142 research outputs found

    Approximation algorithms for mobile multi-agent sensing problem

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    ํ•™์œ„๋…ผ๋ฌธ (์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› : ๊ณต๊ณผ๋Œ€ํ•™ ์‚ฐ์—…๊ณตํ•™๊ณผ, 2020. 8. ๋ฌธ์ผ๊ฒฝ.Multi-agent systems are generally applicable in a wide diversity of domains, such as robot engineering, computer science, the military, and smart cities. In particular, the mobile multi-agent sensing problem can be defined as a problem of detecting events occurring in a large number of nodes using moving agents. In this thesis, we introduce a mobile multi-agent sensing problem and present a mathematical formulation. The model can be represented as a submodular maximization problem under a partition matroid constraint, which is NP-hard in general. The optimal solution of the model can be considered computationally intractable. Therefore, we propose two approximation algorithms based on the greedy approach, which are global greedy and sequential greedy algorithms, respectively. We present new approximation ratios of the sequential greedy algorithm and prove tightness of the ratios. Moreover, we show that the sequential greedy algorithm is competitive with the global greedy algorithm and has advantages of computation times. Finally, we demonstrate the performances of our results through numerical experiments.๋‹ค์ค‘ ์—์ด์ „ํŠธ ์‹œ์Šคํ…œ์€ ์ผ๋ฐ˜์ ์œผ๋กœ ๋กœ๋ด‡ ๊ณตํ•™, ์ปดํ“จํ„ฐ ๊ณผํ•™, ๊ตฐ์‚ฌ ๋ฐ ์Šค๋งˆํŠธ ๋„์‹œ์™€ ๊ฐ™์€ ๋‹ค์–‘ํ•œ ๋ถ„์•ผ์— ์ ์šฉํ•  ์ˆ˜ ์žˆ๋‹ค. ํŠนํžˆ, ๋ชจ๋ฐ”์ผ ๋‹ค์ค‘ ์—์ด์ „ํŠธ ๊ฐ์ง€ ๋ฌธ์ œ๋Š” ์›€์ง์ด๋Š” ์—์ด์ „ํŠธ๋ฅผ ์ด์šฉํ•ด ๋งŽ์€ ์ˆ˜์˜ ๋…ธ๋“œ์—์„œ ๋ฐœ์ƒํ•˜๋Š” ์ด๋ฒคํŠธ๋ฅผ ๊ฐ์ง€ํ•˜๋Š” ๋ฌธ์ œ๋กœ ์ •์˜ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ๋ชจ๋ฐ”์ผ ๋‹ค์ค‘ ์—์ด์ „ํŠธ ๊ฐ์ง€ ๋ฌธ์ œ์˜ ์ˆ˜ํ•™์  ๊ณต์‹์„ ์ œ์•ˆํ•œ๋‹ค. ์ด ๋ฌธ์ œ๋Š” ์ผ๋ฐ˜์ ์œผ๋กœ NP-๋‚œํ•ด ๋ฌธ์ œ์ธ ๋ถ„ํ•  ๋งคํŠธ๋กœ์ด๋“œ ์ œ์•ฝ ํ•˜์—์„œ ํ•˜์œ„ ๋ชจ๋“ˆ ํ•จ์ˆ˜์˜ ์ตœ๋Œ€ํ™” ๋ฌธ์ œ๋กœ ํ‘œํ˜„ํ•  ์ˆ˜ ์žˆ๋‹ค. ๋ฌธ์ œ์˜ ์ตœ์ ํ•ด๋Š” ์ž…๋ ฅ ๋ฐ์ดํ„ฐ์˜ ํฌ๊ธฐ๊ฐ€ ์ปค์งˆ์ˆ˜๋ก ๋ณดํ†ต ํ•ฉ๋ฆฌ์ ์ธ ์‹œ๊ฐ„ ์ด๋‚ด์— ๊ณ„์‚ฐํ•˜๊ธฐ ์–ด๋ ต๋‹ค. ๋”ฐ๋ผ์„œ ๋ณธ ๋…ผ๋ฌธ์—์„œ๋Š” ํƒ์š•์  ์ ‘๊ทผ ๋ฐฉ์‹์— ๊ธฐ์ดˆํ•œ ๋‘ ๊ฐ€์ง€ ๊ทผ์‚ฌ ์•Œ๊ณ ๋ฆฌ์ฆ˜ (์ „์—ญ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜, ์ˆœ์ฐจ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜)์„ ์ œ์•ˆํ•œ๋‹ค. ๋˜ํ•œ, ์ˆœ์ฐจ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜์˜ ์ƒˆ๋กœ์šด ๊ทผ์‚ฌ ๋น„์œจ์„ ์ฆ๋ช…ํ•˜๊ณ  ๊ทผ์‚ฌ ๋น„์œจ์— ์ •ํ™•ํ•˜๊ฒŒ ์ผ์น˜ํ•˜๋Š” ์ธ์Šคํ„ด์Šค๋ฅผ ์ œ์‹œํ•œ๋‹ค. ๋˜ํ•œ, ์ˆ˜์น˜ ์‹คํ—˜ ๊ฒฐ๊ณผ๋กœ ์ˆœ์ฐจ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜์€ ํšจ๊ณผ์ ์ธ ํ•ด๋ฅผ ์ฐพ์•„์ค„ ๋ฟ ์•„๋‹ˆ๋ผ, ์ „์—ญ ํƒ์š• ์•Œ๊ณ ๋ฆฌ์ฆ˜๊ณผ ๋น„๊ตํ•ด ๊ณ„์‚ฐ ์‹œ๊ฐ„์˜ ์ด์ ์„ ๊ฐ€์ง€๊ณ  ์žˆ์Œ์„ ํ™•์ธํ•œ๋‹ค.Chapter 1 Introduction 1 Chapter 2 Literature Review 4 Chapter 3 Problem statement 7 Chapter 4 Algorithms and approximation ratios 11 Chapter 5 Computational Experiments 22 Chapter 6 Conclusions 30 Bibliography 31 ๊ตญ๋ฌธ์ดˆ๋ก 40Maste

    ๊ตญ์ œ์ •์น˜์  ๊ฐˆ๋“ฑ์ด ๋‚จ๋ถํ•œ ๊ฒฝ์ œ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ: ์ฃผ์‹ ์‹œ์žฅ๊ณผ ๋ฌด์—ญ์— ๋Œ€ํ•œ ๋ถ„์„

