Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

Designing highly selective molecules for a drug target protein is a challenging task in drug discovery. This task can be regarded as a multiobjective problem that simultaneously satisfies criteria for various objectives, such as selectivity for a target protein, pharmacokinetic endpoints, and drug-like indices. Recent breakthroughs in artificial intelligence have accelerated the development of molecular structure generation methods, and various researchers have applied them to computational drug designs and successfully proposed promising drug candidates. However, designing efficient selective inhibitors with releasing activities against various homologs of a target protein remains a difficult issue. In this study, we developed a de novo structure generator based on reinforcement learning that is capable of simultaneously optimizing multiobjective problems. Our structure generator successfully proposed selective inhibitors for tyrosine kinases while optimizing 18 objectives consisting of inhibitory activities against 9 tyrosine kinases, 3 pharmacokinetics endpoints, and 6 other important properties. These results show that our structure generator and optimization strategy for selective inhibitors will contribute to the further development of practical structure generators for drug designs.

DOI： 10.1021/acs.jcim.2c00787

PubMed

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Publishing type：Research paper (scientific journal)   Publisher：American Association for the Advancement of Science (AAAS)  

Designing fluorescent molecules requires considering multiple interrelated molecular properties, as opposed to properties that straightforwardly correlated with molecular structure, such as light absorption of molecules. In this study, we have used a de novo molecule generator (DNMG) coupled with quantum chemical computation (QC) to develop fluorescent molecules, which are garnering significant attention in various disciplines. Using massive parallel computation (1024 cores, 5 days), the DNMG has produced 3643 candidate molecules. We have selected an unreported molecule and seven reported molecules and synthesized them. Photoluminescence spectrum measurements demonstrated that the DNMG can successfully design fluorescent molecules with 75% accuracy (
<italic>n</italic>
= 6/8) and create an unreported molecule that emits fluorescence detectable by the naked eye.

DOI： 10.1126/sciadv.abj3906

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Authorship：Lead author,　Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.scriptamat.2021.114335

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

Various sampling and monitoring strategies have been developed to assess marine habitats and life-forms. However, the cost-effectiveness of such survey methods (e.g. line intercept transects and autonomous underwater vehicles) is still not high. In this paper, a practical seafloor habitat mapping method combining a cost-effective survey system (P-SSS: portable speedy sea scanner) and a deep learning-based quantification method were proposed. P-SSS is a highly portable transport system and a towed-type system with five cameras arrayed on its platform. The sea trial was conducted at Pujada Bay, Philippines, on 7 December 2019. The high-quality orthophotos of the seafloor with a high resolution of similar to 3.0 mm/pixel were successfully generated. The attained survey efficiency was 12,900 m(2)/hr. In addition, in this paper, a segmentation method utilizing the U-Net architecture to estimate the coverage of corals, seagrass and sea urchins using a large-scale 2D image is proposed. Overall, this highly portable survey system is expected to become a promising tool for marine environmental surveys, especially in the areas where the rich nature of the oceans is susceptible to environmental changes, such as the remote islands that lack sufficient survey facilities.

DOI： 10.1111/2041-210x.13744

Web of Science

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Other Link： https://onlinelibrary.wiley.com/doi/full-xml/10.1111/2041-210X.13744

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

DOI： 10.1021/acs.jctc.1c00301

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Authorship：Lead author   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

DOI： 10.1021/acs.accounts.0c00713

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1038/s42256-020-00290-y

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Other Link： http://www.nature.com/articles/s42256-020-00290-y

Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

<title>Abstract</title>Closed-cycle aquaculture using hatchery produced seed stocks is vital to the sustainability of endangered species such as Pacific bluefin tuna (<italic>Thunnus orientalis</italic>) because this aquaculture system does not depend on aquaculture seeds collected from the wild. High egg quality promotes efficient aquaculture production by improving hatch rates and subsequent growth and survival of hatched larvae. In this study, we investigate the possibility of a simple, low-cost, and accurate egg quality prediction system based only on photographic images using deep neural networks. We photographed individual eggs immediately after spawning and assessed their qualities, i.e., whether they hatched normally and how many days larvae survived without feeding. The proposed system predicted normally hatching eggs with higher accuracy than human experts. It was also successful in predicting which eggs would produce longer-surviving larvae. We also analyzed the image aspects that contributed to the prediction to discover important egg features. Our results suggest the applicability of deep learning techniques to efficient egg quality prediction, and analysis of early developmental stages of development.

DOI： 10.1038/s41598-020-80001-0

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Other Link： http://www.nature.com/articles/s41598-020-80001-0

Authorship：Lead author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Royal Society of Chemistry (RSC)  

<p>Our developed algorithm, BLOX (BoundLess Objective-free eXploration), successfully found “out-of-trend” molecules potentially useful for photofunctional materials from a drug database.</p>

DOI： 10.1039/d0sc00982b

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Language：English   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AMER PHYSICAL SOC  

We develop a method to efficiently construct phase diagrams using machine learning. Uncertainty sampling (US) in active learning is utilized to intensively sample around phase boundaries. Here, we demonstrate constructions of three known experimental phase diagrams by the US approach. Compared with random sampling, the US approach decreases the number of sampling points to about 20%. In particular, the reduction rate is pronounced in more complicated phase diagrams. Furthermore, we show that using the US approach, undetected new phases can be rapidly found, and smaller numbers of initial sampling points are sufficient. Thus, we conclude that the US approach is useful to construct complicated phase diagrams from scratch and will be an essential tool in materials science.

