Author: "Lu, HJ" - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Lu, HJ"' showing total 610 results

Start Over Author "Lu, HJ"

610 results on '"Lu, HJ"'

1. Behavioral activation for dementia caregivers: scheduling pleasant events and enhancing communications

Author: Au A, Gallagher-Thompson D, Wong MK, Leung J, Chan WC, Chan CC, Lu HJ, Lai MK, and Chan K
Subjects: Behavioral Activation, Telephone-assisted, Caregivers, Paraprofessional, Accessibility, Geriatrics, RC952-954.6
Abstract: Alma Au,1,2 Dolores Gallagher-Thompson,3 Meng-Kong Wong,4 Jess Leung,4 Wai-Chi Chan,5 Chun Chung Chan,6 Hui-Jing Lu,1 Man Kin Lai,1 Kevin Chan11Department of Applied Social Sciences, 2Institute of Active Aging, The Hong Kong Polytechnic University, Hong Kong, People’s Republic of China; 3Department of Psychiatry and Behavioral Sciences and Geriatric Education Centre, School of Medicine, Stanford University, Stanford, CA, USA; 4Department of Psychiatry, United Christian Hospital, 5Department of Psychiatry, University of Hong Kong, 6Department of Medicine and Geriatrics, United Christian Hospital, Hong Kong, People’s Republic of ChinaBackground: Dementia caregiving is often associated with increase in depressive symptoms and strained relationships. This study tested whether telephone-delivered psychoeducation combined with an enhanced behavioral activation (BA) module had a better effect on the well-being of Alzheimer’s caregivers than psychoeducation alone. The focus is on enhancing the competent use of coping skills via BA. The program is delivered by telephone to increase accessibility and sustainability for caregivers. Senior citizens are trained as paraprofessionals to deliver the BA module to increase the potential for sustainability of the program.Methods and subjects: The study compared two telephone interventions using a 4-month longitudinal randomized controlled trial. For the first 4 weeks, all participants received the same psychoeducation program via telephone. Then for the following 4 months, eight biweekly telephone follow-up calls were carried out. For these eight follow-up calls, participants were randomized into either one of the two following groups with different conditions. For the psychoeducation with BA (PsyED-BA) group, participants received eight biweekly sessions of BA practice focused on pleasant event scheduling and improving communications. For the psychoeducation only (PsyED only) group, participants received eight biweekly sessions of general discussion of psychoeducation and related information. A total of 62 family caregivers of persons living with dementia were recruited and 59 (29 in the PsyED-BA group and 30 in the PsyED only group) completed the whole study.Results: As compared to the group with psychoeducation and discussion, the group with enhanced BA had decreased levels of depressive symptoms. The study had a low attrition rate.Conclusion: Results suggested that competence-based training could be effectively administered through the telephone with the help of senior citizens trained and engaged as paraprofessionals. Results contribute to the present literature by offering some framework for developing effective, accessible, sustainable, and less costly interventions.Keywords: telephone-assisted, paraprofessional, accessibility, sustainability
Published: 2015

2. Measurements of the branching fractions of the singly Cabibbo-suppressed decays D-0 -> omega eta, eta(()'())pi(0) and eta(()'())eta

Author: Ablikim, M, Achasov, MN, Ahmed, S, Albayrak, O, Albrecht, M, Ambrose, DJ, Amoroso, A, An, FF, An, Q, Bai, JZ, Bakina, O, Ferroli, RB, Ban, Y, Bennett, DW, Bennett, JV, Berger, N, Bertani, M, Bettoni, D, Bian, JM, Bianchi, F, Boger, E, Boyko, I, Briere, RA, Cai, H, Cai, X, Cakir, O, Calcaterra, A, Cao, GF, Cetin, SA, Chai, J, Chang, JF, Chelkov, G, Chen, G, Chen, HS, Chen, JC, Chen, ML, Chen, PL, Chen, SJ, Chen, XR, Chen, YB, Chu, XK, Cibinetto, G, Dai, HL, Dai, JP, Dbeyssi, A, Dedovich, D, Deng, ZY, Denig, A, Denysenko, I, Destefanis, M, De Mori, F, Ding, Y, Dong, C, Dong, J, Dong, LY, Dong, MY, Dou, ZL, Du, SX, Duan, PF, Fang, J, Fang, SS, Fang, X, Fang, Y, Farinelli, R, Fava, L, Fegan, S, Feldbauer, F, Felici, G, Feng, CQ, Fioravanti, E, Fritsch, M, Fu, CD, Gao, Q, Gao, XL, Gao, Y, Gao, YG, Gao, Z, Garzia, I, Goetzen, K, Gong, L, Gong, WX, Gradl, W, Greco, M, Gu, MH, Gu, S, Gu, YT, Guo, AQ, Guo, LB, Guo, RP, Guo, YP, Haddadi, Z, Hafner, A, Han, S, Hao, XQ, Harris, FA, He, KL, He, XQ, Heinsius, FH, Held, T, Heng, YK, Holtmann, T, Hou, ZL, Hu, C, Hu, HM, Hu, T, Hu, Y, Huang, GS, Huang, JS, Huang, XT, Huang, XZ, Huang, ZL, Hussain, T, Andersson, WI, Ji, Q, Ji, QP, Ji, XB, Ji, XL, Jiang, XS, Jiang, XY, Jiao, JB, Jiao, Z, Jin, DP, Jin, S, Johansson, T, Julin, A, Kalantar-Nayestanaki, N, Kang, XL, Kang, XS, Kavatsyuk, M, Ke, BC, Khan, T, Kiese, P, Kliemt, R, Kloss, B, Kolcu, OB, Kopf, B, Kornicer, M, Kupsc, A, Kuhn, W, Lange, JS, Lara, M, Larin, P, Lavezzi, L, Leithoff, H, Leng, C, Li, C, Li, DM, Li, F, Li, FY, Li, G, Li, HB, Li, HJ, Li, JC, Li, J, Li, K, Li, L, Li, PL, Li, PR, Li, QY, Li, T, Li, WD, Li, WG, Li, XL, Li, XN, Li, XQ, Li, ZB, Liang, H, Liang, YF, Liang, YT, Liao, GR, Lin, DX, Liu, B, Liu, BJ, Liu, CX, Liu, D, Liu, FH, Liu, F, Liu, HB, Liu, HM, Liu, H, Liu, JB, Liu, JP, Liu, JY, Liu, K, Liu, KY, Liu, LD, Liu, PL, Liu, Q, Liu, SB, Liu, X, Liu, YB, Liu, ZA, Liu, Z, Loehner, H, Long, YF, Lou, XC, Lu, HJ, Lu, JG, Lu, Y, Lu, YP, Luo, CL, Luo, MX, Luo, T, Luo, XL, Lyu, XR, Ma, FC, Ma, HL, Ma, LL, Ma, MM, Ma, QM, Ma, T, Ma, XN, Ma, XY, Ma, YM, Maas, FE, Maggiora, M, Malik, QA, Mao, YJ, Mao, ZP, Marcello, S, Messchendorp, JG, Mezzadri, G, Min, J, Min, TJ, Mitchell, RE, Mo, XH, Mo, YJ, Morales, CM, Morello, G, Muchnoi, NY, Muramatsu, H, Musiol, P, Nefedov, Y, Nerling, F, Nikolaev, IB, Ning, Z, Nisar, S, Niu, SL, Niu, XY, Olsen, SL, Ouyang, Q, Pacetti, S, Pan, Y, Papenbrock, M, Patteri, P, Pelizaeus, M, Pellegrino, J, Peng, HP, Peters, K, Pettersson, J, Ping, JL, Ping, RG, Poling, R, Prasad, V, Qi, HR, Qi, M, Qian, S, Qiao, CF, Qin, JJ, Qin, N, Qin, XS, Qin, ZH, Qiu, JF, Rashid, KH, Redmer, CF, Ripka, M, Rong, G, Rosner, C, Sarantsev, A, Savrie, M, Schnier, C, Schoenning, K, Shan, W, Shao, M, Shen, CP, Shen, PX, Shen, XY, Sheng, HY, Song, JJ, Song, WM, Song, XY, Sosio, S, Spataro, S, Sun, GX, Sun, JF, Sun, SS, Sun, XH, Sun, YJ, Sun, YK, Sun, YZ, Sun, ZJ, Sun, ZT, Tang, CJ, Tang, X, Tapan, I, Thorndike, EH, Tiemens, M, Tsednee, B, Uman, I, Varner, GS, Wang, B, Wang, BL, Wang, D, Wang, DY, Wang, K, Wang, LL, Wang, LS, Wang, M, Wang, P, Wang, PL, Wang, WP, Wang, XF, Wang, Y, Wang, YD, Wang, YF, Wang, YQ, Wang, Z, Wang, ZG, Wang, ZH, Wang, ZY, Weber, T, Wei, DH, Weidenkaff, P, Wen, SP, Wiedner, U, Wolke, M, Wu, LH, Wu, LJ, Wu, Z, Xia, L, Xia, Y, Xiao, D, Xiao, H, Xiao, YJ, Xiao, ZJ, Xie, YG, Xie, YH, Xiong, XA, Xiu, QL, Xu, GF, Xu, JJ, Xu, L, Xu, QJ, Xu, QN, Xu, XP, Yan, L, Yan, WB, Yan, WC, Yan, YH, Yang, HJ, Yang, HX, Yang, L, Yang, YH, Yang, YX, Yang, Y, Ye, M, Ye, MH, Yin, JH, You, ZY, Yu, BX, Yu, CX, Yu, JS, Yuan, CZ, Yuan, Y, Yuncu, A, Zafar, AA, Zallo, A, Zeng, Y, Zeng, Z, Zhang, BX, Zhang, BY, Zhang, CC, Zhang, DH, Zhang, HH, Zhang, HY, Zhang, J, Zhang, JL, Zhang, JQ, Zhang, JW, Zhang, JY, Zhang, JZ, Zhang, K, Zhang, L, Zhang, SQ, Zhang, XY, Zhang, YH, Zhang, YT, Zhang, Y, Zhang, ZH, Zhang, ZP, Zhang, ZY, Zhao, G, Zhao, JW, Zhao, JY, Zhao, JZ, Zhao, L, Zhao, MG, Zhao, Q, Zhao, SJ, Zhao, TC, Zhao, YB, Zhao, ZG, Zhemchugov, A, Zheng, B, Zheng, JP, Zheng, WJ, Zheng, YH, Zhong, B, Zhou, L, Zhou, X, Zhou, XK, Zhou, XR, Zhou, XY, Zhou, YX, Zhu, J, Zhu, K, Zhu, KJ, Zhu, S, Zhu, SH, Zhu, XL, Zhu, YC, Zhu, YS, Zhu, ZA, Zhuang, J, Zotti, L, Zou, BS, and Zou, JH
Published: 2018

3. Measurement of the absolute branching fraction of D-vertical bar -> (K)over-bar(0)e(vertical bar) nu(e) via (K)over-bar(0) -> pi(0)pi(0)

Author: Ablikim, M, Achasov, N, Ai, XC, Albayrak, O, Albrecht, M, Ambrose, DJ, Amoroso, A, An, FF, An, Q, Bai, JZ, Ferroli, RB, Ban, Y, Bennett, DW, Bennett, JV, Bertani, M, Bettonin, D, Bian, JM, Bianchi, F, Boger, E, Boyko, I, Briere, RA, Cai, H, Cai, X, Cakir, O, Caleaterra, A, Cao, GF, Cetin, SA, Chang, JF, Chelkov, G, Chen, G, Chen, HS, Chen, HY, Chen, JC, Chen, ML, Chen, S, Chen, SJ, Chen, X, Chen, XR, Chen, YB, Cheng, HP, Chu, XK, Cibinetto, G, Dai, HL, Dai, JP, Dbeyssi, A, Dedovich, D, Deng, ZY, Denig, A, Denysenko, I, Destefanis, M, De Mori, F, Ding, Y, Dong, C, Dong, J, Dong, LY, Dong, MY, Dou, ZL, Du, SX, Duan, PF, Fan, JZ, Fang, J, Fang, SS, Fang, X, Fang, Y, Farinelli, R, Fava, L, Fedorov, O, Feldbauer, F, Felici, G, Feng, CQ, Fioravanti, E, Fritsch, M, Fu, CD, Gao, Q, Gao, XL, Gao, XY, Gao, Y, Gao, Z, Garzia, I, Goetzen, K, Gong, L, Gong, WX, Gradl, W, Greco, M, Gu, MH, Gu, YT, Guan, YH, Guo, AQ, Guo, LB, Guo, RP, Guo, Y, Guo, YP, Haddadi, Z, Hafner, A, Han, S, Hao, XQ, Harris, FA, He, KL, Held, T, Heng, YK, Hou, ZL, Hu, C, Hu, HM, Hu, JF, Hu, T, Hu, Y, Huang, GS, Huang, JS, Huang, XT, Huang, XZ, Huang, Y, Huang, ZL, Hussain, T, Ji, Q, Ji, QP, Ji, XB, Ji, XL, Jiang, LW, Jiang, XS, Jiang, XY, Jiao, JB, Jiao, Z, Jin, DP, Jin, S, Johansson, T, Julin, A, Kalantar-Nayestanaki, N, Kang, XL, Kang, XS, Kavatsyuk, M, Ke, BC, Kiese, P, Kliemt, R, Kloss, B, Kolc, OB, Kopf, B, Kornicer, M, Kupse, A, Kuehn, W, Lange, JS, Lara, M, Larin, P, Leng, C, Li, C, Li, DM, Li, F, Li, FY, Li, G, Li, HB, Li, HJ, Li, JC, Li, J, Li, K, Li, L, Li, PR, Li, QY, Li, T, Li, WD, Li, WG, Li, XL, Li, XN, Li, XQ, Li, YB, Li, ZB, Liang, H, Liang, YF, Liang, YT, Liao, GR, Lin, DX, Liu, B, Liu, BJ, Liu, CX, Liu, D, Liu, FH, Liu, F, Liu, HB, Liu, HH, Liu, HM, Liu, J, Liu, JB, Liu, JP, Liu, JY, Liu, K, Liu, KY, Liu, LD, Liu, PL, Liu, Q, Liu, SB, Liu, X, Liu, YB, Liu, ZA, Liu, Z, Loehner, H, Lou, XC, Lu, HJ, Lu, JG, Lu, Y, Lu, YP, Luo, CL, Luo, MX, Luo, T, Luo, XL, Lu, XR, Ma, FC, Ma, HL, Ma, LL, Ma, MM, Ma, QM, Ma, T, Ma, XN, Ma, XY, Ma, YM, Maas, FE, Maggiora, M, Mao, YJ, Mao, ZP, Marcello, S, Messchendorp, JG, Min, J, Min, TJ, Mitchell, RE, Mo, XH, Mo, YJ, Morales, CM, Muchnoi, NY, Muramatsu, H, Nefedov, Y, Nerling, F, Nikolaev, IB, Ning, Z, Nisar, S, Niu, SL, Niu, XY, Olsen, SL, Ouyang, Q, Pacetti, S, Pan, Y, Patteri, P, Pelizaeus, M, Peng, HP, Peters, K, Pettersson, J, Ping, JL, Ping, RG, Poling, R, Prasad, V, Qi, HR, Qi, M, Qian, S, Qiao, CF, Qin, LQ, Qin, N, Qin, XS, Qin, ZH, Qiu, JF, Rashid, KH, Redmer, CF, Ripka, M, Rong, G, Rosner, C, Ruan, XD, Sarantsev, A, Savrie, M, Schoenning, K, Schumann, S, Shan, W, Shao, M, Shen, CP, Shen, PX, Shen, XY, Sheng, HY, Shi, M, Song, WM, Song, XY, Sosio, S, Spataro, S, Sun, GX, Sun, JF, Sun, SS, Sun, XH, Sun, YJ, Sun, YZ, Sun, ZJ, Sun, ZT, Tang, CJ, Tang, X, Tapan, I, Thorndike, EH, Tiemens, M, Ullrich, M, Uman, I, Varner, GS, Wang, B, Wang, BL, Wang, D, Wang, DY, Wang, K, Wang, LL, Wang, LS, Wang, M, Wang, P, Wang, PL, Wang, W, Wang, WP, Wang, XF, Wang, Y, Wang, YD, Wang, YF, Wang, YQ, Wang, Z, Wang, ZG, Wang, ZH, Wang, ZY, Weber, T, Wei, DH, Weidenkaff, P, Wen, SP, Wiedner, U, Wolke, M, Wu, LH, Wu, LJ, Wu, Z, Xia, L, Xia, Y, Xiao, D, Xiao, H, Xiao, ZJ, Xie, YG, Xiu, QL, Xu, GF, Xu, JJ, Xu, L, Xu, QJ, Xu, QN