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| 1 | Machine learning modeling of superconducting critical temperature显示文摘Superconductivity has been the focus of enormous research effort since its discovery more than a century ago.Yet,some features of this unique phenomenon remain poorly understood;prime among these is the connection between superconductivity and chemical/structural properties of materials.To bridge the gap,several machine learning schemes are developed herein to model the critical temperatures(T_(c))of the 12,000+known superconductors available via the SuperCon database.Materials are first divided into two classes based on their T_(c) values,above and below 10 K,and a classification model predicting this label is trained.The model uses coarse-grained features based only on the chemical compositions.It shows strong predictive power,with out-of-sample accuracy of about 92%.Separate regression models are developed to predict the values of T_(c) for cuprate,iron-based,and low-T_(c) compounds.These models also demonstrate good performance,with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of materials.To improve the accuracy and interpretability of these models,new features are incorporated using materials data from the AFLOW Online Repositories.Finally,the classification and regression models are combined into a single-integrated pipeline and employed to search the entire Inorganic Crystallographic Structure Database(ICSD)for potential new superconductors.We identify>30 non-cuprate and non-iron-based oxides as candidate materials. | Valentin Stanev Corey Oses A.Gilad Kusne Efrain Rodriguez Johnpierre Paglione Stefano Curtarolo Ichiro Takeuchi | 2018 | npj Computational Materials2018，，1： | 21 |
| 2 | Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries显示文摘Machine learning techniques have proven invaluable to manage the ever growing volume of materials research data produced as developments continue in high-throughput materials simulation,fabrication,and characterization.In particular,machine learning techniques have been demonstrated for their utility in rapidly and automatically identifying potential composition-phase maps from structural data characterization of composition spread libraries,enabling rapid materials fabrication-structure-property analysis and functional materials discovery.A key issue in development of an automated phase-diagram determination method is the choice of dissimilarity measure,or kernel function.The desired measure reduces the impact of confounding structural data issues on analysis performance.The issues include peak height changes and peak shifting due to lattice constant change as a function of composition.In this work,we investigate the choice of dissimilarity measure in X-ray diffraction-based structure analysis and the choice of measure’s performance impact on automatic composition-phase map determination.Nine dissimilarity measures are investigated for their impact in analyzing X-ray diffraction patterns for a Fe-Co-Ni ternary alloy composition spread.The cosine,Pearson correlation coefficient,and Jensen-Shannon divergence measures are shown to provide the best performance in the presence of peak height change and peak shifting(due to lattice constant change)when the magnitude of peak shifting is unknown.With prior knowledge of the maximum peak shifting,dynamic time warping in a normalized constrained mode provides the best performance.This work also serves to demonstrate a strategy for rapid analysis of a large number of X-ray diffraction patterns in general beyond data from combinatorial libraries. | Yuma Iwasaki A.Gilad Kusne Ichiro Takeuchi | 2017 | npj Computational Materials2017，，1： | 8 |
| 3 | Role of targeted therapy in metastatic colorectal cancer显示文摘Colorectal cancer(CRC) is a significant cause of cancer-related morbidity and mortality all over the world.Improvements of cytotoxic and biologic agents have prolonged the survival in metastatic CRC(mC RC),with a median overall survival of approximately 2 years and more in the past two decades.The biologic agents that have proven clinical benefits in m CRC mainly target vascular endothelial growth factor(VEGF) and epidermal growth factor receptor(EGFR).In particular,bevacizumab targeting VEGF and cetuximab and panitumumab targeting EGFR have demonstrated sig-nificant survival benefits in combination with cytotoxic chemotherapy in the first-line,second-line,or salvage setting.Aflibercept,ramucirumab,and regorafenib are also used in second-line or salvage therapy.Recent retrospective analyses have shown that KRAS or NRAS mutations were negative predictive markers for anti-EGFR therapy.Based on the evidence from large rand-omized clinical trials,personalized therapy is necessary for patients with m CRC according to their tumor biology and characteristics.The aim of this paper was to summarize the results of the major randomized clinical trials and highlight the benefits of the molecular targeted agents in patients with mC RC. | Yoshihito Ohhara Naoki Fukuda Satoshi Takeuchi Rio Honma Yasushi Shimizu Ichiro Kinoshita Hirotoshi Dosaka-Akita | 2016 | World Journal of Gastrointestinal Oncology2016，8，9： | 8 |
