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Expression analyses of stress‑responsive genes in the hermatypic coral Acropora tenuis and its symbiotic dinofagellates after exposure to the herbicide Diuron

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Marine Life Science & Technology 2023年第3期5卷 289-299页

作者： Hiroshi Ishibashi Seigo Minamide ichiro takeuchi Graduate School of Agriculture Ehime UniversityMatsuyamaEhime 790-8566Japan Center of Advanced Technology for the Environment Graduate School of AgricultureEhime UniversityMatsuyamaEhime 790-8566Japan

Diuron is one of the most frequently applied herbicides in sugarcane farming in southern Japan,and *** addition,it is used as a booster substance in copper-based antifouling *** 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 *** investigated the efects of Diuron on stress-responsive gene expression in the hermatypic coral Acropora tenuis and its symbiotic *** seven days of exposure to 1µg/L and 10µg/L Diuron,no signifcant changes in the body colour of corals were ***,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 ***,aquaglyceroporin was signifcantly downregulated in corals exposed to environmentally relevant concentrations of 1µg/L *** 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 *** exposure had no signifcant efect on the expression levels of HSP90,HSP70,or HSP40 in the symbiotic *** results suggest that stress-responsive genes,such as HSPs,respond diferently to Diuron in corals and their symbiotic dinofagellates and that *** HSPs and CALRs may be useful molecular biomarkers for predicting stress responses induced by the herbicide Diuron.

关键词： Acropora tenuis Calreticulin Coral bleaching Diuron Heat shock protein

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Comparison of dissimilarity measures for cluster analysis of X-ray diffraction data from combinatorial libraries

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npj Computational Materials 2017年第1期3卷 448-456页

作者： Yuma Iwasaki A.Gilad Kusne ichiro takeuchi IoT Devices Research Laboratories NEC CorporationTsukuba 305-8501Japan Spin Quantum Rectification Project ERATOJapan Science and Technology AgencySendai 980-8577Japan National Institute of Standards and Technology GaithersburgMD 20899USA Department of Materials Science and Engineering University of MarylandCollege parkMD 20742USA

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 *** 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 *** desired measure reduces the impact of confounding structural data issues on analysis *** issues include peak height changes and peak shifting due to lattice constant change as a function of *** 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 *** dissimilarity measures are investigated for their impact in analyzing X-ray diffraction patterns for a Fe-Co-Ni ternary alloy composition *** 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 *** prior knowledge of the maximum peak shifting,dynamic time warping in a normalized constrained mode provides the best *** 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.

关键词： alloy shifting analysis

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Machine learning modeling of superconducting critical temperature

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npj Computational Materials 2018年第1期4卷 405-418页

作者： Valentin Stanev Corey Oses A.Gilad Kusne Efrain Rodriguez Johnpierre Paglione Stefano Curtarolo ichiro takeuchi Department of Materials Science and Engineering University of MarylandCollege ParkMD 20742-4111USA Center for Nanophysics and Advanced Materials University of MarylandCollege ParkMD 20742USA Department of Mechanical Engineering and Materials Science Duke UniversityDurhamNC 27708USA Center for Materials Genomics Duke UniversityDurhamNC 27708USA National Institute of Standards and Technology GaithersburgMD 20899USA Department of Chemistry and Biochemistry University of MarylandCollege ParkMD 20742USA Department of Physics University of MarylandCollege ParkMD 20742USA Fritz-Haber-Institut der Max-Planck-Gesellschaft 14195 Berlin-DahlemGermany

Superconductivity has been the focus of enormous research effort since its discovery more than a century ***,some features of this unique phenomenon remain poorly understood;prime among these is the connection between superconductivity and chemical/structural properties of *** 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 *** 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 *** model uses coarse-grained features based only on the chemical *** 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) *** models also demonstrate good performance,with learned predictors offering potential insights into the mechanisms behind superconductivity in different families of *** improve the accuracy and interpretability of these models,new features are incorporated using materials data from the AFLOW Online ***,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 *** identify>30 non-cuprate and non-iron-based oxides as candidate materials.

关键词： learning critical offering

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Deep learning ferroelectric polarization distributions from STEM data via with and without atom finding

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npj Computational Materials 2021年第1期7卷 1342-1352页

作者： Christopher T.Nelson Ayana Ghosh Mark Oxley Xiaohang Zhang Maxim Ziatdinov ichiro takeuchi Sergei V.Kalinin Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTNUSA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTNUSA Department of Materials Science and Engineering University of MarylandCollege ParkMDUSA

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 *** 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 ***,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 *** 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 *** analysis demonstrates the tremendous potential of the DCNN for the analysis of high-resolution STEM imaging and spectral data and highlights the associated limitations.