, Xu, XP, Yan, L, Yan, WB, Yan, WC, Yan, YH, Yang, HJ, Yang, HX, Yang, L, Yang, YX, Ye, M, Ye, MH, Yin, JH, Yu, BX, Yu, CX, Yu, JS, Yuan, CZ, Yuan, WL, Yuan, Y, Yuncu, A, Zafar, AA, Zallo, A, Zeng, Y, Zeng, Z, Zhang, BX, Zhang, BY, Zhang, C, Zhang, CC, Zhang, DH, Zhang, HH, Zhang, HY, Zhang, J, Zhang, JJ, Zhang, JL, Zhang, JQ, Zhang, JW, Zhang, JY, Zhang, JZ, Zhang, K, Zhang, L, Zhang, SQ, Zhang, XY, Zhang, Y, Zhang, YH, Zhang, YN, Zhang, YT, Zhang, ZH, Zhang, ZP, Zhang, ZY, Zhao, G, Zhao, JW, Zhao, JY, Zhao, JZ, Zhao, L, Zhao, MG, Zhao, Q, Zhao, QW, Zhao, SJ, Zhao, TC, Zhao, YB, Zhao, ZG, Zhemchugov, A, Zheng, B, Zheng, JP, Zheng, WJ, Zheng, YH, Zhong, B, Zhou, L, Zhou, X, Zhou, XK, Zhou, XR, Zhou, XY, Zhu, K, Zhu, KJ, Zhu, S, Zhu, SH, Zhu, XL, Zhu, YC, Zhu, YS, Zhu, ZA, Zhuangig, J, Zotti, L, Zou, BS, Zou, JH, Collaboration, BESIII, Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü., Tapan, İlhan, İstanbul Arel Üniversitesi, and [Ablikim, M. -- Ai, X. C. -- An, F. F. -- Bai, J. Z. -- Cai, X. -- Caleaterra, A. -- Cao, G. F. -- Chang, J. F. -- Chen, G. -- Chen, H. S. -- Chen, J. C. -- Chen, M. L. -- Chen, X. -- Chen, Y. B. -- Dai, H. L. -- Deng, Z. Y. -- Dong, J. -- Dong, L. Y. -- Dong, M. Y. -- Duan, P. F. -- Fang, J. -- Fang, S. S. -- Fang, Y. -- Fu, C. D. -- Gao, Q. -- Gong, W. X. -- Gu, M. H. -- Guan, Y. H. -- Guo, A. Q. -- Guo, R. P. -- Guo, Y. -- He, K. L. -- Heng, Y. K. -- Hou, Z. L. -- Hu, H. M. -- Hu, T. -- Hu, Y. -- Ji, Q. -- Ji, X. B. -- Ji, X. L. -- Jiang, X. S. -- Jin, D. P. -- Jin, S. -- Kang, X. L. -- Li, F. -- Li, G. -- Li, H. B. -- Li, H. J. -- Li, J. C. -- Li, W. D. -- Li, W. G. -- Li, X. N. -- Liu, B. J. -- Liu, C. X. -- Liu, Fang -- Liu, H. H. -- Liu, H. M. -- Liu, J. -- Liu, J. Y. -- Liu, P. L. -- Liu, Z. A. -- Lou, X. C. -- Lu, J. G. -- Lu, Y. -- Lu, Y. P. -- Luo, X. L. -- Ma, H. L. -- Ma, M. M. -- Ma, Q. M. -- Ma, T. -- Ma, X. Y. -- Mao, Z. P. -- Min, J. -- Min, T. J. -- Mo, X. H. -- Ning, Z. -- Niu, S. L. -- Niu, X. Y. -- Ouyang, Q. -- Ping, R. G. -- Prasad, V. -- Qian, S. -- Qin, X. S. -- Qin, Z. H. -- Qiu, J. F. -- Rong, G. -- Shen, X. Y. -- Sheng, H. Y. -- Shi, M. -- Song, W. M. -- Song, X. Y. -- Sun, G. X. -- Sun, S. S. -- Sun, X. H. -- Sun, Y. Z. -- Sun, Z. J. -- Tang, X. -- Wang, K. -- Wang, L. L. -- Wang, L. S. -- Wang, P. -- Wang, P. L. -- Wang, W. -- Wang, Y. F. -- Wang, Z. -- Wang, Z. G. -- Wang, Z. Y. -- Wen, S. P. -- Wu, L. H. -- Wu, L. J. -- Wu, Z. -- Xiao, H. -- Xiu, Q. L. -- Xu, G. F. -- Xu, J. J. -- Xu, L. -- Xu, Q. J. -- Yang, H. X. -- Ye, M. -- Yin, J. H. -- Yu, B. X. -- Yuan, C. Z. -- Yuan, Y. -- Zhang, B. X. -- Zhang, B. Y. -- Zhang, C. C. -- Zhang, D. H. -- Zhang, H. Y. -- Zhang, J. -- Zhang, J. J. -- Zhang, J. L. -- Zhang, J. Q. -- Zhang, J. W. -- Zhang, J. Y. -- Zhang, J. Z. -- Zhang, K. -- Zhang, L. -- Zhang, Y. -- Zhang, Y. H. -- Zhao, G. -- Zhao, J. W. -- Zhao, J. Y. -- Zhao, J. Z. -- Zhao, Ling -- Zhao, Q. -- Zhao, Q. W. -- Zhao, T. C. -- Zhao, Y. B. -- Zheng, J. P. -- Zhou, L. -- Zhou, X. Y. -- Zhu, K. -- Zhu, K. J. -- Zhu, S. -- Zhu, Y. S. -- Zhu, Z. A. -- Zhuangig, J. -- Zou, B. S. -- Zou, J. H.] Inst High Energy Phys, Beijing 100049, Peoples R China -- [Chen, H. Y. -- Gao, X. Y. -- Li, Y. B. -- Qi, H. R. -- Shen, C. P.] Beihang Univ, Beijing 100191, Peoples R China -- [Li, Lei] Beijing Inst Petrochem Technol, Beijing 102617, Peoples R China -- [Albrecht, M. -- Chen, S. -- Held, T. -- Huang, G. S. -- Kopf, B. -- Pelizaeus, M. -- Wiedner, U.] Ruhr Univ Bochum, D-44780 Bochum, Germany -- [Albayrak, O. -- Bennett, J. V. -- Briere, R. A. -- Ke, B. C.] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA -- [Liu, Feng -- Mo, Y. J. -- Zhang, Z. H.] Cent China Normal Univ, Wuhan 430079, Peoples R China -- [Ye, M. H.] China Ctr Adv Sci & Technol, Beijing 100190, Peoples R China -- [Nisar, S.] COMSATS Inst Informat Technol, Def Rd,Raiwind Rd, Lahore 54000, Pakistan -- [Achasov, N. -- Muchnoi, N. Yu. -- Nikolaev, I. B.] GI Budker Inst Nucl Phys SB RAS BINP, Novosibirsk 630090, Russia -- [Goetzen, K. -- Peters, K.] GSI Helmholtzctr Heavy Ion Res GmbH, D-64291 Darmstadt, Germany -- [Liao, G. R. -- Wei, D. H. -- Yang, Y. X.] Guangxi Normal Univ, Guilin 541004, Peoples R China -- [Gu, Y. T. -- Liu, H. B. -- Ruan, X. D.] Guangxi Univ, Nanning 530004, Peoples R China -- [Li, K. -- Xu, Q. N.] Hangzhou Normal Univ, Hangzhou 310036, Zhejiang, Peoples R China -- [Dbeyssi, A. -- Fritsch, M. -- Kliemt, R. -- Larin, P. -- Lin, D. X. -- Maas, F. E. -- Morales, C. Morales -- Nerling, F. -- Rosner, Ch. -- Wang, Y. D.] Helmholtz Inst Mainz, Johann Joachim Becher Weg 45, D-55099 Mainz, Germany -- [Hao, X. Q. -- Huang, J. S. -- Sun, J. F.] Henan Normal Univ, Xinxiang 453007, Peoples R China -- [Lara, M. -- Liu, H. H.] Henan Univ Sci & Technol, Luoyang 471003, Peoples R China -- [Cheng, H. P. -- Jiao, Z. -- Lu, H. J.] Huangshan Coll, Huangshan 245000, Peoples R China -- [Xiao, D. -- Zeng, Y.] Hunan Univ, Changsha 410082, Hunan, Peoples R China -- [Bennett, D. W. -- Mitchell, R. E. -- Sun, Z. T.] Indiana Univ, Bloomington, IN 47405 USA -- [Ferroli, R. Baldini -- Bertani, M. -- Caleaterra, A. -- Felici, G. -- Patteri, P. -- Zallo, A.] Ist Nazl Fis Nucl, Lab Nazl Frascati, I-00044 Frascati, Italy -- [Pacetti, S.] Ist Nazl Fis Nucl, I-06100 Perugia, Italy -- [Pacetti, S.] Univ Perugia, I-06100 Perugia, Italy -- [Bettonin, D. -- Cibinetto, G. -- Farinelli, R. -- Fioravanti, E.] Ist Nazl Fis Nucl, Sez Ferrara, I-44122 Ferrara, Italy -- [Farinelli, R. -- Savrie, M.] Univ Ferrara, I-44122 Ferrara, Italy -- [Denig, A. -- Feldbauer, F. -- Fritsch, M. -- Gradl, W. -- Guo, Y. P. -- Hafner, A. -- Kiese, P. -- Kloss, B. -- Liu, Zhiqing -- Redmer, C. F. -- Ripka, M. -- Schumann, S. -- Wang, Y. Q. -- Weber, T. -- Weidenkaff, P.] Johannes Gutenberg Univ Mainz, Johann Joachim Becher Weg 45, D-55099 Mainz, Germany -- [Boger, E. -- Boyko, I. -- Chelkov, G. -- Dedovich, D. -- Denysenko, I. -- Fedorov, O. -- Nefedov, Y. -- Sarantsev, A. -- Zhemchugov, A.] Joint Inst Nucl Res, Dubna 141980, Moscow Region, Russia -- [Kuehn, W. -- Lange, J. S. -- Liang, Y. T. -- Ullrich, M.] Univ Giessen, Inst Phys 2, Heinrich Buff Ring 16, D-35392 Giessen, Germany -- [Haddadi, Z. -- Kalantar-Nayestanaki, N. -- Kavatsyuk, M. -- Loehner, H. -- Messchendorp, J. G. -- Tiemens, M.] Univ Groningen, KVI CART, NL-9747 AA Groningen, Netherlands -- [Chen, X. R. -- Liu, X. -- Yu, J. S.] Lanzhou Univ, Lanzhou 730000, Peoples R China -- [Ding, Y. -- Huang, Z. L. -- Liu, K. Y. -- Ma, F. C.] Liaoning Univ, Shenyang 110036, Peoples R China -- [Guo, L. B. -- Hu, C. -- Luo, C. L. -- Ping, J. L. -- Xie, Y. G. -- Zhong, B.] Nanjing Normal Univ, Nanjing 210023, Jiangsu, Peoples R China -- [Chen, S. J. -- Dou, Z. L. -- Huang, X. Z. -- Huang, Y. -- Qi, M. -- Yuan, W. L. -- Zhang, C.] Nanjing Univ, Nanjing 210093, Jiangsu, Peoples R China -- [Dong, C. -- Gong, L. -- Ji, Q. P. -- Jiang, X. Y. -- Kang, X. S. -- Li, X. Q. -- Liu, Y. B. -- Ma, X. N. -- Shen, P. X. -- Wang, B. -- Yu, C. X. -- Zhang, S. Q. -- Zhao, M. G.] Nankai Univ, Tianjin 300071, Peoples R China -- [Ban, Y. -- Chu, X. K. -- Li, F. Y. -- Liu, L. D. -- Mao, Y. J. -- Shan, W. -- Wang, D. -- Wang, D. Y.] Peking Univ, Beijing 100871, Peoples R China -- [Li, Jin -- Olsen, S. L.] Seoul Natl Univ, Seoul 151747, South Korea -- [Huang, X. T. -- Jiao, J. B. -- Li, K. -- Li, Q. Y. -- Li, T. -- Li, X. L. -- Ma, L. L. -- Ma, Y. M. -- Qin, L. Q. -- Wang, M. -- Zhang, X. Y. -- Zheng, W. J.] Shandong Univ, Jinan 250100, Peoples R China -- [Dai, J. P. -- Liu, B. -- Yang, H. J.] Shanghai Jiao Tong Univ, Shanghai 200240, Peoples R China -- [Liu, F. H.] Shanxi Univ, Taiyuan 030006, Peoples R China -- [Liang, Y. F. -- Tang, C. J.] Sichuan Univ, Chengdu 610064, Peoples R China -- [Wang, Y. -- Yan, L.] Soochow Univ, Suzhou 215006, Peoples R China -- [Li, Z. B. -- Zhang, H. H.] Sun Yat Sen Univ, Guangzhou 510275, Peoples R China -- [Fan, J. Z. -- Gao, Y. -- Liu, K. -- Wang, X. F. -- Xia, Y. -- Zhu, X. L.] Tsinghua Univ, Beijing 100084, Peoples R China -- [Cakir, O.] Ankara Univ, TR-06100 Ankara, Turkey -- [Cetin, S. A. -- Kolc, O. B. -- Yuncu, A.] Istanbul Bilgi Univ, TR-34060 Istanbul, Turkey -- [Tapan, I.] Uludag Univ, TR-16059 Bursa, Turkey -- [Uman, I.] Near East Univ, TR-10 North Cyprus, Mersin, Turkey -- [Chen, S. -- Li, P. R. -- Liu, Q. -- Lu, X. R. -- Qiao, C. F. -- Wang, B. L. -- Xu, X. P. -- Zhang, Y. N. -- Zhang, Yu -- Zheng, Y. H.] Univ Chinese Acad Sci, Beijing 100049, Peoples R China -- [Harris, F. A. -- Kornicer, M. -- Luo, T. -- Varner, G. S.] Univ Hawaii, Honolulu, HI 96822 USA -- [Bian, J. M. -- Julin, A. -- Muramatsu, H. -- Poling, R.] Univ Minnesota, Minneapolis, MN 55455 USA -- [Ambrose, D. J. -- Thorndike, E. H.] Univ Rochester, Rochester, NY 14627 USA -- [Zhu, S. H.] Univ Sci & Technol Liaoning, Anshan 114051, Peoples R China -- [An, Q. -- Fang, X. -- Feng, C. Q. -- Gao, X. L. -- Gao, Z. -- Huang, G. S. -- Li, Cheng -- Liang, H. -- Liu, D. -- Liu, J. B. -- Liu, S. B. -- Pan, Y. -- Peng, H. P. -- Shao, M. -- Sun, Y. J. -- Wang, W. P. -- Wang, Z. H. -- Xia, L. -- Yan, W. C. -- Zeng, Z. -- Zhang, Y. T. -- Zhang, Z. P. -- Zhao, Lei -- Zhao, Z. G. -- Zhou, X. K. -- Zhou, X. R. -- Zhu, Y. C.] Univ Sci & Technol China, Hefei 230026, Peoples R China -- [Xiao, Z. J. -- Zheng, B.] Univ South China, Hengyang 421001, Peoples R China -- [Hussain, T. -- Rashid, K. H. -- Zafar, A. A.] Univ Punjab, Lahore, Pakistan -- [Amoroso, A. -- Bianchi, F. -- Destefanis, M. -- De Mori, F. -- Greco, M. -- Hu, J. F. -- Maggiora, M. -- Marcello, S. -- Sosio, S. -- Spataro, S. -- Yan, W. B. -- Zotti, L.] Univ Turin, I-10125 Turin, Italy -- [Fava, L.] Univ Piemonte Orientale, I-15121 Alessandria, Italy -- [Amoroso, A. -- Bianchi, F. -- Destefanis, M. -- De Mori, F. -- Fava, L. -- Greco, M. -- Hu, J. F. -- Leng, C. -- Maggiora, M. -- Marcello, S. -- Sosio, S. -- Spataro, S. -- Yan, W. B. -- Zotti, L.] Ist Nazl Fis Nucl, I-10125 Turin, Italy -- [Johansson, T. -- Kupse, A. -- Li, C. -- Pettersson, J. -- Schoenning, K. -- Wolke, M.] Uppsala Univ, Box 516, SE-75120 Uppsala, Sweden -- [Cai, H. -- Han, S. -- Jiang, L. W. -- Liu, J. P. -- Qin, N. -- Yang, L. -- Zhang, Z. Y. -- Zhou, X.] Wuhan Univ, Wuhan 430072, Peoples R China -- [Luo, M. X.] Zhejiang Univ, Hangzhou 310027, Zhejiang, Peoples R China -- [Du, S. X. -- Li, D. M. -- Zhao, S. J.] Zhengzhou Univ, Zhengzhou 450001, Peoples R China -- [An, Q. -- Cai, X. -- Chang, J. F. -- Chen, M. L. -- Chen, X. -- Chen, Y. B. -- Dai, H. L. -- Dong, J. -- Dong, M. Y. -- Fang, J. -- Fang, X. -- Gao, Z. -- Garzia, I. -- Gu, M. H. -- Heng, Y. K. -- Hu, T. -- Huang, G. S. -- Ji, X. L. -- Jiang, X. S. -- Jin, D. P. -- Li, F. -- Li, X. N. -- Liang, H. -- Liu, D. -- Liu, J. B. -- Liu, P. L. -- Liu, S. B. -- Liu, Z. A. -- Lou, X. C. -- Lu, J. G. -- Lu, Y. P. -- Luo, X. L. -- Ma, X. Y. -- Min, J. -- Mo, X. H. -- Ning, Z. -- Niu, S. L. -- Ouyang, Q. -- Pan, Y. -- Peng, H. P. -- Qian, S. -- Qin, Z. H. -- Shao, M. -- Sun, Y. J. -- Sun, Z. J. -- Wang, K. -- Wang, W. -- Wang, W. P. -- Wang, Y. F. -- Wang, Z. -- Wang, Z. G. -- Wang, Z. H. -- Wu, Z. -- Xiu, Q. L. -- Xu, G. F. -- Yan, W. C. -- Ye, M. -- Yu, B. X. -- Zeng, Z. -- Zhang, B. Y. -- Zhang, H. Y. -- Zhang, J. W. -- Zhang, Y. H. -- Zhang, Y. T. -- Zhao, J. W. -- Zhao, J. Z. -- Zhao, Lei -- Zhao, Y. B. -- Zhao, Z. G. -- Zheng, J. P. -- Zhou, L. -- Zhou, X. K. -- Zhou, X. R. -- Zhu, K. J. -- Zhu, Y. C. -- Zhuangig, J.] State Key Lab Particle Detect & Elect, Beijing 100049, Peoples R China -- [An, Q. -- Cai, X. -- Chang, J. F. -- Chen, M. L. -- Chen, X. -- Chen, Y. B. -- Dai, H. L. -- Dong, J. -- Dong, M. Y. -- Fang, J. -- Fang, X. -- Gao, Z. -- Garzia, I. -- Gu, M. H. -- Heng, Y. K. -- Hu, T. -- Huang, G. S. -- Ji, X. L. -- Jiang, X. S. -- Jin, D. P. -- Li, F. -- Li, X. N. -- Liang, H. -- Liu, D. -- Liu, J. B. -- Liu, P. L. -- Liu, S. B. -- Liu, Z. A. -- Lou, X. C. -- Lu, J. G. -- Lu, Y. P. -- Luo, X. L. -- Ma, X. Y. -- Min, J. -- Mo, X. H. -- Ning, Z. -- Niu, S. L. -- Ouyang, Q. -- Pan, Y. -- Peng, H. P. -- Qian, S. -- Qin, Z. H. -- Shao, M. -- Sun, Y. J. -- Sun, Z. J. -- Wang, K. -- Wang, W. -- Wang, W. P. -- Wang, Y. F. -- Wang, Z. -- Wang, Z. G. -- Wang, Z. H. -- Wu, Z. -- Xiu, Q. L. -- Xu, G. F. -- Yan, W. C. -- Ye, M. -- Yu, B. X. -- Zeng, Z. -- Zhang, B. Y. -- Zhang, H. Y. -- Zhang, J. W. -- Zhang, Y. H. -- Zhang, Y. T. -- Zhao, J. W. -- Zhao, J. Z. -- Zhao, Lei -- Zhao, Y. B. -- Zhao, Z. G. -- Zheng, J. P. -- Zhou, L. -- Zhou, X. K. -- Zhou, X. R. -- Zhu, K. J. -- Zhu, Y. C. -- Zhuangig, J.] State Key Lab Particle Detect & Elect, Hefei 230026, Peoples R China -- [Yuncu, A.] Bogazici Univ, TR-34342 Istanbul, Turkey -- [Boger, E. -- Tapan, I. -- Zhemchugov, A.] Moscow Inst Phys & Technol, Moscow 141700, Russia -- [Uman, I.] Tomsk State Univ, Funct Elect Lab, Tomsk 634050, Russia -- [Achasov, N. -- Muchnoi, N. Yu. -- Nikolaev, I. B.] Novosibirsk State Univ, Novosibirsk 630090, Russia -- [Sarantsev, A.] PNPI, NRC Kurchatov Inst, Gatchina 188300, Russia -- [Lou, X. C.] Univ Texas Dallas, Richardson, TX 75083 USA -- [Kolc, O. B.] Istanbul Arel Univ, TR-34295 Istanbul, Turkey -- [Peters, K.] Goethe Univ Frankfurt, D-60323 Frankfurt, Germany
Subjects: Semileptonic decays, E(+)E(-), Physics, semileptonic decays, Monte-carlo, Physics, particles & fields, Absolute branching fraction, Physics, nuclear, Charmed mesons, CP Violation, Deterioration, CKM Matrix, charmed mesons, BESIII/BEPCII, absolute branching fraction
Abstract: WOS: 000389144400002, By analyzing 2.93 fb(-1) data collected at the center-of-mass energy root s = 3.773 GeV with the BESIII detector, we measure the absolute branching fraction of the semileptonic decay D+ -> (K) over bar (0)e(+)nu(e) to be B(D (+) -> (K) over bar (0)e(+)nu(e)) = (8.59 +/- 0.14 +/- 0.21)% using (K) over bar (0) -> K-S(0) -> pi(0) pi(0), where the first uncertainty is statistical and the second systematic. Our result is consistent with previous measurements within uncertainties.., National Key Basic Research Program of China [2009CB825204, 2015CB856700]; National Natural Science Foundation of China (NSFC) [10935007, 11125525, 11235011, 11305180, 11322544, 11335008, 11425524, 11475123, 11405046, U1332103]; Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program; CAS Center for Excellence in Particle Physics (CCEPP); Collaborative Innovation Center for Particles and Interactions (CICPI); Joint Large-Scale Scientific Facility Funds of NSFC [11179007, U1232201, U1332201, U1532101]; Joint Large-Scale Scientific Facility Funds of CAS [KJCX2-YW-N29, KJCX2-YW-N45]; 100 Talents Program of CAS; National 1000 Talents Program of China; IN-PAC and Shanghai Key Laboratory for Particle Physics and Cosmology; German Research Foundation DFG (Collaborative Research Center) [CRC-1044]; Istituto Nazionale di Fisica Nucleare, Italy; Koninklijke Nederlandse Akademie van Wetenschappen (KNAW) [530-4CDP03]; Ministry of Development of Turkey [DPT2006K-120470]; National Natural Science Foundation of China (NSFC); Russian Foundation for Basic Research [14-07-91152]; Swedish Resarch Council; U. S. Department of Energy [DE-FG02-04ER41291, DE-FG02-05ER41374, DE-SC0012069, DESC0010118]; U.S. National Science Foundation; University of Groningen (RuG); Helmholtzzentrum fuer Schwerionenforschung GmbH (GSI), Darmstadt; WCU Program of National Research Foundation of Korea [R32-2008-000-10155-0], Supported by National Key Basic Research Program of China (2009CB825204, 2015CB856700), National Natural Science Foundation of China (NSFC) (10935007, 11125525, 11235011, 11305180, 11322544, 11335008, 11425524, 11475123), Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program, CAS Center for Excellence in Particle Physics (CCEPP), Collaborative Innovation Center for Particles and Interactions (CICPI), Joint Large-Scale Scientific Facility Funds of NSFC and CAS (11179007, U1232201, U1332201, U1532101), CAS (KJCX2-YW-N29, KJCX2-YW-N45), 100 Talents Program of CAS, National 1000 Talents Program of China, IN-PAC and Shanghai Key Laboratory for Particle Physics and Cosmology, German Research Foundation DFG (Collaborative Research Center CRC-1044), Istituto Nazionale di Fisica Nucleare, Italy, Koninklijke Nederlandse Akademie van Wetenschappen (KNAW) (530-4CDP03), Ministry of Development of Turkey (DPT2006K-120470), National Natural Science Foundation of China (NSFC) (11405046, U1332103), Russian Foundation for Basic Research (14-07-91152), Swedish Resarch Council, U. S. Department of Energy (DE-FG02-04ER41291, DE-FG02-05ER41374, DE-SC0012069, DESC0010118), U.S. National Science Foundation, University of Groningen (RuG) and Helmholtzzentrum fuer Schwerionenforschung GmbH (GSI), Darmstadt, WCU Program of National Research Foundation of Korea (R32-2008-000-10155-0).
