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  1. AU="Iwasaki, Yuma"
  2. AU="Szczepaniak, Jessica"
  3. AU="Huang, Chun-Mei"
  4. AU="Zhang, Weijng"
  5. AU="Venrdramin, A."
  6. AU="Xu, Hai-Chao"
  7. AU="Imad, Nasser"
  8. AU="Batiridou, Agapi L"
  9. AU="Segal, Matt"
  10. AU="Russell E. Lewis"
  11. AU="Kietselaer, Bas"
  12. AU="Edelson, Brian T"
  13. AU="Elliott, Bruce M"
  14. AU="Pérez, René"
  15. AU="Lourdes Diaz Rodriguez"
  16. AU="Choi, Kai Chow"
  17. AU="Brandolini, Jury"
  18. AU="Yom, Jina"
  19. AU="Sue Casey"
  20. AU="Arimura, Takashi"
  21. AU="Kizilkilic, Osman"

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  1. Artikel ; Online: Skeleton phylogeny reconstructed with transcriptomes for the tribe Drosophilini (Diptera: Drosophilidae).

    Seto, Yosuke / Iwasaki, Yuma / Ogawa, Yoshitaka / Tamura, Koichiro / Toda, Masanori J

    Molecular phylogenetics and evolution

    2023  Band 191, Seite(n) 107978

    Abstract: The family Drosophilidae is one of the most important model systems in evolutionary biology. Thanks to advances in high-throughput sequencing technology, a number of molecular phylogenetic analyses have been undertaken by using large data sets of many ... ...

    Abstract The family Drosophilidae is one of the most important model systems in evolutionary biology. Thanks to advances in high-throughput sequencing technology, a number of molecular phylogenetic analyses have been undertaken by using large data sets of many genes and many species sampled across this family. Especially, recent analyses using genome sequences have depicted the family-wide skeleton phylogeny with high confidence. However, the taxon sampling is still insufficient for minor lineages and non-Drosophila genera. In this study, we carried out phylogenetic analyses using a large number of transcriptome-based nucleotide sequences, focusing on the largest, core tribe Drosophilini in the Drosophilidae. In our analyses, some noise factors against phylogenetic reconstruction were taken into account by removing putative paralogy from the datasets and examining the effects of missing data, i.e. gene occupancy and site coverage, and incomplete lineage sorting. The inferred phylogeny has newly resolved the following phylogenetic positions/relationships at the genomic scale: (i) the monophyly of the subgenus Siphlodora including Zaprionus flavofasciatus to be transferred therein; (ii) the paraphyly of the robusta and melanica species groups within a clade comprised of the robusta, melanica and quadrisetata groups and Z. flavofasciatus; (iii) Drosophila curviceps (representing the curviceps group), D. annulipes (the quadrilineata subgroup of the immigrans group) and D. maculinotata clustered into a clade sister to the Idiomyia + Scaptomyza clade, forming together the expanded Hawaiian drosophilid lineage; (iv) Dichaetophora tenuicauda (representing the lineage comprised of the Zygothrica genus group and Dichaetophora) placed as the sister to the clade of the expanded Hawaiian drosophilid lineage and Siphlodora; and (v) relationships of the subgenus Drosophila and the genus Zaprionus as follows: (Zaprionus, (the quadrilineata subgroup, ((D. sternopleuralis, the immigrans group proper), (the quinaria radiation, the tripunctata radiation)))). These results are to be incorporated into the so-far published phylogenomic tree as a backbone (constraint) tree for grafting much more species based on sequences of a limited number of genes. Such a comprehensive, highly confident phylogenetic tree with extensive and dense taxon sampling will provide an essential framework for comparative studies of the Drosophilidae.
    Mesh-Begriff(e) Animals ; Drosophilidae/genetics ; Phylogeny ; Transcriptome ; Drosophila/genetics ; Biological Evolution ; Skeleton
    Sprache Englisch
    Erscheinungsdatum 2023-11-25
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 33610-5
    ISSN 1095-9513 ; 1055-7903
    ISSN (online) 1095-9513
    ISSN 1055-7903
    DOI 10.1016/j.ympev.2023.107978
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  2. Artikel ; Online: Model-Free Cluster Analysis of Physical Property Data using Information Maximizing Self-Argument Training.

    Sawada, Ryohto / Iwasaki, Yuma / Ishida, Masahiko

    Scientific reports

    2020  Band 10, Heft 1, Seite(n) 7903

    Abstract: We present semi-supervised information maximizing self-argument training (IMSAT), a neural network-based classification method that works without the preparation of labeled data. Semi-supervised IMSAT can amplify specific differences and avoid ... ...

