内在性蛋白質の検出およびエンジニアリングのための新規化学的手法の開発

京都大学0048新制・課程博士博士(工学)甲第18302号工博第3894号新制||工||1597(附属図書館)31160京都大学大学院工学研究科合成・生物化学専攻(主査)教授 濵地 格, 教授 秋吉 一成, 教授 杉野目 道紀学位規則第4条第1項該当Doctor of Philosophy (Engineering)Kyoto UniversityDFA

Hierarchical Clustering of Ensemble Prediction Using LOOCV Predictable Horizon for Chaotic Time Series

Recently, we have presented a method of ensemble prediction of chaotic time series. The method employs strong learners capable of making predictions with small error, where usual ensemble mean does not work well owing to the long term unpredictability of chaotic time series. Thus, we have developed a method to select a representative prediction from a set of plausible predictions by means of using LOOCV (leave-one-out cross-validation) measure to estimate predictable horizon. Although we have shown the effectiveness of the method, it sometimes fails to select the representative prediction with long predictable horizon. In order to cope with this problem, this paper presents a method to select multiple candidates of representative prediction by means of employing hierarchical K-means clustering with K = 2. From numerical experiments, we show the effectiveness of the method and an analysis of the property of LOOCV predictable horizon.The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), November 27 to December 1, 2017, Honolulu, Hawaii, US

Grading Fruits and Vegetables Using RGB-D Images and Convolutional Neural Network

This paper presents a method for grading fruits and vegetables by means of using RGB-D (RGB and depth) images and convolutional neural network (CNN). Here, we focus on grading according to the size of objects. First, the method transforms positions of pixels in RGB image so that the center of the object in 3D space is placed at the position equidistant from the focal point by means of using the corresponding depth image. Then, with the transformed RGB images involving equidistant objects, the method uses CNN for learning to classify the objects or fruits and vegetables in the images for grading according to the size, where the CNN is structured for achieving both size sensitivity for grading and shift invariance for reducing position error involved in images. By means of numerical experiments, we show the effectiveness and the analysis of the present method.The 2017 IEEE Symposium Series on Computational Intelligence (IEEE SSCI 2017), November 27 to December 1, 2017, Honolulu, Hawaii, US

Performance improvement via bagging in probabilistic prediction of chaotic time series using similarity of attractors and LOOCV predictable horizon

Recently, we have presented a method of probabilistic prediction of chaotic time series. The method employs learning machines involving strong learners capable of making predictions with desirably long predictable horizons, where, however, usual ensemble mean for making representative prediction is not effective when there are predictions with shorter predictable horizons. Thus, the method selects a representative prediction from the predictions generated by a number of learning machines involving strong learners as follows: first, it obtains plausible predictions holding large similarity of attractors with the training time series and then selects the representative prediction with the largest predictable horizon estimated via LOOCV (leave-one-out cross-validation). The method is also capable of providing average and/or safe estimation of predictable horizon of the representative prediction. We have used CAN2s (competitive associative nets) for learning piecewise linear approximation of nonlinear function as strong learners in our previous study, and this paper employs bagging (bootstrap aggregating) to improve the performance, which enables us to analyze the validity and the effectiveness of the method

Diffuse Large B-Cell Lymphoma 18 Years After Bilateral Lacrimal Gland IgG4-Related Disease: Case Report and Literature Review

IgG4-related disease is a recently established clinical entity. The disease might serve as the background for later development of systemic lymphoma. This study aims to confirm the diagnosis of IgG4-related disease by re-staining lacrimal gland lesions diagnosed previously with low-grade lymphoma in a patient who developed systemic diffuse large B-cell lymphoma (DLBCL) 18 years later. A 53-year-old man developed bilateral lacrimal gland swelling and right submandibular gland swelling and was diagnosed by excision as low-grade lymphoma. In follow-up, positron emission tomography showed high uptake in the median hyoid 11 years later but no malignancy was detected by laryngeal submucosal biopsy. He was well with no treatment until 18 years later when he had palatal swelling and was diagnosed with DLBCL by oral floor biopsy. He had systemic lymphadenopathy, infiltration in paranasal sinuses, hypopharynx, small intestine, kidney, and prostate. He underwent 8 courses of R-CHOP and 3 courses of high-dose methotrexate and achieved complete remission with no relapse for 1 year thereafter. Re-immunostaining of paraffin blocks of bilateral lacrimal gland lesions showed IgG and IgG4-positive lymphocytes and plasma cells among lymphoid follicles separated by fibrous bundles, with 10 or more IgG4-positive cells in high-power field. The IgG4/IgG-positive cell ratio was 100% and the number of κ chain-positive cells and λ chain-positive cells was the same. The bilateral lacrimal lesions were thus re-diagnosed as IgG4-related disease. In conclusion, systemic DLBCL occurred approximately 20 years after lacrimal gland IgG4-related disease. Literature review revealed 12 patients with IgG4-related disease, including the present patient, who later developed lymphoma in the other organs

Probabilistic Prediction in Multiclass Classification Derived for Flexible Text-Prompted Speaker Verification

So far, we have presented amethod for text-promptedmultistep speaker verification using GEBI (Gibbs-distribution based extended Bayesian inference) for reducing single-step verification error, where we use thresholds for acceptance and rejection but the tuning is not so easy and affects the performance of verification. To solve the problem of thresholds, this paper presents a method of probabilistic prediction in multiclass classification for solving verification problem.We also present loss functions for evaluating the performance of probabilistic prediction. By means of numerical experiments using recorded real speech data, we examine the properties of the present method using GEBI and BI (Bayesian inverence) and show the effectiveness and the risk of probability loss in the present method.22nd International Conference on Neural Information Processing, ICONIP 2015, November 9-12, 2015, Istanbul, Turke

アドレナリンβ2受容体刺激薬の物理的化学的性質及び製剤化に関する基礎的研究

取得学位：博士(薬学)，学位授与番号：博甲第395号，学位授与年月日：平成13年3月22日,学位授与年：200