A Study on Molecular Mechanism of Novel Mitophagy Regulators

학위논문 (박사)-- 서울대학교 대학원 : 생명과학부, 2015. 2. 정용근.CHDH (choline dehydrogenase) is an enzyme catalyzing the dehydrogenation of choline to betaine aldehyde in mitochondria. Apart from this well-known activity, I report here a pivotal role of CHDH in mitophagy. Knockdown of CHDH expression impairs CCCP-induced mitophagy and PARK2/parkin-mediated clearance of mitochondria in mammalian cells, including HeLa cells and SN4741 dopaminergic neuronal cells. Conversely, overexpression of CHDH accelerates PARK2-mediated mitophagy. CHDH is found on both the outer and inner membranes of mitochondria in resting cells. Interestingly, upon induction of mitophagy, CHDH accumulates on the outer membrane in a mitochondrial potential-dependent manner. I found that CHDH is not a substrate of PARK2 but interacts with SQSTM1 independently of PARK2 to recruit SQSTM1 into depolarized mitochondria. The FB1 domain of CHDH is exposed to the cytosol and is required for the interaction with SQSTM1, and overexpression of the FB1 domain only in cytosol reduces CCCP-induced mitochondrial degradation via competitive interaction with SQSTM1. In addition, CHDH, but not the CHDH FB1 deletion mutant, forms a ternary protein complex with SQSTM1 and MAP1LC3 (LC3), leading to loading of LC3 onto the damaged mitochondria via SQSTM1. Further, CHDH is crucial to the mitophagy induced by MPP+ in SN4741 cells. Overall, my results suggest that CHDH is required for PARK2-mediated mitophagy for the recruitment of SQSTM1 and LC3 onto the mitochondria for cargo recognition.ABSTRACT TABLE OF CONTENTS LIST OF FIGURES 1. INTRODUCTION 2. MATERIALS AND METHODS 2.1. Cell culture and transfection 2.2. Plasmids and siRNA 2.3. Measurement of Mito-GFP intensities 2.4. Measurement of enzyme activity and LC-MS 2.5. Immunoprecipitation, western blot and antibodies 2.6. Immunofluorescence and colocalization coefficient 2.7. Flow cytometry 2.8. Mitochondrial DNA quantification 2.9. Mitochondria fractionation 2.10. Proteinase K degradation assay 3. RESULTS 3.1. CHDH is required for PARK2-mediated mitophagy 3.2. Mitophagic activity of CHDH is independent of enzyme activity 3.3. CHDH accumulates on the outer membrane following mitochondrial damage 3.4. CHDH interacts with SQSTM1 independently of PARK2 during mitophagy 3.5. The interaction of CHDH with SQSTM1 brings LC3 to damaged mitochondria for cargo recognition during mitophagy 3.6. CHDH is implicated in MPP+-induced mitophagy in SN4741 dopaminergic cells 4. DISCUSSION 5. REFFERENCES 국문 초록Docto

