Structural and Functional Studies of Enhanced Intracellular Survival Proteins from Mycobacterium tuberculosis and Mycobacterium smegmatis

학위논문 (박사)-- 서울대학교 대학원 : 화학부, 2014. 2. 서세원.The intracellular pathogen Mycobacterium tuberculosis causes tuberculosis. Enhanced intracellular survival (Eis) protein, secreted by M. tuberculosis, enhances survival of Mycobacterium smegmatis in macrophages. M. tuberculosis Eis was shown to suppress host immune defenses by negatively modulating autophagy, inflammation, and cell death through c-Jun N-terminal kinase (JNK)-dependent inhibition of reactive oxygen species generation. M. tuberculosis Eis was demonstrated to contribute to drug resistance by acetylating multiple amines of aminoglycosides. However, the mechanism of enhanced intracellular survival by M. tuberculosis Eis remains unanswered. Therefore, I have characterized both M. tuberculosis and M. smegmatis Eis proteins biochemically and structurally. I have discovered that M. tuberculosis Eis is an efficient N-acetyltransferase, rapidly acetylating Lys55 of dual-specificity protein phosphatase 16 (DUSP16)/mitogen-activated protein kinase phosphatase-7 (MKP-7), a JNK-specific phosphatase. In contrast, M. smegmatis Eis is more efficient as an N-acetyltransferase. I also show that M. smegmatis Eis acetylates aminoglycosides as readily as M. tuberculosis Eis. Furthermore, M. tuberculosis Eis, but not M. smegmatis Eis, inhibits lipopolysaccharide-induced JNK phosphorylation. This functional difference against DUSP16/MKP-7 can be understood by comparing the structures of two Eis proteins. The active site of M. tuberculosis Eis with a narrow channel appears to be more suitable for sequence-specific recognition of the protein substrate than the pocket-shaped active site of M. smegmatis Eis. I propose that M. tuberculosis Eis initiates the inhibition of JNK-dependent autophagy, phagosome maturation, and reactive oxygen species generation by acetylating Lys55 of DUSP16/MKP-7. My work thus provides new insights into the mechanism of suppressing host immune responses and enhancing mycobacterial survival within macrophages by M. tuberculosis Eis.1. Introduction 2. Material and Methods 2.1 Protein expression, purification, and mutagenesis 2.2 Crystallization and X-ray data collection 2.3 Structure solution and refinement 2.4 Spectrophotometric acetylation assay of aminoglycosides and steady-state kinetic measurements 2.5 In vitro acetylation of synthetic peptides and mass spectrometry 2.6 In vitro protein N-acetylation assay 2.7 Cell culture and Western blotting 3. Results and discussion 3.1 Overall structure and structural similarity search 3.2 Quaternary structure of Eis proteins 3.3 Ligand binding at the active site 3.4 Comparison of aminoglycosides acetyltransferase activity of Eis proteins from M. tuberculosis and M. smegmatis 3.5 Comparison of overall structure of Eis proteins from M. tuberculosis and M. smegmatis 3.6 Identification of DUSP16/MKP-7 as the N-acetylation target of M. tuberculosis Eis. 3.7 Comparison of substrate binding sites in Eis proteins 3.8 M. tuberculosis Eis, but not M. smegmatis Eis, inhibits lipopolysaccharide-induced JNK phosphorylation 3.9 Mechanism of acetylation by M. tuberculosis Eis 3.10 Mechanism of M. tuberculosis Eis protein in immune system of host cell 4. References Abstract (in Korean) Acknowledgements Appendix: Printouts of the first author publications.Docto

