36 research outputs found
ํฌ๊ธฐ๊ฐ ์กฐ์ ๋ ์ฐํ ๊ทธ๋ํ ํ๋ ์ดํฌ๋ฅผ ์ด์ฉํ ์ธ๊ฐ์ค๊ธฐ์ธํฌ์ ์ธํฌ ํ์ฑ ๋ฐ ๋ถํ ์กฐ์
ํ์๋
ผ๋ฌธ(๋ฐ์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ํํ์๋ฌผ๊ณตํ๋ถ, 2023. 2. ๋ฐํํ.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.์ธํฌ ์ธ์ ์ฅ๋ ฅ์ผ๋ก ๋ฒ์ญ๋ ์ ์๋ ์ธํฌ ๋ฌผ๋ฆฌ์ ์๊ทน์ ํตํ ์ค๊ธฐ ์ธํฌ ๋ถํ ์ฐ๊ตฌ์ ๋ํ ๊ด์ฌ์ด ์ฆ๊ฐํ๊ณ ์๋ค. ๋ฌผ๋ฆฌ์ ์๊ทน์ ๊ธฐ๊ณ์ ํ์ง๋์
(mechanotransduction)์ด๋ผ๊ณ ํ๋ ์ธ๊ฐ ์ค๊ฐ์ฝ์ค๊ธฐ์ธํฌ ๋ถํ๋ฅผ ์กฐ์ ํ ์ ์๋ ๊ฒ์ผ๋ก ์๋ ค์ ธ ์๋ค. ์ต๊ทผ ๊ทธ๋ํ์ ์ฃผ์ ์ ๋์ฒด์ธ ์ฐํ ๊ทธ๋ํ์ด ์ค๊ธฐ์ธํฌ ๊ณํต ์ฌ์์ ์ ํฉํ ๋ฌผ๋ฆฌํํ์ ํน์ฑ์ ๊ฐ๋ ์ ๋งํ ๋ฌผ์ง๋ก ์ฃผ๋ชฉ๋๊ณ ์๋ค. ์ฐํ ๊ทธ๋ํ์ ์ ์ ๊ธฐ์ ์์์ฑ ์ํธ์์ฉ์ ํตํด ๋งํก๋จ ์์ฉ์ฒด ๋จ๋ฐฑ์ง์ธ ์ธํ
๊ทธ๋ฆฐ๊ณผ ์ํธ์์ฉํ ์ ์๋ค. ๊ทธ๋ฌ๋ ๊ธฐ์กด ์ค๊ธฐ์ธํฌ ์ฐ๊ตฌ์์ ์ฌ์ฉ๋ ๋ฌผ์ง์ ํํ์ ํฌ๊ธฐ๊ฐ ๋ถ๊ท์นํ ์ฐํ ๊ทธ๋ํ์ ์ฌ์ฉํ๊ณ ์๋ค. ์ด๋ฌํ ์ฐํ ๊ทธ๋ํ ๋ฌผ์ฑ์ ๋ถ๊ท์น์ฑ์ ์ฐํ ๊ทธ๋ํ์ ์ธก๋ฉด ํฌ๊ธฐ์ ๋ฐ๋ผ ํต์ ๋๊ธฐ ์ด๋ ค์ด ๋ค์ํ ์ธํฌ ๋ฐ์์ ์ผ์ผํจ๋ค. ์ด ์ฐ๊ตฌ์์๋ ๋ณผ ๋ฐ๋ง ์๊ฐ์ ์กฐ์ ํ์ฌ ์ข์ ํฌ๊ธฐ ๋ถํฌ๋ฅผ ๊ฐ์ง ํ์ฐ์ ๊ธฐ๊ณ์ ์ผ๋ก ์ ์ํ์๋ค. ๊ทธ๋ฐ ๋ค์ ์์ ๋ Hummers์ ๋ฐฉ๋ฒ์ ์ฌ์ฉํ์ฌ ๋ณผ ๋ฐ๋ง๋ ํ์ฐ์์ ํฌ๊ธฐ๊ฐ ์ ์ด๋ ์ฐํ ๊ทธ๋ํ ํ๋ ์ดํฌ๋ฅผ ํํ์ ์ผ๋ก ํฉ์ฑํ์๋ค. ํฉ์ฑ๋ ์ฐํ ๊ทธ๋ํ์ ํฌ๊ธฐ ๋ถํฌ๋ ์์ฉ์ก ํ๊ฒฝ์์ ์ธก์ ๋์๋ค. ์ธ๊ฐ ์ค๊ธฐ ์ธํฌ์์ ํฌ๊ธฐ ์กฐ์ ๋ ์ฐํ ๊ทธ๋ํ ํ๋ ์ดํฌ์ ์ฉ๋ ์์กด์ ์ธํฌ๋
