Faculty Profiles - TOKUNAGA Terumasa

Conference Prsentations (Oral, Poster) -

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Simultaneous measurements of membrane voltage and intracellular Ca2+ of AWA neurons by a gene encoded voltage indicator and GCaMP

Takeshi Ishihara, Noriko Sato, Terumasa Tokunaga

23rd International C.elegans conference Genetics Society of America

Event date： 2021.06.21 - 2021.06.24 Language：English

Measurement of neuronal activities in non-invasive and unanesthetized condition is important for understanding neuronal function in intact animals. Ca2+ imaging by fluorescent gene encoded calcium indicators (GECI) are a powerful way to measure neuronal activities in C. elegans. Although Ca2+ imaging revealed important aspects in neuronal functions, the measurement of neuronal membrane voltage is important to understand the neuronal functions. Furthermore, the relations of change of membrane voltages and changes of Ca2+ has not been fully understood. Recently, several types of gene encoded voltage indicators (GEVI) that are derived from 7TM proteins used for optogenetics has been developed to measure changes of membrane voltage in living animals. Even though the fluorescence of these GEVIs is dim, they showed fast time constants and relatively high fluorescent change depend on voltages. Among those GEVIs, we use paQuasAr3 for the voltage measurement, because it shows relatively higher fluorescence with other superior characteristics.
Since AWA, one of the olfactory sensory neurons, which is responsible for diacetyl sensation, was reported to show all-or-none action potentials (Liu et al. 2018), we firstly analyzed AWA voltage changes induced by diacetyl. We found that fluorescence of paQuasAr3 expressed in AWA cell body is changed in response to diacetyl stimulation with high reproducibility. At the beginning of the stimulation, the transient increase and decrease of fluorescence intensity was observed, whereas the relatively higher fluorescence intensity was sustained during the stimulation. To elucidate relations between the Ca2+ responses and the voltage responses, we made wild-type animals expressing paQuasAr3 and GCaMP6f in AWA neurons, and measured both fluorescence at a cell body simultaneously. We found that the changes of paQuasAr3 started faster than the changes of GCaMP. These analyses will give insights on the neuronal functions in informational processing.

Development of training data with collaboration of observation, numerical simulation and machine leaning for space plasma phenomena forecast model

Keiichiro FUKAZAWA, Tomoki KIMURA Terumasa TOKUNAGA, Shinya NAKANO

Japan Geoscience Union Meeting 2021 Japan Geoscience Union

線虫C.elegansの細胞レベルの膜電位/カルシウム同時イメージング確立に向けて Invited

徳永旭将, 石原健, 佐藤則子, 岩崎唯史

第2回分子サイバネティクス研究会，第46回分子ロボティクス定例研究会学術変革領域(A)「分子サイバネティクス」

Image-based Plant Disease Diagnosis with Unsupervised Anomaly Detection Based on Reconstructability of Colors

Ryoya Katafuchi, Terumasa Tokunaga

International Conference on Image Processing and Vision Engineering Institute for Systems and Technologies of Information, Control and Communication

Event date： 2021.04.28 - 2021.04.30 Language：English

This paper proposes an unsupervised anomaly detection technique for image-based plant disease diagnosis. The construction of large and publicly available datasets containing labeled images of healthy and diseased crop plants led to growing interest in computer vision techniques for automatic plant disease diagnosis. Al- though supervised image classifiers based on deep learning can be a powerful tool for plant disease diagnosis, they require a huge amount of labeled data. The data mining technique of anomaly detection includes un- supervised approaches that do not require rare samples for training classifiers. We propose an unsupervised anomaly detection technique for image-based plant disease diagnosis that is based on the reconstructability of colors; a deep encoder-decoder network trained to reconstruct the colors of healthy plant images should fail to reconstruct colors of symptomatic regions. Our proposed method includes a new image-based framework for plant disease detection that utilizes a conditional adversarial network called pix2pix and a new anomaly score based on CIEDE2000 color difference. Experiments with PlantVillage dataset demonstrated the superiority of our proposed method compared to an existing anomaly detector at identifying diseased crop images in terms of accuracy, interpretability and computational efficiency.

