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1. The Detector System of The Daya Bay Reactor Neutrino Experiment

Author

An, F. P., Bai, J. Z., Balantekin, A. B., Band, H. R., Beavis, D., Beriguete, W., Bishai, M., Blyth, S., Brown, R. L., Butorov, I., Cao, D., Cao, G. F., Cao, J., Carr, R., Cen, W. R., Chan, W. T., Chan, Y. L., Chang, J. F., Chang, L. C., Chang, Y., Chasman, C., Chen, H. Y., Chen, H. S., Chen, M. J., Chen, Q. Y., Chen, S. J., Chen, S. M., Chen, X. C., Chen, X. H., Chen, X. S., Chen, Y. X., Chen, Y., Cheng, J. H., Cheng, J., Cheng, Y. P., Cherwinka, J. J., Chidzik, S., Chow, K., Chu, M. C., Cummings, J. P., de Arcos, J., Deng, Z. Y., Ding, X. F., Ding, Y. Y., Diwan, M. V., Dong, L., Dove, J., Draeger, E., Du, X. F., Dwyer, D. A., Edwards, W. R., Ely, S. R., Fang, S. D., Fu, J. Y., Fu, Z. W., Ge, L. Q., Ghazikhanian, V., Gill, R., Goett, J., Gonchar, M., Gong, G. H., Gong, H., Gornushkin, Y. A., Grassi, M., Greenler, L. S., Gu, W. Q., Guan, M. Y., Guo, R. P., Guo, X. H., Hackenburg, R. W., Hahn, R. L., Han, R., Hans, S., He, M., He, Q., He, W. S., Heeger, K. M., Heng, Y. K., Higuera, A., Hinrichs, P., Ho, T. H., Hoff, M., Hor, Y. K., Hsiung, Y. B., Hu, B. Z., Hu, L. M., Hu, L. J., Hu, T., Hu, W., Huang, E. C., Huang, H. Z., Huang, H. X., Huang, P. W., Huang, X., Huang, X. T., Huber, P., Hussain, G., Isvan, Z., Jaffe, D. E., Jaffke, P., Jen, K. L., Jetter, S., Ji, X. P., Ji, X. L., Jiang, H. J., Jiang, W. Q., Jiao, J. B., Johnson, R. A., Joseph, J., Kang, L., Kettell, S. H., Kohn, S., Kramer, M., Kwan, K. K., Kwok, M. W., Kwok, T., Lai, C. Y., Lai, W. C., Lai, W. H., Langford, T. J., Lau, K., Lebanowski, L., Lee, J., Lee, M. K. P., Lei, R. T., Leitner, R., Leung, J. K. C., Leung, K. Y., Lewis, C. A., Li, B., Li, C., Li, D. J., Li, F., Li, G. S., Li, J., Li, N. Y., Li, Q. J., Li, S. F., Li, S. C., Li, W. D., Li, X. B., Li, X. N., Li, X. Q., Li, Y., Li, Y. F., Li, Z. B., Liang, H., Liang, J., Lin, C. J., Lin, G. L., Lin, P. Y., Lin, S. X., Lin, S. K., Lin, Y. C., Ling, J. J., Link, J. M., Littenberg, L., Littlejohn, B. R., Liu, B. J., Liu, C., Liu, D. W., Liu, H., Liu, J. L., Liu, J. C., Liu, S., Liu, S. S., Liu, X., Liu, Y. B., Lu, C., Lu, H. Q., Lu, J. S., Luk, A., Luk, K. B., Luo, T., Luo, X. L., Ma, L. H., Ma, Q. M., Ma, X. Y., Ma, X. B., Ma, Y. Q., Mayes, B., McDonald, K. T., McFarlane, M. C., McKeown, R. D., Meng, Y., Mitchell, I., Mohapatra, D., Kebwaro, J. Monari, Morgan, J. E., Nakajima, Y., Napolitano, J., Naumov, D., Naumova, E., Newsom, C., Ngai, H. Y., Ngai, W. K., Nie, Y. B., Ning, Z., Ochoa-Ricoux, J. P., Olshevskiy, A., Pagac, A., Pan, H. -R., Patton, S., Pearson, C., Pec, V., Peng, J. C., Piilonen, L. E., Pinsky, L., Pun, C. S. J., Qi, F. Z., Qi, M., Qian, X., Raper, N., Ren, B., Ren, J., Rosero, R., Roskovec, B., Ruan, X. C., Sands III, W. R., Seilhan, B., Shao, B. B., Shih, K., Song, W. Y., Steiner, H., Stoler, P., Stuart, M., Sun, G. X., Sun, J. L., Tagg, N., Tam, Y. H., Tanaka, H. K., Tang, W., Tang, X., Taychenachev, D., Themann, H., Torun, Y., Trentalange, S., Tsai, O., Tsang, K. V., Tsang, R. H. M., Tull, C. E., Tung, Y. C., Viaux, N., Viren, B., Virostek, S., Vorobel, V., Wang, C. H., Wang, L. S., Wang, L. Y., Wang, L. Z., Wang, M., Wang, N. Y., Wang, R. G., Wang, T., Wang, W., Wang, W. W., Wang, X. T., Wang, X., Wang, Y. F., Wang, Z., Wang, Z. M., Webber, D. M., Wei, H. Y., Wei, Y. D., Wen, L. J., Wenman, D. L., Whisnant, K., White, C. G., Whitehead, L., Whitten Jr., C. A., Wilhelmi, J., Wise, T., Wong, H. C., Wong, H. L. H., Wong, J., Wong, S. C. F., Worcester, E., Wu, F. F., Wu, Q., Xia, D. M., Xia, J. K., Xiang, S. T., Xiao, Q., Xing, Z. Z., Xu, G., Xu, J. Y., Xu, J. L., Xu, J., Xu, W., Xu, Y., Xue, T., Yan, J., Yang, C. G., Yang, L., Yang, M. S., Yang, M. T., Ye, M., Yeh, M., Yeh, Y. S., Yip, K., Young, B. L., Yu, G. Y., Yu, Z. Y., Zeng, S., Zhan, L., Zhang, C., Zhang, F. H., Zhang, H. H., Zhang, J. W., Zhang, K., Zhang, Q. X., Zhang, Q. M., Zhang, S. H., Zhang, X. T., Zhang, Y. C., Zhang, Y. H., Zhang, Y. M., Zhang, Y. X., Zhang, Z. J., Zhang, Z. Y., Zhang, Z. P., Zhao, J., Zhao, Q. W., Zhao, Y. F., Zhao, Y. B., Zheng, L., Zhong, W. L., Zhou, L., Zhou, N., Zhou, Z. Y., Zhuang, H. L., Zimmerman, S., and Zou, J. H.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment
Abstract