빅데이터 기술을 이용한 해양구조물의 데이터 마이닝 방법

학위논문 (석사)-- 서울대학교 대학원 : 공과대학 협동과정해양플랜트엔지니어링전공, 2019. 2. 노명일.As many products as ships and offshore structures are constructed in the shipyard, and various data are generated and stored in the design or construction stage. Big data technology needs to be applied to process data of large size quickly, obtain meaningful results and use it for decision making. In this paper, we propose a solution to two of the problems that may occur in the shipyard. One of the two problems which can arise in the shipyard has mainly happened in the design stage. Engineers can make the mistake of choosing the wrong material in the design process, and the wrong material selection in the design process can directly lead to a design error. Another problem may arise during the procurement and purchase process. In the absence of additional information such as lead time of material or inventory at the time of procurement, additional time is required to retrieve the data. Both problems arise predominantly from the unskilled. Therefore, the purpose of this study is to establish a 8 system that can inform the engineers about the relationships between materials which can be obtained by association analysis and material requirements which can be obtained by regression analysis. This kind of system can help the engineers to reduce design errors and time consuming due to the procurement process. The information of piping materials used in an offshore structure can be regarded as big data because of their various types and size, and the data mining algorithms based on the big data technology are applied to data related to the offshore structures. To analyze the relationship between materials for design, frequent pattern growth algorithm was used. For material requirement analysis, big data technology-based regression analysis was used to generate a regression model, respectively. Finally, the proposed method was used to check the relationship between materials, and to predict material requirement, and verified the effectiveness of the proposed method by comparing each result with actual cases.조선소에 많은 선박과 해양 구조물들이 건조되면서 다양한 데이터들이 설계 및 건조 과정에서 생성되고 누적된다. 누적된 데이터를 빠르게 처리하여 의사설정에 이용하려는 필요성에 발생함에 따라 빅데이터 기술의 도입 필요성도 함께 커지고 있다. 본 연구에서는 조선소에서 발생할 수 있는 두 가지 사례에 대하여 빅데이터 기반 데이터 마이닝 방법을 통한 해결책을 제안하고자 한다. 첫 번째 문제점은 설계 단계에서 발생할 수 있는 문제로, 설계 과정에서 적절하지 못한 자재를 선정하여 그것이 오작으로 이어지는 경우이다. 또 다른 한가지는 구매 및 조달 과정에서 발생할 수 있는 문제로, 조달 과정을 관리하기 위한 자재 관련 추가적인 정보를 검색하는데 추가적인 시수가 소요된다는 점이다. 두 가지 문제 모두 미숙련자에게서 주로 발생하며, 본 연구에서는 연관성 분석을 이용한 자재 추천과 회귀 분석을 이용한 소요량 예측이라는 90Contents Abstract 7 1. Introduction 9 1.1. Research background 9 1.2. Related works 14 1.3. Target of the study 15 1.4. System configuration 17 2. Data mining method for pipng material 19 2.1. Piping materials of an offshore structure 19 2.2. Association analysis 23 2.2.1. Assotication between piping material for recommendation of associated materials 23 2.2.2. Comparison of association analysis algorithms 25 2.3. Regression analysis 31 2.3.1. Assistance of purchase process by forecasting material requirement 32 2.3.2. Regression model training 35 2.4. Estabilishment of big data framework 46 2.4.1. Big data framework 46 2.4.2. Data processing 47 3. Application to big data framework 49 3.1. Recommendation of associated materials 49 3.1.1. Method of association analysis of piping material 49 3.1.2. Result of analysis and validation 56 3.2. Forecasting of material requirement 63 3.2.1. Method of training regression model 63 3.2.2. Prediction of material requirement and validation 69 4. Conclusion and future works 85 Related Works 86 국문 초록 89Maste

Re-defining Strausss Political Philosophy through Strausss Interpretation of Nietzsche: Theologico-Political Problem and Plato

스트라우스는 근대정치철학의 전개 과정에서 퇴행의 마지막 물결을 일으킨 장본인으로 니체를 지목한다. 이런 맥락에서 스트라우스의 니체 해석은 니체 철학에 대한 신랄한 비판으로 이뤄진다. 스트라우스의 니체 해석이 합당한 것인가는 논란의 여지가 있으나, 이 논문의 목적은 스트라우스의 니체 해석을 객관적으로 평가하는 것보다 스트라우스의 니체 해석을 통해 그의 정치철학을 깊이 있고 정확하게 이해하는 데 있다. 이를 위해 이 논문은 스트라우스에게 지속적으로 영향을 미친 신학·정치적 문제와 플라톤 해석을 중심으로 스트라우스의 니체 해석이 어떻게 전개되고 있는가를 검토하였다. 논문의 2장은 1920년대와 1930년대에 스트라우스의 신학·정치적 문제가 어떤 변화를 겪고 있으며 그 변화는 스트라우스의 니체 해석과 어떤 연관성이 있는가를 밝혔으며 3장은 파라비안 전회를 경험한 스트라우스의 플라톤 해석이 스트라우스의 니체 해석에 어떤 영향을 미쳤는가를 설명했다. 특히 비전주의 전통에 대한 스트라우스의 해석을 주목하며, 왜 스트라우스의 니체 해석이 신랄한 비판에서 긍정적 평가로 전환했는가를 해명하고자 했다. 마지막으로 논문은 이러한 스트라우스의 니체 해석이 스트라우스 정치철학의 정체성과 어떤 연관성이 있는가를 정리하였다.N

