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2,290 results on '"Takahashi, Yu"'

1. Cancer risk by length of Barrett’s esophagus in Japanese population: a nationwide multicenter retrospective cohort study

Author

Fukuda, Sho, Watanabe, Kenta, Kubota, Dai, Yamamichi, Nobutake, Takahashi, Yu, Watanabe, Yoshitaka, Adachi, Kyoichi, Ishimura, Norihisa, Koike, Tomoyuki, Sugawara, Hideyuki, Asanuma, Kiyotaka, Abe, Yasuhiko, Kon, Takashi, Ihara, Eikichi, Haraguchi, Kazuhiro, Otsuka, Yoshihiro, Yoshimura, Rie, Iwaya, Yugo, Okamura, Takuma, Manabe, Noriaki, Horiuchi, Akira, Matsumoto, Mio, Onochi, Kengo, Takahashi, So, Yoshida, Tatsuki, Shimodaira, Yosuke, and Iijima, Katsunori

Published
2024
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8. Marked intestinal trans-differentiation by autoimmune gastritis along with ectopic pancreatic and pulmonary trans-differentiation

Author

Takeuchi, Chihiro, Sato, Junichi, Yamamichi, Nobutake, Kageyama-Yahara, Natsuko, Sasaki, Akiko, Akahane, Takemi, Aoki, Rika, Nakajima, Shigemi, Ito, Masayoshi, Yamamichi, Mitsue, Liu, Yu-Yu, Sakuma, Nobuyuki, Takahashi, Yu, Sakaguchi, Yoshiki, Tsuji, Yosuke, Sakurai, Kouhei, Tomida, Shuta, Niimi, Keiko, Ushijima, Toshikazu, and Fujishiro, Mitsuhiro

Published
2024
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18. Differentiable Digital Signal Processing Mixture Model for Synthesis Parameter Extraction from Mixture of Harmonic Sounds

Author

Kawamura, Masaya, Nakamura, Tomohiko, Kitamura, Daichi, Saruwatari, Hiroshi, Takahashi, Yu, and Kondo, Kazunobu

Subjects
Computer Science - Sound, Computer Science - Machine Learning, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

A differentiable digital signal processing (DDSP) autoencoder is a musical sound synthesizer that combines a deep neural network (DNN) and spectral modeling synthesis. It allows us to flexibly edit sounds by changing the fundamental frequency, timbre feature, and loudness (synthesis parameters) extracted from an input sound. However, it is designed for a monophonic harmonic sound and cannot handle mixtures of harmonic sounds. In this paper, we propose a model (DDSP mixture model) that represents a mixture as the sum of the outputs of multiple pretrained DDSP autoencoders. By fitting the output of the proposed model to the observed mixture, we can directly estimate the synthesis parameters of each source. Through synthesis parameter extraction experiments, we show that the proposed method has high and stable performance compared with a straightforward method that applies the DDSP autoencoder to the signals separated by an audio source separation method., Comment: 5 pages, 2 figures, to appear in 2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP 2022)

Published
2022

26. Multichannel Audio Source Separation with Independent Deeply Learned Matrix Analysis Using Product of Source Models

Author

Hasumi, Takuya, Nakamura, Tomohiko, Takamune, Norihiro, Saruwatari, Hiroshi, Kitamura, Daichi, Takahashi, Yu, and Kondo, Kazunobu

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Independent deeply learned matrix analysis (IDLMA) is one of the state-of-the-art multichannel audio source separation methods using the source power estimation based on deep neural networks (DNNs). The DNN-based power estimation works well for sounds having timbres similar to the DNN training data. However, the sounds to which IDLMA is applied do not always have such timbres, and the timbral mismatch causes the performance degradation of IDLMA. To tackle this problem, we focus on a blind source separation counterpart of IDLMA, independent low-rank matrix analysis. It uses nonnegative matrix factorization (NMF) as the source model, which can capture source spectral components that only appear in the target mixture, using the low-rank structure of the source spectrogram as a clue. We thus extend the DNN-based source model to encompass the NMF-based source model on the basis of the product-of-expert concept, which we call the product of source models (PoSM). For the proposed PoSM-based IDLMA, we derive a computationally efficient parameter estimation algorithm based on an optimization principle called the majorization-minimization algorithm. Experimental evaluations show the effectiveness of the proposed method., Comment: 8 pages, 5 figures, accepted for Asia-Pacific Signal and Information Processing Association Annual Summit and Conference 2021 (APSIPA ASC 2021)

