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In Vivo Development of Fetal Pig Kidneys in Mature Monkeys under Clinically Approved Immunosuppressant Drugs

Engineering 2022年第3期8卷 65-73页

作者： Tsuyoshi Takamura Kenji Matsui Naoto Matsumoto Yatsumu Saito Toshinari Fujimoto Susumu Tajiri Shuichiro Yamanaka Kei Matsumoto Akimitsu Kobayashi Izumi Yamamoto Hiroshi Sasaki haruyuki hirayama Hitomi Matsunari Kazuaki Nakano Hiroshi Nagashima Akihiko Kiyoshi Takao Kuroda Makoto Inoue Takeshi Miyawaki Takashi Yokoo Eiji Kobayashi Division of Nephrology and Hypertension Department of Internal MedicineThe Jikei University School of MedicineTokyo 105-8461Japan Department of Urology The Jikei University School of MedicineTokyo 105-8461Japan Department of Plastic and Reconstructive Surgery The Jikei University School of MedicineTokyo 105-8461Japan Meiji University International Institute for Bio-Resource Research Kanagawa 214-8571Japan Sumitomo Dainippon Pharma Co. Ltd.Osaka-fu 564-0053Japan Department of Kidney Regenerative Medicine The Jikei University School of MedicineTokyo 105-8461Japan

Controlling the immune response with only clinically approved immunosuppressant drugs is difficult in renal heterotra ns plantation from pigs to nonhuman ***,to the best of our knowledge,no reports exist on the use of fetal pigs as kidney *** study aimed to compare the degree of transplant rejection between neonatal and fetal kidneys,with genetically unmodified pigs as donors and cynomolgus monkeys as *** left kidneys of the recipient monkeys were removed,followed by transplantation of neonatal as well as fetal pig kidneys,which had undergone vascular anastomosis at the same site,into the *** was performed with only US Food and Drug Administration-approved *** fetal kidneys were transplanted into the omentum and paraaortic regions of cynomolgus ***,the engraftment and development of the transplanted tissues were pathologically examined by sampling over time(twice in each experiment).An acute rejection was observed after a few weeks in neonatal renal grafts with vascular ***,fetal pig kidneys were spared from rejection despite the administration of the same immunosuppressive protocol to the monkeys and the recipient blood vessels flowing into the fetal *** immunogenicity of fetal kidneys in pig-monkey renal heterotransplantation was lower than that of neonatal kidneys.

关键词： Cynomolgus monkey Pig Kidney Fetal kidney Immunosuppression

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Using Cross Entropy as a Performance Metric for Quantifying Uncertainty in DNN Image Classifiers: An Application to Classification of Lung Cancer on CT Images

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Journal of Biomedical Science and Engineering 2024年第1期17卷 1-12页

作者： Eri Matsuyama Masayuki Nishiki Noriyuki Takahashi haruyuki Watanabe Faculty of Informatics University of Fukuchiyama Kyoto Japan Graduate School of Radiological Sciences International University of Health and Welfare Tochigi Japan School of Health Sciences Fukushima Medical University Fukushima Japan School of Radiological Technology Gunma Prefectural College of Health Sciences Gunma Japan

Cross entropy is a measure in machine learning and deep learning that assesses the difference between predicted and actual probability distributions. In this study, we propose cross entropy as a performance evaluation metric for image classifier models and apply it to the CT image classification of lung cancer. A convolutional neural network is employed as the deep neural network (DNN) image classifier, with the residual network (ResNet) 50 chosen as the DNN archi-tecture. The image data used comprise a lung CT image set. Two classification models are built from datasets with varying amounts of data, and lung cancer is categorized into four classes using 10-fold cross-validation. Furthermore, we employ t-distributed stochastic neighbor embedding to visually explain the data distribution after classification. Experimental results demonstrate that cross en-tropy is a highly useful metric for evaluating the reliability of image classifier models. It is noted that for a more comprehensive evaluation of model perfor-mance, combining with other evaluation metrics is considered essential. .

