2006年から2017年まで日本の新規発症の成人部分てんかん患者に対する抗てんかん薬処方パターンに関する研究

京都大学新制・課程博士博士(医学)甲第24881号医博第5015号京都大学大学院医学研究科医学専攻(主査)教授 古川 壽亮, 教授 髙橋 良輔, 教授 阪上 優学位規則第4条第1項該当Doctor of Medical ScienceKyoto UniversityDFA

Novelty Detection in Sequential Data by Informed Clustering and Modeling

Novelty detection in discrete sequences is a challenging task, since deviations from the process generating the normal data are often small or intentionally hidden. Novelties can be detected by modeling normal sequences and measuring the deviations of a new sequence from the model predictions. However, in many applications data is generated by several distinct processes so that models trained on all the data tend to over-generalize and novelties remain undetected. We propose to approach this challenge through decomposition: by clustering the data we break down the problem, obtaining simpler modeling task in each cluster which can be modeled more accurately. However, this comes at a trade-off, since the amount of training data per cluster is reduced. This is a particular problem for discrete sequences where state-of-the-art models are data-hungry. The success of this approach thus depends on the quality of the clustering, i.e., whether the individual learning problems are sufficiently simpler than the joint problem. While clustering discrete sequences automatically is a challenging and domain-specific task, it is often easy for human domain experts, given the right tools. In this paper, we adapt a state-of-the-art visual analytics tool for discrete sequence clustering to obtain informed clusters from domain experts and use LSTMs to model each cluster individually. Our extensive empirical evaluation indicates that this informed clustering outperforms automatic ones and that our approach outperforms state-of-the-art novelty detection methods for discrete sequences in three real-world application scenarios. In particular, decomposition outperforms a global model despite less training data on each individual cluster

Endoderm Differentiationin VitroIdentifies a Transitional Period for Endoderm Ontogeny in the Sea Urchin Embryo

AbstractThe vegetal plate of the sea urchin embryo is specified during early cleavage divisions of the embryo as shown by the classical experiments of Horstadius (reviewed in “Experimental Embryology of Echinoderms,” 1973, Clarendon, Oxford). Not until gastrulation, though, do the cells within this territory differentiate into their characteristic cell types. Vegetal plate descendents comprise the coelomic epithelium, circumesophageal muscle, basal cells, pigment cells, and endodermal epithelium. We report here that cells of the endodermal lineage acquire the ability to differentiate autonomously several hours prior to gastrulation, between the late blastula and early mesenchyme blastula stages. Cells dissociated from whole embryos after the late blastula stage have the ability to differentiatein vitro,independent of cell contacts and of the embryonic environment. In contrast, preendoderm cells removed from the embryo prior to the late blastula stage show no ability to differentiate when culturedin vitroeven though cells of other lineages, e.g., ectoderm and skeletogenic mesenchyme, show morphological and molecular differentiation in these same cultures. We have used the expression of the endoderm-specific gene products Endo 1 and LvN1.2, detected by RNase protection assays and byin situimmunolabeling, to quantify endoderm differentiation independent of embryonic or cellular morphology. These studies define a transitional period in the ontogeny of the endoderm, from cells reliant on interactions to promote fate specification and organization of territories to later events involved in morphogenesis that result from cell-type-specific gene expression

Multi-View Representation is What You Need for Point-Cloud Pre-Training

A promising direction for pre-training 3D point clouds is to leverage the massive amount of data in 2D, whereas the domain gap between 2D and 3D creates a fundamental challenge. This paper proposes a novel approach to point-cloud pre-training that learns 3D representations by leveraging pre-trained 2D networks. Different from the popular practice of predicting 2D features first and then obtaining 3D features through dimensionality lifting, our approach directly uses a 3D network for feature extraction. We train the 3D feature extraction network with the help of the novel 2D knowledge transfer loss, which enforces the 2D projections of the 3D feature to be consistent with the output of pre-trained 2D networks. To prevent the feature from discarding 3D signals, we introduce the multi-view consistency loss that additionally encourages the projected 2D feature representations to capture pixel-wise correspondences across different views. Such correspondences induce 3D geometry and effectively retain 3D features in the projected 2D features. Experimental results demonstrate that our pre-trained model can be successfully transferred to various downstream tasks, including 3D shape classification, part segmentation, 3D object detection, and semantic segmentation, achieving state-of-the-art performance.Comment: 14 pages, 6 figure

On Chip Counting and Localisation of Magnetite Pollution Nanoparticles

Magnetic nanoparticles are generally smaller than 200 nm surrounding our environment and can easily enter the human brain through the respiratory system. The harm of such nanoparticles may endanger people’s health. This paper focuses on modelling and simulation based on a new kind of magnetic sensors, which can count and localize these magnetite nanoparticles. The proposed sensors could help to prevent these nanoparticles from the polluted environment and undoubtedly reduce their adverse risks to humans. The modelled magnetic system consists of a tunnelling magnetoresistive (TMR) sensor array, a conducting line, and the detected magnetite nanoparticles. The localization and quantization of these nanoparticles can be achieved by analysing total output voltages from the TMR sensor array