์ฑ์ด ๊ด์ฐฐ๋์๋ค. ์ฐํ ๊ทธ๋ํ ํ๋ ์ดํฌ์ ์ธํฌ ์ฌ์ด์ ์ํธ ์์ฉ์ ์ ์ํ๋ฏธ๊ฒฝ ๋ถ์๋์๊ณ ์ธ๊ฐ ์ค๊ฐ์ฝ์ค๊ธฐ์ธํฌ์ ๊ณจํ์ฑ ๋ฐ ์ ๊ฒฝ ๋ถํ์ ํจ๊ป ์ฐํ ๊ทธ๋ํ์ ํจ๊ณผ๋ฅผ ๋ถ์ํ๊ธฐ ์ํด ์ผ์ ๋ฐ ์ ์ ์ ๋ฐํ์ ์ธก์ ํ์๋ค. ๋ํ ๋ฉด์ญ์ผ์์ ํตํด ์ธํฌ์ ๋ชจ์๊ณผ ํฌ๊ธฐ๋ฅผ ๋ถ์ํจ์ผ๋ก์จ ๊ตญ์์ ์ ์ฐฉ์ด ์ค๊ธฐ์ธํฌ ๋ถํ ์ด์ง๊ณผ ์ธํฌ์๋ฉธ์ฌ ์ด์ง ํ๊ฒฝ์์ ์ธํฌ ์์กด๋ ฅ ํฅ์์ ๊ด์ฌํ๋ ํต์ฌ ์์์ด๋ฉฐ ์ด๋ ์ธํฌ ์ข
๋ฅ์ ๋ฐฐ์ ํ๊ฒฝ์ ๋ฐ๋ผ ๋ค๋ฅธ ํฌ๊ธฐ์ ์ฐํ ๊ทธ๋ํ์ด ํจ์จ์ ์์ ํ์ธํ์๋ค. ์ด๋ก์ ํฌ๊ธฐ ์กฐ์ ์ฐํ ๊ทธ๋ํ ํ๋ ์ดํฌ๊ฐ ์ธ๊ฐ ์ค๊ธฐ ์ธํฌ์ ๋ถํ ๊ณํต ๊ฒฐ์ ๋ฐ ์น๋ฃ์ ์์ฉ์ ์ํ ํจ์จ์ ์ธ ํ๋ณด๊ฐ ๋ ๊ฒ์ด๋ผ๊ณ ์ ์ํ๋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 ์ด ๋ก 109๋ฐ
์ ํํ๊ณ ํ์ต ๊ธฐ๋ฐ ์ ๋ ฅ ๋ถ์์ ๊ธฐ๋ฐ์ผ๋ก ํ๋ ํด๋ก ๊ฒ์ดํ ์ ํฉ์ฑ
ํ์๋
ผ๋ฌธ(์์ฌ) -- ์์ธ๋ํ๊ต๋ํ์ : ๊ณต๊ณผ๋ํ ์ ๊ธฐยท์ ๋ณด๊ณตํ๋ถ, 2023. 2. ๊นํํ.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 ๋
ผ๋ฌธ์์๋ ํฉ์ฑ ๋จ๊ณ์์ ํด๋ก ๊ฒ์ดํ
์ ํจ์จ์ ์ผ๋ก ์ ์ฉํ๊ธฐ ์ํ ๋ ๊ฐ์ง
๊ธฐ๋ฒ์ ์๊ฐํ๋ค.
์ฒซ์งธ๋ก, ํด๋ก ๊ฒ์ดํ
๊ธฐ๋ฐ์ ๊ธฐ์กด ๋ก์ง ๊ตฌ์กฐ์ ๋นํจ์จ์ฑ์ ๊ทน๋ณตํ๊ธฐ ์ํด ์ ๋ฐ
ํ ์ ์ ๋ถ์์ ๊ธฐ๋ฐ์ผ๋ก ํ ์๋ก์ด ํด๋ก ๊ฒ์ดํ
๋ฐฉ๋ฒ๋ก ์ ์ ์ํ๋ค. ์ ์๋ ํด๋ก
๊ฒ์ดํ
๋ฐฉ๋ฒ์์ ํ์ฉ๋๋ ๋ ๊ฐ์ง ์๋ก์ด ๊ธฐ๋ฅ์ (i) ํผ๋๋ฐฑ ๋ฃจํ๊ฐ ์๋ ํ๋ฆฝํ๋กญ
์ ๋ฉํฐํ๋ ์ ์ ํ ์ ํธ ํ๋ฅ ๋ฐ (ii) ์๋ก ๋ค๋ฅธ ๋ฉํฐํ๋ ์ ์ ํ ์ ํธ๋ฅผ ๊ฐ๋ ๋
ํ๋ฆฝํ๋กญ์ ๋ฉํฐํ๋ ์ ์ ํ ์ ํธ ๊ฒฐํฉ ํ๋ฅ ์ด๋ค. ์ ๋ ฅ ์ด๋์ด ์๋ ๊ฒฝ์ฐ์๋ง ํด๋ก
๊ฒ์ดํ
์ ์ ์ฉํ๊ณ ์๋ก ๋ค๋ฅธ ํด๋ก ๊ฒ์ดํ
๊ทธ๋ฃน์ ํตํฉํจ์ผ๋ก์ ์ ์ฒด ๋์ ์ ๋ ฅ๋ฅผ
์ค์ด๊ณ ์ ํ์๋ค. ์คํ์ ํตํด ๊ธฐ์กด์ ํด๋ก ๊ฒ์ดํ
๋ฐฉ๋ฒ์ ๋นํด ํ๊ท 2.46%(์ต๋
5.00%)์ ์ด ์ ๋ ฅ ์๋น๋ฅผ ์ค์ด๋ ๊ฒ์ ํ์ธํ์๋ค.