The Daya Bay experiment was the first to report simultaneous measurements of reactor antineutrinos at multiple baselines leading to the discovery of $\bar{\nu}_e$ oscillations over km-baselines. Subsequent data has provided the world's most precise measurement of $\rm{sin}^22\theta_{13}$ and the effective mass splitting $\Delta m_{ee}^2$. The experiment is located in Daya Bay, China where the cluster of six nuclear reactors is among the world's most prolific sources of electron antineutrinos. Multiple antineutrino detectors are deployed in three underground water pools at different distances from the reactor cores to search for deviations in the antineutrino rate and energy spectrum due to neutrino mixing. Instrumented with photomultiplier tubes (PMTs), the water pools serve as shielding against natural radioactivity from the surrounding rock and provide efficient muon tagging. Arrays of resistive plate chambers over the top of each pool provide additional muon detection. The antineutrino detectors were specifically designed for measurements of the antineutrino flux with minimal systematic uncertainty. Relative detector efficiencies between the near and far detectors are known to better than 0.2%. With the unblinding of the final two detectors' baselines and target masses, a complete description and comparison of the eight antineutrino detectors can now be presented. This paper describes the Daya Bay detector systems, consisting of eight antineutrino detectors in three instrumented water pools in three underground halls, and their operation through the first year of eight detector data-taking., Comment: 52 pages, 51 figures

Published
2015
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2. Observation of electron-antineutrino disappearance at Daya Bay

