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449 results on '"Saito, Kazuyuki"'

3. 3D Surface-to-Structure Translation using Deep Convolutional Networks

Author

Moriya, Takumi, Saito, Kazuyuki, and Tanaka, Hiroya

Subjects
Computer Science - Computer Vision and Pattern Recognition
Abstract

Our demonstration shows a system that estimates internal body structures from 3D surface models using deep convolutional neural networks trained on CT (computed tomography) images of the human body. To take pictures of structures inside the body, we need to use a CT scanner or an MRI (Magnetic Resonance Imaging) scanner. However, assuming that the mutual information between outer shape of the body and its inner structure is not zero, we can obtain an approximate internal structure from a 3D surface model based on MRI and CT image database. This suggests that we could know where and what kind of disease a person is likely to have in his/her body simply by 3D scanning surface of the body. As a first prototype, we developed a system for estimating internal body structures from surface models based on Visible Human Project DICOM CT Datasets from the University of Iowa Magnetic Resonance Research Facility., Comment: 2 pages, 3 figures

Published
2017

6. Terrestrial ecosystem model performance in simulating productivity and its vulnerability to climate change in the northern permafrost region

Author

Xia, Jianyang, McGuire, A David, Lawrence, David, Burke, Eleanor, Chen, Guangsheng, Chen, Xiaodong, Delire, Christine, Koven, Charles, MacDougall, Andrew, Peng, Shushi, Rinke, Annette, Saito, Kazuyuki, Zhang, Wenxin, Alkama, Ramdane, Bohn, Theodore J, Ciais, Philippe, Decharme, Bertrand, Gouttevin, Isabelle, Hajima, Tomohiro, Hayes, Daniel J, Huang, Kun, Ji, Duoying, Krinner, Gerhard, Lettenmaier, Dennis P, Miller, Paul A, Moore, John C, Smith, Benjamin, Sueyoshi, Tetsuo, Shi, Zheng, Yan, Liming, Liang, Junyi, Jiang, Lifen, Zhang, Qian, and Luo, Yiqi

Subjects
Earth Sciences, Atmospheric Sciences, Climate Action, Geophysics
Abstract

Realistic projection of future climate-carbon (C) cycle feedbacks requires better understanding and an improved representation of the C cycle in permafrost regions in the current generation of Earth system models. Here we evaluated 10 terrestrial ecosystem models for their estimates of net primary productivity (NPP) and responses to historical climate change in permafrost regions in the Northern Hemisphere. In comparison with the satellite estimate from the Moderate Resolution Imaging Spectroradiometer (MODIS; 246 ± 6 g C m−2 yr−1), most models produced higher NPP (309 ± 12 g C m−2 yr−1) over the permafrost region during 2000–2009. By comparing the simulated gross primary productivity (GPP) with a flux tower-based database, we found that although mean GPP among the models was only overestimated by 10% over 1982–2009, there was a twofold discrepancy among models (380 to 800 g C m−2 yr−1), which mainly resulted from differences in simulated maximum monthly GPP (GPPmax). Most models overestimated C use efficiency (CUE) as compared to observations at both regional and site levels. Further analysis shows that model variability of GPP and CUE are nonlinearly correlated to variability in specific leaf area and the maximum rate of carboxylation by the enzyme Rubisco at 25°C (Vcmax_25), respectively. The models also varied in their sensitivities of NPP, GPP, and CUE to historical changes in climate and atmospheric CO2 concentration. These results indicate that model predictive ability of the C cycle in permafrost regions can be improved by better representation of the processes controlling CUE and GPPmax as well as their sensitivity to climate change.

Published
2017

7. On Defining AW*-algebras and Rickart C*-algebras

Author

Saitô, Kazuyuki and Wright, J. D. Maitland

Subjects
Mathematics - Operator Algebras, [2010]Primary46L99, 37B99
Abstract

Let A be a C*-algebra. It is shown that A is an AW*-algebra if, and only if, each maximal abelian self--adjoint subalgebra of A is monotone complete. An analogous result is proved for Rickart C*-algebras; a C*-algebra is a Rickart C*-algebra if, and only if, it is unital and each maximal abelian self--adjoint subalgebra of A is monotone {\sigma}-complete., Comment: 10 pages

Published
2015

9. Variability in the sensitivity among model simulations of permafrost and carbon dynamics in the permafrost region between 1960 and 2009

Author

McGuire, A David, Koven, Charles, Lawrence, David M, Clein, Joy S, Xia, Jiangyang, Beer, Christian, Burke, Eleanor, Chen, Guangsheng, Chen, Xiaodong, Delire, Christine, Jafarov, Elchin, MacDougall, Andrew H, Marchenko, Sergey, Nicolsky, Dmitry, Peng, Shushi, Rinke, Annette, Saito, Kazuyuki, Zhang, Wenxin, Alkama, Ramdane, Bohn, Theodore J, Ciais, Philippe, Decharme, Bertrand, Ekici, Altug, Gouttevin, Isabelle, Hajima, Tomohiro, Hayes, Daniel J, Ji, Duoying, Krinner, Gerhard, Lettenmaier, Dennis P, Luo, Yiqi, Miller, Paul A, Moore, John C, Romanovsky, Vladimir, Schädel, Christina, Schaefer, Kevin, Schuur, Edward AG, Smith, Benjamin, Sueyoshi, Tetsuo, and Zhuang, Qianlai

Subjects
Earth Sciences, Geochemistry, Geoinformatics, Climate Action, carbon cycle, climate change, permafrost, permafrost carbon feedback, sensitivity, soil carbon, Atmospheric Sciences, Oceanography, Meteorology & Atmospheric Sciences, Climate change impacts and adaptation
Abstract

A significant portion of the large amount of carbon (C) currently stored in soils of the permafrost region in the Northern Hemisphere has the potential to be emitted as the greenhouse gases CO2 and CH4 under a warmer climate. In this study we evaluated the variability in the sensitivity of permafrost and C in recent decades among land surface model simulations over the permafrost region between 1960 and 2009. The 15 model simulations all predict a loss of near-surface permafrost (within 3 m) area over the region, but there are large differences in the magnitude of the simulated rates of loss among the models (0.2 to 58.8 × 103 km2 yr−1). Sensitivity simulations indicated that changes in air temperature largely explained changes in permafrost area, although interactions among changes in other environmental variables also played a role. All of the models indicate that both vegetation and soil C storage together have increased by 156 to 954 Tg C yr−1 between 1960 and 2009 over the permafrost region even though model analyses indicate that warming alone would decrease soil C storage. Increases in gross primary production (GPP) largely explain the simulated increases in vegetation and soil C. The sensitivity of GPP to increases in atmospheric CO2 was the dominant cause of increases in GPP across the models, but comparison of simulated GPP trends across the 1982–2009 period with that of a global GPP data set indicates that all of the models overestimate the trend in GPP. Disturbance also appears to be an important factor affecting C storage, as models that consider disturbance had lower increases in C storage than models that did not consider disturbance. To improve the modeling of C in the permafrost region, there is the need for the modeling community to standardize structural representation of permafrost and carbon dynamics among models that are used to evaluate the permafrost C feedback and for the modeling and observational communities to jointly develop data sets and methodologies to more effectively benchmark models.

