36 research outputs found

    ํฌ๊ธฐ๊ฐ€ ์กฐ์ ˆ๋œ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€ ํ”Œ๋ ˆ์ดํฌ๋ฅผ ์ด์šฉํ•œ ์ธ๊ฐ„์ค„๊ธฐ์„ธํฌ์˜ ์„ธํฌ ํ™œ์„ฑ ๋ฐ ๋ถ„ํ™” ์กฐ์ ˆ

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ํ™”ํ•™์ƒ๋ฌผ๊ณตํ•™๋ถ€, 2023. 2. ๋ฐ•ํƒœํ˜„.There is increasing interest in studying stem cell differentiation through cellular physical stimulation which can be translated into cell-recognized tension. It has been known that physical stimulation can direct human mesenchymal stem cell differentiation which called mechanotransduction. Recently, graphene oxide (GO), major derivative of graphene, has been synthesized as promising material which has suitable physico-chemical characteristics for stem cell lineage specification. GO can interact with integrin, the transmembrane receptor protein, through electrostatic, hydrophobic interactions. However, GO used in previous stem cell research has used GO with an irregular morphology and size. Such irregularity of GO causes diverse cellular responses according to lateral sizes of GO. In this study, we fabricated graphite mechanically with narrow size distribution by adjusting the ball-milling time. Then, size-controlled GO flakes were chemically synthesized from ball-milled graphite using modified Hummers method. Size distribution of GO were measured by hydrodynamic situations. Dose-dependent cytotoxicity of the size-controlled GO flakes on human stem cells was observed. The interaction between GO flakes and cells was analysed with electron microscopy. Also, effect of GO with osteogenic and neural differentiation of hMSCs were measured by staining and gene expression level. Also, by analyzing the shape and size of the cells through immunostaining, we confirmed that focal adhesion was key component involved in promoting stem cell differentiation and enhanced cell viability in apoptotic circumstances. We suggest that the size-controlled GO sheets would be efficient candidate for enhancement of lineage determination of human stem cells and therapeutic applications.์„ธํฌ ์ธ์‹ ์žฅ๋ ฅ์œผ๋กœ ๋ฒˆ์—ญ๋  ์ˆ˜ ์žˆ๋Š” ์„ธํฌ ๋ฌผ๋ฆฌ์  ์ž๊ทน์„ ํ†ตํ•œ ์ค„๊ธฐ ์„ธํฌ ๋ถ„ํ™” ์—ฐ๊ตฌ์— ๋Œ€ํ•œ ๊ด€์‹ฌ์ด ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ๋‹ค. ๋ฌผ๋ฆฌ์  ์ž๊ทน์€ ๊ธฐ๊ณ„์  ํ˜•์งˆ๋„์ž…(mechanotransduction)์ด๋ผ๊ณ  ํ•˜๋Š” ์ธ๊ฐ„ ์ค‘๊ฐ„์—ฝ์ค„๊ธฐ์„ธํฌ ๋ถ„ํ™”๋ฅผ ์กฐ์ ˆํ•  ์ˆ˜ ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ์•Œ๋ ค์ ธ ์žˆ๋‹ค. ์ตœ๊ทผ ๊ทธ๋ž˜ํ•€์˜ ์ฃผ์š” ์œ ๋„์ฒด์ธ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€์ด ์ค„๊ธฐ์„ธํฌ ๊ณ„ํ†ต ์‚ฌ์–‘์— ์ ํ•ฉํ•œ ๋ฌผ๋ฆฌํ™”ํ•™์  ํŠน์„ฑ์„ ๊ฐ–๋Š” ์œ ๋งํ•œ ๋ฌผ์งˆ๋กœ ์ฃผ๋ชฉ๋˜๊ณ  ์žˆ๋‹ค. ์‚ฐํ™” ๊ทธ๋ž˜ํ•€์€ ์ •์ „๊ธฐ์  ์†Œ์ˆ˜์„ฑ ์ƒํ˜ธ์ž‘์šฉ์„ ํ†ตํ•ด ๋ง‰ํšก๋‹จ ์ˆ˜์šฉ์ฒด ๋‹จ๋ฐฑ์งˆ์ธ ์ธํ…Œ๊ทธ๋ฆฐ๊ณผ ์ƒํ˜ธ์ž‘์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด ์ค„๊ธฐ์„ธํฌ ์—ฐ๊ตฌ์—์„œ ์‚ฌ์šฉ๋œ ๋ฌผ์งˆ์€ ํ˜•ํƒœ์™€ ํฌ๊ธฐ๊ฐ€ ๋ถˆ๊ทœ์น™ํ•œ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€์„ ์‚ฌ์šฉํ•˜๊ณ  ์žˆ๋‹ค. ์ด๋Ÿฌํ•œ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€ ๋ฌผ์„ฑ์˜ ๋ถˆ๊ทœ์น™์„ฑ์€ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€์˜ ์ธก๋ฉด ํฌ๊ธฐ์— ๋”ฐ๋ผ ํ†ต์ œ๋˜๊ธฐ ์–ด๋ ค์šด ๋‹ค์–‘ํ•œ ์„ธํฌ ๋ฐ˜์‘์„ ์ผ์œผํ‚จ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๋ณผ ๋ฐ€๋ง ์‹œ๊ฐ„์„ ์กฐ์ •ํ•˜์—ฌ ์ข์€ ํฌ๊ธฐ ๋ถ„ํฌ๋ฅผ ๊ฐ€์ง„ ํ‘์—ฐ์„ ๊ธฐ๊ณ„์ ์œผ๋กœ ์ œ์ž‘ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฐ ๋‹ค์Œ ์ˆ˜์ •๋œ Hummers์˜ ๋ฐฉ๋ฒ•์„ ์‚ฌ์šฉํ•˜์—ฌ ๋ณผ ๋ฐ€๋ง๋œ ํ‘์—ฐ์—์„œ ํฌ๊ธฐ๊ฐ€ ์ œ์–ด๋œ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€ ํ”Œ๋ ˆ์ดํฌ๋ฅผ ํ™”ํ•™์ ์œผ๋กœ ํ•ฉ์„ฑํ•˜์˜€๋‹ค. ํ•ฉ์„ฑ๋œ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€์˜ ํฌ๊ธฐ ๋ถ„ํฌ๋Š” ์ˆ˜์šฉ์•ก ํ™˜๊ฒฝ์—์„œ ์ธก์ •๋˜์—ˆ๋‹ค. ์ธ๊ฐ„ ์ค„๊ธฐ ์„ธํฌ์—์„œ ํฌ๊ธฐ ์กฐ์ ˆ๋œ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€ ํ”Œ๋ ˆ์ดํฌ์˜ ์šฉ๋Ÿ‰ ์˜์กด์  ์„ธํฌ๋…์„ฑ์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ์‚ฐํ™” ๊ทธ๋ž˜ํ•€ ํ”Œ๋ ˆ์ดํฌ์™€ ์„ธํฌ ์‚ฌ์ด์˜ ์ƒํ˜ธ ์ž‘์šฉ์€ ์ „์žํ˜„๋ฏธ๊ฒฝ ๋ถ„์„๋˜์—ˆ๊ณ  ์ธ๊ฐ„ ์ค‘๊ฐ„์—ฝ์ค„๊ธฐ์„ธํฌ์˜ ๊ณจํ˜•์„ฑ ๋ฐ ์‹ ๊ฒฝ ๋ถ„ํ™”์™€ ํ•จ๊ป˜ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€์˜ ํšจ๊ณผ๋ฅผ ๋ถ„์„ํ•˜๊ธฐ ์œ„ํ•ด ์—ผ์ƒ‰ ๋ฐ ์œ ์ „์ž ๋ฐœํ˜„์„ ์ธก์ •ํ•˜์˜€๋‹ค. ๋˜ํ•œ ๋ฉด์—ญ์—ผ์ƒ‰์„ ํ†ตํ•ด ์„ธํฌ์˜ ๋ชจ์–‘๊ณผ ํฌ๊ธฐ๋ฅผ ๋ถ„์„ํ•จ์œผ๋กœ์จ ๊ตญ์†Œ์  ์ ‘์ฐฉ์ด ์ค„๊ธฐ์„ธํฌ ๋ถ„ํ™” ์ด‰์ง„๊ณผ ์„ธํฌ์ž๋ฉธ์‚ฌ ์ด‰์ง„ ํ™˜๊ฒฝ์—์„œ ์„ธํฌ ์ƒ์กด๋ ฅ ํ–ฅ์ƒ์— ๊ด€์—ฌํ•˜๋Š” ํ•ต์‹ฌ ์š”์†Œ์ด๋ฉฐ ์ด๋Š” ์„ธํฌ ์ข…๋ฅ˜์™€ ๋ฐฐ์–‘ ํ™˜๊ฒฝ์— ๋”ฐ๋ผ ๋‹ค๋ฅธ ํฌ๊ธฐ์˜ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€์ด ํšจ์œจ์ ์ž„์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ด๋กœ์„œ ํฌ๊ธฐ ์กฐ์ ˆ ์‚ฐํ™” ๊ทธ๋ž˜ํ•€ ํ”Œ๋ ˆ์ดํฌ๊ฐ€ ์ธ๊ฐ„ ์ค„๊ธฐ ์„ธํฌ์˜ ๋ถ„ํ™” ๊ณ„ํ†ต ๊ฒฐ์ • ๋ฐ ์น˜๋ฃŒ์  ์‘์šฉ์„ ์œ„ํ•œ ํšจ์œจ์ ์ธ ํ›„๋ณด๊ฐ€ ๋  ๊ฒƒ์ด๋ผ๊ณ  ์ œ์•ˆํ•œ๋‹ค.Chapter 1. Research background and objectives 2 Chapter 2. Literature review 6 2.1. Human stem cells 6 2.1.1. Human mesenchymal stem cells 6 2.1.2. Human embryonic stem cells 6 2.2. Stem cell research utilizing graphene oxide 7 Chapter 3. Experimental procedures 11 3.1. Preparation of GO 11 3.1.1. Ball-milling of graphite 11 3.1.2. Preparation of GO by ball-milled graphite 11 3.2. Characterization of GO 11 3.3. Preparation of magnetic nanoparticles 12 3.4. Cultivation and differentiation of hMSCs 12 3.5. Cultivation of hESCs 13 3.6. Generation of hEBs and neural differentiation 14 3.7. Cell adhesion assay of GO 14 3.8. Cell viability assay 15 3.8.1. CCK8 assay 15 3.8.2 Fluorescence-based live and dead assay 15 3.9. qRT-PCR analysis 15 3.10. Alkaline phosphatase staining and Alizarin Red S staining 18 3.11. Immunocytochemistry 18 3.12. Western blotting 18 3.13. Statistical analysis 19 Chapter 4. Material characteristics and cellular interactions of size-controlled graphene oxide flakes 21 4.1. Introduction 21 4.2. Characterization of GO processed by ball-milling 21 4.3. Morphology and cytotoxic effect of GO attached to hMSCs 27 4.4. Conclusions 30 Chapter 5. Enhanced osteogenic differentiation of bone marrow-derived human mesenchymal stem cells using size-controlled graphene oxide flakes 31 5.1. Introduction 32 5.2. Enhancing effect of GO on osteogenic differentiation of hMSCs 35 5.3. Promotion of early cell spreading and focal adhesion complex formation of hMSCs by GO-1.7 39 5.4. Expression and localization of osteogenic marker proteins by GO-1.7 43 5.5. Proposed mechanism of osteogenic differentiation enhanced by GO 47 5.6. Conclusions 52 Chapter 6. Enhancing effect of graphene oxide flakes on stem cell viability in single-cell detachment and shear stress-caused apoptotic circumstances 53 6.1. Introduction 54 6.2. Viability of hMSCs treated with GO-1.7 in non-adhesive condition and shear stress 56 6.3. Viability of hESCs treated with size-controlled GO flakes in non-adhesive condition 60 6.4. Conclusions 62 Chapter 7. Enhanced neural differentiation of adipose-derived human mesenchymal stem cells using size-controlled graphene oxide flakes 64 7.1. Introduction 65 7.2. 3D culture of ADSCs 67 7.3. Gene expression of neural induction markers in ADSCs 69 7.4. Expression of neural induction markers in ADSCs 72 7.5. Conclusions 75 Chapter 8. Overall discussion and further suggestions 77 Appendix. Enhanced neural differentiation of 3D human embryonic stem cells via magnetic nanoparticle-based physical stimuli 80 A.1. Introduction 81 A.2. Improved neural induction of MNP-incorporated hEBs, manufactured through a concentrated magnetic force system 84 A.3. Morphological analysis of neurally induced hESCs 87 A.4. Genetical analysis of neural induction marker genes 91 A.5. Related mechanisms to accelerated neural induction of hEBs 94 A.6. Conclusions 96 Bibliography 97 ๊ตญ ๋ฌธ ์ดˆ ๋ก 109๋ฐ•