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์‚ฌํšŒ๊ณผํ•™๋Œ€ํ•™ ๊ฒฝ์ œํ•™๋ถ€,2020. 2. ๊น€๋ณ‘์—ฐ.๋ณธ ๋…ผ๋ฌธ์€ ๊ตญ์ œ์ •์น˜์  ๊ฐˆ๋“ฑ์˜ ๊ฒฝ์ œ์  ์˜ํ–ฅ์— ๋Œ€ํ•ด ๋‚จ๋ถํ•œ์˜ ์‚ฌ๋ก€๋ฅผ ์ค‘์‹ฌ์œผ๋กœ ์‚ดํŽด๋ณธ๋‹ค. ํŠนํžˆ ๋ถํ•œ ๊ด€๋ จ ๋ฆฌ์Šคํฌ๊ฐ€ ๋‚จํ•œ์˜ ์ฃผ์‹ ์‹œ์žฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ๊ณผ ๊ฒฝ์ œ ์ œ์žฌ๊ฐ€ ๋ถํ•œ์˜ ๋ฌด์—ญ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์— ์ฃผ๋ชฉํ•˜์˜€๋‹ค. ์ „์ฒด ๋…ผ๋ฌธ์€ ๊ฐœ๋ณ„์ ์ธ ์†Œ์ฃผ์ œ๋ฅผ ๋‹ค๋ฃจ๋Š” ์„ธ ํŽธ์˜ ์‹ค์ฆ ์—ฐ๊ตฌ๋กœ ๊ตฌ์„ฑ๋œ๋‹ค. ์ฒซ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ๋‚จํ•œ์˜ ๊ธฐ์—… ์ฃผ๊ฐ€ ์ˆ˜์ต๋ฅ ์ด ๋ถํ•œ ๋ฆฌ์Šคํฌ์— ์–ด๋–ป๊ฒŒ ๋ฐ˜์‘ํ•˜๋Š”์ง€ ๋ถ„์„ํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด ๋‚จํ•œ ์–ธ๋ก ์˜ ๋ถํ•œ ๊ด€๋ จ ๋ณด๋„ ์ž๋ฃŒ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ์›”๋ณ„ ๋ถํ•œ ๋ฆฌ์Šคํฌ ์ง€์ˆ˜๋ฅผ ์ž‘์„ฑํ•˜์˜€๋‹ค. ์ด ์ง€์ˆ˜๋Š” ๋‚จ๋ถ ๊ด€๊ณ„์˜ ๊ธด์žฅ์ด ํ™•๋Œ€๋˜๊ฑฐ๋‚˜ ์™„ํ™”๋˜๋Š” ๊ฒฝ์šฐ ์–ธ๋ก ๋ณด๋„์— ๋“ฑ์žฅํ•  ๊ฒƒ์œผ๋กœ ๊ธฐ๋Œ€๋˜๋Š” ํ‚ค์›Œ๋“œ๋ฅผ ํฌํ•จํ•œ ๊ธฐ์‚ฌ์˜ ๋นˆ๋„์ˆ˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ์‚ฐ์ถœ๋œ๋‹ค. 1999~2018๋…„์˜ ์–ธ๋ก  ๋ณด๋„์ž๋ฃŒ๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, ๋ถํ•œ ๋ฐœ ๋ฆฌ์Šคํฌ๋Š” ํ•ต/๋ฏธ์‚ฌ์ผ ์‹คํ—˜, ๊ตฐ์‚ฌ๋„๋ฐœ ๋“ฑ ์ด๋ฒคํŠธ ์‹œ์ ์— ๊ธ‰์ฆํ•˜๋ฉฐ, ๋ฐ˜๋Œ€๋กœ ์ •์ƒํšŒ๋‹ด, 6์ž ํšŒ๋‹ด ๋“ฑ ๋Œ€ํ™”์˜ ์‹œ๊ธฐ์—๋Š” ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ธฐ์—… ์ฃผ๊ฐ€ ์ˆ˜์ต๋ฅ ์„ ์ข…์†๋ณ€์ˆ˜๋กœ ํ•œ ํšŒ๊ท€ ๋ถ„์„์—์„œ๋Š” ๊ตญ๋‚ด ํˆฌ์ž์ž์˜ ์ฃผ์‹ ๋ณด์œ  ๋น„์œจ์ด ๋†’์€ ๊ธฐ์—…์ผ์ˆ˜๋ก, ์ž์‚ฐ ๊ทœ๋ชจ๊ฐ€ ํฌ๊ณ  ๊ณ ์ •์ž์‚ฐ์˜ ๋น„์ค‘์ด ๋†’์€ ๊ธฐ์—…์ผ์ˆ˜๋ก, ๋‚จ๋ถ๊ฒฝํ˜‘์— ๊ด€์—ฌํ•œ ๊ฒฝํ—˜์ด ์žˆ๋Š” ๊ธฐ์—…์ผ์ˆ˜๋ก ๋ถํ•œ ๊ด€๋ จ ๋ฆฌ์Šคํฌ์— ๋ฏผ๊ฐํ•˜๊ฒŒ ๋ฐ˜์‘ํ•˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‘ ๋ฒˆ์งธ ์žฅ์—์„œ๋Š” ๋ถํ•œ์— ๋ถ€๊ณผ๋œ ์ฃผ์š” ๊ฒฝ์ œ ์ œ์žฌ๊ฐ€ ๋ฌด์—ญ์— ์ค€ ์˜ํ–ฅ์„ ๋ฌด์—ญ์˜ ์งˆ์  ์ธก๋ฉด์„ ์ค‘์‹ฌ์œผ๋กœ ๋ถ„์„ํ•œ๋‹ค. ์šฐ์„  1998~2018๋…„์˜ ๋ถํ•œ-์ค‘๊ตญ ๊ฐ„ ๋ฌด์—ญ์„ ์™ธ์—ฐ์  ํ™•์žฅ ์ˆ˜์ค€(extensive margin), ์ƒ๋Œ€ ๊ฐ€๊ฒฉ(relative unit price), ๋ฌผ๋Ÿ‰(quantity)์œผ๋กœ ๋ถ„ํ•ดํ•˜๊ณ , ์ด ์ค‘ ๋ฌด์—ญ์˜ ์งˆ์  ์ธก๋ฉด์œผ๋กœ ๋ณผ ์ˆ˜ ์žˆ๋Š” ์™ธ์—ฐ์  ํ™•์žฅ ์ˆ˜์ค€๊ณผ ์ƒ๋Œ€ ๊ฐ€๊ฒฉ ์ง€์ˆ˜์˜ ๋ณ€ํ™”์— ์ฃผ๋ชฉํ•˜์˜€๋‹ค. ์ด๋ฅผ ํ†ตํ•ด, ๋ถํ•œ์˜ ๋Œ€์ค‘ ์ˆ˜์ถœ์ด ์ง€๋‚œ 20๋…„๊ฐ„ ์–‘์ ์œผ๋กœ ์„ฑ์žฅํ•˜์˜€์„ ๋ฟ ์งˆ์ ์œผ๋กœ๋Š” ์ •์ฒด๋˜์–ด ์žˆ๊ฑฐ๋‚˜ ์˜คํžˆ๋ ค ํ›„ํ‡ดํ–ˆ๋‹ค๋Š” ์‚ฌ์‹ค์„ ํ™•์ธํ•˜์˜€๋‹ค. ํšŒ๊ท€๋ถ„์„์—์„œ๋Š” ๋ถํ•œ์˜ ๋ฌด์—ญ์„ ์ง์ ‘์ ์œผ๋กœ ํƒ€๊ฒฉํ•˜๊ณ ์ž ํ•œ ํ•œ๊ตญ๊ณผ ์ผ๋ณธ์˜ ๋…์ž ์ œ์žฌ ๋ฐ 2017๋…„ UN์•ˆ๋ณด๋ฆฌ์—์„œ ๊ฒฐ์˜๋œ ๋‹ค์ž ์ œ์žฌ๋ฅผ ํ•ต์‹ฌ ์„ค๋ช… ๋ณ€์ˆ˜๋กœ ์„ค์ •ํ•˜์˜€๊ณ , ๋ถ„์„ ๋ฐฉ๋ฒ•์œผ๋กœ๋Š” ์ „๊ธฐ ์ข…์† ๋ณ€์ˆ˜๊ฐ€ ํฌํ•จ๋œ ๋™์  ํŒจ๋„ ๋ชจํ˜•(dynamic panel model)์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ์ถ”์ • ๊ฒฐ๊ณผ, 2017๋…„ UN์˜ ์ œ์žฌ๊ฐ€ ๋ถํ•œ์˜ ๋Œ€์ค‘ ์ˆ˜์ถœ์—์„œ ํ’ˆ๋ชฉ์˜ ์™ธ์—ฐ์  ํ™•์žฅ ์ˆ˜์ค€์„ ์ถ•์†Œ ์‹œํ‚จ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ 2003๋…„ ์ผ๋ณธ์˜ ์ œ์žฌ๋Š” ์ค‘๊ตญ ์ˆ˜์ž… ์‹œ์žฅ์—์„œ ๋ถํ•œ ์ƒ์‚ฐํ’ˆ์˜ ์ƒ๋Œ€ ๊ฐ€๊ฒฉ์„ ์œ ์˜ํ•˜๊ฒŒ ํ•˜๋ฝ์‹œํ‚จ ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค. ์ถ”๊ฐ€์ ์ธ ํšŒ๊ท€๋ถ„์„์— ๋”ฐ๋ฅด๋ฉด, ์ด๋Ÿฌํ•œ ์ƒ๋Œ€ ๊ฐ€๊ฒฉ ํ•˜๋ฝ์€ ๋ถ-์ค‘ ๊ฐ„ ๊ฐ€๊ฒฉ ํ˜‘์ƒ๋ ฅ์˜ ์ฐจ์ด์—์„œ ๊ธฐ์ธํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ ๊ฒฐ๊ณผ๋Š” ๋ถํ•œ์— ๋Œ€ํ•œ ๋ฌด์—ญ ์ œ์žฌ๊ฐ€ ๋‹ค๋ฅธ ์ฃผ์š” ๊ต์—ญ๊ตญ๊ณผ์˜ ๊ฑฐ๋ž˜ ๊ด€๊ณ„๋ฅผ ์ฐจ๋‹จํ•˜๊ณ  ์ค‘๊ตญ์— ๋Œ€ํ•œ ์˜์กด๋„๋ฅผ ์ง€๋‚˜์น˜๊ฒŒ ๋†’์ด๋ฉด์„œ ์•”๋ฌต์  ๋น„์šฉ์„ ๋ฐœ์ƒ์‹œํ‚ค๊ณ  ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. ๋งˆ์ง€๋ง‰ ์žฅ์—์„œ๋Š” ๋‚จํ•œ์˜ 5.24 ์กฐ์น˜๋ฅผ ํšŒํ”ผํ•˜๊ธฐ ์œ„ํ•œ ๋ถ-์ค‘ ๊ฐ„์˜ ์šฐํšŒ๋ฌด์—ญ ๊ทœ๋ชจ๋ฅผ ์ถ”์ •ํ•˜์˜€๋‹ค. ์ด ์—ฐ๊ตฌ๋Š” ์ค‘๊ตญ์˜ ๊ธฐ์—…-ํ’ˆ๋ชฉ ๋‹จ์œ„์˜ ์ž๋ฃŒ๋ฅผ ์‚ฌ์šฉํ•˜์—ฌ ํ˜„์žฌ๊นŒ์ง€ ๋ถ-์ค‘ ๋ฌด์—ญ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ ์ค‘ ๊ฐ€์žฅ ๋ฏธ์‹œ์  ์ˆ˜์ค€์˜ ์‹ค์ฆ๋ถ„์„ ๊ฒฐ๊ณผ๋ฅผ ์ œ์‹œํ•œ๋‹ค. ๋ถ„์„ ๋ฐฉ๋ฒ•์€ ์ด์ค‘ ์ฐจ๋ถ„๋ฒ•(difference-in-difference estimation)์„ ์‚ฌ์šฉํ•˜์˜€์œผ๋ฉฐ, ๋Œ€ ๋‚จํ•œ ์ˆ˜์ถœ๊ณผ ๋Œ€ ๋ถํ•œ ์ˆ˜์ž…์ด ๋™์‹œ์— ๋ฐœ์ƒํ•œ ๊ธฐ์—…-ํ’ˆ๋ชฉ๋“ค์„ ์ฒ˜์น˜๊ทธ๋ฃน์œผ๋กœ ์„ค์ •ํ•˜์—ฌ 2010๋…„ ์ „ํ›„์˜ ๋ณ€ํ™”๋ฅผ ์ถ”์ •ํ•˜์˜€๋‹ค. ๋ถ„์„ ๊ฒฐ๊ณผ, ๋ถํ•œ์˜ ์ค‘๊ตญ์„ ๊ฒฝ์œ ํ•œ ๋‚จํ•œ์œผ๋กœ์˜ ๊ฐ„์ ‘ ์ˆ˜์ถœ์€ 2010๋…„ 5.24 ์กฐ์น˜ ์ดํ›„ ์œ ์˜ํ•˜๊ฒŒ ์ฆ๊ฐ€ํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ์‚ฐ์—…๋ณ„๋กœ ๋‚˜๋ˆ„์–ด ๋ณด๋ฉด, ์ด๋Ÿฌํ•œ ์šฐํšŒ ๋ฌด์—ญ์€ ์ฃผ๋กœ ์˜๋ฅ˜ ์ž„๊ฐ€๊ณต ๋ถ€๋ฌธ์— ์ง‘์ค‘๋˜์–ด ์žˆ์œผ๋ฉฐ ๊ทธ ๊ทœ๋ชจ๋Š” ์ œ์žฌ ์ดํ›„ ๋ถํ•œ์˜ ๋Œ€๋‚จ ์ง์ ‘ ์ˆ˜์ถœ ๊ฐ์†Œ๋ถ„์˜ 25%์— ๋‹ฌํ•˜๋Š” ๊ฒƒ์œผ๋กœ ์ถ”์ •๋˜์—ˆ๋‹ค.This dissertation investigates the economic impacts of international conflict, focusing on the cases of the two Koreas. Specifically, it examines the effects of North Korea-related risks on the South Korean stock market and economic