TY - JOUR A1 - Ijiri, Akira A1 - Inagaki, Fumio A1 - Kubo, Yusuke A1 - Adhikari, Rishi Ram A1 - Hattori, Shohei A1 - Hoshino, Tatsuhiko A1 - Imachi, Hiroyuki A1 - Kawagucci, Shinsuke A1 - Morono, Yuki A1 - Ohtomo, Yoko A1 - Ono, Shuhei A1 - Sakai, Sanae A1 - Takai, Ken A1 - Toki, Tomohiro A1 - Wang, David T. A1 - Yoshinaga, Marcos Y. A1 - Arnold, Gail L. A1 - Ashi, Juichiro A1 - Case, David H. A1 - Feseker, Tomas A1 - Hinrichs, Kai-Uwe A1 - Ikegawa, Yojiro A1 - Ikehara, Minoru A1 - Kallmeyer, Jens A1 - Kumagai, Hidenori A1 - Lever, Mark Alexander A1 - Morita, Sumito A1 - Nakamura, Ko-ichi A1 - Nakamura, Yuki A1 - Nishizawa, Manabu A1 - Orphan, Victoria J. A1 - Roy, Hans A1 - Schmidt, Frauke A1 - Tani, Atsushi A1 - Tanikawa, Wataru A1 - Terada, Takeshi A1 - Tomaru, Hitoshi A1 - Tsuji, Takeshi A1 - Tsunogai, Urumu A1 - Yamaguchi, Yasuhiko T. A1 - Yoshida, Naohiro T1 - Deep-biosphere methane production stimulated by geofluids in the Nankai accretionary complex JF - Science Advances Y1 - 2018 U6 - https://doi.org/10.1126/sciadv.aao4631 SN - 2375-2548 VL - 4 IS - 6 PB - American Assoc. for the Advancement of Science CY - Washington ER - TY - GEN A1 - Ijiri, Akira A1 - Inagaki, Fumio A1 - Kubo, Yusuke A1 - Adhikari, Rishi Ram A1 - Hattori, Shohei A1 - Hoshino, Tatsuhiko A1 - Imachi, Hiroyuki A1 - Kawagucci, Shinsuke A1 - Morono, Yuki A1 - Ohtomo, Yoko A1 - Ono, Shuhei A1 - Sakai, Sanae A1 - Takai, Ken A1 - Toki, Tomohiro A1 - Wang, David T. A1 - Yoshinaga, Marcos Y. A1 - Arnold, Gail L. A1 - Ashi, Juichiro A1 - Case, David H. A1 - Feseker, Tomas A1 - Hinrichs, Kai-Uwe A1 - Ikegawa, Yojiro A1 - Ikehara, Minoru A1 - Kallmeyer, Jens A1 - Kumagai, Hidenori A1 - Lever, Mark Alexander A1 - Morita, Sumito A1 - Nakamura, Ko-ichi A1 - Nakamura, Yuki A1 - Nishizawa, Manabu A1 - Orphan, Victoria J. A1 - Røy, Hans A1 - Schmidt, Frauke A1 - Tani, Atsushi A1 - Tanikawa, Wataru A1 - Terada, Takeshi A1 - Tomaru, Hitoshi A1 - Tsuji, Takeshi A1 - Tsunogai, Urumu A1 - Yamaguchi, Yasuhiko T. A1 - Yoshida, Naohiro T1 - Deep-biosphere methane production stimulated by geofluids in the Nankai accretionary complex T2 - Postprints der Universität Potsdam Mathematisch-Naturwissenschaftliche Reihe N2 - Microbial life inhabiting subseafloor sediments plays an important role in Earth’s carbon cycle. However, the impact of geodynamic processes on the distributions and carbon-cycling activities of subseafloor life remains poorly constrained. We explore a submarine mud volcano of the Nankai accretionary complex by drilling down to 200 m below the summit. Stable isotopic compositions of water and carbon compounds, including clumped methane isotopologues, suggest that ~90% of methane is microbially produced at 16° to 30°C and 300 to 900 m below seafloor, corresponding to the basin bottom, where fluids in the accretionary prism are supplied via megasplay faults. Radiotracer experiments showed that relatively small microbial populations in deep mud volcano sediments (10 2 to 10 3 cells cm −3 ) include highly active hydrogenotrophic methanogens and acetogens. Our findings indicate that subduction-associated fluid migration has stimulated microbial activity in the mud reservoir and that mud volcanoes may contribute more substantially to the methane budget than previously estimated. T3 - Zweitveröffentlichungen der Universität Potsdam : Mathematisch-Naturwissenschaftliche Reihe - 802 KW - multiply-substituted isotopologues KW - marine subsurface sediments KW - carbon isotopic composition KW - submarine mud volcano KW - intact polar lipids KW - fore-arc basin KW - subseafloor sediments KW - microbial lipids KW - Cascadia margin KW - organic-acids Y1 - 2019 U6 - http://nbn-resolving.de/urn/resolver.pl?urn:nbn:de:kobv:517-opus4-427002 SN - 1866-8372 IS - 802 ER - TY - JOUR A1 - Rezaei, Mina A1 - Näppi, Janne J. A1 - Lippert, Christoph A1 - Meinel, Christoph A1 - Yoshida, Hiroyuki T1 - Generative multi-adversarial network for striking the right balance in abdominal image segmentation JF - International journal of computer assisted radiology and surgery N2 - Purpose: The identification of abnormalities that are relatively rare within otherwise normal anatomy is a major challenge for deep learning in the semantic segmentation of medical images. The small number of samples of the minority classes in the training data makes the learning of optimal classification challenging, while the more frequently occurring samples of the majority class hamper the generalization of the classification boundary between infrequently occurring target objects and classes. In this paper, we developed a novel generative multi-adversarial network, called Ensemble-GAN, for mitigating this class imbalance problem in the semantic segmentation of abdominal images. Method: The Ensemble-GAN framework is composed of a single-generator and a multi-discriminator variant for handling the class imbalance problem to provide a better generalization than existing approaches. The ensemble model aggregates the estimates of multiple models by training from different initializations and losses from various subsets of the training data. The single generator network analyzes the input image as a condition to predict a corresponding semantic segmentation image by use of feedback from the ensemble of discriminator networks. To evaluate the framework, we trained our framework on two public datasets, with different imbalance ratios and imaging modalities: the Chaos 2019 and the LiTS 2017. Result: In terms of the F1 score, the accuracies of the semantic segmentation of healthy spleen, liver, and left and right kidneys were 0.93, 0.96, 0.90 and 0.94, respectively. The overall F1 scores for simultaneous segmentation of the lesions and liver were 0.83 and 0.94, respectively. Conclusion: The proposed Ensemble-GAN framework demonstrated outstanding performance in the semantic segmentation of medical images in comparison with other approaches on popular abdominal imaging benchmarks. The Ensemble-GAN has the potential to segment abdominal images more accurately than human experts. KW - imbalanced learning KW - generative multi-discriminative networks KW - semantic KW - segmentation KW - abdominal imaging Y1 - 2020 U6 - https://doi.org/10.1007/s11548-020-02254-4 SN - 1861-6410 SN - 1861-6429 VL - 15 IS - 11 SP - 1847 EP - 1858 PB - Springer CY - Berlin ER -