情報数理学専攻 平成29年度情報数理学セミナー
第1回
| 日時: | 4月27日（木） 14:40～16:10 |
| 会場: | 大阪大学 吹田キャンパス 応用物理学 講義棟 P1-311 |
| 内容: | 安全教育講演Ⅰ |
| 司会: | 吉川 裕之 助教 |
| 概要: |
博士前期・後期課程の教育・研究において、またその後の社会活動においても重要な事柄である「安全」について、その概略を講義する。
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第2回
| 日時: | 5月11日（木） 14:40～16:10 |
| 会場: | 大阪大学 吹田キャンパス 応用物理学 講義棟 P1-311 |
| 内容: | 安全教育講演ⅠI |
| 概要: |
博士前期・後期課程の教育・研究において、またその後の社会活動においても重要な事柄である「安全」について、その概略を講義する。
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第3回
| 日時: | 5月18日（木） 13:00～15:40 |
| 会場: | 情報科学研究科A棟 A109講義室 |
| 内容: | 博士論文中間発表会 |
| 講演者: | Md Sohel Ahmed（森田研） 13：00～13：30 |
| 講演題目: | Earthquake Disaster Management Analysis in Dhaka |
| 概要: | A massive earthquake has been forecast for Bangladesh in the near future due to its position at the junction of three continental plates. Should it occur, the earthquake is expected to be devastating, particularly in the capital city of Dhaka, due to the large number of high-rise buildings constructed on relatively unstable ground and the high population density of the metropolitan area. In this paper, we use statistics-based methodology to identify correlations among the occupancy classifications, emergency response, and population density. Multiple regression analysis indicates that the level of satisfaction regarding disaster management is fairly low with respect to emergency response and medical care. |
| 講演者: | 滝本直也（森田研） 13：30〜14：00 |
| 講演題目: | The Black-box Optimization via Multi-points Criterion |
| 概要: | The optimization of expensive-to-evaluate functions generally relies on meta model based exploration strategies. Many global optimization algorithms used in the field of computer experiments are based on Kriging method (Gaussian process regression). They proceed by choosing iteratively the point maximizing a criterion which balances the predicted value and the uncertainty. Then, multi-points sampling methods can achieve an effective search by less iteration. Finally, we investigate the multi-point criterion which aims at distributing sampling points depending on the number of processors which enable us to evaluate the different settings of simulations simultaneously. |
| 講演者: | Jian Yang （鈴木研） 14：00〜14：30 |
| 講演題目: | A Study on A Framework for A Self-Organizing Agent Model Tackling Multi-Target Detection and Gravitation |
| 概要: | Swarm intelligence is an emergent property that arises from a group of locally interacting individuals. It is often observed in self-organized "intelligent" systems where autonomy, emergence and distributed functions replace control, pre-programming and centralization. Examples of such systems range from bacterial colonies to insect colonies, fish schools and bird flocks, which demonstrate intelligent behaviors including collective foraging, nest building and pattern formation (e.g., biofilm formation by bacteria). Inspired by how a group of individuals in nature exhibit such intelligent behavior, in this research we investigate the design of a framework for such self-organizing agent model for multiple possible applications, including drone clusters' autonomic controlling, nanoscale drug delivery by bio-nanomachines and even load balancing for networks. Here we define the rules by which agents interact to meet the application goals. We focus on a swarm of agents that interact by secreting signals called attractants and repellents to detect and localize to targets that may exist in the domain of our concern. This multi-target detection and gravitation problem was investigated in our previous work, in which, agents release both