Ataxin-1 occupies the promoter region of E-cadherin in vivo and activates CtBP2-repressed promoter

AbstractAtaxin-1 is a polyglutamine protein of unknown function that is encoded by the ATXN1 gene in humans. To gain insight into the function of ataxin-1, we sought to identify proteins that interact with ataxin-1 through yeast two-hybrid screening. In this study, transcriptional corepressor CtBP2 was identified as a protein that interacted with ataxin-1. CtBP2 and ataxin-1 colocalized in the nucleus of mammalian cells. Since the E-cadherin promoter is a target of CtBP-mediated repression, the relationship between ataxin-1 and the E-cadherin promoter was investigated. Chromatin immunoprecipitation assays showed that CtBP2 and ataxin-1 were recruited to the E-cadherin promoter in mammalian cells. Luciferase assays using E-cadherin promoter reporter constructs revealed that the luciferase activity was enhanced as the level of ataxin-1 protein expression increased. CtBP2 overexpression decreased E-cadherin expression, but expression of ataxin-1 inversely increased the mRNA and protein levels of endogenous E-cadherin. Interestingly, siRNA experiments showed that the transcriptional activation of ataxin-1 was associated with the presence of CtBP2. This study demonstrates that ataxin-1 occupies the promoter region of E-cadherin in vivo and that ataxin-1 activates the promoter in a CtBP2-mediated transcriptional regulation manner

A Unified Compression Framework for Efficient Speech-Driven Talking-Face Generation

Virtual humans have gained considerable attention in numerous industries, e.g., entertainment and e-commerce. As a core technology, synthesizing photorealistic face frames from target speech and facial identity has been actively studied with generative adversarial networks. Despite remarkable results of modern talking-face generation models, they often entail high computational burdens, which limit their efficient deployment. This study aims to develop a lightweight model for speech-driven talking-face synthesis. We build a compact generator by removing the residual blocks and reducing the channel width from Wav2Lip, a popular talking-face generator. We also present a knowledge distillation scheme to stably yet effectively train the small-capacity generator without adversarial learning. We reduce the number of parameters and MACs by 28$\times$ while retaining the performance of the original model. Moreover, to alleviate a severe performance drop when converting the whole generator to INT8 precision, we adopt a selective quantization method that uses FP16 for the quantization-sensitive layers and INT8 for the other layers. Using this mixed precision, we achieve up to a 19$\times$ speedup on edge GPUs without noticeably compromising the generation quality.Comment: MLSys Workshop on On-Device Intelligence, 2023; Demo: https://huggingface.co/spaces/nota-ai/compressed_wav2li

Atomic-Scale Scanning of Domain Network in the Ferroelectric HfO2 Thin Film

Ferroelectric HfO2-based thin films have attracted much interest in the utilization of ferroelectricity at the nanoscale for next-generation electronic devices. However, the structural origin and stabilization mechanism of the ferroelectric phase are not understood because the film is typically nanocrystalline with active yet stochastic ferroelectric domains. Here, electron microscopy is used to map the in-plane domain network structures of epitaxially grown ferroelectric Y:HfO2 films in atomic resolution. The ferroelectricity is confirmed in free-standing Y:HfO2 films, allowing for investigating the structural origin for their ferroelectricity by 4D-STEM, high-resolution STEM, and iDPC-STEM. At the grain boundaries of <111>-oriented Pca21 orthorhombic grains, a high-symmetry mixed-(R3m, Pnm21) phase is induced, exhibiting enhanced polarization due to in-plane compressive strain. Nanoscale Pca21 orthorhombic grains and their grain boundaries with mixed-(R3m, Pnm21) phases of higher symmetry cooperatively determine the ferroelectricity of the Y:HfO2 film. It is also found that such ferroelectric domain networks emerge when the film thickness is beyond a finite value. Furthermore, in-plane mapping of oxygen positions overlaid on ferroelectric domains discloses that polarization is suppressed at vertical domain walls, while it is active when domains are aligned horizontally with subangstrom domain walls. In addition, randomly distributed 180° charged domain walls are confined by spacer layers