关键词： Cross Entropy Performance Metrics DNN Image Classifiers Lung Cancer Prediction Uncertainty

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Nanotwin-assisted nitridation of quenched FeNi alloy nanopowders for rare-earth-free magnets

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Journal of Materials Science & Technology 2023年第34期165卷 244-252页

作者： Jian Wang Yusuke hirayama Kazuyuki Suzuki Kwangjae Park Zheng Liu Kenta Takagi Kimihiro Ozaki Innovative Functional Materials Research Institute National Institute of Advanced Industrial Science and Technology4-205SakurazakaMoriyamaNagoya463-8560Japan

L1_(0)-ordered FeNi alloy with a high uniaxial magnetic anisotropy and large magnetic moment is a promising candidate for rare-earth-free permanent magnets ***,the synthesis of this chemically ordered phase remains a longstanding challenge because of its low chemical order-disorder transition temperature(200-320℃).Although a non-equilibrium synthetic route based on a nitrogen topotactic reaction has been proposed as a valid approach,the volume fraction and degree of chemical ordering of the product phase are ***,we propose a promising approach that promotes the efficient formation of L1_(0)-ordered nitride phase in FeNi nanopowders by introducing a quenching treatment during a low-oxygen induction thermal plasma *** quenched FeNi nanopowders possessed much smaller powder sizes(40.4 vs 74.0 nm),exhibited higher number densities of nanotwins(39.8%vs 24.1%)and formed much larger volume fraction(33.6 wt.%vs 0.6 wt.%)of ordered phase than the unquenched ***,quenching-induced high-density nanotwins led to the dominant coverage of serrated{001}crystal facets over the surfaces of the FeNi *** unique features substantially accelerated the formation of the L1_(0)-ordered nitride phase in the FeNi nanopowders because the{001}crystallographic orientation had the highest nitrogen *** work provides not only a valid synthetic approach for mass production of the L10-ordered nitride phase in FeNi nanopowders but also novel insights into the crystal-defect-assisted nitridation of nanomaterials.

关键词： Rare-earth-free Permanent magnet FeNi Nitridation Nanotwin

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Explainable Analysis of Deep Learning Models for Coronavirus Disease (COVID-19) Classification with Chest X-Ray Images: Towards Practical Applications

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Open Journal of Medical Imaging 2022年第3期12卷 83-102页

作者： Eri Matsuyama haruyuki Watanabe Noriyuki Takahashi Faculty of Informatics University of Fukuchiyama Kyoto Japan School of Radiological Technology Gunma Prefectural College of Health Sciences Gunma Japan School of Hearth Sciences Fukushima Medical University Fukushima Japan

In recent years, with numerous developments of convolutional neural network (CNN) classification models for medical diagnosis, the issue of misrecognition/misclassification has become more and more important. Thus, research on misrecognition/misclassification has been progressing. This study focuses on the problem of misrecognition/misclassification of CNN classification models for coronavirus disease (COVID-19) using chest X-ray images. We construct two models for COVID-19 pneumonia classification by fine-tuning ResNet-50 architecture, i.e., a model retrained with full-sized original images and a model retrained with segmented images. The present study demonstrates the uncertainty (misrecognition/misclassification) of model performance caused by the discrepancy in the shapes of images at the phase of model construction and that of clinical applications. To achieve it, we apply three XAI methods to demonstrate and explain the uncertainty of classification results obtained from the two constructed models assuming for clinical applications. Experimental results indicate that the performance of classification models cannot be maintained when the type of constructed model and the geometric shape of input images are not matched, which may bring about misrecognition in clinical applications. We also notice that the effect of adversarial attack might be induced if the method of image segmentation is not performed properly. The results suggest that the best approach to obtaining a highly reliable prediction in the classification of COVID-19 pneumonia is to construct a model using full-sized original images as training data and use full-sized original images as the input when utilized in clinical applications.