다중 사용자 다중 송수신 안테나 시스템에서 제로포싱 빔포밍과 결합된 스케줄링 기법

Thesis(master`s)--서울대학교 대학원 :전기공학부,2005.Maste

A module system independent of base languages

1

선형논리에 기반한 불확실성 데이터베이스 의미론

22kc

Calculation of Semileptonic Form Factor for Bˉ→D∗ℓνˉ\bar B\to D^\ast \ell \bar \nu Decay Using the Oktay-Kronfeld Action

학위논문 (박사)-- 서울대학교 대학원 : 자연과학대학 물리·천문학부(물리학전공), 2018. 8. 이원종.We study the calculation of $\bar{B}\to D^\ast \ell \bar{\nu}$ semileptonic form factor using the lattice QCD technique. Our first target is the zero recoil process which is gold-plated channel to determine the flavor mixing between bottom and charm: $V_{cb}$ of the Cabbibo-Kobayashi-Maskawa (CKM) matrix. The simulation is done on the $N_{f}=2+1+1$ MILC Highly-improved staggered-quark (HISQ) lattices where the lattice spacing a\approx 0.12~\fm, and the sea pion mass M_\pi\approx 300~\MeV. For valence quarks, we use the HISQ action for the light and strange quarks and the Oktay-Kronfeld (OK) action for the bottom and charm quarks. Here the OK action is improved version of the Fermilab action such that the bilinear operators are tree-level matched to QCD through \CO(\lambda^3) in HQET power counting where $\lambda\sim a\Lambda\sim \Lambda/(2m_Q)$ and $m_Q$ is the heavy quark mass. We present preliminary results of the \BtoDst semileptonic form factor at zero recoil. For the OK action inputs, we determine the critical hopping parameter \kcrit nonperturbatively, and tune the hopping parameters $\kappa_b$ and $\kappa_c$ for physical bottom and charm.1 Introduction 1 1.1 CKM Matrix . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.1.1 Complex phase in CKM matrix . . . . . . . . . . . . . . . . 2 1.1.2 CP violation phase . . . . . . . . . . . . . . . . . . . . . . . 3 1.1.3 Wolfenstein parametrization . . . . . . . . . . . . . . . . . . 3 1.2 CKM Matrix Determination . . . . . . . . . . . . . . . . . . . . . . 4 1.2.1 Leptonic decay of charged pseudoscalar meson . . . . . . . . 5 1.2.2 Semi-leptonic decay . . . . . . . . . . . . . . . . . . . . . . 6 1.2.3 $V_{tq}$ . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 1.2.4 More on exclusive $\bar{B}\to D^\ast \ell \bar{\nu}$ decays . . . . . . . . . . . . . . 7 1.3 Lattice Gauge Theory . . . . . . . . . . . . . . . . . . . . . . . . . 8 1.3.1 QCD on the lattice . . . . . . . . . . . . . . . . . . . . . . . 8 1.3.2 $V_{cb}$ on the lattice . . . . . . . . . . . . . . . . . . . . . . . . 9 2 Lattice Fermion Actions 13 2.1 Naive Fermion Action . . . . . . . . . . . . . . . . . . . . . . . . . 13 2.1.1 Doubling symmetry . . . . . . . . . . . . . . . . . . . . . . 14 2.1.2 Naive staggered action . . . . . . . . . . . . . . . . . . . . . 15 2.2 Highly Improved Staggered Quarks . . . . . . . . . . . . . . . . . . 15 2.2.1 FN-type action . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.2 Fat7 smearing . . . . . . . . . . . . . . . . . . . . . . . . . . 16 2.2.3 Lepage term . . . . . . . . . . . . . . . . . . . . . . . . . . . 18 2.2.4 Naik term . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19 2.2.5 HISQ action . . . . . . . . . . . . . . . . . . . . . . . . . . . 20 2.2.6 Phase factor . . . . . . . . . . . . . . . . . . . . . . . . . . . 