37 research outputs found
์ ์ฌ๊ณ์ธต๋ชจํ์ ํ์ฉํ์ฌ
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ๊ณต๊ณผ๋ํ ํ๋๊ณผ์ ๊ธฐ์ ๊ฒฝ์ยท๊ฒฝ์ ยท์ ์ฑ
์ ๊ณต, 2019. 2. ์ด์ข
์.๊ธ๋ก๋ฒ ์ ๋ ฅ์์ฅ์ ์ ์ฌ์์๋์ง ๋ฐ์ ์ ์ค์ฌ์ผ๋ก ๊ธ๊ฒฉํ๊ฒ ์ ํ๋๊ณ ์์ผ๋ฉฐ, ํ๊ตญ ์ ๋ถ๋ 2030๋
๊น์ง ๋ฐ์ ๋์ 20%๋ฅผ ์ ์ฌ์์๋์ง๋ก ํ๋ํ๋ ํ๊ธฐ์ ์ธ ์๋์ง ์ ํ ์ ์ฑ
์ ์๋ฆฝํ์๋ค.
๊ธ๋ก๋ฒ ์ ์ฌ์์๋์ง์ ํ๋๋ ๊ตญ๊ฐ๋ณ๋ก ๋ค์ ์ฐจ์ด๋ ์์ผ๋ ๊ตญ๋ฏผ ์ํ ๋ฐ ์์ ๊ณผ ๋ฐ์ ํ ์ ๋ ฅ์ฐ์
์ด๋ผ๋ ํน์ฑ๊ณผ ์ ๋ถ์ ์คโ์ฅ๊ธฐ์ ์ธ ํ๋ ๋ชฉํ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ๊ณต๊ฒฉ์ ์ง์ ์ ์ฑ
์ ์ํด ์ถ์ง๋๋ ์ ๋ถ ์์กดํ ์ฐ์
์ด๋ผ๋ ์ ์์ ๊ด๋ จ ์ ์ฑ
์ ๋ํ ๊ตญ๋ฏผ ์์ฉ์ฑ์ด ์ ์ฐจ ์ค์ํ๊ฒ ๋ค๋ค์ง๋ ๊ณตํต์ ์ ๊ฐ์ง๊ณ ์๋ค.
์ด๋ฌํ ๋งฅ๋ฝ์์ ๋ณธ ์ฐ๊ตฌ์์๋ ์ ๋ถ๊ฐ 2012๋
๋์
ํ ์ ์ฌ์์๋์ง ๊ณต๊ธ์๋ฌดํ(Renewable Portfolio StandardRPS) ์ ์ฑ
์์ฑ์ ๋ํ ๊ตญ๋ฏผ์ ์ด์ง์ ์ ํธ์ ์์ฉ์ฑ์ ์ปจ์กฐ์ธํธ ์กฐ์ฌ(Conjoint Survey)์ ์ ์ฌ๊ณ์ธต๋ชจํ(latent class model)์ ํ์ฉํ์ฌ ๋ถ์ํ์๋ค. ์ดํ, ์ถ์ ๊ฒฐ๊ณผ๋ฅผ ๋ฐํ์ผ๋ก ์๋๋ฆฌ์ค ๋ถ์์ ์ฌ์ฉํ์ฌ, ํ๊ตญ ์ ๋ถ๊ฐ ์ถ์งํ๋ ์ ์ฌ์์๋์ง ํ๋ ์ ์ฑ
์ ๋ํ 2020๋
, 2025๋
2030๋
๋จ๊ณ๋ณ ๊ตญ๋ฏผ์ ๊ณ์ธต๋ณ ์์ฉ๋ฅ ์ ์๋ฎฌ๋ ์ด์
ํ์๋ค.
์์ธ๋ฌ, ์ ์ฑ
์์ฉ์ฑ์ ์ํฅ์ ๋ฏธ์น๋ ์ ์ฑ
์ธ์ง์์ค ์์ธ๊ณผ ์ปค๋ฎค๋์ผ์ด์
๋งค์ฒด๊ฐ์ ์๊ด๊ด๊ณ ๋ถ์์ ํตํด ๊ณ์ธต๋ณ ํจ๊ณผ์ ์ธ ์ ๋ณดํ๋ ๊ฒฝ๋ก๋ฅผ ์๋ณํ์๋ค.