Published
2016

10. Evaluation of air–soil temperature relationships simulated by land surface models during winter across the permafrost region

Author

Wang, Wenli, Rinke, Annette, Moore, John C, Ji, Duoying, Cui, Xuefeng, Peng, Shushi, Lawrence, David M, McGuire, A David, Burke, Eleanor J, Chen, Xiaodong, Decharme, Bertrand, Koven, Charles, MacDougall, Andrew, Saito, Kazuyuki, Zhang, Wenxin, Alkama, Ramdane, Bohn, Theodore J, Ciais, Philippe, Delire, Christine, Gouttevin, Isabelle, Hajima, Tomohiro, Krinner, Gerhard, Lettenmaier, Dennis P, Miller, Paul A, Smith, Benjamin, Sueyoshi, Tetsuo, and Sherstiukov, Artem B

Subjects
Hydrology, Earth Sciences, Oceanography, Physical Geography and Environmental Geoscience, Meteorology & Atmospheric Sciences, Physical geography and environmental geoscience
Abstract

A realistic simulation of snow cover and its thermal properties are important for accurate modelling of permafrost. We analyse simulated relationships between air and near-surface (20 cm) soil temperatures in the Northern Hemisphere permafrost region during winter, with a particular focus on snow insulation effects in nine land surface models, and compare them with observations from 268 Russian stations. There are large cross-model differences in the simulated differences between near-surface soil and air temperatures (ΔT; 3 to 14 °C), in the sensitivity of soil-to-air temperature (0.13 to 0.96 °C °C-1), and in the relationship between ΔT and snow depth. The observed relationship between ΔT and snow depth can be used as a metric to evaluate the effects of each model's representation of snow insulation, hence guide improvements to the model's conceptual structure and process parameterisations. Models with better performance apply multilayer snow schemes and consider complex snow processes. Some models show poor performance in representing snow insulation due to underestimation of snow depth and/or overestimation of snow conductivity. Generally, models identified as most acceptable with respect to snow insulation simulate reasonable areas of near-surface permafrost (13.19 to 15.77 million km2). However, there is not a simple relationship between the sophistication of the snow insulation in the acceptable models and the simulated area of Northern Hemisphere near-surface permafrost, because several other factors, such as soil depth used in the models, the treatment of soil organic matter content, hydrology and vegetation cover, also affect the simulated permafrost distribution.

Published
2016

13. Monotone Complete C*-algebras and Generic Dynamics

Author

Saito, Kazuyuki and Wright, J. D. Maitland

Subjects
Mathematics - Operator Algebras, Mathematics - Dynamical Systems, Mathematics - Functional Analysis, 46L99, 37B99
Abstract

Let S be the Stone space of a complete, non-atomic Boolean algebra. Let G be a countably infinite group of homeomorphisms of S. Let the action of G on S have a free dense orbit. Then we prove that, on a generic subset of S, the orbit equivalence relation coming from this action can also be obtained as an action of the (abelian) Dyadic Group. For the special case where the complete Boolean algebra is the algebra of regular open subsets of the real numbers, this reduces to a theorem of Sullivan-Weiss-Wright. By applying our new dynamical results we improve on an earlier paper by constructing a family of monotone complete C*-algebras, {B(t):t in T} with the following properties. First,T is large; it can be identified with the set of all subsets of the reals. Secondly each B(t) is a small C*-algebra,which is a monotone complete factor of Type III, is hyperfinite and is not a von Neumann algebra.Thirdly, when t is not equal to s, then B(s) is not isomorphic to B(t). In fact, B(s) and B(t) take different values in the classification semi-group for small monotone complete C*-algebras., Comment: 44 pages: To be published in the Proceedings of the London Mathematical Society