DOI： 10.1103/PhysRevMaterials.3.033802

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Language：English   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press  

Motivation Fast and accurate prediction of protein-ligand binding structures is indispensable for structure-based drug design and accurate estimation of binding free energy of drug candidate molecules in drug discovery. Recently, accurate pose prediction methods based on short Molecular Dynamics (MD) simulations, such as MM-PBSA and MM-GBSA, among generated docking poses have been used. Since molecular structures obtained from MD simulation depend on the initial condition, taking the average over different initial conditions leads to better accuracy. Prediction accuracy of protein-ligand binding poses can be improved with multiple runs at different initial velocity. Results This paper shows that a machine learning method, called Best Arm Identification, can optimally control the number of MD runs for each binding pose. It allows us to identify a correct binding pose with a minimum number of total runs. Our experiment using three proteins and eight inhibitors showed that the computational cost can be reduced substantially without sacrificing accuracy. This method can be applied for controlling all kinds of molecular simulations to obtain best results under restricted computational resources.

DOI： 10.1093/bioinformatics/btx638

Scopus

PubMed

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Language：English   Publishing type：Research paper (scientific journal)  

Automatic design of organic materials requires black-box optimization in a vast chemical space. In conventional molecular design algorithms, a molecule is built as a combination of predetermined fragments. Recently, deep neural network models such as variational autoencoders and recurrent neural networks (RNNs) are shown to be effective in de novo design of molecules without any predetermined fragments. This paper presents a novel Python library ChemTS that explores the chemical space by combining Monte Carlo tree search and an RNN. In a benchmarking problem of optimizing the octanol-water partition coefficient and synthesizability, our algorithm showed superior efficiency in finding high-scoring molecules. ChemTS is available at https://github.com/tsudalab/ChemTS.

DOI： 10.1080/14686996.2017.1401424

PubMed

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

Recent advancements in artificial intelligence (AI)-based molecular design methodologies have offered synthetic chemists new ways to design functional molecules with their desired properties. While various AI-based molecule generators have significantly advanced toward practical applications, their effective use still requires specialized knowledge and skills concerning AI techniques. Here, we develop a large language model (LLM)-powered chatbot, ChatChemTS, that assists users in designing new molecules using an AI-based molecule generator through only chat interactions, including automated construction of reward functions for the specified properties. Our study showcases the utility of ChatChemTS through de novo design cases involving chromophores and anticancer drugs (epidermal growth factor receptor inhibitors), exemplifying single- and multiobjective molecule optimization scenarios, respectively. ChatChemTS is provided as an open-source package on GitHub at https://github.com/molecule-generator-collection/ChatChemTS.

Scientific contribution

ChatChemTS is an open-source application that assists users in utilizing an AI-based molecule generator, ChemTSv2, solely through chat interactions. This study demonstrates that LLMs possess the potential to utilize advanced software, such as AI-based molecular generators, which require specialized knowledge and technical skills.

DOI： 10.1186/s13321-025-00984-8

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Other Link： https://link.springer.com/article/10.1186/s13321-025-00984-8/fulltext.html

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Oxford University Press (OUP)  

Abstract

Biological systems are composed of and regulated by intricate and diverse biomolecular interactions. Experimental and computational approaches have been developed to elucidate the mechanisms of these interactions; however, owing to cost, time, and accuracy issues, large-scale spatiotemporal analyses of molecular pairs remains challenging. Thus, the molecular recognition mechanisms underlying these diverse interactions remain unclear. We successfully simulated the large-scale molecular dynamics (MD) of 4,275 protein–compound pairs by combining a method to accelerate the MD simulations with the supercomputer Fugaku. Our spatiotemporal analysis of generated big MD data revealed universal features underlying molecular recognition and binding processes. This study expands our understanding of the concept of MD simulations from a technique to investigate the dynamic properties of individual protein–drug pairs to an approach to perform large-scale spatiotemporal analysis and compound screening. This study opens an avenue in biological research for subsequent drug discovery.

DOI： 10.1093/pnasnexus/pgaf094

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

ABSTRACT

QCforever is a wrapper designed to automatically and simultaneously calculate various physical quantities using quantum chemical (QC) calculation software for blackbox optimization in chemical space. We have updated it to QCforever2 to search the conformation and optimize density functional parameters for a more accurate and reliable evaluation of an input molecule. In blackbox optimization, QCforever2 can work as compactly arranged surrogate models for costly chemical experiments. QCforever2 is the future of QC calculations and would be a good companion for chemical laboratories, providing more reliable search and exploitation in the chemical space.

DOI： 10.1002/jcc.70017

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Authorship：Last author   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.atech.2024.100577

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Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.calphad.2024.102745

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Authorship：Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

Obtaining a fine approximation of a black-box function is important for understanding and evaluating innovative materials. Active learning aims to improve the approximation of black-box functions with fewer training data. In this study, we investigate whether active learning based on uncertainty sampling enables the efficient approximation of black-box functions in regression tasks using various material databases. In cases where the inputs are provided uniformly and defined in a relatively low-dimensional space, the liquidus surfaces of the ternary systems are the focus. The results show that uncertainty-based active learning can produce a better black-box function with higher prediction accuracy than that by random sampling. Furthermore, in cases in which the inputs are distributed discretely and unbalanced in a high-dimensional feature space, datasets extracted from materials databases for inorganic materials, small molecules, and polymers are addressed, and uncertainty-based active learning is occasionally inefficient. Based on the dependency on the material descriptors, active learning tends to produce a better black-box functions than random sampling when the dimensions of the descriptor are small. The results indicate that active learning is occasionally inefficient in obtaining a better black-box function in materials science.

