인간 threonyl-tRNA synthetase의 혈관신생인자 및 뮤신 생합성 조절 기전에 관한 연구

학위논문 (박사)-- 서울대학교 대학원 약학대학 약학과, 2017. 8. 김성훈.Aminoacyl-tRNA synthetases (ARSs) are essential enzymes for protein synthesis to link specific amino acids to their cognate tRNAs. Recent studies have shown that ARSs, considered as a sort of housekeeping enzyme, are now involved in a variety of functions such as transcription, translation, proliferation, inflammation, angiogenesis and cell death. This study is focused on the human threonyl-tRNA synthetase (TRS) and its potential role. In chapter I, the results show that human TRS functions as a translational initiation factor to regulate vertebrate-specific translation initiation via eIF4E homologous protein (4EHP). TRS selectively interacts with 4EHP in a manner similar to the eIF4G interaction with eIF4E. In this way, TRS acts as a scaffold protein to assemble eIF4A, consequently forming eIF4F-like complex. Importantly, complex formation is evolutionary gain-of-function to control protein synthesis of a subset of mRNAs necessary for development of the vertebrate system, verified by endothelial cell migration and vessel formation as well as in vivo zebrafish embryo vascularization assays. In chapter II, the results show that TRS specifically regulates biosynthesis of mucin1 (MUC1) through catalytic activity. The levels of MUC1 protein are affected by threonine and biosynthesis of MUC1 in pancreatic cancer is sensitive to the activity and expression of TRS that incorporates threonine to MUC1. TRS catalytic inhibitors and threonine starvation attenuate MUC1-dependent pancreatic cancer cell migration. In addition, tissue levels of TRS and MUC1 are positively correlated in clinical tumor specimen and expression of both proteins at high level is associated with poor survival outcome of the patients. To summarize, chapter I study discovers an unexpected role of TRS in regulating translation initiation in vertebrates and uncovers a previously unidentified cap-dependent translation initiation mechanism that represents an evolutionary gain of function in vertebrates. Chapter II study provides several evidences showing the potential role of TRS in the migration of human pancreatic cancer cells by enhancing MUC1 biosynthesis, suggesting a novel insight into targeting TRS as a new way to pancreatic cancer.Chapter I. Threonyl-tRNA synthetase activates vertebrate-specific translational initiation via eIF4E homologous protein 1 Title 1 Abbreviation 2 Abstract 3 Introduction 4 Results 7 Specific interaction of TRS UNE-T region with 4EHP 7 Structure of the TRS UNE-T and 4EHP complex 8 Structural details of the TRS UNE-T and 4EHP interaction 10 Interaction between 4EHP and TRS as a gain of function in vertebrates 12 eIF4G-like function of TRS 13 Functional implication of the TRS and 4EHP complex 16 Vertebrate-specific translational control of angiogenesis via the TRS and 4EHP complex 19 Discussion 53 Materials and Methods 56 References 69 Chapter II. Inhibition of MUC1 biosynthesis via threonyl-tRNA synthetase suppresses pancreatic cancer cell migration 76 Title 7 Abbreviation 77 Abstract 78 Introduction 79 Results 81 Threonine deprivation reduces MUC1 levels in pancreatic cancer cells 81 Threonine deprivation suppresses pancreatic cancer cell migration 82 TRS regulates MUC1 biosynthesis 83 TRS inhibitors suppress pancreatic cancer cell migration 84 TRS affects pancreatic cancer cell migration 85 TRS and MUC1 levels are positively correlated with pancreatic cancer 86 Discussion 106 Materials and Methods 108 References 115Docto