    Abstract We present semi-supervised information maximizing self-argument training (IMSAT), a neural network-based classification method that works without the preparation of labeled data. Semi-supervised IMSAT can amplify specific differences and avoid undesirable misclassification in accordance with the purpose. We demonstrate that semi-supervised IMSAT has a comparable performance with existing methods for semi-supervised learning of image classification and can also classify real experimental data (X-ray diffraction patterns and thermoelectric hysteresis curves) in the same way even though their shape and dimensions are different. Our algorithm will contribute to the automation of big data processing and artificial intelligence-driven material development.
    Sprache Englisch
    Erscheinungsdatum 2020-05-13
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-020-64281-0
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  3. Artikel ; Online: Early Clinical Outcomes of the First Commercialized Human Autologous Ex Vivo Cultivated Oral Mucosal Epithelial Cell Transplantation for Limbal Stem Cell Deficiency: Two Case Reports and Literature Review.

    Toshida, Hiroshi / Kasahara, Tomoto / Kiriyama, Masamichi / Iwasaki, Yuma / Sugita, Jobu / Ichikawa, Kohei / Ohta, Toshihiko / Miyahara, Katsumi

    International journal of molecular sciences

    2023  Band 24, Heft 10

    Abstract: The first product in the world for ex vivo cultivated oral mucosal epithelial cell transplantation (COMET) to treat limbal stem cell deficiency (LSCD), named ... ...

    Abstract The first product in the world for ex vivo cultivated oral mucosal epithelial cell transplantation (COMET) to treat limbal stem cell deficiency (LSCD), named Ocural
    Mesh-Begriff(e) Humans ; Mouth Mucosa/pathology ; Corneal Diseases/metabolism ; Limbal Stem Cells ; Cell Transplantation ; Epithelial Cells/metabolism ; Transplantation, Autologous ; Stem Cell Transplantation/methods ; Cells, Cultured
    Sprache Englisch
    Erscheinungsdatum 2023-05-18
    Erscheinungsland Switzerland
    Dokumenttyp Review ; Case Reports
    ZDB-ID 2019364-6
    ISSN 1422-0067 ; 1422-0067 ; 1661-6596
    ISSN (online) 1422-0067
    ISSN 1422-0067 ; 1661-6596
    DOI 10.3390/ijms24108926
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  4. Artikel: Predicting material properties by integrating high-throughput experiments, high-throughput ab-initio calculations, and machine learning.

    Iwasaki, Yuma / Ishida, Masahiko / Shirane, Masayuki

    Science and technology of advanced materials

    2019  Band 21, Heft 1, Seite(n) 25–28

    Abstract: High-throughput experiments (HTEs) have been powerful tools to obtain many materials data. However, HTEs often require expensive equipment. Although high-throughput ab-initio calculation (HTC) has the potential to make materials big data easier to ... ...

    Abstract High-throughput experiments (HTEs) have been powerful tools to obtain many materials data. However, HTEs often require expensive equipment. Although high-throughput ab-initio calculation (HTC) has the potential to make materials big data easier to collect, HTC does not represent the actual materials data obtained by HTEs in many cases. Here we propose using a combination of simple HTEs, HTC, and machine learning to predict material properties. We demonstrate that our method enables accurate and rapid prediction of the Kerr rotation mapping of an Fe
    Sprache Englisch
    Erscheinungsdatum 2019-12-20
    Erscheinungsland United States
    Dokumenttyp Journal Article
    ZDB-ID 2027985-1
    ISSN 1878-5514 ; 1468-6996
    ISSN (online) 1878-5514
    ISSN 1468-6996
    DOI 10.1080/14686996.2019.1707111
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  5. Artikel ; Online: Individual multidisciplinary clinical phenotypes of nasal and ocular symptoms in hay fever: Crowdsourced cross-sectional study using AllerSearch.

    Inomata, Takenori / Sung, Jaemyoung / Fujio, Kenta / Nakamura, Masahiro / Akasaki, Yasutsugu / Nagino, Ken / Okumura, Yuichi / Iwagami, Masao / Fujimoto, Keiichi / Ebihara, Nobuyuki / Midorikawa-Inomata, Akie / Shokirova, Hurramhon / Huang, Tianxiang / Hirosawa, Kunihiko / Miura, Maria / Ohno, Mizu / Morooka, Yuki / Iwata, Nanami / Iwasaki, Yuma /
    Murakami, Akira

    Allergology international : official journal of the Japanese Society of Allergology

    2023  Band 72, Heft 3, Seite(n) 418–427

    Abstract: Background: Multidisciplinary efforts to prospectively collect and analyze symptoms of hay fever are limited. We aimed to identify the characteristics of nasal and ocular symptoms of hay fever, using the AllerSearch smartphone application.: Methods: ... ...