๋ ๋ฒ์งธ๋ก ํ๋ฆฝํ๋กญ์ ํด๋ก ์ฃผ๊ธฐ๋ณ ์ํ๋ฅผ ๋ํ๋ด๋ ๊ธด ํ ๊ธ๋ง/์ธํ ๊ธ๋ง ์ํ์ค
๋ฅผ ์งง์ ์๋ฒ ๋ฉ ๋ฒกํฐ๋ก ๋ณํํ๋ ๋ฌธ์ ๋ฅผ ํด๊ฒฐํ์๋ค. ์ด๋ฅผ ํ ๊ธ๋ง ๊ธฐ๋ฐ ํด๋ก ๊ฒ์ด
ํ
์ ์ํ ํ๋ฆฝํ๋กญ ๊ทธ๋ฃนํ์ ์ ์ฉํ์ฌ ํ๋ฆฝํ๋กญ ๊ฐ์ ์ํ ์ ์ฌ์ฑ ํ์ธ์ด ๋ฉ๋ชจ๋ฆฌ
์ฌ์ฉ๋ ๋ฐ ์คํ ์๊ฐ ์ธก๋ฉด์์ ์ค์ง์ ์ผ๋ก ์คํ ๊ฐ๋ฅํ๊ฒ ํ์๋ค. ์ด๋ฅผ ์ํด ๊ธฐ๊ณ
ํ์ต ๊ธฐ๋ฐ์ผ๋ก ์๋์ ํ๋ฆฝํ๋กญ ํ ๊ธ ์ํ์ค๋ฅผ ์์ธกํ๊ธฐ์ ์ถฉ๋ถํ ์ ํํ ์ ์ฐจ์์
์๋ฒ ๋ฉ ๋ฒกํฐ์ ์์ฑ์ ์ ์ํ๋ค. ์ฐ๋ฆฌ๋ ํ ๊ธ๋ง ์ํ์ค ๊ฐ์ ์๊ณ์ด ์ ์ฌ์ฑ์ ๊ณ ๋ ค
ํ๊ธฐ ์ํด ๋๋
ธ์ด์ฆ ์คํ ์ธ์ฝ๋๋ฅผ ์ด์ฉํ์ฌ 5000 ํด๋ก ์ฌ์ดํด์ ํ ๊ธ๋ง ์ํ์ค๋ฅผ
10์ฐจ์์ผ๋ก ์์ถํ๊ณ ์ด๋ฅผ ์ฅ๋จ๊ธฐ ๋ฉ๋ชจ๋ฆฌ ์คํ ์ธ์ฝ๋์ ์
๋ ฅํ์ฌ ์ ์ฒด ์ํ์ค๋ฅผ
๋๋ณํ๋ ์ ์ฐจ์ ์๋ฒ ๋ฉ ๋ฒกํฐ๋ฅผ ์์ฑํ๋ ์ ๊ฒฝ๋ง ๋ชจ๋ธ์ ๊ฐ๋ฐํ์๋ค. ๋ํ ์ฐ๋ฆฌ๋
ํด๋ก ๊ฒ์ดํ
์ ์ํ ๋ ๊ฐ์ง ๋ถ๊ฐ์ ์ธ ์ ๊ฒฝ๋ง ๋ชจ๋ธ์ธ (1) 2๊ฐ์ ์๋ฒ ๋ฉ ๋ฒกํฐ์ 0-
์ํ ํ๋ฅ ์์ฑ์ ์ํ ๊ฒฐํฉ ํ๋ฅ ์์ธก ๋ชจ๋ธ๊ณผ (2) ๋ ๊ฐ์ ์๋ฒ ๋ฉ ๋ฒกํฐ๋ฅผ ๊ฒฐํฉํ์ฌ
์๋ก์ด ์๋ฒ ๋ฉ ๋ฒกํฐ๋ฅผ ์์ธกํ๋ ๊ฒฐํฉ ํน์ง ์์ธก ๋ชจ๋ธ์ ์ ์ํ๋ค. IWLS ๋ฒค์น๋งํฌ
ํ๋ก๋ฅผ ์ด์ฉํ ์คํ์ ํตํด, ๋๋
ธ์ด์ฆ ์คํ ์ธ์ฝ๋๋ง ์ฌ์ฉํ์๋๋ณด๋ค ์ฅ๋จ๊ธฐ ๋ฉ๋ชจ๋ฆฌ
๊ธฐ๋ฐ์ ์คํ ์ธ์ฝ๋๋ฅผ ๊ฒฐํฉํ์ ๋ ์
๋ ฅ ๋ฐ์ดํฐ๋ฅผ ๋ณต์ ์ ํ๋๊ฐ ๋ ์ฐ์ํ ๊ฒ์ ํ
์ธํ์๋ค. ๋ํ ์ฐ๋ฆฌ์ ๋ฐฉ๋ฒ์ด ๊ธฐ์กด์ ํ ๊ธ๋ง ๊ธฐ๋ฐ ํด๋ก ๊ฒ์ดํ