sanctions on North Korea's foreign trade. It consists of three empirical studies covering subtopics. The first chapter analyzes how South Korean stock returns respond to North Korea-related risk. To do this, a monthly index for geopolitical risk from North Korea is constructed using South Korean media coverage database. The index is based on the frequency of articles containing keywords that are likely to appear in the media when inter-Korean tensions escalate or ease. Analysis of the media coverage from 1999 to 2018 show that the geopolitical risk index sharply increases in the occurrences of nuclear tests, missile launches, and military confrontations, while decreases significantly at around the times of summit meetings and multilateral talks. In the regression analysis, it is found that geopolitical risk related to North Korea has more negative effects on stock returns of firms with a higher share of domestic investors, larger assets and a higher proportion of fixed assets. It is also found that stock prices of companies involved in inter-Korean economic cooperation exhibit a more sensitive response to the North Korea risk. Chapter โ…ก explores the impact of economic sanctions on North Koreas foreign trade, focusing on the quality of trade. It decomposes the trade between North Korea and China into the extensive margin, relative unit price and quantity, over the periods 1998-2018. Then it estimates sanction-induced changes in the former two elements of North Koreas export to China. The decomposition results show that the growth of North Koreas export to China is mostly attributed to the growth in quantity rather than quality. In the regression analysis, sanctions imposed by South Korea, Japan and the United Nations Security Council (UNSC) are used as key treatments. It is found that the UN sanctions in 2017 reduce the extensive margin in North Korean exports, and Japanese sanctions in 2003 have lowered the relative prices of North Korean products in the Chinese import market. The price impacts of sanctions are found to be associated with the bargaining power of China over North Korea. The findings suggest that trade sanctions against North Korea have created implicit costs by preventing North Korea from trading with alternative partners and increasing reliance on China. The last chapter estimates the size of the transit trade between North Korea and China to circumvent the sanctions imposed by South Korea. It exploits firm-product level variations in Chinese trade data to present micro evidence of the sanction-bypassing trade. Specifically, the transit trade is identified only when a firm import a product from North Korea and export the same product to South Korea in the same period. The difference-in-difference estimation results show that indirect exports from North Korea to South Korea via China are increased significantly by the 5.24 measures in 2010. The increase in North Koreas indirect export of apparels, in particular, accounts for a 25% of the decrease in North Korea's direct exports to South Korea.Introduction 1 Chapter โ… . Geopolitical Risk from North Korea and Stock Market Reaction 4 1. Introduction 4 2. Related Literature 6 2.1. News-based Uncertainty Index 6 2.2. The Effects of Geopolitical Risk from North Korea 7 3. Measuring Geopolitical Risk from North Korea 7 3.1. Definition and Scope of Geopolitical Risk 7 3.2. Data and Methodology 9 3.3. Evaluating the GPRNK Index 12 4. Geopolitical Risk and Firm-level Stock Returns 20 4.1. Empirical Framework 20 4.2. Data and Descriptive Statistics 23 4.3. Baseline Results 26 4.4. Robustness Check 32 5. Conclusion 40 Chapter โ…ก. Decomposing North Koreas Trade with China and Revisiting Sanction Effects 41 1. Introduction 41 2. Decomposing North Koreas Trade with China 46 2.1. Data 47 2.2. Methodology 50 2.3. Decomposition Results 52 3. Panel Regression Analysis 59 3.1. Empirical Framework 59 3.2. Baseline Results 63 3.3. Possible Channels 66 3.4. Robustness Check 71 4. Conclusion 73 Chapter โ…ข. The Role of Chinese firms in Bypassing Sanctions on North Korea 75 1. Introduction 75 2. Data 78 2.1. Chinese Custom Trade Data 78 2.2. Stylized Facts 81 3. Empirical Strategy 83 4. Regression Results 86 4.1. North Koreas Indirect Exports via China 86 4.2. The Effects of Sanctions on the Indirect Exports 90 5. Conclusion 93 Concluding Remarks 95 References 98 Appendices 107 A1. Supplementary Materials for Chapter 1 107 A2. Supplementary Materials for Chapter โ…ก 117 A3. Supplementary Materials for Chapter โ…ข 121Docto