์ฐ๊ตฌ๊ฒฐ๊ณผ, ์ ์ฌ์์๋์ง ํ๋ ์ ์ฑ
์ ๋ํ ๊ตญ๋ฏผ์ ์ ํธ๋ 2๊ฐ์ ์ด์ง์ ๊ณ์ธต์ผ๋ก ์๋ณ๋์์ผ๋ฉฐ, ์ฐ๊ฐ ์ ์ ์๊ฐ ๊ฐ์๋ฅผ ์ ํธํ๋ ํ์์ ์ง๊ณ์ธต๊ณผ ์จ์ค๊ฐ์ค ๋ฐฐ์ถ ๊ฐ์๋ฅผ ์ ํธํ๋ ํฉ๋ฆฌ์ ์ ํ๊ณ์ธต์ผ๋ก ๋๋ ์ง๋ ๊ฒ์ผ๋ก ๋ถ์ํ์๋ค. ์ ํธ์ ์ด์ง์ฑ์ ์ํฅ์ ๋ฏธ์น ์ ์ฑ
์ธ์ง ์์ธ์ ์ ์ฌ์์๋์ง ํ์์ฑ ๊ณผ ์ ์ฌ์์๋์ง์ ๋น์ฉ๊ณผ ํธ์ต์ ๋ํ ํ๊ฐ์๋ค.
์ ์ฌ์์๋์ง ํ๋ ์ ์ฑ
์ ๋ํ ๊ตญ๋ฏผ ์์ฉ๋ฅ ์๋ฎฌ๋ ์ด์
๊ฒฐ๊ณผ๋ 2020๋
63.2%, 2025๋
48.95%, 2030๋
38.15%๋ก ๋ํ๋ฌ๋ค. ๋จ๊ณ์ ์งํ์ ๋ฐ๋ฅธ ๊ตญ๋ฏผ ์์ฉ๋ฅ ํ๋ฝ์ ๊ฐ์ฅ ํฐ ๋์ธ์ ์ ๊ธฐ์๊ธ ์์น์ธ ๊ฒ์ผ๋ก ๋ถ์๋์๋ค.
๊ทธ๋ฆฌ๊ณ , ์ ์ฑ
์ธ์ง์ ์ฐ๊ด์ฑ์ด ๋์ ์ปค๋ฎค๋์ผ์ด์
์ฑ๋์ ์ธํฐ๋ท, ์ฃผ๋ณ์ง์ธ, ๊ทธ๋ฆฌ๊ณ ์ ๋ฌธ/์ก์ง๋ก ๋ ๊ณ์ธต์ด ๋์ผํ๊ฒ ๋ํ๋ฌ์ผ๋, ์ปค๋ฎค๋์ผ์ด์
์ถ์ฒ๋ ๊ณ์ธต๋ณ๋ก ์์ดํ๊ฒ ์๋ณ๋์๋ค.
๊ฒฐ๋ก ์ ์ผ๋ก, ๋ณธ ์ฐ๊ตฌ๊ฒฐ๊ณผ๋ฅผ ๊ธฐ๋ฐ์ผ๋ก ์ ๋ถ์ ์ ์ฌ์์๋์ง ํ๋ ์ ์ฑ
์ถ์ง์ ๋ํด ๋ค์๊ณผ ๊ฐ์ด ์ ์ฑ
์ ํจ์๋ฅผ ์ ๊ณตํ ์ ์์ ๊ฒ์ด๋ค. ์ฐ์ , ์ ๋ถ๋ ์ ์ฑ
์ด๊ธฐ์๋ ์ ์ฑ
์์ฑ์ ๋ํ ๊ตญ๋ฏผ์ ์ด์ง์ ์ ํธ๋ฅผ ๋ฐ๋์ ๊ณ ๋ คํ์ฌ ์ฌํ์ ๊ฐ๋ฑ์ ์ต์ํํ์ฌ์ผ ํ ๊ฒ์ด๋ค. ๋ํ, ์ค์ฅ๊ธฐ์ ์ผ๋ก๋ ์ ๊ธฐ์๊ธ ์์น์ ๋ํ ๊ตญ๋ฏผ ์์ฉ๋ฅ ๊ฐ์์ ๋๋นํ ์ ์๋๋ก ์ง์์ ์ธ ๊ธฐ์ ๊ฐ๋ฐ์ ํตํด ๊ฐ๊ฒฉ๊ฒฝ์๋ ฅ์ ํ๋ณดํ๊ณ , ์ ์ฌ์์๋์ง์ ํ๊ฐ์ ๋ํ ์ ํํ ์ ๋ณด ์ ๊ณต์ ํตํด ๊ตญ๋ฏผ ์์ฉ์ฑ์ ์ ๊ณ ํด์ผ ํ ๊ฒ์ด๋ค. ๋ง์ง๋ง์ผ๋ก, ๊ณ์ธต๋ณ๋ก ํจ๊ณผ์ ์ธ ์ ์ฑ
์ธ์ง์์ธ๊ณผ ์ปค๋ฎค๋์ผ์ด์
๋งค์ฒด๋ฅผ ํ์ฉํ ์ ๋ต์ ์ ์ฑ
ํ๋ณด๋ฅผ ํตํด ์ ์ฑ
์งํ์ ํจ์จ์ฑ์ ์ ๊ณ ํด์ผ ํ ๊ฒ์ด๋ค.The global electricity market is rapidly shifting toward renewable energy, and the Korean government has also established a landmark energy transition policy to expand the proportion of renewable energy in electricity generation to 20% by 2030.