의사 레이블링된 조건적 적대적 생성 신경망을 이용한 단절된 매니폴드 학습

학위논문 (석사) -- 서울대학교 대학원 : 공과대학 컴퓨터공학부, 2020. 8. 장병탁.Previous studies pointed out that the generation process via a simply connected prior and a single generator may lead to the mode collapse problem in GANs. Introducing multiple generators and a classifier mitigated mode collapse problem. However, in many cases, we do not know the number of manifolds in advance. If the number of generators is greater than the number of manifolds, the classifier inevitably lays its decision boundaries on manifolds. Even if we do know the number of manifolds, the decision boundaries may be laid on manifolds. In these cases, generators do not sample data near decision boundaries. To remedy this problem, we propose pseudo- labeled cGANs which match two joint distributions p_r (x, c) and p_g (x, c) instead of two marginal distributions p_r(x) and p_g(x). Our model samples data near decision boundaries while maintaining the strength of classifier-based GANs. Empirically, our model is insensitive to the number of generators on Moons, MNIST, and CIFAR10 datasets.이전 연구들은 단순 연결된 사전 확률과 하나의 생성자를 통한 생성 과정은 GAN에서의 모드 붕괴 문제를 야기할 수 있음을 지적하였다. 여러 개의 생성자와 하나의 분류기를 사용하여 각각의 생성자가 다른 데이터 공간에 집중하게 하는 것 은 모드 붕괴 문제를 완화시켰다. 그러나, 많은 경우에 우리는 매니폴드의 갯수를 미리 알지 못한다. 생성자의 갯수가 매니폴드의 갯수보다 많으면 분류기는 결정 경 계를 매니폴드 위에 두게 된다. 우리가 매니폴드의 갯수를 미리 안다고 하더라도, 결정 경계는 매니폴드 위에 놓일 수 있다. 이러한 경우에 생성자는 결정 경계 근처 에서 데이터를 추출하지 않는다. 이 문제를 해결하기 위해, 우리는 의사 레이블링된 조건적 적대적 생성 신경망을 제안한다. 제안된 신경망은 두 주변확률분포 p_r(x) 와 p_g(x)를 가깝게 하는 대신 두 결합확률분포 p_r(x,c) 와 p_g(x,c)를 가깝게 한다. 우리 모델은 결정 경계 근처 에서도 데이터를 추출하면서도 분류기 기반의 적대적 생성 신경망의 강점을 유지한다. 경험적으로 우리 모델은 Moons, MNIST 그리고 CIFAR10 데이터셋에서 생성자의 숫자에 덜 민감함을 보였다.1 Introduction 1 2 Preliminaries 3 2.1 GenerativeAdversarialNets 3 2.2 LearningDisconnectedManifolds 3 2.3 FlawsofClassifier-basedGANs 8 3 Method 10 4 Experiments 14 4.1 Moons 17 4.2 MNIST . 18 4.3 CIFAR10 20 4.4 CelebA . 21 5 Conclusion 28 Abstract (In Korean) 33 A Network Architecture and Hyperparameters 34Maste

Source depth estimation in shallow sloped bottom based on channel impulse response

수동 소나를 통해 음원의 위치를 알아내고자 하는 연구는 국방 안보로부터 해상공사에 이르기까지 다양한 분야에서 지속되고 있다. 그중 2019년 조성일 등은 채널 임펄스 응답의 교차점을 통하여 음원의 깊이를 추정하고자 하는 연구를 진행하였다. 해당 연구는 수평한 해저면을 가정하여, 경사가 없는 해역에만 적용할 수 있다는 제약이 있다. 본 연구에서는 경사가 있는 조건에서 채널 임펄스 응답의 교차점과 음원의 깊이간의 상관관계에 대해서 연구하였고, 채널 임펄스 응답의 교차점 깊이가 음원의 깊이, 음원 위치의 수심, 수신기 위치의 수심을 통해 비율적으로 계산될 수 있음을 확인하였다. 또한 KAM08 실험의 현장 자료에 본 알고리즘을 적용하여 수심대비 1.15% 이내의 오차를 확인하였다. 한편, 수신기에 기록된 신호만을 가지고 알고리즘을 운영하였을 때 음원의 깊이가 수심에서 차지하는 비율만을 알 수 있다는 제약을 확인하였다.1. 서 론 1 1.1. 연구 배경 1 1.2. 연구 동향 1 1.3. 연구 목적 3 1.4. 논문의 구성 3 2. 배경 이론 4 2.1. 음선 모델 (Ray model) 4 2.2. 채널 임펄스 응답의 교차점 6 3. 시뮬레이션 9 3.1. 송신신호 (Source signal) 9 3.2. 거리 독립 모델 - 1 (Pekeris Waveguide model) 11 3.2.1. 환경 조건 11 3.2.2. BELLHOP model 12 3.2.3. KRAKEN model 14 3.2.4. 비교 및 검토 15 3.3. 거리 독립 모델 - 2 (Flat bottom model) 17 3.3.1. 환경 조건 17 3.3.2. BELLHOP model 18 3.4. 거리 종속 모델 - 1 (Up slope model) 20 3.4.1. 환경 조건 20 3.4.2. BELLHOP model 21 3.5. 거리 종속 모델 - 2 (Down slope model) 23 3.5.1. 환경 조건 23 3.5.2. BELLHOP model 24 3.6. 거리 종속 모델 - 3 (Concave model) 26 3.6.1. 환경 조건 26 3.6.2. BELLHOP model 27 3.7. 거리 종속 모델 - 4 (Convex model) 28 3.7.1. 환경 조건 28 3.7.2. BELLHOP model 29 3.8. 거리 종속 모델 - 5 (Up slope model) 30 3.8.1. 환경 조건 30 3.8.2. BELLHOP model 31 3.9. 거리 종속 모델 - 6 (Down slope model) 33 3.9.1. 환경 조건 33 3.9.2. BELLHOP model 34 3.10. 거리 종속 모델 - 7 (Concave model) 36 3.10.1. 환경 조건 36 3.10.2. BELLHOP model 37 3.11. 거리 종속 모델 - 8 (Convex model) 38 3.11.1. 환경 조건 38 3.11.2. BELLHOP model 39 3.12. 시뮬레이션 결과 40 4. 해상 자료 적용 42 4.1. 환경 조건 42 4.2. 채널 임펄스 응답 44 4.3. 결과 47 5. 결론 48Maste