기계학습 시스템 설계를 위한 방법

학위논문 (박사)-- 서울대학교 대학원 : 전기·컴퓨터공학부, 2016. 2. 최기영.Machine learning has been paid attention because intelligence such as recognition, decision making, and recommendation is a helpful utility in industrial, medical, transportation, entertainment systems, and others that human need to interact with. As machine learning techniques are extensively applied to various areas, the needs for more robust algorithms and more efficient hardware have been increased. In order to develop an efficient machine learning system, we have researched from high-level algorithm down to low-level hardware logicthe main focus of our work is on ensemble machine learning and stochastic computing (SC). The first work is to combine multiple components, i.e., multiple feature extractors (FE) and multiple classifiers in the aspect of pattern recognition. Ensemble of multiple components is one of challenging approaches for constructing a more accurate classifier. It can handle difficult problems where a single classifier easily makes a wrong decision due to lack of training or parameter optimization. Combining the decisions of participating classifiers statistically reduces the risk of wrong decision. We suggest a hierarchical ensemble framework of multiple feature extractors and multiple classifiers (MFMC). The second work is to construct efficient hardware building blocks for machine learning in order to reduce system complexity and generate high area- and energy-efficient logic, where we exploit the property of machine learning systems that does not require accurate computations. We select stochastic computing (SC), which is an alternative paradigm to conventional binary arithmetic computing. SC can boost efficiency in terms of area, power, and error tolerance, while relaxing the accuracy of computation. The third work is to combine both machine learning and stochastic computing, where we select deep learning. This work presents an efficient DNN design with stochastic computing. Observing that directly adopting stochastic computing to DNN has some challenges including random error fluctuation, range limitation, and overhead in accumulation, we address these problems by removing near-zero weights, applying weight-scaling, and integrating the activation function with the accumulator. The approach allows an easy implementation of early decision termination with a fixed hardware design by exploiting the progressive precision characteristics of stochastic computing, which was not easy with existing approaches. Experimental results show that our approach outperforms the conventional binary logic in terms of gate area, latency, and power consumption.1. Introduction 1 1.1 Hierarchical Ensemble Learning Framework 1 1.2 Hardware Building Block for Machine Learning By Using Stochastic Computing 1 1.2.1 Dynamic energy-accuracy trade-off using stochastic computing in deep neural networks 5 2. A Design Framework for Hierarchical Ensemble of Multiple Feature Extractors and Multiple Classifiers 7 2.1 Introduction 7 2.2 Related work 9 2.3 Proposed hierarchical ensemble system 12 2.3.1 Local Mapping Block and Global Mapping Block 12 2.3.2 Complexity comparison according to composition of LMB 15 2.3.3 Motivation for differentiating local and global mappings17 2.3.4 Reinforcement learning for LMB 19 2.3.5 Construction of Bayesian network from GMB 24 2.4 Experimental results 32 2.4.1 Measure of effectiveness for WMV and RL 33 2.4.2 Pedestrian detection dataset 35 2.4.3 Comparison between GMB and AdaBoost 41 2.4.4 UCI Multiple Features dataset 42 2.4.5 LMB selection 44 2.4.6 Discussion 45 2.5 Conclusion 46 3. Synthesis of Efficient Stochastic Logic for Many-Variable Expressions 49 3.1 Introduction 49 3.2 Related Work 52 3.3 SC Logic Synthesis for Multivariate Expressions 54 3.3.1 Probabilistic Logic 55 3.3.2 Definitions 58 3.3.3 Overview of the Proposed Method 60 3.3.4 Direct Synthesis VS. Kernel-based Synthesis 60 3.3.5 SC Kernel 63 3.3.6 Prime SC Kernel 65 3.3.7 iSC Kernel 68 3.3.8 Relationship Between iSC Kernels 70 3.3.9 Hybrid Scheme 75 3.3.10 Cost Function 76 3.3.11 SC Synthesis Algorithm 78 3.4 Experimental Results 82 3.4.1 Performance of SC Logic Synthesis Algorithm 83 3.4.2 Quality of Synthesis Results 84 3.4.3 Comparison of Accuracy 89 3.5 Conclusion 90 4. An Energy-Efficient Random Number Generator for Stochastic Circuits 91 4.1 Introduction 91 4.2 II. Background 92 4.2.1 Preliminaries 92 4.2.2 Shortcomings of Conventional Approaches 93 4.3 III. Proposed Stochastic Number Generator 96 4.3.1 Overview of the Proposed SNG 96 4.3.2 Even-distribution Encoding 96 4.3.3 Inter-group Randomization 98 4.3.4 Proposed Building Block for Bit Shuffling 100 4.3.5 Intra-group Randomization 102 4.4 Experimental Results 103 4.4.1 Accuracy of Generated Stochastic Bit Stream 104 4.4.2 Area, Delay, Power, Energy and SCC Average 104 4.4.3 Energy Efficiency When Operated under Maximal Precision 105 4.5 Conclusion 106 5. Approximate De-randomizer for Stochastic Circuits 107 5.1 Introduction 107 5.2 Proposed Approximate Parallel Counter 108 5.2.1 Analysis for Gate Count in 1-layer Approximate PC 109 5.2.2 Analysis for Error in 1-layer Approximate PC 110 5.3 Experimental Results 111 5.4 Conclusion 112 6. Dynamic Energy-Accuracy Trade-off Using Stochastic Computing in Deep Neural Networks 113 6.1 Introduction 113 6.2 Background 115 6.4 DNN Using Stochastic Circuit 117 6.4.1 Overview of the Proposed DNN using SC 117 6.4.2 Removing Near-Zero Weights 119 6.4.3 Applying Weight Scaling 120 6.4.4 Activation Function with Accumulation 121 6.5 Early Decision Termination 125 6.5.1 Moving Average Tracking Output Trends 126 6.6 Experimental Results 127 6.6.1 Accuracy of DNN Using SC 128 6.6.2 Effectiveness of Early Decision Termination 129 6.6.3 Comparison of Synthesis Results 130 6.7 Conclusion 132 7. Conclusion 134 Bibliography 136 요약(국문초록) 144Docto