DOI： 10.1038/s41598-024-76800-4

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Other Link： https://www.nature.com/articles/s41598-024-76800-4

Publishing type：Research paper (scientific journal)   Publisher：Informa UK Limited  

DOI： 10.1080/14686996.2024.2423600

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Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

Developing non-contact, non-destructive monitoring methods for marine life is crucial for sustainable resource management. Recent monitoring technologies and machine learning analysis advancements have enhanced underwater image and acoustic data acquisition. Systems to obtain 3D acoustic data from beneath the seafloor are being developed; however, manual analysis of large 3D datasets is challenging. Therefore, an automatic method for analyzing benthic resource distribution is needed. This study developed a system to estimate benthic resource distribution non-destructively by combining high-precision habitat data acquisition using high-frequency ultrasonic waves and prediction models based on a 3D convolutional neural network (3D-CNN). The system estimated the distribution of asari clams (Ruditapes philippinarum) in Lake Hamana, Japan. Clam presence and count were successfully estimated in a voxel with an ROC-AUC of 0.9 and a macro-average ROC-AUC of 0.8, respectively. This system visualized clam distribution and estimated numbers, demonstrating its effectiveness for quantifying marine resources beneath the seafloor.

DOI： 10.1038/s41598-024-77893-7

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Other Link： https://www.nature.com/articles/s41598-024-77893-7

Publishing type：Research paper (scientific journal)   Publisher：Wiley  

The cluster analysis of materials categorizes them according to similarities based on the features of materials, providing insight into the relationship between the materials. Conventional cluster analyses typically use basic features derived from the chemical composition and crystal structure without considering target material properties such as the bandgap and dielectric constant. However, such approaches do not meet demands for grading materials according to properties of interest simultaneously with chemical and structural similarities. Herein, a clustering method grouping similar materials in terms of both the target properties and basic features is proposed. The clustering is compared considering the cohesive energy with that considering the bandgap of metal oxides, showing that their categorizations are clearly different. Further, several clusters classified by the bandgap are analyzed, and coordination environments related to each range of the bandgap are revealed. The clustering for the electronic static dielectric constant identifies a cluster involving several perovskite‐type oxides and balancing with the bandgap near the Pareto front. The method enables analyses with different viewpoints from those of the conventional clustering and feature importance analyses by taking the relationship between the target property and the basic features into account.

DOI： 10.1002/aisy.202400253

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：The Royal Society  

The seafloor is inhabited by a large number of benthic invertebrates, and their importance in mediating carbon mineralization and biogeochemical cycles is recognized. However, the majority of fauna live below the sediment surface, so most means of survey rely on destructive sampling methods that are limited to documenting species presence rather than event driven activity and functionally important aspects of species behaviour. We have developed and tested a laboratory-based three-dimensional acoustic coring system that is capable of non-invasively visualizing the presence and activity of invertebrates within the sediment matrix. Here, we present reconstructed three-dimensional acoustic images of the sediment profile, with strong backscatter revealing the presence and position of individual benthic organisms. These data were used to train a three-dimensional convolutional neural network model and, using a combination of data augmentation and data correction techniques, we were able to identify individual species with an 88% accuracy. Combining three-dimensional acoustic coring with deep learning forms an effective and non-invasive means of providing detailed mechanistic information of in situ species–sediment interactions, opening new opportunities to quantify species-specific contributions to ecosystems.

DOI： 10.1098/rsos.240042

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Other Link： https://royalsocietypublishing.org/doi/full-xml/10.1098/rsos.240042

Authorship：Lead author,　Last author   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

Background

Liquid–liquid phase separation (LLPS) by biomolecules plays a central role in various biological phenomena and has garnered significant attention. The behavior of LLPS is strongly influenced by the characteristics of RNAs and environmental factors such as pH and temperature, as well as the properties of proteins. Recently, several databases recording LLPS-related biomolecules have been established, and prediction models of LLPS-related phenomena have been explored using these databases. However, a prediction model that concurrently considers proteins, RNAs, and experimental conditions has not been developed due to the limited information available from individual experiments in public databases.

Results

To address this challenge, we have constructed a new dataset, RNAPSEC, which serves each experiment as a data point. This dataset was accomplished by manually collecting data from public literature. Utilizing RNAPSEC, we developed two prediction models that consider a protein, RNA, and experimental conditions. The first model can predict the LLPS behavior of a protein and RNA under given experimental conditions. The second model can predict the required conditions for a given protein and RNA to undergo LLPS.

Conclusions

RNAPSEC and these prediction models are expected to accelerate our understanding of the roles of proteins, RNAs, and environmental factors in LLPS.

DOI： 10.1186/s12859-024-05764-z

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Other Link： https://link.springer.com/article/10.1186/s12859-024-05764-z/fulltext.html

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

Abstract

Developing compounds with novel structures is important for the production of new drugs. From an intellectual perspective, confirming the patent status of newly developed compounds is essential, particularly for pharmaceutical companies. The generation of a large number of compounds has been made possible because of the recent advances in artificial intelligence (AI). However, confirming the patent status of these generated molecules has been a challenge because there are no free and easy-to-use tools that can be used to determine the novelty of the generated compounds in terms of patents in a timely manner; additionally, there are no appropriate reference databases for pharmaceutical patents in the world. In this study, two public databases, SureChEMBL and Google Patents Public Datasets, were used to create a reference database of drug-related patented compounds using international patent classification. An exact structure search system was constructed using InChIKey and a relational database system to rapidly search for compounds in the reference database. Because drug-related patented compounds are a good source for generative AI to learn useful chemical structures, they were used as the training data. Furthermore, molecule generation was successfully directed by increasing and decreasing the number of generated patented compounds through incorporation of patent status (i.e., patented or not) into learning. The use of patent status enabled generation of novel molecules with high drug-likeness. The generation using generative AI with patent information would help efficiently propose novel compounds in terms of pharmaceutical patents. Scientific contribution: In this study, a new molecule-generation method that takes into account the patent status of molecules, which has rarely been considered but is an important feature in drug discovery, was developed. The method enables the generation of novel molecules based on pharmaceutical patents with high drug-likeness and will help in the efficient development of effective drug compounds.