실시간 가열 및 액상 투과전자현미경 기법을 통한 촉매 나노입자의 구조 변화 및 확산 과정

학위논문(박사) -- 서울대학교대학원 : 공과대학 화학생물공학부, 2024. 2. 박정원.최근 수십 년 동안 나노 입자는 다양한 과학 분야와 응용 분야에서 중요한 구성 요소가 되었다. 이러한 응용 분야 중 이종 촉매는 작은 크기의 독특한 특성과 금속 종의 분산을 극대화할 수 있는 능력으로 인해 나노 입자가 사용되는 가장 중요한 분야 중 하나이다. 원하는 구조와 특성을 가진 촉매 나노입자를 설계하기 위해서는 나노입자의 원자 구조와 열처리 및 액체와 같은 반응 조건에서의 구조적 변화에 대한 이해가 필요하다. 그러나 촉매 나노입자의 구조적 변화는 이러한 화학적 환경에서 개별 나노입자를 원자 단위로 직접 관찰하는 것이 어렵기 때문에 이해하기 어렵다. 실시간 투과전자현미경(TEM)은 반응 조건에서 개별 나노 입자를 원자 해상도로 관찰할 수 있는 유망한 도구이다. 본 연구에서는, 열처리 중 담지된 촉매 나노입자의 구조 변화 과정과 액체 내 촉매 나노입자의 확산 과정을 이해하기 위해 촉매에서 기초적이면서도 실용적으로 중요한 세 가지 나노입자 시스템을 연구했으며, 이를 위해 실시간 투과전자현미경법을 사용했다. 세리아에 담지된 백금의 응집 메커니즘을 이해하는 것은 내구성 있는 촉매를 개발하는 데 있어 근본적이고 실질적인 중요성을 갖는다. 세리아에 지지된 백금의 응집은 고온 환원 시 담지체인 세리아 나노입자의 응집과 함께 발생하는 것으로 알려져 있지만, 원자 수준 과정에 대한 실시간 직접 관찰이 부족하여 그 메커니즘에 대해 알려진 바가 거의 없다. 제어된 공간 분포와 다양한 입자 경계 형태를 가진 세리아 입자와 담지된 백금으로 구성된 모델 촉매에서 백금의 열 거동을 조사하기 위해 실시간 및 일반 투과전자현미경 기법을 이용했으며, 그 결과 백금의 응집이 발생하는 정도는 담지체 세리아 입자의 응집과 상관관계가 있는 것을 밝혔다. 세리아 입자의 응집은 잘 정렬된 결정립계를 가진 세리아 입자 간 접촉에서 발생할 가능성이 가장 높음을 밝혀냈다. 본 결과는 세리아 응집 중 표면 재구성이 백금의 응집을 유도할 가능성이 있음을 시사합니다. 루테늄은 암모니아 탈수소화 반응에 가장 활성이 높은 금속 원소이며, 특히 Ru(0001) 표면의 원자 수준 계단 구조인 B5 자리가 활성이 높은 것으로 알려져 있다. 하지만 일반적으로 제조된 촉매에서 B5 자리의 밀도는 일반적으로 낮다. 육방정계 질화붕소(h-BN)와 루테늄의 결정성 대칭이 유사하다는 점에 착안하여 B5 부위가 풍부한 루테늄 나노입자를 합성했으며, 구면수차보정 투과전자현미경을 이용하여 육각형 모양의 루테늄 나노입자가 h-BN 시트에서 에피택시얼하게 합성된 것을 확인했다. B5 자리는 루테늄 나노입자의 가장자리를 따라 발견되었으며, 반응 조건에서 촉매를 활성화하면 Ru 나노입자의 모양이 더 잘 발달된 육각형으로 변형되어 B5 부위의 수가 증가하는 것을 밝혀냈다. 콜로이드 촉매 나노 입자의 열 운동과 그 상호작용은 나노 과학에서 근본적으로 중요하지만, 나노 규모에서 개별 입자의 동역학을 연구할 수 있는 적절한 분석 도구가 없기 때문에 정량적으로 접근하기 어렵다. 여기서는 액체 속에 분산되어 있는 2 nm 크기의 금 나노입자의 열운동과 응집 역학을 그래핀 액상셀 투과전자현미경을 사용하여 직접 관찰했다. 작은 크기의 나노입자는 브라운 운동을 보이지만 가우시안 변위 분포를 따르지 않았으며 이는 액상 환경의 동적 이질성으로 인한 나노입자의 확산 계수의 변동으로 인한 것임을 밝혀냈다. 또한, 두 나노입자 간 유착 과정은 확산에 제한된 나노입자 복합체의 형성과 표면 리간드가 중요한 역할을 하는 배향 부착이라는 두 가지 과정에 의해 발생하는 것을 발견하여 제안하였다. 주요어 : 실시간 투과전자현미경, 나노입자, 촉매, 구조 변화, 확산 학 번 : 2018-21563 성 명 : 강성수In recent decades, nanoparticles have become important building blocks for a wide range of scientific fields and applications. Among these applications, heterogeneous catalysis is one of the most important areas where nanoparticles are used, due to their unique properties at small sizes and their ability to maximize the dispersion of metal species. The design of catalytic nanoparticles with desired structures and properties requires an understanding of the atomic structures of the nanoparticles and the structural evolution under reaction conditions, such as thermal treatments and in liquid. However, the structural evolution of catalytic nanoparticles is less understood due to the challenges of directly observing individual nanoparticles in such chemically active environments at the atomic scale. In situ transmission electron microscopy (TEM) is a promising tool that allows the observation of individual nanoparticles under reaction conditions at atomic resolution. Here, in situ and ex situ TEM are used to study three nanoparticle systems of fundamental and practical importance in catalysis to understand the structural annealing of supported catalytic nanoparticles during heat treatments and the diffusion of catalytic nanoparticles in liquid. Understanding the sintering mechanism of platinum (Pt) supported on ceria (CeO2) is of fundamental and practical importance for the development of durable catalysts. The sintering of Pt supported on CeO2 is known to