Author

An, F. P., Bai, J. Z., Balantekin, A. B., Band, H. R., Beavis, D., Beriguete, W., Bishai, M., Blyth, S., Boddy, K., Brown, R. L., Cai, B., Cao, G. F., Cao, J., Carr, R., Chan, W. T., Chang, J. F., Chang, Y., Chasman, C., Chen, H. S., Chen, H. Y., Chen, S. J., Chen, S. M., Chen, X. C., Chen, X. H., Chen, X. S., Chen, Y., Chen, Y. X., Cherwinka, J. J., Chu, M. C., Cummings, J. P., Deng, Z. Y., Ding, Y. Y., Diwan, M. V., Dong, L., Draeger, E., Du, X. F., Dwyer, D. A., Edwards, W. R., Ely, S. R., Fang, S. D., Fu, J. Y., Fu, Z. W., Ge, L. Q., Ghazikhanian, V., Gill, R. L., Goett, J., Gonchar, M., Gong, G. H., Gong, H., Gornushkin, Y. A., Greenler, L. S., Gu, W. Q., Guan, M. Y., Guo, X. H., Hackenburg, R. W., Hahn, R. L., Hans, S., He, M., He, Q., He, W. S., Heeger, K. M., Heng, Y. K., Hinrichs, P., Ho, T. H., Hor, Y. K., Hsiung, Y. B., Hu, B. Z., Hu, T., Huang, H. X., Huang, H. Z., Huang, P. W., Huang, X., Huang, X. T., Huber, P., Isvan, Z., Jaffe, D. E., Jetter, S., Ji, X. L., Ji, X. P., Jiang, H. J., Jiang, W. Q., Jiao, J. B., Johnson, R. A., Kang, L., Kettell, S. H., Kramer, M., Kwan, K. K., Kwok, M. W., Kwok, T., Lai, C. Y., Lai, W. C., Lai, W. H., Lau, K., Lebanowski, L., Lee, J., Lee, M. K. P., Leitner, R., Leung, J. K. C., Leung, K. Y., Lewis, C. A., Li, B., Li, F., Li, G. S., Li, J., Li, Q. J., Li, S. F., Li, W. D., Li, X. B., Li, X. N., Li, X. Q., Li, Y., Li, Z. B., Liang, H., Liang, J., Lin, C. J., Lin, G. L., Lin, S. K., Lin, S. X., Lin, Y. C., Ling, J. J., Link, J. M., Littenberg, L., Littlejohn, B. R., Liu, B. J., Liu, C., Liu, D. W., Liu, H., Liu, J. C., Liu, J. L., Liu, S., Liu, X., Liu, Y. B., Lu, C., Lu, H. Q., Luk, A., Luk, K. B., Luo, T., Luo, X. L., Ma, L. H., Ma, Q. M., Ma, X. B., Ma, X. Y., Ma, Y. Q., Mayes, B., McDonald, K. T., McFarlane, M. C., McKeown, R. D., Meng, Y., Mohapatra, D., Morgan, J. E., Nakajima, Y., Napolitano, J., Naumov, D., Nemchenok, I., Newsom, C., Ngai, H. Y., Ngai, W. K., Nie, Y. B., Ning, Z., Ochoa-Ricoux, J. P., Oh, D., Olshevski, A., Pagac, A., Patton, S., Pearson, C., Pec, V., Peng, J. C., Piilonen, L. E., Pinsky, L., Pun, C. S. J., Qi, F. Z., Qi, M., Qian, X., Raper, N., Rosero, R., Roskovec, B., Ruan, X. C., Seilhan, B., Shao, B. B., Shih, K., Steiner, H., Stoler, P., Sun, G. X., Sun, J. L., Tam, Y. H., Tanaka, H. K., Tang, X., Themann, H., Torun, Y., Trentalange, S., Tsai, O., Tsang, K. V., Tsang, R. H. M., Tull, C., Viren, B., Virostek, S., Vorobel, V., Wang, C. H., Wang, L. S., Wang, L. Y., Wang, L. Z., Wang, M., Wang, N. Y., Wang, R. G., Wang, T., Wang, W., Wang, X., Wang, Y. F., Wang, Z., Wang, Z. M., Webber, D. M., Wei, Y. D., Wen, L. J., Wenman, D. L., Whisnant, K., White, C. G., Whitehead, L., Whitten Jr., C. A., Wilhelmi, J., Wise, T., Wong, H. C., Wong, H. L. H., Wong, J., Worcester, E. T., Wu, F. F., Wu, Q., Xia, D. M., Xiang, S. T., Xiao, Q., Xing, Z. Z., Xu, G., Xu, J., Xu, J. L., Xu, W., Xu, Y., Xue, T., Yang, C. G., Yang, L., Ye, M., Yeh, M., Yeh, Y. S., Yip, K., Young, B. L., Yu, Z. Y., Zhan, L., Zhang, C., Zhang, F. H., Zhang, J. W., Zhang, Q. M., Zhang, K., Zhang, Q. X., Zhang, S. H., Zhang, Y. C., Zhang, Y. H., Zhang, Y. X., Zhang, Z. J., Zhang, Z. P., Zhang, Z. Y., Zhao, J., Zhao, Q. W., Zhao, Y. B., Zheng, L., Zhong, W. L., Zhou, L., Zhou, Z. Y., Zhuang, H. L., and Zou, J. H.

Subjects
High Energy Physics - Experiment
Abstract

The Daya Bay Reactor Neutrino Experiment has measured a non-zero value for the neutrino mixing angle $\theta_{13}$ with a significance of 5.2 standard deviations. Antineutrinos from six 2.9 GW$_{\rm th}$ reactors were detected in six antineutrino detectors deployed in two near (flux-weighted baseline 470 m and 576 m) and one far (1648 m) underground experimental halls. With a 43,000 ton-GW_{\rm th}-day livetime exposure in 55 days, 10416 (80376) electron antineutrino candidates were detected at the far hall (near halls). The ratio of the observed to expected number of antineutrinos at the far hall is $R=0.940\pm 0.011({\rm stat}) \pm 0.004({\rm syst})$. A rate-only analysis finds $\sin^22\theta_{13}=0.092\pm 0.016({\rm stat})\pm0.005({\rm syst})$ in a three-neutrino framework., Comment: 5 figures. Version to appear in Phys. Rev. Lett

Published
2012
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3. A side-by-side comparison of Daya Bay antineutrino detectors

