アデリック曲線上の算術的ヒルベルト・サミュエル関数とχ-体積

京都大学新制・課程博士博士(理学)甲第24387号理博第4886号新制||理||1699(附属図書館)京都大学大学院理学研究科数学・数理解析専攻(主査)教授 森脇 淳, 教授 雪江 明彦, 教授 吉川 謙一学位規則第4条第1項該当Doctor of ScienceKyoto UniversityDFA

Spin diffusion and dynamics studies of the channel forming membrane proteins by solid-state nuclear magnetic resonance

Solid-state nuclear magnetic resonance (SSNMR) is an important tool for the structure, function and dynamics study of many chemical and biological systems, especially powerful in studying membrane proteins, whose structures have been difficult to analyze by traditional x-ray crystallography or solution NMR techniques. In this thesis, various NMR techniques are used to study the structure and dynamics of membrane proteins within lipid bilayers. The main technique applied in this thesis is spin diffusion experiments. We study the structural rearrangement upon membrane binding of colicin Ia by the proton-driven 13C spin diffusion (PDSD) 13C-13C 2D correlation experiment. Membrane bound colicin Ia turns out to have a more extended structure compared to the soluble state. Then a 1D 1H detected 1H spin diffusion experiment is developed to provide the same membrane protein topology information as the 2D 13C detected version, but with significant sensitivity enhancement. We demonstrated this new technique on the colicin Ia channel-forming domain and achieved about 200 fold time saving. Further, the data analysis method is developed to extract the intermolecular distance as long as 12 y from 19F spin diffusion experiment CODEX, where the oligomeric state is obtained at the same time. Demonstrated on the M2 proton channel system, this method is applied to extract the intermolecular distances between a key residue Trp41 in different states of the M2 proton channel. Finally, the water accessibility of the M2 proton channel in different states is studied by the 1H spin diffusion experiment and 3D low resolution models are proposed for this proton channel system by simulating the 1H spin diffusion process between the water and protein. The second focus of this thesis is the dynamics of the M2 peptide in a complex membrane system. Compared to the single component model lipid bilayers, this composite membrane is shown to reduce the rotational rate of the membrane protein by 2 orders of magnitude, which is explained by a rotational diffusion model. The advantage of this immobilization is the ability to acquire high resolution SSNMR spectra at physiological temperatures

A Review on The Professional Development of Elementary Education Teachers in China

The school field is the main field of teachers' study and work, which creates the field space for teachers' professional development. In order to explore the situation of teacher professional development in school field,based on the general condition of the research, from the research scope, the research perspective, the fit between "teaching" and "learning" in the study and the discussion that the theoretical research focuses on the value of practice, we have sorted out the hot spots and trends of the research on the professional development of Primary and high school teachers in China from the perspective of school standard.In the subsequent analysis, we need to make further thinking or exploration in the five aspects of the origin and fulcrum of the research, the positioning of the study, the establishment of the research thinking, the consideration of the influential factors in the research and the enlightenment of the research methodology

Vector-based Efficient Data Hiding in Encrypted Images via Multi-MSB Replacement

As an essential technique for data privacy protection, reversible data hiding in encrypted images (RDHEI) methods have drawn intensive research interest in recent years. In response to the increasing demand for protecting data privacy, novel methods that perform RDHEI are continually being developed. We propose two effective multi-MSB (most significant bit) replacement-based approaches that yield comparably high data embedding capacity, improve overall processing speed, and enhance reconstructed images' quality. Our first method, Efficient Multi-MSB Replacement-RDHEI (EMR-RDHEI), obtains higher data embedding rates (DERs, also known as payloads) and better visual quality in reconstructed images when compared with many other state-of-the-art methods. Our second method, Lossless Multi-MSB Replacement-RDHEI (LMR-RDHEI), can losslessly recover original images after an information embedding process is performed. To verify the accuracy of our methods, we compared them with other recent RDHEI techniques and performed extensive experiments using the widely accepted BOWS-2 dataset. Our experimental results showed that the DER of our EMR-RDHEI method ranged from 1.2087 bit per pixel (bpp) to 6.2682 bpp with an average of 3.2457 bpp. For the LMR-RDHEI method, the average DER was 2.5325 bpp, with a range between 0.2129 bpp and 6.0168 bpp. Our results demonstrate that these methods outperform many other state-of-the-art RDHEI algorithms. Additionally, the multi-MSB replacement-based approach provides a clean design and efficient vectorized implementation.Comment: 14 pages; journa

Online Deep Metric Learning

Metric learning learns a metric function from training data to calculate the similarity or distance between samples. From the perspective of feature learning, metric learning essentially learns a new feature space by feature transformation (e.g., Mahalanobis distance metric). However, traditional metric learning algorithms are shallow, which just learn one metric space (feature transformation). Can we further learn a better metric space from the learnt metric space? In other words, can we learn metric progressively and nonlinearly like deep learning by just using the existing metric learning algorithms? To this end, we present a hierarchical metric learning scheme and implement an online deep metric learning framework, namely ODML. Specifically, we take one online metric learning algorithm as a metric layer, followed by a nonlinear layer (i.e., ReLU), and then stack these layers modelled after the deep learning. The proposed ODML enjoys some nice properties, indeed can learn metric progressively and performs superiorly on some datasets. Various experiments with different settings have been conducted to verify these properties of the proposed ODML.Comment: 9 page