์ ๋นํด ํ๊ท 14.0%
์ ๋์ ์ ๋ ฅ์ ์ค์ผ ์ ์์์ ํ์ธํ์๋ค.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
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
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
ํ๊ตญ ์ฒญ์๋ ์ ์ง๋จ ๋ฐ๋๋ฆผ์ ๋ํ ์ฌ์ธต์ธํฐ๋ทฐ ์ฐ๊ตฌ: ๋ฐ๋๋ฆผ์ ์ ํํ ๋ฐ ์์ ๋ฏธ๋์ด์ ์ญํ ์ ์ค์ฌ์ผ๋ก
๋ณธ ์ฐ๊ตฌ๋ ํ๊ตญ์ ์ฌํ์ ๋ฌธ์ ์ธ ์ฒญ์๋
์ง๋จ ๋ฐ๋๋ฆผ ํ์์ ์ฒญ์๋
๋ค์ ๊ด๊ณ์ ํน์ฑ์ ์ค์ฌ์ผ๋ก ์ฌ์กฐ๋ช
ํ๊ณ ์ ํ์๋ค. ๊ตฌ์ฒด์ ์ผ๋ก๋ ์ง๋จ ๋ฐ๋๋ฆผ ํ์์ด ์ฒญ์๋
๋ค์ ์ฌํ๊ด๊ณ ๊ณผ์ ์์์ ์ด๋ ํ ์์์ผ๋ก ๋ํ๋๊ณ ์๋์ง, ์ด๋ ํ ํน์ง์ ์ง๋๊ณ ์๋์ง, ๊ทธ ๊ณผ์ ์์ ๋ฏธ๋์ด๋ ์ด๋ค ์ญํ ์ ํ๊ณ ์๋์ง ํ์์ ์ผ๋ก ์ดํด๋ณด๊ณ ์ ํ์๋ค. ์ด๋ฅผ ์ํ์ฌ ์์ธ ๋ฐ ๊ฒฝ๊ธฐ๋ ๊ฑฐ์ฃผ ์คํ์ 14๋ช
์ ๋์์ผ๋ก ์ฌ์ธต์ธํฐ๋ทฐ๋ฅผ ์ํํ์๋ค. ์ฐ๊ตฌ๊ฒฐ๊ณผ, ๋ฐ๋๋ฆผํ์์ ์ฒญ์๋
๋ค์ ๋๋ ๊ด๊ณ(๋ฌด๋ฆฌ)์ ๋งฅ๋ฝ์์ ์ธํจ์ดํ ๋ฐ๋๋ฆผ๊ณผ ๊ด๊ณํ ๋ฐ๋๋ฆผ์ผ๋ก ๊ตฌ๋ถํ์ฌ ์ ๊ทผํ ํ์๊ฐ ์์์ ํ์ธํ์๋ค. ์ฒญ์๋
๋ค์ ๋ฐ์ด๋ผ๋ ํน์ํ ํ๊ฒฝ ์์์ ์๊ท๋ชจ์ ๋ฌด๋ฆฌ๋ฅผ ์ง์ด ์น๊ตฌ๊ด๊ณ๋ฅผ ํ์ฑํ๊ณ ์์์ผ๋ฉฐ, ๋ฌด๋ฆฌ๊ฐ ํ์ฑ๋๋ ํ๊ธ ์ด๊ธฐ์ ์ด๋ค ๋ฌด๋ฆฌ์๋ ์ํ์ง ๋ชปํ ํ์์ ๋ค๋ฅธ ํ์๋ค๋ก๋ถํฐ ๋ฐฐ์ ๋์ด ๋ฐ๋๋ฆผ์ ๋์์ด ๋ ์ ์์๋ค. ๋ฐ๋ฉด, ๋ค์ํ ์น๊ตฌ ๋ฌด๋ฆฌ ์์์ ๋ฐ์ํ๋ ๊ฐ๋ฑ์ ๊ณผ์ ์์๋ ๋ฐ๋๋ฆผ์ ๋น๋ฒํ ์ผ์ด๋๊ณ ์์๋ค. ํ์์ ํด๋นํ๋ ๊ด๊ณํ ๋ฐ๋๋ฆผ์ ์ธํจ์ดํ ๋ฐ๋๋ฆผ๊ณผ ๊ตฌ๋ณ๋๋ ๋ช๋ช ํน์ง๋ค์ด ๊ด์ฐฐ๋์๋ค. ๋ณธ ์ฐ๊ตฌ๋ ๋ฐ๋๋ฆผ์ด ๋ฐ์ํ๋ ์ํฉ์ ์์ธ์ธ ๊ตฌ์กฐ์ ํน์ฑ์ ๋ฐ๋ผ ๋ฐ๋๋ฆผ์ ์ ํํ๋ฅผ ์๋ํ ๋ค, ๋ฐ๋๋ฆผ ์ ํ์ ๋ฐ๋ฅธ ํน์ง๊ณผ ๋ฐ์์์ธ, ๊ทธ๋ฆฌ๊ณ ์ด ๊ณผ์ ์์ ๋ฏธ๋์ด๊ฐ ์ด๋ ํ ๊ธฐํญ์ ์ญํ ์ ํ๊ณ ์๋์ง๋ฅผ ๋ฐํ๋ค. ์ด๋ฅผ ํตํด ๋ณธ ์ฐ๊ตฌ๋ ์ฒญ์๋
๋ค ์ฌ์ด์์ ๋ฐ์ํ๋ ๋ฐ๋๋ฆผ ํ์์ ๋๋ฌ๋๋ ๊ฐ์์ ์ธ ์ํฉ๋ง์ ๋ณด๊ณ ๋ชจ๋ ์ง๋จ ๋ฐ๋๋ฆผ์ผ๋ก ๋ช
๋ช
ํ์ฌ ๋จ์ ์ง์ด์๋ ์ ๋๋ฉฐ, ๋ณด๋ค ๊ทผ๋ณธ์ ์ธ ์ฐจ์์์์ ๊ต์ก์ด ์ ํ๋๊ธฐ ์ํด ๋จผ์ ๋ฐ๋๋ฆผ ํ์์ ์ดํดํ๊ธฐ ์ํ ์๊ฐ์ ์ ๋ฐ์ฑ์ด ์ ์คํ ํ์ํจ์ ์ ์ํ๋ค
Insulin resistance and bone age advancement in girls with central precocious puberty
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
ํ๊ตญ์ด ๋จ๋ ์ธ์ด ๋ณํ์ ๊ดํ ์ฐ๊ตฌ : 1960๋ ๋, 2000๋ ๋ ๋ฉ๋ก ์ํ์ ๋ํ๋ ๋จ๋ ์ธ์ด๋ฅผ ๋์์ผ๋ก
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์ธ์ดํ๊ณผ ์ธ์ดํ์ ๊ณต,2004.Maste
Comparative studies in 'Kim Yun-Deok's Gayageum Sanjo and Geomungo Sanjo
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์์
๊ณผ, 2013. 8. ์ด์ง์.๊ฐ์ผ๊ธ์ฐ์กฐ๋ 19์ธ๊ธฐ ๋ง๊ฒฝ์ ๊ฐ๋ฝ์ด ํ์ฑ๋ ์ดํ, ์ฌ๋ฌ ์ธ๋์ ๊ฑธ์ณ ๊ณ์นโคํ๋ ๋์๋ค. ๊ฐ์ผ๊ธ๋ช