| 4 | Identification of advanced spin-driven thermoelectric materials via interpretable machine learning显示文摘Machine learning is becoming a valuable tool for scientific discovery.Particularly attractive is the application of machine learning methods to the field of materials development,which enables innovations by discovering new and better functional materials.To apply machine learning to actual materials development,close collaboration between scientists and machine learning tools is necessary.However,such collaboration has been so far impeded by the black box nature of many machine learning algorithms.It is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and physics.Here,we demonstrate the development of spin-driven thermoelectric materials with anomalous Nernst effect by using an interpretable machine learning method called factorized asymptotic Bayesian inference hierarchical mixture of experts(FAB/HMEs).Based on prior knowledge of material science and physics,we were able to extract from the interpretable machine learning some surprising correlations and new knowledge about spin-driven thermoelectric materials.Guided by this,we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric material.This material shows the largest thermopower to date. | Yuma Iwasaki Ryohto Sawada Valentin Stanev Masahiko Ishida Akihiro Kirihara Yasutomo Omori Hiroko Someya Ichiro Takeuchi Eiji Saitoh Shinichi Yorozu | 2019 | npj Computational Materials2019，，1： | 7 |
| 5 | Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering显示文摘Analyzing large X-ray diffraction(XRD)datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials libraries.Optimizing and automating this task can help accelerate the process of discovery of materials with novel and desirable properties.Here,we report a new method for pattern analysis and phase extraction of XRD datasets.The method expands the Nonnegative Matrix Factorization method,which has been used previously to analyze such datasets,by combining it with custom clustering and cross-correlation algorithms.This new method is capable of robust determination of the number of basis patterns present in the data which,in turn,enables straightforward identification of any possible peak-shifted patterns.Peak-shifting arises due to continuous change in the lattice constants as a function of composition and is ubiquitous in XRD datasets from composition spread libraries.Successful identification of the peak-shifted patterns allows proper quantification and classification of the basis XRD patterns,which is necessary in order to decipher the contribution of each unique single-phase structure to the multi-phase regions.The process can be utilized to determine accurately the compositional phase diagram of a system under study.The presented method is applied to one synthetic and one experimental dataset and demonstrates robust accuracy and identification abilities. | Valentin Stanev Velimir V.Vesselinov A.Gilad Kusne Graham Antoszewski Ichiro Takeuchi Boian S.Alexandrov | 2018 | npj Computational Materials2018，，1： | 5 |
| 6 | Magnifying Narrowband Imaging Is More Accurate Than Conventional White-Light Imaging in Diagnosis of Gastric Mucosal Cancer显示文摘 | Yasumasa Ezoe Manabu Muto Noriya Uedo Hisashi Doyama Kenshi Yao Ichiro Oda Kazuhiro Kaneko Yoshiro Kawahara Chizu Yokoi Yasushi Sugiura Hideki Ishikawa Yoji Takeuchi Yoshibumi Kaneko Yutaka Saito | 2011 | Gastroenterology2011，，6： | 4 |
| 7 | A High-Precision US-Guided Robot-Assisted HIFU Treatment System for Breast Cancer显示文摘乳腺癌是女性中最常见的癌症。高强度聚焦超声(波)(HIFU)是一种很有前景的非介入式治疗方法。为了提高HIFU治疗的精确度和降低治疗成本,开发了一套基于超声引导(US)的五自由度辅助HIFU系统。搭建了一套功能齐全的样机,它可以轻松地实现三维超声图像重建、目标分割、治疗路径生成和HIFU自动照射等功能。利用线模型实现点定位,并且在异构组织模型上对凝固区域进行评估。在超声引导下,HIFU治疗区域的中心偏离目标治疗区域中心不到2 mm。规划治疗区域周围出现的超调值远低于在临床使用的允许值。对于乳腺癌的治疗,本系统有足够的准确性。 | Tianhan Tang Takashi Azuma Toshihide Iwahashi Hideki Takeuchi Etsuko Kobayashi Ichiro Sakuma | 2018 | Engineering2018，4，5： | 3 |