关键词： Deep network ferroelectric

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Unsupervised phase mapping of X-ray diffraction data by nonnegative matrix factorization integrated with custom clustering

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npj Computational Materials 2018年第1期4卷 295-304页

作者： Valentin Stanev Velimir V.Vesselinov A.Gilad Kusne Graham Antoszewski ichiro takeuchi Boian S.Alexandrov Department of Materials Science and Engineering University of MarylandCollege ParkMD 20742USA Center for Nanophysics and Advanced Materials University of MarylandCollege ParkMD 20742USA Joint Quantum Institute University of MarylandCollege ParkMD 20742USA Los Alamos National Laboratory Earth Science DivisionLos AlamosNM 87545USA National Institute of Standards and Technology GaithersburgMD 20899USA Department of Mathematics University of MarylandCollege ParkMD 20742USA Los Alamos National Laboratory Theoretical DivisionLos AlamosNM 87545USA

Analyzing large X-ray diffraction(XRD)datasets is a key step in high-throughput mapping of the compositional phase diagrams of combinatorial materials *** and automating this task can help accelerate the process of discovery of materials with novel and desirable ***,we report a new method for pattern analysis and phase extraction of XRD *** 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 *** 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 ***-shifting arises due to continuous change in the lattice constants as a function of composition and is ubiquitous in XRD datasets from composition spread *** 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 *** process can be utilized to determine accurately the compositional phase diagram of a system under *** presented method is applied to one synthetic and one experimental dataset and demonstrates robust accuracy and identification abilities.

关键词： properties. phase nonnegative

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Identification of advanced spin-driven thermoelectric materials via interpretable machine learning

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npj Computational Materials 2019年第1期5卷 232-237页

作者： Yuma Iwasaki Ryohto Sawada Valentin Stanev Masahiko Ishida Akihiro Kirihara Yasutomo Omori Hiroko Someya ichiro takeuchi Eiji Saitoh Shinichi Yorozu Central Research Laboratories NEC CorporationTsukuba 305-8501Japan PREST JSTSaitama 322-0012Japan Department of Materials Science and Engineering University of MarylandCollege ParkMD 20742USA Center for Nanophysics and Advanced Materials University of MarylandCollege ParkMD 20742USA Joint Quantum Institute University of MarylandCollege ParkMD 20742USA Institute for Materials Research Tohoku UniversitySendai 908-8577Japan WPI Advanced Institute for Materials Research Tohoku UniversitySendai 908-8577Japan Advanced Science Research Center Japan Atomic Energy AgencyTokai 319-1195Japan RIKEN Center for Emergent Matter Science Wako 351-0198Japan

Machine learning is becoming a valuable tool for scientific *** attractive is the application of machine learning methods to the field of materials development,which enables innovations by discovering new and better functional *** apply machine learning to actual materials development,close collaboration between scientists and machine learning tools is ***,such collaboration has been so far impeded by the black box nature of many machine learning *** is often difficult for scientists to interpret the data-driven models from the viewpoint of material science and ***,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 *** by this,we carried out an actual material synthesis that led to the identification of a novel spin-driven thermoelectric *** material shows the largest thermopower to date.

关键词： becoming learning attractive

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Causal analysis of competing atomistic mechanisms in ferroelectric materials from high-resolution scanning transmission electron microscopy data

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npj Computational Materials 2020年第1期6卷 570-578页

作者： Maxim Ziatdinov Christopher T.Nelson Xiaohang Zhang Rama K.Vasudevan Eugene Eliseev Anna N.Morozovska ichiro takeuchi Sergei V.Kalinin The Center for Nanophase Materials Sciences Oak Ridge National LaboratoryOak RidgeTN 37831USA Computational Sciences and Engineering Division Oak Ridge National LaboratoryOak RidgeTN 37831USA Department of Materials Science and Engineering University of MarylandCollege ParkMD 20742USA Institute for Problems of Materials Science National Academy of Sciences of UkraineKrjijanovskogo 3Kyiv 03142Ukraine Institute of Physics National Academy of Sciences of Ukraine46pr.NaukyKyiv 03028Ukraine

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 ***,the fundamental limitation of machine learning methods is their correlative nature,leading to extreme susceptibility to confounding ***,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.