As countries around the world have actively implemented renewable energy policies, public acceptance became more important than before. This is because the renewable energy sector is a government-led policy-dependent industry and an electricity industry closely linked to people's lives and national security.
In this context, this study used a conjoint survey and a latent class model to examine the publics heterogeneous preference and acceptance of the Renewable Portfolio Standard(RPS) policy adopted by the government in 2012. Furthermore, this study conducted simulation analysis based on the estimation results and possible policy scenarios to investigate the public acceptance rate of the Korean government 's renewalble energy policy as of 2020, 2025, and 2030
In addition, by analyzing the correlation between perceptions and information searching patterns of the public, the effective communication channels for information campaign were identified.
As a result, it was found that the public's preference for renewable energy expansion policy can be classified into two heterogeneous classesone is a careful stabilizer preferring to reduce annual power outage and the other is a rational pragmatist who prefers to reduce greenhouse gas emissions. The significant factors that affect public preferences were need for renewable energy and perception of costs and benefits of renewable energy.
From the simulation analysis, it was found that public acceptance for renewable energy expansion policies are 63.2% in 2020, 48.95% in 2025, and 38.15% in 2030. The biggest driver of declining public acceptance rate was the increase in electricity prices.
In conclusion, based on the results of this study, the following policy implications can be provided for the government's policy of expanding renewable energy.
First, the government should minimize the social conflict by considering the heterogeneous preference of the people for the policy attributes when implementing the renewable energy policy. In addition, the government needs to ensure price competitiveness through continuous technological development of renewable energy and to provide accurate information on the estimation of renewable energy in order to avoid or minimize the public resistance for the rise of electricity prices in the medium and long term. Finally, the efficiency of policy implementation should be improved through strategic Public Relation, which utilizes effective communication media for positively changing the public perceptions.์ด ๋ก iii