Published
2012

14. Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry

Author

Kakkar, Ajay K., Bassand, Jean-Pierre, John Camm, A., Fitzmaurice, David A., Goldhaber, Samuel Z., Goto, Shinya, Haas, Sylvia, Hacke, Werner, Mantovani, Lorenzo G., Misselwitz, Frank, Pieper, Karen S., Turpie, Alexander G.G., van Eickels, Martin, Verheugt, Freek W.A., Kayani, Gloria, Fox, Keith A.A., Gersh, Bernard J., Luciardi, Hector Lucas, Gibbs, Harry, Brodmann, Marianne, Cools, Frank, Barretto, Antonio Carlos Pereira, Connolly, Stuart J., Spyropoulos, Alex, Eikelboom, John, Corbalan, Ramon, Hu, Dayi, Jansky, Petr, Nielsen, Jørn Dalsgaard, Ragy, Hany, Raatikainen, Pekka, Le Heuzey, Jean-Yves, Darius, Harald, Keltai, Matyas, Kakkar, Sanjay, Sawhney, Jitendra Pal Singh, Agnelli, Giancarlo, Ambrosio, Giuseppe, Koretsune, Yukihiro, Sánchez Díaz, Carlos Jerjes, Ten Cate, Hugo, Atar, Dan, Stepinska, Janina, Panchenko, Elizaveta, Lim, Toon Wei, Jacobson, Barry, Oh, Seil, Viñolas, Xavier, Rosenqvist, Marten, Steffel, Jan, Angchaisuksiri, Pantep, Oto, Ali, Parkhomenko, Alex, Al Mahmeed, Wael, Fitzmaurice, David, Hu, D.Y., Chen, K.N., Zhao, Y.S., Zhang, H.Q., Chen, J.Z., Cao, S.P., Wang, D.W., Yang, Y.J., Li, W.H., Yin, Y.H., Tao, G.Z., Yang, P., Chen, Y.M., He, S.H., Wang, Ying, Wang, Yong, Fu, G.S., Li, X., Wu, T.G., Cheng, X.S., Yan, X.W., Zhao, R.P., Chen, M.S., Xiong, L.G., Chen, P., Jiao, Y., Guo, Y., Xue, L., Wang, F.Z., Li, H., Yang, Z.M., Bai, C.L., Chen, J., Chen, J.Y., Chen, X., Feng, S., Fu, Q.H., Gao, X.J., Guo, W.N., He, R.H., He, X.A., Hu, X.S., Huang, X.F., Li, B., Li, J., Li, L., Li, Y.H., Liu, T.T., Liu, W.L., Liu, Y.Y., Lu, Z.C., Luo, X.L., Ma, T.Y., Peng, J.Q., Sheng, X., Shi, X.J., Sun, Y.H., Tian, G., Wang, K., Wang, L., Wu, R.N., Xie, Q., Xu, R.Y., Yang, J.S., Yang, L.L., Yang, Q., Ye, Y., Yu, H.Y., Yu, J.H., Yu, T., Zhai, H., Zhan, Q., Zhang, G.S., Zhang, Q., Zhang, R., Zhang, Y., Zheng, W.Y., Zhou, B., Zhou, Z.H., Zhu, X.Y., Kakkar, S., Sawhney, J.P.S., Jadhav, P., Durgaprasad, R., Ravi Shankar, A.G., Rajput, R.K., Bhargava, K., Sarma, R., Srinivas, A., Roy, D., Nagamalesh, U.M., Chopda, M., Kishore, R., Kulkarni, G., Chandwani, P., Pothiwala, R.A., Padinhare Purayil, M., Shah, S., Chawla, K., Kothiwale, V.A., Raghuraman, B., Vijayaraghavan, G., Vijan, V.M., Bantwal, G., Bisne, V., Khan, A., Gupta, J.B., Kumar, S., Jain, D., Abraham, S., Adak, D., Barai, A., Begum, H., Bhattacharjee, P., Dargude, M., Davies, D., Deshpande, B., Dhakrao, P., Dhyani, V., Duhan, S., Earath, M., Ganatra, A., Giradkar, S., Jain, V., Karthikeyan, R., Kasala, L., Kaur, S., Krishnappa, S., Lawande, A., Lokesh, B., Madarkar, N., Meena, R., More, P., Naik, D., Prashanth, K., Rao, M., Rao, N.M., Sadhu, N., Shah, D., Sharma, M., Shiva, P., Singhal, S., Suresh, S., Vanajakshamma, V., Panse, S.G., Koretsune, Y., Kanamori, S., Yamamoto, K., Kumagai, K., Katsuda, Y., Sadamatsu, K., Toyota, F., Mizuno, Y., Misumi, I., Noguchi, H., Ando, S., Suetsugu, T., Minamoto, M., Oda, Hiroshi, Shiraishi, K., Adachi, S., Chiba, K., Norita, H., Tsuruta, M., Koyanagi, T., Ando, H., Higashi, T., Okada, K., Azakami, S., Komaki, S., Kumeda, K., Murayama, T., Matsumura, J., Oba, Y., Sonoda, R., Goto, K., Minoda, K., Haraguchi, Y., Suefuji, H., Miyagi, H., Kato, H., Nakamura, Tadashi, Nakamura, Tsugihiro, Nandate, H., Zaitsu, R., Fujiura, Yoshihisa, Yoshimura, A., Numata, H., Ogawa, J., Tatematsu, H., Kamogawa, Y., Murakami, K., Wakasa, Y., Yamasawa, M., Maekawa, H., Abe, S., Kihara, H., Tsunoda, S., Saito, Katsumi, Saito, Kazuyuki, Fudo, T., Obunai, K., Tachibana, H., Oba, I., Kuwahata, T., Higa, S., Gushiken, M., Eto, T., Yoshida, H., Ikeda, D., Fujiura, Yoshitake, Ishizawa, M., Nakatsuka, M., Murata, K., Ogurusu, C., Shimoyama, M., Akutsu, M., Takamura, I., Hoshino, F., Yokota, N., Iwao, T., Tsuchida, K., Takeuchi, M., Hatori, Y., Kitami, Y., Nakamura, Yoichi, Oyama, R., Ageta, M., Oda, Hiroyuki, Go, Y., Mishima, K., Unoki, T., Morii, S., Shiga, Yuhei, Sumi, H., Nagatomo, T., Sanno, K., Fujisawa, K., Atsuchi, Y., Nagoshi, T., Seto, T., Tabuchi, T., Kameko, M., Nii, K., Oshiro, K., Takezawa, H., Nagano, S., Miyamoto, N., Iwaki, M., Nakamura, Yuichiro, Fujii, M., Okawa, M., Abe, Masahiko, Abe, Masatake, Abe, Mitsunori, Saito, T., Mito, T., Nagao, K., Minami, J., Mita, T., Sakuma, I., Taguchi, T., Marusaki, S., Doi, H., Tanaka, M., Fujito, T., Matsuta, M., Kusumoto, T., Kakinoki, S., Ashida, K., Yoshizawa, N., Agata, J., Arasaki, O., Manita, M., Ikemura, M., Fukuoka, S., Murakami, H., Matsukawa, S., Hata, Y., Taniguchi, T., Ko, T., Kubo, H., Imamaki, M., Akiyama, M., Inagaki, M., Odakura, H., Ueda, T., Katsube, Y., Nakata, A., Watanabe, H., Techigawara, M., Igarashi, M., Taga, K., Kimura, T., Tomimoto, S., Shibuya, M., Nakano, M., Ito, K., Seo, T., Hiramitsu, S., Hosokawa, H., Hoshiai, M., Hibino, M., Miyagawa, K., Horie, Hajime, Sugishita, N., Shiga, Yukio, Soma, A., Neya, K., Yoshida, Tetsuro, Yoshida, Tomoki, Mizuguchi, M., Ishiguro, M., Minagawa, T., Wada, M., Mukawa, H., Okuda, F., Nagasaka, S., Abe, Y., Adachi, Sen, Adachi, Susumu, Adachi, T., Akahane, K., Amano, T., Aoki, K., Aoyama, T., Arai, H., Arima, S., Arino, T., Asano, H., Asano, T., Azuma, J., Baba, T., Betsuyaku, T., Chibana, H., Date, H., Doiuchi, J., Emura, Y., Endo, M., Fujii, Y., Fujiki, R., Fujisawa, A., Fujisawa, Y., Fukuda, T., Fukui, T., Furukawa, N., Furukawa, T., Furumoto, W., Goto, T., Hamaoka, M., Hanazono, N., Hasegawa, K., Hatsuno, T., Hayashi, Y., Higuchi, K., Hirasawa, K., Hirayama, H., Hirose, M., Hirota, S., Honda, M., Horie, Hideki, Ido, T., Iiji, O., Ikeda, H., Ikeda, K., Ikeoka, K., Imaizumi, M., Inaba, H., Inoue, T., Iseki, F., Ishihara, A., Ishioka, N., Ito, N., Iwase, T., Kakuda, H., Kamata, J., Kanai, H., Kanda, H., Kaneko, M., Kano, H., Kasai, T., Kato, T., Kato, Y., Kawada, Y., Kawai, K., Kawakami, K., Kawakami, S., Kawamoto, T., Kawano, S., Kim, J., Kira, T., Kitazawa, H., Kitazumi, H., Kito, T., Kobayashi, T., Koeda, T., Kojima, J., Komatsu, H., Komatsu, I., Koshibu, Y., Kotani, T., Kozuka, T., Kumai, Y., Kumazaki, T., Maeda, I., Maeda, K., Maruyama, Y., Matsui, S., Matsushita, K., Matsuura, Y., Mineoi, K., Mitsuhashi, H., Miura, N., Miyaguchi, S., Miyajima, S., Miyamoto, H., Miyashita, A., Miyata, S., Mizuguchi, I., Mizuno, A., Mori, T., Moriai, O., Morishita, K., Murai, O., Nagai, Sho, Nagai, Shunichi, Nagata, E., Nagata, H., Nakagomi, A., Nakahara, S., Nakamura, M., Nakamura, R., Nakanishi, N., Nakayama, T., Nakazato, R., Nanke, T., Nariyama, J., Niijima, Y., Niinuma, H., Nishida, Y., Nishihata, Y., Nishino, K., Nishioka, H., Nishizawa, K., Niwa, I., Nomura, K., Nomura, S., Nozoe, M., Ogawa, T., Ohara, N., Okada, M., Okamoto, K., Okita, H., Okuyama, M., Ono, H., Ono, T., Onuki Pearce, Y., Oriso, S., Ota, A., Otaki, E., Saito, Y., Sakai, H., Sakamoto, N., Sakamoto, Y., Samejima, Y., Sasagawa, Y., Sasaguri, H., Sasaki, A., Sasaki, T., Sato, Kazuki, Sato, Kiyoharu, Sawano, M., Seki, S., Sekine, Y., Seta, Y., Sezaki, K., Shibata, N., Shiina, Y., Shimono, H., Shimoyama, Y., Shindo, T., Shinohara, H., Shinohe, R., Shinozuka, T., Shirai, T., Shiraiwa, T., Shozawa, Y., Suga, T., Sugimoto, C., Suzuki, Kazuo, Suzuki, Keita, Suzuki, Shu, Suzuki, Shunji, Suzuki, Susumu, Suzuki, Y., Tada, M., Taguchi, A., Takagi, T., Takagi, Y., Takahashi, K., Takahashi, S., Takai, H., Takanaka, C., Take, S., Takeda, H., Takei, K., Takenaka, K., Tana, T., Tanabe, G., Taya, K., Teragawa, H., Tohyo, S., Toru, S., Tsuchiya, Y., Tsuji, T., Tsuzaki, K., Uchiyama, H., Ueda, O., Ueyama, Y., Wakaki, N., Wakiyama, T., Washizuka, T., Watanabe, M., Yamada, T., Yamagishi, T., Yamaguchi, H., Yamamoto, Kenichi, Yamamoto, Kentaro, Yamamoto, Kunihiko, Yamamoto, T., Yamaura, M., Yamazoe, M., Yasui, K., Yokoyama, Y., Yoshida, K., Lim, T.W., Ching, C.K., Foo, C.G., Chow, J.H., Chen, D.D., Jaufeerally, F.R., Lee, Y.M., Lim, G., Lim, W.T., Thng, S., Yap, S.Y., Yeo, C., Oh, S., Pak, H.N., Kim, J.-B., Kim, J.H., Jang, S.-W., Kim, D.H., Ryu, D.R., Park, S.W., Kim, D.-K., Choi, D.J., Oh, Y.S., Cho, M.-C., Kim, S.-H., Jeon, H.-K., Shin, D.-G., Park, J.S., Park, H.K., Han, S.-J., Sung, J.H., Cho, J.-G., Nam, G.-B., On, Y.K., Lim, H.E., Kwak, J.J., Cha, T.-J., Hong, T.J., Park, S.H., Yoon, J.H., Kim, N.-H., Kim, K.-S., Jung, B.C., Hwang, G.-S., Kim, C.-J., Kim, D.B., Ahn, J.J., An, H.J., Bae, H., Baek, A.L., Chi, W.J., Choi, E.A., Choi, E.H., Choi, H.K., Choi, H.S., Han, S., Heo, E.S., Her, K.O., Hwang, S.W., Jang, E.M., Jang, H.-S., Jang, S., Jeon, H.-G., Jeon, S.R., Jeon, Y.R., Jeong, H.K., Jung, I.-A., Kim, Hyeon Jeong, Kim, Hyun Ju, Kim, Ji Seon, Kim, Jung Sook, Kim, J.A., Kim, K.T., Kim, M.S., Kim, Sang Hee, Kim, Sang Hyun, Kim, Y.-I., Lee, C.S., Lee, E.H., Lee, G.H., Lee, H.Y., Lee, H.-Y., Lee, K.H., Lee, K.R., Lee, M.S., Lee, M.-Y., Lee, R.W., Lee, S.E., Lee, S.H., Lee, S., Lee, W.Y., Noh, I.K., Park, A.R., Park, B.R., Park, H.N., Park, J.H., Park, M., Park, Y., Seo, S.-Y., Shim, J., Sim, J.H., Sohn, Y.M., Son, W.S., Son, Y.S., Song, H.J., Wi, H.K., Woo, J.J., Ye, S., Yim, K.H., Yoo, K.M., Yoon, E.J., Yun, S.Y., Angchaisuksiri, P., Chawanadelert, S., Mongkolwongroj, P., Kanokphatcharakun, K., Cheewatanakornkul, S., Laksomya, T., Pattanaprichakul, S., Chantrarat, T., Rungaramsin, S., Silaruks, S., Wongcharoen, W., Siriwattana, K., Likittanasombat, K., Katekangplu, P., Boonyapisit, W., Cholsaringkarl, D., Chatlaong, B., Chattranukulchai, P., Santanakorn, Y., Hutayanon, P., Khunrong, P., Bunyapipat, T., Jai-Aue, S., Kaewsuwanna, P., Bamungpong, P., Gunaparn, S., Hongsuppinyo, S., Inphontan, R., Khattaroek, R., Khunkong, K., Kitmapawanont, U., Kongsin, C., Naratreekoon, B., Ninwaranon, S., Phangyota, J., Phrommintikul, A., Phunpinyosak, P., Pongmorakot, K., Poomiphol, S., Pornnimitthum, N., Pumprueg, S., Ratchasikaew, S., Sanit, K., Sawanyawisuth, K., Silaruks, B., Sirichai, R., Sriwichian, A., Suebjaksing, W., Sukklad, P., Suttana, T., Tangsirira, A., Thangpet, O., Tiyanon, W., Vorasettakarnkij, Y., Wisaratapong, T., Wongtheptien, W., Wutthimanop, A., Yawila, S., Oto, A., Altun, A., Ozdogru, I., Ozdemir, K., Yilmaz, O., Aydinlar, A., Yilmaz, M.B., Yeter, E., Ongen, Z., Cayli, M., Pekdemir, H., Ozdemir, M., Sucu, M., Sayin, T., Demir, M., Yorgun, H., Ersanli, M., Okuyan, E., Aras, D., Abdelrahman, H., Aktas, O., Alpay, D., Aras, F., Bireciklioglu, M.F., Budeyri, S., Buyukpapuc, M., Caliskan, S., Esen, M., Felekoglu, M.A., Genc, D., Ikitimur, B., Karaayvaz, E.B., Kılıç Karataş, S., Okutucu, S., Ozcelik, E., Quisi, A., Sag, H., Sahiner, L., Sayin, B.Y., Seker, T., Uzun Alkan, D., Yildirim, E., Yildirim, R., Yilmaz, F., Yuksekdag, V., Luciardi, H.L., Vensentini, N., Ingaramo, A.C., Sambadaro, G.A., Fernandez Caputi, V., Berman, S.G., Dragotto, P., Kleiban, A.J., Centurion, N., Giacomi, G., Ahuad Guerrero, R.A., Conde, D., Zapata, G., Di Paola, L.A., Ramos, J.L., Dran, R.D., Egido, J., Fernandez, A.A., Fosco, M.J., Sassone, S., Sinisi, V.A., Cartasegna, L.R., Berli, M.A., Gomez Vilamajo, O.A., Ferroni, F., Alaguibe, E.D., Alvarez D'Amelio, A., Arabetti, C., Arias, L., Belardi, J.A., Bergesio, L., Berli, F., Berli, M., Borchowiec, S., Buzzetti, C., Cabrini, R., Campisi, V., Cappi, A.L., Carrizo, R., Colombo Berra, F., Costabel, J.P., Costamagna, O.J.A., Damonte, A.A., De Urquiza, I.N., Diez, F., Edén, M.F., Fanuele, M., Fernandez Voena, F., Foa Torres, M., Funosas, C., Giacomi, M.P., Gimenez, C.H., Gurfinkel, E.P., de L.M. 