์ธ๋ค์ ์ค์น์๊ฒ ๋ฐฐ์ด ๊ฐ๋ฝ์ ์์ ์ ๊ฐ๋ฝ์ ์ถ๊ฐํ๊ณ ๋ค๋ฌ์ด ์์
์ ์ผ๋ก ์์ฑ๋๋ฅผ ๋์ฌ๊ฐ์ผ๋ฉฐ, ์ด๋ ๊ฒ ์์ฑ๋ ๊ฐ๊ฐ์ ๊ฐ๋ฝ์ ๊ทธ ๋ช
์ธ์ ์ด๋ฆ์ ๋ถ์ฌ โโโ๋ฅ๋ผ ๋ถ๋ฅด๊ณ ์๋ค. ์ฐ์กฐ์ ๋ช
์ธ๋ค์ ๋ณดํต ํ ๊ฐ์ง์ ์
๊ธฐ๋ก ์ฐ์กฐ ๊ฐ๋ฝ์ ๊ตฌ์ฑํ์ฌ ์ ํ๋ฅผ ๋จ๊ธฐ๋ ๊ฒ์ด ์ผ๋ฐ์ ์ด๋, ๊น์ค๋์ ์์ ์ ์ด๋ฆ์ผ๋ก ํ๋์ ์
๊ธฐ๊ฐ ์๋ ๊ฐ์ผ๊ธ๊ณผ ๊ฑฐ๋ฌธ๊ณ ๋ ์
๊ธฐ๋ก ๊ฐ๊ธฐ ์ฐ์กฐ์ ์ ํ๋ฅผ ํ์ฑํด ๊ฐ๋ฝ์ ๋จ๊ฒผ๋ค. ๋ณธ ๋
ผ๋ฌธ์์๋ ํ ์ฐ์ฃผ์๊ฐ ๊ฐ๊ธฐ ๋ค๋ฅธ ์
๊ธฐ๋ก ์ฐ์กฐ๋ฅผ ํ์ฑํ ๊ฒฝ์ฐ ๊ทธ ์ ์จ๋ค์ด ์๋ก ์ํฅ์ ๋ฐ์์ ๊ฒ์ด๋ผ๋ ์ ์ ํ์ ๊ทธ ์ฐ๊ณ์ฑ์ ์ดํด๋ณด๊ณ ์ ํ์๋ค.
โ
ก์ฅ์์๋ ๊น์ค๋์ ์์ ์ ์ฐ์กฐ์ ์ ์น๊ณผ์ ์ ์ดํด ๋ณด์๋ค. โ
ข์ฅ์์ ๋ ์ฐ์กฐ์ ๊ณตํต์ ์ผ๋ก ํฌํจ๋ ์ง์์กฐโค์ค๋ชจ๋ฆฌโค์์ง๋ชจ๋ฆฌ์ฅ๋จ์ ๋น๊ตโค๋ถ์ํ์๋ค. โ
ฃ์ฅ์์๋ ์์ดํ ์ฅ๋จ์ด์ง๋ง ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ์ ์์ง๋ชจ๋ฆฌ์ ๊ฐ์ ๋ฐ์์ฒด๊ณ๋ก ๋ณผ ์ ์๋ ๊ฐ์ผ๊ธ์ฐ์กฐ์ ์ค์ค๋ชจ๋ฆฌ์ ํ๋ชจ๋ฆฌ๋ฅผ ๋น๊ตโค๋ถ์ํ์๋ค. ๊ทธ๋ฌ๋ ๊ฐ์ผ๊ธ์ฐ์กฐ ๋จ๋ชจ๋ฆฌ์ ๊ฒฝ์ฐ ๋ฐ์์ฒด๊ณ๊ฐ ๋ฌ๋ผ ๋น๊ต ๊ฐ๋ฅํ ์ฅ๋จ์ด ์์ผ๋ฏ๋ก, ์ฐ๊ตฌ์ ๋ฒ์์์ ์ ์ธํ์๋ค.
๊น์ค๋์ด ์ง์ ๋จ๊ธด ์์์ ๊ธฐ์ค์ผ๋ก ๊ฐ์ผ๊ธ์ฐ์กฐ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ๋ฅผ ๋น๊ตํ์ฌ ๊ณตํต์ ์จ๊ณผ ์ ์ฌ์ ์จ์ ์ดํด๋ณธ ๊ฒฐ๊ณผ๋ ๋ค์๊ณผ ๊ฐ๋ค.