22 2.2.6.1 Phase-absorbed staples . . . . . . . . . . . . . . . 22 2.2.6.2 Phase-absorbed longlink . . . . . . . . . . . . . . . 23 2.2.7 HISQ coefficients for the MILC code . . . . . . . . . . . . . 24 2.2.7.1 Tadpole improvement . . . . . . . . . . . . . . . . 25 2.2.7.2 Boundary condition . . . . . . . . . . . . . . . . . 25 2.3 Fermilab Action . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26 2.3.1 Wilson fermion action . . . . . . . . . . . . . . . . . . . . . 26 2.3.2 Clover action . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.3 $\gamma_5$-hermiticity . . . . . . . . . . . . . . . . . . . . . . . . . . 27 2.3.4 Fermilab action . . . . . . . . . . . . . . . . . . . . . . . . . 29 2.3.5 Tadpole improved action . . . . . . . . . . . . . . . . . . . . 30 2.4 Oktay-Kronfeld Action . . . . . . . . . . . . . . . . . . . . . . . . . 31 2.4.1 $\gamma_5$-hermiticity . . . . . . . . . . . . . . . . . . . . . . . . . . 32 2.4.2 Tadpole improvement . . . . . . . . . . . . . . . . . . . . . 33 3 Correlation Functions 39 3.1 Quark Propagators . . . . . . . . . . . . . . . . . . . . . . . . . . . 39 3.1.1 Naive fermion propagator . . . . . . . . . . . . . . . . . . . 39 3.1.2 Naive fermion propagator for staggered quark . . . . . . . . 40 3.2 Meson Propagator . . . . . . . . . . . . . . . . . . . . . . . . . . . 41 3.2.1 Wilson-Wilson meson . . . . . . . . . . . . . . . . . . . . . 41 3.2.2 Wilson-Staggered meson . . . . . . . . . . . . . . . . . . . . 42 3.2.3 Signal-to-noise ratio . . . . . . . . . . . . . . . . . . . . . . 44 3.3 3-point Correlation Function . . . . . . . . . . . . . . . . . . . . . . 45 3.3.1 Naive spectator with sequential inversion . . . . . . . . . . 45 3.3.2 Coherent sequential source . . . . . . . . . . . . . . . . . . . 47 3.3.3 3-point function with a Staggered spectator . . . . . . . . . 49 3.3.4 3-point function: heavy-quark spectator . . . . . . . . . . . 49 3.3.5 Current improvement . . . . . . . . . . . . . . . . . . . . . 50 3.4 Discrete Symmetry . . . . . . . . . . . . . . . . . . . . . . . . . . . 52 3.4.1 Reflection parity for 3-point functions . . . . . . . . . . . . 52 3.5 Fitting . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54 3.5.1 Constant fit of the ratio of the correlation function . . . . . 55 3.5.2 Covariance matrix estimation . . . . . . . . . . . . . . . . . 56 4 Tuning of the Hopping Parameters in Oktay-Kronfeld Action 59 4.1 Nonperturbative Determination of $\kappa_{crit}$ . . . . . . . . . . . . . . . . 60 4.1.1 Simulation details: Iteration . . . . . . . . . . . . . . . . . . 61 4.1.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 62 4.1.3 Correlator fit results . . . . . . . . . . . . . . . . . . . . . . 65 4.2 Nonperturbative Tuning of $\kappa_b$ and _x0014_$\kappa_c$ . . . . . . . . . . . . . . . . 67 4.2.1 Source . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 68 4.2.2 Fermilab interpretation: Kinetic mass . . . . . . . . . . . . 68 4.2.3 Tuning strategy . . . . . . . . . . . . . . . . . . . . . . . . . 69 4.2.4 More on HQET inspired fitting function . . . . . . . . . . . 70 4.2.4.1 Error propagation . . . . . . . . . . . . . . . . . . 72 4.2.5 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 72 4.2.6 Correlator fit results . . . . . . . . . . . . . . . . . . . . . . 