39 research outputs found

    ํด๋ ™์‹œ์—˜๋ผ ๋‰ด๋ชจ๋‹ˆ์• ๋ฅผ ์ €ํ•ดํ•˜๋Š” ๋ฐ•ํ…Œ๋ฆฌ์˜คํŒŒ์ง€ KPP2018๊ณผ KPP2020์˜ ๋ถ„๋ฆฌ ๋ฐ ํŠน์„ฑ ๋ถ„์„๊ณผ ์นตํ…Œ์ผ์„ ์ด์šฉํ•œ ์‹ํ’ˆ์—์„œ์˜ ์ƒ๋ฌผํ•™์  ์ œ์–ด

    Get PDF
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ) -- ์„œ์šธ๋Œ€ํ•™๊ต๋Œ€ํ•™์› : ๋†์—…์ƒ๋ช…๊ณผํ•™๋Œ€ํ•™ ๋†์ƒ๋ช…๊ณตํ•™๋ถ€, 2023. 2. ์ด์ฃผํ›ˆ.Klebsiella pneumoniae is a well-known opportunistic human pathogen causing chronic pulmonary obstruction and primarily infects the immunocompromised individuals in nosocomial environment. This pathogen can produce extended-spectrum beta-lactamases (ESBL), which are resistant to almost all beta-lactam antibiotics, and nowadays carbapenem-resistant strains are increasing. Recently, Klebsiella pneumoniae is detected in food samples, especially in poultry products or in raw vegetables. Therefore, the development of new agent is urgently needed and to control this pathogen, 12 Klebsiella-infecting phages were isolated from sewage samples. The analysis of host range revealed that the isolated phage KPP2020 has high host specificity among them, inhibiting only K. pneumoniae. The phage KPP2018 infects K. pneumoniae mainly, also infects Shigella spp., and Salmonella serovars. Morphological observation using TEM showed that both phages belong to the family Siphoviridae. The stability of KPP2020 and KPP2018 was maintained for 12 h under stress conditions (-20~60โ„ƒ and pH 3~11 for KPP2020 and -20~65โ„ƒ and pH 3~12 for KPP2018). Bacterial challenge assay of KPP2020 showed 3.51 log reduction of K. pneumoniae KCTC 2242 within 2 h. The complete genomes of KPP2020 and KPP2018 were analyzed and revealed that KPP2020 consists of 49,044 bp containing 95 ORFs with a GC content 51.33%, while KPP2018 consists of 137,988 bp DNA with 228 ORFs. Subsequent bioinformatics analysis revealed no toxin genes or virulence factor, suggesting the safety for human applications. Comparative genome analysis about tail gene cluster was conducted and there was no identity between KPP2020 and KPP2018 tail-related genes, indicating that differences in host range results may related to this. Application of KPP2020 using cutting board showed about 4 log reduction for at least 7 h, indicating that KPP2020 has potential to control K. pneumoniae effectively. Food application of the phage cocktail consisting of KPP2020 and KPP2018 in a 1:1 ratio