    Abstract Background: Multidisciplinary efforts to prospectively collect and analyze symptoms of hay fever are limited. We aimed to identify the characteristics of nasal and ocular symptoms of hay fever, using the AllerSearch smartphone application.
    Methods: This mobile health-based prospective observational study using the AllerSearch smartphone application was conducted between February 1, 2018, and May 1, 2020. Individuals who downloaded AllerSearch from Japan and provided comprehensive self-assessments (including 17 items related to quality of life [QoL]-related items) were included. The characteristics and risk factors for allergic rhinitis (AR) and allergic conjunctivitis (AC) were identified using hierarchical heat maps and multivariate logistic regression.
    Results: Of the 9041 participants with hay fever, 58.8% had AR and AC, 22.2% had AR, and 5.7% had AC. The AR-AC comorbid cohort showed worse symptoms of hay fever and QoL scores than the other cohorts. Factors (odds ratio, 95% confidence interval) associated with AR-AC included a lower age (0.98, 0.97-0.98), female sex (1.31, 1.19-1.45), liver disease (1.58, 1.26-2.35), dry eye disease (1.45, 1.30-1.63), unknown dry eye disease status (1.46, 1.31-1.62), contact lens use discontinuation during the hay fever season (1.69, 1.28-2.23), and bedroom flooring material other than hardwood, carpet, tatami, or vinyl (1.91, 1.16-3.14).
    Conclusions: Analysis of medical big data for hay fever performed using a mobile health app helped identify risk factors and characteristics of AC, AR, and AR-AC. Phenotyping of highly variable symptoms of hay fever, such as nasal and ocular symptoms, can facilitate better-quality clinical care.
    Mesh-Begriff(e) Female ; Humans ; Rhinitis, Allergic, Seasonal/diagnosis ; Rhinitis, Allergic, Seasonal/epidemiology ; Quality of Life ; Cross-Sectional Studies ; Crowdsourcing ; Rhinitis, Allergic/diagnosis ; Rhinitis, Allergic/epidemiology ; Conjunctivitis, Allergic ; Dry Eye Syndromes/diagnosis ; Dry Eye Syndromes/epidemiology ; Dry Eye Syndromes/etiology
    Sprache Englisch
    Erscheinungsdatum 2023-02-03
    Erscheinungsland England
    Dokumenttyp Observational Study ; Journal Article
    ZDB-ID 1336498-4
    ISSN 1440-1592 ; 1323-8930
    ISSN (online) 1440-1592
    ISSN 1323-8930
    DOI 10.1016/j.alit.2023.01.001
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  6. Buch ; Online: Materials development by interpretable machine learning

    Iwasaki, Yuma / Sawada, Ryoto / Stanev, Valentin / Ishida, Masahiko / Kirihara, Akihiro / Omori, Yasutomo / Someya, Hiroko / Takeuchi, Ichiro / Saitoh, Eiji / Shinichi, Yorozu

    2019  

    Abstract: Machine learning technologies are expected to be great tools for scientific discoveries. In particular, materials development (which has brought a lot of innovation by finding new and better functional materials) is one of the most attractive scientific ... ...

    Abstract Machine learning technologies are expected to be great tools for scientific discoveries. In particular, materials development (which has brought a lot of innovation by finding new and better functional materials) is one of the most attractive scientific fields. To apply machine learning to actual materials development, collaboration between scientists and machine learning is becoming inevitable. However, such collaboration has been restricted so far due to black box machine learning, in which it is difficult for scientists to interpret the data-driven model from the viewpoint of material science and physics. Here, we show a material development success story that was achieved by good collaboration between scientists and one type of interpretable (explainable) machine learning called factorized asymptotic Bayesian inference hierarchical mixture of experts (FAB/HMEs). Based on material science and physics, we interpreted the data-driven model constructed by the FAB/HMEs, so that we discovered surprising correlation and knowledge about thermoelectric material. Guided by this, we carried out actual material synthesis that led to identification of a novel spin-driven thermoelectric material with the largest thermopower to date.