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ํ†ต๊ณ„ํ•™๊ณผ, 2022. 8. ์˜คํฌ์„.Over the decades, parametric dimension reduction methods have been actively developed for non-Euclidean data analysis. Examples include Fletcher et al., 2004; Huckemann et al., 2010; Jung et al., 2011; Jung et al., 2012; Zhang et al., 2013. Sometimes the methods are not enough to capture the structure of data. This dissertation presents newly developed nonparametric dimension reductions for data observed on manifold, resulting in more flexible fits. More precisely, the main focus is on the generalizations of principal curves into Riemannian manifold. The principal curve is considered as a nonlinear generalization of principal component analysis (PCA). The dissertation consists of four main parts as follows. First, the approach given in Chapter 3 lie in the same lines of Hastie (1984) and Hastie and Stuetzle (1989) that introduced the definition of original principal curve on Euclidean space. The main contributions of this study can be summarized as follows: (a) We propose both extrinsic and intrinsic approaches to form principal curves on spheres. (b) We establish the stationarity of the proposed principal curves on spheres. (c) In extensive numerical studies, we show the usefulness of the proposed method through real seismological data and real Human motion capture data as well as simulated data on 2-sphere, 4-sphere. Secondly, As one of further work in the previous approach, a robust nonparametric dimension reduction is proposed. To this ends, absolute loss and Huber loss are used rather than L2 loss. The contributions of Chapter 4 can be summarized as follows: (a) We study robust principal curves on spheres that are resistant to outliers. Specifically, we propose absolute-type and Huber-type principal curves, which go through the median of data, to robustify the principal curves for a set of data which may contain outliers. (b) For a theoretical aspect, the stationarity of the robust principal curves is investigated. (c) We provide practical algorithms for implementing the proposed robust principal curves, which are computationally feasible and more convenient to implement. Thirdly, An R package 'spherepc' comprehensively providing dimension reduction methods on a sphere is introduced with details for possible reproducible research. To the best of our knowledge, no available R packages offer the methods of dimension reduction and principal curves on a sphere. The existing R packages providing principal curves, such as 'princurve' and 'LPCM', are available only on Euclidean space. In addition, most nonparametric dimension reduction methods on manifold involve somewhat complex intrinsic optimizations. The proposed R package 'spherepc' provides the state-of-the-art principal curve technique on the sphere and comprehensively collects and implements the existing techniques. Lastly, for an effective initial estimate of complex structured data on manifold, local principal geodesics are first provided and the method is applied to various simulated and real seismological data. For variance stabilization and theoretical investigations for the procedure, nextly, the focus is on the generalization of Kรฉgl (1999); Kรฉgl et al., (2000), which provided the new definition of principal curve on Euclidean space, into generic Riemannian manifolds. Theories including consistency and convergence rate of the procedure by means of empirical risk minimization principle, are further established on generic Riemannian manifolds. The consequences on the real data analysis and simulation study show the promising characteristics of the proposed approach.๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์€ ๋‹ค์–‘์ฒด ์ž๋ฃŒ์˜ ๋ณ€๋™์„ฑ์„ ๋”์šฑ ํšจ๊ณผ์ ์œผ๋กœ ์ฐพ์•„๋‚ด๊ธฐ ์œ„ํ•ด, ๋‹ค์–‘์ฒด ์ž๋ฃŒ์˜ ์ƒˆ๋กœ์šด ๋น„๋ชจ์ˆ˜์  ์ฐจ์›์ถ•์†Œ๋ฐฉ๋ฒ•๋ก ์„ ์ œ์‹œํ•˜์˜€๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ์ฃผ๊ณก์„ (principal curves) ๋ฐฉ๋ฒ•์„ ์ผ๋ฐ˜์ ์ธ ๋‹ค์–‘์ฒด ๊ณต๊ฐ„์œผ๋กœ ํ™•์žฅํ•˜๋Š” ๊ฒƒ์ด ์ฃผ์š” ์—ฐ๊ตฌ ์ฃผ์ œ์ด๋‹ค. ์ฃผ๊ณก์„ ์€ ์ฃผ์„ฑ๋ถ„๋ถ„์„(PCA)์˜ ๋น„์„ ํ˜•์  ํ™•์žฅ ์ค‘ ํ•˜๋‚˜์ด๋ฉฐ, ๋ณธ ํ•™์œ„๋…ผ๋ฌธ์€ ํฌ๊ฒŒ ๋„ค ๊ฐ€์ง€์˜ ์ฃผ์ œ๋กœ ์ด๋ฃจ์–ด์ ธ ์žˆ๋‹ค. ์ฒซ ๋ฒˆ์งธ๋กœ, Hastie (1984), Hastie and Stuetzle (1989}์˜ ๋ฐฉ๋ฒ•์„ ์ž„์˜์˜ ์ฐจ์›์˜ ๊ตฌ๋ฉด์œผ๋กœ ํ‘œ์ค€์ ์ธ ๋ฐฉ์‹์œผ๋กœ ํ™•์žฅํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ ์ฃผ์ œ์˜ ๊ณตํ—Œ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. (a) ์ž„์˜์˜ ์ฐจ์›์˜ ๊ตฌ๋ฉด์—์„œ ๋‚ด์žฌ์ , ์™ธ์žฌ์ ์ธ ๋ฐฉ์‹์˜ ์ฃผ๊ณก์„  ๋ฐฉ๋ฒ•์„ ๊ฐ๊ฐ ์ œ์•ˆํ•œ๋‹ค. (b) ๋ณธ ๋ฐฉ๋ฒ•์˜ ์ด๋ก ์  ์„ฑ์งˆ(์ •์ƒ์„ฑ)์„ ๊ทœ๋ช…ํ•œ๋‹ค. (c) ์ง€์งˆํ•™์  ์ž๋ฃŒ ๋ฐ ์ธ๊ฐ„ ์›€์ง์ž„ ์ž๋ฃŒ ๋“ฑ์˜ ์‹ค์ œ ์ž๋ฃŒ์™€ 2์ฐจ์›, 4์ฐจ์› ๊ตฌ๋ฉด์œ„์˜ ์‹œ๋ฎฌ๋ ˆ์ด์…˜ ์ž๋ฃŒ์— ๋ณธ ๋ฐฉ๋ฒ•์„ ์ ์šฉํ•˜์—ฌ, ๊ทธ ์œ ์šฉ์„ฑ์„ ๋ณด์ธ๋‹ค. ๋‘ ๋ฒˆ์งธ๋กœ, ์ฒซ ๋ฒˆ์งธ ์ฃผ์ œ์˜ ํ›„์† ์—ฐ๊ตฌ ์ค‘ ํ•˜๋‚˜๋กœ์„œ, ๋‘๊บผ์šด ๊ผฌ๋ฆฌ ๋ถ„ํฌ๋ฅผ ๊ฐ€์ง€๋Š” ์ž๋ฃŒ์— ๋Œ€ํ•˜์—ฌ ๊ฐ•๊ฑดํ•œ ๋น„๋ชจ์ˆ˜์  ์ฐจ์›์ถ•์†Œ ๋ฐฉ๋ฒ•์„ ์ œ์•ˆํ•œ๋‹ค. ์ด๋ฅผ ์œ„ํ•ด, L2 ์†์‹คํ•จ์ˆ˜ ๋Œ€์‹ ์— L1 ๋ฐ ํœด๋ฒ„(Huber) ์†์‹คํ•จ์ˆ˜๋ฅผ ํ™œ์šฉํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ ์ฃผ์ œ์˜ ๊ณตํ—Œ์€ ๋‹ค์Œ๊ณผ ๊ฐ™๋‹ค. (a) ์ด์ƒ์น˜์— ๋ฏผ๊ฐํ•˜์ง€ ์•Š์€ ๊ฐ•๊ฑดํ™”์ฃผ๊ณก์„ (robust principal curves)์„ ์ •์˜ํ•œ๋‹ค. ๊ตฌ์ฒด์ ์œผ๋กœ, ์ž๋ฃŒ์˜ ๊ธฐํ•˜์  ์ค‘์‹ฌ์ ์„ ์ง€๋‚˜๋Š” L1 ๋ฐ ํœด๋ฒ„ ์†์‹คํ•จ์ˆ˜์— ๋Œ€์‘๋˜๋Š” ์ƒˆ๋กœ์šด ์ฃผ๊ณก์„ ์„ ์ œ์•ˆํ•œ๋‹ค. (b) ์ด๋ก ์ ์ธ ์ธก๋ฉด์—์„œ, ๊ฐ•๊ฑดํ™”์ฃผ๊ณก์„ ์˜ ์ •์ƒ์„ฑ์„ ๊ทœ๋ช…ํ•œ๋‹ค. (c) ๊ฐ•๊ฑดํ™”์ฃผ๊ณก์„ ์„ ๊ตฌํ˜„ํ•˜๊ธฐ ์œ„ํ•ด ๊ณ„์‚ฐ์ด ๋น ๋ฅธ ์‹ค์šฉ์ ์ธ ์•Œ๊ณ ๋ฆฌ์ฆ˜์„ ์ œ์•ˆํ•œ๋‹ค. ์„ธ ๋ฒˆ์งธ๋กœ, ๊ธฐ์กด์˜ ์ฐจ์›์ถ•์†Œ๋ฐฉ๋ฒ• ๋ฐ ๋ณธ ๋ฐฉ๋ฒ•๋ก ์„ ์ œ๊ณตํ•˜๋Š” R ํŒจํ‚ค์ง€๋ฅผ ๊ตฌํ˜„ํ•˜์˜€์œผ๋ฉฐ ์ด๋ฅผ ๋‹ค์–‘ํ•œ ์˜ˆ์ œ ๋ฐ ์„ค๋ช…๊ณผ ํ•จ๊ป˜ ์†Œ๊ฐœํ•œ๋‹ค. ๋ณธ ๋ฐฉ๋ฒ•๋ก ์˜ ๊ฐ•์ ์€ ๋‹ค์–‘์ฒด ์œ„์—์„œ์˜ ๋ณต์žกํ•œ ์ตœ์ ํ™” ๋ฐฉ์ •์‹์„ ํ’€์ง€์•Š๊ณ , ์ง๊ด€์ ์ธ ๋ฐฉ์‹์œผ๋กœ ๊ตฌํ˜„ ๊ฐ€๋Šฅํ•˜๋‹ค๋Š” ์ ์ด๋‹ค. R ํŒจํ‚ค์ง€๋กœ ๊ตฌํ˜„๋˜์–ด ์ œ๊ณต๋œ๋‹ค๋Š” ์ ์ด ์ด๋ฅผ ๋ฐฉ์ฆํ•˜๋ฉฐ, ๋ณธ ํ•™์œ„ ๋…ผ๋ฌธ์˜ ์—ฐ๊ตฌ๋ฅผ ์žฌํ˜„๊ฐ€๋Šฅํ•˜๊ฒŒ ๋งŒ๋“ ๋‹ค. ๋งˆ์ง€๋ง‰์œผ๋กœ, ๋ณด๋‹ค ๋ณต์žกํ•œ ๊ตฌ์กฐ๋ฅผ ๊ฐ€์ง€๋Š” ๋‹ค์–‘์ฒด ์ž๋ฃŒ์˜ ๊ตฌ์กฐ๋ฅผ ์ถ”์ •ํ•˜๊ธฐ์œ„ํ•ด, ๊ตญ์†Œ์ฃผ์ธก์ง€์„ ๋ถ„์„(local principal geodesics) ๋ฐฉ๋ฒ•์„ ์šฐ์„  ์ œ์•ˆํ•œ๋‹ค. ์ด ๋ฐฉ๋ฒ•์„ ์‹ค์ œ ์ง€์งˆํ•™ ์ž๋ฃŒ ๋ฐ ๋‹ค์–‘ํ•œ ๋ชจ์˜์‹คํ—˜ ์ž๋ฃŒ์— ์ ์šฉํ•˜์—ฌ ๊ทธ ํ™œ์šฉ์„ฑ์„ ๋ณด์˜€๋‹ค. ๋‹ค์Œ์œผ๋กœ, ์ถ”์ •์น˜์˜ ๋ถ„์‚ฐ์•ˆ์ •ํ™” ๋ฐ ์ด๋ก ์  ์ •๋‹นํ™”๋ฅผ ์œ„ํ•˜์—ฌ Kรฉgl (1999), Kรฉgl et al., (2000) ๋ฐฉ๋ฒ•์„ ์ผ๋ฐ˜์ ์ธ ๋ฆฌ๋งŒ๋‹ค์–‘์ฒด๋กœ ํ™•์žฅํ•œ๋‹ค. ๋” ๋‚˜์•„๊ฐ€, ๋ฐฉ๋ฒ•๋ก ์˜ ์ผ์น˜์„ฑ, ์ˆ˜๋ ด์†๋„์™€ ๊ฐ™์€ ์ ๊ทผ์  ์„ฑ์งˆ์„ ๋น„๋กฏํ•˜์—ฌ ๋น„์ ๊ทผ์  ์„ฑ์งˆ์ธ ์ง‘์ค‘๋ถ€๋“ฑ์‹(concentration inequality)์„ ํ†ต๊ณ„์ ํ•™์Šต์ด๋ก ์„ ์ด์šฉํ•˜์—ฌ ๊ทœ๋ช…ํ•œ๋‹ค.1 Introduction 1 2 Preliminaries 8 2.1 Principal curves 8 2.1 Riemannian manifolds and centrality on manifold 10 2.1 Principal curves on Riemannian manifolds 14 3 Spherical principal curves 15 3.1 Enhancement of principal circle for initialization 16 3.2 Proposed principal curves 25 3.3 Numerical experiments 34 3.4 Proofs 45 3.5 Concluding remarks 62 4 Robust spherical principal curves 64 4.1 The proposed robust principal curves 64 4.2 Stationarity of robust spherical principal curves 72 4.3 Numerical experiments 74 4.4 Summary and future work 80 5 spherepc: An R package for dimension reduction on a sphere 84 5.1 Existing methods 85 5.2 Spherical principal curves 91 5.3 Local principal geodesics 94 5.4 Application 99 5.5 Conclusion 101 6 Local principal curves on Riemannian manifolds 112 6.1 Preliminaries 116 6.2 Local principal geodesics 118 6.3 Local principal curves 125 6.4 Real data analysis 133 6.5 Further work 133 7 Conclusion 139 A. Appendix 141 A.1. Appendix for Chapter 3 141 A.2. Appendix for Chapter 4 145 A.3. Appendix for Chapter 6 152 Abstract in Korean 176 Acknowledgement in Korean 179๋ฐ•