using chicken meat showed higher lytic activity (4.35 log reduction within 2 h) and phage resistance of indicator strain developed slower than that of single phages. KPP2020 can lower the secretion of pro-inflammatory cytokines of K. pneumoniae infected RAW 264.7 cells, suggesting that KPP2020 has therapeutic effect against bacterial infection. KPP2020 does not induced inflammatory response of RAW 264.7 cells and not involved in the response induced by LPS, suggesting that KPP2020 can be an effective therapeutic agent against bacterial infection. Therefore, these two novel bacteriophages KPP2020 and KPP2018 can be used as natural food preservatives for food safety, and KPP2020 can be a therapeutic agent against K. pneumoniae infection.ํด๋ ™์‹œ์—˜๋ผ ๋‰ด๋ชจ๋‹ˆ์• ๋Š” ๋งŒ์„ฑ ํ ์žฅ์• ๋ฅผ ์ผ์œผํ‚ค๋Š” ๊ท ์ฃผ๋กœ, ์„ ํƒ์ ์œผ๋กœ ๋ณ‘์›์„ฑ์„ ๊ฐ€์ง€๋Š”๋ฐ ์ฃผ๋กœ ๋ณ‘์› ํ™˜๊ฒฝ์—์„œ ๋ฉด์—ญ์ด ์•ฝํ™”๋œ ๊ฐœ์ธ์„ ๊ฐ์—ผํ•œ๋‹ค. ์ด ๋ณ‘์›๊ท ์€ ๋Œ€๋ถ€๋ถ„์˜ ๋ฒ ํƒ€-๋ฝํƒ ํ•ญ์ƒ์ œ์— ๋‚ด์„ฑ์ด ์žˆ๋Š” ESBL์„ ์ƒ์„ฑํ•  ์ˆ˜ ์žˆ๊ณ , ์ตœ๊ทผ์—๋Š” ์‹ํ’ˆ, ํŠนํžˆ ๊ฐ€๊ธˆ๋ฅ˜๋‚˜ ์•ผ์ฑ„์—์„œ ํด๋ ™์‹œ์—˜๋ผ ๋‰ด๋ชจ๋‹ˆ์• ๊ฐ€ ๊ฒ€์ถœ๋˜๋Š” ์‚ฌ๋ก€๊ฐ€ ์ฆ๊ฐ€ํ•˜๊ณ  ์žˆ์–ด์„œ ์ด๋ฅผ ์ œ์–ดํ•˜๊ธฐ ์œ„ํ•œ ์ƒˆ๋กœ์šด ์ œ์ œ์˜ ๊ฐœ๋ฐœ์ด ์‹œ๊ธ‰ํ•˜๋‹ค. ์ด๋ฅผ ์ œ์–ดํ•˜๊ธฐ ์œ„ํ•ด์„œ ํด๋ ™์‹œ์—˜๋ผ๋ฅผ ๊ฐ์—ผํ•˜๋Š” ํŒŒ์ง€๋ฅผ ํ•˜์ˆ˜ ์ฒ˜๋ฆฌ์žฅ์—์„œ ๋ถ„๋ฆฌํ–ˆ๋‹ค. ๊ฐ ํŒŒ์ง€์˜ ๊ฐ์—ผ ๋ฒ”์œ„๋ฅผ ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, KPP2020์€ ํด๋ ™์‹œ์—˜๋ผ ๋‰ด๋ชจ๋‹ˆ์• ๋งŒ์„ ์–ต์ œํ•˜๋Š” ์•„์ฃผ ๋†’์€ ์ˆ™์ฃผ ํŠน์ด์„ฑ์„ ๋ณด์ด๋Š” ๊ฒƒ์„ ํ™•์ธํ–ˆ๊ณ , KPP2018์€ ์ฃผ๋กœ ํด๋ ™์‹œ์—˜๋ผ ๋‰ด๋ชจ๋‹ˆ์• ๋ฅผ ๊ฐ์—ผํ•˜๋ฉฐ, ์‹œ๊ฒ”๋ผ ์†, ์‚ด๋ชจ๋„ฌ๋ผ๋„ ๊ฐ์—ผํ•˜๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํˆฌ๊ณผ ์ „์ž ํ˜„๋ฏธ๊ฒฝ์„ ์‚ฌ์šฉํ•œ ํ˜•ํƒœํ•™์  ๊ด€์ฐฐ์„ ํ†ตํ•ด, ๋‘ ํŒŒ์ง€ ๋ชจ๋‘ Siphoviridae ๊ณผ์— ์†ํ•œ๋‹ค๋Š” ๊ฒƒ์„ ์•Œ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ๋‹ค์–‘ํ•œ ์ŠคํŠธ๋ ˆ์Šค ์กฐ๊ฑด์—์„œ KPP2020๊ณผ KPP2018์˜ ์•ˆ์ •์„ฑ์„ ํ™•์ธํ•ด๋ณธ ๊ฒฐ๊ณผ, KPP2020์˜ ๊ฒฝ์šฐ, -20โ„ƒ์—์„œ 60โ„ƒ ๊ทธ๋ฆฌ๊ณ  pH 3์—์„œ 11๊นŒ์ง€, KPP2018์˜ ๊ฒฝ์šฐ, -20โ„ƒ์—์„œ 65โ„ƒ ๊ทธ๋ฆฌ๊ณ  pH 3์—์„œ 12๊นŒ์ง€ ๋ฒ”์œ„ ์•ˆ์—์„œ 12์‹œ๊ฐ„ ๋™์•ˆ ์•ˆ์ •์ ์ด์—ˆ๋‹ค. KPP2020์˜ ์ˆ™์ฃผ ์ œ์–ด ๋Šฅ๋ ฅ์„ ํ™•์ธํ•œ ๊ฒฐ๊ณผ, ํด๋ ™์‹œ์—˜๋ผ ๋‰ด๋ชจ๋‹ˆ์• ๋ฅผ 2์‹œ๊ฐ„ ์ด๋‚ด์— 3.51 ๋กœ๊ทธ ๊ฐ์†Œ์‹œํ‚ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. KPP2020 ๋ฐ KPP2018์˜ ์œ ์ „์ฒด๋ฅผ ๋ถ„์„ํ•˜์˜€๊ณ , ํŒŒ์ง€ ๊ผฌ๋ฆฌ ๋ถ€๋ถ„์— ํ•ด๋‹นํ•˜๋Š” ์œ ์ „์ž๊ตฐ์„ ๋น„๊ต ๋ถ„์„ํ•œ ๊ฒฐ๊ณผ, KPP2020๊ณผ KPP2018 ์‚ฌ์ด์—๋Š” ์ƒ๋™์„ฑ์ด ์—†์—ˆ์œผ๋ฉฐ, ์ด๋Š” ๋‘ ํŒŒ์ง€์˜ ๊ฐ์—ผ ๋ฒ”์œ„์˜ ์ฐจ์ด์™€ ์—ฐ๊ด€์ด ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค. ํด๋ ™์‹œ์—˜๋ผ ๋‰ด๋ชจ๋‹ˆ์• ๊ฐ€ ์˜ค์—ผ๋œ ๋„๋งˆ์— KPP2020์„ ์ ์šฉํ•˜์—ฌ ๊ท ์ฃผ ์ €ํ•ด ํšจ๊ณผ๋ฅผ ํ™•์ธํ•œ ๊ฒฐ๊ณผ, ์ตœ์†Œ 7์‹œ๊ฐ„ ๋™์•ˆ ์ง€์†์ ์œผ๋กœ 4 ๋กœ๊ทธ ์ด์ƒ์˜ ๊ท ์„ ๊ฐ์†Œ์‹œํ‚ค๋Š” ๊ฒƒ์„ ํ™•์ธํ•˜์˜€๋‹ค. ์‹ํ’ˆ์—์„œ ํŒŒ์ง€๋ฅผ ๋” ํšจ๊ณผ์ ์œผ๋กœ ์ ์šฉํ•˜๊ธฐ ์œ„ํ•ด KPP2020๊ณผ KPP2018์„ 1:1 ๋น„์œจ๋กœ ํ˜ผํ•ฉํ•˜์—ฌ ํŒŒ์ง€ ์นตํ…Œ์ผ์„ ์ œ์ž‘ํ•˜์˜€๊ณ , ์ด๋ฅผ ๋‹ญ๊ณ ๊ธฐ์— ์ ์šฉํ•ด ๋ณด์•˜์„ ๋•Œ, ๋‹จ์ผ ํŒŒ์ง€๋ณด๋‹ค ๋” ๋†’์€ ์šฉ๊ท  ํ™œ์„ฑ์„ ๋ณด์˜€๊ณ  (2์‹œ๊ฐ„ ๋‚ด 4.35 ๋กœ๊ทธ ๊ฐ์†Œ), ํŒŒ์ง€์— ๋Œ€ํ•œ ๊ท ์ฃผ์˜ ์ €ํ•ญ์„ฑ์ด ๋” ๋Š๋ฆฌ๊ฒŒ ์ƒ์„ฑ๋˜์—ˆ๋‹ค. KPP2020์€ ํด๋ ™์‹œ์—˜๋ผ ๋‰ด๋ชจ๋‹ˆ์• ๊ฐ€ ๊ฐ์—ผ๋œ RAW 264.7 ์„ธํฌ์—์„œ ์ „ ์—ผ์ฆ์„ฑ ์‚ฌ์ดํ† ์นด์ธ์˜ ๋ถ„๋น„๋ฅผ ๋‚ฎ์ถœ ์ˆ˜ ์žˆ์–ด KPP2020์ด ์„ธ๊ท  ๊ฐ์—ผ์— ๋Œ€ํ•œ ์น˜๋ฃŒ ํšจ๊ณผ๊ฐ€ ์žˆ์Œ์„ ์‹œ์‚ฌํ•œ๋‹ค. KPP2020์„ RAW 264.7 ์„ธํฌ์— ์ฒ˜๋ฆฌํ•˜์˜€์„ ๋•Œ ์ „ ์—ผ์ฆ์„ฑ ์‚ฌ์ดํ† ์นด์ธ์ด ๋ฐœํ˜„๋˜์ง€ ์•Š์•˜๊ณ , LPS๋กœ ์ธํ•ด ์—ผ์ฆ ๋ฐ˜์‘์ด ํ™œ์„ฑํ™”๋œ RAW 264.7 ์„ธํฌ์— ํŒŒ์ง€๋ฅผ ์ฒ˜๋ฆฌํ•˜์˜€์„ ๋•Œ ์‚ฌ์ดํ† ์นด์ธ์˜ ๋ฐœํ˜„์— ๊ด€์—ฌํ•˜์ง€ ์•Š๊ธฐ ๋•Œ๋ฌธ์— KPP2020์€ ๋” ํšจ๊ณผ์ ์ธ ์น˜๋ฃŒ์ œ๋กœ์„œ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ๋‹ค. ๋ณธ ์—ฐ๊ตฌ๋ฅผ ํ†ตํ•ด ์ƒˆ๋กœ์šด ๋ฐ•ํ…Œ๋ฆฌ์˜คํŒŒ์ง€ KPP2018๊ณผ KPP2020์€ ์‹ํ’ˆ ์•ˆ์ „์„ ์œ„ํ•œ ์ฒœ์—ฐ ์‹ํ’ˆ ์ฒจ๊ฐ€์ œ๋กœ ์‚ฌ์šฉ๋  ์ˆ˜ ์žˆ์œผ๋ฉฐ, KPP2020์€ ์„ธ๊ท  ๊ฐ์—ผ์— ๋Œ€ํ•œ ์น˜๋ฃŒ์ œ๊ฐ€ ๋  ์ˆ˜ ์žˆ์Œ์„ ํ™•์ธํ•˜์˜€๋‹ค.1. Introduction 1 2. Materials and methods 5 2.1. Bacterial strains and growth conditions 5 2.2. Bacteriophage isolation, purification, and propagation 7 2.3. Transmission Electron Microscopy 8 2.4. Host range test 8 2.5. Stability test under various stress conditions 8 2.6. Bacterial challenge assay 9 2.7. Genome sequencing and bioinformatics analysis 9 2.8. Cutting board application 10 2.9. Food application 11 2.10. Secretion of cytokines with RAW 264.7 cell 12 2.11. Nucleotide sequence accession number 15 3. Results 16 3.1. Host range and morphological observation 16 3.2. Phage stability under various stress conditions 19 3.3. Bacterial challenge assay 22 3.4. Phage genome characterization 24 3.5. Comparative genome analysis 27 3.6. Cutting board application 33 3.7. Food application with phage cocktail 35 3.8. Inflammatory alleviation of RAW 264.7 cells 37 4. Discussion 44 5. References 48 ๊ตญ๋ฌธ์ดˆ๋ก 54์„