attractants and repellents to induce gravitational forces toward target locations. In the ongoing work, first we examine three modified approaches by which agents coordinate their behavior through releasing (1) attractants, (2) repellents or (3) both attractants and repellents. We examine the primary behavior of agents in these approaches and evaluate the performance. Second, under different initial spatial distributions of the agents over the entire domain, interesting behaviors of the agents emerge including repetitive oscillations alternately among the targets' locations. These intrigue us to study the model using methodology of non-linearity. Third, we will step further to demonstrate the validity and quality of this framework when applied to multiple practical problems, for example, the drone clusters' target detecting process. This research is also expected to shed light on performance optimization process of the model according to different requests in different applications. A comprehensively exhaustive method over parameter space should be averted. |
| 講演者: | Juan Lorenzo Hagad（沼尾研） 14：40～15：10 |
| 講演題目: | Learning Robust Representations of Mental Stress Using Spatio-Temporal Features of Physiological Signals and Deep Learning |
| 概要: | Driven by advancements in wearable technology, physiological signals are proving to be invaluable tools able to unobtrusively gather insights about a wide range of physical and mental states. For instance, consumer-grade sensors can now track physiological signs associated with stress and potentially use these observations towards enhancing quality of life. Automated models stand to benefit the most due to their ability to learn from and process the vast amount of data that will result from the proliferation of wearables. However, the dynamics of physiological data has been proven to be inherently complex, and existing models still struggle to provide reliable performance especially across multiple users and practical application domains. Deep learning could theoretically close this gap. However, existing approaches are still unable to make full use of the capacity of certain deep learning structures such as convolutional neural networks (CNN). This is primarily due to the nature of the traditional parameters used in these models, which typically lack meaningful local spatial correlations. It would therefore be beneficial to construct models using data representations with meaningful spatial characteristics, provided that they draw from the same data sources as established features. This work proposes a more robust representation of authentic mental stress using a deep network structure trained on spatio-temporal representations of traditional physiological data features. Specifically, it applies CNNs to discover latent features from spectral and fractal representations of physiological signals. This is in contrast to the flat statistical feature vectors typically seen in existing methods. The final model will be trained on a combination of multiple physiological signals; however, priority will be placed on optimizing certain individual modalities. In the case of heart rate, the temporal nature of heart rate dynamics and psychological states should entail that CNNs should be able to discover salient features from heart rate time-frequency spectrograms. These raw spectrograms, while inherently more complex, should be more expressive than the statistical measures used to summarize entire windows of data, provided that data noise can be minimized. An additional challenge is the use of