    ์ •ํ™•ํ•˜๊ณ  ํ•™์Šต ๊ธฐ๋ฐ˜ ์ „๋ ฅ ๋ถ„์„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•˜๋Š” ํด๋ก ๊ฒŒ์ดํŒ…์˜ ํ•ฉ์„ฑ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์ „๊ธฐยท์ •๋ณด๊ณตํ•™๋ถ€, 2023. 2. ๊น€ํƒœํ™˜.In this paper, we introduce two techniques to efficiently apply clock gating in the synthesis stage. First, We propose a new clock gating methodology based on a precise power saving analysis to overcome the ineffectiveness of the conventional logic structure based clock gating. Two new features exploited in our proposed clock gating are (i) the multiplexer selection signal probability that a flip-flop with multiplexer feedback loop receives a new input and (ii) the joint probability of selection signals that two flip-flops with different multiplexor selection signals both receive new inputs at the same clock cycle. In summary, our method reduces the total power consumption by 2.46% on average (up to 5.00%) over the conventional clock gating method. In the second work, we address a new problem of transforming the long toggling/untoggling sequences of flip-flops cycle-accurate activities into short embedding vectors, so that the flip-flop grouping for clock gating is practically feasible in terms of the memory usage and run time for checking activity similarity among flip-flops. To this end, we propose a machine learning based generation of embedding vectors which are accurate enough to predict the original flip-flop toggling sequences. Precisely, we develop a neural network model of LSTM (long short-term memory) based AE(autoencoder) model combined with SDAE (stacked denoising autoencoder) to take into account the time-series (i.e., clock cycle) similarity feature among the toggling sequences, which is essential to determine which flip-flops should be grouped together for clock gating. By integrating (1) our LSTM based embedding vector generation model, we propose two additional ML models for clock gating: (2) joint state probability predictor (JSP) model for generating 0-state probability of two embedding vectors, and (3) joint feature predictor (JFP) model for generating a new embedding vector that combines two embedding vectors. Through experiments, it is confirmed that our proposed LSTM combined with AutoEnc improves the toggling sequence prediction accuracy up to 0.88 while an LSTM (long short-term memory) based AE model produces accuracy to 0.72, thereby enabling our ML based clock gating framework to save the dynamic power consumption further over that by the state-of-the-art commercial clock gating tool, which relies on the flip-flops toggling probability for grouping flip-flops. Through experiments with benchmark circuits in IWLS, it is shown that our method is able to reduce the dynamic power by 14.0% on average over that by the conventional toggling-driven clock gating.๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•ฉ์„ฑ ๋‹จ๊ณ„์—์„œ ํด๋ก ๊ฒŒ์ดํŒ…์„ ํšจ์œจ์ ์œผ๋กœ ์ ์šฉํ•˜๊ธฐ ์œ„ํ•œ ๋‘ ๊ฐ€์ง€ ๊ธฐ๋ฒ•์„ ์†Œ๊ฐœํ•œ๋‹ค. ์ฒซ์งธ๋กœ, ํด๋ก ๊ฒŒ์ดํŒ… ๊ธฐ๋ฐ˜์˜ ๊ธฐ์กด ๋กœ์ง ๊ตฌ์กฐ์˜ ๋น„ํšจ์œจ์„ฑ์„ ๊ทน๋ณตํ•˜๊ธฐ ์œ„ํ•ด ์ •๋ฐ€ ํ•œ ์ ˆ์ „ ๋ถ„์„์„ ๊ธฐ๋ฐ˜์œผ๋กœ ํ•œ ์ƒˆ๋กœ์šด ํด๋ก ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์•ˆํ•œ๋‹ค. ์ œ์•ˆ๋œ ํด๋ก ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•์—์„œ ํ™œ์šฉ๋˜๋Š” ๋‘ ๊ฐ€์ง€ ์ƒˆ๋กœ์šด ๊ธฐ๋Šฅ์€ (i) ํ”ผ๋“œ๋ฐฑ ๋ฃจํ”„๊ฐ€ ์žˆ๋Š” ํ”Œ๋ฆฝํ”Œ๋กญ ์˜ ๋ฉ€ํ‹ฐํ”Œ๋ ‰์„œ ์„ ํƒ ์‹ ํ˜ธ ํ™•๋ฅ  ๋ฐ (ii) ์„œ๋กœ ๋‹ค๋ฅธ ๋ฉ€ํ‹ฐํ”Œ๋ ‰์„œ ์„ ํƒ ์‹ ํ˜ธ๋ฅผ ๊ฐ–๋Š” ๋‘ ํ”Œ๋ฆฝํ”Œ๋กญ์˜ ๋ฉ€ํ‹ฐํ”Œ๋ ‰์„œ ์„ ํƒ ์‹ ํ˜ธ ๊ฒฐํ•ฉ ํ™•๋ฅ ์ด๋‹ค. ์ „๋ ฅ ์ด๋“์ด ์žˆ๋Š” ๊ฒฝ์šฐ์—๋งŒ ํด๋ก ๊ฒŒ์ดํŒ…์„ ์ ์šฉํ•˜๊ณ  ์„œ๋กœ ๋‹ค๋ฅธ ํด๋ก ๊ฒŒ์ดํŒ… ๊ทธ๋ฃน์„ ํ†ตํ•ฉํ•จ์œผ๋กœ์„œ ์ „์ฒด ๋™์  ์ „๋ ฅ๋ฅผ ์ค„์ด๊ณ ์ž ํ•˜์˜€๋‹ค. ์‹คํ—˜์„ ํ†ตํ•ด ๊ธฐ์กด์˜ ํด๋ก ๊ฒŒ์ดํŒ… ๋ฐฉ๋ฒ•์— ๋น„ํ•ด ํ‰๊ท  2.46%(์ตœ๋Œ€ 5.00%)์˜ ์ด ์ „๋ ฅ ์†Œ๋น„๋ฅผ ์ค„์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ ํ”Œ๋ฆฝํ”Œ๋กญ์˜ ํด๋ก ์ฃผ๊ธฐ๋ณ„ ์ƒํƒœ๋ฅผ ๋‚˜ํƒ€๋‚ด๋Š” ๊ธด ํ† ๊ธ€๋ง/์–ธํ† ๊ธ€๋ง ์‹œํ€€์Šค ๋ฅผ ์งง์€ ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ๋กœ ๋ณ€ํ™˜ํ•˜๋Š” ๋ฌธ์ œ๋ฅผ ํ•ด๊ฒฐํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ† ๊ธ€๋ง ๊ธฐ๋ฐ˜ ํด๋ก ๊ฒŒ์ด ํŒ…์„ ์œ„ํ•œ ํ”Œ๋ฆฝํ”Œ๋กญ ๊ทธ๋ฃนํ™”์— ์ ์šฉํ•˜์—ฌ ํ”Œ๋ฆฝํ”Œ๋กญ ๊ฐ„์˜ ์ƒํƒœ ์œ ์‚ฌ์„ฑ ํ™•์ธ์ด ๋ฉ”๋ชจ๋ฆฌ ์‚ฌ์šฉ๋Ÿ‰ ๋ฐ ์‹คํ–‰ ์‹œ๊ฐ„ ์ธก๋ฉด์—์„œ ์‹ค์งˆ์ ์œผ๋กœ ์‹คํ˜„ ๊ฐ€๋Šฅํ•˜๊ฒŒ ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๊ธฐ๊ณ„ ํ•™์Šต ๊ธฐ๋ฐ˜์œผ๋กœ ์›๋ž˜์˜ ํ”Œ๋ฆฝํ”Œ๋กญ ํ† ๊ธ€ ์‹œํ€€์Šค๋ฅผ ์˜ˆ์ธกํ•˜๊ธฐ์— ์ถฉ๋ถ„ํžˆ ์ •ํ™•ํ•œ ์ €์ฐจ์›์˜ ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ์˜ ์ƒ์„ฑ์„ ์ œ์•ˆํ•œ๋‹ค. ์šฐ๋ฆฌ๋Š” ํ† ๊ธ€๋ง ์‹œํ€€์Šค ๊ฐ„์˜ ์‹œ๊ณ„์—ด ์œ ์‚ฌ์„ฑ์„ ๊ณ ๋ ค ํ•˜๊ธฐ ์œ„ํ•ด ๋””๋…ธ์ด์ฆˆ ์˜คํ† ์ธ์ฝ”๋”๋ฅผ ์ด์šฉํ•˜์—ฌ 5000 ํด๋ก ์‚ฌ์ดํด์˜ ํ† ๊ธ€๋ง ์‹œํ€€์Šค๋ฅผ 10์ฐจ์›์œผ๋กœ ์••์ถ•ํ•˜๊ณ  ์ด๋ฅผ ์žฅ๋‹จ๊ธฐ ๋ฉ”๋ชจ๋ฆฌ ์˜คํ† ์ธ์ฝ”๋”์— ์ž…๋ ฅํ•˜์—ฌ ์ „์ฒด ์‹œํ€€์Šค๋ฅผ ๋Œ€๋ณ€ํ•˜๋Š” ์ €์ฐจ์› ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ๋ฅผ ์ƒ์„ฑํ•˜๋Š” ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์„ ๊ฐœ๋ฐœํ•˜์˜€๋‹ค. ๋˜ํ•œ ์šฐ๋ฆฌ๋Š” ํด๋ก ๊ฒŒ์ดํŒ…์„ ์œ„ํ•œ ๋‘ ๊ฐ€์ง€ ๋ถ€๊ฐ€์ ์ธ ์‹ ๊ฒฝ๋ง ๋ชจ๋ธ์ธ (1) 2๊ฐœ์˜ ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ์˜ 0- ์ƒํƒœ ํ™•๋ฅ  ์ƒ์„ฑ์„ ์œ„ํ•œ ๊ฒฐํ•ฉ ํ™•๋ฅ  ์˜ˆ์ธก ๋ชจ๋ธ๊ณผ (2) ๋‘ ๊ฐœ์˜ ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ๋ฅผ ๊ฒฐํ•ฉํ•˜์—ฌ ์ƒˆ๋กœ์šด ์ž„๋ฒ ๋”ฉ ๋ฒกํ„ฐ๋ฅผ ์˜ˆ์ธกํ•˜๋Š” ๊ฒฐํ•ฉ ํŠน์ง• ์˜ˆ์ธก ๋ชจ๋ธ์„ ์ œ์•ˆํ•œ๋‹ค. IWLS ๋ฒค์น˜๋งˆํฌ ํšŒ๋กœ๋ฅผ ์ด์šฉํ•œ ์‹คํ—˜์„ ํ†ตํ•ด, ๋””๋…ธ์ด์ฆˆ ์˜คํ† ์ธ์ฝ”๋”๋งŒ ์‚ฌ์šฉํ–ˆ์„๋•Œ๋ณด๋‹ค ์žฅ๋‹จ๊ธฐ ๋ฉ”๋ชจ๋ฆฌ ๊ธฐ๋ฐ˜์˜ ์˜คํ† ์ธ์ฝ”๋”๋ฅผ ๊ฒฐํ•ฉํ–ˆ์„ ๋•Œ ์ž…๋ ฅ ๋ฐ์ดํ„ฐ๋ฅผ ๋ณต์› ์ •ํ™•๋„๊ฐ€ ๋” ์šฐ์ˆ˜ํ•œ ๊ฒƒ์„ ํ™• ์ธํ•˜์˜€๋‹ค. ๋˜ํ•œ ์šฐ๋ฆฌ์˜ ๋ฐฉ๋ฒ•์ด ๊ธฐ์กด์˜ ํ† ๊ธ€๋ง ๊ธฐ๋ฐ˜ ํด๋ก ๊ฒŒ์ดํŒ…์— ๋น„ํ•ด ํ‰๊ท  14.0% ์˜ ๋™์  ์ „๋ ฅ์„ ์ค„์ผ ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.1 Selective Clock Gating Based on Comprehensive Power Saving Analysis 1 1.1 Introduction 1 1.2 Preliminary and Motivation 1 1.3 Selective Clock Gating 3 1.3.1 Concept of Selective Clock Gating 3 1.3.2 Joint probability of selection signals 5 1.4 Experimental Results 6 1.4.1 Experimental Setup 6 1.4.2 Experimental Result 7 1.5 Conclusion 10 2 Machine Learning Based Flip-Flop Grouping for Toggling Driven Clock Gating 11 2.1 Introduction 11 2.2 Preliminaries and Prior Works 13 2.2.1 Preliminary and Motivation 13 2.2.2 Prior Works 14 2.3 Machine Learning Based Clock Gating Framework 14 2.3.1 Primary Model: Embedding Vector Generation 14 2.3.2 Secondary Models: Joint State Probability and Joint Feature Prediction 17 2.3.3 Distance Analysis Between Embedding Vectors 18 2.3.4 Power Analysis Model 19 2.3.5 Overall Flow of Flip-flop Grouping 19 2.4 Experimental Results 19 2.4.1 Comparison of Dynamic Power Saving 20 2.4.2 Performance of Auto-encoder Reconstruction Model 21 2.5 Conclusion 21 Abstract (In Korean) 26์„