    Expression patterns of tenascin-N in the developing mandible

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    Previous studies have demonstrated that tenascin-N belongs to the family of tenascins, which are found in the extracellular matrix of various embryonic tissues, wounds, and tumors. Tenascin is expressed in the embryonic epithelium, including the neural epithelium from which neural crest cells emerge. However, the expression pattern and role of tenascin-N in the craniofacial region remains unknown. In this study, expression patterns of tenascin-N were confirmed in the mouse craniofacial region from embryonic day 12.5 (E12.5) to postnatal 11. In the diastema region, tenascin-N was strongly expressed in the mesenchyme from E12.5 to E14.5. Tenascin-N expression was also detected in the developing tooth germ. From the bell stage to the premature stage, tenascin- N was expressed in the odontoblasts and ameloblasts of the molar tooth germ, and the ameloblasts of the incisor tooth germ. These findings indicate that the spatial and temporal expression of tenascin-N might have a role in proper mouse craniofacial development, especially tooth developmentope

    ํ™ฉํ•ด ์—ฐ์•ˆ ๊ฐฏ๋ฒŒ ํ‡ด์ ํ™˜๊ฒฝ๋‚ด ์œ ๊ธฐํƒ„์†Œ์˜ ์‹œ๊ณต๊ฐ„ ๋ถ„ํฌ ๋ฐ ๊ฑฐ๋™ํŠน์„ฑ ๊ทœ๋ช…