a small data-set, a common problem with many medical datasets, which necessitates the use of regularization methods to avoid over-fitting. Furthermore, in an effort to build towards a practical real-world model, the data collection methodology in this work prioritizes the elicitation of authentic mental stress through mentally fatiguing activities, as opposed to the frustration and physical stressors used by many traditional methodologies. Wearable sensors such as a chest-worn electrocardiogram (ECG) and a wristband sensor will be used to track the subject's physiological states while subjective user annotations will establish the ground truth stress levels. Finally, class activation maps (CAM) will be used to analyze important feature regions in the spectra which are most relevant to different stress-related states. This will be used to validate the model vis-a-vis known mechanisms about physiological stress signals or could potentially be used to improve the learning stages of the model. |
| 講演者: | Wasin Kalintha（沼尾研） 15：10～15：40 |
| 講演題目: | Step Toward the Next Generation of Evolutionary Distance Metric Learning |
| 概要: | Many machine learning and data mining algorithms, such as classification, clustering and regression tasks, heavily rely on the distance metric for the input data patterns. Distance metric learning (DML) attempts to optimize a metric to improve data mining task. Many research studies on DML reiterate that the definition of distance between two data points substantially affects clustering tasks. Most of recent DML methods have been proposed based on Mahalanobis distance to improve the accuracy of clustering by formulating a penalty function for constraints into an objective function to learn a distance metric. However, these techniques sometimes lead to decreasing in the cluster validity index. Evolutionary distance metric learning (EDML) has been proposed to address this problem by directly improve cluster validity index as an objective function. Although EDML provides outstanding results over other semi-supervised clustering in many data set, it can only perform a linear transformation, like most of the DML techniques, which yields insignificant to non-linear separable data. Moreover, this unifies view of evolutionary algorithm and data mining has only few work recently, this will be a challenging problem to solve. This study proposes a non-linear Evolutionary Distance Metric learning. First, we address the non-linear separable by Kernelized Evolutionary Distance MetricLearning. Specifically, DML method which provides an integration of kernelization technique with Mahalanobis-based DML and EDML. Therefore, the non-linear transformation of the distance metric can be performed while maintain the optimized cluster validity index by an evolutionary algorithm. Furthermore, rather than just apply to clustering, I would like to expand this work to all other data mining technique, e.g., classification, regression and so on. |
第4回
| 日時: | 6月1日（木） 14:40～16:10 |
| 会場: | 情報科学研究科A棟 A109講義室 |
| 内容: | 講演会 |
| 講演者: | 田嶋達裕氏（ジュネーブ大学研究員） |
| 講演題目: | Consciousness and Cross-Embedding (意識と相互埋め込み) |
| 概要: |
