ข‹์€ ํ”„๋กœ์„ธ์Šค ๋ชจ๋ธ์„ ๋„์ถœํ•จ์„ ํ™•์ธํ•  ์ˆ˜ ์žˆ์—ˆ๋‹ค

    Expression of myosin heavy chain isoforms in the inferior oblique muscles in patients with inferior oblique overaction

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    ์˜ํ•™๊ณผ/๋ฐ•์‚ฌ[ํ•œ๊ธ€] ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์€ ๋น„๊ต์  ํ”ํ•˜๊ฒŒ ๊ด€์ฐฐ๋˜๋Š” ์•ˆ๊ตฌ ์šด๋™์งˆํ™˜์œผ๋กœ ์•ˆ๊ตฌ๊ฐ€ ๋‚ด์ „ํ•  ๋•Œ ์ƒ์ „๋˜๋Š” ํ˜„์ƒ์ด๋ฉฐ ํŠนํžˆ ํ•˜์‚ฌ๊ทผ์˜ ์ž‘์šฉ ๋ฐฉํ–ฅ์œผ๋กœ ์•ˆ๊ตฌ๊ฐ€ ์›€์ง์ผ ๋•Œ ๋ฐ˜๋Œ€ํŽธ ์•ˆ๊ตฌ์— ๋น„ํ•ด ๋” ๋งŽ์€ ์ƒ์ „์ด ๋‚˜ํƒ€๋‚˜๋Š” ๊ฒƒ์ด ํŠน์ง•์ด๋‹ค. ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์€ ์ž„์ƒ์ ์œผ๋กœ ์›๋ฐœ์„ฑ๊ณผ ์†๋ฐœ์„ฑ ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์œผ๋กœ ๋ถ„๋ฅ˜ํ•œ๋‹ค. ์›๋ฐœ์„ฑ ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์€ ๊ทธ ์›์ธ์ด ๋ฐํ˜€์ ธ ์žˆ์ง€ ์•Š์œผ๋ฉฐ, ์ œ1์•ˆ์œ„์—์„œ๋Š” ์ƒ์‚ฌ์‹œ๊ฐ€ ์กฐ๊ธˆ ์žˆ๊ฑฐ๋‚˜ ํ˜น์€ ๊ฑฐ์˜ ๋‚˜ํƒ€๋‚˜์ง€ ์•Š๋Š”๋‹ค. ๋ฐ˜๋ฉด ์†๋ฐœ์„ฑ ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์€ ์ฃผ๋กœ ํ•œ์ชฝ ๋ˆˆ์— ๋‚˜ํƒ€๋‚˜๋ฉฐ, ๋™์ธก ์ œ4๋‡Œ์‹ ๊ฒฝ ๋งˆ๋น„๋กœ ์ธํ•œ ์ƒ์‚ฌ๊ทผ ๋งˆ๋น„๋‚˜ ๋ฐ˜๋Œ€์ชฝ ๋ˆˆ์˜ ์ƒ์ง๊ทผ ๋งˆ๋น„๋กœ ์ธํ•ด ์ƒ๊ธด๋‹ค. ์ œ1์•ˆ์œ„์—์„œ ์ˆ˜์ง์‚ฌ์‹œ์˜ ํŽธ์œ„๊ฐ€ ํฌ๊ณ  ํšŒ์„ ์‚ฌ์‹œ๋„ ๋™๋ฐ˜๋˜๋Š” ๊ฒƒ์ด ์›๋ฐœ์„ฑ ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„๊ณผ์˜ ์ฐจ์ด์ ์ด๋‹ค. ์ด์™€ ๊ฐ™์€ ์ž„์ƒ ์–‘์ƒ์˜ ์ฐจ์ด๋กœ ๋‘ ์œ ํ˜•์˜ ๊ตฌ๋ถ„์ด ๊ฐ€๋Šฅํ•˜๊ฒŒ ๋˜๋Š”๋ฐ ์†๋ฐœ์„ฑ ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์˜ ์›์ธ์ด ์ƒ์‚ฌ๊ทผ์˜ ๋งˆ๋น„ ๋“ฑ์— ์ธํ•œ ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ง„ ๋ฐ˜๋ฉด, ์›๋ฐœ์„ฑ ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์˜ ์›์ธ์€ ์•„์ง ๋šœ๋ ท์ด ๋ฐํ˜€์ง€์ง€ ์•Š์€ ์‹ค์ •์ด๋‹ค. ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ์›๋ฐœ์„ฑ๊ณผ ์†๋ฐœ์„ฑ ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์ด ์žˆ๋Š” ํ™˜์ž์˜ ํ•˜์‚ฌ๊ทผ์—์„œ ๊ฐ๊ฐ ๋ฏธ์˜ค์‹  ์ค‘์‡„์˜ ์•„ํ˜•๊ณผ ๊ทธ ๋ถ„ํฌ๋ฅผ ์กฐ์‚ฌํ•˜๊ณ  ๋น„๊ตํ•˜์˜€์œผ๋ฉฐ, ์ •์ƒ ํ•˜์‚ฌ๊ทผ๊ณผ ๋น„๊ต ๊ด€์ฐฐํ•œ ๊ฒฐ๊ณผ ๋‹ค์Œ๊ณผ ๊ฐ™์€ ๊ฒฐ๋ก ์„ ์–ป์—ˆ๋‹ค. 1. ์•ˆ์™€์ธต์€ ๊ทผ์„ฌ์œ ๊ฐ€ ์ง๊ฒฝ์ด ์ž‘๊ณ  ์›ํ˜•์ด๋ฉฐ ํ•˜์‚ฌ๊ทผ์˜ ๋ฐ”๊นฅ์ชฝ์„ U์ž ๋ชจ์–‘์œผ๋กœ ๋‘˜๋Ÿฌ์‹ธ๊ณ  ์žˆ๋Š”๋ฐ, ๊ทผ์„ฌ์œ ์˜ ์ง๊ฒฝ์ด ํฐ ์ค‘์‹ฌ๋ถ€์˜ ์•ˆ๊ตฌ์ธต๊ณผ ๋šœ๋ ท์ด ๊ตฌ๋ณ„์ด ๋˜์—ˆ๋‹ค. 2. fast ์•„ํ˜•์€ ๊ฐ€์žฅ ํ’๋ถ€ํ•œ ์•„ํ˜•์œผ๋กœ ์•ˆ์™€์ธต๊ณผ ์•ˆ๊ตฌ์ธต์—์„œ ๋™์ผํ•œ ์—ผ์ƒ‰์–‘์ƒ์„ ๋‚˜ํƒ€๋‚ด์—ˆ๋Š”๋ฐ, ์ •์ƒ ํ•˜์‚ฌ๊ทผ์— ๋น„ํ•ด ์›๋ฐœ์„ฑ ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์˜ ํ•˜์‚ฌ๊ทผ์—์„œ fast ์•„ํ˜•์˜ ๋ฐœํ˜„์ด ๊ฐ์†Œ๋˜์–ด ์žˆ์—ˆ๊ณ , ์ด ์ฐจ์ด๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•˜์˜€๋‹ค(๊ทผ์‚ฌ์œ ์˜ํ™•๋ฅ =0.000). ์†๋ฐœ์„ฑ ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์˜ ํ•˜์‚ฌ๊ทผ์—์„œ๋Š” ์ •์ƒ์— ๋น„ํ•ด ์˜๋ฏธ์žˆ๋Š” ์ฐจ์ด๊ฐ€ ์—†์—ˆ๋‹ค. 3. slow ์•„ํ˜•์€ ์•ˆ์™€์ธต๊ณผ ์•ˆ๊ตฌ์ธต์— ๊ด‘๋ฒ”์œ„ํ•˜๊ฒŒ ํฉ์–ด์ ธ ์—ผ์ƒ‰๋˜์—ˆ์œผ๋‚˜ ์•ˆ์™€์ธต์—์„œ ์ข€ ๋” ์šฐ์„ธํ•˜๊ฒŒ ์—ผ์ƒ‰๋˜์–ด ๋‚˜ํƒ€๋‚ฌ๊ณ  ์ •์ƒ์— ๋น„ํ•ด ์›๋ฐœ์„ฑ ๊ธฐ๋Šฅํ•ญ์ง„, ์†๋ฐœ์„ฑ ๊ธฐ๋Šฅํ•ญ์ง„์œผ๋กœ ๊ฐˆ์ˆ˜๋ก ํ†ต๊ณ„์ ์œผ๋กœ ์˜๋ฏธ์žˆ๊ฒŒ ์ฆ๊ฐ€๋˜์–ด ๋ฐœํ˜„๋˜์—ˆ๋‹ค(๊ทผ์‚ฌ์œ ์˜ํ™•๋ฅ =0.000). 4. developmental ์•„ํ˜•์€ ์•ˆ์™€์ธต์—๋งŒ ๋‚˜ํƒ€๋‚˜๊ณ  ์•ˆ๊ตฌ์ธต์—๋Š” ๋‚˜ํƒ€๋‚˜์ง€ ์•Š์•˜๋Š”๋ฐ, ์ •์ƒ ํ•˜์‚ฌ๊ทผ๊ณผ ์›๋ฐœ์„ฑ ๊ธฐ๋Šฅํ•ญ์ง„์— ๋น„ํ•ด ์†๋ฐœ์„ฑ ๊ธฐ๋Šฅํ•ญ์ง„์˜ ๊ฒฝ์šฐ์— ํ†ต๊ณ„์ ์œผ๋กœ ์˜๋ฏธ์žˆ๊ฒŒ ์ฆ๊ฐ€๋˜์–ด ๋ฐœํ˜„๋˜์—ˆ๋‹ค(๊ทผ์‚ฌ์œ ์˜ํ™•๋ฅ =0.000). ์ •์ƒ ํ•˜์‚ฌ๊ทผ๊ณผ ์›๋ฐœ์„ฑ ๋ฐ ์†๋ฐœ์„ฑ ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์ด ์žˆ๋Š” ํ•˜์‚ฌ๊ทผ์—์„œ ๋‹ค์–‘ํ•œ ๋ฏธ์˜ค์‹  ์ค‘์‡„ ์•„ํ˜•์˜ ๋ฐœํ˜„์—๋Š” ํ†ต๊ณ„์ ์œผ๋กœ ์œ ์˜ํ•œ ์ฐจ์ด๊ฐ€ ์žˆ์—ˆ๋‹ค. ์ด๋Ÿฌํ•œ ์ฐจ์ด๊ฐ€ ์›๋ฐœ์„ฑ ํ˜น์€ ์†๋ฐœ์„ฑ ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์˜ ๋ณ‘์ธ์— ์–ด๋–ค ์—ญํ• ์„ ํ•˜๋Š”์ง€๋Š” ๊ตฌ์ฒด์ ์œผ๋กœ ํ™•์ธํ•  ์ˆ˜๋Š” ์—†์—ˆ์ง€๋งŒ ์ด๋Ÿฌํ•œ ์—ฐ๊ด€์„ฑ์˜ ์ธ๊ณผ๊ด€๊ณ„์— ๋Œ€ํ•ด ์ถ”๊ฐ€์ ์ธ ์—ฐ๊ตฌ๊ฐ€ ์ˆ˜ํ–‰๋œ๋‹ค๋ฉด ํ•˜์‚ฌ๊ทผ ๊ธฐ๋Šฅํ•ญ์ง„์˜ ๋ณ‘์ธ์„ ์ดํ•ดํ•˜๋Š” ๋ฐ์— ๋„์›€์ด ๋˜๋ฆฌ๋ผ ์ƒ๊ฐ๋œ๋‹ค. [์˜๋ฌธ]Inferior oblique overaction (IOOA), an elevation of an eye as it moves toward adduction, is a common oculomotor disease. It has been customary to distinguish between primary and secondary overactions of this muscle. Primary overaction in which there is no evidence for a past or present ipsilateral superior oblique muscle paralysis or paresis is difficult to explain. Secondary overaction is caused by paresis or paralysis of the ipsilateral superior oblique muscle or by paresis or paralysis of the contralateral superior rectus