Al doped zinc oxide films on the plastic substrate by inductively coupled plasma assisted sputtering

학위논문(박사)--서울대학교 대학원 :재료공학부,2006.Docto

금융 시장 클러스터 분석에 기반한 포트폴리오 관리

학위논문 (박사)-- 서울대학교 대학원 : 산업공학과, 2016. 8. 장우진.Decades have passed since the financial market began to receive attention from academia. Financial Economics became a solid branch of Economics, and statistical tools and Econometrics were exhaustively employed to analyze the financial data from every possible directions. A framework proposed using mathematical models was a catalyst for expansion of the market. Analytical tools from other fields such as Signal Processing and Physics discovered phenomena ubiquitous in financial data and established the stylized facts. Though these studies did deepen the understanding of the financial market, they werent sufficient to prevent financial crises. Institutional investors and policy makers helplessly watched the market tumbles and the academia was unable to provide a clear answer and often held responsible for crises. An apparent conclusion was that the financial market is far from being fully grasped, and further study is necessary. Recently, Network theory gained popularity as a tool to interpret the financial market structure. Analytical methods found in this field such as hierarchical tree and Minimum Spanning Tree were effective to visualize relative positions and interaction between assets. A network analysis begins with a similarity/dissimilarity measure to represent the system of objects. Statistical correlation between assets were extensively studied and accepted as a good quantity to measure the similarity/dissimilarity of assets. Another approach to utilize a dissimilarity measure is data mining. Data mining methods are particularly useful to process large amount of data in an exploratory research. Given that the financial market is yet to be explained such approach might provide an insight which was overlooked before. Therefore, clustering analysis, one of the most well-known data mining methods, was applied to the financial market. In this study, correlation coefficients between stocks were measured and transformed using a distance function. A well-established distance function preserves the topology of the original correlation matrix. It is a good metric to see the stocks as they were. Clustering analysis was then performed on dissimilarity matrices, which are correlation matrices transformed by a distance function. Clustering analysis is designed to put similar objects together in a cluster. Though not in a quantitative way as the clustering analysis, the investors and market participants already have a framework to group similar firms together. The categorization of firms using industrial sector such as the one given by MSCIs Global Industry Classification Standard is accepted as the standard approach to group firms together. Investors would compare the firms in the same sector and add the most promising ones to their stock portfolio. One of the objectives of this study is to test whether the quantitative methods agree with the traditional classification by sectors. Firms were grouped by their correlations in stock returns and the members of the cluster were individually identified by their industrial sector. When a small data set of largest 30 firms by market capitalization in Korea were used to create a dendrogram, firms in the same sector were often found next to each other on the tree which suggests they are close to each other. There were few exceptions and the overall structure of trees varies for different correlation coefficients but a large part of the data would agree with the classification by the sector. However, when a clustering analysis was performed on a larger data set of 200 firms in Korea, most clusters were made of firms from different sectors and clusters rarely had more than 75% covered by a single sector which implies even within a sector, there is no clear dominant pattern which the members of the sector follow. A portfolio of stocks were constructed based on the clustering analysis. A hypothesis was that if the clustering analysis was able to capture the market structure, the portfolio created based on this information should outperform benchmarks such as the market index. The largest 200 firms by market capitalization in Korea were used to for the analysis, and portfolios of 10, 20, and 30 stocks were built and their performance was recorded. Stocks were chosen randomly from each clusters and the average performances of 1000 such portfolios were compared to the benchmarks. Since the stocks were chosen randomly, another benchmark, a portfolio of stocks randomly chosen from the entire data set was created. The purpose of the random portfolio is to determine whether