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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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6. Electrically tunable valley-dependent transport in strained silicene constrictions.

Author

Li, Yuan, Jiang, W. Q., Ding, G. Y., Peng, Y. Z., Wen, Z. C., Wang, G. Q., Bai, R., Qian, Z. H., Xiao, X. B., and Zhou, G. H.

Subjects
*ELECTRIC admittance, *ELECTRIC potential, *POTENTIAL energy, *ELECTRIC fields, *ENERGY bands
Abstract

We adopt the tight-binding model and the mode-matching method to study the electrical modulation of the valley polarization in strained silicene constrictions. The effects of the potential energy, the external electric field, and the strain on the band structures and the transport property are investigated. The conductance shows a clear valley polarization and zero conductance in the absence of the electric field and the strain. It is found that the external electric field can open a clear bandgap and result in an extra zero conductance. However, the strain can not induce a bandgap, which only slightly change the energy band. Correspondingly, the strain has little effect on the system's conductance. The transport behavior of zero conductance is elucidated in terms of band structures of the silicene constriction. Therefore, one can realize an effective modulation of the valley-dependent transport of the silicene constriction by combining the potential energy and the electric field. The electrical modulation of the valley polarization and zero conductance can be exploited for silicene-based valleytronics devices. [ABSTRACT FROM AUTHOR]

Published
2019
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17. 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

18. 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. 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., and Zou, J. H.

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

19. Observation of Electron-Antineutrino Disappearance at Daya Bay

Author

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

Published
2012
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39. 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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42. 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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44. Preparation of ZnS-PSA Nanocomposites by in situSimultaneous Polymerization-precipitation of ZnS Nanoparticles Using γ-Radiation

Author

Cheng, B, Jiang, W Q, Zhu, Y R, and Chen, Z Y

Abstract

Poly(St-co-AA)(PSA)-semiconductor ZnS nanocomposites with well homogeneously dispersed semiconductor nanoparticles in the copolymer matrix were prepared by in situ simultaneous polymerization-precipitation of ZnS nanoparticles using γ-radiation. These synthesized nanocomposites were further characterized by XRD and TEM measurements.

Published
1999
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45. 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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46. 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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47. [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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48. [Relative factors and follow up research of asymptomatic osteonecrosis of the femoral head].

Author

Wang CS, Jiang WQ, Zhang SP, Yang P, and Wang KZ

Subjects
Adult, Aged, Disease Progression, Female, Femur Head Necrosis classification, Femur Head Necrosis etiology, Follow-Up Studies, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Young Adult, Femur Head diagnostic imaging, Femur Head Necrosis diagnostic imaging
Abstract

Objective: To evaluate the progress and influence factors of asymptomatic osteonecrosis of the femoral head(ONFH). Methods: MRI was performed on the contralateral hips of 174 patients with unilateral symptomatic ONFH who admitted at Department of Orthopaedics, the Second Affiliated Hospital of Xi'an Jiaotong University from January 2012 to December 2018. Eighty-three of 174 patients with unilateral ONFH were found suffering from contralateral ONFH(47.7%), of which 77 patients were followed up.There were 28 males and 49 females with age of 48.6 years (range: 21-73 years). The pathogenesis, ARCO classfication, areas and position of osteonecrosis were collected.Independent sample t test, χ(2) test, Fisher exact test, multivariate Logistic regression were used to analyze the potential influence factors. Results: Patients were followed up for 36.7 months. During the following up period, ARCO classification of 28 patients (36.4%) progressed.The progress of asymptomatic ONFH was not related to the gender, age and original ARCO classification, but related to the pathogenesis, position and area of osteonecrosis (all P< 0.05). Conclusion: The progress of asymptomatic osteonecrosis is related to the pathogenesis, position and area of osteonecrosis,but most of asymptomatic ONFH will not progress.

Published
2019
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49. 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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50. To build a prognostic score model containing indispensible tumour markers for metastatic nasopharyngeal carcinoma in an epidemic area.

Author

Jin Y, Cai XY, Cai YC, Cao Y, Xia Q, Tan YT, Jiang WQ, and Shi YX

Subjects
Adolescent, Adult, Age Factors, Aged, Aged, 80 and over, Analysis of Variance, Carcinoma mortality, Child, China epidemiology, Female, Humans, Male, Middle Aged, Nasopharyngeal Neoplasms mortality, Predictive Value of Tests, Prognosis, Retrospective Studies, Sex Factors, Survival Analysis, Young Adult, Biomarkers, Tumor blood, Carcinoma blood, Carcinoma secondary, Nasopharyngeal Neoplasms blood
Abstract

Background and Objective: The survival outcomes of patients with metastatic nasopharyngeal carcinoma (NPC) differ significantly between individuals. The aim of this study is to build a prognostic score model (PSM) incorporating circulating tumour markers for metastatic NPC in an epidemic area., Methods: Seven hundred and ninety-nine patients with disseminated NPC were analysed retrospectively. Univariate and multivariable analyses were conducted using the Cox proportion hazards model. Factors analysed included patients' characteristics (gender, age group, performance status), circulating tumour-marker characteristics (Epstein-Barr virus (EBV) DNA level, EBV VCA-IgA level, lactate dehydrogenase (LDH) level, alkaline phosphatase (ALP) level), basic laboratory characteristics (leucocyte count, haemoglobin level, albumin level), and disease characteristics (presence of metastasis at presentation, disease-free interval, number of metastatic sites, specific metastatic sites). The PSM was built according to numerical score derived from the regression coefficients of each independent prognostic variable. The prognostic score of each patient was calculated by totalling up the scores of each independent variable., Results: Independent prognostic factors included performance status, age, haemoglobin level, LDH level, ALP level and EBV DNA level. Three prognostic groups based on PSM were obtained: low risk (total score=0-4); intermediate risk (5-8); high risk (9-12). Median survivals of the three groups were 25.5, 15.1 and 7 months, respectively, (P<0.001)., Conclusion: Clinical and laboratory characteristics can help guide the prognostication of patients with metastatic NPC in epidemic areas., (Copyright © 2011 Elsevier Ltd. All rights reserved.)

Published
2012
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