Brain-wide, complex neural dynamics are considered crucial for cognitive functions and consciousness in the human and animals. The system complexity, however, often defies conventional mechanistic analyses via reductionist modelings. I propose that such a dilemma could be resolved partially by using theorems in general dynamical systems. In this lecture, I introduce Takens' embedding theorem in general dynamical systems and its application to neural data analyses. The method enables us to characterize the dynamical structures and causal interactions among variables without relying on equation-based models. Applying this method to wide-field electrophysiological data reveals a universal hierarchy of cross-area interactions and dynamical complexity in the conscious brain, demonstrating its potentially wide application to deciphering complex biological systems.
(*The lecture will be given in Japanese with English slides. The discussion could be in either English or Japanese.)
References: [1] Tajima, S., Yanagawa, T., Fujii, N., & Toyoizumi, T. Untangling brain-wide dynamics in consciousness by cross-embedding. PLOS Computational Biology, 11(11), e1004537:1-28, (2015). [2] Tajima, S., & Kanai, R. Integrated information and dimensionality in continuous attractor dynamics. Neuroscience of Consciousness, (in press). Preprint: arXiv:1701.05157 [q-bio.NC] |
第5回
| 日時: | 6月8日（木） 14:40～16:10 |
| 会場: | 情報科学研究科A棟 A109講義室 |
| 内容: | 講演会 |
| 講演者: | 宮本裕一郎先生（上智大学 理工学部 情報科学科 准教授） |
| 講演題目: | ブラックボックス最適化とその手法紹介 |
| 概要: | 数理最適化問題のうち，目的関数がブラックボックス関数であるものをブラックボックス最適化問題という． ブラックボックス最適化はシミュレーションのパラメーター調整などに使われ，近年その重要性が増している． 今回は，ブラックボックス最適化問題に対する様々な手法（解法）を紹介し，その特徴を概観する． 簡単な計算実験結果なども紹介する． |
第6回
| 日時: | 7月6日（木）13:00～14:30 |
| 会場: | 大阪大学産業科学研究所 管理棟１階講堂 (研究所建物案内図⑤の１階となります。） |
| 内容: | 講演会 |
| 講演者: | 小野 智司先生 （鹿児島大学 理工学研究科/准教授） |
| 講演題目: | 応用志向の進化計算 |
| 概要: | 非線形計画法に代表される最適化技術に対して，生物の進化過程を模倣した進化計算は，対象となる目的関数が非凸，微分不可， 大域的多峰性，ノイズを含むなどの性質を持っている場合であっても最適化を行える点に特長がある． 本講演では，多点探索型のメタヒューリスティクスである進化計算の利点を活かした3種類の最適化技術――多峰性関数最適化， 多目的最適化，対話型最適化――について，応用事例を交えて紹介する． |
第7回
| 日時: | 7月13日（木）14:40〜16:10 |
| 会場: | 大阪大学産業科学研究所 管理棟１階講堂 (研究所建物案内図⑤の１階となります。） |
| 内容: | 講演会 |
| 講演者: | 犬塚 信博 教授（名古屋工業大学） |
| 講演題目: | つながりと論理を重視した知識発見アルゴリズム |
| 概要: | データから隠れた知識やパターンを見つけることは、人工知能研究の重要な課題である。 論理に基づいた知識の表現は人の言葉と本質的に同等であり、発見の手段として有望である。 論理に基づいて複雑なデータや人々のつながり関係からパターンを発見するアルゴリズムを検討する。 アルゴリズムの設計や知識表現の観点から議論を行いたい。 |
第8回
| 日時: | 7月20日（木）14:40〜16:10 |
| 会場: | 情報科学研究科A棟 A109講義室 |
| 内容: | 講演会 |
| 講演者: | Andrew L Johnson先生 （情報科学研究科/特任准教授） |
| 講演題目: | Regression as a Special Case of Quadratic Programming |
| 概要: | Students in operations research and industrial engineering typically study linear and non-linear programming. Whereas regression is more commonly used in the fields of statistics and econometrics. This lecture will describe the relationship between the two methodologies. Specifically, standard ordinary least squares regression can be formulated as a quadratic programming problem and solved by finding a solution to a linear set of equations. I will demonstrate how to formulate and solve regression problems in a number of ways using the software GAMS. A temporary license for GAMS will be provided and students should come to class with GAMS already installed on their computers. |
第9回
| 日時: | 7月27日（木）14:40〜16:10 |
| 会場: | 情報科学研究科Ａ棟 Ａ１０９室 |
| 内容: | 講演会 |
| 講演者: | Andrew L Johnson（特任准教授/情報科学研究科） |
| 講演題目: | Shape Constrained Functional Estimation |
| 概要: | Domain knowledge or theory developed for specific fields often provides us with qualitative information on the properties of the functions in a model, but rarely indicates their explicit functional form. I will discuss how an optimization framework can be used for shape restricted functional estimation. Specifically I will discuss how different loss functions lead to linear, quadratic or generally nonlinear programming problems. Further, I will demonstrate how restrictions such as monotonicity, convexity, and supermdularity can be imposed in an optimization framework. We will implement these estimators in GAMS doing exercise both in class and have problems to take home. |