occur along with the aggregation of CeO2 support nanoparticles during high temperature reduction, but little is known about its mechanism due to the lack of direct in situ observations of the atomic scale processes. In situ and ex situ TEM are used to investigate the thermal behavior of Pt on CeO2 in model catalysts consisting of Pt on CeO2 particles with controlled spatial distributions and different grain boundary conformations. The extent to which Pt sintering occurs is correlated with the aggregation of the supporting CeO2 particles. CeO2 particle aggregation is most likely to occur at particle contacts with well-aligned grain boundaries, which serve as sites for the most significant Pt sintering. The results suggest that surface reconstruction during CeO2 aggregation is likely to induce Pt sintering. Ruthenium (Ru) is the most active metal element for the ammonia dehydrogenation reaction. The B5 site, an atomic level on Ru(0001) of Ru, is known to exhibit the highest catalytic activity for ammonia dehydrogenation, but the populations of B5 sites in prepared catalysts are typically low. Here, Ru nanoparticles rich in B5 sites are synthesized based on the similarity in the crystal symmetries of hexagonal boron nitride (h-BN) and Ru. Aberration-corrected TEM shows that hexagonal Ru nanoparticles are epitaxially synthesized on h-BN sheets. The B5 sites are identified along the edges of the Ru nanoparticles. Activation of the catalyst under reaction conditions increases the population of B5 sites by transforming the shape of the Ru nanoparticles into better developed hexagons. The electron density of the Ru nanoparticles also increases during activation. The thermal motion of colloidal catalytic nanoparticles and their interactions are of fundamental importance in nanoscience, but are difficult to access quantitatively, mainly due to the lack of appropriate analytical tools to study the dynamics of individual particles at the nanoscale. Here, the stochastic thermal motion and coalescence dynamics of gold nanoparticles as small as 2 nm in liquid are directly observed using graphene liquid cell (GLC) TEM. The small nanoparticles show Brownian but non-Gaussian motion, indicating the fluctuation of the diffusion coefficient of the nanoparticles due to the dynamic heterogeneity of the liquid environment. Furthermore, the coalescence of two nanoparticles occurs by two processes: the diffusion-limited formation of a transient nanoparticle complex and subsequent coalescence by oriented attachment, in which surface-passivating ligands play a critical role. Keyword: In situ transmission electron microscopy (TEM), nanoparticle, catalyst, structural annealing, diffusion Student Number: 2018-21563Chapter 1 Introduction . 1 1.1. Structural dynamics of catalytic nanoparticles in reaction 1 1.2. In situ transmission electron microscopy for mechanistic understanding of single nanoparticle reactions 2 1.3. Purpose of research . 4 Chapter 2 Aggregation of CeO2 particles with aligned grains drives sintering of Pt single atoms in Pt/CeO2 catalysts 6 2.1. Introduction . 6 2.2. Methods . 7 2.2.1. Sample preparation . 7 2.2.2. H2-temperature programmed reduction (H2-TPR) 8 2.2.3. X-ray diffraction (XRD) 8 2.2.4. Raman spectroscopy 8 2.2.5. Surface area measurement 9 2.2.6. Transmission electron microscopy 9 2.3. Results and discussion. 