    A Study on Application of Neural Network using Genetic Algorithm in Container Traffic Prediction

    Get PDF
    ๋ณธ ์—ฐ๊ตฌ์—์„œ๋Š” ๋น„์„ ํ˜•์˜ˆ์ธก๊ธฐ๋ฒ•์œผ๋กœ์„œ ๋งŽ์€ ๊ด€์‹ฌ์„ ๋ฐ›๊ณ  ์žˆ๋Š” ์ธ๊ณต์‹ ๊ฒฝ๋ง์„ ์‚ฌ์šฉํ•˜์—ฌ ์ปจํ…Œ์ด๋„ˆ๋ฌผ๋™๋Ÿ‰ ์˜ˆ์ธก์„ ์ˆ˜ํ–‰ํ–ˆ์œผ๋ฉฐ, ์ด๋ฅผ ์ „ํ†ต์ ์ธ ์˜ˆ์ธก๊ธฐ๋ฒ• ์ค‘ ํ•˜๋‚˜์ธ ARIMA๋ชจํ˜•๊ณผ ๋น„๊ตํ•˜์˜€๋‹ค. ์ธ๊ณต์‹ ๊ฒฝ๋ง์„ ์ ์šฉํ•  ๋•Œ ๋ฌธ์ œ์ ์ด ๋˜๋Š” ๊ฒƒ ์ค‘ ํ•˜๋‚˜์ธ ๋„คํŠธ์›Œํฌ ๊ตฌ์กฐ์„ค๊ณ„์— ์žˆ์–ด ๊ธฐ์กด์˜ ์„ ํ–‰์—ฐ๊ตฌ๋“ค์€ ์ด๋ก ์ ์œผ๋กœ ์ •๋ฆฝ๋œ ๋ฐฉ๋ฒ•์ด ์•„๋‹Œ ๊ฒฝํ—˜์ด๋‚˜ ์‹คํ—˜์— ๋ฐ”ํƒ•์„ ๋‘” ๋ฐฉ๋ฒ•๋ก ์„ ์‚ฌ์šฉํ•˜๋Š”๋ฐ, ๋ณธ ์—ฐ๊ตฌ์—์„œ ๊ทธ ๋Œ€์•ˆ์œผ๋กœ ์ธ๊ณต์‹ ๊ฒฝ๋ง์˜ ๊ตฌ์กฐ์„ค๊ณ„ ๋ฌธ์ œ์— ์žˆ์–ด ๋ฐฉ๋Œ€ํ•˜๋ฉฐ ๋ณต์žกํ•œ ํƒ์ƒ‰๊ณต๊ฐ„์— ํšจ๊ณผ์ ์œผ๋กœ ์•Œ๋ ค์ง„ ์œ ์ „์•Œ๊ณ ๋ฆฌ์ฆ˜(GA)์„ ์‚ฌ์šฉํ•˜์˜€๋‹ค. ๊ทธ๋ฆฌ๊ณ  ์ธ๊ณต์‹ ๊ฒฝ๋ง์˜ ๋Œ€ํ‘œ์ ์ธ ๋ชจํ˜•์ธ ๋‹ค์ธตํผ์…‰ํŠธ๋ก (MLP)์˜ ๋Œ€์•ˆ์œผ๋กœ ์‹œ๊ฐ„์ง€์—ฐ๋„คํŠธ์›Œํฌ(TDNN)๋ฅผ ์‚ฌ์šฉํ•˜์˜€๊ณ , ์ตœ์ข…์ ์œผ๋กœ ์„ ํ˜•๊ธฐ๋ฒ•์ธ ARIMA๋ชจํ˜•๊ณผ ์ธ๊ณต์‹ ๊ฒฝ๋ง๋ชจํ˜•์˜ ์žฅ์ ๋“ค์„ ๊ฒฐํ•ฉํ•œ Hybrid ARIMA-ANN์„ ์‚ฌ์šฉํ•ด ์ธ๊ณต์‹ ๊ฒฝ๋ง์˜ ์˜ˆ์ธก๋ ฅ์„ ๋†’์˜€๋‹ค.On this study, the artificial neural network(ANN), one of the most popular nonlinear forecasting methods, is compared with autoregressive integrated moving average(ARIMA) model through performing a prediction of container traffic. The existing studies have been used the rule of thumb in topology design for network which had a great effect on forecasting performance of the artificial neural network. However, this study applies the genetic algorithm, known as the effectively optimal algorithm in the huge and complex sample space, as the alternative. And we use the time delayed neural network(TDNN) instead of multi-layer perceptron(MLP) which is the most popular neural network model. Finally, we use the hybrid methodology that combines both the linear ARIAM and the nonlinear ANN models, and compare the methodology with other models in performance for prediction.ABSTRACT ์ œ 1 ์žฅ ์„œ ๋ก  1 1.1 ์—ฐ๊ตฌ์˜ ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์  1 1.2 ์„ ํ–‰ ์—ฐ๊ตฌ 3 1.3 ์—ฐ๊ตฌ์˜ ๋ฐฉ๋ฒ• ๋ฐ ๊ตฌ์„ฑ 7 ์ œ 2 ์žฅ ๋ฐฉ ๋ฒ• ๋ก  8 2.1 ์ž๊ธฐํšŒ๊ท€์ด๋™ํ‰๊ท ๋ชจํ˜•(ARIMA) 8 2.2 ์ธ๊ณต์‹ ๊ฒฝ๋ง(ANN) 13 2.2.1 ๊ฐœ์š” 14 2.2.2 ์‹œ๊ฐ„์ง€์—ฐ๋„คํŠธ์›Œํฌ(TDNN) 21 2.3 ์œ ์ „์•Œ๊ณ ๋ฆฌ์ฆ˜(GA) 26 2.4 Hybrid ARIMA-ANN 30 ์ œ 3 ์žฅ ์‹ค์ฆ๋ถ„์„ 33 3.1 ์ปจํ…Œ์ด๋„ˆ๋ฌผ๋™๋Ÿ‰ ์ž๋ฃŒ 36 3.2 ARIMA ๋ถ„์„ ๊ฒฐ๊ณผ 39 3.3 ์ธ๊ณต์‹ ๊ฒฝ๋ง ๋ถ„์„ ๊ฒฐ๊ณผ 41 3.4 Hybrid ARIMA-ANN ๋ถ„์„ ๊ฒฐ๊ณผ 49 ์ œ 4 ์žฅ ๊ฒฐ ๋ก  ๋ฐ ํ–ฅํ›„ ์—ฐ๊ตฌ๋ฐฉํ–ฅ 51 ์ฐธ๊ณ ๋ฌธํ—Œ 5