第10回
| 日時: | 10月12日（木）13:00〜17:00 |
| 会場: | 情報科学研究科Ａ棟 Ａ210/212会議室 |
| 内容: | 修士論文中間発表会 |
第11回
| 日時: | 11月30日（木）13:00〜14:40 |
| 会場: | 情報科学研究科Ａ棟 Ａ１０９室 |
| 内容: | D2・M1中間発表会，インターンシップ報告会 |
| 講演者: | 植松直哉（D2） |
| 講演題目: | An Exact Algorithm for Submodular Maximization |
| 概要: | Many problems in computer science can be formulated as submodular set-function maximization, such as sensor locations, influence spread, et al. Although many approximate algorithms exist but not many exact algorithms have been studied since the problem is known as NP-hard. First, we consider an exact algorithm for monotone submodular maximization. We propose a heuristic algorithm which is a substitute for column generation, and "branch and enumeration" for more general problems. The current result shows that our algorithm is more efficient than the existing algorithm in large-scale problems. An exact algorithm will be proposed for more general cases, such as approximate submodular or non-monotone submodular function in our future work. |
| 講演者: | Sanjjamts Amartaivan (M1) |
| 講演題目: | 2D Bin-Packing problem with free rotation |
第12回
| 日時: | 12月6日（水） 13:00～16:10 |
| 会場: | 情報科学研究科 B棟 1階 B101講義室 |
| 内容: | 博士論文公聴会 |
| 講演者: | 李 浩鎮 |
| 講演題目: | Convergence analysis of network-of-networks with hierarchical structure （階層構造を持つnetwork-of-networksの収束性解析） |
| 概要: | マルチエージェントシステムは、その構造と対応するネットワークを考え、そのグラフラプラシアンのスペクトルを用いてダイナミックスを特徴づけることができる。例えば、二番目に小さい固有値である代数的連結度はグラフの連結状態の尺度となり、マルチエージェントシステムにおける合意の収束率の最悪値を与えるため解析手段として用いられる。 一方、大規模システムに対しては、それをサブシステムが相互結合した階層構造を持つネットワークとして表現して解析する様々な手法が研究されてきた。しかしながら、大規模システムに対応するグラフラプラシアンは次元の大きい行列となるためその固有値を求めることが容易ではない。 本研究ではそのような問題に着目し大規模システム、特に階層構造を持つnetwork-of-networksの収束解析のために代数的連結度をより容易に求める方法を与えることを目的とする。最初に、同じ無向グラフ構造を持つ複数のネットワークの同じ点同士がつながることで得られる、無向グラフの積でその構造を表せるネットワークについてその方法を与える。さらに、無向グラフで表せるがその構造は少し異なる複数のネットワーク同士がつながることで得られるネットワークについてその方法を与える。 有向グラフで構造を表せるネットワークに関しては、まず平衡グラフで表せる構造を持つ複数のネットワーク同士が平衡グラフを介してつながることで得られる、平衡グラフの積でその構造を表せるネットワークについて考える。収束率を解析する際には、元の有向グラフのミラーグラフを考え、その代数的連結度を用いる。さらに、平衡グラフで表せるがその構造は少し異なる複数のネットワーク同士がつながることで得られるネットワークについてその方法を与える。 |
| 講演者: | Nattapong Thammasan |
| 講演題目: | Practical Emotion Recognition using Wearable Brain and Physiological Sensors （ウェアラブル脳波および生体センサを用いた実用的な感情認識） |
| 概要: | Detecting user emotion will play an important role in bridging the gap between human and computers. Recently, brain and physiological signals have been employed to detect emotional cues of human subjects with the assumption that bodily signals would provide information of intrinsic emotions better than conventional approaches including computer vision and speech analysis. However, conventional devices for brain imaging and physiological recording featured in previous works tend to be obtrusive since they were originally designed for use in clinical and controlled environments. This drawback limits the practicability of emotion recognition systems. Recently, a variety of wearable brain and physiological sensors have been developed which demonstrate potential in the emotion detection domain but come with the significant challenges regarding signal quality and stability. Hence, the objective of this study is to improve the practicability of emotion recognition system by using wearable brain and physiological sensors without significantly degrading the performance. In particular, this study has two main focuses. Firstly, the study employs multiple wearable sensors, including electroencephalographic (EEG) headset, chest-attachable electrocardiogram (ECG) patch, and wrist-worn galvanic skin response (GSR) band, with the aim of improving the robustness of the system by implementing efficient multimodal integration. Hereby, this study proposes making use of the reliability information of each modality, quantified by signal quality and accelerometer data, to regulate the information ensemble. The empirical results from experiments with 30 subjects performing music-listening tasks demonstrate that the context-aware system significantly outperforms traditional approaches in arousal and valence classification. Secondly, this study addresses limitations of existing systems with regards to accommodating new users by minimizing the amount of calibration data required to make use of the system. Conventional generalized systems designed to detect emotions tend to suffer from degraded performance due to inter-subject variability in bodily signals, especially with regards to EEG. This necessitates collection of calibration data recordings which can be time-consuming, annoying and reducing the practicability of the entire system. To mitigate this shortcoming, an emerging technique called transfer learning is adopted. This technique can reduce calibration data requirements by allowing the use of information collected from other subjects to build a model for a new subject. This reduced data requirement streamlines the process of adding new users to the system, and empirical results also demonstrate the method's potential for enabling subject-independent emotion recognition. The proposed method may shed light on developing more practical emotion recognition systems for real-world applications. |
| 講演者: | 陰山 真矢 |
| 講演題目: | 恒常性自己調節モデルに対する数理的研究 |
| 概要: | 1972年にJ.E. Lovelockは地球における自己調節恒常性システムという概念を提唱した。 これは、生物相とそれを取り巻く環境が相互に作用することによって、地球システム全体を安定化に向けて自己調節しているという考え方である。 このようなシステムを単純化・理想化したものがLovelockによるデイジーワールドである。 デイジーワールドは恒星の周りを公転している仮想の惑星である。 デイジーワールドには黒色と白色の２種類のデイジーしか存在せず、これらは地球の植物と同様に生育域を争っている。 また、惑星の温度は恒星からの放射熱と地表面の光反射率によってのみ決まる。 つまり、地表面の色が濃いほど恒星からの光を吸収して温度が上昇しやすく、色が薄いほど光を反射して温度が低下しやすい。 WatsonとLovelockによって導入されたデイジーワールドの方程式は、非常に単純なものであったにもかかわらず、２種類のデイジーが互いの生育域を争いながら、惑星の大域的な気温を自らの成長に最適な値へと自律的に調節していくような結果を示した。 現在、地球上の生物が環境に何らかの影響を与えていることについてはもはや議論の余地はないが、デイジーワールドモデルは生物と環境の相互作用を再現する最も単純な地球システムモデルのひとつとしてその有用性が期待されており、それに対する様々な関連研究や拡張研究が行われている。 本研究では、既存モデルに対して球面上でのデイジーと温度の拡散効果を加えた、２次元モデルを扱う。 先ず、球面上でモデルの定式化を行い、それに対して局所解、大域解、力学系を構成し、有限次元アトラクタの存在を示す。 また、このモデルに対する数値計算結果を紹介する。 次に、２次元長方形領域上でモデルを考え、それに対する数学解析の結果と恒星からの光の強さを表すパラメータ L を変動させた場合における数値計算結果を示す。 それにより、我々の２次元モデルにおいても温度の恒常性機能が維持されていることを示すとともに、さらに、温度の恒常性が出現する L の領域で２種類のデイジーの共存が見られ、白・黒デイジーの生存競争によって生成される棲み分けパターンについて説明する。 この結果から、デイジーの棲み分けパターンと温度調節機構との関係性の可視化を試みる。 |
| 講演者: | Hongle Wu |
| 講演題目: | Learning Sleep Pattern based on Audio Data （音データに基づく睡眠パターンの学習） |
| 概要: | A good sleep is important for a healthy life. Recently, several consumer sleep devices have emerged on the market claiming that they can provide personal sleep monitoring; however, many of them require additional hardware or there is a lack of scientific evidence regarding their reliability. The objective of our research is to develop a more practical and economical approach that provides acceptable accuracy of sleep study. We have achieved two major achievements. Firstly, we propose a method to discover sleep patterns via clustering of audio events recorded during sleep. The proposed method extends the conventional self-organizing map algorithm by kernelization and sequence-based technologies to obtain a fine-grained map that visualizes the