Published
2021

27. Prior Distribution Design for Music Bleeding-Sound Reduction Based on Nonnegative Matrix Factorization

Author

Mizobuchi, Yusaku, Kitamura, Daichi, Nakamura, Tomohiko, Saruwatari, Hiroshi, Takahashi, Yu, and Kondo, Kazunobu

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

When we place microphones close to a sound source near other sources in audio recording, the obtained audio signal includes undesired sound from the other sources, which is often called cross-talk or bleeding sound. For many audio applications including onstage sound reinforcement and sound editing after a live performance, it is important to reduce the bleeding sound in each recorded signal. However, since microphones are spatially apart from each other in this situation, typical phase-aware blind source separation (BSS) methods cannot be used. We propose a phase-insensitive method for blind bleeding-sound reduction. This method is based on time-channel nonnegative matrix factorization, which is a BSS method using only amplitude spectrograms. With the proposed method, we introduce the gamma-distribution-based prior for leakage levels of bleeding sounds. Its optimization can be interpreted as maximum a posteriori estimation. The experimental results of music bleeding-sound reduction indicate that the proposed method is more effective for bleeding-sound reduction of music signals compared with other BSS methods., Comment: Accepted and will be presented at APSIPA2021

Published
2021

34. Intestinal commensal microbiota and cytokines regulate Fut2+ Paneth cells for gut defense

Author

Kamioka, Mariko, Goto, Yoshiyuki, Nakamura, Kiminori, Yokoi, Yuki, Sugimoto, Rina, Ohira, Shuya, Kurashima, Yosuke, Umemoto, Shingo, Sato, Shintaro, Kunisawa, Jun, Takahashi, Yu, Domino, Steven E, Renauld, Jean-Christophe, Nakae, Susumu, Iwakura, Yoichiro, Ernst, Peter B, Ayabe, Tokiyoshi, and Kiyono, Hiroshi

Subjects
Microbiology, Biochemistry and Cell Biology, Biomedical and Clinical Sciences, Biological Sciences, Stem Cell Research, Prevention, Vaccine Related, Digestive Diseases, Biodefense, Aetiology, 2.1 Biological and endogenous factors, Oral and gastrointestinal, Animals, Cytokines, Fucosyltransferases, Gastrointestinal Microbiome, Ileum, Interleukin-17, Interleukins, Mice, Paneth Cells, Symbiosis, alpha-Defensins, Interleukin-22, Galactoside 2-alpha-L-fucosyltransferase, Paneth cell, alpha-defensin, fucosyltransferase 2, IL-22, IL-17a, α-defensin
Abstract

Paneth cells are intestinal epithelial cells that release antimicrobial peptides, such as α-defensin as part of host defense. Together with mesenchymal cells, Paneth cells provide niche factors for epithelial stem cell homeostasis. Here, we report two subtypes of murine Paneth cells, differentiated by their production and utilization of fucosyltransferase 2 (Fut2), which regulates α(1,2)fucosylation to create cohabitation niches for commensal bacteria and prevent invasion of the intestine by pathogenic bacteria. The majority of Fut2- Paneth cells were localized in the duodenum, whereas the majority of Fut2+ Paneth cells were in the ileum. Fut2+ Paneth cells showed higher granularity and structural complexity than did Fut2- Paneth cells, suggesting that Fut2+ Paneth cells are involved in host defense. Signaling by the commensal bacteria, together with interleukin 22 (IL-22), induced the development of Fut2+ Paneth cells. IL-22 was found to affect the α-defensin secretion system via modulation of Fut2 expression, and IL-17a was found to increase the production of α-defensin in the intestinal tract. Thus, these intestinal cytokines regulate the development and function of Fut2+ Paneth cells as part of gut defense.