๋ชฉ ์ฐจ v
ํ ๋ชฉ์ฐจ vii
๊ทธ๋ฆผ ๋ชฉ์ฐจ viii
1. ์๋ก 1
1.1 ์ฐ๊ตฌ๋ฐฐ๊ฒฝ ๋ฐ ๋ชฉ์ 1
1.2 ์ฐ๊ตฌ๋ฒ์ ๋ฐ ๋ฐฉ๋ฒ 3
1.3 ๋
ผ๋ฌธ์ ๊ตฌ์ฑ 5
2. ๋ฌธํ ์ฐ๊ตฌ 6
2.1 ์ฐ๋ฆฌ๋๋ผ ์ ์ฌ์์๋์ง ๋ฐ ์ ์ฑ
ํํฉ 6
2.2 ์ ์ฌ์์๋์ง์ ๋ํ ์ฌํ์ ์์ฉ์ฑ๊ณผ ์ ํธ ์ฐ๊ตฌ 10
2.2.1 ์ ์ฌ์์๋์ง์ ๋ํ ์ฌํ์ ์์ฉ์ฑ ์ฐ๊ตฌ 10
2.2.2 ์ ์ฌ์์๋์ง์ ๋ํ ์๋น์ ์ ํธ ๋ถ์ 11
2.2.3 ์ ์ฌ์์๋์ง ์์ฉ์ฑ์ ์ํฅ์ ๋ฏธ์น๋ ์์ธ ์ฐ๊ตฌ 15
2.3 ์ ์ฑ
์ ์ดํด๋์ ์ปค๋ฎค๋์ผ์ด์
๋งค์ฒด์์ ๊ด๊ณ ์ฐ๊ตฌ 18
2.4 ๊ธฐ์กด ์ฐ๊ตฌ์ ํ๊ณ์ ์ฐ๊ตฌ ๋๊ธฐ 19
3. ์ฐ๊ตฌ๋ชจํ ๋ฐ ๋ฐฉ๋ฒ๋ก 23
3.1 ์ฐ๊ตฌ์ค๊ณ 23
3.2 ์๋ฃ์ ๊ตฌ์ฑ 26
3.2.1 ํ๋ณธ ๊ธฐ์ด ํต๊ณ๋ 26
3.2.2 ์ ํ์คํ 27
3.2.3 ๊ธฐ์ด ์ค๋ฌธ์กฐ์ฌ 31
3.2.4 ์ ์ฌ์์๋์ง ํ๋ ์ ์ฑ
์ ๊ธฐ๋ํธ์ต๊ณผ ๊ธฐ๋๋น์ฉ 36
3.3 ์ค์ฆ๋ชจํ 45
3.3.1 ์ ์ฌ๊ณ์ธต๋ชจํ(Latent class model) 45
3.3.2 ์ ์ฑ
์ธ์ง์์ค๊ณผ ์ปค๋ฎค๋์ผ์ด์
๋งค์ฒด์์ ์๊ด๊ด๊ณ ๋ถ์ 53
4. ์ฐ๊ตฌ๊ฒฐ๊ณผ ๋ฐ ํด์ 55
4.1 ์ ์ฌ๊ณ์ธต๋ชจํ ๋ถ์ ๊ฒฐ๊ณผ 56
4.1.1 ์ ํธ ๊ณ์ธต ๋ฐ ๊ณ์ธต๋ณ ์ ํธ๊ตฌ์กฐ ๋ถ์ 56
4.1.2 ๋ฏผ๊ฐ๋ ๋ถ์ 61
4.2 ์ ์ฌ์์๋์ง ํ๋ ์ ์ฑ
์ ๊ตญ๋ฏผ ์์ฉ๋ฅ ์๋ฎฌ๋ ์ด์
๋ถ์ 69
4.3 ๊ณ์ธต๋ณ ์ ์ฑ
์ธ์ง์์ค๊ณผ ์ปค๋ฎค๋์ผ์ด์
๋งค์ฒด์์ ์๊ด๊ด๊ณ ๋ถ์ 75
5. ๊ฒฐ๋ก ๋ฐ ์์ฌ์ 81
์ฐธ ๊ณ ๋ฌธ ํ 91
๋ถ๋ก : ์ค๋ฌธ์ง 98
Abstract 109Maste
An Intrarenal Adrenocortical Carcinoma Arising in an Adrenal Rest
We describe a case of a 61-year-old Korean man who was diagnosed with renal cell carcinoma that was discovered on abdominopelvic computed tomography obtained after the patient complained of back pain. A radical nephrectomy was performed, and the surgical specimen showed a relatively well-circumscribed and yellowish lobulated hard mass. Microscopically, the tumor showed sheets and nests of hypercellular pleomorphic cells with thick fibrous septation, frequent mitoses, and areas of adrenal cortical-like tissue. Immunohistochemical staining revealed that the tumor cells were positive for inhibin-ฮฑ, vimentin, synaptophysin, and melan A. It also revealed that the tumor cells were negative for pan-cytokeratin, epithelial membrane antigen, paired box 8, ฮฑ-methylacyl-coenzyme A racemase, CD10, cytokeratin 7, carbonic anhydrase 9, c-Kit, renal cell carcinoma, transcription factor E3, human melanoma black 45, desmin, smooth muscle actin, S-100, chromogranin A, CD34, anaplastic lymphoma kinase, and integrase interactor 1. Based on these histopathological and immunohistochemical findings, we diagnosed the tumor as intrarenal adrenocortical carcinoma arising in an adrenal rest. Several cases of intrarenal adrenocortical carcinoma have been reported, although they are very rare. Due to its poor prognosis and common recurrence or metastasis, clinicians and pathologists must be aware of this entity.ope