機械学習・数値シミュレーション・観測融合による宇宙プラズマ現象予測モデル開発に向けた学習データの整備 Invited

深沢圭一郎, 木村智樹, 徳永旭将, 中野慎也

2020年度ISEE研究集会「太陽地球圏環境予測のためのモデル研究の展望」名古屋大学宇宙地球環境研究所

Overlapping Cluster Analysis of Whole-Brain Imaging Data of C.elegans: Detection of Functional Hub Neuron

独立成分分析を用いた人工衛星干渉磁場の分離: 伸展物と事前情報を用いない磁場観測

今城峻, 能勢正仁, 相田真里, 松本晴久, 東尾奈々, 徳永旭将, 松岡彩子

統計数理研究所共同研究集会「宇宙地球環境の理解に向けての統計数理的アプローチ」統計数理研究所, 名古屋大学宇宙地球環境研究所，名古屋大学数理データ科学教育研究センター

An image processing pipeline for quantifying spatiotemporal evolution of voltage responses inside a single cell of C. elegans

Terumasa Tokunaga, Noriko Sato, Yuishi Iwasaki, Takeshi Ishihara

The 43rd Annual Meeting of the Molecular Biology Society of Japan The Molecular Biology Society of Japan

非対称的な相互作用を持つマルコフ確率場を変形モデルとした非剛体イメージレジストレーション技術の開発

長村徹, 徳永旭将

第23回情報論的学習理論ワークショップ電子情報通信学会情報論的学習理論と機械学習研究会

GLCICによる欠損補間に基づく教師なし画像異常検知法の提案

深町悠貴, 徳永旭将

第23回情報論的学習理論ワークショップ電子情報通信学会情報論的学習理論と機械学習研究会

宇宙プラズマ現象予測モデル開発に向けた機械学習・数値シミュレーション・観測による学習データの整備

深沢圭一郎, 木村智樹, 徳永旭将, 中野慎也

第148回地球電磁気・地球惑星圏学会総会及び講演会地球電磁気・地球惑星圏学会

Event date： 2020.11.01 - 2020.11.04 Language：English

The machine learning has become a powerful tool to find the relation between variables thanks to the deep learning technique. This performs greatly in the classification, regression and recently generative modeling in the engineering and commercial areas. However, due to the satisfaction of physical laws in the scientific research area, the application of machine learning has some difficulties. In particular, the generative modeling is very sensitive to scientific data since the generated data is not guaranteed by the physical laws.
To overcome these problems, we have tried to apply machine learning to space plasma physics. In the observation there are many lacks data in space and time. Using the technique of GAN (Generative Adversarial Networks), we have challenged to represent the lack data of aurora image by ASI (All-Sky Imager) of THEMIS. Now we use the natural training data not only the observation data and we have obtained the smooth represented data, however these data cannot satisfy the physical laws. Then we prepare the training data of only observation.
From this thought the preparing the training data is the most important for machine learning. Then we have prepared the global simulation data of magnetosphere using real solar wind data for the generation and forecast the configuration of the magnetosphere. These data are the very large size and time elapsed data so that the data set cannot be stored in often case and usual machine learning cannot treat these data set. However recently there are 3D CNN (convolutional neural network) and RNN (recurrent neural network) which can be trained by 3D data set and these data set may become very important. In this study, we show the database of this data set data, representation of the auroral image and their status.

Development of Deformable Image Registration Technique using MRFs Based Deformation Model with Asymmetric Interaction

IIBMP2020

GANに基づくCIEDE2000異常度スコアを用いた色異常検知方法の提案

片渕凌也, 徳永旭将

第23回画像の認識・理解シンポジウム電子情報通信学会パターン認識・メディア理解(PRMU)研究専門委員会

Event date： 2020.08.02 - 2020.08.05 Language：Japanese

本論文では, 色に現れる異常の検出を目的とした, 教師なし異常検出方法を提案する. 提案手法では, 敵対的生成ネットワークに基づく教師なし学習に基づき, 色の再構成可能性を評価することで, 正常データでは見られなかった色の異常の検知を行う. また, 色の再構成可能性の評価のため, CIEDE2000 色差に基づく異常度スコアを提案する. 実験では, PlantVillage データセットを用いた病気の植物とその病変領域の検出に対して提案手法の性能評価を行い,ベースラインである AnoGAN との比較を行う. 実験の結果, 提案手法が色の異常検知問題に対して検出性能, 解釈可能性および計算効率性の観点から AnoGAN より優れた性能を発揮することを示す.