๊ฐ์ผ๊ธ์ฐ์กฐ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ์ ๊ฐ ์ ์จ์ ์ฅ๋จ ๋จ์๋ก ๋น๊ตํ ๊ฒฐ๊ณผ ํ๋ชจ๋ฆฌ๋ฅผ ์ ์ธํ ๋ชจ๋ ์ฅ๋จ์์ ๊ณตํต์ ์จ๊ณผ ์ ์ฌ์ ์จ์ด ๋ฐ๊ฒฌ๋์๋ค. ํ๋ชจ๋ฆฌ์์๋ ๋จ ํ๋์ ๊ณตํต์ ์จ๋ง ๋ฐ๊ฒฌ๋์์ผ๋ ๋ฆฌ๋ฌ์ ์ ์ธํ ์๊ณ์ ์กฐํฉ์ด ๋ฐ๋ณตโค๋ณํ๋๋ ๋ถ๋ถ๋ค๋ก ์ ์ฌ์ฑ์ ์ฐพ์๋ณผ ์ ์์๋ค. ์ด๋ ์๋์ ์ผ๋ก ๋๋ฆฐ ์ฅ๋จ์ผ์๋ก ์ ์ฌํ ์์๊ฐ ๋ง์ด ๋ฐ๊ฒฌ๋๊ณ , ์ฅ๋จ์ด ๋นจ๋ผ์ง๋ฉด ๊ฐ ์
๊ธฐ์ ํน์ฑ์ ๋ง์ถ์ด ์ฐ์ฃผํ๋ ์ ์จ์ด ๋ง์์ง๊ธฐ ๋๋ฌธ์ ์ ์จ์ ์ ์ฌ์ฑ์ด ์ ์ด์ง๋ ๊ฒ์ผ๋ก ๋ณด์ธ๋ค.
์ด๋ฒ ์ฐ๊ตฌ์์๋ ๊น์ค๋๋ฅ ๊ฐ์ผ๊ธ์ฐ์กฐ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ ์ ์จ๋ง์ ๋ฒ์๋ก ํ์ฌ ์ ์ฌํ ์ ์จ๋ค์ ์ฐพ์๋ด์์ง๋ง ์ถํ ์๊ณโค๋ฆฌ๋ฌโคํ์โค์ฃผ๋ฒ์ ํน์ง ๋ฑ ๊ฐ ์์
์ ์์๋ณ๋ก ํน์ง์ ์ฐพ์ ๋น๊ตํด ๋ณด๋ ์ฐ๊ตฌ๋ฅผ ์งํํ๋ค๋ฉด ๋์ฑ ์ฌ๋ ๊น์ ๊ฒฐ๊ณผ๋ฅผ ๋์ถํด ๋ผ ์ ์์ ๊ฒ์ผ๋ก ๊ธฐ๋๋๋ค.I. ์๋ก 1
1. ๋ฌธ์ ์ ๊ธฐ ๋ฐ ์ฐ๊ตฌ๋ชฉ์ 1
2. ์ฐ๊ตฌ๋ฒ์ ๋ฐ ์ฐ๊ตฌ๋ฐฉ๋ฒ 3
โ
ก. ๊น์ค๋์ ์์ ์ ์ฐ์กฐ์ ์ ์น๊ณผ์ 7
1. ๊น์ค๋์ ์์ ์ ์์
ํ๋ 7
2. ๊น์ค๋๋ฅ ์ฐ์กฐ์ ์ ์น๊ณผ์ 8
1) ๊น์ค๋๋ฅ ๊ฐ์ผ๊ธ์ฐ์กฐ 8
2) ๊น์ค๋๋ฅ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ 10
โ
ข. ๋์ผ ์ฅ๋จ ๊ฐ ์ ์จ๋น๊ต 12
1. ์ง์์กฐ 12
1) ๊ฐ์ผ๊ธ์ฐ์กฐ ์ง์์กฐ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ ์ง์์กฐ์ ๊ณตํต์ ์จ 13
2) ๊ฐ์ผ๊ธ์ฐ์กฐ ์ง์์กฐ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ ์ง์์กฐ์ ์ ์ฌ์ ์จ 22
2. ์ค๋ชจ๋ฆฌ 42
1) ๊ฐ์ผ๊ธ์ฐ์กฐ ์ค๋ชจ๋ฆฌ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ ์ค๋ชจ๋ฆฌ์ ๊ณตํต์ ์จ 43
2) ๊ฐ์ผ๊ธ์ฐ์กฐ ์ค๋ชจ๋ฆฌ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ ์ค๋ชจ๋ฆฌ์ ์ ์ฌ์ ์จ 46
3. ์์ง๋ชจ๋ฆฌ 64
1) ๊ฐ์ผ๊ธ์ฐ์กฐ ์์ง๋ชจ๋ฆฌ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ ์์ง๋ชจ๋ฆฌ์ ๊ณตํต์ ์จ 65
2) ๊ฐ์ผ๊ธ์ฐ์กฐ ์์ง๋ชจ๋ฆฌ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ ์์ง๋ชจ๋ฆฌ์ ์ ์ฌ์ ์จ 70
โ
ฃ. ์์ด ์ฅ๋จ ๊ฐ ์ ์จ๋น๊ต 78
1. ์ค์ค๋ชจ๋ฆฌ 78