    ์šฐ๋ฆฌ์˜ ์–ธ๋ก 

    Get PDF
    ์ด ๊ธ€์€ ๋งˆ๋ˆ„์—˜ ๊ณค์‚ด๋ ˆ์Šค ํ”„๋ผ๋‹ค์˜ ใ€Žํˆฌ์Ÿ์˜ ์‹œ๊ฐ„ใ€์— ์ˆ˜๋ก๋œ ใ€Œ์šฐ๋ฆฌ ์–ธ๋ก ใ€(Nuestro periodismo)๋ฅผ ์˜ฎ๊ธด ๊ฒƒ์ด๋‹ค.๊ตญ์ œํ˜•์‚ฌ๊ธฐ๊ตฌ๋Š” ํ•œ ๋‚˜๋ผ์˜ ์ฟ ๋ฐํƒ€๋ฅผ ํ†ต์ œํ•˜๊ณ  ์œ„๊ธฐ์— ๋†“์ธ ์ œ๊ตญ์˜ ๋…์žฌ์ฒด์ œ๋ฅผ ์ข…์‹์‹œํ‚ค๋Š” ๋ฐ ํ•„์š”ํ•ฉ๋‹ˆ๋‹ค. ์ด๋Ÿฌํ•œ ๊ตญ์ œ๊ธฐ๊ตฌ์˜ ์„ค๋ฆฝ์„ ์ถ”์ง„ํ•˜์ง€ ์•Š๋Š” ์ด์œ ๋Š” ์šฐ๋ฆฌ์˜ ๋ฌด๋ถ„๋ณ„ํ•˜๊ณ  ์•…ํ•œ ํƒ€์„ฑ์— ๊ธธ๋“ค์—ฌ์ง„ ์˜ค๋งŒํ•จ ๋•Œ๋ฌธ์ผ ๊ฒƒ์ž…๋‹ˆ๋‹ค. ์ด๋•Œ ์šฐ๋ฆฌ๊ฐ€ ํ•  ์ˆ˜ ์žˆ๋Š” ์ผ์ด๋ž€, ์ €์ž๋“ค, ํŠนํžˆ, ๊ธฐ์ž๋“ค์ด ์œ„์ƒ๊ธฐ๊ตฌ๋ฅผ ๊ฒฐ์„ฑํ•˜๋„๋ก ๋„์™€์ฃผ๋Š” ๊ฒƒ์ด๋ฉฐ, ์ด๋“ค๋กœ ํ•˜์—ฌ๊ธˆ ์ •์น˜์  ๋…๊ฐ€์Šค๋กœ ์˜ค์—ผ๋˜์–ด ์žˆ๋Š” ๋Œ€๊ธฐ๋ฅผ ์ •ํ™”ํ•˜๋„๋ก ์œ ๋„ํ•˜๋Š” ๊ฒƒ์ž…๋‹ˆ๋‹ค. ๊ทธ๋Ÿฌ๋‚˜ ๊ณผ์—ฐ, ์šฐ๋ฆฌ๋‚˜๋ผ์˜ ๊ธฐ์ž๋“ค์ด ์ด์™€ ๊ฐ™์€ ์œ„์—…์„ ์ˆ˜ํ–‰ํ•˜๋Š”๋ฐ ํ•„์š”ํ•œ ์ถฉ๋ถ„ํ•œ ์„ฑํ’ˆ์„ ์†Œ์œ ํ•˜๊ณ  ์žˆ๋Š”๊ฐ€๊ฐ€ ๋ฌธ์ œ์ž…๋‹ˆ๋‹ค. ํ™•๊ณ ํ•œ ์‹ ๋…๋„ ์—†๋Š” ๊ธ€์Ÿ์ด๋ฉฐ, ์ •์‹ ์  ํ˜ผ๋ˆ ๊ฐ€์šด๋ฐ ์ง„์‹ค์„ ๋ง๊ฐํ•œ ์‚ฌ๋žŒ๋“ค์ด๋‹ˆ๊นŒ ๋ง์ž…๋‹ˆ๋‹ค. ์ƒˆ๋กœ์šด ์œ„์ƒ๊ธฐ๊ตฌ์— ํ•„์š”ํ•œ ์ง€์‹์„ ์Šต๋“ํ•˜๊ธฐ ์œ„ํ•ด์„œ๋ผ๋„ ์ถฉ์‹คํ•œ ์ธํ’ˆ์ด ์š”๊ตฌ๋ฉ๋‹ˆ๋‹ค. ์ด๋•Œ์˜ ์ถฉ์‹คํ•จ์ด๋ž€, ํ•˜๋‚˜์˜ ์›์น™์„ ๊ณ ์ˆ˜ํ•˜๋Š” ๊ฒƒ, ํ˜น์€ ์ ์–ด๋„ ์ž๊ธฐ๊ฐ€ ์†ํ•œ ์ •๋‹น์ธ์— ๋Œ€ํ•œ ์ถฉ์„ฑ์‹ฌ์„ ์ง€ํ‚ค๋Š” ๊ฒƒ์„ ์˜๋ฏธํ•ฉ๋‹ˆ๋‹ค

    Teachers` perception of teacher evaluation and school organization

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(๋ฐ•์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :๊ต์œกํ•™๊ณผ ๊ต์œกํ–‰์ •์ „๊ณต,2000.Docto