Published: 2016

4. Measurement of the branching fraction for psi(3770) -> gamma chi c0

Author: Ablikim, M, Achasov, MN, Ai, XC, Albayrak, O, Albrecht, M, Ambrose, DJ, Amoroso, A, An, FF, An, Q, Bai, JZ, Ferroli, RB, Ban, Y, Bennett, DW, Bennett, JV, Bertani, M, Bettoni, D, Bian, JM, Bianchi, E, Boger, E, Boyko, I, Briere, RA, Cai, H, Cai, X, Cakir, O, Calcaterra, A, Cao, GF, Cetin, SA, Chang, JF, Chelkov, G, Chen, G, Chen, HS, Chen, HY, Chen, JC, Chen, ML, Chen, SJ, Chen, X, Chen, XR, Chen, YB, Cheng, HP, Chu, XK, Cibinetto, G, Dai, HL, Dai, JP, Dbeyssi, A, Dedovich, D, Deng, ZY, Denig, A, Denysenko, I, Destefanis, M, De Mori, F, Ding, Y, Dong, C, Dong, J, Dong, LY, Dong, MY, Dou, ZL, Du, SX, Duan, PF, Eren, EE, Fan, JZ, Fang, J, Fang, SS, Fang, X, Fang, Y, Farinelli, R, Fava, L, Fedorov, O, Feldbauer, F, Felici, G, Feng, CQ, Fioravanti, E, Fritsch, M, Fu, CD, Gao, Q, Gao, XL, Gao, XY, Gao, Y, Gao, Z, Garzia, I, Goetzen, K, Gong, L, Gong, WX, Gradl, W, Greco, M, Gu, MH, Gu, YT, Guan, YH, Guo, AQ, Guo, LB, Guo, Y, Guo, YP, Haddadi, Z, Hafner, A, Han, S, Hao, XQ, Harris, FA, He, KL, Held, T, Heng, YK, Hou, ZL, Hu, C, Hu, HM, Hu, JF, Hu, T, Hu, Y, Huang, GS, Huang, JS, Huang, XT, Huang, Y, Hussain, T, Ji, Q, Ji, QP, Ji, XB, Ji, XL, Jiang, LW, Jiang, XS, Jiang, XY, Jiao, JB, Jiao, Z, Jin, DP, Jin, S, Johansson, T, Julin, A, Kalantar-Nayestanaki, N, Kang, XL, Kang, XS, Kavatsyuk, M, Ke, BC, Kiese, R, Kliemt, R, Kloss, B, Kolcu, OB, Kopf, B, Kornicer, M, Kuehn, W, Kupsc, A, Lange, JS, Lara, M, Larin, R, Leng, C, Li, C, Li, DM, Li, F, Li, FY, Li, G, Li, HB, Li, JC, Li, J, Li, K, Li, L, Li, PR, Li, QY, Li, T, Li, WD, Li, WG, Li, XL, Li, XM, Li, XN, Li, XQ, Li, ZB, Liang, H, Liang, YF, Liang, YT, Liao, GR, Lin, DX, Liu, BJ, Liu, CX, Liu, D, Liu, FH, Liu, F, Liu, HB, Liu, HH, Liu, HM, Liu, J, Liu, JB, Liu, JP, Liu, JY, Liu, K, Liu, KY, Liu, LD, Liu, PL, Liu, Q, Liu, SB, Liu, X, Liu, YB, Liu, ZA, Liu, Z, Loehner, H, Lou, XC, Lu, HJ, Lu, JG, Lu, Y, Lu, YP, Luo, CL, Luo, MX, Luo, T, Luo, XL, Lyu, XR, Ma, FC, Ma, HL, Ma, LL, Ma, QM, Ma, T, Ma, XN, Ma, XY, Ma, YM, Maas, FE, Maggiura, M, Mao, YJ, Mao, ZP, Marcello, S, Messchendorp, JG, Min, J, Mitchell, RE, Mo, XH, Mo, YJ, Morales, CM, Muchnoi, NY, Muramatsu, H, Nefedov, Y, Nerling, F, Nikolaev, IB, Ning, Z, Nisar, S, Niu, SL, Niu, XY, Olsen, SL, Ouyang, Q, Pacetti, S, Pan, Y, Patteri, R, Pelizaeus, M, Peng, HP, Peters, K, Pettersson, J, Ping, JL, Ping, RG, Poling, R, Prasad, V, Qi, HR, Qi, M, Qian, S, Qiao, CF, Qin, LQ, Qin, N, Qin, XS, Qin, ZH, Qiu, JF, Rashid, KH, Redmer, CF, Ripka, M, Rong, G, Rosner, C, Ruan, XD, Santoro, V, Sarantsev, A, Savrie, M, Schoenning, K, Schumann, S, Shan, W, Shao, M, Shen, CP, Shen, PX, Shen, XY, Sheng, HY, Song, WM, Song, XY, Sosio, S, Spataro, S, Sun, GX, Sun, JF, Sun, SS, Sun, YJ, Sun, YZ, Sun, ZJ, Sun, ZT, Tang, CJ, Tang, X, Tapan, I, Thorndike, EH, Tiemens, M, Ullrich, M, Uman, I, Varner, GS, Wang, B, Wang, BL, Wang, D, Wang, DY, Wang, K, Wang, LL, Wang, LS, Wang, M, Wang, R, Wang, PL, Wang, SG, Wang, W, Wang, WP, Wang, XF, Wang, YD, Wang, YF, Wang, YQ, Wang, Z, Wang, ZG, Wang, ZH, Wang, ZY, Weber, T, Wei, DH, Wei, JB, Weidenkaff, R, Wen, SP, Wiedner, U, Wolke, M, Wu, LH, Wu, Z, Xia, L, Xia, LG, Xia, Y, Xiao, D, Xiao, H, Xiao, ZJ, Xie, YG, Xiu, QL, Xu, GF, Xu, L, Xu, QJ, Xu, QN, Xu, XP, Yan, L, Yan, WB, Yan, WC, Yan, YH, Yang, HJ, Yang, HX, Yang, L, Yang, YX, Ye, M, Ye, MH, Yin, JH, Yu, BX, Yu, CX, Yu, JS, Yuan, CZ, Yuan, WL, Yuan, Y, Yuncu, A, Zafar, AA, Zallo, A, Zeng, Y, Zeng, Z, Zhang, BX, Zhang, BY, Zhang, C, Zhang, CC, Zhang, DH, Zhang, HH, Zhang, HY, Zhang, JJ, Zhang, JL, Zhang, JQ, Zhang, JW, Zhang, JY, Zhang, JZ, Zhang, K, Zhang, L, Zhang, XY, Zhang, Y, Zhang, YH, Zhang, YN, Zhang, YT, Zhang, ZH, Zhang, ZP, Zhang, ZY, Zhao, G, Zhao, JW, Zhao, JY, Zhao, JZ, Zhao, L, Zhao, MG, Zhao, Q, Zhao, QW, Zhao, SJ, Zhao, TC, Zhao, YB, Zhao, ZG, Zhemchugov, A, Zheng, B, Zheng, JP, Zheng, WJ, Zheng, YH, Zhong, B, Zhou, L, Zhou, X, Zhou, XK, Zhou, XR, Zhou, XY, Zhu, K, Zhu, KJ, Zhu, S, Zhu, SH, Zhu, XL, Zhu, YC, Zhu, YS, Zhu, ZA, Zhuang, J, Zotti, L, Zou, BS, Zou, JH, Collaboration, BESIII, İstanbul Arel Üniversitesi, and [Ablikim, M. -- Ai, X. C. -- An, F. F. -- Bai, J. Z. -- Cai, X. -- Cao, G. F. -- Chang, J. F. -- Chen, G. -- Chen, H. S. -- Chen, J. C. -- Chen, M. L. -- Chen, X. -- Chen, Y. B. -- Dai, H. L. -- Deng, Z. Y. -- Dong, J. -- Dong, L. Y. -- Dong, M. Y. -- Duan, P. F. -- Fang, J. -- Fang, S. S. -- Fang, Y. -- Fu, C. D. -- Gao, Q. -- Gong, W. X. -- Gu, M. H. -- Guan, Y. H. -- Guo, A. Q. -- Guo, Y. -- He, K. L. -- Heng, Y. K. -- Hou, Z. L. -- Hu, H. M. -- Hu, T. -- Hu, Y. -- Ji, Q. -- Ji, X. B. -- Ji, X. L. -- Jiang, X. S. -- Jin, D. P. -- Jin, S. -- Kang, X. L. -- Li, F. -- Li, G. -- Li, H. B. -- Li, J. C. -- Li, W. D. -- Li, W. G. -- Li, X. N. -- Liu, B. J. -- Liu, C. X. -- Liu, Fang -- Liu, H. H. -- Liu, H. M. -- Liu, J. -- Liu, J. Y. -- Liu, P. L. -- Liu, Z. A. -- Lou, X. C. -- Lu, J. G. -- Lu, Y. -- Lu, Y. P. -- Luo, X. L. -- Ma, H. L. -- Ma, Q. M. -- Ma, T. -- Ma, X. Y. -- Mao, Z. P. -- Min, J. -- Mo, X. H. -- Ning, Z. -- Niu, S. L. -- Niu, X. Y. -- Ouyang, Q. -- Ping, R. G. -- Prasad, V. -- Qian, S. -- Qin, X. S. -- Qin, Z. H. -- Qiu, J. F. -- Rong, G. -- Shen, X. Y. -- Sheng, H. Y. -- Song, W. M. -- Song, X. Y. -- Sun, G. X. -- Sun, S. S. -- Sun, Y. Z. -- Sun, Z. J. -- Tang, X. -- Wang, K. -- Wang, L. L. -- Wang, L. S. -- Wang, R. -- Wang, P. L. -- Wang, W. -- Wang, Y. F. -- Wang, Z. -- Wang, Z. G. -- Wang, Z. Y. -- Wen, S. P. -- Wu, L. H. -- Wu, Z. -- Xiao, D. -- Xie, Y. G. -- Xiu, Q. L. -- Xu, G. F. -- Xu, L. -- Yang, H. X. -- Ye, M. -- Yin, J. H. -- Yu, B. X. -- Yuan, C. Z. -- Yuan, Y. -- Zhang, B. X. -- Zhang, B. Y. -- Zhang, C. C. -- Zhang, D. H. -- Zhang, H. Y. -- Zhang, J. J. -- Zhang, J. L. -- Zhang, J. Q. -- Zhang, J. W. -- Zhang, J. Y. -- Zhang, J. Z. -- Zhang, K. -- Zhang, L. -- Zhang, Y. -- Zhang, Y. H. -- Zhao, G. -- Zhao, J. W. -- Zhao, J. Y. -- Zhao, J. Z. -- Zhao, Ling -- Zhao, Q. -- Zhao, Q. W. -- Zhao, T. C. -- Zhao, Y. B. -- Zheng, J. P. -- Zhou, L. -- Zhou, X. Y. -- Zhu, K. -- Zhu, K. J. -- Zhu, S. -- Zhu, Y. S. -- Zhu, Z. A. -- Zhuang, J. -- Zou, B. S. -- Zou, J. H.] Inst High Energy Phys, Beijing 100049, Peoples R China -- [Chen, H. Y. -- Gao, X. Y. -- Qi, H. R. -- Shen, C. P.] Beihang Univ, Beijing 100191, Peoples R China -- [Li, Lei] Beijing Inst Petrochem Technol, Beijing 102617, Peoples R China -- [Albrecht, M. -- Held, T. -- Kopf, B. -- Pelizaeus, M. -- Wiedner, U.] Ruhr Univ Bochum, D-44780 Bochum, Germany -- [Albayrak, O. -- Bennett, J. V. -- Briere, R. A. -- Ke, B. C.] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA -- [Liu, Feng -- Mo, Y. J. -- Zhang, Z. H.] Cent China Normal Univ, Wuhan 430079, Peoples R China -- [Ye, M. H.] China Ctr Adv Sci & Technol, Beijing 100190, Peoples R China -- [Nisar, S.] COMSATS Inst Informat Technol, Def Rd,Off Raiwind Rd, Lahore 54000, Pakistan -- [Achasov, M. N. -- Muchnoi, N. Yu. -- Nikolaev, I. B.] GI Budker Inst Nucl Phys SB RAS BINP, Novosibirsk 630090, Russia -- [Goetzen, K. -- Peters, K.] GSI Helmholtzctr Heavy Ion Res GmbH, D-64291 Darmstadt, Germany -- [Liao, G. R. -- Wei, D. H. -- Yang, Y. X.] Guangxi Normal Univ, Guilin 541004, Peoples R China -- [Li, X. M. -- Liu, H. B. -- Ruan, X. D. -- Xia, Y.] Guangxi Univ, Nanning 530004, Peoples R China -- [Li, K. -- Xu, Q. J.] Hangzhou Normal Univ, Hangzhou 310036, Zhejiang, Peoples R China -- [Dbeyssi, A. -- Fritsch, M. -- Kliemt, R. -- Larin, R. -- Lin, D. X. -- Morales, C. Morales -- Nerling, F. -- Rosner, Ch. -- Wang, Y. D.] Helmholtz Inst Mainz, Johann Joachim Becher Weg 45, D-55099 Mainz, Germany -- [Hao, X. Q. -- Huang, J. S. -- Sun, J. F.] Henan Normal Univ, Xinxiang 453007, Peoples R China -- [Liu, H. H.] Henan Univ Sci & Technol, Luoyang 471003, Peoples R China -- [Cheng, H. P. -- Jiao, Z. -- Lu, H. J.] Huangshan Coll, Huangshan 245000, Peoples R China -- [Yan, Y. H. -- Zeng, Y.] Hunan Univ, Changsha 410082, Hunan, Peoples R China -- [Bennett, D. W. -- Lara, M. -- Mitchell, R. E. -- Sun, Z. T.] Indiana Univ, Bloomington, IN 47405 USA -- [Ferroli, R. Baldini -- Bertani, M. -- Calcaterra, A. -- Felici, G. -- Patteri, R. -- Zallo, A.] Ist Nazl Fis Nucl, Lab Nazl Frascati, I-00044 Frascati, Italy -- [Pacetti, S.] INFN, I-06100 Perugia, Italy -- [Pacetti, S.] Univ Perugia, I-06100 Perugia, Italy -- [Bettoni, D. -- Cibinetto, G. -- Farinelli, R. -- Fioravanti, E. -- Garzia, I. -- Santoro, V.] INFN, Sez Ferrara, I-44122 Ferrara, Italy -- [Farinelli, R. -- Savrie, M.] Univ Ferrara, I-44122 Ferrara, Italy -- [Denig, A. -- Feldbauer, F. -- Fritsch, M. -- Gradl, W. -- Guo, Y. P. -- Hafner, A. -- Kiese, R. -- Kloss, B. -- Liu, Zhiqing -- Redmer, C. F. -- Ripka, M. -- Schumann, S. -- Ullrich, M. -- Wang, Y. Q. -- Weber, T. -- Weidenkaff, R.] Johannes Gutenberg Univ Mainz, Johann Joachim Becher Weg 45, D-55099 Mainz, Germany -- [Boger, E. -- Boyko, I. -- Chelkov, G. -- Dedovich, D. -- Denysenko, I. -- Fedorov, O. -- Nefedov, Y. -- Sarantsev, A. -- Zhemchugov, A.] Joint Inst Nucl Res, Dubna 141980, Moscow Region, Russia -- [Kuehn, W. -- Lange, J. S. -- Liang, Y. T.] Univ Giessen, Inst Phys 2, Heinrich Buff Ring 16, D-35392 Giessen, Germany -- [Haddadi, Z. -- Kalantar-Nayestanaki, N. -- Kavatsyuk, M. -- Loehner, H. -- Messchendorp, J. G. -- Tiemens, M.] Univ Groningen, KVI CART, NL-9747 AA Groningen, Netherlands -- [Chen, X. R. -- Liu, X.] Lanzhou Univ, Lanzhou 730000, Peoples R China -- [Ding, Y. -- Liu, K. Y. -- Ma, F. C.] Liaoning Univ, Shenyang 110036, Peoples R China -- [Guo, L. B. -- Hu, C. -- Luo, C. L. -- Ping, J. L. -- Xiao, Z. J. -- Zhong, B.] Nanjing Normal Univ, Nanjing 210023, Jiangsu, Peoples R China -- [Chen, S. J. -- Dou, Z. L. -- Huang, Y. -- Qi, M. -- Yuan, W. L. -- Zhang, C.] Nanjing Univ, Nanjing 210093, Jiangsu, Peoples R China -- [Dong, C. -- Gong, L. -- Ji, Q. P. -- Jiang, X. Y. -- Kang, X. S. -- Li, X. Q. -- Liu, Y. B. -- Ma, X. N. -- Shen, P. X. -- Wang, B. -- Yu, C. X. -- Zhao, M. G.] Nankai Univ, Tianjin 300071, Peoples R China -- [Ban, Y. -- Chu, X. K. -- Li, F. Y. -- Liu, L. D. -- Mao, Y. J. -- Shan, W. -- Wang, D. -- Wang, D. Y. -- Wang, S. G. -- Wei, J. B.] Peking Univ, Beijing 100871, Peoples R China -- [Li, Jin -- Olsen, S. L.] Seoul Natl Univ, Seoul 151747, South Korea -- [Huang, X. T. -- Jiao, J. B. -- Li, K. -- Li, Q. Y. -- Li, T. -- Li, X. L. -- Ma, L. L. -- Ma, Y. M. -- Wang, M. -- Zhang, X. Y. -- Zheng, W. J.] Shandong