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    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ์ž์—ฐ๊ณผํ•™๋Œ€ํ•™ ์ง€๊ตฌํ™˜๊ฒฝ๊ณผํ•™๋ถ€, 2022. 8. ๊น€์ข…์„ฑ.์ „ ์„ธ๊ณ„์ ์œผ๋กœ ์—ผ์Šต์ง€, ๋งน๊ทธ๋กœ๋ธŒ, ์ž˜ํ”ผ๋ฅผ ํฌํ•จํ•œ ๋ธ”๋ฃจ์นด๋ณธ ์ƒํƒœ๊ณ„๋Š” ์ง€๊ตฌ์˜จ๋‚œํ™”๊ฐ€ ๊ฐ€์†ํ™”๋˜๋Š” ์ƒํ™ฉ์—์„œ ๋†’์€ ์ด์‚ฐํ™”ํƒ„์†Œ ํก์ˆ˜์œจ๋กœ ๊ธฐํ›„ ๋ณ€ํ™”๋ฅผ ์™„ํ™”ํ•˜๋Š”๋ฐ ์ค‘์š”ํ•œ ์—ญํ• ์„ ํ•œ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ธฐ์กด์˜ ๋ธ”๋ฃจ์นด๋ณธ ์ƒํƒœ๊ณ„์˜ ์ด์‚ฐํ™”ํƒ„์†Œ ํก์ˆ˜ ๋Šฅ๋ ฅ์— ๋Œ€ํ•ด ๋งŽ์€ ์—ฐ๊ตฌ๊ฐ€ ์ง„ํ–‰๋˜์—ˆ์œผ๋‚˜, ์ž ์žฌ์  ํƒ„์†Œํก์ˆ˜์›์ธ ๊ฐฏ๋ฒŒ์˜ ํƒ„์†Œ ์ €์žฅ ๋Šฅ๋ ฅ๊ณผ ๊ทธ ์กฐ์ ˆ ์š”์ธ์— ๋Œ€ํ•œ ์—ฐ๊ตฌ๋Š” ๋ฏธ๋น„ํ•œ ์‹ค์ •์ด๋‹ค. ์ด์— ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ํ™ฉํ•ด ๊ฐฏ๋ฒŒ ํ‡ด์ ๋ฌผ๋‚ด ์œ ๊ธฐํƒ„์†Œ์˜ ์‹œ๊ณต๊ฐ„๋ถ„ํฌ์™€ ๊ฑฐ๋™์š”์ธ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์ฒซ์งธ๋กœ, ํ•œ๊ตญ ์กฐ๊ฐ„๋Œ€ ํ‘œ์ธตํ‡ด์ ๋ฌผ๋‚ด ์ด์œ ๊ธฐํƒ„์†Œ์˜ ์‹œ๊ณต๊ฐ„์  ๋ถ„ํฌ์™€ ๊ฑฐ๋™์„ ํ‰๊ฐ€ํ•˜์˜€๋‹ค. ์กฐ๊ฐ„๋Œ€ ํ™˜๊ฒฝ์˜ ๋Œ€ํ‘œ์„ฑ๊ณผ ๊ฐ๊ด€์  ๋น„๊ต๋ฅผ ๋‹ด๋ณดํ•˜๊ธฐ ์œ„ํ•ด ์ „ํ˜•์  ์ž์—ฐ ๊ฐฏ๋ฒŒ 4๊ฐœ์†Œ์™€ ๋‹ซํžŒ ํ•˜๊ตฌ 1๊ฐœ์†Œ๋ฅผ ๋Œ€์ƒ์œผ๋กœ 2018๋…„ 1์›”๋ถ€ํ„ฐ 12์›”๊นŒ์ง€ ์›”๋ณ„ ์กฐ์‚ฌ๋ฅผ ์ˆ˜ํ–‰ํ•˜์˜€๋‹ค. ์กฐ์‚ฌ ๋ฐ ๋ถ„์„๊ฒฐ๊ณผ, ์ด ์œ ๊ธฐํƒ„์†Œ ํ•จ๋Ÿ‰์€ ํ‡ด์ ๋ฌผ ์ž…์žํฌ๊ธฐ(์ž…๋„)๋ฅผ ๋Œ€๋ณ€ํ•˜๋Š” ๋‹ˆ์งˆ ํ•จ๋Ÿ‰์— ๋”ฐ๋ผ ๊ฒฐ์ •๋จ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๊ฐ€๋กœ๋ฆผ๋งŒ๊ณผ ์ˆœ์ฒœ๋งŒ ํ‡ด์ ๋ฌผ์€ ์ €์„œ๋ฏธ์„ธ์กฐ๋ฅ˜ ๋Œ€๋ฐœ์ƒ์— ๊ธฐ์ธํ•˜์—ฌ ๊ฒจ์šธ์ฒ  ์ด ์œ ๊ธฐํƒ„์†Œ ํ•จ๋Ÿ‰์ด ๋†’์•˜๊ณ , ํŠนํžˆ ฮด13C ๊ฐ’์ด ํฌ๊ฒŒ ์ฆ๊ฐ€ํ–ˆ๋‹ค. ๋ฐ˜๋ฉด ๋‚™๋™๊ฐ• ํ•˜๊ตฌ ํ‡ด์ ๋ฌผ์€ ์žฅ๋งˆ์ฒ (9โ€“10์›”)์— ์œก์ƒ์œผ๋กœ๋ถ€ํ„ฐ์˜ ๋‹ด์ˆ˜๋ฐฉ๋ฅ˜ ์˜ํ–ฅ์œผ๋กœ ์ธํ•ด ฮด13C์™€ ฮด15N ๊ฐ’์ด ๊ฐ์†Œํ•˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๋‘˜์งธ๋กœ, ํ•œ๊ตญ ์ „ ์—ฐ์•ˆ ๊ฐฏ๋ฒŒ ํ‡ด์ ๋ฌผ๋‚ด ์œ ๊ธฐํƒ„์†Œ ์ €์žฅ๋Ÿ‰๊ณผ ์œ ๊ธฐํƒ„์†Œ ์นจ์ ๋ฅ ์˜ ์‚ฐ์ •์„ ์œ„ํ•ด ํ˜„์žฅ์กฐ์‚ฌ ์ž๋ฃŒ์™€ ์›๊ฒฉํƒ์‚ฌ ๊ธฐ๋ฒ•์„ ํ™œ์šฉํ•˜์˜€๋‹ค. ์กฐ์‚ฌ์ง€์—ญ์€ ๋™์„œ๋‚จํ•ด 7๊ฐœ ์‹œ๋„(๊ฒฝ๊ธฐ, ์ถฉ๋‚จ, ์ „๋ถ, ์ „๋‚จ, ๊ฒฝ๋‚จ, ๊ฒฝ๋ถ, ๊ฐ•์›) ๋‚ด 21๊ฐœ ์ง€์—ญ์ด์—ˆ์œผ๋ฉฐ, 2017๋…„๋ถ€ํ„ฐ 2020๋…„๊นŒ์ง€ ์ฝ”์–ดํ‡ด์ ๋ฌผ์„ ๋ถ„์„ํ•˜์˜€๊ณ , ์›๊ฒฉ ํƒ์‚ฌ๊ธฐ๋ฒ•์„ ํ†ตํ•ด ๊ฐฏ๋ฒŒ์˜ ํ‡ด์ ๋ฌผ ์„ฑ์ƒ๊ณผ ๋ฉด์ ์„ ์‚ฐ์ •ํ•˜์˜€๋‹ค. ์—ผ์ƒ์‹๋ฌผ์ด ์„œ์‹ํ•˜๋Š” ์—ผ์Šต์ง€์—์„œ๋Š” ์‹๋ฌผ์˜ ์ผ์ฐจ์ƒ์‚ฐ์„ ํ†ตํ•œ ๋†’์€ ํƒ„์†Œ๊ณ ์ • ๋Šฅ๋ ฅ์œผ๋กœ ์ธํ•ด, ๋น„์‹์ƒ ๊ฐฏ๋ฒŒ๋ณด๋‹ค ์ƒ๋Œ€์ ์œผ๋กœ ๋†’์€ ์œ ๊ธฐํƒ„์†Œ ์ €์žฅ๋Ÿ‰์„ ๋ณด์˜€๋‹ค. ํ˜„์žฅ์กฐ์‚ฌ ์ž๋ฃŒ์™€ ์›๊ฒฉํƒ์‚ฌ ๊ธฐ๋ฒ•์„ ํ†ตํ•ด, ๊ตญ๊ฐ€ ์ˆ˜์ค€์—์„œ ํ•œ๊ตญ ์ „ ์—ฐ์•ˆ์˜ ์กฐ๊ฐ„๋Œ€ ๊ฐฏ๋ฒŒ์˜ ์ด ์œ ๊ธฐํƒ„์†Œ ์ €์žฅ๋Ÿ‰ ๋ฐ ์—ฐ๊ฐ„ ์œ ๊ธฐํƒ„์†Œ ์นจ์ ๋ฅ ์„ ์‚ฐ์ •ํ•˜์˜€๋‹ค. ์…‹์งธ๋กœ, ์™ธ๋ž˜์‹๋ฌผ ๊ฐฏ๋ˆํ’€๊ณผ ํ† ์ฐฉ์‹๋ฌผ ๊ฐˆ๋Œ€, ์น ๋ฉด์ดˆ๊ฐ€ ์œ ๊ธฐํƒ„์†Œ ์ฆ๊ฐ€์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ์„ ๋น„๊ตํ•˜๊ธฐ ์œ„ํ•ด ์—ผ์ƒ์‹๋ฌผ ์ข…๋ณ„ ์œ ๊ธฐํƒ„์†Œ ์ €์žฅ๋Ÿ‰ ์ฆ๊ฐ€์œจ์„ ๋ถ„์„ํ•˜์˜€๋‹ค. ์ค‘๊ตญ 7๊ฐœ์ง€์—ญ๊ณผ ํ•œ๊ตญ 12๊ฐœ์ง€์—ญ์„ ๋Œ€์ƒ์œผ๋กœ, ๊ฐ ์ง€์—ญ์˜ ์—ผ์ƒ์‹๋ฌผ์ด ์„œ์‹ํ•˜๋Š” ์—ผ์Šต์ง€์™€ ๋น„์‹์ƒ ๊ฐฏ๋ฒŒ์—์„œ ์กฐ์‚ฌ๋ฅผ ์‹ค์‹œํ•˜์˜€๋‹ค. ์™ธ๋ž˜์‹๋ฌผ ๊ฐฏ๋ˆํ’€์ด ์šฐ์ ํ•˜๋Š” ์ค‘๊ตญ ์—ผ์Šต์ง€๊ฐ€ ํ† ์ฐฉ์‹๋ฌผ ๊ฐˆ๋Œ€์™€ ์น ๋ฉด์ดˆ๊ฐ€ ์šฐ์ ํ•˜๋Š” ํ•œ๊ตญ ์—ผ์Šต์ง€๋ณด๋‹ค ๋†’์€ ์œ ๊ธฐํƒ„์†Œ ์ €์žฅ๋Ÿ‰์„ ๋ณด์˜€๋‹ค. ๋™์ผ ๊ธฐ๊ฐ„ ๋™์•ˆ ๊ฐฏ๋ˆํ’€์˜ ์œ ๊ธฐํƒ„์†Œ ์ €์žฅ๋Ÿ‰ ์ฆ๊ฐ€์œจ์€ ์น ๋ฉด์ดˆ์™€ ๊ฐˆ๋Œ€์— ๋น„ํ•ด ๋†’์•˜์œผ๋ฉฐ, ์ด๋Š” ์ƒ๋Œ€์ ์œผ๋กœ ๋†’์€ ์ผ์ฐจ์ƒ์‚ฐ๋Ÿ‰๊ณผ ์ง€ํ•˜๋ถ€ ๋ฟŒ๋ฆฌ ์ƒ๋ฌผ๋Ÿ‰์œผ๋กœ ์ธํ•œ ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋˜ํ•œ ๊ฐฏ๋ˆํ’€ ์„œ์‹์ง€๋Š” ๋น„์‹์ƒ ๊ฐฏ๋ฒŒ๊ณผ ๊ฐˆ๋Œ€ ์„œ์‹์ง€์— ๋น„ํ•ด ์˜จ์‹ค๊ฐ€์Šค ๋ฐฐ์ถœ, ๋Œ€ํ˜•์ €์„œ๋™๋ฌผ ๋จน์ด๋ง, ํ‡ด์ ๋ฌผ ์•ˆ์ •๋„, ํƒ„์†Œ์นจ์ ์˜ ๊ด€์ ์—์„œ ์ด์ ์ด ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ํ™•์ธ๋˜์—ˆ๋‹ค. ๋์œผ๋กœ, ๋Œ€๊ทœ๋ชจ ํ˜„์žฅ์กฐ์‚ฌ๋ฅผ ํ†ตํ•ด ํ™ฉํ•ด ์ „ ์—ฐ์•ˆ ๊ฐฏ๋ฒŒ์˜ ์œ ๊ธฐํƒ„์†Œ ์ €์žฅ๋Ÿ‰๊ณผ ์œ ๊ธฐํƒ„์†Œ ์นจ์ ๋ฅ ์„ ์‚ฐ์ •ํ•˜์˜€๋‹ค. ์กฐ์‚ฌ์ง€์—ญ์€ ์ค‘๊ตญ 5๊ฐœ ์‹œ๋„(๋žด์˜ค๋‹์„ฑ, ํ—ˆ๋ฒ ์ด์„ฑ, ํ†ˆ์ง„์‹œ, ์‚ฐ๋‘ฅ์„ฑ, ์žฅ์‘ค์„ฑ)๋‚ด 19๊ฐœ ์ง€์—ญ, ํ•œ๊ตญ 5๊ฐœ ์‹œ๋„(๊ฒฝ๊ธฐ, ์ถฉ๋‚จ, ์ „๋ถ, ์ „๋‚จ, ๊ฒฝ๋‚จ)๋‚ด 18๊ฐœ ์ง€์—ญ์—์„œ ์ฝ”์–ดํ‡ด์ ๋ฌผ์„ ์ฑ„์ง‘ํ•˜์˜€๋‹ค. ๋ถ„์„๊ฒฐ๊ณผ, ์–‘์‹์žฅ, ๋„์‹œ ๋ฐ ์‚ฐ์—…๋‹จ์ง€๋กœ๋ถ€ํ„ฐ ๊ฐ•์„ ํ†ตํ•œ ์œ ๊ธฐ๋ฌผ์งˆ์˜ ์œ ์ž…์ด ์œ ๊ธฐํƒ„์†Œ ์นจ์ ์— ๊ธฐ์—ฌํ•˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ํ™ฉํ•ด ๊ฐฏ๋ฒŒ ํ‡ด์ ๋ฌผ๋‚ด ์œ ๊ธฐํƒ„์†Œ ํ•จ๋Ÿ‰์€ ํ‡ด์ ๋ฌผ ์ž…๋„์™€ ์—ผ์ƒ์‹๋ฌผ์˜ ์œ ๋ฌด์— ๋”ฐ๋ผ ๊ฒฐ์ •๋˜๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๋‹จ์œ„๋ฉด์ ๋‹น ์œ ๊ธฐํƒ„์†Œ ์ €์žฅ๋Ÿ‰๊ณผ ๊ฐฏ๋ฒŒ ๋ฉด์ ์ž๋ฃŒ๋ฅผ ๊ธฐ๋ฐ˜์œผ๋กœ ํ™ฉํ•ด ์ „ ์—ฐ์•ˆ ๊ฐฏ๋ฒŒ์˜ ์ด ์œ ๊ธฐํƒ„์†Œ ์ €์žฅ๋Ÿ‰ 21โ€“171 Tg C๊ณผ ์—ฐ๊ฐ„ ์œ ๊ธฐํƒ„์†Œ ์นจ์ ๋ฅ  0.08โ€“0.61 Tg C yr-1; 0.29โ€“2.24 Tg CO2 eq. yr-1์„ ์ถ”์ •ํ•˜์˜€๋‹ค. ๊ธฐ์กด์˜ ๋ธ”๋ฃจ์นด๋ณธ ์ƒํƒœ๊ณ„์™€ ๋น„๊ตํ•˜์˜€์„ ๋•Œ ๊ฐฏ๋ฒŒ์€ ์ƒ๋Œ€์ ์€ ๋‚ฎ์€ ๋‹จ์œ„๋ฉด์ ๋‹น ์œ ๊ธฐํƒ„์†Œ ์ €์žฅ๋Ÿ‰๊ณผ ์นจ์ ๋ฅ ์„ ๋ณด์ด๋‚˜, ์ „ ์„ธ๊ณ„์ ์œผ๋กœ ๊ด‘ํ™œํ•œ ๋ฉด์ ๊ณผ ํ•ด๋‹น ์„œ์‹์ง€์˜ ์ผ์ฐจ์ƒ์‚ฐ์ž์ธ ์ €์„œ๋ฏธ์„ธ์กฐ๋ฅ˜๋ฅผ ๊ณ ๋ คํ•  ๋•Œ ๊ฐฏ๋ฒŒ๋„ ๋˜ํ•œ ์ค‘์š”ํ•œ ํƒ„์†Œํก์ˆ˜์›์ž„์„ ์‹œ์‚ฌํ•œ๋‹ค. ์ด์ƒ์˜ ์—ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ์ข…ํ•ฉ ์š”์•ฝํ•˜๋ฉด, ํ™ฉํ•ด ๊ฐฏ๋ฒŒ ํ‡ด์ ๋ฌผ๋‚ด ์œ ๊ธฐํƒ„์†Œ ๋ถ„ํฌ๋Š” ํ‡ด์ ๋ฌผ ์ž…๋„์™€ ์‹์ƒ์˜ ์œ ๋ฌด์— ์˜ํ•ด ๊ฐ€์žฅ ๋งŽ์€ ์˜ํ–ฅ์„ ๋ฐ›์œผ๋ฉฐ, ์œ ๊ธฐํƒ„์†Œ ๊ธฐ์›์€ ์—ผ์ƒ์‹๋ฌผ ์ข…๊ณผ ์œก์ƒ-ํ•ด์–‘๊ธฐ์› ์œ ๊ธฐ๋ฌผ ์œ ์ž…์— ๋”ฐ๋ผ ๋ณ€ํ™”๋˜๋Š” ๊ฒƒ์ด ํ™•์ธ๋˜์—ˆ๋‹ค. ๊ฒฐ๊ณผ์ ์œผ๋กœ, ๋ณธ ์—ฐ๊ตฌ๋Š” ํ™ฉํ•ด ๊ฐฏ๋ฒŒ์˜ ๋ธ”๋ฃจ์นด๋ณธ ์ž ์žฌ์„ฑ๊ณผ ์ด์— ์˜ํ–ฅ์„ ๋ฏธ์น˜๋Š” ์ƒํƒœํ•™์  ํŠน์„ฑ์— ๋Œ€ํ•œ ์ •๋ณด๋ฅผ ์ œ๊ณตํ•˜๋ฉฐ, ํ–ฅํ›„ ํ™ฉํ•ด ๊ฐฏ๋ฒŒ ํ‡ด์ ๋ฌผ๋‚ด ํƒ„์†Œ์ˆœํ™˜ ์—ฐ๊ตฌ์— ๋Œ€ํ•œ ์ค‘์š”ํ•œ ๊ธฐ์ดˆ์ž๋ฃŒ๋กœ ํ™œ์šฉ๋  ์ˆ˜ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค.Recently, blue carbon ecosystems (BCEs), including salt marshes, mangrove forests, and seagrass meadows, have been highlighted for their capacity to fix high quantities of carbon under global warming. Although these conventional BCEs are widely studied for their role as highly efficient CO2 sinks, holistic data analysis of carbon sink capacity and its controlling factors remain limited in the tidal flat ecosystems of the Yellow Sea. Thus, the current study evaluated the spatiotemporal distribution and fate of sedimentary organic carbon of tidal flat ecosystems in the Yellow Sea. Sedimentary organic carbon in the surface sediments of typical intertidal areas were investigated to address year-round monthly distributions and site-specific sources. Target areas included four natural tidal flats (Ganghwa, Garolim, Sinan, and Suncheon) and one artificially closed estuary (Nakdong River) in South Korea during 2018. Among the parameters monitored, mud content was a key factor controlling organic matter content, across varying habitats, with significant positive correlations to total organic carbon (TOC). Elevated TOC content and heavier carbon stable isotope ratios (ฮด13C) in the sediments of Garolim and Suncheon from February to April of 2018 reflected microphybenthos blooms during winter, indicating a primary influence of marine sources. In comparison, ฮด13C and ฮด15N were depleted in the sediments of Nakdong River estuary during the flood season (Septemberโ€“October), indicating the