关键词： Explainable AI CNN CXR Image COVID-19 Pneumonia

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SPECIES-AREA RELATIONSHIP FOR POWER-LAW SPECIES ABUNDANCE DISTRIBUTION

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International Journal of Biomathematics 2012年第3期5卷 215-223页

作者： haruyuki IRIE KEI TOKITA Information Media Center and Graduate School of Science Hiroshima University 1-4-2 Kagamiyama Higashi-Hiroshima Hiroshima 739-8511 Japan Cybermcdia Center Graduate School of Science and Graduate School of Frontier Biosciences Osaka University 1-32 Machikaneyamacho Toyonaka Osaka 560-0043 Japan

We studied the mathematical relations between species abundance distributions (SADs) and species-area relationships (SARs) and found that a power-law SAR can be generally derived from a power-law SAD without a special assumption such as the "canonical hypothesis". In the present analysis, an SAR-exponent is obtained as a function of an SAD-exponent for a finite number of species. We also studied the inverse problem, from SARs to SADs, and found that a power-SAD can be derived from a power-SAR under the condition that the functional form of the corresponding SAD is invariant for changes in the number of species. We also discuss general relationships among lognormal SADs, the broken-stick model (exponential SADs), linear SARs and logarithmic SARs. These results suggest the existence of a common mechanism for SADs and SARs, which could prove a useful tool for theoretical and experimental studies on biodiversity and species coexistence.

关键词：物种多度分布面积关系对数正态分布特别行政区幂律 SARS 物种数量生物多样性

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Atomically precise thiolate-protected gold nanoclusters:Current status of designability of the structure and physicochemical properties

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聚集体(英文) 2023年第2期4卷 1-41页

作者： Sakiat Hossain Daisuke hirayama Ayaka Ikeda Mai Ishimi Sota Funaki Arpan Samanta Tokuhisa Kawawaki Yuichi Negishi Research Institute for Science and Technology Tokyo University of ScienceShinjuku-kuJapan Department of Applied Chemistry Faculty of ScienceTokyo University of ScienceShinjuku-kuJapan

Thiolate(SR)-protected gold nanoclusters(Aun(SR)m NCs)are a rare type of material capable of simultaneously exhibiting multiple physicochemical properties well-suited to specific applications,including photoluminescence,thermocatalysis,electrocatalysis,photocatalysis,magnetism,and optical *** the past several decades,there has been tremendous progress in our understanding of the structure and physicochemical properties of Aun(SR)m NCs,resulting in the ability to fine-tune the characteristics of these *** is therefore helpful to examine the extent to which the properties of Aun(SR)m and related metal NCs can now be adjusted based on *** review presents representative examples of previous studies concerning the geometry,electronic structure,luminescence properties,catalysis,magnetism and optical activity of Aun(SR)m and related metal NCs and discusses the current status of the designability of these NCs to impart specific structural and physicochemical *** information is expected to accelerate the fabrication of highly functional materials based on Aun(SR)m and related metal NCs.

关键词： atomically precise metal nanoclusters gold nanoclusters physicochemical properties structures thiolate

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Exact Bosonic Solutions of the Truncated Skyrme Model

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Chinese Physics Letters 2010年第6期27卷 35-36页

作者： SHI Chang-Guang hirayama Minoru Department of Mathematics and Physics Shanghai University of Electric Power Shanghai 200090 Department of Physics University of Toyama Gofuku 3190 Toyama Japan

A model defined by half the Lagrangian （the highly nonlinear part） of the Skyrme model is investigated. Two classes of bosonic solutions are obtained. It is shown that, although the field configurations of these two... 详细信息

关键词： Models Solutions Model

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A Breast Density Classification System for Mammography Considering Reliability Issues in Deep Learning

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Open Journal of Medical Imaging 2023年第3期13卷 63-83页

作者： Eri Matsuyama Megumi Takehara Noriyuki Takahashi haruyuki Watanabe Faculty of Informatics University of Fukuchiyama Kyoto Japan Breast Center Dokkyo Medical University Hospital Tochigi Japan School of Hearth Sciences Fukushima Medical University Fukushima Japan School of Radiological Technology Gunma Prefectural College of Health Sciences Gunma Japan