All-Sky Imagerデータの複数の脈動パッチを包括的に追跡するためのパイプライン

野見山陸, 三好由純, 遠山航平, 小川泰信, 細川敬祐, 徳永旭将

第23回画像の認識・理解シンポジウム電子情報通信学会パターン認識・メディア理解(PRMU)研究専門委員会

Signal and Noise Separation from Satellite Magnetic Field Data Using Independent Component Analysis: Prospect of Magnetic Measurements without Mast and Noise Source Information

Shun Imajo, Masahito Nosé, Mari Aida, Haruhisa Matsumoto, Nana Higashio, Terumasa Tokunaga, Ayako Matsuoka

JpGU-AGU Joint Meeting 2020 Japan Geoscience Union Meeting

Event date： 2020.07.12 - 2020.07.16 Language：English

We propose an application of the independent component analysis (ICA) to separate satellite- induced time-varying noises from magnetic data obtained by onboard multiple magnetometers. The ICA is a method of estimating source signals observed at multiple sites so that estimated source signals are statistically independent of each other. Since satellite noises are clearly independent of external natural variations, the ICA is expected to separate the satellite noises. We applied the ICA to the magnetic data measured by the first Quasi-Zenith Satellite (QZS-1) that has two triaxial fluxgate magnetometers without an extendable mast. First, we removed the long-period trend from the original data. Then, we applied the FastICA to the detrended data and obtained six independent components (ICs). Satellite-induced noises were successfully classified into three ICs (noise ICs). Natural signals are represented by the rest three ICs (signal ICs). Finally, we restored the observed signals from the signal ICs. We confirmed that amplitude and waveform of natural phenomena were not altered by the processing. We also proposed the method of automatic determination of noise ICs using the D score, which is similar to the normalized coefficient of variance of the mixing vectors. We confirmed that the three largest D scores, which give the noise ICs, are much larger than the three smallest D, which give the signal ICs. The automatic determination of noise ICs by this method was 95% identical to that by visual inspection. These results demonstrated that the ICA method can provide for mast-less magnetic observations in future satellite missions.

Identifying the Snowfall Cloud at Syowa Station, Antarctica via a Convolutional Neural Network

Kazue Suzuki, Masaki Shimomura, Kazuyuki Nakamura, Naohiko Hirasawa, Hironori Yabuki, Takashi Yamanouchi, Terumasa Tokunaga

The 34th Annual Conference of the Japanese Society for Artificial Intelligence The Japanese Society for Artificial Intelligence

A Pipeline for Comprehensive Tracking of Pulsating Patches in All-Sky Imager Data

JpGU - AGU Joint Meeting 2020 Japan Geoscience Union

映像IoT技術による赤ちゃん見守りシステム

村田健史, 深沢圭一郎, 徳永旭将, 水原隆道, 野見山陸, Somnuk Phon-Amnuaisuk

第151回情報システムと社会環境研究発表会

Whole-brain calcium imaging analyses of dynamics of neural network in C. elegans

Yuko Murakami, Suzu Oe, Motonari Ichinose, Takayuki Teramoto, Yu Toyoshima, Terumasa Tokunaga, Osamu Hirose, Stephan Wu, Jang Moon-Song, Hirofumi Sato, Sayuri Kuge, Yuishi Iwasaki, Ryo Yoshida, Yuichi Iino, Takeshi Ishihara

Whole neuronal analyses of the behavioral switching depending on associative learning in C. elegans

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Conference Prsentations (Oral, Poster) -