muscle. Secondary overaction is different from primary overaction in that the vertical deviation is large in the primary position with the accompanied torsional deviation. And Bielschowsky head tilt test is positive. Although clinical distinction between primary and secondary overaction is not so difficult, the explanations given for apparent primary overaction in the old literature are vague. In this study, the expression of myosin heavy chain (MyHC) isoforms in inferior oblique muscles (IO) were investigated in primary and secondary IOOA, and the distribution of MyHC isoforms in IOOA is compared with that in normal control so as to further understand the pathogenesis of IOOA. Two patients (4 eyes) had primary IOOA and 2 patients (2 eyes) had secondary IOOA. Additional IOs, as normal control, were obtained from two 4-year-old patients (2 eyes) who underwent enucleation due to retinoblastoma. Immunohistochemical assay, confocal microscopy, gel electrophoretic analysis and western blotting analysis were performed. The results were as follows: 1. The U-shaped orbital layer (OL) on the orbital surface of the IO was readily distinguished from the central global layer (GL) on the basis on smaller fiber size in OL. 2. The most abundant fast isoform had identical staining pattern in OL and in GL. The expression of fast MyHC isoform was decreased significantly in primary IOOA. 3. Slow MyHC isoform was scattered through both layers, but it was detected predominantly in the fibers of OL. There was increasing tendency in expression of the slow MyHC isoform among normal, primary and secondary IOOA. 4. Developmental isoform was detected only in the fibers of OL. The expression of developmental MyHC isoform was increased significantly in secondary IOOA. It was shown that there is a significant difference in the expression of MyHC isoforms among different IOOAs. The cause-and-effect relationship of this difference has to be further investigated so as to understand the exact pathogenesis of primary IOOA.ope

    A Study of sentence-terminating endings in middle Korean

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