    The Socioeconomic Status Model and Voter Turnout in Korean Local Elections

    No full text
    ๊ธฐ์กด์˜ ๋Œ€๋ถ€๋ถ„์˜ ์—ฐ๊ตฌ๋“ค์€ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„๋ชจ๋ธ์ด ํ•œ๊ตญ ์„ ๊ฑฐ์˜ ํˆฌํ‘œ์œจ์„ ์„ค๋ช…ํ•˜์ง€ ๋ชปํ•œ๋‹ค๊ณ  ์ฃผ์žฅํ•œ๋‹ค. ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„๋ชจ๋ธ์˜ ๊ธฐ๋ณธ ๊ฐ€์ •๊ณผ๋Š” ๋‹ฌ๋ฆฌ ๋†’์€ ์†Œ๋“์ˆ˜์ค€์ด๋‚˜ ๊ต์œก์ˆ˜์ค€์ด ์œ ๊ถŒ์ž์˜ ํˆฌํ‘œ๊ฐ€๋Šฅ์„ฑ์„ ๋†’์—ฌ์ฃผ์ง€ ๋ชปํ•œ๋‹ค๋Š” ๊ฒƒ์ด๋‹ค. ๋ณธ ๋…ผ๋ฌธ์€ ์ด ๊ฐ™์€ ์ฃผ์žฅ์ด 1990๋…„๋Œ€ ์ดํ›„ ๋งŽ์€ ์ •์น˜์  ๊ฒฝ์ œ์  ๋ณ€ํ™”๋ฅผ ๊ฒช์€ 2000๋…„๋Œ€์˜ ํ•œ๊ตญ ์‚ฌํšŒ์—์„œ ์—ฌ์ „ํžˆ ์œ ํšจํ•œ์ง€ ์•Œ๊ธฐ ์œ„ํ•ด์„œ 2006๋…„๋„์™€ 2010๋…„๋„ ์ „๊ตญ๋™์‹œ์ง€๋ฐฉ์„ ๊ฑฐ์—์„œ์˜ ํˆฌํ‘œ์œจ์˜ ๊ฒฐ์ •์š”์ธ๋“ค์„ ์‹œ๋„๊ตฐ ์ง‘ํ•ฉ์ž๋ฃŒ๋ฅผ ์ด์šฉํ•ด์„œ ๋ถ„์„ํ•˜์˜€๋‹ค. ๊ทธ ๊ฒฐ๊ณผ ๋ถ€๋ถ„์ ์œผ๋กœ๋‚˜๋งˆ ์„ ๊ฑฐ๊ตฌ๋ฏผ์˜ ํˆฌํ‘œ์—ฌ๋ถ€๋ฅผ ์‚ฌํšŒ๊ฒฝ์ œ์  ์ง€์œ„๋ชจ๋ธ๋กœ ์„ค๋ช…ํ•  ์ˆ˜ ์žˆ๋‹ค๋Š” ์‚ฌ์‹ค์„ ๋ฐœ๊ฒฌํ•˜์˜€๋‹ค. ๋‹ค๋ฅธ ๋ณ€์ˆ˜๋“ค์˜ ์˜ํ–ฅ๋ ฅ์ด ํ†ต์ œ๋˜์—ˆ์„ ๋•Œ, ์„ ๊ฑฐ๊ตฌ๋ฏผ์˜ ๋Œ€์กธ๋น„์œจ์ด ๋†’์œผ๋ฉด ๋†’์„์ˆ˜๋ก ์„ ๊ฑฐ๊ตฌ์˜ ํˆฌํ‘œ์œจ์ด ์ฆ๊ฐ€ํ•œ ๊ฒƒ์ด๋‹ค. ์ด๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ฐœ์ธ์˜ ๊ต์œก์ˆ˜์ค€์ด ๋†’์„์ˆ˜๋ก ํˆฌํ‘œ๋ฅผ ํ•  ๊ฐ€๋Šฅ์„ฑ์ด ๋†’๋‹ค๋Š” ๊ฒƒ์„ ์œ ์ถ”ํ•ด๋ณผ ์ˆ˜ ์žˆ์—ˆ๋‹ค. ํ•˜์ง€๋งŒ ์„ ํ–‰์—ฐ๊ตฌ๋“ค๊ณผ ๋งˆ์ฐฌ๊ฐ€์ง€๋กœ ์„ ๊ฑฐ๊ตฌ๋ฏผ์˜ ์†Œ๋“์ˆ˜์ค€์ด ๋†’์•„์งˆ์ˆ˜๋ก ํˆฌํ‘œ์œจ์ด ์ƒ์Šนํ•œ๋‹ค๋Š” ์ฆ๊ฑฐ๋Š” ๋ฐœ๊ฒฌํ•˜์ง€ ๋ชปํ–ˆ๋‹ค. Since the 1990s, many political scientists have argued that the Socioeconomic Status (SES) Model cannot explain voter turnout in Korea. According to their studies, there is no evidence supporting that more educated and/or wealthier people are more likely to go to the polls than less educated and/or poorer ones. This paper attempts to reexamine the impact of the socioeconomic status on voter turnout by analyzing the aggregated data for the 4th and 5th Local Elections. Unlike previous studies, my findings provide partial evidence for the association between socioeconomic status and voter turnout. Holding other variables constant, as the proportion of college graduates in the electoral district increases, voter turnout grows. It indicates that higher education leads to higher voter turnout. However, any positive evidence for the impact of income on voter turnout is not found

    ๋Œ€๋‘๋‹จ๋ฐฑ์งˆ๊ณผ ์†Œ๋””์›€์ œํ•œ์ด streptozotocin์œผ๋กœ ์œ ๋„๋œ ๋‹น๋‡จํฐ์ฅ์˜ ์ง€์งˆ๋Œ€์‚ฌ ๋ฐ ์‹ ์žฅ์— ๋ฏธ์น˜๋Š” ์˜ํ–ฅ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์‹ํ’ˆ์˜์–‘ํ•™๊ณผ,1999.Maste