토론

제가 1976년부터 1977년 사이에 1년간，그리고 지난 학기에 한 6개월간 가 있었는데 연구차 갔었기 때문에 실제로 가르쳐본 경험은 전혀없고，독일에서 가장 큰 한국학센터인 Bochum대학에서 교수，조교，학생을과 얘기를 할 기회가 있었습니다. 이번에는 남쪽의 Konstanz대학에 몇 개월 있다가，오는 길에 Tübingen대학에 갔었는데 이곳에 최근 에 한국학과가 생겨 아이캠만이라는분이 정교수로서 한국학을 육성하고 있었습니다. 서독에 서 한국어 교육은， 그 수강생 이 대부분 한국간호원과 결혼한 대학생 들이거나 혹은 한국에 초청을 받은 사람 이 미리 예비적으로 배우는 경우가 많았습니다. Bochum과 Tübingen대 학 외 에 München, Göttingen, 자유베를린, Köln, Frankfurt, Bonn 대학에서 한국학 강좌를 개설하고 있는데 학생 수는 적다고 합니다. 그 중에 Bonn대학이 학생이 상당히 많아서 40~50명되는데 구기성교수가 담당을 하고 있습니다. 덧붙여서 말씀드릴 것은 제가 지난 4월 중순에 영국중부에 있는 더럼(Durham)이라는 도시에서 열린 유렵한국학회에 참석한 일이 있는데，서구라파사람뿐 아니 라 동구라파에 117론서도 소련학자 둘을 포함해서 많이 참석했습니다. 그런데 동구라파 사람들과 서구라파사람들의 한국어 사용관이 완전히달라서，대부분 북한에 유학을 다녀 온 동구라파사람들은 처음부터 한국 사람을 대하면 한국말로 이야기하려고하고 발표를 할때도한국말로 발표하는 사람이 많았는데, 서구라파 출신들은 한국말을 거의 쓰질 않고 발표도 물론 영어로 하여 대조를 이루었습니다