In a convolutional neural network (CNN) classification model for diagnosing medical images, transparency and interpretability of the model’s behavior are crucial in addition to high classification accuracy, and it is highly important to explicitly demonstrate them. In this study, we constructed an interpretable CNN-based model for breast density classification using spectral information from mammograms. We evaluated whether the model’s prediction scores provided reliable probability values using a reliability diagram and visualized the basis for the final prediction. In constructing the classification model, we modified ResNet50 and introduced algorithms for extracting and inputting image spectra, visualizing network behavior, and quantifying prediction ambiguity. From the experimental results, our proposed model demonstrated not only high classification accuracy but also higher reliability and interpretability compared to the conventional CNN models that use pixel information from images. Furthermore, our proposed model can detect misclassified data and indicate explicit basis for prediction. The results demonstrated the effectiveness and usefulness of our proposed model from the perspective of credibility and transparency.

关键词： Explainable AI t-SNE Entropy Wavelet Transform Mammogram

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Degradation of dibromophenols by UV irradiation

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Journal of Environmental Sciences 2014年第6期26卷 1284-1288页

作者： Keiko Katayama-hirayama Naoki Toda Akihiko Tauchi Atsushi Fujioka Tetsuya Akitsu Hidehiro Kaneko Kimiaki hirayama Interdisciplinary Graduate School of Medicine and Engineering University of Yamanashi 4-3-11 Takeda Kofu 400-8511 Japan Technology Development Division Toshiba Lighting and Technology Corporation Asahimachi 5-2-1 Imabari Ehime 794-8510 Japan

We examined the degradation of dibromophenols （DBPs）, i.e. 2,4-DBP, 2,6-DBP and 3,5-DBP by ultraviolet （UV） irradiation and estimated the relationship between degradability and molecular orbital properties of each dibromopbenol. The removal of DBPs under a UV lamp system was successfully performed in an aqueous solution. After 5 min of irradiation, the initial DBPs concentration of 20 mg/L was decreased to below 1 mg/L, and about 60% of bromide ion was released. A decrease in the concentration of dissolved organic carbon （DOC） suggested the mineralization of DBPs, The mineralization may occur after release of bromide ions because the decrease of DOC was slower than the release of bromide ions. The degradability of 3,5-DBP was slightly lower than 2,6-DBP and 2,4-DBE Molecular orbital calculation suggested that the electrophilic frontier density and the highest occupied molecular orbital （HOMO） energy may be related to the degradability of DBPs.

关键词： bromide ion dibromophenol DOC mineralization UV irration

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Machine Learning in Predicting Printable Biomaterial Formulations for Direct Ink Writing

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Research 2023年第4期2023卷 105-115页

作者： Hongyi Chen Yuanchang Liu Stavroula Balabani Ryuji hirayama Jie Huang Department of Mechanical Engineering University College LondonLondonUK Department of Computer Science University College LondonLondonUK Wellcome-EPSRC Centre for Interventional Surgical Sciences(WEISS) University College LondonLondonUK.

Three-dimensional(3D)printing is emerging as a transformative technology for biomedical *** 3D printed product can be patient-specific by allowing customizability and direct control of the *** trial-and-error approach currently used for developing the composition of printable inks is time-and resource-consuming due to the increasing number of variables requiring expert *** intelligence has the potential to reshape the ink development process by forming a predictive model for printability from experimental *** this paper,we constructed machine learning(ML)algorithms including decision tree,random forest(RF),and deep learning(DL)to predict the printability of biomaterials.A total of 210 formulations including 16 different bioactive and smart materials and 4 solvents were 3D printed,and their printability was *** ML methods were able to learn and predict the printability of a variety of inks based on their biomaterial *** particular,the RF algorithm has achieved the highest accuracy(88.1%),precision(90.6%),and F1 score(87.0%),indicating the best overall performance out of the 3 algorithms,while DL has the highest recall(87.3%).Furthermore,the ML algorithms have predicted the printability window of biomaterials to guide the ink *** printability map generated with DL has finer granularity than other *** has proven to be an effective and novel strategy for developing biomaterial formulations with desired 3D printability for biomedical engineering applications.

关键词： printing formulation biomaterials

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