    Myocardial Rotation and Torsion in Child Growth

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    BACKGROUND: The speckle tracking echocardiography can benefit to assess the regional myocardial deformations. Although, previous reports suggested no significant change in left ventricular (LV) torsion with aging, there are certain differences in LV rotation at the base and apex. The purpose of this study was to evaluate the change and relationship of LV rotation for torsion with aging in children. METHODS: Forty healthy children were recruited and divided into two groups of twenty based on whether the children were preschool-age (2-6 years of age) or school-age (7-12 years of age). After obtaining conventional echocardiographic data, apical and basal short axis rotation were assessed with speckle tracking echocardiography. LV rotation in the basal and apical short axis planes was determined using six myocardial segments along the central axis. RESULTS: Apical and basal LV rotation did not show the statistical difference with increased age between preschool- and school-age children. Apical radial strain showed significant higher values in preschool-age children, especially at the anterior (52.8 ยฑ 17.4% vs. 34.7 ยฑ 23.2%, p < 0.02), lateral (55.8 ยฑ 20.4% vs. 36.1 ยฑ 22.7%, p < 0.02), and posterior segments (57.1 ยฑ 17.6% vs. 38.5 ยฑ 21.7%, p < 0.01). The torsion values did not demonstrate the statistical difference between two groups. CONCLUSION: This study revealed the tendency of higher rotation values in preschool-age children than in school-age children. The lesser values of rotation and torsion with increased age during childhood warrant further investigation.ope

    Relationship between serum sodium level and coronary artery abnormality in Kawasaki disease