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A., Colquhoun, D., Kiat, H., Eccleston, D., French, J., Zimmett, L., Ayres, B., Phan, T., Blombery, P., Crimmins, D., O'Donnell, D., Choi, A., Astridge, P., Arstall, M., Jepson, N., Binnekamp, M., Lee, A., Rogers, J., Starmer, G., Carroll, P., Faunt, J., Aggarwala, A., Barry, L., Batta, C., Beveridge, R., Black, A., Bonner, M., Boys, J., Buckley, E., Campo, M., Carlton, L., Connelly, A., Conway, B., Cresp, D., Dimitri, H., Dixon, S., Dolman, M., Duroux, M., Eskandari, M., Eslick, R., Ferreira-Jardim, A., Fetahovic, T., Fitzpatrick, D., Geraghty, R., Gibbs, J., Grabek, T., Modi, M.H., Hayes, K., Hegde, M.P., Hesketh, L., Hoffmann, B., Jacobson, B., Johnson, K., Juergens, C., Kassam, I., Lawlor, V., Lehman, M., Lehman, S., Leung, D., Mackay, S., MacKenzie, M., McCarthy, C., McIntosh, C., McKeon, L., Morrison, H., Mussap, C., Myers, J.-D., Nagalingam, V., Oldfield, G., O'May, V., Palmer, J., Parsons, L., Patching, K., Patching, T., Paul, V., Plotz, M., Preston, S., Rashad, H., Ratcliffe, M., Raynes, S., Rose, J., Sanders, L., Seremetkoska, M., Setio, H., Shone, S., Shrestha, P., Singh, C., Singleton, C., Stoyanov, N., Sutcliffe, S., Swaraj, K., Tarrant, J., Thompson, S., Tsay, I.M., Vorster, M., Waldman, A., Wallis, L., Wilford, E., Wong, K., Connolly, S.J., Spyropoulos, A., Eikelboom, J., Luton, R., Gupta, M., Pandey, A.S., Cheung, S., Leader, R., Beaudry, P., Ayala-Paredes, F., Berlingieri, J., Heath, J., Poirier, G., Du Preez, M., Nadeau, R., Dresser, G., Dhillon, R., Hruczkowski, T., Schweitzer, B., Coutu, B., Angaran, P., MacDonald, P., Vizel, S., Fikry, S., Parkash, R., Lavoie, A., Cha, J., Ramjattan, B., Bonet, J., Ahmad, K., Aro, L., Aves, T., Beaudry, K., Bergeron, C., Bigcanoe, J., Bignell, N., Breakwell, L., Burke, E., Carroll, L., Clarke, B., Cleveland, T., Daheb, S., Dehghani, P., Denis, I., Djaidani, Z., Dorian, P., Douglass, S., Dunnigan, J., Ewert, A., Farquhar, D., Fearon, A., Ferleyko, L., Fournier, D., Fox, B., Grenier, M.-C., Gulliver, W., Haveman, K., Hines, C., Hines, K., Jackson, A.M., Jean, C., Jethoo, G., Kahlon, R., Kelly, S., Kim, R., Korley, V., Kornder, J., Kwan, L., Largy, J., Lewis, C., Lewis, S., Mangat, I., Moor, R., Navratil, J., Neas, I., Otis, J., Otis, R., Pandey, M., Petrie, F., Pinter, A., Raines, M., Roberts, P., Robinson, M., Sas, G., Schulman, S., Snell, L., Spearson, S., Stevenson, J., Trahey, T., Wong, S., Wright, D., Ragy, H., Abd El-Aziz, A., Abou Seif, S.K., El Din, M.G., El Etriby, S., Elbahry, A., El-Etreby, A., Elkhadem, M., Katta, A., Khairy, T., Mowafy, A., Nawar, M., Ohanissian, A., Reda, A., Reda, M., Salem, H., Sami, N., Samir, S., Setiha, M., Sobhy, M., Soliman, A., Taha, N., Tawfik, M., Zaatout, E., Kettles, D., Bayat, J., Siebert, H., Horak, A., Kelfkens, Y., Garda, R., Pillay, T., Guerra, M., van Zyl, L., Theron, H., Murray, A., Louw, R., Greyling, D., Mntla, P., Ueckermann, V., Loghdey, R., Ismail, S., Ahmed, F., Engelbrecht, J., Ramdass, A., Maharajh, S., Oosthuysen, W., Angel, G., Bester, C., Booysen, M., Boshoff, C., Cannon, C., Cassimjee, S., Chami, C., Conway, G., Davids, A., de Meyer, L., Du Plessis, G., Ellis, T., Henley, L., Karsten, M., Loyd, E., Marks, J., Mavhusa, L., Mostert, M., Page, A., Rikhotso, L., Salie, M., Sasto, J., Shaik, F., Skein, A., Smith, L., Tarr, G., Tau, T., van Zyl, F., Al Mahmeed, W., Yousef, G., Agrawal, A., Nathani, M., Ibrahim, M., Esheiba, E.M., Singh, R., Naguib, A., Abu-Mahfouz, M., Al Omairi, M., Al Naeemi, A., Maruthanayagam, R., Bazargani, N., Wassef, A., Gupta, R., Khan, M., Subbaraman, B., Abdul, A., Al Mulla, A., El Bardisy, S., Haridas, P., Jadhav, S., Magdaluyo, K., Makdad, M., Maqsood, I., Mohamed, R., Sharma, N., Sharma, R., Thanzeel, M., Goldhaber, S.Z., Canosa, R., Rama, P., Blumberg, E., Garcia, J., Mullen, P., Wilson, V., Quick, A., Ferrick, K., Kutayli, W.M., Cox, M., Franco, M., Falkowski, S., Mendelson, R., Williams, M., Miller, S., Beach, S., Alfieri, A., Gutowski, T., Haque, I., Reddy, R., Ahmed, W., Delafontaine, P., Diercks, D., Theodoro, D., Remmel, K., Alberts, M., Ison, R., Noveck, H., Duffy, P., Pitta, S., Nishijima, D., Treasure, C., Asafu-Adjaye, N., Ball, K., Bartlett, M., Bentley, M., Bowers, S., Brown, A., Browne, A., Cameron-Watts, J., Canova, M., Cassidy, D., Cervellione, K., Congal, S., DePauw, J., Dickerson, A., Eley, M., Evans, L., Felpel, S., Ferdinand, K., Fielder, D., Gentry, P., Haideri, A., Hakimi, F., Harbour, T., Hartranft, E., Hawkins, B., Headlee, M., Henson, L., Herrick, C., Hicks, T., Jasinski, S., Jones, A., Jones, L., Jones, P., Karl, S., Keeling, M., Kerr, J., Knowles, P., Langdon, J., Lay, M., Lee, J.A., Lincoln, T., Malone, E., Merliss, A., Merritt, D., Minardo, J., Mooso, B., Orosco, C., Palumbo, V., Parker, M., Parrott, T., Paserchia, S., Pearl, G., Peterson, J., Pickelsimer, N., Purcell, T., Raynor, J., Raziano, S., Richard, C., Richardson, T., Robertson, C., Sage, A., Sanghera, T., Shaw, P., Shoemaker, J., Smith, K., Stephanie, B., Thatcher, A., Theobald, H., Thompson, N., Treasure, L., Tripti, T., Verdi, C., Worthy, V., Sawhney, Jitendra PS., Kothiwale, Veerappa A., Bisne, Vikas, Durgaprasad, Rajashekhar, Jadhav, Praveen, Chopda, Manoj, Vanajakshamma, Velam, Meena, Ramdhan, Vijayaraghavan, Govindan, Chawla, Kamaldeep, and Allu, Jagan