Author

Daya Bay Collaboration, An, F. P., An, Q., Bai, J. Z., Balantekin, A. B., Band, H. R., Beriguete, W., Bishai, M., Blyth, S., Brown, R. L., Cao, G. F., Cao, J., Carr, R., Chang, J. F., Chang, Y., Chasman, C., Chen, H. S., Chen, S. J., Chen, S. M., Chen, X. C., Chen, X. H., Chen, X. S., Chen, Y., Cherwinka, J. J., Chu, M. C., Cummings, J. P., Deng, Z. Y., Ding, Y. Y., Diwan, M. V., Draeger, E., Du, X. F., Dwyer, D., Edwards, W. R., Ely, S. R., Fang, S. D., Fu, J. Y., Fu, Z. W., Ge, L. Q., Gill, R. L., Gonchar, M., Gong, G. H., Gong, H., Gornushkin, Y. A., Greenler, L. S., Gu, W. Q., Guan, M. Y., Guo, X. H., Hackenburg, R. W., Hahn, R. L., Hans, S., Hao, H. F., He, M., He, Q., He, W. S., Heeger, K. M., Heng, Y. K., Hinrichs, P., Ho, T. H., Hor, Y. K., Hsiung, Y. B., Hu, B. Z., Hu, T., Huang, H. X., Huang, H. Z., Huang, P. W., Huang, X., Huang, X. T., Huber, P., Jaffe, D. E., Jetter, S., Ji, X. L., Ji, X. P., Jiang, H. J., Jiang, W. Q., Jiao, J. B., Johnson, R. A., Kang, L., Kettell, S. H., Kramer, M., Kwan, K. K., Kwok, M. W., Kwok, T., Lai, C. Y., Lai, W. C., Lai, W. H., Lau, K., Lebanowski, L., Lee, M. K. P., Leitner, R., Leung, J. K. C., Leung, K. Y., Lewis, C. A., Li, F., Li, G. S., Li, J., Li, Q. J., Li, S. F., Li, W. D., Li, X. B., Li, X. N., Li, X. Q., Li, Y., Li, Z. B., Liang, H., Lin, C. J., Lin, G. L., Lin, S. K., Lin, S. X., Lin, Y. C., Ling, J. J., Link, J. M., Littenberg, L., Littlejohn, B. R., Liu, B. J., Liu, D. W., Liu, J. C., Liu, J. L., Liu, S., Liu, X., Liu, Y. B., Lu, C., Lu, H. Q., Luk, A., Luk, K. B., Luo, X. L., Ma, L. H., Ma, Q. M., Ma, X. Y., Ma, Y. Q., Mayes, B., McDonald, K. T., McFarlane, M. C., McKeown, R. D., Meng, Y., Mohapatra, D., Nakajima, Y., Napolitano, J., Naumov, D., Nemchenok, I., Newsom, C., Ngai, H. Y., Ngai, W. K., Nie, Y. B., Ning, Z., Ochoa-Ricoux, J. P., Olshevski, A., Pagac, A., Patton, S., Pec, V., Peng, J. C., Piilonen, L. E., Pinsky, L., Pun, C. S. J., Qi, F. Z., Qi, M., Qian, X., Rosero, R., Roskovec, B., Ruan, X. C., Seilhan, B., Shao, B. B., Shih, K., Steiner, H., Stoler, P., Sun, G. X., Sun, J. L., Tam, Y. H., Tanaka, H. K., Tang, X., Torun, Y., Trentalange, S., Tsai, O., Tsang, K. V., Tsang, R. H. M., Tull, C., Viren, B., Vorobel, V., Wang, C. H., Wang, L. S., Wang, L. Y., Wang, M., Wang, N. Y., Wang, R. G., Wang, W., Wang, X., Wang, Y. F., Wang, Z., Wang, Z. M., Webber, D. M., Wei, Y. D., Wen, L. J., Wenman, D. L., Whisnant, K., White, C. G., Whitehead, L., Wilhelmi, J., Wise, T., Wong, H. L. H., Wong, J., Wu, F. F., Wu, Q., Xi, J. B., Xia, D. M., Xiao, Q., Xing, Z. Z., Xu, G., Xu, J., Xu, J. L., Xu, Y., Xue, T., Yang, C. G., Yang, L., Ye, M., Yeh, M., Yeh, Y. S., Young, B. L., Yu, Z. Y., Zhan, L., Zhang, C., Zhang, F. H., Zhang, J. W., Zhang, Q. M., Zhang, S. H., Zhang, Y. C., Zhang, Y. H., Zhang, Y. X., Zhang, Z. J., Zhang, Z. P., Zhang, Z. Y., Zhao, H., Zhao, J., Zhao, Q. W., Zhao, Y. B., Zheng, L., Zhong, W. L., Zhou, L., Zhou, Y. Z., Zhou, Z. Y., Zhuang, H. L., and Zou, J. H.

Subjects
Physics - Instrumentation and Detectors, High Energy Physics - Experiment, Nuclear Experiment
Abstract

The Daya Bay Reactor Neutrino Experiment is designed to determine precisely the neutrino mixing angle $\theta_{13}$ with a sensitivity better than 0.01 in the parameter sin$^22\theta_{13}$ at the 90% confidence level. To achieve this goal, the collaboration will build eight functionally identical antineutrino detectors. The first two detectors have been constructed, installed and commissioned in Experimental Hall 1, with steady data-taking beginning September 23, 2011. A comparison of the data collected over the subsequent three months indicates that the detectors are functionally identical, and that detector-related systematic uncertainties exceed requirements., Comment: 24 pages, 36 figures

Published
2012
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9. Comparison of Psychological Symptoms and Serum Levels of Neurotransmitters in Shanghai Adolescents with and without Internet Addiction Disorder: A Case-Control Study

Author

Laks, J, Zhang, H-X, Jiang, W-Q, Lin, Z-G, Du, Y-S, Vance, A, Laks, J, Zhang, H-X, Jiang, W-Q, Lin, Z-G, Du, Y-S, and Vance, A

Abstract

BACKGROUND: Internet addiction disorder (IAD) is now recognized internationally and is known to be linked with academic and social impairment. To date, we know little about its associated main biological factors. This study aimed to collect a carefully defined group of adolescents with IAD and an age- and gender-matched typically developing comparison group. We hypothesized that the young people with IAD would have higher rates of self-reported anxiety and depressive symptoms, have altered levels of peripheral blood dopamine, norepinephrine and serotonin. In addition, we hypothesized the hours spent online are correlated with the severity of depression and anxiety among these young people with IAD. METHODOLOGY/PRINCIPAL FINDING: A cross-sectional study of 20 adolescents who met Beard's criteria for IAD and 15 typically developing adolescents (comparison group) was conducted. All the participants completed the Self Rating Depression Scale (SDS), Self Rating Anxiety Scale (SAS), and the Screen for Child Anxiety Related Emotional Disorders (SCARED). Peripheral blood dopamine, serotonin and norepinephrine were assayed. The mean level of norepinephrine was lower in the IAD group than that in the typically developing participants, while dopamine and serotonin levels did not differ. The SDS, SAS and SCARED symptom scores were increased in the adolescents with IAD. A logistic regression analysis revealed that a higher SAS score and lower level of norepinephrine independently predicted IAD group membership. There was no significant correlation between hours spent online and scores of SAS/SDS in IAD group. CONCLUSIONS/SIGNIFICANCE: Increased self-reported anxiety and lower peripheral blood norepinephrine are independently associated with IAD.