    Comment: 17 pages, 5 figures
    Schlagwörter Condensed Matter - Materials Science ; Physics - Computational Physics
    Thema/Rubrik (Code) 670
    Erscheinungsdatum 2019-03-06
    Erscheinungsland us
    Dokumenttyp Buch ; Online
    Datenquelle BASE - Bielefeld Academic Search Engine (Lebenswissenschaftliche Auswahl)

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  7. Artikel ; Online: Machine-learning guided discovery of a new thermoelectric material.

    Iwasaki, Yuma / Takeuchi, Ichiro / Stanev, Valentin / Kusne, Aaron Gilad / Ishida, Masahiko / Kirihara, Akihiro / Ihara, Kazuki / Sawada, Ryohto / Terashima, Koichi / Someya, Hiroko / Uchida, Ken-Ichi / Saitoh, Eiji / Yorozu, Shinichi

    Scientific reports

    2019  Band 9, Heft 1, Seite(n) 2751

    Abstract: Thermoelectric technologies are becoming indispensable in the quest for a sustainable future. Recently, an emerging phenomenon, the spin-driven thermoelectric effect (STE), has garnered much attention as a promising path towards low cost and versatile ... ...

    Abstract Thermoelectric technologies are becoming indispensable in the quest for a sustainable future. Recently, an emerging phenomenon, the spin-driven thermoelectric effect (STE), has garnered much attention as a promising path towards low cost and versatile thermoelectric technology with easily scalable manufacturing. However, progress in development of STE devices is hindered by the lack of understanding of the fundamental physics and materials properties responsible for the effect. In such nascent scientific field, data-driven approaches relying on statistics and machine learning, instead of more traditional modeling methods, can exhibit their full potential. Here, we use machine learning modeling to establish the key physical parameters controlling STE. Guided by the models, we have carried out actual material synthesis which led to the identification of a novel STE material with a thermopower an order of magnitude larger than that of the current generation of STE devices.
    Sprache Englisch
    Erscheinungsdatum 2019-02-26
    Erscheinungsland England
    Dokumenttyp Journal Article
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/s41598-019-39278-z
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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  8. Artikel ; Online: Flexible heat-flow sensing sheets based on the longitudinal spin Seebeck effect using one-dimensional spin-current conducting films.

    Kirihara, Akihiro / Kondo, Koichi / Ishida, Masahiko / Ihara, Kazuki / Iwasaki, Yuma / Someya, Hiroko / Matsuba, Asuka / Uchida, Ken-ichi / Saitoh, Eiji / Yamamoto, Naoharu / Kohmoto, Shigeru / Murakami, Tomoo

    Scientific reports

    2016  Band 6, Seite(n) 23114

    Abstract: Heat-flow sensing is expected to be an important technological component of smart thermal management in the future. Conventionally, the thermoelectric (TE) conversion technique, which is based on the Seebeck effect, has been used to measure a heat flow ... ...

    Abstract Heat-flow sensing is expected to be an important technological component of smart thermal management in the future. Conventionally, the thermoelectric (TE) conversion technique, which is based on the Seebeck effect, has been used to measure a heat flow by converting the flow into electric voltage. However, for ubiquitous heat-flow visualization, thin and flexible sensors with extremely low thermal resistance are highly desired. Recently, another type of TE effect, the longitudinal spin Seebeck effect (LSSE), has aroused great interest because the LSSE potentially offers favourable features for TE applications such as simple thin-film device structures. Here we demonstrate an LSSE-based flexible TE sheet that is especially suitable for a heat-flow sensing application. This TE sheet contained a Ni0.2Zn0.3Fe2.5O4 film which was formed on a flexible plastic sheet using a spray-coating method known as "ferrite plating". The experimental results suggest that the ferrite-plated film, which has a columnar crystal structure aligned perpendicular to the film plane, functions as a unique one-dimensional spin-current conductor suitable for bendable LSSE-based sensors. This newly developed thin TE sheet may be attached to differently shaped heat sources without obstructing an innate heat flux, paving the way to versatile heat-flow measurements and management.
    Sprache Englisch
    Erscheinungsdatum 2016-03-15
    Erscheinungsland England
    Dokumenttyp Journal Article ; Research Support, Non-U.S. Gov't
    ZDB-ID 2615211-3
    ISSN 2045-2322 ; 2045-2322
    ISSN (online) 2045-2322
    ISSN 2045-2322
    DOI 10.1038/srep23114
    Datenquelle MEDical Literature Analysis and Retrieval System OnLINE

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