| 8 | Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data显示文摘Machine learning has emerged as a powerful tool for the analysis of mesoscopic and atomically resolved images and spectroscopy in electron and scanning probe microscopy,with the applications ranging from feature extraction to information compression and elucidation of relevant order parameters to inversion of imaging data to reconstruct structural models.However,the fundamental limitation of machine learning methods is their correlative nature,leading to extreme susceptibility to confounding factors.Here,we implement the workflow for causal analysis of structural scanning transmission electron microscopy(STEM)data and explore the interplay between physical and chemical effects in a ferroelectric perovskite across the ferroelectric–antiferroelectric phase transitions. | Maxim Ziatdinov Christopher T.Nelson Xiaohang Zhang Rama K.Vasudevan Eugene Eliseev Anna N.Morozovska Ichiro Takeuchi Sergei V.Kalinin | 2020 | npj Computational Materials2020，，1： | 2 |
| 9 | MorphologyBased Prediction of Osteogenic Differentiation Potential of Human Mesenchymal Stem Cells显示文摘 | Fumiko Matsuoka Ichiro Takeuchi Hideki Agata | 2013 | PLOS ONE2013，8，55： | 1 |
| 10 | Advanced Glycation Endproduct-Induced Calcium Handling Impairment in Mouse Cardiac Myocytes显示文摘 | Ralica Petrova Yasuhiko Yamamoto Katsuhiko Muraki Hideto Yonekura Shigeru Sakurai Takuo Watanabe Hui Li Masayoshi Takeuchi Zenji Makita Ichiro Kato Shin Takasawa Hiroshi Okamoto Yuji Imaizumi Hiroshi Yamamoto | 2002 | Journal of Molecular and Cellular Cardiology2002，，10： | 1 |
| 11 | An efficient diagnostic strategy for small, depressed early gastric cancer with magnifying narrow-band imaging: a post-hoc analysis of a prospective randomized controlled trial显示文摘 | Shinya Yamada Hisashi Doyama Kenshi Yao Noriya Uedo Yasumasa Ezoe Ichiro Oda Kazuhiro Kaneko Yoshiro Kawahara Chizu Yokoi Yasushi Sugiura Hideki Ishikawa Yoji Takeuchi Yutaka Saito Manabu Muto | 2013 | Gastrointestinal Endoscopy2013，，： | 1 |
| 12 | Accumulation of Pathogenesis-Related Proteins in Tobacco Leaves Irradiated with UV-B显示文摘 | Takahiro Fujibe Kaori Watanabe Nobuyoshi Nakajima Yuko Ohashi Ichiro Mitsuhara Kotaro T Yamamoto Yuichi Takeuchi | 2000 | Journal of Plant Research2000，，4： | 1 |
| 13 | Mixed Duct-Acinar-Islet Cell Tumor of the Pancreas: Report of a Case显示文摘 | Kohji Tanakaya Norihiro Teramoto Eiji Konaga Hitoshi Takeuchi Yoshimasa Yasui Akira Takeda Yasuhiro Yunoki Ichiro Murakami | 2001 | Surgery Today2001，，2： | 1 |
| 14 | Rapid quantitative screening assay for SARS-CoV-2 neutralizing antibodies using HiBiT-tagged virus-like particles显示文摘Due to the unavailability of any specific countermeasure,the constantly spreading C0VID-19 pandemic could only be partially and temporarily slowed down by implementing regional lockdowns that force people to stay at home and prevent their movement.With the progression of the pandemic,a considerable subset of the population would have acquired post-infection immunity and the tests that reveal the postinfection immune status of individuals are the need of the hour. | Kei Miyakawa Sundararaj Stanleyraj Jeremiah Norihisa Ohtake Satoko Matsunaga Yutaro Yamaoka Mayuko Nishi Takeshi Morita Ryo Saji Mototsugu Nishii Hirokazu Kimura Hideki Hasegawa Ichiro Takeuchi Akihide Ryo | 2020 | Journal of Molecular Cell Biology2020，12，12： | 1 |
| 15 | Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding显示文摘Over the last decade,scanning transmission electron microscopy(STEM)has emerged as a powerful tool for probing atomic structures of complex materials with picometer precision,opening the pathway toward exploring ferroelectric,ferroelastic,and chemical phenomena on the atomic scale.Analyses to date extracting a polarization signal from lattice coupled distortions in STEM imaging rely on discovery of atomic positions from intensity maxima/minima and subsequent calculation of polarization and other order parameter fields from the atomic displacements.Here,we explore the feasibility of polarization mapping directly from the analysis of STEM images using deep convolutional neural networks(DCNNs).In this approach,the DCNN is trained on the labeled part of the image(i.e.,for human labelling),and the trained network is subsequently applied to other images.We explore the effects of the choice of the descriptors(centered on atomic columns and grid-based),the effects of observational bias,and whether the network trained on one composition can be applied to a different one.This analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations. | Christopher T.Nelson Ayana Ghosh Mark Oxley Xiaohang Zhang Maxim Ziatdinov Ichiro Takeuchi Sergei V.Kalinin | 2021 | npj Computational Materials2021，，1： | 1 |