대수함수와 지수함수를 통한 전송신로 손실을 모사한 고속신호 전송회로

학위논문 (박사)-- 서울대학교 대학원 : 공과대학 전기·컴퓨터공학부, 2018. 8. 김재하.This paper describes a transmitter that can emulate a wide variety of frequency-dependent loss characteristics of high-speed DRAM channels, with an aim to facilitate an automated test procedure for DRAM interface that does not require physical reconfiguration of channels. Specifically, the proposed transmitter can generate the waveform of an NRZ data stream that experienced the adjustable amounts of skin-effect loss and dielectric loss of electrical channels. To save the hardware cost of implementing a high-speed, high-resolution digital-to-analog converter, the transmitter constructs the waveform using a set of logarithmic and exponential basis functions, each of which is implemented using a pseudo-logarithmic amplifier and low-bandwidth amplifier with adjustable gain and bandwidth, respectively. The prototype chip fabricated in 65um CMOS consumes 52,000um2 and operates over 1.4~7Gbps while dissipating 38mW at 7Gbps. It is demonstrated that the implemented transmitter can emulate 10~40-long microstrip lines on FR4 material with the peak error less than 6.25% in the pulse response.CHAPTER 1 INTRODUCTION 1 1.1 DRAM INTERFACE. 3 1.1.1. INTERFACE OF THE COMPUTING MEMORY . 4 1.1.2. INTERFACE OF THE MOBILE MEMORY 5 1.1.3. HIGH BANDWIDTH MEMORY . 8 1.2 MEMORY TEST. 9 1.2.1. DRAM TEST SEQUENCE 10 1.3 CHANNEL TESTING ENVIRONMENT IN HIGH-SPEED SERIAL LINK . 13 CHAPTER 2 CHANNEL ATTENUATION AND MODELING 16 2.1 CHANNEL NOISE. 16 2.1.1. SKIN-EFFECT 18 2.1.2. DIELECTRIC LOSS . 20 2.1.3. PCB LOSS MECHANISM . 21 2.2 MEMORY CHANNEL CHARACTERISTICS 25 CHAPTER 3 CHANNEL-EMULATING MODEL 28 3.1 CHANNEL EQUALIZATION AND EMULATION . 30 3.1.1. DIELECTRIC EQUALIZER AND EMULATOR 40 3.1.2. SKIN-EFFECT EQUALIZER 35 3.1.3. SKIN-EFFECT EMULATOR 38 3.2 EMULATION MODEL PROPOSAL 40 CHAPTER 4 THE ARCHITECTURE OF CHANNEL-EMULATING TRANSMITTER 48 4.1 LOGARITHMIC APPROXIMATION . 49 4.1.1. PSEUDO-LOGARITHMIC APPROXIMATION 50 4.1.2. PSEUDO-LOGARITHMIC FOR DATA INPUT. 52 4.2 DIELECTRIC LOSS EMULATOR 56 4.3 EMULATING RESULTS. 57 CHAPTER 5 CIRCUIT IMPLEMENTATION 61 5.1 PROPOSED EMULATION ARCHITECTURE . 62 5.1.1. TRANSMITTER 63 5.1.2. PROGRAMMABLE SHIFT REGISTER AND SERIALIZER . 66 5.1.3. PATTERN GENERATOR 68 5.1.4. CLOCK GENERATOR 69 CHAPTER 6 EXPERIMENTAL RESULTS 73 6.1 EMULATION PROCEDURE 77 6.2 MEASUREMENT ENVIRONMENTS 78 6.3 EMULATION WITH ACTUAL CHANNELS. 84 6.4 SIMULATION WITH S-PARAMETERS 87 CHAPTER 7 CONCLUSION 88 BIBLIOGRAPHY 91 초 록 95Docto