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    PURPOSE: Kawasaki disease (KD) is an immune-related multisystemic vasculitis that occurs in children, especially ensuing from a coronary artery abnormality. Sodium level is known to be related to vascular injury, which could affect the progress of KD. The purpose of this study was to determine the serum sodium levels that could predict the occurrence of cardiac and coronary artery events in KD. METHODS: We conducted a retrospective review of medical records for 104 patients with KD from January 2015 to December 2015. Patients with serum Na levels of <135 mEq/L at the time of initial diagnosis were assigned to the hyponatremia group. Laboratory findings and echocardiographic data were analyzed for various aspects. RESULTS: Among the 104 patients with KD, 91 were included in the study, of whom 48 (52.7%) had hyponatremia. The degree of fever, white blood cell count, percentage of neutrophils, percentage of lymphocytes, total bilirubin level, brain natriuretic peptide level, erythrocyte sedimentation rate, and C-reactive protein level were higher in the patients with hyponatremia. They also demonstrated a trend of larger coronary artery diameters based on Z scores. CONCLUSION: The severity of vascular inflammation in acute KD with hyponatremia might worsen the prognosis of coronary vasculature. Although no statistically significant correlation was found between the initial serum sodium levels and coronary arteriopathy in the patients with KD in this study, a long-term follow-up study with a larger number of enrolled patients should be designed in the future to elucidate the relationship between serum sodium level and coronary arteriopathy in patients with KD.ope

    ํ•œ๊ตญ ์ฒญ์†Œ๋…„์˜ ์ง‘๋‹จ ๋”ฐ๋Œ๋ฆผ์— ๋Œ€ํ•œ ์‹ฌ์ธต์ธํ„ฐ๋ทฐ ์—ฐ๊ตฌ: ๋”ฐ๋Œ๋ฆผ์˜ ์œ ํ˜•ํ™” ๋ฐ ์†Œ์…œ๋ฏธ๋””์–ด์˜ ์—ญํ• ์„ ์ค‘์‹ฌ์œผ๋กœ

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    ๋ณธ ์—ฐ๊ตฌ๋Š” ํ•œ๊ตญ์˜ ์‚ฌํšŒ์  ๋ฌธ์ œ์ธ ์ฒญ์†Œ๋…„ ์ง‘๋‹จ ๋”ฐ๋Œ๋ฆผ ํ˜„์ƒ์„ ์ฒญ์†Œ๋…„๋“ค์˜ ๊ด€๊ณ„์  ํŠน์„ฑ์„ ์ค‘์‹ฌ์œผ๋กœ ์žฌ์กฐ๋ช…ํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ๋Š” ์ง‘๋‹จ ๋”ฐ๋Œ๋ฆผ ํ˜„์ƒ์ด ์ฒญ์†Œ๋…„๋“ค์˜ ์‚ฌํšŒ๊ด€๊ณ„ ๊ณผ์ • ์†์—์„œ ์–ด๋– ํ•œ ์–‘์ƒ์œผ๋กœ ๋‚˜ํƒ€๋‚˜๊ณ  ์žˆ๋Š”์ง€, ์–ด๋– ํ•œ ํŠน์ง•์„ ์ง€๋‹ˆ๊ณ  ์žˆ๋Š”์ง€, ๊ทธ ๊ณผ์ •์—์„œ ๋ฏธ๋””์–ด๋Š” ์–ด๋–ค ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ๋Š”์ง€ ํƒ์ƒ‰์ ์œผ๋กœ ์‚ดํŽด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. ์ด๋ฅผ ์œ„ํ•˜์—ฌ ์„œ์šธ ๋ฐ ๊ฒฝ๊ธฐ๋„ ๊ฑฐ์ฃผ ์ค‘ํ•™์ƒ 14๋ช…์„ ๋Œ€์ƒ์œผ๋กœ ์‹ฌ์ธต์ธํ„ฐ๋ทฐ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์—ฐ๊ตฌ๊ฒฐ๊ณผ, ๋”ฐ๋Œ๋ฆผํ˜„์ƒ์„ ์ฒญ์†Œ๋…„๋“ค์˜ ๋˜๋ž˜ ๊ด€๊ณ„(๋ฌด๋ฆฌ)์˜ ๋งฅ๋ฝ์—์„œ ์™ธํ†จ์ดํ˜• ๋”ฐ๋Œ๋ฆผ๊ณผ ๊ด€๊ณ„ํ˜• ๋”ฐ๋Œ๋ฆผ์œผ๋กœ ๊ตฌ๋ถ„ํ•˜์—ฌ ์ ‘๊ทผํ•  ํ•„์š”๊ฐ€ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์ฒญ์†Œ๋…„๋“ค์€ ๋ฐ˜์ด๋ผ๋Š” ํŠน์ˆ˜ํ•œ ํ™˜๊ฒฝ ์†์—์„œ ์†Œ๊ทœ๋ชจ์˜ ๋ฌด๋ฆฌ๋ฅผ ์ง€์–ด ์นœ๊ตฌ๊ด€๊ณ„๋ฅผ ํ˜•์„ฑํ•˜๊ณ  ์žˆ์—ˆ์œผ๋ฉฐ, ๋ฌด๋ฆฌ๊ฐ€ ํ˜•์„ฑ๋˜๋Š” ํ•™๊ธ‰ ์ดˆ๊ธฐ์— ์–ด๋–ค ๋ฌด๋ฆฌ์—๋„ ์†ํ•˜์ง€ ๋ชปํ•œ ํ•™์ƒ์€ ๋‹ค๋ฅธ ํ•™์ƒ๋“ค๋กœ๋ถ€ํ„ฐ ๋ฐฐ์ œ๋˜์–ด ๋”ฐ๋Œ๋ฆผ์˜ ๋Œ€์ƒ์ด ๋  ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋ฐ˜๋ฉด, ๋‹ค์–‘ํ•œ ์นœ๊ตฌ ๋ฌด๋ฆฌ ์†์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๊ฐˆ๋“ฑ์˜ ๊ณผ์ •์—์„œ๋„ ๋”ฐ๋Œ๋ฆผ์€ ๋นˆ๋ฒˆํžˆ ์ผ์–ด๋‚˜๊ณ  ์žˆ์—ˆ๋‹ค. ํ›„์ž์— ํ•ด๋‹นํ•˜๋Š” ๊ด€๊ณ„ํ˜• ๋”ฐ๋Œ๋ฆผ์€ ์™ธํ†จ์ดํ˜• ๋”ฐ๋Œ๋ฆผ๊ณผ ๊ตฌ๋ณ„๋˜๋Š” ๋ช‡๋ช‡ ํŠน์ง•๋“ค์ด ๊ด€์ฐฐ๋˜์—ˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” ๋”ฐ๋Œ๋ฆผ์ด ๋ฐœ์ƒํ•˜๋Š” ์ƒํ™ฉ์  ์š”์ธ์ธ ๊ตฌ์กฐ์  ํŠน์„ฑ์— ๋”ฐ๋ผ ๋”ฐ๋Œ๋ฆผ์˜ ์œ ํ˜•ํ™”๋ฅผ ์‹œ๋„ํ•œ ๋’ค, ๋”ฐ๋Œ๋ฆผ ์œ ํ˜•์— ๋”ฐ๋ฅธ ํŠน์ง•๊ณผ ๋ฐœ์ƒ์›์ธ, ๊ทธ๋ฆฌ๊ณ  ์ด ๊ณผ์ •์—์„œ ๋ฏธ๋””์–ด๊ฐ€ ์–ด๋– ํ•œ ๊ธฐํญ์ œ ์—ญํ• ์„ ํ•˜๊ณ  ์žˆ๋Š”์ง€๋ฅผ ๋ฐํ˜”๋‹ค. ์ด๋ฅผ ํ†ตํ•ด ๋ณธ ์—ฐ๊ตฌ๋Š” ์ฒญ์†Œ๋…„๋“ค ์‚ฌ์ด์—์„œ ๋ฐœ์ƒํ•˜๋Š” ๋”ฐ๋Œ๋ฆผ ํ˜„์ƒ์„ ๋“œ๋Ÿฌ๋‚˜๋Š” ๊ฐ€์‹œ์ ์ธ ์ƒํ™ฉ๋งŒ์„ ๋ณด๊ณ  ๋ชจ๋‘ ์ง‘๋‹จ ๋”ฐ๋Œ๋ฆผ์œผ๋กœ ๋ช…๋ช…ํ•˜์—ฌ ๋‹จ์ • ์ง€์–ด์„œ๋Š” ์•ˆ ๋˜๋ฉฐ, ๋ณด๋‹ค ๊ทผ๋ณธ์ ์ธ ์ฐจ์›์—์„œ์˜ ๊ต์œก์ด ์„ ํ–‰๋˜๊ธฐ ์œ„ํ•ด ๋จผ์ € ๋”ฐ๋Œ๋ฆผ ํ˜„์ƒ์„ ์ดํ•ดํ•˜๊ธฐ ์œ„ํ•œ ์‹œ๊ฐ์˜ ์ •๋ฐ€์„ฑ์ด ์ ˆ์‹คํžˆ ํ•„์š”ํ•จ์„ ์ œ์•ˆํ•œ๋‹ค