1) ๊ฐ์ผ๊ธ์ฐ์กฐ ์ค์ค๋ชจ๋ฆฌ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ ์์ง๋ชจ๋ฆฌ์ ๊ณตํต์ ์จ 79
2) ๊ฐ์ผ๊ธ์ฐ์กฐ ์ค์ค๋ชจ๋ฆฌ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ ์์ง๋ชจ๋ฆฌ์ ์ ์ฌ์ ์จ 83
2. ํ๋ชจ๋ฆฌ 87
1) ๊ฐ์ผ๊ธ์ฐ์กฐ ํ๋ชจ๋ฆฌ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ ์์ง๋ชจ๋ฆฌ์ ๊ณตํต์ ์จ 88
2) ๊ฐ์ผ๊ธ์ฐ์กฐ ํ๋ชจ๋ฆฌ์ ๊ฑฐ๋ฌธ๊ณ ์ฐ์กฐ ์์ง๋ชจ๋ฆฌ์ ์ ์ฌ์ ์จ 89
โ
ค. ๊ฒฐ๋ก 97
์ฐธ๊ณ ๋ฌธํ 99
Abstract 101
์ฒจ๋ถ์
๋ณด 104Maste
Decomposition of Gender Pay Gap By using Censored Quantile Regression Analysis on Counterfactual Inference
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ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ฒฝ์ ํ๋ถ, 2013. 8. ์ด์๋ฐฐ.ํ๊ตญ์ฌํ์์ ๋จ๋
์๊ธ๋ถํฌ๋ ๊ฐ ๋ถ์๋ณ๋ก ๋งค์ฐ ์ด์ง์ ์ธ ์์์ ๋ณด์ธ๋ค. ๋ฐ๋ผ์ ๊ธฐ์กด์ ์กฐ๊ฑด๋ถ ๊ธฐ๋๊ฐ์ ๊ธฐ๋ฐํ ํ๊ท๋ถ์์ ํตํด์๋ ์ค์ ์๊ธ๋ถํฌ๋ฅผ ํจ๊ณผ์ ์ผ๋ก ๋ถ์ํด๋ผ ์ ์๋ค. ๊ฒ์ด ๋ถ์ ํ๊ท๋ถ์(CQR)์ ์กฐ๊ฑด๋ถ ๋ถ์์ ๋ํ ํ๊ท๋ถ์์ ํตํด ๋ถํฌ์ ์ฒด์ ๋ํ ์ ๋ณด๋ฅผ ๋ณด์กดํ ๋ฟ ์๋๋ผ, ๊ฒ์ด๊ฐ์ผ๋ก์จ ์ต์ ์๊ธ์ ๊ณ ๋ คํ๋ฏ๋ก ์์ ๊ฐ์ ๊ฒฝ์ฐ์ ํจ๊ณผ์ ์ธ ๋ฐฉ๋ฒ๋ก ์ ๋์์ผ๋ก ๊ณ ๋ ค๋ ์ ์๋ค. ๋ณธ ์ฐ๊ตฌ๋ Chernozhukov์ Hong์ 2002๋
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ผ๋ฌธ์์ ์ ์๋ ๋ชจํ์ ์ ์ฉํ์ฌ 2000๋
๊ณผ 2008๋
๋ ํด์ ํ๊ตญ๋
ธ๋ํจ๋(KLIPS) ์๋ฃ๋ฅผ ๋ถ์ํ์๋ค. ๋ํ, ๋ฐ์ฌ์ค ๋ถ์ ๋ฐ ๋ถํด ๋ถ์๋ฒ์ ํตํด ์๊ธ๋ณํ์ ๊ณ ๋ฑ๊ต์ก์ด ์๊ธ์ ๋ฏธ์น๋ ์ํฅ์ ๊ด์ฐฐํ์๋ค. ๋ถ์์ ๊ฒฐ๊ณผ๋ก, ์ฌ์ฑ ๊ณ ๋ฑ๊ต์ก ์ง์์ ์ฑ
์ด ๋น๋ถ๊ฒฉ์ฐจํด์์ ์ฑ๋ณ์๊ธ๊ฒฉ์ฐจ ์ํ์ ๊ธฐ์ฌํ ์ ์์์ ๊ฒฝํ์ ์ผ๋ก ๋
ผ์ฆํ๋ค.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
CClโ์ ์ํ ๊ธ์ฑ ๊ฐ์ฅํด ๋ชจ๋ธ์์ tributylmethyl ammonium ์ ๋ด๊ด๋ง ํฌ๊ณผ๊ณผ์ ์ ๊ธฐ์ ์ฐ๊ตฌ
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ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ๋ํ์ :์ ์ฝํ๊ณผ ์ฝ์ ๊ณผํ์ ๊ณต,2004.Maste