Published
2013

10. Observation of Electron-Antineutrino Disappearance at Daya Bay

Author

Center for Neutrino Physics, Physics, An, F. P., Bai, J. Z., Balantekin, A. B., Band, H. R., Beavis, D., Beriguete, W., Bishai, M., Blyth, S., Boddy, K., Brown, R. L., Cai, B., Cao, G. F., Cao, J., Carr, Rachel E., Chan, W. T., Chang, J. F., Chang, Y., Chasman, C., Chen, H. S., Chen, H. Y., Chen, S. J., Chen, S. M., Chen, X. C., Chen, X. H., Chen, X. S., Chen, Y., Chen, Y. X., Cherwinka, J. J., Chu, M. C., Cummings, J. P., Deng, Z. Y., Ding, Y. Y., Diwan, M. V., Dong, L., Draeger, E., Du, X. F., Dwyer, D. A., Edwards, W. R., Ely, S. R., Fang, S. D., Fu, J. Y., Fu, Z. W., Ge, L. Q., Ghazikhanian, V., Gill, R. L., Goett, J., Gonchar, M., Gong, G. H., Gong, H., Gornushkin, Y. A., Greenler, L. S., Gu, W. Q., Guan, M. Y., Guo, X. H., Hackenburg, R. W., Hahn, R. L., Hans, S., He, M., He, Q., He, W. S., Heeger, K. M., Heng, Y. K., Hinrichs, P., Ho, T. H., Hor, Y. K., Hsiung, Y. B., Hu, B. Z., Hu, T., Huang, H. X., Huang, H. Z., Huang, P. W., Huang, X., Huang, X. T., Huber, Patrick, Isvan, Z., Jaffe, D. E., Jetter, S., Ji, X. L., Ji, X. P., Jiang, H. J., Jiang, W. Q., Jiao, J. B., Johnson, R. A., Kang, L., Kettell, S. H., Kramer, M., Kwan, K. K., Kwok, M. W., Kwok, T., Lai, C. Y., Lai, W. C., Lai, W. H., Lau, K., Lebanowski, L., Lee, J., Lee, M. K. P., Leitner, R., Leung, J. K. C., Leung, K. Y., Lewis, C. A., Li, B., Li, F., Li, G. S., Li, J., Li, Q. J., Li, S. F., Li, W. D., Li, X. B., Li, X. N., Li, X. Q., Li, Y., Li, Z. B., Liang, H., Liang, J., Lin, C. J., Lin, G. L., Lin, S. K., Lin, S. X., Lin, Y. C., Ling, J. J., Link, Jonathan M., Littenberg, L., Littlejohn, B. R., Liu, B. J., Liu, C., Liu, D. W., Liu, H., Liu, J. C., Liu, J. L., Liu, S., Liu, X., Liu, Y. B., Lu, C., Lu, H. Q., Luk, A., Luk, K. B., Luo, T., Luo, X. L., Ma, L. H., Ma, Q. M., Ma, X. B., Ma, X. Y., Ma, Y. Q., Mayes, B., McDonald, K. T., McFarlane, M. C., McKeown, R. D., Meng, Y., Mohapatra, D., Morgan, J. E., Nakajima, Y., Napolitano, J., Naumov, D., Nemchenok, I., Newsom, C., Ngai, H. Y., Ngai, W. K., Nie, Y. B., Ning, Z., Ochoa-Ricoux, J. P., Oh, D., Olshevski, A., Pagac, A., Patton, S., Pearson, C., Pec, V., Peng, J. C., Piilonen, Leo E., Pinsky, L., Pun, C. S. J., Qi, F. Z., Qi, M., Qian, X., Raper, N., Rosero, R., Roskovec, B., Ruan, X. C., Seilhan, B., Shao, B. B., Shih, K., Steiner, H., Stoler, P., Sun, G. X., Sun, J. L., Tam, Y. H., Tanaka, H. K., Tang, X., Themann, H., Torun, Y., Trentalange, S., Tsai, O., Tsang, K. V., Tsang, R. H. M., Tull, C., Viren, B., Virostek, S., Vorobel, V., Wang, C. H., Wang, L. S., Wang, L. Y., Wang, L. Z., Wang, M., Wang, N. Y., Wang, R. G., Wang, T., Wang, W., Wang, X., Wang, Y. F., Wang, Z., Wang, Z. M., Webber, D. M., Wei, Y. D., Wen, L. J., Wenman, D. L., Whisnant, K., White, C. G., Whitehead, L., Whitten, C. A., Wilhelmi, J., Wise, T., Wong, H. C., Wong, H. L. H., Wong, J., Worcester, E., Wu, F. F., Wu, Q., Xia, D. M., Xiang, S. T., Xiao, Q., Xing, Z. Z., Xu, G., Xu, J., Xu, J. L., Xu, W., Xu, Y., Xue, T., Yang, C. G., Yang, L., Ye, M., Yeh, M., Yeh, Y. S., Yip, K., Young, B. L., Yu, Z. Y., Zhan, L., Zhang, C., Zhang, F. H., Zhang, J. W., Zhang, Q. M., Zhang, K., Zhang, Q. X., Zhang, S. H., Zhang, Y. C., Zhang, Y. H. Percival, Zhang, Y. X., Zhang, Z. J., Zhang, Z. P., Zhang, Z. Y., Zhao, J., Zhao, Q. W., Zhao, Y. B., Zheng, L., Zhong, W. L., Zhou, L., Zhou, Z. Y., Zhuang, H. L., Zou, J. H., Center for Neutrino Physics, Physics, An, F. P., Bai, J. Z., Balantekin, A. B., Band, H. R., Beavis, D., Beriguete, W., Bishai, M., Blyth, S., Boddy, K., Brown, R. L., Cai, B., Cao, G. F., Cao, J., Carr, Rachel E., Chan, W. T., Chang, J. F., Chang, Y., Chasman, C., Chen, H. S., Chen, H. Y., Chen, S. J., Chen, S. M., Chen, X. C., Chen, X. H., Chen, X. S., Chen, Y., Chen, Y. X., Cherwinka, J. J., Chu, M. C., Cummings, J. P., Deng, Z. Y., Ding, Y. Y., Diwan, M. V., Dong, L., Draeger, E., Du, X. F., Dwyer, D. A., Edwards, W. 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Abstract