Published
2022

35. Empirical Bayesian Independent Deeply Learned Matrix Analysis For Multichannel Audio Source Separation

Author

Hasumi, Takuya, Nakamura, Tomohiko, Takamune, Norihiro, Saruwatari, Hiroshi, Kitamura, Daichi, Takahashi, Yu, and Kondo, Kazunobu

Subjects
Computer Science - Sound, Electrical Engineering and Systems Science - Audio and Speech Processing
Abstract

Independent deeply learned matrix analysis (IDLMA) is one of the state-of-the-art supervised multichannel audio source separation methods. It blindly estimates the demixing filters on the basis of source independence, using the source model estimated by the deep neural network (DNN). However, since the ratios of the source to interferer signals vary widely among time-frequency (TF) slots, it is difficult to obtain reliable estimated power spectrograms of sources at all TF slots. In this paper, we propose an IDLMA extension, empirical Bayesian IDLMA (EB-IDLMA), by introducing a prior distribution of source power spectrograms and treating the source power spectrograms as latent random variables. This treatment allows us to implicitly consider the reliability of the estimated source power spectrograms for the estimation of demixing filters through the hyperparameters of the prior distribution estimated by the DNN. Experimental evaluations show the effectiveness of EB-IDLMA and the importance of introducing the reliability of the estimated source power spectrograms., Comment: 5 pages, 4 figures, accepted for European Signal Processing Conference 2021 (EUSIPCO 2021)

Published
2021

41. Feasibility of S-1 adjuvant chemotherapy after major hepatectomy for biliary tract cancers: An exploratory subset analysis of JCOG1202

Author

Kobayashi, Shogo, Nakachi, Kohei, Ikeda, Masafumi, Konishi, Masaru, Ogawa, Gakuto, Sugiura, Teiichi, Yanagimoto, Hiroaki, Morinaga, Soichiro, Wada, Hiroshi, Shimada, Kazuaki, Takahashi, Yu, Nakagohri, Toshio, Kamata, Ken, Shimizu, Yasuhiro, Ajiki, Tetsuo, Hirano, Satoshi, Gotohda, Naoto, Ueno, Makoto, Okusaka, Takuji, and Furuse, Junji

Published
2024
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49. Transcriptome analysis reveals the essential role of NK2 homeobox 1/thyroid transcription factor 1 (NKX2-1/TTF-1) in gastric adenocarcinoma of fundic-gland type

Author

Fukagawa, Kazushi, Takahashi, Yu, Yamamichi, Nobutake, Kageyama-Yahara, Natsuko, Sakaguchi, Yoshiki, Obata, Miho, Cho, Rina, Sakuma, Nobuyuki, Nagao, Sayaka, Miura, Yuko, Tamura, Naoki, Ohki, Daisuke, Mizutani, Hiroya, Yakabi, Seiichi, Minatsuki, Chihiro, Niimi, Keiko, Tsuji, Yosuke, Yamamichi, Mitsue, Shigi, Narumi, Tomida, Shuta, Abe, Hiroyuki, Ushiku, Tetsuo, Koike, Kazuhiko, and Fujishiro, Mitsuhiro

Published
2023
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50. Comprehensive behavioral study of C57BL/6.KOR-ApoEshl mice

Author

Ueno Hiroshi, Takahashi Yu, Murakami Shinji, Wani Kenta, Miyazaki Tetsuji, Matsumoto Yosuke, Okamoto Motoi, and Ishihara Takeshi

Subjects
apolipoprotein, behavior, behavioral test, central nervous system, mouse, Neurosciences. Biological psychiatry. Neuropsychiatry, RC321-571
Abstract

Apolipoprotein E (ApoE) is associated with Alzheimer’s disease (AD) and cognitive dysfunction in elderly individuals. There have been extensive studies on behavioral abnormalities in ApoE-deficient (Apoeshl) mice, which have been described as AD mouse models. Spontaneously hyperlipidemic mice were discovered in 1999 as ApoE-deficient mice due to ApoE gene mutations. However, behavioral abnormalities in commercially available Apoeshl mice remain unclear. Accordingly, we aimed to investigate the behavioral abnormalities of Apoeshl mice.

Published
2023
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