    Insulin resistance and bone age advancement in girls with central precocious puberty

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    PURPOSE: Precocious puberty has significantly increased recently. While obesity is associated with puberty timing, the relationship between obesity and central precocious puberty (CPP) remains controversial. The purpose of this study was to determine whether insulin resistance is associated with bone age (BA) advancement in girls with CPP. METHODS: We retrospectively analyzed the records of 804 girls referred for puberty evaluation. Anthropometric measurements, BA, sex hormone, sex hormone binding globulin (SHBG), and insulin levels, lipid profiles, and gonadotropin releasing hormone stimulation tests were assessed. Insulin resistance parameters were calculated using the homeostasis model assessment-insulin resistance (HOMA-IR) and quantitative insulin sensitivity check index (QUICKI) models. RESULTS: BA, BA advancement, free estradiol index, insulin, and HOMA-IR increased significantly in girls with high body mass index (BMI) compared with that of girls with low BMI in cases of CPP. HOMA-IR was positively correlated with BA advancement and BMI but negatively correlated with SHBG. QUICKI was negatively correlated with BA advancement and BMI and positively correlated with SHBG. When HOMA-IR increased by 1, the odds for BA advancement increased 120% after adjusting for age and BMI (P=0.033). CONCLUSIONS: Insulin resistance could be associated with BA advancement in girls with CPP.ope

    ํ•œ๊ตญ์–ด ๋‚จ๋…€ ์–ธ์–ด ๋ณ€ํ™”์— ๊ด€ํ•œ ์—ฐ๊ตฌ : 1960๋…„๋Œ€, 2000๋…„๋Œ€ ๋ฉœ๋กœ ์˜ํ™”์— ๋‚˜ํƒ€๋‚œ ๋‚จ๋…€ ์–ธ์–ด๋ฅผ ๋Œ€์ƒ์œผ๋กœ

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    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์–ธ์–ดํ•™๊ณผ ์–ธ์–ดํ•™์ „๊ณต,2004.Maste

