T
Takashi Kubo
Researcher at Osaka University
Publications - 331
Citations - 9948
Takashi Kubo is an academic researcher from Osaka University. The author has contributed to research in topics: Diradical & Singlet state. The author has an hindex of 50, co-authored 286 publications receiving 8642 citations. Previous affiliations of Takashi Kubo include University of Hyogo & Université de Namur.
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Journal Article
[Rhabdomyosarcoma of the prostate: report of a case and review of the literature].
TL;DR: Forty-two cases of rhabdomyosarcoma of the prostate including the authors' case were collected from the Japanese literature and reviewed with respect to the multimodal treatment and prognosis.
Journal Article
Environmental kuznets curve (ekc) relationships between pollutant discharge per capita (pdc) of domestic wastewater and income indicators
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Theoretical Study on Open-Shell Nonlinear Optical Systems
Masayoshi Nakano,Ryohei Kishi,Nozomi Nakagawa,Tomoshige Nitta,Takashi Kubo,Kazuhiro Nakasuji,Kenji Kamada,Koji Ohta,Benoît Champagne,Edith Botek,Satoru Yamada,Kizashi Yamaguchi +11 more
TL;DR: In this paper, the static second hyperpolarizabilities of open-shell organic nonlinear optical (NLO) systems composed of singlet diradical molecules are investigated using ab initio molecular orbital (MO) and density functional theory (DFT) methods.
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Outcomes of non-ischaemic coronary lesions with high-risk plaque characteristics on coronary CT angiography.
Seokhun Yang,Masahiro Hoshino,Taishi Yonetsu,Jinlong Zhang,Doyeon Hwang,Eun-Seok Shin,Joon Hyung Doh,Chang-Wook Nam,Jian-an Wang,Shao-Liang Chen,Nobuhiro Tanaka,Hiroshi Matsuo,Takashi Kubo,Tsunekazu Kakuta,Bon-Kwon Koo +14 more
TL;DR: In non-ischaemic lesions, ql- HRP and qn-HRP showed a synergistic impact on risk assessment and had prognostic interactions with FFR and treatment modalities.
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Semantic segmentation of superficial layer in intracoronary optical coherence tomography based on cropping-merging and deep learning
TL;DR: In this article , a semantic segmentation method was presented based on deep learning on the OCT image analysis of the human vessel to reduce the diagnosis pressure of cardiovascular doctors. But this method is not suitable for the segmentation of lesion tissues.