关键词： ferroelectric analysis founding

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Rapid quantitative screening assay for SARS-CoV-2 neutralizing antibodies using HiBiT-tagged virus-like particles

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Journal of Molecular Cell Biology 2020年第12期12卷 987-990页

作者： 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 Department of Microbiology Yokohama City University School of MedicineKanagawa 236-0004Japan Advanced Medical Research Center Yokohama City UniversityKanagawa 236-0004Japan Bioscience Division Reagent Development DepartmentTosoh CorporationKanagawa 252-1123Japan Life Science Laboratory Technology and Development DivisionKanto Chemical Co.Inc.Kanagawa 259-1146Japan Department of Emergency Medicine Yokohama City University HospitalKanagawa 236-0004Japan School of Medical Technology Faculty of Health SciencesGunma Paz UniversityGunma 370-0006Japan Influenza Research Center National Institute of Infectious DiseasesTokyo 208-0011Jlapan

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 *** 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.

关键词： immunity infection antibodies

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Strain Induced Metastable Phase and Phase Revolution in PbTiO3-CoFe204 Nanocomposite Film

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Chinese Physics Letters 2014年第1期31卷 174-177页

作者：胡传圣罗震林孙霞潘国强何庆文闻周兴泰 ichiro takeuchi 高琛 National Synchrotron Radiation Laboratory & School of Nuclear Science and Technology University of Science and Technology of China Hefei 230026 Department of Physics University of Science and Technology of China Hefei 230026 Shanghai Synchrotron Radiation Facility Shanghai Institute of Applied Physics Chinese Academy of Sciences P. O. Box 800-204 Shanghai 201800 Department of Materials Science and Engineering University of Maryland College Park Maryland 20742 USA Department of Materials Science and Engineering & CAS Key Laboratory of Materials for Energy Conversion University of Science and Technology of China Hefei 230026

An inter-component epitaxial strain-induced PbTiOa metastable phase is observed in a PbTiO3-GoFe2O4 epitaxial composite film, corresponding to the dielectric anomaly reported previously. High-resolution synchrotron radiation X-ray diffraction and first principles calculation demonstrate the coexistence of different PbTi03 phases, even a possible morphotropie phase boundary in the film, elucidating the underlying microscopic rneehanism of the formation of Pb TiO3 metastable phase. This sheds light on the design and manipulation of electromechanical properties of epitaxial films, through the strain engineering.

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Design of Medical Devices Using Spongy Sheet Composed of Hyaluronic Acid and Collagen

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Open Journal of Regenerative Medicine 2020年第2期9卷 71-84页

作者： Yoshimitsu Kuroyanagi Haruka Ohno Ryusuke Suzuki Misato Kuroyanagi ichiro takeuchi R & D Center for Skin Regenerative Medicine Technosurg Ltd. Sagamihara Japan School of Allied Health Sciences Kitasato University Sagamihara Japan New Business Development Division Mitsui Fine Chemicals Inc. Tokyo Japan Department of Emergency Medicine Yokohama City University Graduate School of Medicine Yokohama Japan

This research aims to obtain useful information for development of medical devices such as wound dressing and tissue anti-adhesive product, using a spongy sheet composed of hyaluronic acid (HA) and collagen (Col). The spongy sheets were manufactured by freeze vacuum drying of HA and Col aqueous solution, followed by UV irradiation to introduce intermolecular crosslinks between Col molecules. These spongy sheets are referred to as Sponge-A (ratio of HA/Col = 5/1) and Sponge-B (ratio of HA/Col = 5/5). Both surfaces of Sponge-A and Sponge-B treated with UV irradiation for 15 minutes are referred to as Sponge-A-15 and Sponge-B-15, respectively. The weight change of spongy sheet was determined by immersing a peace of spongy sheet in water at 37°C. The weight of sponge-A-15 collected 1/2, 1, 3, 7 days after immersion in water was 63.5%, 62.1%, 56.6%, 54.4% of the original weight, respectively. The weight of Sponge-B-15 was 78.3%, 76.7%, 79.1%, 71.9% of the original weight, respectively. The weight change of spongy sheet was determined by immersing a peace of spongy sheet in water containing collagenase at 37°C. The weight of Sponge-A-15 collected 6, 8, 10, 12 hours after immersion in water containing collagenase (0.0005 w/v%) was 65.7%, 59.8%, 57.9%, 55.2% of the original weight, respectively. The weight of Sponge-B-15 was 63.5%, 52.1%, 42.0%, 43.2% of the original weight, respectively. This spongy sheet is considered to have the unique structure, where HA molecules are entrapped in an intermolecular cross-linked network structure of Col molecules. When immersed in water containing collagenase,

关键词： Hyaluronic Acid Collagen Wound Dressing Tissue Non-Adhesive Product Intermolecular Crosslinks

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