The Daya Bay Reactor Neutrino Experiment has measured a nonzero value for the neutrino mixing angle 0(13) with a significance of 5.2 standard deviations. Antineutrinos from six 2.9 GW(th) reactors were detected in six antineutrino detectors deployed in two near (flux-weighted baseline 470 m and 576 m) and one far (1648 m) underground experimental halls. With a 43 000 ton-GW(th)-day live-time exposure in 55 days, 10 416 (80 376) electron-antineutrino candidates were detected at the far hall (near halls). The ratio of the observed to expected number of antineutrinos at the far hall is R = 0.940 +/- 0.011(stat.) +/- 0.004(syst.). A rate-only analysis finds sin(2)2 theta(13) = 0.092 +/- 0.016(stat.) +/- 0.005(syst.) in a three-neutrino framework.

Published
2012

15. CUDA IMPLEMENTATION IN THE EM SCATTERING OF A THREE-LAYER CANOPY.

Author

Jiang, W.-Q., Zhang, M., Chen, H., and Lu, Y.-G.

Subjects
ALGORITHMS, SCATTERING (Physics), GRAPHICS processing units, ELECTROMAGNETISM, MONTE Carlo method
Abstract

Calculation of the EM scattered fields from a three-layer canopy faces intensive computational burden, when the area becomes large and obviously lames the application of the traditional serial algorithm. With the development of graphics hardware, the Graphics Processing Unit (GPU) can be used to calculate the electromagnetic (EM) scattering problems parallelly. In this paper, the Compute Unified Device Architecture (CUDA) is combined with the four-path method and the reciprocity theorem to improve the speed of calculating the EM scattering properties from a three layer-canopy which are simulated by employing the Monte-Carlo method. We get a highest speedup of 294 times in comparison with the original serial algorithm on a Core (TM) i5 CPU with a GTS460 GPU as a coprocessor. [ABSTRACT FROM AUTHOR]

Published
2011
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17. Nanomagnetic Particles for SQUID-Based Magnetically Labeled Immunoassay.

Author

Horng, H. E., Yang, S. Y., Huang, Y. W., Jiang, W. Q., Hong, C.-Y., and Yang, H. C.

Subjects
MAGNETIC fields, IMMUNOASSAY, IMMUNOGLOBULINS, NANOPARTICLES, VITAMIN B complex, ALBUMINS
Abstract

With the increasing importance of SQUID-based magnetically labeled immunoassay, the study on the synthesis of controllable sizes of magnetic nanoparticles plays a role to promote the accuracy of the immunoassay. In this work, Fe3 O4 nano-particles coated with a suitable bio-probe (biotin) are synthesized through chemical co-precipitation process to probe the bio-target (avidin). Through the synthesis developed here, the particle hydrodynamic diameter can be adjusted from 30 to 90 mu, which provide candidates for probing various bio-targets in the future. The amount of the magnetically labeled avidin is then analyzed via measuring the saturated magnetization or the remanence of the sample by using a scum magnetometer. [ABSTRACT FROM AUTHOR]

Published
2005
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18. Technical note: Identification of suitable normalizers for microRNA expression analysis in milk somatic cells of the yak (Bos grunniens).

Author

Bai, W. L., Yin, R. H., Yang, R. J., Khan, W. A., Ma, Z. J., Zhao, S. J., Jiang, W. Q., Wang, Z. Y., Zhu, Y. B., Luo, G. B., and Zhao, Z. H.