공공기관 복수노조의 통합에 관한 연구 -도로교통공단, 한국환경공단, 한국토지주택공사의 비교를 중심으로-

학위논문 (석사)-- 서울대학교 행정대학원 : 공기업정책학과, 2016. 8. 정광호.본 연구는 현재 복수노조체제하에 있는 공공기관중 노조통합 기 관과 노조 미통합 기관의 비교분석을 통해 노조통합에 결정적으로 미치는 요인을 분석하는데 그 목적이 있다. 선행연구의 검토를 통 해 노조통합 추진에 있어서 영향을 미치는 주요 요인인 조합원수 추세, 교섭권 문제, 위원장의 의지, 대내외 환경변화를 잠정적 독 립변수로 설정하고 이를 노조통합 기관인 도로교통공단, 한국환경 공단과 노조 미통합 기관인 한국토지주택공사를 대상으로 최대유 사체계분석안을 활용하여 비교분석을 실시하였다. 비교분석 결과 조합원수 추세, 교섭권 문제, 대내외 환경변화에 있어서는 3개 기관 모두 유사성이 발견되었고, 위원장의 의지에 있어서는 한국토지주택공사에 한해 상이성이 발견되었다. 따라서 각 사례간 상이성을 보인 위원장의 의지를 노조통합에 영향을 미 치는 주요 요인으로 분석하였다. 또한, 위의 비교분석으로 도출한 결론은 다음과 같다. 첫째, 노 조통합 추진에 있어서 위원장의 의지가 무엇보다 중요하다. 둘째, 노조통합 추진에 앞서 양 노조간의 주요 갈등양상 해소가 선행되 어야 한다는 점이다. 셋째, 실질적인 노조통합은 어느 한가지 요인 으로 이루어지지 않는다는 점을 확인할 수 있었다. 아울러, 연구결 과를 토대로 공공기관을 통합함에 있어 조직의 안정을 위해서는 통합후 발생가능한 갈등을 사전에 방지하는 등 신중한 접근이 필 요하며, 노조통합을 통한 진정한 조직의 발전을 위해서는 기득권 을 내려놓는 등 조합원들의 의식 제고가 뒷받침 되어야 할 것이 다.제 1 장 서론 1 제 1 절 연구목적 및 필요성 1 1. 연구목적 1 2. 연구필요성 2 제 2 절 연구대상 및 방법 3 1. 연구대상 3 2. 연구방법 4 제 2 장 이론적 논의 및 선행연구 검토 5 제 1 절 이론적 논의 5 1. 공공기관 노사관계 5 2. 복수노조제도 8 제 2 절 선행연구 검토 10 1. 해외 노조통합에 관한 연구 10 2. 국내 노조통합에 관한 연구 12 3. 선행연구의 한계 14 제 3 장 비교분석을 위한 방법론 16 제 1 절 최대유사체계분석안 16 제 2 절 연구의 분석틀 18 제 4 장 공공기관 복수노조 통합사례 연구 20 제 1 절 도로교통공단 사례 20 1. 통합기관 및 노동조합 20 2. 주요 갈등양상 23 3. 통합추진 경과 24 4. 통합의 주요 원인 검토 26 제 2 절 한국환경공단 사례 29 1. 통합기관 및 노동조합 29 2. 주요 갈등양상 30 3. 통합추진 경과 34 4. 통합의 주요 원인 검토 36 제 3 절 한국토지주택공사 사례 39 1. 통합기관 및 노동조합 39 2. 주요 갈등양상 41 3. 통합논의 경과 43 4. 통합과 관련된 주요 원인 검토 44 제 4 절 최대유사체계분석안을 통한 비교분석 47 제 5 장 결 론 51 제 1 절 연구결과의 요약 51 제 2 절 연구의 함의 53 제 3 절 연구의 한계 및 향후 연구과제 54 참고 문헌 55 Abstract 57Maste

무선 LAN 환경에서 에너지효율 향상을 위한 스케쥴링

학위논문(석사)--서울대학교 대학원 :전기컴퓨터공학부,2005.Maste

The optimal mixing ratio of forest topsoils for hydroseeding materials and their performance in restoring rock cut-slopes

학위논문(박사)--서울대학교 대학원 :산림자원학과,1999.Docto

Analysis of minimal checkpoint coordination on 2-processor system

Maste

PCB 공장의 실시간 특성을 고려한 스케쥴링

학위논문(석사)--서울대학교 대학원 :산업공학과,1998.Maste