Expression of DNA methylation-related proteins in breast phyllodes tumor
The purpose of this study is to research the expression of DNA methylation-related proteins in phyllodes tumors of the breast and to study the implication on patient outcomes. We generated tissue microarrays (TMAs)
of 196 phyllodes tumors (PT) and performed immunohistochemical staining for 5-meC and the DNA methylationrelated proteins DNMT1 and ISL-1. The staining results were analyzed and compared with clinicopathologic parameters. A total of 196 cases were included in this study, of which 153 were benign, 27 were borderline, and 16 were malignant. The levels of DNMT1, 5 meC, and ISL-1 in the stromal component of tumors increased with increasing grade (P<0.001). Especially, high stromal positivity of DNMT1 and ISL-1 were associated with increased distant metastasis (P=0.001, and P=0.013, respectively). Univariate analysis for factors associated with decreased disease free survival and overall survival identified DNMT1 high positivity (P=0.002 and P<0.001, respectively) and stromal ISL-1 high positivity (P<0.001 and P<0.001, respectively). Among borderline phyllodes tumors, stromal DNMT1 high positivity was associated with decreased OS (P=0.015). In conclusion, DNA methylation and expression of methylation-related proteins in the stromal component increased with increasing histologic grade in phyllodes tumors. In addition, overexpression stromal expression of DNMT1 and ISL-1 was associated with poor prognosis.ope
The value of phosphohistone H3 as a proliferation marker for evaluating invasive breast cancers: A comparative study with Ki67
BACKGROUND:
Established measurements of proliferation in breast cancer are Ki67 and mitotic-activity-index (MAI), with problems in reproducibility and prognostic accuracy. Phosphohistone H3 (PHH3), a relatively novel IHC marker is specific for mitosis with good reproducibility. We hypothesized that PHH3 would be more reproducible and better represent proliferation than Ki67.
RESULTS:
PHH3 identified easily-missed mitosis by MAI, as demonstrated by upgrading M grade at diagnosis (n = 29/218, evenly distributed). PHH3 accurately found hot-spots, supported by mitotic count agreement between low-power and 10HPFs (R2 = 0.999; P = 0.001). PHH3 was more reproducible than Ki67, measured by five-rater inter-class correlation coefficient (0.904 > 0.712; P = 0.008). Finally, despite a relatively short follow-up (median 46 months; 7 recurrences) PHH3 was the only variable correlated with disease-free survival (P = 0.043), while all other conventional clinicopathologic variables, including Ki67 (P = 0.356), did not.