    Comparative studies in 'Kim Yun-Deok's Gayageum Sanjo and Geomungo Sanjo

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ์Œ์•…๊ณผ, 2013. 8. ์ด์ง€์˜.๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ๋Š” 19์„ธ๊ธฐ ๋ง๊ฒฝ์— ๊ฐ€๋ฝ์ด ํ˜•์„ฑ๋œ ์ดํ›„, ์—ฌ๋Ÿฌ ์„ธ๋Œ€์— ๊ฑธ์ณ ๊ณ„์Šนโ€คํ™•๋Œ€ ๋˜์—ˆ๋‹ค. ๊ฐ€์•ผ๊ธˆ๋ช…์ธ๋“ค์€ ์Šค์Šน์—๊ฒŒ ๋ฐฐ์šด ๊ฐ€๋ฝ์— ์ž์‹ ์˜ ๊ฐ€๋ฝ์„ ์ถ”๊ฐ€ํ•˜๊ณ  ๋‹ค๋“ฌ์–ด ์Œ์•…์ ์œผ๋กœ ์™„์„ฑ๋„๋ฅผ ๋†’์—ฌ๊ฐ”์œผ๋ฉฐ, ์ด๋ ‡๊ฒŒ ์™„์„ฑ๋œ ๊ฐ๊ฐ์˜ ๊ฐ€๋ฝ์— ๊ทธ ๋ช…์ธ์˜ ์ด๋ฆ„์„ ๋ถ™์—ฌ โ—‹โ—‹โ—‹๋ฅ˜๋ผ ๋ถ€๋ฅด๊ณ  ์žˆ๋‹ค. ์‚ฐ์กฐ์˜ ๋ช…์ธ๋“ค์€ ๋ณดํ†ต ํ•œ ๊ฐ€์ง€์˜ ์•…๊ธฐ๋กœ ์‚ฐ์กฐ ๊ฐ€๋ฝ์„ ๊ตฌ์„ฑํ•˜์—ฌ ์œ ํŒŒ๋ฅผ ๋‚จ๊ธฐ๋Š” ๊ฒƒ์ด ์ผ๋ฐ˜์ ์ด๋‚˜, ๊น€์œค๋•์€ ์ž์‹ ์˜ ์ด๋ฆ„์œผ๋กœ ํ•˜๋‚˜์˜ ์•…๊ธฐ๊ฐ€ ์•„๋‹Œ ๊ฐ€์•ผ๊ธˆ๊ณผ ๊ฑฐ๋ฌธ๊ณ  ๋‘ ์•…๊ธฐ๋กœ ๊ฐ๊ธฐ ์‚ฐ์กฐ์˜ ์œ ํŒŒ๋ฅผ ํ˜•์„ฑํ•ด ๊ฐ€๋ฝ์„ ๋‚จ๊ฒผ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํ•œ ์—ฐ์ฃผ์ž๊ฐ€ ๊ฐ๊ธฐ ๋‹ค๋ฅธ ์•…๊ธฐ๋กœ ์‚ฐ์กฐ๋ฅผ ํ˜•์„ฑํ•  ๊ฒฝ์šฐ ๊ทธ ์„ ์œจ๋“ค์ด ์„œ๋กœ ์˜ํ–ฅ์„ ๋ฐ›์•˜์„ ๊ฒƒ์ด๋ผ๋Š” ์ „์ œํ•˜์— ๊ทธ ์—ฐ๊ณ„์„ฑ์„ ์‚ดํŽด๋ณด๊ณ ์ž ํ•˜์˜€๋‹ค. โ…ก์žฅ์—์„œ๋Š” ๊น€์œค๋•์˜ ์ƒ์• ์™€ ์‚ฐ์กฐ์˜ ์ „์Šน๊ณผ์ •์„ ์‚ดํŽด ๋ณด์•˜๋‹ค. โ…ข์žฅ์—์„œ ๋‘ ์‚ฐ์กฐ์— ๊ณตํ†ต์ ์œผ๋กœ ํฌํ•จ๋œ ์ง„์–‘์กฐโ€ค์ค‘๋ชจ๋ฆฌโ€ค์ž์ง„๋ชจ๋ฆฌ์žฅ๋‹จ์„ ๋น„๊ตโ€ค๋ถ„์„ํ•˜์˜€๋‹ค. โ…ฃ์žฅ์—์„œ๋Š” ์ƒ์ดํ•œ ์žฅ๋‹จ์ด์ง€๋งŒ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ์˜ ์ž์ง„๋ชจ๋ฆฌ์™€ ๊ฐ™์€ ๋ฐ•์ž์ฒด๊ณ„๋กœ ๋ณผ ์ˆ˜ ์žˆ๋Š” ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ์˜ ์ค‘์ค‘๋ชจ๋ฆฌ์™€ ํœ˜๋ชจ๋ฆฌ๋ฅผ ๋น„๊ตโ€ค๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ ๋‹จ๋ชจ๋ฆฌ์˜ ๊ฒฝ์šฐ ๋ฐ•์ž์ฒด๊ณ„๊ฐ€ ๋‹ฌ๋ผ ๋น„๊ต ๊ฐ€๋Šฅํ•œ ์žฅ๋‹จ์ด ์—†์œผ๋ฏ€๋กœ, ์—ฐ๊ตฌ์˜ ๋ฒ”์œ„์—์„œ ์ œ์™ธํ•˜์˜€๋‹ค. ๊น€์œค๋•์ด ์ง์ ‘ ๋‚จ๊ธด ์Œ์›์„ ๊ธฐ์ค€์œผ๋กœ ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ๋ฅผ ๋น„๊ตํ•˜์—ฌ ๊ณตํ†ต์„ ์œจ๊ณผ ์œ ์‚ฌ์„ ์œจ์„ ์‚ดํŽด๋ณธ ๊ฒฐ๊ณผ๋Š” ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ์˜ ๊ฐ ์„ ์œจ์„ ์žฅ๋‹จ ๋‹จ์œ„๋กœ ๋น„๊ตํ•œ ๊ฒฐ๊ณผ ํœ˜๋ชจ๋ฆฌ๋ฅผ ์ œ์™ธํ•œ ๋ชจ๋“  ์žฅ๋‹จ์—์„œ ๊ณตํ†ต์„ ์œจ๊ณผ ์œ ์‚ฌ์„ ์œจ์ด ๋ฐœ๊ฒฌ๋˜์—ˆ๋‹ค. ํœ˜๋ชจ๋ฆฌ์—์„œ๋Š” ๋‹จ ํ•˜๋‚˜์˜ ๊ณตํ†ต์„ ์œจ๋งŒ ๋ฐœ๊ฒฌ๋˜์—ˆ์œผ๋‚˜ ๋ฆฌ๋“ฌ์„ ์ œ์™ธํ•œ ์Œ๊ณ„์˜ ์กฐํ•ฉ์ด ๋ฐ˜๋ณตโ€ค๋ณ€ํ˜•๋˜๋Š” ๋ถ€๋ถ„๋“ค๋กœ ์œ ์‚ฌ์„ฑ์„ ์ฐพ์•„๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ์ด๋Š” ์ƒ๋Œ€์ ์œผ๋กœ ๋Š๋ฆฐ ์žฅ๋‹จ์ผ์ˆ˜๋ก ์œ ์‚ฌํ•œ ์š”์†Œ๊ฐ€ ๋งŽ์ด ๋ฐœ๊ฒฌ๋˜๊ณ , ์žฅ๋‹จ์ด ๋นจ๋ผ์ง€๋ฉด ๊ฐ ์•…๊ธฐ์˜ ํŠน์„ฑ์— ๋งž์ถ”์–ด ์—ฐ์ฃผํ•˜๋Š” ์„ ์œจ์ด ๋งŽ์•„์ง€๊ธฐ ๋•Œ๋ฌธ์— ์„ ์œจ์˜ ์œ ์‚ฌ์„ฑ์ด ์ ์–ด์ง€๋Š” ๊ฒƒ์œผ๋กœ ๋ณด์ธ๋‹ค. ์ด๋ฒˆ ์—ฐ๊ตฌ์—์„œ๋Š” ๊น€์œค๋•๋ฅ˜ ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ ์„ ์œจ๋งŒ์„ ๋ฒ”์œ„๋กœ ํ•˜์—ฌ ์œ ์‚ฌํ•œ ์„ ์œจ๋“ค์„ ์ฐพ์•„๋‚ด์—ˆ์ง€๋งŒ ์ถ”ํ›„ ์Œ๊ณ„โ€ค๋ฆฌ๋“ฌโ€คํ˜•์‹โ€ค์ฃผ๋ฒ•์  ํŠน์ง• ๋“ฑ ๊ฐ ์Œ์•…์  ์š”์†Œ๋ณ„๋กœ ํŠน์ง•์„ ์ฐพ์•„ ๋น„๊ตํ•ด ๋ณด๋Š” ์—ฐ๊ตฌ๋ฅผ ์ง„ํ–‰ํ•œ๋‹ค๋ฉด ๋”์šฑ ์‹ฌ๋„ ๊นŠ์€ ๊ฒฐ๊ณผ๋ฅผ ๋„์ถœํ•ด ๋‚ผ ์ˆ˜ ์žˆ์„ ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋œ๋‹ค.I. ์„œ๋ก  1 1. ๋ฌธ์ œ ์ œ๊ธฐ ๋ฐ ์—ฐ๊ตฌ๋ชฉ์  1 2. ์—ฐ๊ตฌ๋ฒ”์œ„ ๋ฐ ์—ฐ๊ตฌ๋ฐฉ๋ฒ• 3 โ…ก. ๊น€์œค๋•์˜ ์ƒ์• ์™€ ์‚ฐ์กฐ์˜ ์ „์Šน๊ณผ์ • 7 1. ๊น€์œค๋•์˜ ์ƒ์• ์™€ ์Œ์•…ํ™œ๋™ 7 2. ๊น€์œค๋•๋ฅ˜ ์‚ฐ์กฐ์˜ ์ „์Šน๊ณผ์ • 8 1) ๊น€์œค๋•๋ฅ˜ ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ 8 2) ๊น€์œค๋•๋ฅ˜ ๊ฑฐ๋ฌธ๊ณ  ์‚ฐ์กฐ 10 โ…ข. ๋™์ผ ์žฅ๋‹จ ๊ฐ„ ์„ ์œจ๋น„๊ต 12 1. ์ง„์–‘์กฐ 12 1) ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ ์ง„์–‘์กฐ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ ์ง„์–‘์กฐ์˜ ๊ณตํ†ต์„ ์œจ 13 2) ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ ์ง„์–‘์กฐ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ ์ง„์–‘์กฐ์˜ ์œ ์‚ฌ์„ ์œจ 22 2. ์ค‘๋ชจ๋ฆฌ 42 1) ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ ์ค‘๋ชจ๋ฆฌ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ ์ค‘๋ชจ๋ฆฌ์˜ ๊ณตํ†ต์„ ์œจ 43 2) ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ ์ค‘๋ชจ๋ฆฌ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ ์ค‘๋ชจ๋ฆฌ์˜ ์œ ์‚ฌ์„ ์œจ 46 3. ์ž์ง„๋ชจ๋ฆฌ 64 1) ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ ์ž์ง„๋ชจ๋ฆฌ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ ์ž์ง„๋ชจ๋ฆฌ์˜ ๊ณตํ†ต์„ ์œจ 65 2) ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ ์ž์ง„๋ชจ๋ฆฌ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ ์ž์ง„๋ชจ๋ฆฌ์˜ ์œ ์‚ฌ์„ ์œจ 70 โ…ฃ. ์ƒ์ด ์žฅ๋‹จ ๊ฐ„ ์„ ์œจ๋น„๊ต 78 1. ์ค‘์ค‘๋ชจ๋ฆฌ 78 1) ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ ์ค‘์ค‘๋ชจ๋ฆฌ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ ์ž์ง„๋ชจ๋ฆฌ์˜ ๊ณตํ†ต์„ ์œจ 79 2) ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ ์ค‘์ค‘๋ชจ๋ฆฌ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ ์ž์ง„๋ชจ๋ฆฌ์˜ ์œ ์‚ฌ์„ ์œจ 83 2. ํœ˜๋ชจ๋ฆฌ 87 1) ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ ํœ˜๋ชจ๋ฆฌ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ ์ž์ง„๋ชจ๋ฆฌ์˜ ๊ณตํ†ต์„ ์œจ 88 2) ๊ฐ€์•ผ๊ธˆ์‚ฐ์กฐ ํœ˜๋ชจ๋ฆฌ์™€ ๊ฑฐ๋ฌธ๊ณ ์‚ฐ์กฐ ์ž์ง„๋ชจ๋ฆฌ์˜ ์œ ์‚ฌ์„ ์œจ 89 โ…ค. ๊ฒฐ๋ก  97 ์ฐธ๊ณ ๋ฌธํ—Œ 99 Abstract 101 ์ฒจ๋ถ€์•…๋ณด 104Maste