    Issues and Tasks m Teacher Evaluation

    No full text
    ์ด ์—ฐ๊ตฌ์—์„œ๋Š” ๊ต์‚ฌํ‰๊ฐ€๋ฅผ ๋‘˜๋Ÿฌ์‹ผ ๋‹ค์–‘ํ•œ ์Ÿ์ ๋“ค์„ ๋…ผ์˜ํ•˜๊ณ ๏ผŒ๊ทธ๋Ÿฌํ•œ ๋…ผ์˜๋ฅผ ๋ฐ”ํƒ•์œผ๋กœ ๊ต์‚ฌํ‰๊ฐ€์˜ ๊ณผ์ œ๋ฅผ ์ œ์‹œํ•˜๊ณ ์ž ํ•˜์˜€๋‹ค. ๊ต์‚ฌํ‰๊ฐ€์˜ ๋ชฉ์ ๏ผŒ๋Œ€์ƒ๏ผŒ๋‚ด์šฉ๏ผŒ์ฐธ์—ฌ์ž๏ผŒ๋ฐฉ๋ฒ• ๋ฐ ์ ˆ์ฐจ๏ผŒ๊ฒฐ๊ณผ ํ™œ์šฉ ๋“ฑ์„ ๋‘˜๋Ÿฌ์‹ธ๊ณ  ๋‹ค์–‘ํ•œ ์Ÿ์ ๋“ค์ด ์ œ๊ธฐ๋˜๊ณ  ์žˆ๋Š” ๊ฒƒ์œผ๋กœ ๋‚˜ํƒ€๋‚ฌ๋‹ค. ๊ทธ๋Ÿฌํ•œ ์Ÿ์ ๋“ค์„ ๊ณ ๋ คํ•˜์—ฌ ๊ต์‚ฌํ‰๊ฐ€์˜ ์ฃผ์š” ๊ณผ์ œ๋“ค์„ ๊ต์‚ฌํ‰๊ฐ€ ์ฒด์ œ์˜ ๊ธฐ๋ณธ ์ฒ ํ•™ ๋ฐ ์‹ ๋… ํ™•๋ฆฝ๏ผŒ๊ต์‚ฌํ‰๊ฐ€ ์ฒด์ œ์˜ ๋ชฉ์  ๋ฐ ๊ตฌ์กฐ ์žฌ์ •๋ฆฝ๏ผŒ๊ต์‚ฌํ‰๊ฐ€ ๋Œ€์ƒ์˜ ๋ถ„ํ™” ๋ฐ ์ฐจ๋ณ„ํ™”๏ผŒ๊ต์‚ฌํ‰๊ฐ€ ๋‚ด์šฉ์˜ ์žฌ๊ตฌ์„ฑ๏ผŒ๋‹ค๋ฉดํ‰๊ฐ€์˜ ํ•ฉ๋ฆฌ์  ์ •์ฐฉ๏ผŒ๊ต์‚ฌํ‰๊ฐ€ ๊ณผ์ •์—์„œ์˜ ๊ต์‚ฌ ๊ถŒํ•œ ๋ฐ ์ž๊ธฐ์ฃผ๋„์„ฑ ๊ฐ•ํ™”๏ผŒํ‰๊ฐ€ ๋ฐฉ๋ฒ•์˜ ๊ฐœ์„ ๏ผŒํ‰๊ฐ€ ๊ฒฐ๊ณผ์˜ ๊ณต๊ฐœ ๋ฐ ์ ์ ˆํ•œ ํ™œ์šฉ๏ผŒ๊ต์‚ฌํ‰๊ฐ€์— ๋Œ€ํ•œ ์ฃผ๊ธฐ์  ์ž๋ฃŒ ์ˆ˜์ง‘๏ผŒ๊ต์‚ฌํ‰๊ฐ€์™€ ๊ด€๋ จํ•œ ํ–‰ยท์žฌ์ • ์ง€์›์ œ์ฒด ๊ตฌ์ถ•๏ผŒ๊ต์‚ฌํ‰๊ฐ€์™€ ํ•™๊ตํ‰๊ฐ€๏ผŒ์žฅํ•™๏ผŒ์ปจ์„คํŒ… ๋“ฑ ์—ฌํƒ€ ์งˆ ๊ด€๋ฆฌ ๊ธฐ์ œ์™€์˜ ๊ด€๊ณ„ ์ •๋ฆฝ๏ผŒ๊ต์‚ฌํ‰๊ฐ€์— ์ž„ํ•˜๋Š” ํ•™๊ต๊ตฌ์„ฑ์›๋“ค์˜ ๋Šฅ๋™์  ์ž์„ธ ํ™•๋ฆฝ ๋“ฑ์œผ๋กœ ์ œ์‹œํ•˜์˜€๋‹ค. ๊ทธ๋Ÿฌํ•œ ๊ณผ์ œ๋“ค์ด ์ง€ํ–ฅํ•˜๋Š” ๊ต์‚ฌํ‰๊ฐ€์˜ ๋ฐฉํ–ฅ์„ ์ข…ํ•ฉํ•˜๋ฉด ํ•™๊ต๊ตฌ์„ฑ์› ๊ฐ„์˜ ๋ฐ˜์„ฑ์  ๋Œ€ํ™”๋กœ์„œ์˜ ๊ต์‚ฌํ‰๊ฐ€๋ผ๊ณ  ํ•  ์ˆ˜ ์žˆ๋‹ค