there is a statistical difference in choosing stocks from portfolio or choosing in a completely random fashion. All clustering portfolios were able to outperform the market index but many failed to beat the random portfolio in terms of return-to-risk ratio. One of the possible explanation was found that the clustering analysis was able to identify a group of underperforming stocks and by choosing equal number of stocks from each clusters, the clustering portfolio had a relatively larger number of underperforming stocks. For an investor, the purpose of creating a stock portfolio is not to analyze the market structure but to buy diverse stocks with varying risk profiles thereby generating positive excess returns with an acceptable level of downside risk. Therefore, a trading simulation was performed to see if the clustering portfolios can be used to serve this purpose. Correlations of stocks were estimated using the historical data before a portfolio was launched, and then the portfolios were constructed in the same manner as the previous section. The portfolios were launched after the period of correlation estimation, so no information regarding the period of investment was incorporated in the portfolios. Although the clustering portfolios did outperform the market index, none of them were able to beat the random portfolio. A marked underperformance of clustering portfolios was detected for most of the portfolios. Detailed analyses of each portfolios and their clusters revealed that there were clusters of underperforming stocks and the portfolios had a disproportionately large number of underperforming stocks in their portfolio. By trimming down the underperforming clusters and thereby removing them from the portfolio construction step, the clustering portfolios were able to beat the random portfolio. The framework was formalized and using the US market, an extended portfolio management over 20 years were simulated. Clustering portfolios were constructed in 1990 and were managed until the end of 2015. Three rebalancing periods of 3 months, 6 months and 12 months were chosen and the assets were reallocating every rebalancing period to study the effect of rebalancing frequency. Three correlation estimation periods of 1 year, 3 years and 5 years were chosen and correlation coefficients were estimated over a given period to study how changing correlation estimation period would affect the performance of portfolio. Many clustering portfolios were unable to outperform the market index, and it was found that neither rebalancing period nor correlation estimation period had a linear relationship with the performance of portfolios. The cluster trimming process was formalized with rules and when a cluster with the most firms with net earnings loss over a long correlation estimation period of 3 years or 5 years was removed, the average return and return-to-risk ratio was improved. The result makes sense because firms with persistent earnings loss are likely to be struggling and adding them to portfolio is likely to be detrimental for portfolios performance. Another rule found was that when clusters with more firms with net earnings loss than firms with net earnings gain were removed, the performance of portfolios was improved significantly. The two rules were applied simultaneously and all clusters which satisfied the conditions were removed. The portfolios created without those clusters were able to outperform other clustering portfolios and the benchmark index. The purpose of this research was to analyze the market structure using clustering analysis based on correlation coefficients and propose a framework to create a stock portfolio. It was found that the classification by sector is insufficient to create a diversified portfolio. A framework to construct a portfolio based on clustering analysis was proposed and the trimming process to remove clusters of inferior stocks was introduced.Chapter 1 Introduction 1 1.1 Background and Motivation 1 1.2 Research Objectives 6 1.3 Organization of the Research 9 Chapter 2 Literature Review 10 2.1 Financial Time Series and Correlation 10 2.2 Clustering Financial Time Series 18 2.3 Modern Portfolio Theory and Portfolio Analysis 23 Chapter 3 Dissimilarity Metrics 27 3.1 Correlation Analysis of Financial Time Series 27 3.2 Random Matrix Theory and Filter Correlation Matrix 37 3.3 Dissimilarity Metrics and Dendrogram 42 Chapter 4 Clustering and Portfolio Analysis 48 4.1 Return Clusters and Industrial Composition 48 4.2 Market Structure and Portfolio Analysis 59 4.3 Trading Simulation 74 4.4 The US market 91 Chapter 5 Conclusion 113 5.1 Summary and Implications 113 5.2 Contributions 120 5.3 Limitations and Future Research 121 Bibliography 123 Appendix 132 Abstract in Korean 151Docto