MATERIALS AND METHODS:
We compared Ki67 and PHH3 for 218 breast cancer surgical cases diagnosed from 2012 to 2013 at Severance hospital. The most representative invasive breast cancer surgical slides were immunohistochemically stained for Ki67 and PHH3.
CONCLUSIONS:
Poor reproducibility and inadequate representation of proliferation of Ki67 and MAI may be improved by PHH3, allowing better accuracy in breast cancer diagnostics.ope
Study on Korean atists studied in Paris in the 1920s and 1930s
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ์์ํ๊ณผ ๋ฏธ์ ์ด๋ก ์ ๊ณต, 2011.8. ์ ํ๋ฏผ.Maste
์ข ์ ์ฐ๊ด ์ฌ์ ์์ธํฌ ์ํ์ ๋ฐ๋ฅธ ์ ๋ฐฉ์ ๋์ฌ ํน์ฑ
In the breast cancer which characteristically produces tumor stroma, reverse Warburg effect is suggested as its tumor metabolism. It is the theory that the metabolic interaction between tumor cells and stromal cells plays an important role in survival and growth of tumor. Cancer-associated fibroblast (CAF), the stromal cell which plays a key role in the theory, is the most important component of tumor microenvironment (TME) and it is recently classified into several subtypes (FAP, S100A4, PDGFRฮฑ, and PDGFRฮฒ), each of which has separate functional characteristics. In this study, we aimed to investigate differences in metabolic interaction between tumor cells and stromal cells depending on subtypes of CAF and investigate impacts of tumor stroma-targeted material on tumorigenesis and growth of tumor depending on subtypes of CAF and finally recognize the possibility of tumor stroma-targeted material as a targeted therapy of breast cancer. We produced four cancer-associated fibroblast (CAF) subtype cell lines, which were stably expressing each CAF marker (FAP, S100A4, PDGFRฮฑ, and PDGFRฮฒ) and analyzed differences in metabolic interaction of each CAF subtype with breast cancer molecular subtypes. We also examined migration assay and invasion assay to investigate the effect of each CAF subtype on metastasis and invasion ability of cancer cells. Then, we produced a tumor xenograft model using BALB/C nude mice to confirm cell line studies. Among four CAF subtypes, we identified that FB-PDGFRฮฒ activated glycolysis, mitophagy, and autophagy of MDA-MB-231 and FB-FAP activated glycolysis and autophagy of triple negative breast cancer (TNBC) cell lines. FB-FAP especially played an important role in cancer behavior of TNBC cells; MDA-MB-231 and MDA-MB-468. Also, in mice xenograft model, it showed that FB-FAP activated glycolysis and autophagy metabolism of TNBC cell lines. In inhibition study with knocked down fibroblasts, FB-siPDGFRฮฒ and FB-siFAP cells, the tumor metabolism and its behavior of MDA-MB-231, MDA-MB-468, and mice xenograft models was consistent with the results from studies of TNBC cell lines and mice xenograft models co-cultured with FB-PDGFRฮฒ and FB-FAP. In conclusion, CAF has heterogeneous characteristic in that each breast cancer molecular subtype shows differences in the expression of metabolic related markers and different cancer behavior depending on co-cultured CAF subtype. Among CAF subtypes, FB-FAP seems to promote tumor formation and growth through glycolysis and autophagy of TNBC and we suggest it has a potential role as a therapeutic target for TNBC.
์ข
์ ๊ธฐ์ง์ ํน์ง์ ์ผ๋ก ํ์ฑํ๋ ์ ๋ฐฉ์์์๋ ์ข
์ ๋์ฌ๋ก ๋ฆฌ๋ฒ์ค ์๋ฒ๊ทธ ํจ๊ณผ๊ฐ ์ ์๋๊ณ ์๋ค. ์ด๋ ์ข
์ ์ธํฌ์ ๊ธฐ์ง ์ธํฌ ๊ฐ ๋์ฌ ์ํธ์์ฉ์ด ์์ผ๋ฉฐ, ์ข
์์ ์์กด ๋ฐ ์ฑ์ฅ์ ์ค์ํ ์ญํ ์ ํ๋ค๋ ์ด๋ก ์ด๋ค. ๋ฆฌ๋ฒ์ค ์๋ฒ๊ทธ ํจ๊ณผ ์ด๋ก ์์ ํต์ฌ ์ญํ ์ ํ๋ ๊ธฐ์ง ์ธํฌ์ธ ์ข
์ ์ฐ๊ด ์ฌ์ ์์ธํฌ๋ ์ข
์ ๋ฏธ์ธ ํ๊ฒฝ์ ๊ตฌ์ฑํ๋ ๊ฐ์ฅ ์ค์ํ ๊ตฌ์ฑ ์์๋ก, ์ต๊ทผ ์ด๊ฒ์ด ์ฌ๋ฌ ๊ฐ์ ์ํ (FAP, S100A4, PDGFRฮฑ, and PDGFRฮฒ) ์ผ๋ก ๋ถ๋ฅ๋๋ฉฐ ๊ทธ๊ฒ๋ค ๊ฐ๊ฐ์ ์๋ก ๋ค๋ฅธ ๊ธฐ๋ฅ์ฑ ํน์ฑ์ด ์๋ ๊ฒ์ผ๋ก ์๋ ค์ก๋ค. ๋ณธ ์ฐ๊ตฌ์์, ์ฐ๋ฆฌ๋ ์ ๋ฐฉ์์์ ์ข
์ ์ฐ๊ด ์ฌ์ ์์ธํฌ์ ์ํ์ ๋ฐ๋ฅธ ์ข
์ ์ธํฌ์ ๊ธฐ์ง ์ธํฌ ๊ฐ ๋์ฌ ์ํธ ์์ฉ์ ์ฐจ์ด๋ฅผ ๊ท๋ช
ํ๊ณ , ์ข
์ ๊ธฐ์ง ํ์ ๋ฌผ์ง์ด ์ข
์ ์ฐ๊ด ์ฌ์ ์์ธํฌ์ ์ํ์ ๋ฐ๋ผ ์ข
์์ ๋ฐ์ ๋ฐ ์ฑ์ฅ์ ๋ฏธ์น๋ ์ํฅ์ ์กฐ์ฌํ์ฌ, ์ต์ข
์ ์ผ๋ก๋ ์ข
์ ๊ธฐ์ง ํ์ ๋ฌผ์ง์ ์ ๋ฐฉ์ ํ์ ์น๋ฃ์ ๋ก์ ๊ฐ๋ฅ์ฑ์ ์กฐ์ฌํ๋ ๊ฒ์ ๋ชฉํ๋ก ํ์๋ค. ๊ฐ๊ฐ ์์ ์ ์ผ๋ก ์ข
์ ์ฐ๊ด ์ฌ์ ์์ธํฌ ํ์ง์ (FAP, S100A4, PDGFRฮฑ, and PDGFRฮฒ) ๋ฅผ ๋ฐํํ๋ ๋ค ์ข
๋ฅ์ ์ข
์ ์ฐ๊ด ์ฌ์ ์์ธํฌ ์ํ๋ค์ ์ ์ํ์ฌ, ๊ฐ ์ข
์ ์ฐ๊ด ์ฌ์ ์์ธํฌ ์ํ๊ณผ ๊ฐ ์ ๋ฐฉ์ ๋ถ์ ์ํ๋ณ ์ธํฌ ๊ฐ ๋์ฌ ์ํธ์์ฉ์ ์ฐจ์ด๋ฅผ ์์๋ณด์๋ค. ๋ํ ์ด๋ ๋ถ์๊ณผ ์นจ์ค ๋ถ์์ ํตํด ๊ฐ ์ข
์ ์ฐ๊ด ์ฌ์ ์์ธํฌ ์ํ์ด ์์ธํฌ์ ์ ์ด ๋ฐ ์นจ์ค ๋ฅ๋ ฅ์ ๋ผ์น๋ ์ํฅ์ ์์๋ณด์๋ค. ๊ทธ๋ฆฌ๊ณ ๋์๋ BALB/C ๋๋ ๋ง์ฐ์ค๋ฅผ ์ด์ฉํ์ฌ ์ข
์ ์ด์ข
์ด์ ๋ชจ๋ธ์ ์ ์ํ์ฌ ์ธํฌ์ฃผ ์คํ๋ค์ ํ์ธํด ๋ณด์๋ค. ์ ์ํ ๋ค ๊ฐ์ง ์ข
์ ์ฐ๊ด ์ฌ์ ์์ธํฌ ์ํ ์ค์์, FB-PDGFRฮฒ๊ฐ MDA-MB-231์ธํฌ์ ํด๋น์์ฉ, ๋ฏธํ ํ์ง, ์๊ฐํฌ์ ํ์ฑ์ ์ฆ๊ฐ์ํค๊ณ FB-FAP๋ ์ผ์ค ์์ฑ ์ ๋ฐฉ์ ์ธํฌ์ฃผ์ ํด๋น์์ฉ๊ณผ ์๊ฐํฌ์ ํ์ฑ์ ์ฆ๊ฐ์ํด์ ํ์ธํ์๋ค. FB-FAP๋ ํนํ ์ผ์ค ์์ฑ ์ ๋ฐฉ์ ์ธํฌ์ธ MDA-MB-231๊ณผ MDA-MB-468์ ์ข