DOI： 10.1186/s13321-023-00791-z

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Other Link： https://link.springer.com/article/10.1186/s13321-023-00791-z/fulltext.html

Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.ecoinf.2023.102371

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Authorship：Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：Informa UK Limited  

DOI： 10.1080/27660400.2023.2261834

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Publishing type：Research paper (scientific journal)   Publisher：American Physical Society (APS)  

DOI： 10.1103/physrevmaterials.7.093804

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Other Link： http://harvest.aps.org/v2/journals/articles/10.1103/PhysRevMaterials.7.093804/fulltext

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.jbi.2023.104448

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.matdes.2023.112111

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.modpat.2023.100296

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.atech.2022.100087

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Publishing type：Research paper (international conference proceedings)  

Semantic segmentation of marine images can be used to describe seafloor scenes and monitor marine creatures. However, preparing human-annotated datasets for image segmentation is time-consuming task. Therefore, this paper proposes a semi-supervised semantic segmentation algorithm based on the combination of Mean-Teacher and U-Net models to classify seafloor images collected in Philippines. The method will train and validate on two parts of the image. On the one hand, for images containing categories of coral, sea urchin, sea stars, and others (including sediment and seagrass), ordinary labeling is used for training and validation. On the other hand, for images only including seagrass and sediment categories, manual labeling of seagrass categories is particularly difficult. In order to overcome this barrier, based on the characteristics of this type of images, K-means clustering algorithm is used to obtain labeled dataset for training and validation. Compared with the U-Net based supervised method, the semi-supervised method proposed in this paper achieves good results and accuracy values even with fewer labeled images.

DOI： 10.1109/UT49729.2023.10103432

Scopus

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Biophysical Society of Japan  

DOI： 10.2142/biophysico.bppb-v20.0022

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.ijpx.2022.100135

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Publishing type：Research paper (scientific journal)   Publisher：Hindawi Limited  

Occupational therapists evaluate various aspects of a client’s occupational performance. Among these, postural control is one of the fundamental skills that need assessment. Recently, several methods have been proposed to estimate postural control abilities using deep-learning-based approaches. Such techniques allow for the potential to provide automated, precise, fine-grained quantitative indices simply by evaluating videos of a client engaging in a postural control task. However, the clinical applicability of these assessment tools requires further investigation. In the current study, we compared three deep-learning-based pose estimators to assess their clinical applicability in terms of accuracy of pose estimations and processing speed. In addition, we verified which of the proposed quantitative indices for postural controls best reflected the clinical evaluations of occupational therapists. A framework using deep-learning techniques broadens the possibility of quantifying clients’ postural control in a more fine-grained way compared with conventional coarse indices, which can lead to improved occupational therapy practice.

DOI： 10.1155/2022/6952999

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Other Link： http://downloads.hindawi.com/journals/oti/2022/6952999.xml

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Informa UK Limited  

DOI： 10.1080/27660400.2022.2146470

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Language：Japanese   Publishing type：Research paper (scientific journal)  

DOI： 10.34565/seibutsukogaku.100.11_599

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

DOI： 10.1021/acs.jcim.2c00812

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.aquaeng.2022.102274

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Publishing type：Research paper (scientific journal)  

DOI： 10.1021/acs.jcim.2c00660

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Language：English   Publishing type：Research paper (scientific journal)  

BACKGROUND & AIMS: Tissue-clearing and three-dimensional (3D) imaging techniques aid clinical histopathological evaluation; however, further methodological developments are required prior to use in clinical practice. METHODS: We sought to develop a novel fluorescence staining method based on the classical PAS stain. We further attempted to develop a 3D imaging system based on this staining method and evaluated whether the system can be employed for quantitative 3D pathological evaluation and deep learning-based automatic diagnosis of inflammatory bowel diseases. RESULTS: We successfully developed a novel periodic acid-FAM hydrazide (PAFhy) staining method for 3D imaging when combined with a tissue-clearing technique (PAFhy-3D). This strategy enabled clear and detailed imaging of the 3D architectures of crypts in human colorectal mucosa. PAFhy-3D imaging also revealed abnormal architectural changes in crypts in ulcerative colitis tissues and identified the distributions of neutrophils in cryptitis and crypt abscesses. PAFhy-3D revealed novel pathological findings including "spiral staircase-like crypts" specific to inflammatory bowel diseases. Quantitative analysis of crypts based on 3D morphological changes enabled differential diagnosis of ulcerative colitis, Crohn's disease, and non-inflammatory bowel disease; such discrimination could not be achieved by pathologists. Furthermore, a deep learning-based system employing PAFhy-3D images was used to distinguish these diseases The accuracies were excellent (macro-average AUC = 0.94; F1 scores = 0.875 for ulcerative colitis, 0.717 for Crohn's disease, and 0.819 for non-inflammatory bowel disease). CONCLUSIONS: PAFhy staining and PAFhy-3D imaging are promising approaches for next-generation experimental and clinical histopathology.