    Decomposition of Gender Pay Gap By using Censored Quantile Regression Analysis on Counterfactual Inference

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ)-- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ฒฝ์ œํ•™๋ถ€, 2013. 8. ์ด์„๋ฐฐ.ํ•œ๊ตญ์‚ฌํšŒ์—์„œ ๋‚จ๋…€์ž„๊ธˆ๋ถ„ํฌ๋Š” ๊ฐ ๋ถ„์œ„๋ณ„๋กœ ๋งค์šฐ ์ด์งˆ์ ์ธ ์–‘์ƒ์„ ๋ณด์ธ๋‹ค. ๋”ฐ๋ผ์„œ ๊ธฐ์กด์˜ ์กฐ๊ฑด๋ถ€ ๊ธฐ๋Œ€๊ฐ’์— ๊ธฐ๋ฐ˜ํ•œ ํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•ด์„œ๋Š” ์‹ค์ œ ์ž„๊ธˆ๋ถ„ํฌ๋ฅผ ํšจ๊ณผ์ ์œผ๋กœ ๋ถ„์„ํ•ด๋‚ผ ์ˆ˜ ์—†๋‹ค. ๊ฒ€์—ด ๋ถ„์œ„ ํšŒ๊ท€๋ถ„์„(CQR)์€ ์กฐ๊ฑด๋ถ€ ๋ถ„์œ„์— ๋Œ€ํ•œ ํšŒ๊ท€๋ถ„์„์„ ํ†ตํ•ด ๋ถ„ํฌ์ „์ฒด์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ๋ณด์กดํ•  ๋ฟ ์•„๋‹ˆ๋ผ, ๊ฒ€์—ด๊ฐ’์œผ๋กœ์จ ์ตœ์ €์ž„๊ธˆ์„ ๊ณ ๋ คํ•˜๋ฏ€๋กœ ์œ„์™€ ๊ฐ™์€ ๊ฒฝ์šฐ์— ํšจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ•๋ก ์  ๋Œ€์•ˆ์œผ๋กœ ๊ณ ๋ ค๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋Š” Chernozhukov์™€ Hong์˜ 2002๋…„ ๋…ผ๋ฌธ์—์„œ ์ œ์‹œ๋œ ๋ชจํ˜•์„ ์ ์šฉํ•˜์—ฌ 2000๋…„๊ณผ 2008๋…„ ๋‘ ํ•ด์˜ ํ•œ๊ตญ๋…ธ๋™ํŒจ๋„(KLIPS) ์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•˜์˜€๋‹ค. ๋˜ํ•œ, ๋ฐ˜์‚ฌ์‹ค ๋ถ„์„ ๋ฐ ๋ถ„ํ•ด ๋ถ„์„๋ฒ•์„ ํ†ตํ•ด ์ž„๊ธˆ๋ณ€ํ™”์™€ ๊ณ ๋“ฑ๊ต์œก์ด ์ž„๊ธˆ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๊ด€์ฐฐํ•˜์˜€๋‹ค. ๋ถ„์„์˜ ๊ฒฐ๊ณผ๋กœ, ์—ฌ์„ฑ ๊ณ ๋“ฑ๊ต์œก ์ง€์›์ •์ฑ…์ด ๋นˆ๋ถ€๊ฒฉ์ฐจํ•ด์†Œ์™€ ์„ฑ๋ณ„์ž„๊ธˆ๊ฒฉ์ฐจ ์™„ํ™”์— ๊ธฐ์—ฌํ•  ์ˆ˜ ์žˆ์Œ์„ ๊ฒฝํ—˜์ ์œผ๋กœ ๋…ผ์ฆํ•œ๋‹ค.Wage distribution of both female and male labor force in Korea show apparently heterogeneous features between different quantile groups. For this reason, ordinary conditional expectation such as mean or median cannot explain the data effectively. Censored quantile regression (CQR) which considers also the minimum wage as well as quantile issue could be plausible alternative in this case. By employing and developing CQR procedure of Chernozhukov and Hong (2002), Korean Labor and Income Panel Study (KLIPS) of year 2000 and 2008 were analyzed. Furthermore, the effect of higher education level for women and men were derived from the total change of wage from 2000 to 2008 based on counterfactual distribution theory and through the decomposition method. The final results imply that policy which can guarantee higher education level for the destitute poor female workers could be efficient policy to reduce the gap between men and women, and between the rich and the poor simultaneously.1 Introduction 2 Theoretical background 2.1 General idea 2.2 Censored quantile regression 3 Data description and Analysis 3.1 Data description 3.2 Regression Analysis 3.3 Decomposition and counterfactual analysis 4 ConclusionMaste

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