78 4.3 Inconsistency . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 90 4.4 Hyperfine Splitting . . . . . . . . . . . . . . . . . . . . . . . . . . . 93 4.4.1 HQET expansion . . . . . . . . . . . . . . . . . . . . . . . . 93 4.4.2 Generalized masses . . . . . . . . . . . . . . . . . . . . . . . 95 4.4.3 Fit and results . . . . . . . . . . . . . . . . . . . . . . . . . 96 5 _x0016_$\bar{B}\to D^\ast \ell \bar{\nu}$ Semileptonic Form Factor at Zero Recoil 99 5.1 Simulation Details . . . . . . . . . . . . . . . . . . . . . . . . . . . 99 5.1.1 Valence light quark: HISQ . . . . . . . . . . . . . . . . . . . 100 5.1.2 Valence heavy quark: OK action . . . . . . . . . . . . . . . 100 5.1.3 Improved currents . . . . . . . . . . . . . . . . . . . . . . . 101 5.2 Spectral Decomposition . . . . . . . . . . . . . . . . . . . . . . . . 102 5.3 Ground State Analysis . . . . . . . . . . . . . . . . . . . . . . . . . 104 5.3.1 Analysis on $C^{B\to D^\ast}_{A_1}$ . . . . . . . . . . . . . . . . . . . . . . 104 5.3.2 Analysis of R . . . . . . . . . . . . . . . . . . . . . . . . . . 107 5.3.3 Summary on ground state analysis . . . . . . . . . . . . . . 107 5.4 Excited State Analysis . . . . . . . . . . . . . . . . . . . . . . . . 109 5.4.1 $B$ and $D^\ast$ meson 2 + 2 excited states . . . . . . . . . . . . 109 5.4.2 $C^{X\to Y}_J$ Simultaneous fit . . . . . . . . . . . . . . . . . . . . 110 5.4.3 Result . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110 6 Conclusion and Future Works 117 A Gauge Ensemble 119 B MILC Library Convention 121 B.1 Standard MILC Phases . . . . . . . . . . . . . . . . . . . . . . . . 121 B.2 Gamma Matrices in MILC Code . . . . . . . . . . . . . . . . . . . 121 B.3 Gamma Matrices of OK Inverter in QOPQDP . . . . . . . . . . . . 122 B.4 Conversion From FNAL to MILC Representation . . . . . . . . . . 123 C Heavy-heavy Current Improvement 125 C.1 Convention in the Code . . . . . . . . . . . . . . . . . . . . . . . . 125 C.2 Comments . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 128 D Staggered Chiral Perturbation for $B\to D^\ast$ Form Factor 129 D.1 $B\to D^\ast$_x0003_ Zero Recoil . . . . . . . . . . . . . . . . . . . . . . . . . . 129 D.1.1 NLO PQ rSChPT . . . . . . . . . . . . . . . . . . . . . . . . 129 D.1.2 Inputs for NLO: Summary . . . . . . . . . . . . . . . . . . . 131 D.1.3 Beyond the NLO . . . . . . . . . . . . . . . . . . . . . . . . 131 D.2 $B\to D^\ast$ Nonzero Recoil . . . . . . . . . . . . . . . . . . . . . . . . . 132 D.2.1 NLO full QCD rSChPT . . . . . . . . . . . . . . . . . . . . . 132 D.2.2 NLO full QCD fit function . . . . . . . . . . . . . . . . . . . 133 D.2.3 Beyond the NLO full QCD fit function . . . . . . . . . . . . 134 D.2.4 NLO PQ rSChPT . . . . . . . . . . . . . . . . . . . . . . . . 134 D.2.5 Beyond the NLO PQ fit function . . . . . . . . . . . . . . . 135 D.3 Valence Spectators for the Production . . . . . . . . . . . . . . . . 135 E Computational Cost of Oktay-Kronfeld Action 137 F Decoupling of m1 139 F.1 HQET Lagrangian at the Leading Order . . . . . . . . . . . . . . 139 F.2 m1 in the HQET Hamiltonian . . . . . . . . . . . . . . . . . . . . . 139 Bibliography 143Docto