11 2.3.1. Catalyst preparation and structural analysis . 11 2.3.2. Relationship between CeO2 aggregation and Pt sintering investigated by the control of interparticle distance 13 2.3.3. Effects of grain boundaries in aggregated CeO2 domains for Pt sintering . 14 2.3.4. Conclusion 17 Chapter 3 Heteroepitaxial growth of B5-site-rich Ru nanoparticles guided by h- BN for low-temperature NH3 dehydrogenation 38 3.1. Introduction 38 3.2. Methods 40 vi 3.2.1. Catalyst synthesis 40 3.2.2. Adsorption and desorption isotherms 42 3.2.3. Inductively coupled plasma-optical emission spectroscopy (ICP-OES) 43 3.2.4. X-ray photoelectron spectroscopy (XPS) 44 3.2.5. Transmission electron microscopy (TEM) 44 3.2.6. Catalytic activity test 45 3.2.7. Density functional theory (DFT) calculation 47 3.3. Results and discussion. 48 3.3.1. Epitaxial growth of B5-site-rich Ru nanoparticles on h-BN. 48 3.3.2. Activation of Ru/h-BN catalyst 53 3.3.3. Catalytic performances 56 3.3.4. Conclusion 60 Chapter 4 Real-space imaging of nanoparticle transport and interaction dynamics in graphene liquid cell TEM 95 4.1. Introduction 95 4.2. Methods 98 4.2.1. GLC preparation 98 4.2.2. Liquid-phase TEM 99 4.2.3. Single-particle tracking and drift correction . 100 4.2.4. Off-lattice random walk model in dynamically heterogeneous environment 103 4.2.5. Kinetics of two-step coalescence between ligand-passivated nanoparticles 120 4.2.6. Survival probability calculation 129 4.3. Results and discussion 131 4.3.1. GLC TEM of nanoparticles in liquid 131 4.3.2. Fickian diffusion of nanoparticles in a GLC . 132 4.3.3. Fluctuation in diffusion coefficient of nanoparticles in a GLC 134 4.3.4. Origin of diffusion coefficient fluctuations 137 vii 4.3.5. Precise prediction of displacement distribution of nanoparticles in a finite pixel size 139 4.3.6. Encounter complex formation and coalescence of nanoparticles 140 4.3.7. Conclusion . 144 Chapter 5 Summary and Conclusions 192 Bibliography 194 국 문 초 록 . 206 List of Publications 209 viii박

Machine Learning-Based Anomaly Detection on Seawater Temperature Data with Oversampling

This study deals with a method for anomaly detection in seawater temperature data using machine learning methods with oversampling techniques. Data were acquired from 2017 to 2023 using a Conductivity–Temperature–Depth (CTD) system in the Pacific Ocean, Indian Ocean, and Sea of Korea. The seawater temperature data consist of 1414 profiles including 1218 normal and 196 abnormal profiles. This dataset has an imbalance problem in which the amount of abnormal data is insufficient compared to that of normal data. Therefore, we generated abnormal data with oversampling techniques using duplication, uniform random variable, Synthetic Minority Oversampling Technique (SMOTE), and autoencoder (AE) techniques for the balance of data class, and trained Interquartile Range (IQR)-based, one-class support vector machine (OCSVM), and Multi-Layer Perceptron (MLP) models with a balanced dataset for anomaly detection. In the experimental results, the F1 score of the MLP showed the best performance at 0.882 in the combination of learning data, consisting of 30% of the minor data generated by SMOTE. This result is a 71.4%-point improvement over the F1 score of the IQR-based model, which is the baseline of this study, and is 1.3%-point better than the best-performing model among the models without oversampling data