DOI： 10.1016/j.jcmgh.2022.07.001

PubMed

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Authorship：Lead author   Publishing type：Research paper (scientific journal)   Publisher：Informa UK Limited  

DOI： 10.1080/14686996.2022.2075240

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Language：Japanese   Publishing type：Research paper (scientific journal)   Publisher：Biophysical Society of Japan  

DOI： 10.2142/biophys.62.193

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Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

PHYSBO (optimization tools for PHYSics based on Bayesian Optimization) is a Python library for fast and scalable Bayesian optimization. It has been developed mainly for application in the basic sciences such as physics and materials science. Bayesian optimization is used to select an appropriate input for experiments/simulations from candidate inputs listed in advance in order to obtain better output values with the help of machine learning prediction. PHYSBO can be used to find better solutions for both single and multi-objective optimization problems. At each cycle in the Bayesian optimization, a single proposal or multiple proposals can be obtained for the next experiments/simulations. These proposals can be obtained interactively for use in experiments. PHYSBO is available at https://github.com/issp-center-dev/PHYSBO. Program summary: Program Title: PHYSBO CPC Library link to program files: https://doi.org/10.17632/22d72yb6k6.1 Developer's repository link: https://github.com/issp-center-dev/PHYSBO Licensing provisions: GNU General Public License version 3 Programming language: Python3 External routines/libraries: NumPy, SciPy, MPI for Python. Nature of problem: Bayesian optimization (BO) can be used to select inputs that will yield better outputs from a list of candidate inputs with the help of machine learning prediction through a Gaussian process. Although BO is a powerful tool, two of its components, training the Gaussian process regression and optimizing the acquisition function, are generally computationally expensive. Moreover, hyperparameter tuning is necessary for the former process. Solution method: PHYSBO is a Python library for performing fast and scalable Bayesian optimization. To avoid the computationally expensive training process, PHYSBO uses a random feature map, Thompson sampling, and a one-rank Cholesky update. In addition, PHYSBO performs hyperparameter tuning automatically by maximizing the Type II likelihood, and MPI parallelization is used to reduce the calculation time for optimizing the acquisition function.

DOI： 10.1016/j.cpc.2022.108405

Scopus

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Authorship：Last author,　Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：Informa UK Limited  

DOI： 10.1080/27660400.2022.2076548

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Authorship：Corresponding author   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

DOI： 10.1021/acs.jctc.1c01074

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

Computer-aided synthesis planning (CASP) aims to assist chemists in performing retrosynthetic analysis for which they utilize their experiments, intuition, and knowledge. Recent breakthroughs in machine learning (ML) techniques, including deep neural networks, have significantly improved data-driven synthetic route designs without human intervention. However, learning chemical knowledge by ML for practical synthesis planning has not yet been adequately achieved and remains a challenging problem. In this study, we developed a data-driven CASP application integrated with various portions of retrosynthesis knowledge called "ReTReK" that introduces the knowledge as adjustable parameters into the evaluation of promising search directions. The experimental results showed that ReTReK successfully searched synthetic routes based on the specified retrosynthesis knowledge, indicating that the synthetic routes searched with the knowledge were preferred to those without the knowledge. The concept of integrating retrosynthesis knowledge as adjustable parameters into a data-driven CASP application is expected to enhance the performance of both existing data-driven CASP applications and those under development.

DOI： 10.1021/acs.jcim.1c01074

PubMed

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Publishing type：Research paper (scientific journal)   Publisher：Elsevier BV  

DOI： 10.1016/j.matdes.2022.110497

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Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

<title>Abstract</title>Gesture and multimodal communication researchers typically annotate video data manually, even though this can be a very time-consuming task. In the present work, a method to detect gestures is proposed as a fundamental step towards a semi-automatic gesture annotation tool. The proposed method can be applied to RGB videos and requires annotations of part of a video as input. The technique deploys a pose estimation method and active learning. In the experiment, it is shown that if about 27% of the video is annotated, the remaining parts of the video can be annotated automatically with an F-score of at least 0.85. Users can run this tool with a small number of annotations first. If the predicted annotations for the remainder of the video are not satisfactory, users can add further annotations and run the tool again. The code has been released so that other researchers and practitioners can use the results of this research. This tool has been confirmed to work in conjunction with ELAN.

DOI： 10.1007/s10579-022-09586-4

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Other Link： https://link.springer.com/article/10.1007/s10579-022-09586-4/fulltext.html

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Informa UK Limited  

Stimuli-responsive polymers with complicated but controllable shape-morphing behaviors are critically desirable in several engineering fields. Among the various shape-morphing materials, cross-linked polymers with exchangeable bonds in dynamic network topology can undergo on-demand geometric change via solid-state plasticity while maintaining the advantageous properties of cross-linked polymers. However, these dynamic polymers are susceptible to creep deformation that results in their dimensional instability, a highly undesirable drawback that limits their service longevity and applications. Inspired by the natural ice strategy, which realizes creep reduction using crystal structure transformation, we evaluate a dynamic cross-linked polymer with tunable creep behavior through topological alternation. This alternation mechanism uses the thermally triggered disulfide-ene reaction to convert the network topology - from dynamic to static - in a polymerized bulk material. Thus, such a dynamic polymer can exhibit topological rearrangement for thermal plasticity at 130 degrees C to resemble typical dynamic cross-linked polymers. Following the topological alternation at 180 degrees C, the formation of a static topology reduces creep deformation by more than 85% in the same polymer. Owing to temperature-dependent selectivity, our cross-linked polymer exhibits a shape-morphing ability while enhancing its creep resistance for dimensional stability and service longevity after sequentially topological alternation. Our design enriches the design of dynamic covalent polymers, which potentially expands their utility in fabricating geometrically sophisticated multifunctional devices.