Published
2018
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15. Diurnal glycemic fluctuation is associated with severity of coronary artery disease in prediabetic patients: Possible role of nitrotyrosine and glyceraldehyde-derived advanced glycation end products

Author

Watanabe, Makoto, Kawai, Yasuyuki, Kitayama, Michihiko, Akao, Hironubu, Motoyama, Atsushi, Wakasa, Minoru, Saito, Ryuhei, Aoki, Hirofumi, Fujibayashi, Kousuke, Tsuchiya, Taketsugu, Nakanishi, Hiroaki, Saito, Kazuyuki, Takeuchi, Masayoshi, and Kajinami, Kouji

Published
2017
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24. Development of an Okinawa Panel for biogeographic inference of Okinawans

Author

Nakanishi, Hiroaki, Pereira, Vania, Børsting, Claus, Tvedebrink, Torben, Takada, Aya, Saito, Kazuyuki, Nakanishi, Hiroaki, Pereira, Vania, Børsting, Claus, Tvedebrink, Torben, Takada, Aya, and Saito, Kazuyuki

Abstract

Background The Precision ID Ancestry Panel with 165 SNP markers was unable to differentiate between mainland Japanese and Okinawa Japanese or to distinguish either of them from other East Asian populations. Aim An Okinawa panel was developed with the aim of further separating Okinawa Japanese individuals from mainland Japanese and other Asian groups. Seventy-five SNPs were selected using the most informative markers from the literature. Further, 22 SNPs were selected to separate Okinawa Japanese at minimum SNPs. Subjects and methods Samples were collected from 48 unrelated individuals from mainland Japan and 46 unrelated residents of the Okinawa prefecture. Data were evaluated by STRUCTURE, principal component, and GenoGeographer analyses. Results The 22 SNP set had similar levels of differentiation in STRUCTURE and PCA analyses as the 75 SNP set. GenoGeographer analysis showed that, out of the 46 Okinawa Japanese individuals, the 75 SNP and 22 SNP sets correctly assigned the Okinawan population as the most likely population of origin for 32 and 31 individuals, respectively. Conclusion Neither SNP set could completely differentiate between Okinawa Japanese and other Asian groups, however, these sets should be useful for crime investigation, when the sample, cost and time are limited.

Published
2023

25. Measurement report: Method for evaluating CO2 emissions from a cement plant using atmospheric δ(O2/ N2) and CO2 measurements and its implication for future detection of CO2 capture signals.