direct influence of terrestrial organic input through freshwater discharge. To estimate current organic carbon stocks and sequestration rates in the coastal areas of the West Sea, South Sea, and East Sea of South Korea, field surveys were conducted over 4 years combined with remote sensing technology were conducted encompassing entire intertidal areas. Twenty-one intertidal flats were targeted across seven provinces (Gyeonggi, Chungnam, Jeonbuk, Jeonnam, Gyeongnam, Gyeongbuk, and Gangwon). Organic carbon stocks measured in salt marshes (i.e., upper intertidal zone) reflected the high carbon fixation capacity of halophytes through primary production. The texture of different sediments was classified based on remotely sensed imagery, and was confirmed to be closely correlated with field-based classification data. Using field and remote sensing results, total organic carbon stocks and sequestration rates were estimated in the tidal flats of South Korea. This investigation was conducted to address the effects of an invasive halophyte (i.e., Spartina alterniflora) on sedimentary organic carbon compared to native halophyte habitats (i.e., Suaeda japonica and Phragmites australis) in South Korea and China. Out of the two countries, salt marshes in China tended to have higher organic carbon stocks compared to those in Korea, which was attributed to different rates of increase in TOC by halophyte species. Spartina alterniflora contributed to higher carbon accumulation rates in sediments (3.4 times), through higher primary production and greater root biomass, compared to S. japonica (2.5 times) and P. australis (2.4 times) over the same period. In addition, compared to P. australis and bare tidal flats, S. alterniflora had advantages with respect to greenhouse gas emissions, the food web, sediment erodibility, and carbon burial. Finally, a large-scale investigation was conducted to demonstrate the distribution of total organic carbon stocks and sequestration rates of coastal sediments along the Yellow Sea. Riverine inputs of anthropogenic organic matter from aquaculture, municipal, and industrial areas contributed to the burial of sedimentary organic carbon. Out of the evaluated environmental parameters, sediment mud contents and halophytes were confirmed as key factors affecting organic carbon levels in coastal sediment. Based on the assimilated data, total organic carbon stocks (21โ€“171 Tg C) and sequestration rates (0.08โ€“0.61 Tg C yr-1; 0.29โ€“2.24 Tg CO2 eq. yr-1) were evaluated in the Yellow Sea. Of note, tidal flats had relatively lower carbon stocks due to having lower net primary production (NPP) compared to conventional BCEs. Nevertheless, given the extensive areal coverage and microphytobenthos (MPB), tidal flats could be significant carbon sinks, and also terminal reservoirs of detritus organic matter from adjacent vegetated coastal ecosystems. Overall, the distribution of sedimentary organic carbon varied in the sediment mud content and vegetation of tidal flats in the Yellow Sea. Furthermore, the sources affecting the differences in its origin included halophyte species and terrestrial-marine inputs. In conclusion, the present study provides a relatively large-scale baseline on the carbon dynamics of coastal sediments along the Yellow Sea, contributing to the global database of โ€œBlue Carbonโ€ science.CHAPTER. 1. Introduction 1 1.1. Backgrounds 2 1.2. Objectives 8 CHAPTER. 2. Natural and anthropogenic signatures on sedimentary organic matters across varying intertidal habitats in the Korean waters 11 2.1. Introduction 12 2.2. Materials and methods 15 2.2.1. Study area 15 2.2.2. Sampling and laboratory analyses 18 2.2.3. Data analysis 21 2.3. Results and discussion 22 2.3.1. Spatiotemporal distributions of sedimentary organic matter 22 2.3.2. Effects of the mud contents on sedimentary TOC and TN 29 2.3.3. Effects of benthic microalgae on sedimentary TOC and TN 32 2.3.4. Site-specific variabilities in sources of sedimentary organic matters 36 2.3.5. Factors affecting complex dynamics of sedimentary organic matter 39 CHAPTER. 3. The first national scale evaluation of organic carbon stocks and sequestration rates of coastal sediments along the West Sea, South Sea, and East Sea of South Korea 42 3.1. Introduction 43 3.2. Materials and methods 46 3.2.1. Study area 46 3.2.2. Tidal flat delineation using remote sensing 58 3.2.3. Validation of sediment textural types using remote sensing 62 3.2.4. Sampling and laboratory analyses 63 3.2.5. Calculation of organic carbon stock 69 3.2.6. Calculation of organic carbon sequestration rate 73 3.2.7. Statistical analyses 74 3.3. Results and discussion 75 3.3.1. Spatiotemporal distribution of organic carbon stocks per unit area 75 3.3.2. Environmental factors affecting the complex dynamics of sedimentary organic carbon stocks 78 3.3.3. Effects of mud content and vegetation on sedimentary TOC 81 3.3.4. Validation of tidal flat areas and sediment textural types using remote sensing classification 86 3.3.5. Estimation of organic carbon stocks and sequestration rates in South Korea 90 CHAPTER. 4. The effect of exotic S. alterniflora invasion on sedimentary organic carbon across the coastal areas of the Yellow Sea 99 4.1. Introduction 100 4.2. Materials and methods 103 4.2.1. Data packages 103 4.2.2. Air temperature anomaly 105 4.2.3. Sedimentary organic carbon stocks per unit area 106 4.2.4. Benthic community after Spartina alterniflora eradication 109 4.2.5. CO2 and CH4 emissions 110 4.2.6. Relatively contribution of primary diet 111 4.2.7. Statistical analysis 112 4.3. Results and discussion 113 4.3.1. Elevated temperature affecting the spread of Spartina alterniflora in the Yellow Sea 113 4.3.2. Effects of Spartina alterniflora invasion on sedimentary organic carbon 117 4.3.3. Effect of eradication on Spartina alterniflora and macrobenthos community 124 4.3.4. Comparision of ecological functions between bare tidal flat, native Phragmites australis, and invasive Spartina alterniflora 127 CHAPTER. 5. Spatial variation of sedimentary organic carbon in the coastal areas of the Yellow Sea 135 5.1. Introduction 136 5.2. Materials and methods 138 5.2.1. Study area 138 5.2.2. Sampling and laboratory analyses 140 5.2.3. Data analyses 146 5.3. Results and discussion 147 5.3.1. Spatial distribution of organic carbon stocks per unit area 147 5.3.2. Environmental factors affecting the complex dynamics of sedimentary organic carbon stocks 149 5.3.3. Halophyte species-specific variability in the sources of sedimentary organic matter 151 5.3.4. Organic carbon stocks and carbon sequestration rates in the coastal areas of the Yellow Sea 153 CHAPTER. 6. Conclusions 157 6.1. Summary 158 6.2. Environmental implications and limitations 163 6.3. Future research directions 166 BIBLIOGRAPHY 169 ABSTRACT (IN KOREAN) 186๋ฐ•