DOI： 10.1080/14686996.2021.2025426

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Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

DOI： 10.1021/acsmedchemlett.1c00439

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

The construction of a deep learning model and visualization of judgment regions were conducted for X-ray diffraction and scattering images of aliphatic polyesters. Due to recent progress in measurement methods, a large amount of image data can be obtained in a short time; therefore, machine learning methods are useful to determine the important regions for a given objective. Although techniques to visualize the judgment regions using deep learning have recently been developed, there have been few reports discussing whether such models can determine the important regions of X-ray diffraction and scattering images of polymeric materials. Herein, we demonstrate classification models based on convolutional neural networks (CNNs) for wide-angle X-ray diffraction and small-angle X-ray scattering images of aliphatic polyesters to predict the types of polymers and several crystallization temperatures. Furthermore, the judgment regions of the X-ray images used by the CNNs were visualized using the Grad-CAM, LIME, and SHAP methods. The main regions were diffraction and scattering peaks recognized by experts. Other areas, such as the beam centers were recognized when the intensity of the images was randomly changed. This result may contribute to developing important features in deep learning models, such as the recognition of structure-property relationships.

DOI： 10.1038/s41428-021-00531-w

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Other Link： http://www.nature.com/articles/s41428-021-00531-w

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Informa UK Limited  

DOI： 10.1080/27660400.2021.1943171

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Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

DOI： 10.1021/acs.jcim.1c00679

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Fuji Technology Press Ltd.  

We propose a pose estimation method using a National Advisory Committee for Aeronautics (NACA) airfoil model for fish schools. This method allows one to understand the state in which fish are swimming based on their posture and dynamic variations. Moreover, their collective behavior can be understood based on their posture changes. Therefore, fish pose is a crucial indicator for collective behavior analysis. We use the NACA model to represent the fish posture; this enables more accurate tracking and movement prediction owing to the capability of the model in describing posture dynamics. To fit the model to video data, we first adopt the DeepLabCut toolbox to detect body parts (i.e., head, center, and tail fin) in an image sequence. Subsequently, we apply a particle filter to fit a set of parameters from the NACA model. The results from DeepLabCut, i.e., three points on a fish body, are used to adjust the components of the state vector. This enables more reliable estimation results to be obtained when the speed and direction of the fish change abruptly. Experimental results using both simulation data and real video data demonstrate that the proposed method provides good results, including when rapid changes occur in the swimming direction.

DOI： 10.20965/jrm.2021.p0547

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：AIP Publishing  

DOI： 10.1063/5.0051902

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Authorship：Lead author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

BACKGROUND: Although deep learning algorithms for clinical cytology have recently been developed, their application to practical assistance systems has not been achieved. In addition, whether deep learning systems (DLSs) can perform diagnoses that cannot be performed by pathologists has not been fully evaluated. METHODS: The authors initially obtained low-power field cytology images from archived Papanicolaou-stained urinary cytology glass slides from 232 patients. To aid in the development of a diagnosis support system that could identify suspicious atypical cells, the images were divided into high-power field panel image sets for training and testing of the 16-layer Visual Geometry Group convolutional neural network. The DLS was trained using linked information pertaining to whether urothelial carcinoma (UC) in the corresponding histology specimen was invasive or noninvasive, or high-grade or low-grade, followed by an evaluation of whether the DLS could diagnose these characteristics. RESULTS: The DLS achieved excellent performance (eg, an area under the curve [AUC] of 0.9890; F1 score, 0.9002) when trained on high-power field images of malignant and benign cases. The DLS could diagnose whether the lesions were invasive UC (AUC, 0.8628; F1 score, 0.8239) or high-grade UC (AUC, 0.8661; F1 score, 0.8218). Gradient-weighted class activation mapping of these images indicated that the diagnoses were based on the color of tumor cell nuclei. CONCLUSIONS: The DLS could accurately screen UC cells and determine the malignant potential of tumors more accurately than classical cytology. The use of a DLS during cytopathology screening could help urologists plan therapeutic strategies, which, in turn, may be beneficial for patients.

DOI： 10.1002/cncy.22443

PubMed

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Other Link： https://onlinelibrary.wiley.com/doi/full-xml/10.1002/cncy.22443

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

DOI： 10.1111/infa.12377

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Other Link： https://onlinelibrary.wiley.com/doi/full-xml/10.1111/infa.12377

Authorship：Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

In computer-assisted synthesis planning (CASP) programs, providing as many chemical synthetic routes as possible is essential for considering optimal and alternative routes in a chemical reaction network. As the majority of CASP programs have been designed to provide one or a few optimal routes, it is likely that the desired one will not be included. To avoid this, an exact algorithm that lists possible synthetic routes within the chemical reaction network is required, alongside a recommendation of synthetic routes that meet specified criteria based on the chemist's objectives. Herein, we propose a chemical-reaction-network-based synthetic route recommendation framework called "CompRet" with a mathematically guaranteed enumeration algorithm. In a preliminary experiment, CompRet was shown to successfully provide alternative routes for a known antihistaminic drug, cetirizine. CompRet is expected to promote desirable enumeration-based chemical synthesis searches and aid the development of an interactive CASP framework for chemists.