Author

Ishidoya, Shigeyuki, Tsuboi, Kazuhiro, Kondo, Hiroaki, Ishijima, Kentaro, Aoki, Nobuyuki, Matsueda, Hidekazu, and Saito, Kazuyuki

Subjects
ATMOSPHERIC carbon dioxide, CARBON sequestration, CEMENT plants, CARBON emissions, MOLE fraction, CEMENT industries, FLUIDIZED-bed combustion, POWER plants
Abstract

Continuous observations of atmospheric δ(O2/N2) and CO 2 amount fractions have been carried out at Ryori (RYO), Japan, since August 2017. In these observations, the O 2 : CO 2 exchange ratio (ER, -Δy(O2)Δy(CO2)-1) has frequently been lower than expected from short-term variations in emissions from terrestrial biospheric activities and combustion of liquid, gas, and solid fuels. This finding suggests a substantial effect of CO 2 emissions from a cement plant located about 6 km northwest of RYO. To evaluate this effect quantitatively, we simulated CO 2 amount fractions in the area around RYO by using a fine-scale atmospheric transport model that incorporated CO 2 fluxes from terrestrial biospheric activities, fossil fuel combustion, and cement production. The simulated CO 2 amount fractions were converted to O 2 amount fractions by using the respective ER values of 1.1, 1.4, and 0 for the terrestrial biospheric activities, fossil fuel combustion, and cement production. Thus obtained O 2 and CO 2 amount fraction changes were used to derive a simulated ER for comparison with the observed ER. To extract the contribution of CO 2 emissions from the cement plant, we used y(CO2∗) as an indicator variable, where y(CO2∗) is a conservative variable for terrestrial biospheric activities and fossil fuel combustion obtained by simultaneous analysis of observed δ(O2/N2) and CO 2 amount fractions and simulated ERs. We confirmed that the observed and simulated ER values and also the y(CO2∗) values and simulated CO 2 amount fractions due only to cement production were generally consistent. These results suggest that combined measurements of δ(O2/N2) and CO 2 amount fractions will be useful for evaluating CO 2 capture from flue gas at carbon capture and storage (CCS) plants, which, similar to a cement plant, change CO 2 amount fractions without changing O 2 values, although CCS plants differ from cement plants in the direction of CO 2 exchange with the atmosphere. [ABSTRACT FROM AUTHOR]

Published
2024
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28. Tumor Budding as a Predictive Marker of Relapse and Survival in Patients With Stage II Colon Cancer

Author

SAITO, KAZUYUKI, OKUYAMA, TAKASHI, MIYAZAKI, SHUNYA, OI, HARUKA, MITSUI, TAKASHI, NORO, TAKUJI, TAKESHITA, EMIKO, ONO, YUKO, URAHASHI, TAIZEN, TAJIMA, HIDEHIRO, and YOSHITOMI, HIDEYUKI

Subjects
Pharmacology, Cancer Research, Colonic Neoplasms, Humans, Neoplasm Recurrence, Local, Prognosis, Biomarkers, Disease-Free Survival, General Biochemistry, Genetics and Molecular Biology, Research Article, Neoplasm Staging, Retrospective Studies
Abstract

Background/Aim: Tumor budding (TB) has recently been recognized worldwide as a prognostic predictor in several solid cancers. The objective of this study was to explore the relationship between TB and clinicopathological characteristics, postoperative relapse, and survival in patients with stage II colon cancer. Patients and Methods: A total of 213 patients with stage II colon cancer were retrospectively enrolled at Saitama Medical Center, Dokkyo Medical University from 2010 to 2016. TB was evaluated in hotspot areas on hematoxylin and eosin-stained slides at the invasive front of the tumor to define a low-grade group (BD1) and a high-grade group (BD2 or BD3). Results: High-grade TB was found in 38.3% of cases, and was associated with pT4, presence of lymphovascular invasion, and tumor relapse (p=0.02, p=0.03, p=0.002, respectively). Patients with high-grade TB showed worse relapse-free survival (RFS) and overall survival (OS) rates than patients with low-grade TB (5-year RFS: High 75.6% vs. Low 92.1%, p=0.001; 5-year OS: High 93.7% vs. Low 93.7%, p=0.001). On multivariate analysis for predictors of RFS and OS, high-grade TB was significant for both RFS and OS (RFS, p=0.003; OS, p=0.005). Patients with high-grade TB experienced lung and liver relapses significantly more frequently than patients with low-grade TB (p=0.03 each). Among patients who received adjuvant chemotherapy (AC), no patients showed lung or liver relapse even in the presence of high-grade TB. Conclusion: TB may offer a useful predictor of relapse in patients with stage II colon cancer after surgery, and AC should be considered for patients with high-grade TB.

Published
2022
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36. Glyceraldehyde-derived advanced glycation end-products are associated with left ventricular ejection fraction and brain natriuretic peptide in patients with diabetic adverse cardiac remodeling

Author

Yasuda, Yuushi, primary, Aoki, Hirofumi, additional, Fujita, Wataru, additional, Fujibayashi, Kousuke, additional, Wakasa, Minoru, additional, Kawai, Yasuyuki, additional, Nakanishi, Hiroaki, additional, Saito, Kazuyuki, additional, Takeuchi, Masayoshi, additional, and Kajinami, Kouji, additional

Published
2022
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38. Spatiotemporal variations of the δ(O2 ∕ N2), CO2 and δ(APO) in the troposphere over the western North Pacific

Author

Ishidoya, Shigeyuki, Tsuboi, Kazuhiro, Niwa, Yosuke, Matsueda, Hidekazu, Murayama, Shohei, Ishijima, Kentaro, and Saito, Kazuyuki

Abstract

We analyzed air samples collected on board a C-130 cargo aircraft over the western North Pacific from May 2012 to March 2020 for atmospheric δ(O2 / N2) and CO2 amount fraction. Observations were corrected for significant artificial fractionation of O2 and N2 caused by thermal diffusion during the air sample collection using the simultaneously measured δ(Ar / N2). The observed seasonal cycles of the δ(O2 / N2) and atmospheric potential oxygen (δ(APO)) varied nearly in opposite phase to the cycle of the CO2 amount fraction at all latitudes and altitudes. Seasonal amplitudes of δ(APO) decreased with latitude from 34 to 25∘ N, as well as with increasing altitude from the surface to 6 km by 50 %–70 %, while those of the CO2 amount fraction decreased by less than 20 %. By comparing the observed values with the simulated δ(APO) and CO2 amount fraction values generated by an atmospheric transport model, we found that the seasonal δ(APO) cycle in the middle troposphere was modified significantly by a combination of the northern and southern hemispheric seasonal cycles due to the interhemispheric mixing of air. The simulated δ(APO) underestimated the observed interannual variation in δ(APO) significantly, probably due to the interannual variation in the annual mean air–sea O2 flux. Interannual variation in δ(APO) driven by the net marine biological activities, obtained by subtracting the assumed solubility-driven component of δ(APO) from the total variation, indicated a clear influence on annual net sea–air marine biological O2 flux during El Niño and net air–sea flux during La Niña. By analyzing the observed secular trends of δ(O2 / N2) and the CO2 amount fraction, global average terrestrial biospheric and oceanic CO2 uptakes for the period 2012–2019 were estimated to be (1.8±0.9) and (2.8±0.6) Pg a−1 (C equivalents), respectively.