    Expression of p75(NGFR), a Proliferative and Basal Cell Marker, in the Buccal Mucosa Epithelium during Re-epithelialization

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    We investigated the expression of p75NGFR, a proliferative and basal cell marker, in the mouse buccal mucosa epithelium during wound healing in order to elucidate the role of epithelial stem cells. Epithelial defects were generated in the epithelium of the buccal mucosa of 6-week-old mice using CO2 laser irradiation. BrdU was immediately administered to mice following laser irradiation. They were then sacrificed after 1, 3, 7, and 14 days. Paraffin sections were prepared and the irradiated areas were analyzed using immunohistochemistry with anti-p75NGFR, BrdU, PCNA, and CK14 antibodies. During re-epithelialization, PCNA (โ€“)/p75NGFR (+) cells extended to the wound, which then closed, whereas PCNA (+)/p75NGFR (+) cells were not observed at the edge of the wound. In addition, p75NGFR (โ€“)/CK14 (+), which reflected the presence of post-mitotic differentiating cells, was observed in the supra-basal layers of the extended epithelium. BrdU (+)/p75NGFR (+), which reflected the presence of epithelial stem cells, was detected sparsely in buccal basal epithelial cells after healing, and disappeared after 7 days. These results suggest that p75NGFR (+) keratinocytes are localized in the basal layer, which contains oral epithelial stem cells, and retain the ability to proliferate in order to regenerate the buccal mucosal epithelium.ope

    FGF10 Is Required for Circumvallate Papilla Morphogenesis by Maintaining Lgr5 Activity

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    Taste buds develop in different regions of the mammal oral cavity. Adult stem cells in various organs including the tongue papillae are marked by leucine-rich repeat-containing G protein-coupled receptor 5 (Lgr5) and its homolog, Lgr6. Recent studies have reported that adult taste stem/progenitor cells in circumvallate papilla (CVP) on the posterior tongue are Lgr5-positive. In this study, we confirm the Lgr5 expression pattern during CVP development. A previous study reported that mesenchymal Fgf10 is necessary for maintaining epithelial Lgr5-positive stem/progenitor cells. To confirm the interaction between Lgr5-positive CVP epithelium and mesenchymal factor FGF10, reverse recombination (180-degree) was performed after tongue epithelium detachment. FGF10 protein-soaked bead implantation was performed after reverse recombination to rescue CVP development. Moreover, we reduced mesenchymal Fgf10 by BIO and SU5402 treatment which disrupted CVP morphogenesis. This study suggests that the crosstalk between epithelial Lgr5 and mesenchymal Fgf10 plays a pivotal role in CVP epithelium invagination during mouse tongue CVP development by maintaining Lgr5-positive stem/progenitor cells.ope

    Runx3 regulates iron metabolism via modulation of BMP signalling

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    Objectives: Runx3, a member of the Runx family of transcription factors, has been studied as a tumour suppressor and key player of organ development. In a previous study, we reported differentiation failure and excessive angiogenesis in the liver of Runx3 knock-out (KO) mice. Here, we examined a function of the Runx3 in liver, especially in iron metabolism. Methods: We performed histological and immunohistological analyses of the Runx3 KO mouse liver. RNA-sequencing analyses were performed on primary hepatocytes isolated from Runx3 conditional KO (cKO) mice. The effect of Runx3 knock-down (KD) was also investigated using siRNA-mediated KD in functional human hepatocytes and human hepatocellular carcinoma cells. Result: We observed an iron-overloaded liver with decreased expression of hepcidin in Runx3 KO mice. Expression of BMP6, a regulator of hepcidin transcription, and activity of the BMP pathway were decreased in the liver tissue of Runx3 KO mice. Transcriptome analysis on primary hepatocytes isolated from Runx3 cKO mice also revealed that iron-induced increase in BMP6 was mediated by Runx3. Similar results were observed in Runx3 knock-down experiments using HepaRG cells and HepG2 cells. Finally, we showed that Runx3 enhanced the activity of the BMP6 promoter by responding to iron stimuli in the hepatocytes. Conclusion: In conclusion, we suggest that Runx3 plays important roles in iron metabolism of the liver through regulation of BMP signalling.ope

    Postoperative developed intra-abdominal desmoid tumor after surgical resection of gastrointestinal malignancy: A review of 10 cases

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    Purpose Desmoid tumors are locally aggressive tumors with no known potential for metastasis. They tend to recur even after complete excision. Sometimes it is not easy to differentiate between intra-abdominal desmoid and tumor recurrence, especially after gastrointestinal (GI) tumor resection. The current study aims to review the characteristics, management, and outcomes of patients with intra-abdominal desmoid tumor post GI resection. Methods During the period between 2007 and 2018, after a retrospective review of patientsโ€™ clinical data, 10 patients were finally included. Medical records were screened for demographic, clinical, pathological data, management strategy, postoperative morbidity, mortality, recurrence rate and follow-up. Results The study comprised 10 patients (8 males). The median age was 53.5 years (range, 35โ€“68 years). Two patients diagnosed as familial adenomatous polyposis (FAP). All the patients underwent previous GI resection: three (30%) for colon cancer, three (30%) gastrectomy, two (20%) total proctocolectomy with ileal pouch-anal anastomosis (TPC+IPAA) for FAP, one (10%) low anterior resection (three rectal cancers) and one (10%) distal pancreatectomy. The tumor was found to be in bowel mesentery in eight cases (80%). The median tumor size was 5.3 cm (range, 2.6โ€“19.0 cm). Six patients (60%) underwent open resection, while four patients (40%) underwent laparoscopic surgery. Complications occurred in five cases (50%) and ranged from Clavien-Dindo (IIโ€“III). The median follow-up period was 16.5 months (1.5โ€“136.0 months) with recurrence in one case (10%). Pathology came out to be desmoid tumor fibromatosis in all cases. Conclusion When a mass develops after surgical resection for abdominal GI malignancy and tends to be large in size, located in the bowel mesentery and away from previous primary tumor site, most probably it is desmoid rather than tumor recurrence.ope

    Ahnak is required to balance calcium ion homeostasis and smooth muscle development in the urinary system

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    Background: Various renal abnormalities, including hydronephrosis, polycystic kidney disease, and hydroureter, have been reported, and these abnormalities are present in DiGeorge syndrome, renal dysplasia, and acute kidney failure. Previous studies have shown that various genes are associated with renal abnormalities. However, the major target genes of nonobstructive hydronephrosis have not yet been elucidated. Results: We examined neuroblast differentiation-associated protein Ahnak localization and analyzed morphogenesis in developing kidney and ureter. To investigated function of Ahnak, RNA-sequencing and calcium imaging were performed in wild type and Ahnak knockout (KO) mice. Ahnak localization was confirmed in the developing mouse kidneys and ureter. An imbalance of calcium homeostasis and hydronephrosis, which involves an expanded renal pelvis and hydroureter, was observed in Ahnak KO mice. Gene Ontology enrichment analysis on RNA-seq results indicated that 'Channel Activity', 'Passive Transmembrane Transporter Activity' and 'Cellular Calcium Ion Homeostasis' were downregulated in Ahnak KO kidney. 'Muscle Tissue Development', 'Muscle Contraction', and 'Cellular Calcium Ion Homeostasis' were downregulated in Ahnak KO ureter. Moreover, peristaltic movement of smooth muscle in the ureter was reduced in Ahnak KO mice. Conclusions: Abnormal calcium homeostasis causes renal disease and is regulated by calcium channels. In this study, we focused on Ahnak, which regulates calcium homeostasis in several organs. Our results indicate that Ahnak plays a pivotal role in kidney and ureter development, and in maintaining the function of the urinary system.ope
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