DOI： 10.1186/s13321-020-00452-5

PubMed

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Other Link： http://link.springer.com/article/10.1186/s13321-020-00452-5/fulltext.html

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1038/s41598-020-69400-5

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Other Link： http://www.nature.com/articles/s41598-020-69400-5

Language：English   Publishing type：Research paper (scientific journal)   Publisher：American Chemical Society (ACS)  

Biomolecular imaging using X-ray free-electron lasers (XFELs) has been successfully applied to serial femtosecond crystallography. However, the application of single-particle analysis for structure determination using XFELs with 100 nm or smaller biomolecules has two practical problems: the incomplete diffraction data sets for reconstructing 3D assembled structures and the heterogeneous conformational states of samples. A new diffraction template matching method is thus presented here to retrieve a plausible 3D structural model based on single noisy target diffraction patterns, assuming candidate structures. Two concepts are introduced here: prompt candidate diffraction, generated by enhanced sampled coarse-grain (CG) candidate structures, and efficient molecular orientation searching for matching based on Bayesian optimization. A CG model-based diffraction-matching protocol is proposed that achieves a 100-fold speed increase compared to exhaustive diffraction matching using an all-atom model. The conditions that enable multiconformational analysis were also investigated by simulated diffraction data for various conformational states of chromatin and ribosomes. The proposed method can enable multiconformational analysis, with a structural resolution of at least 20 Å for 270-800 Å flexible biomolecules, in experimental single-particle structure analyses that employ XFELs.

DOI： 10.1021/acs.jcim.0c00131

PubMed

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Authorship：Last author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：Hindawi Limited  

This study aimed to leverage computer vision (CV) technology to develop a technique for quantifying postural control. A conventional quantitative index, occupational therapists’ qualitative clinical evaluations, and CV-based quantitative indices using an image analysis algorithm were applied to evaluate the postural control of 34 typically developed preschoolers. The effectiveness of the CV-based indices was investigated relative to current methods to explore the clinical applicability of the proposed method. The capacity of the CV-based indices to reflect therapists’ qualitative evaluations was confirmed. Furthermore, compared to the conventional quantitative index, the CV-based indices provided more detailed quantitative information with lower costs. CV-based evaluations enable therapists to quantify details of motor performance that are currently observed qualitatively. The development of such precise quantification methods will improve the science and practice of occupational therapy and allow therapists to perform to their full potential.

DOI： 10.1155/2020/8542191

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Other Link： http://downloads.hindawi.com/journals/oti/2020/8542191.xml

Language：English   Publishing type：Research paper (scientific journal)  

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Authorship：Last author,　Corresponding author   Language：English   Publishing type：Research paper (scientific journal)   Publisher：MDPI AG  

Accompanied with an increase of revealed biomolecular structures owing to advancements in structural biology, the molecular dynamics (MD) approach, especially coarse-grained (CG) MD suitable for macromolecules, is becoming increasingly important for elucidating their dynamics and behavior. In fact, CG-MD simulation has succeeded in qualitatively reproducing numerous biological processes for various biomolecules such as conformational changes and protein folding with reasonable calculation costs. However, CG-MD simulations strongly depend on various parameters, and selecting an appropriate parameter set is necessary to reproduce a particular biological process. Because exhaustive examination of all candidate parameters is inefficient, it is important to identify successful parameters. Furthermore, the successful region, in which the desired process is reproducible, is essential for describing the detailed mechanics of functional processes and environmental sensitivity and robustness. We propose an efficient search method for identifying the successful region by using two machine learning techniques, Bayesian optimization and active learning. We evaluated its performance using F1-ATPase, a biological rotary motor, with CG-MD simulations. We successfully identified the successful region with lower computational costs (12.3% in the best case) without sacrificing accuracy compared to exhaustive search. This method can accelerate not only parameter search but also biological discussion of the detailed mechanics of functional processes and environmental sensitivity based on MD simulation studies.

DOI： 10.3390/biom10030482

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Informa UK Limited  

DOI： 10.1080/14686996.2020.1793382

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Language：English   Publishing type：Research paper (scientific journal)  

Computational techniques for accurate and efficient prediction of protein-protein complex structures are widely used for elucidating protein-protein interactions, which play important roles in biological systems. Recently, it has been reported that selecting a structure similar to the native structure among generated structure candidates (decoys) is possible by calculating binding free energies of the decoys based on all-atom molecular dynamics (MD) simulations with explicit solvent and the solution theory in the energy representation, which is called evERdock. A recent version of evERdock achieves a higher-accuracy decoy selection by introducing MD relaxation and multiple MD simulations/energy calculations; however, huge computational cost is required. In this paper, we propose an efficient decoy selection method using evERdock and the best arm identification (BAI) framework, which is one of the techniques of reinforcement learning. The BAI framework realizes an efficient selection by suppressing calculations for nonpromising decoys and preferentially calculating for the promising ones. We evaluate the performance of the proposed method for decoy selection problems of three protein-protein complex systems. Their results show that computational costs are successfully reduced by a factor of 4.05 (in the best case) compared to a standard decoy selection approach without sacrificing accuracy.

DOI： 10.1063/1.5129551

PubMed

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Language：English   Publishing type：Research paper (scientific journal)  

DOI： 10.1021/acs.jcim.9b00538

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Other Link： https://dblp.uni-trier.de/db/journals/jcisd/jcisd59.html#IshidaTKTO19

Language：English   Publishing type：Research paper (scientific journal)   Publisher：Springer Science and Business Media LLC  

DOI： 10.1038/s41524-019-0262-4

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Other Link： http://www.nature.com/articles/s41524-019-0262-4

Language：English   Publishing type：Research paper (scientific journal)  

© 1976-2012 IEEE. Various methods have been developed and used for monitoring marine benthic habitats, such as coral reefs and seagrass meadows. However, the efficiency of general survey methods [e.g., line intercept transects and autonomous underwater vehicles (AUVs)] still is not high. In this article, we propose a practical coral-coverage estimation method combining an effective survey system [Speedy Sea Scanner (SSS)] and a deep-learning-based estimation method. The SSS is a towed-type system with six cameras arrayed on the platform. The depth rating of the system in our trial was 50 m. The length of the array baseline was 4.4 m, and six cameras were placed on the platform with equal spacing. The sea trial was conducted at Kujuku-Shima, Japan, on September 30, 2017. We successfully generated 3-D models and high-quality orthophotos of the seafloor with high resolution of about 1.5 mm/pixel. The survey efficiency of the SSS was about 7000 m2/h. In addition, the experimental results of coral-coverage estimation showed that the corals can be distinguished with accuracy of about 80% in places with relatively high transparency, and the error of coverage estimation was 10% or less. The proposed coral-coverage estimation method is more efficient than other survey techniques and costs less than AUV surveying; therefore, it is expected to become a promising tool for marine environmental surveying.