    ๋‚ญ์ข… ์ œ๊ฑฐ๋ฅผ ์œ„ํ•œ ๊ธ€๋ฆฌ์„ธ๋กค-๋กœ์ฆˆ๋ฒต๊ฐˆ-ํด๋ฆฌ๋„์นด๋†€(GRP) ๊ฒฝํ™”์š”๋ฒ• ํผ์˜ ๊ฐœ๋ฐœ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์œตํ•ฉ๊ณผํ•™๊ธฐ์ˆ ๋Œ€ํ•™์› ๋‚˜๋…ธ์œตํ•ฉํ•™๊ณผ,2020. 2. ์ด๊ฐ•์›.Polycystic kidney disease (PKD) is a common genetic disorder that comes with a proliferating and enlarging cyst that ultimately leads to loss of kidney function. Since an enlarged cyst is the primary factor for limited kidney function, the vast cyst is surgically removed by laparoscopic deroofing or by sclerosant, which is a relatively nascent treatment method that entails complications and sometimes failure due to cyst fluid refilling and infection. In this study, we suggest a more stable and effective polidocanol foam with glycerol and Rose Bengal (GRP form) to prevent cyst regeneration and irritation that is caused by required body movement during treatment. GRP form inhibits cellular proliferation and disrupts cellular junction, e-cadherin, and cyst formation. This advanced form also elongates foam retention time and retards foam degeneration in comparison to polidocanol foam only. The GRP foam shows to be a safe and effective treatment as a commercial grade polidocanol foam form from an in vivo study. Thus, this study provides an advanced polidocanol form by adding glycerol and rose-Bengal to help existing sclerotherapy.๋‹ค๋‚ญ์„ฑ ์‹ ์žฅ ์งˆํ™˜ (PKD) ์€ ๊ถ๊ทน์ ์œผ๋กœ ์‹ ์žฅ ๊ธฐ๋Šฅ ์ƒ์‹ค์„ ์ดˆ๋ž˜ํ•˜๋Š” ๊ฐ€์žฅ ํ”ํ•˜๊ฒŒ ๋ฐœ์ƒํ•˜๋Š” ์œ ์ „์  ์žฅ์• ๋กœ, ๋‚ญ์ข…์ด ์ฆ์‹ํ•˜๊ณ  ํ™•๋Œ€ํ•œ๋‹ค. ํ™•๋Œ€ ๋ฐ ์ฆ์‹ํ•œ ๋‚ญ์ข…์€ ์‹ ์žฅ์˜ ๊ธฐ๋Šฅ์„ ์ €ํ•ดํ•˜๋Š” ์ฃผ์š” ์š”์ธ์ด๊ธฐ ๋•Œ๋ฌธ์— ๊ฒฝํ™”์ œ๋กœ ๊ด‘๋Œ€ํ•œ ๋‚ญ์ข…์„ ์™ธ๊ณผ์ ์œผ๋กœ ์ œ๊ฑฐํ•œ๋‹ค. ์ด ์—ฐ๊ตฌ์—์„œ ๊ธ€๋ฆฌ์„ธ๋กค๊ณผ ๋กœ์ฆˆ๋ฒต๊ฐˆ์„ ํด๋ฆฌ๋„์นด๋†€ ํผ์— ์ฒจ๊ฐ€ํ•˜์—ฌ ์•ˆ์ •์ ์ด๊ณ  ํšจ๊ณผ์ ์ธ, ๊ธ€๋ฆฌ์„ธ๋กค-๋กœ์ฆˆ๋ฒต๊ฐˆ-ํด๋ฆฌ๋„์นด๋†€ ํผ์„ ๊ฐญ๋ผํ•˜์˜€๋‹ค(GRP vํผ). GRP ํผ์€ ์„ธํฌ ์ฆ์‹์„ ์–ต์ œํ•˜๊ณ  ์„ธํฌ ์ ‘ํ•ฉ์— ์“ฐ์ด๋Š” E-cadherin์˜ ๋ฐœํ˜„์„ ์–ต์ œํ•œ๋‹ค. ์ด GRPํผ์€ ํผ์˜ ์œ ์ง€ ์‹œ๊ฐ„์„ ์—ฐ์žฅํ•˜๊ณ , ๊ธฐ์กด์˜ ํผ๋ณด๋‹ค ์„ธํฌ๋…์„ฑ์ด ๋›ฐ์–ด๋‚จ์„ ์ฆ๋ช…ํ–ˆ๋‹ค. GRPํผ์€ ๋˜ํ•œ ๊ธฐ์กด์˜ ์ƒ์—…์  ๋“ฑ๊ธ‰์˜ ํด๋ฆฌ๋„์นด๋†€ ํผ๋ณด๋‹ค ํšจ๊ณผ์ ์ด๊ณ  ์ด์™€ ๋™๋“ฑํ•˜๊ฒŒ ์•ˆ์ „ํ•จ์„ ์‹คํ—˜์ ์œผ๋กœ ๋ฐํ˜€๋ƒˆ๋‹ค.Thesis and Dissertation Deposit Agreement 3 Abstract 4 Introduction 7 Result and Discussion 9 Conclusion 17 Material and Methods 18 Figures and Supplementary Materials 20 Reference 29 Abstract (Korean) 32Maste

    Maurice Ravel์˜ ใ€ŒLe tombeau de couperinใ€์— ๋Œ€ํ•œ ์—ฐ๊ตฌ

    No full text
    ํ•™์œ„๋…ผ๋ฌธ(์„์‚ฌ)--์„œ์šธ๋Œ€ํ•™๊ต ๋Œ€ํ•™์› :์Œ์•…๊ณผ ํ”ผ์•„๋…ธ์ „๊ณต,1996.Maste
    corecore