| 16 | High-throughput research on superconductivity显示文摘As an essential component of the Materials Genome Initiative aiming to shorten the period of materials research and development, combinatorial synthesis and rapid characterization technologies have been playing a more and more important role in exploring new materials and comprehensively understanding materials properties. In this review, we discuss the advantages of high-throughput experimental techniques in researches on superconductors. The evolution of combinatorial thin-film technology and several high-speed screening devices are briefly introduced. We emphasize the necessity to develop new high-throughput research modes such as a combination of high-throughput techniques and conventional methods. | 秦明阳 林泽丰 魏忠旭 朱北沂 袁洁 Ichiro Takeuchi 金魁 | 2018 | Chinese Physics B2018，27，12： | 0 |
| 17 | Strain Induced Metastable Phase and Phase Revolution in PbTiO3-CoFe204 Nanocomposite Film显示文摘 | 胡传圣 罗震林 孙霞 潘国强 何庆 文闻 周兴泰 Ichiro Takeuchi 高琛 | 2014 | Chinese Physics Letters2014，31，1： | 0 |
| 18 | Combinatorial Synthesis and High-Throughput Characterization of Microstructure and Phase Transformation in Ni-Ti-Cu-V Quaternary Thin-Film Library显示文摘Ni-Ti-based shape memory alloys(SMAs)have found widespread use in the last 70 years,but improving their functional stability remains a key quest for more robust and advanced applications.Named for their ability to retain their processed shape as a result of a reversible martensitic transformation,SMAs are highly sensitive to compositional variations.Alloying with ternary and quaternary elements to finetune the lattice parameters and the thermal hysteresis of an SMA,therefore,becomes a challenge in materials exploration.Combinatorial materials science allows streamlining of the synthesis process and data management from multiple characterization techniques.In this study,a composition spread of Ni-Ti-Cu-V thin-film library was synthesized by magnetron co-sputtering on a thermally oxidized Si wafer.Composition-dependent phase transformation temperature and microstructure were investigated and determined using high-throughput wavelength dispersive spectroscopy,synchrotron X-ray diffraction,and temperature-dependent resistance measurements.Of the 177 compositions in the materials library,32 were observed to have shape memory effect,of which five had zero or near-zero thermal hysteresis.These compositions provide flexibility in the operating temperature regimes that they can be used in.A phase map for the quaternary system and correlations of functional properties are discussed w让h respect to the local microstructure and composition of the thin-film library. | Naila M.Al Hasan Huilong Hou Suchismita Sarkar Sigurd Thienhaus Apurva Mehta Alfred Ludwig Ichiro Takeuchi | 2020 | Engineering2020，6，6： | 0 |
| 19 | Expression analyses of stress‑responsive genes in the hermatypic coral Acropora tenuis and its symbiotic dinofagellates after exposure to the herbicide Diuron显示文摘Diuron is one of the most frequently applied herbicides in sugarcane farming in southern Japan,and Australia.In addition,it is used as a booster substance in copper-based antifouling paints.Due to these various uses,Diuron is released into the marine environment;however,little information is available on gene expression in corals and their symbiotic algae exposed to Diuron.We investigated the efects of Diuron on stress-responsive gene expression in the hermatypic coral Acropora tenuis and its symbiotic dinofagellates.After seven days of exposure to 1µg/L and 10µg/L Diuron,no signifcant changes in the body colour of corals were observed.However,quantitative reverse transcription-polymerase chain reaction analyses revealed that the expression levels of stress-responsive genes,such as heat shock protein 90(HSP90),HSP70,and calreticulin(CALR),were signifcantly downregulated in corals exposed to 10µg/L of Diuron for seven days.Moreover,aquaglyceroporin was signifcantly downregulated in corals exposed to environmentally relevant concentrations of 1µg/L Diuron.In contrast,no such efects were observed on the expression levels of other stress-responsive genes,such as oxidative stress-responsive proteins,methionine adenosyltransferase,and green/red fuorescent proteins.Diuron exposure had no signifcant efect on the expression levels of HSP90,HSP70,or HSP40 in the symbiotic dinofagellates.These results suggest that stress-responsive genes,such as HSPs,respond diferently to Diuron in corals and their symbiotic dinofagellates and that A.tenuis HSPs and CALRs may be useful molecular biomarkers for predicting stress responses induced by the herbicide Diuron. | Hiroshi Ishibashi Seigo Minamide Ichiro Takeuchi | 2023 | Marine Life Science & Technology2023，5，3： | 0 |
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