DOI： 10.1109/JOE.2019.2938717

Scopus

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Language：English   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (scientific journal)   Publisher：Wiley  

DOI： 10.1002/jcc.25715

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Language：English   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Comprehensive and effective survey methods of coral distribution are indispensable for environmental conservation in the sea. Observation methods by divers, autonomous underwater vehicles (AUVs), and aerial imagery have been investigated for decades. However, effective methods in turbid water have not been developed sufficiently. In this paper, we propose a practical coral-coverage estimation method by combining an effective survey system (SSS: Speedy Sea Scanner) and a deep-learning based estimation method. We tested the performance of the proposed method in Kujuku-shima islands, Nagasaki, Japan. Experimental results showed that corals can be distinguished with accuracy of about 80% in places with relatively high transparency, and the error of coverage estimation is 10% or less.

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：SPIE  

We propose a measurement method of Tail Beat Frequency (TBF) and Coast Phase (CP) of fish swimming for isolated fish in a school of fish with visual tracking. For analysis of fish swimming behaviors, features that represent fish movements, e.g., TBF and CP, have been commonly used in the fields of biological and fisheries researches. We propose a measurement method for such features using particle filter and apply the method to a large school of fish in an aquarium. Experimental results show that the TBFs and the CPs are measured with our method accurately enough for further analysis of fish behaviors. The average errors of the TBFs was 0.126 (Hz) and the precision and recall of the classification for CP detection were 0.945 and 0.879 respectively.

DOI： 10.1117/12.2266447

Scopus

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Language：English   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (scientific journal)  

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：Institute of Electrical and Electronics Engineers Inc.  

We propose a measurement method for the mean speed distribution of collective motions of highly dense groups with optical flow in this paper. This measurement is fundamental for ecological investigations and mathematical modeling of collective animal behaviors, including human crowds. Our method is applicable to highly dense homogeneous groups wherein individual movements are approximately uniform locally. To measure speed distributions, we partition a group into regions and estimate mean speeds in each region by extracting only flows that are relevant to collective motions and averaging them over a period of time. We experimentally find that our method works well even when we cannot reliably track individuals. We specifically apply our method to schools of sardines to measure a kind of speed distribution called rotation curve (RC). Experimental results obtained by simulation demonstrate that our method can estimate flows and RCs accurately. We also performed experiments with videos of real fish. The RCs were estimated by manual tracking and by our method. The results are approximately equal, and the average difference is less than 4% of the mean body length of fish in the observed schools. These results indicate that our method is practically useful for measuring RCs.

DOI： 10.1109/ISM.2014.26

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

We propose a visual tracking method for dense fish schools in which occlusions occur frequently. Although much progress has been made for tracking multiple objects in video images, it is challenging to track individuals in highly dense groups. For occluded fishes, estimation of their positions and directions is difficult. However, if we know the number of fishes in a local area, we can accurately estimate their states by matching all of the combinations of possible parameters on the basis of our appearance model. We apply the idea to track multiple fishes in a school. Experimental results show that multiple fishes are practically tracked with our method compared to a well-known tracking method, and the average difference is less than 4% of the mean body length of the school.

DOI： 10.1109/ACPR.2015.7486526

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Language：English   Publishing type：Research paper (international conference proceedings)   Publisher：IEEE  

Classification of digital images into photographs and various kinds of non-photographic images has not been sufficiently studied but has many applications such as retrieval of real scene photographs from web sites and image databases. In this paper, we show that the combination of Bag of Visual Words of SURF features and histograms of LBPs for HSV and Luminance components (SURF+LBP(HSVL)) is simple, but works well as visual features for photographs and non-photographic image classification. We found that a classifier trained with SURF+LBP(HSVL) was the best among all the classifiers we tested using various visual features. Our classifier attained an accuracy of 96.8% for our image dataset and outperformed the other state-of-the-art classifiers.

DOI： 10.1109/MVA.2015.7153192

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Language：English   Publishing type：Research paper (international conference proceedings)  

A calculus XPCF of 1âŠ\-sequences, which are infinite sequences of {0,1,âŠ\} with at most one copy of bottom, is proposed and investigated. It has applications in real number computation in that the unit interval I is topologically embedded in the set ΣâŠ\,1ω of 1âŠ\-sequences and a real function on I can be written as a program which inputs and outputs 1âŠ\-sequences. In XPCF, one defines a function on ΣâŠ\,1ω only by specifying its behaviors for the cases that the first digit is 0 and 1. Then, its value for a sequence starting with a bottom is calculated by taking the meet of the values for the sequences obtained by filling the bottom with 0 and 1. The validity of the reduction rule of this calculus is justified by the adequacy theorem to a domain-theoretic semantics. Some example programs including addition and multiplication are shown. Expressive powers of XPCF and related languages are also investigated. © 2013 Elsevier B.V.

DOI： 10.1016/j.entcs.2013.09.023

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J-GLOBAL

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Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (scientific journal)   Publisher：分子シミュレーション研究会  

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media)  

J-GLOBAL

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J-GLOBAL

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Language：Japanese   Publishing type：Article, review, commentary, editorial, etc. (other)  

J-GLOBAL

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