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2. 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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4. Measurement and calculation of neutron leakage spectra from slab samples of beryllium, gallium and tungsten irradiated with 14.8MeV neutrons.

Author

Nie, Y. B., Ruan, X. C., Ren, J., Zhang, S., Han, R., Bao, J., Huang, H. X., Ding, Y. Y., Wu, H. C., Liu, P., and Zhou, Z. Y.

Subjects
*NUCLEAR reactions, *TUNGSTEN, *DATA libraries, *SCINTILLATION counters
Abstract

In order to make benchmark validation of the nuclear data for gallium (Ga), tungsten (W) and beryllium (Be) in existing modern evaluated nuclear data files, neutron leakage spectra in the range from 0.8 to 15MeV from slab samples were measured by time-of-flight technique with a BC501 scintillation detector. The measurements were performed at China Institute of Atomic Energy (CIAE) using a D-T neutron source. The thicknesses of the slabs were 0.5 to 2.5 mean free path for 14.8MeV neutrons, and the measured angles were chosen to be 60° and 120°. The measured spectra were compared with those calculated by the continuous energy Monte-Carlo transport code MCNP, using the data from the CENDL-3.1, ENDF/B-VII.1 and JENDL-4.0 nuclear data files, the comparison between the experimental and calculated results show that: The results from all three libraries significantly underestimate the cross section in energy range of 10-13MeV for Ga; For W, the calculated spectra using data from CENDL-3.1 and JENDL-4.0 libraries show larger discrepancies with the measured ones, especially around 8.5-13.5MeV; and for Be, all the libraries led to underestimation below 3MeV at 120°. [ABSTRACT FROM AUTHOR]

Published
2017
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5. Cloning and Characterization of Disease Resistance Protein RPM1Genes against Powdery Mildew in Wheat Line N9134

Author

Nie, Y. B. and Ji, W. Q.

Abstract

Powdery mildew (Blumeria graminisf. sp. tritici) is one of the most devastating wheat diseases. The wheat line N9134 contains PmAS846that was transferred to N9134 from wild emmer wheat, and is still one of the most effective resistance genes in China. A full-length wheat RPM1gene was obtained by rapid amplification of cDNA ends (RACE) based on the up-regulated probe sequence from differentially expressed transcripts during the N9134 and powdery mildew interaction. The gene was named TaRPM1, and the open reading frame (ORF) is 2721 nucleotides and encodes a polypeptide of 907 amino acids with a predicted isoelectric point of 4.86. Phylogenetic analysis revealed that TaRPM1was highly homologous on both Aegilops tauschiiand Triticum urartuat both the nucleotide and protein level. Using real-time quantitative PCR, the TaRPM1gene expression level in wheat leaves was found to be sharply up-regulated, while the transcript level was lowly induced in the root and stem. Under the powdery mildew treatment, the transcription profile of TaRPM1was very strongly expressed at 48 hour post inoculation (hpi), which increased again to 96 hpi and reaching a high level at 120 hpi. Based on sequence similarities and positions, we inferred that the TaRPM1gene was on wheat chromosome 3D. These results suggested that TaRPM1plays an important role in the mechanism of innate immunity to infection by the powdery mildew pathogen.

Published
2019
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6. Neutron-gamma discrimination with broaden the lower limit of energy threshold using BP neural network.

Author

Zhang SY, Wei Z, Zhang PQ, Zhao Q, Li M, Bai XH, Wu K, Nie YB, Ding YY, Wang JR, Zhang Y, Su XD, and Yao ZE

Abstract

Neutron-gamma discrimination is a tough and significative in experimental neutrons measurements procedure, especially for low-energy neutrons signal discrimination. In this work, based on the Pulse Shape Discrimination (PSD) and Back-Propagation (BP) artificial neural networks, a neutron-gamma discrimination method is developed to broaden the lower limit of energy threshold with the hidden layer of 20 neurons. Compared with neutron-gamma discrimination method based on PSD only, the developed neutron-gamma discrimination method based on the PSD and BP-ANN can discriminate neutron and gamma-ray signals with low energy threshold, which can discriminate signals up to 99.93%. Moreover, this work can reduce the energy threshold from 350 keV to 70 keV, as well as the acquired data utilization increased from 60% to more than 99.9%, which overcome the hardware limitations and distinguish neutron and gamma-ray signals, effectively. The developed neutron-gamma discrimination method and the trained neural network can be directly used to other experimental neutrons measurements., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Published
2024
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7. [The value of platelet count in predicting the efficacy of rituximab treatment in adult patients with chronic primary immune thrombocytopenia].