Subjects
*NON-coding RNA, *MICRORNA, *SOMATIC cells, *YAK, *MILK, *LACTATION, *COLOSTRUM
Abstract

MicroRNA are approximately 18- to 22-nucleotide nonprotein coding molecules that play important roles in the regulation of gene expression at the posttranscriptional level. In the present study, we assessed the suitability of 8 noncoding small RNA as normalizers for microRNA (miR) quantitative analysis in milk somatic cells of lactating yaks, including 3 small nuclear RNA (snRNA; RNU1A, RNU5A, and RNU6B), 3 small nucleolar RNA (snoRNA; SNORA73A, Z30, and SNORA74A), 1 rRNA (5S), and 1 transfer RNA (Met-tRNA). The snRNA RNU1A, RNU5A, and SNORA73A were identified as the most stable references in milk somatic cells of lactating yaks. Also, a minimum of 3 reference RNA (RNU1A, RNU5A, and SNORA73A) were required for the normalization of microRNA expression data in milk somatic cells of the lactating yak. We further evaluated the suitability of the combination of RNU1A, RNU5A, and SNORA73A as reference RNA in milk somatic cells of lactating yaks via detecting the relative expression of miR 16b, miR 21-5p, miR 145, and miR 155 as microRNA of putative interest. In comparison to the colostrum period, on the whole, the expressions of the 4 microRNA were found to be upregulated at an early period and, thereafter, a declining pattern was exhibited from early to final periods in all microRNA investigated. Based on the results from this study, we recommend that the combination of RNU1A, RNU5A, and SNORA73A can be used as normalizers for microRNA quantitative analysis in future longitudinal studies on milk somatic cells of lactating yaks in relation to lactation. [ABSTRACT FROM AUTHOR]

Published
2013
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19. Rapid detection of bovine milk in yak milk using a polymerase chain reaction technique.

Author

Bai, W. L., Yin, R. H., Zhao, S. J., Dou, Q. L., Yang, J. C., Jiang, W. Q., Zhao, Z. H., and Luo, G. B.

Subjects
*POLYMERASE chain reaction, *YAK, *MILK, *GENE amplification, *QUALITY control
Abstract

Yak milk contains a greater percentage of protein and has better quality than bovine milk. There has been an increasing focus on yak milk and milk products during the last few years. In the present study, a PCR-based assay was developed for the specific identification of bovine milk in yak milk by designing 3 primers targeting the mitochondrial ND1 gene. The use of 3 primers in a single PCR reaction set yielded 2 amplification fragments of 293 and 190 bp from bovine milk DNA, whereas only 1 amplification fragment of 293 bp was obtained in yak milk DNA. The technique was applied to raw and heat-treated binary mixtures of yak and bovine milks and enabled the specific detection of bovine milk with a detection limit of 0.1%. The assay developed is sensitive, fast, and straightforward, and it might be useful in the quality control of yak milk and milk products. [ABSTRACT FROM AUTHOR]

Published
2009
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20. [A prospective study on the development and application verification of the quantitative evaluation software for three-dimensional morphology of pathological scars based on photo modeling technology].

Author

Jiang WQ, Pan F, Chai M, Wulan LKS, Yu XD, and Guo LL

Subjects
Female, Humans, Male, Asian People, Extremities, Prospective Studies, Adult, Cicatrix, Hypertrophic diagnostic imaging, Keloid diagnostic imaging
Abstract

Objective: To develop a quantitative evaluation software for three-dimensional morphology of pathological scars based on photo modeling technology, and to verify its accuracy and feasibility in clinical application. Methods: The method of prospective observational study was adopted. From April 2019 to January 2022, 59 patients with pathological scars (totally 107 scars) who met the inclusion criteria were admitted to the First Medical Center of Chinese PLA General Hospital, including 27 males and 32 females, aged 33 (26, 44) years. Based on photo modeling technology, a software for measuring three-dimensional morphological parameters of pathological scars was developed with functions of collecting patients' basic information, and scar photography, three-dimensional reconstruction, browsing the models, and generating reports. This software and the clinical routine methods (vernier calipers, color Doppler ultrasonic diagnostic equipment, and elastomeric impression water injection method measurement) were used to measure the longest length, maximum thickness, and volume of scars, respectively. For scars with successful modelling, the number, distribution of scars, number of patients, and the longest length, maximum thickness, and volume of scars measured by both the software and clinical routine methods were collected. For scars with failed modelling, the number, distribution, type of scars, and the number of patients were collected. The correlation and consistency of the software and clinical routine methods in measuring the longest length, maximum thickness, and volume of scars were analyzed by unital linear regression analysis and the Bland-Altman method, respectively, and the intraclass correlation coefficients (ICCs), mean absolute error (MAE), and mean absolute percentage error (MAPE) were calculated. Results: A total of 102 scars from 54 patients were successfully modeled, which located in the chest (43 scars), in the shoulder and back (27 scars), in the limb (12 scars), in the face and neck (9 scars), in the auricle (6 scars), and in the abdomen (5 scars). The longest length, maximum thickness, and volume measured by the software and clinical routine methods were 3.61 (2.13, 5.19) and 3.53 (2.02, 5.11) cm, 0.45 (0.28, 0.70) and 0.43 (0.24, 0.72) cm, 1.17 (0.43, 3.57) and 0.96 (0.36, 3.26) mL. The 5 hypertrophic scars and auricular keloids from 5 patients were unsuccessfully modeled. The longest length, maximum thickness, and volume measured by the software and clinical routine methods showed obvious linear correlation (with r values of 0.985, 0.917, and 0.998, P <0.05). The ICCs of the longest length, maximum thickness, and volume of scars measured by the software and clinical routine methods were 0.993, 0.958, and 0.999 (with 95% confidence intervals of 0.989-0.995, 0.938-0.971, and 0.998-0.999, respectively). The longest length, maximum thickness, and volume of scars measured by the software and clinical routine methods had good consistency. The Bland-Altman method showed that 3.92% (4/102), 7.84% (8/102), and 8.82% (9/102) of the scars with the longest length, maximum thickness, and volume respectively were outside the 95% consistency limit. Within the 95% consistency limit, 2.04% (2/98) scars had the longest length error of more than 0.5 cm, 1.06% (1/94) scars had the maximum thickness error of more than 0.2 cm, and 2.15% (2/93) scars had the volume error of more than 0.5 mL. The MAE and MAPE of the longest length, maximum thickness, and volume of scars measured by the software and clinical routine methods were 0.21 cm, 0.10 cm, 0.24 mL, and 5.75%, 21.21%, 24.80%, respectively. Conclusions: The quantitative evaluation software for three-dimensional morphology of pathological scars based on photo modeling technology can realize the three-dimensional modeling and measurement of morphological parameters of most pathological scars. Its measurement results were in good consistency with those of clinical routine methods, and the errors were acceptable in clinic. This software can be used as an auxiliary method for clinical diagnosis and treatment of pathological scars.