Published
2022

44. Additional file 1 of Assessing and projecting surface air temperature conditions required to sustain permafrost in Japan

Author

Yokohata, Tokuta, Iwahana, Go, Saito, Kazuyuki, Ishizaki, Noriko N., Matsushita, Taiga, and Sueyoshi, Tetsuo

Abstract

Additional file 1, Information for locations of permafrost occurrence based on observations (Table S1), distribution of altitude and annual mean surface air temperature in Japan (Figure S1), and time series of annual mean surface air temperatures at the locations where we detected environmentally conditioned permafrost regions (Figure S2).

Published
2022
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48. Near-real-time estimation of fossil fuel CO2 emissions from China based on atmospheric observations on Hateruma and Yonaguni Islands, Japan.

Author

Tohjima, Yasunori, Niwa, Yosuke, Patra, Prabir K., Mukai, Hitoshi, Machida, Toshinobu, Sasakawa, Motoki, Tsuboi, Kazuhiro, Saito, Kazuyuki, and Ito, Akihiko

Subjects
COVID-19 pandemic, ATMOSPHERIC transport, ABATEMENT (Atmospheric chemistry), FOSSIL fuels, COVID-19, ATMOSPHERIC models, ISLANDS
Abstract

We developed a near-real-time estimation method for temporal changes in fossil fuel CO2 (FFCO2) emissions from China for 3 months [January, February, March (JFM)] based on atmospheric CO2 and CH4 observations on Hateruma Island (HAT, 24.06° N, 123.81° E) and Yonaguni Island (YON, 24.47° N, 123.01° E), Japan. These two remote islands are in the downwind region of continental East Asia during winter because of the East Asian monsoon. Previous studies have revealed that monthly averages of synoptic-scale variability ratios of atmospheric CO2 and CH4 (ΔCO2/ΔCH4) observed at HAT and YON in JFM are sensitive to changes in continental emissions. From the analysis based on an atmospheric transport model with all components of CO2 and CH4 fluxes, we found that the ΔCO2/ΔCH4 ratio was linearly related to the FFCO2/CH4 emission ratio in China because calculating the variability ratio canceled out the transport influences. Using the simulated linear relationship, we converted the observed ΔCO2/ΔCH4 ratios into FFCO2/CH4 emission ratios in China. The change rates of the emission ratios for 2020–2022 were calculated relative to those for the preceding 9-year period (2011–2019), during which relatively stable ΔCO2/ΔCH4 ratios were observed. These changes in the emission ratios can be read as FFCO2 emission changes under the assumption of no interannual variations in CH4 emissions and biospheric CO2 fluxes for JFM. The resulting average changes in the FFCO2 emissions in January, February, and March 2020 were 17 ± 8%, − 36 ± 7%, and − 12 ± 8%, respectively, (− 10 ± 9% for JFM overall) relative to 2011–2019. These results were generally consistent with previous estimates. The emission changes for January, February, and March were 18 ± 8%, − 2 ± 10%, and 29 ± 12%, respectively, in 2021 (15 ± 10% for JFM overall) and 20 ± 9%, − 3 ± 10%, and − 10 ± 9%, respectively, in 2022 (2 ± 9% for JFM overall). These results suggest that the FFCO2 emissions from China rebounded to the normal level or set a new high record in early 2021 after a reduction during the COVID-19 lockdown. In addition, the estimated reduction in March 2022 might be attributed to the influence of a new wave of COVID-19 infections in Shanghai. [ABSTRACT FROM AUTHOR]

Published
2023
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50. Spatiotemporal variations of the δ(O2/N2), CO2 and δ(APO) in the troposphere over the Western North Pacific

Author

Ishidoya, Shigeyuki, Tsuboi, Kazuhiro, Niwa, Yosuke, Matsueda, Hidekazu, Murayama, Shohei, Ishijima, Kentaro, and Saito, Kazuyuki

Abstract

We analyzed air samples collected onboard a cargo aircraft C-130 over the western North Pacific from May 2012 to March 2020 for atmospheric δ(O2/N2) and CO2 amount fraction. We corrected for significant artificial fractionation of O2 and N2 caused by thermal diffusion during the air sample collection by using the simultaneously-measured δ(Ar/N2). The observed seasonal cycles of the δ(O2/N2) and atmospheric potential oxygen (δ(APO)) varied nearly in opposite phase to that of the CO2 amount fraction at all latitudes and altitudes. Seasonal amplitudes of δ(APO) decreased with latitude from 34 to 25° N, as well as with increasing altitude from the surface to 6 km by 50–70 %, while those of CO2 amount fraction decreased by less than 20 %. By comparing the observed values with the simulated δ(APO) and CO2 amount fraction values generated by an atmospheric transport model, we found that the seasonal δ(APO) cycle in the middle troposphere was modified significantly by a superposition of the northern and southern hemispheric seasonal cycles due to the inter-hemispheric mixing of air. The simulated δ(APO) underestimated the observed interannual variation in δ(APO) significantly, probably due to the interannual variation in the annual mean air-sea O2 flux. Interannual variation in δ(APO) driven by the net marine biological activities, obtained by subtracting the assumed solubility-driven component of δ(APO) from the total variation, indicated a clear evidence of influence on annual sea-to-air (air-to-sea) marine biological O2 flux during El Niño (La Niña). By analyzing the observed secular trends of δ(O2/N2) and CO2 amount fraction, global average terrestrial biospheric and oceanic CO2 uptakes for the period 2012–2019 were estimated to be (1.8 ± 0.9) and (2.8 ± 0.6) Pg a−1 (C equivalents), respectively.

Published
2021

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