Author

Wang SX, Nie YB, Ju MK, Sun T, Li HY, Zhang DL, Zhang L, and Yang RC

Subjects
Adolescent, Adult, Aged, Female, Humans, Male, Middle Aged, Platelet Count, Retrospective Studies, Treatment Outcome, Young Adult, Purpura, Thrombocytopenic, Idiopathic drug therapy, Rituximab therapeutic use
Abstract

Objective: To investigate the value of platelet count in predicting the efficacy of rituximab treatment in chronic primary immune thrombocytopenia (ITP). Methods: A retrospective study was conducted in 103 chronic ITP patients hospitalized in our medical center between January 2011 and December 2014. The sensitivity, specificity, positive predictive value (PPV), negative predictive value (NPV) of platelet count in different time points were analyzed for the predictor of treatment response. Optimal cutoff values were established using ROC analysis. Results: A total of 103 patients were included in the study. There were 46 males and 57 females, with a median age of 30 (18-67) years. At day 1, 3 and 7 after the first dose of rituximab, there was no significant difference in platelet counts between the success group (PLT≥50×10(9)/L after treatment) and the failure group (PLT≤50×10(9)/L after treatment) ( P >0.05). At day 14 after rituximab treatment (PTD 14), platelet counts became significantly different in the success and failure groups[41(8-384)×10(9)/L vs 23(0-106)×10(9)/L, P =0.003], and remained different thereafter, with increasing significance in the subsequent follow-ups. Patients were divided further using an optimal cut-off platelet count of 50×10(9)/L on PTD 14, PTD 30, and PTD 60, and PPV and NPV values were calculated for predicting eventual success and failure. Conclusion: Response can be predicted by obtaining platelet counts at 14, 30 and 60 days after rituximab treatment. The study proposed a protocol that guides patient monitoring and management planning.

Published
2018
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8. [Retrospectively analysis of the difference of bleeding frequency and hemophilic arthropathy between hemophilia A and hemophilia B patients].

Author

Wang SX, Guan Y, Nie YB, Li HY, Sun BY, Wang XY, and Yang RC

Subjects
Disease Progression, Hemarthrosis, Hemorrhage, Humans, Phenotype, Retrospective Studies, Hemophilia A, Hemophilia B
Abstract

Objective: To analyze the difference of bleeding frequency, plain radiographic (X-ray) , risk factors in hemophilic arthropathy progression and the Arnold-Hilgartner classification. Methods: A retrospective study was conducted in 211 hemophilia patients hospitalized in our medical center between January 2007 and December 2010, some patients with hemarthrosis were followed up for 5 years. Results: All patients were male, including 150 hemophilia A (HA) and 61 hemophilia B (HB) . The HA patients bled more frequently than HB patients with annualized total bleeding rate 20.5 (0-48) vs 13 (1-40) ; annualized joint bleeding rate 13.5 (0-38) vs 8 (0-33) , especially in moderate hemophilia [26 (1-48) vs 12 (1-36) , P <0.001; 18 (0-36) vs 7.5 (0-26) , P =0.001], but severe hemophilia had no difference in bleeding frequency [33 (1-41) vs 26 (1-40) , P =0.702; 22 (0-36) vs 18 (0-33) , P =0.429]. The condition of the affected joints of 108 HA and 54 HB was evaluated on roentgenography. In HA patients, the Arnold-Hilgartner classification increased with the severity ratings ( r =0.063, P =0.004) . However, similar associations were not found in HB patients ( r =0.045, P =0.082) . Five years later, 36 HA and 19 HB patients received the same joint X-ray, there were no significant differences in joints radiographic progression between the total HA and HB groups ( z =1.941, P =0.052) . However, significant difference between moderate HA and HB was observed ( z =0.076, P =0.002) . Multivariate unconditioned Logistic analysis showed that annualized joint bleeding rate [ P <0.001, OR =1.166 (95% CI 1.097-1.239) ] and articular structural injuries [ P= 0.018, OR =2.842 (95% CI 1.196-6.755) ] were independent risk factors for the joints radiographic progression. Conclusion: The study suggests that there was a difference in bleeding phenotype between HA and HB, especially in moderate hemophilia. HB patients showed mild but progressive development over time, compared with HA patients. Annualized joint bleeding rate and articular structural injuries were independent risk factors for the joints radiographic progression.

Published
2017
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