distribution and changes of sleep-related events. We introduced features widely applied in audio processing and popular kernel functions to the proposed method to evaluate and compare performance. By visualizing the transition of cluster dynamics, sleep-related audio events were found to relate to the various stages of sleep. In addition, we calculated the conditional probabilities of an audio event given the current sleep stage, quantified the correlation between sleep-related sound events and sleep stages. The conditional probabilities demonstrate that snore events have the strongest relationship with deep sleep and body movement is more related to rapid eye movement (REM) and light sleep than deep sleep. Teeth grinding most frequently occurs during Non-REM sleep. The proposed method provides a new aspect of sleep monitoring because the results demonstrate that audio events can be directly correlated to an individual's sleep patterns, and empirically warrant future study into the assessment of personal sleep quality using audio data. Our second research topic is assessing the sleep quality through audio events. We used subjective sleep quality as training label, combined several machine learning approaches including kernelized self-organizing map, hierarchical clustering and hidden Markov model, obtained the models to indicate the sleep pattern of specific quality level. We found there is no significant difference on sleep stage sequence HMMs between different sleep quality, on the contrary, the HMMs of sound events from different sleep quality level have obvious difference. This evidence is interesting that sleep stage sequence is useless for assessing sleep quality. Therefore, the HMMs of sound events from good and poor sleep quality were used to model the sleep quality. The likelihoods between an input audio event sequence and HMMs are calculated as input vectors, then several classification methods are applied, including support vector machines (SVM), adaptive Boosting (Adaboost), majority decision, etc. According to the experiment, the classifier by HMMs obtained a feasible result, which empirically warrants our approach on the assessment of personal sleep quality by audio data. |
第13回
| 日時: | 12月21日（木） 14:40～16:10 |
| 会場: | 情報科学研究科A棟 A109講義室 |
| 内容: | 講演会 |
| 講演者: | 桜間一徳准教授（鳥取大学 工学研究科 機械宇宙工学専攻） |
| 講演題目: | マルチエージェントシステムに対する制御系設計理論の体系化 |
| 概要: | 近年，IoT (Internet of Things) が普及することで，モバイル端末，電気機器，建築物，乗用車など様々な要素システムが，情報交換を通じて大規模につながりつつある． このような情報と物理システムが融合したサイバーフィジカルシステムの効率的な運用法を生み出すことは，将来，社会インフラを中心に多大な利益をもたらすことが期待される． このため，制御工学の分野では，サイバーフィジカルシステムの抽象モデルとして，マルチエージェントシステムに対する制御系設計が盛んに行われている． マルチエージェントシステムとは，ネットワーク上で情報交換する多数のエージェントから構成されるシステムのことである． 本講演では，マルチエージェントシステムに対するタスクの一般的な定式化法，およびそれに対する系統的な制御系設計法について，講演者の最近の研究結果を中心に解説する． さらに，この結果を一般化フォーメーション制御へ応用し，タスクの実現可能性が，タスク内の自由度とネットワークの一般化連結度の関係から決まることを明らかにする． |
第15回
| 日時: | 1月25日（木） 14:40～16:10 |
| 会場: | 情報科学研究科A棟 A109講義室 |
| 内容: | 講演会 |
| 講演者: | 玉田 洋介 助教 （自然科学研究機構 基礎生物学研究所） |
| 講演題目: | 補償光学を用いた生物の深部イメージング |
| 概要: | 蛍光プローブを用いて生体分子や生体構造を標識し、蛍光顕微鏡を用いてその分子や 構造を観察する技術はライブセルイメージングと呼ばれている。これにより、DNAやタンパク質などの生体分子や細胞内小器官などの生体構造のダイナミクスを、生きた生物内でとらえられるようになった。 現在では、光の理論限界を超えた解像度を達成する超解像イメージングにより、実際の生体分子のサイズに近いナノスケールの解像度でライブセルイメージングを行うことも可能となっている。 こうした技術の進歩の一方で、未だ解決されていないのが、生きた生物の深部を観察する際の「ぼけ」の問題である。 生きた生物の内部には、屈折率の異なる構造体が大小複雑に混在しており、そこを通過することで光は複雑に乱れる。 この光の乱れのため、生きた生物を深く観察しようとすればするほど像がぼやけてしまい、生体内部で起きる重要な生命現象が観察できなくなっている。 本セミナーでは、この問題に対して現在とられている対策を紹介するとともに、天文学にて発展してきた「補償光学」を顕微鏡に適用する最新の研究について紹介する。 補償光学は、光の乱れを計測してリアルタイムで補正する技術であり、これをライブ セルイメージングに適用することで、生きた生物の深部に対しても高解像イメージングが可能になると期待される。 |