์ ์ธํฌ ํน์ฑ์ ์ค์ํ ์ญํ ์ ํ๋ ๊ฒ์ ํ์ธํ์๋ค. ๋ํ, ๋ง์ฐ์ค ์ด์ข
์ด์ ๋ชจ๋ธ์์๋ FB-FAP๊ฐ MDA-MB-231๊ณผ MDA-MB-468 ์ธํฌ์ ํด๋น์์ฉ๊ณผ ์๊ฐํฌ์ ๋์ฌ ๊ธฐ์ ์ ํ์ฑํ ์ํด์ ํ์ธํ์๋ค. Knocked down ์ฌ์ ์์ธํฌ์ธ FB-siPDGFRฮฒ์ FB-siFAP ์ธํฌ๋ฅผ ์ด์ฉํ ์ต์ ์คํ์์, MDA-MB-231, MDA-MB-468 ๋ฐ ๋ง์ฐ์ค ์ด์ข
์ด์ ๋ชจ๋ธ์ ์ข
์ ๋์ฌ ๋ฐ ์ข
์ ์ธํฌ ํน์ฑ์ FB-PDGFRฮฒ ์ FB-FAP๋ฅผ ํจ๊ป ๋ฐฐ์ํ ์ผ์ค ์์ฑ ์ ๋ฐฉ์ ์ธํฌ์ฃผ ๋ฐ ๋ง์ฐ์ค ์ด์ข
์ด์ ๋ชจ๋ธ ์ฐ๊ตฌ์์ ๋ณด์ฌ์ค ๊ฒฐ๊ณผ์ ์ผ๊ด๋๊ฒ ๋ํ๋ฌ๋ค. ์ข
ํฉํ๋ฉด, ๊ฐ ์ ๋ฐฉ์ ๋ถ์ ์ํ์ด ํจ๊ป ๋ฐฐ์ํ ์ข
์ ์ฐ๊ด ์ฌ์ ์์ธํฌ ์ํ์ ๋ฐ๋ผ ๋์ฌ ๊ด๋ จ ํ์ง์์ ๋ฐํ ๋ฐ ์ข
์ ์ธํฌ ํน์ฑ์ ์ฐจ์ด๋ฅผ ๋ณด์ธ๋ค๋ ์๋ค๋ ์ ์์ ์ข
์ ์ฐ๊ด ์ฌ์ ์์ธํฌ๋ ์ด์ง์ ์ธ ํน์ฑ์ ๊ฐ์ง๋ค. ์ข
์ ์ฐ๊ด ์ฌ์ ์์ธํฌ ์ํ๋ค ์ค, FB-FAP ๋ ์ผ์ค ์์ฑ ์ ๋ฐฉ์ ์ธํฌ์ฃผ์์ ํด๋น์์ฉ๊ณผ ์๊ฐํฌ์์ ํตํด ์ข
์์ ํ์ฑ ๋ฐ ์ฑ์ฅ์ ์ด์งํ๋ ๊ฒ์ผ๋ก ํ๋จํ์๊ณ , ์ด๊ฒ์ด ์ผ์ค ์์ฑ ์ ๋ฐฉ์์ ์น๋ฃ ํ์ ์ผ๋ก์ ์ ์ฌ์ ์ธ ์ญํ ์ด ์์์ ์ ์ํ๋ค.open๋ฐ
A Study on issue management strategy in policy decision making process : focused on building policy for a radioactive waste repository
ํ์๋
ผ๋ฌธ(์์ฌ)--์์ธ๋ํ๊ต ํ์ ๋ํ์ :ํ์ ํ๊ณผ(ํ์ ํ์ ๊ณต),2007.Maste
๊ฑฐ๋ฆฌํ๋ ฌ์ ์ด์ฉํ ๋ค์ฐจ์ ์ฒ๋๋ฒ๊ณผ Twitter ๋คํธ์ํฌ ์๋ฃ์์ ์์ฉ
ํ์๋
ผ๋ฌธ (์์ฌ)-- ์์ธ๋ํ๊ต ๋ํ์ : ํต๊ณํ๊ณผ, 2013. 2. ์์ํ.Multidimensional scaling is applied in varied fields such as marketing, genetics, ecology, molecular biology, psychology and social networks. In majority, multidimensional scaling has its main purpose to verify the relationship between individuals by embedding high-dimensional observations on a sphere to points on a lower-dimensional sphere. Simply put, multidimensional scaling makes it possible to look through the large data by illustrating them with a simple plot. In the process of applying multidimensional scaling to the data, we need to define a dissimilarity matrix, which reflects the distance between the each pair of the entities. Under the certain restrictions, there can be a variety of distance measures to construct the dissimilarity matrix. In this paper, we introduce several different distance measures possibly used for multidimensional scaling and categorize those measures so that they can be used in an appropriate circumstance. An application to the actual data has been done with the network data from Twitter. By implementing different types of measures to the specific data, we would like to show the importance of selecting an appropriate distance measure for the data.1. Introduction
2. Review of Literature
3. Multidimensional Scaling
4. Application to Twitter Network Data
5. ConclusionMaste