Published
2023
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21. Technical note: Selection of suitable reference genes for studying gene expression in milk somatic cell of yak (Bos grunniens) during the lactation cycle.

Author

Bai WL, Yin RH, Zhao SJ, Jiang WQ, Yin RL, Ma ZJ, Wang ZY, Zhu YB, Luo GB, Yang RJ, and Zhao ZH

Subjects
Animals, Colostrum, Female, Lactation genetics, Milk cytology, Polymerase Chain Reaction, Pregnancy, RNA genetics, Real-Time Polymerase Chain Reaction, Cattle physiology, Gene Expression Regulation physiology, Lactation physiology
Abstract

Quantitative real-time PCR is the most sensitive technique for gene expression analysis. Data normalization is essential to correct for potential errors incurred in all steps from RNA isolation to PCR amplification. The commonly accepted approach for normalization is the use of reference gene. Until now, no suitable reference genes have been available for data normalization of gene expression in milk somatic cells of lactating yaks across lactation. In the present study, we evaluated the transcriptional stability of 10 candidate reference genes in milk somatic cells of lactating yak, including ACTB, B2M, GAPDH, GTP, MRPL39, PPP1R11, RPS9, RPS15, UXT, and RN18S1. Four genes, RPS9, PPP1R11, UXT, and MRPL39, were identified as being the most stable genes in milk somatic cells of lactating yak. Using the combination of RPS9, PPP1R11, UXT, and MRPL39 as reference genes, we further assessed the relative expression of 4 genes of interest in milk somatic cells of yak across lactation, including ELF5, ABCG2, SREBF2, and DGAT1. Compared with expression in colostrum, the overall transcription levels of ELF5, ABCG2, and SREBF2 in milk were found to be significantly upregulated in early, peak, and late lactation, and significantly downregulated thereafter, before the dry period. A similar pattern was observed in the relative expression of DGAT1, but no significant difference was revealed in its expression in milk from late lactation compared with colostrum. Based on these results, we suggest that the geometric mean of RPS9, PPP1R11, UXT, and MRPL39 can be used for normalization of real-time PCR data in milk somatic cells of lactating yak, if similar experiments are performed., (Copyright © 2014 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.)

Published
2014
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22. Intranuclear distribution of Epstein-Barr virus-encoded nuclear antigens EBNA-1, -2, -3 and -5.

Author

Jiang WQ, Wendel-Hansen V, Lundkvist A, Ringertz N, Klein G, and Rosén A

Subjects
Animals, Antibodies, Viral, Cell Line, Transformed, Cell Nucleus immunology, Epstein-Barr Virus Nuclear Antigens, Fluorescent Antibody Technique, Humans, Immunohistochemistry, Antigens, Viral metabolism, Herpesvirus 4, Human immunology
Abstract

Epstein-Barr virus (EBV)-transformed lymphoblastoid cell lines (LCLs) express at least seven virally encoded proteins. Their functional role, and their relationships to each other and to normal nuclear constituents are virtually unknown. As the first step towards a topographical study, the intranuclear distribution of EBV-encoded nuclear antigens EBNA-1, -2, -3 and -5 (abbreviated E1, E2 etc.) was examined in EBV-transformed LCLs by immunofluorescence and digital image analysis of fluorescence patterns. E1-E3 showed a finely granular distribution. The E2 patterns were virtually identical when comparing indirect staining using an E2-specific mouse monoclonal antibody with anticomplement immunofluorescence using a human antibody, rendered monospecific to E2 by absorption. The E1/E2 patterns showed 32% overlap and the E2/E3 10% overlap in the high overlap category (66.7-100%), while the E2/E2 comparison with two reagents showed 61% overlap in this category. This suggests that E2 and E3 largely appear in different nuclear structures, whereas E1 appears to be randomly distributed with regard to E2. The E5 pattern was radically different from that of E1, E2 and E3. The anti-E5 mouse monoclonal antibody detected 4-10 huge, globular, sharply circumscribed dots, located in dispersed chromatin areas, while the distribution of E1, E2 and E3 showed no obvious relationship to chromatin distribution. The methods described here allow a more refined topographical analysis of the EBNA protein family, mostly in relation to each other, in relation to other nuclear proteins, and with respect to specialized functional domains in interphase chromatin.(ABSTRACT TRUNCATED AT 250 WORDS)

Published
1991
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