Univ, Jinan 250100, Peoples R China -- [Dai, J. P. -- Yang, H. J.] Shanghai Jiao Tong Univ, Shanghai 200240, Peoples R China -- [Liu, F. H.] Shanxi Univ, Taiyuan 030006, Peoples R China -- [Liang, Y. F. -- Qin, L. Q. -- Tang, C. J.] Sichuan Univ, Chengdu 610064, Peoples R China -- [Xu, X. P.] Soochow Univ, Suzhou 215006, Peoples R China -- [Li, Z. B. -- Zhang, H. H.] Sun Yat Sen Univ, Guangzhou 510275, Guangdong, Peoples R China -- [Fan, J. Z. -- Gao, Y. -- Liu, K. -- Wang, X. F. -- Xia, L. G. -- Zhu, X. L.] Tsinghua Univ, Beijing 100084, Peoples R China -- [Cakir, O.] Istanbul Aydin Univ, TR-34295 Istanbul, Turkey -- [Cetin, S. A. -- Eren, E. E. -- Kolcu, O. B. -- Yuncu, A.] Istanbul Bilgi Univ, TR-34060 Istanbul, Turkey -- [Dong, C. -- Gong, L. -- Ji, Q. P. -- Jiang, X. Y. -- Kang, X. S. -- Li, X. Q. -- Liu, Y. B. -- Ma, X. N. -- Shen, P. X. -- Wang, B. -- Yu, C. X. -- Zhao, M. G.] Uludag Univ, TR-16059 Bursa, Turkey -- [Uman, I.] Near East Univ, TR-10 North Cyprus, Mersin, Turkey -- [Li, P. R. -- Liu, Q. -- Lyu, X. R. -- Qiao, C. F. -- Wang, B. L. -- Xu, Q. N. -- Zhang, Y. N. -- Zhang, Yu -- Zheng, Y. H.] Univ Chinese Acad Sci, Beijing 100049, Peoples R China -- [Harris, F. A. -- Kornicer, M. -- Luo, T. -- Varner, G. S.] Univ Hawaii, Honolulu, HI 96822 USA -- [Bian, J. M. -- Julin, A. -- Muramatsu, H. -- Poling, R.] Univ Minnesota, Minneapolis, MN 55455 USA -- [Ambrose, D. J. -- Thorndike, E. H.] Univ Rochester, Rochester, NY 14627 USA -- [Zhu, S. H.] Univ Sci & Technol Liaoning, Anshan 114051, Peoples R China -- [An, Q. -- Fang, X. -- Feng, C. Q. -- Gao, X. L. -- Gao, Z. -- Huang, G. S. -- Li, Cheng -- Liang, H. -- Liu, D. -- Liu, J. B. -- Liu, S. B. -- Pan, Y. -- Peng, H. P. -- Shao, M. -- Sun, Y. J. -- Wang, W. P. -- Wang, Z. H. -- Xia, L. -- Yan, W. B. -- Yan, W. C. -- Zeng, Z. -- Zhang, Y. T. -- Zhang, Z. P. -- Zhao, Lei -- Zhao, Z. G. -- Zhou, X. K. -- Zhou, X. R. -- Zhu, Y. C.] Univ Sci & Technol China, Hefei 230026, Peoples R China -- [Xiao, H. -- Zheng, B.] Univ South China, Hengyang 421001, Peoples R China -- [Hussain, T. -- Rashid, K. H. -- Zafar, A. A.] Univ Punjab, Lahore 54590, Pakistan -- [Amoroso, A. -- Bianchi, E. -- Destefanis, M. -- De Mori, F. -- Greco, M. -- Hu, J. F. -- Maggiura, M. -- Marcello, S. -- Sosio, S. -- Spataro, S. -- Yan, L. -- Zotti, L.] Univ Turin, I-10125 Turin, Italy -- [Fava, L. -- Johansson, T.] Univ Piemonte Orientale, I-15121 Alessandria, Italy -- [Amoroso, A. -- Bianchi, E. -- Destefanis, M. -- De Mori, F. -- Fava, L. -- Greco, M. -- Hu, J. F. -- Leng, C. -- Maggiura, M. -- Marcello, S. -- Sosio, S. -- Spataro, S. -- Yan, L. -- Zotti, L.] INFN, I-10125 Turin, Italy -- [Kupsc, A. -- Li, C. -- Pettersson, J. -- Schoenning, K. -- Wolke, M.] Uppsala Univ, Box 516, SE-75120 Uppsala, Sweden -- [Cai, H. -- Han, S. -- Jiang, L. W. -- Liu, J. P. -- Qin, N. -- Yang, L. -- Zhang, Z. Y. -- Zhou, X.] Wuhan Univ, Wuhan 430072, Peoples R China -- [Luo, M. X.] Zhejiang Univ, Hangzhou 310027, Zhejiang, Peoples R China -- [Du, S. X. -- Li, D. M. -- Zhao, S. J.] Zhengzhou Univ, Zhengzhou 450001, Peoples R China -- [An, Q. -- Cai, X. -- Chang, J. F. -- Chen, Y. B. -- Dai, H. L. -- Fang, X. -- Feng, C. Q. -- Gao, X. L. -- Gao, Z. -- Gong, W. X. -- Gu, Y. T. -- Heng, Y. K. -- Hu, T. -- Huang, G. S. -- Ji, X. L. -- Jiang, X. S. -- Jin, D. P. -- Lange, J. S. -- Li, F. -- Li, X. N. -- Liang, H. -- Liu, D. -- Liu, P. L. -- Liu, S. B. -- Liu, Z. A. -- Lou, X. C. -- Lu, J. G. -- Luo, X. L. -- Min, J. -- Mo, X. H. -- Ning, Z. -- Niu, S. L. -- Ouyang, Q. -- Pan, Y. -- Peng, H. P. -- Prasad, V. -- Shao, M. -- Sun, Y. J. -- Sun, Z. J. -- Wang, K. -- Wang, W. -- Wang, W. P. -- Wang, Y. F. -- Wang, Z. -- Wang, Z. G. -- Wang, Z. H. -- Wu, Z. -- Xia, L. -- Xie, Y. G. -- Xiu, Q. L. -- Yan, W. B. -- Yan, W. C. -- Ye, M. -- Yu, B. X. -- Zeng, Z. -- Zhang, B. Y. -- Zhang, H. Y. -- Zhang, J. W. -- Zhang, Y. H. -- Zhang, Y. T. -- Zhao, J. W. -- Zhao, J. Z. -- Zhao, Lei -- Zhao, Y. B. -- Zhao, Z. G. -- Zhou, L. -- Zhou, X. K. -- Zhou, X. R. -- Zhu, K. J. -- Zhu, Y. C. -- Zhuang, J.] State Key Lab Particle Detect & Elect, Beijing 100049, Peoples R China -- [Cakir, O.] Ankara Univ, TR-06100 Ankara, Turkey -- [Yuncu, A.] Bogazici Univ, TR-34342 Istanbul, Turkey -- [Boger, E. -- Chelkov, G. -- Zhemchugov, A.] Moscow Inst Phys & Technol, Moscow 141700, Russia -- [Chelkov, G.] Tomsk State Univ, Funct Elect Lab, Tomsk 634050, Russia -- [Muchnoi, N. Yu. -- Nikolaev, I. B.] Novosibirsk State Univ, Novosibirsk 630090, Russia -- [Sarantsev, A.] NRC Kurchatov Inst, PNPI, Gatchina 188300, Russia -- [Lou, X. C.] Univ Texas Dallas, Richardson, TX 75083 USA -- [Kolcu, O. B.] Istanbul Arel Univ, TR-34295 Istanbul, Turkey
Abstract: WOS: 000368783600015, By analyzing a data set of 2.92 fb(-1) of e(+) e(-) collision data taken at root s = 3.773 GeVand 106.41 x 10(6) psi(3686) decays taken at root s = 3.686 GeVwith the BESIII detector at the BEPCII collider, we measure the branching fraction and the partial decay width for psi(3770)->gamma chi c0 to be B(psi(3770)->gamma chi c0) = (6.88 +/- 0.28 +/- 0.67) x 10(-3) and Gamma[psi(3770)->gamma chi c0] = (187 +/- 8 +/- 19) keV, respectively. These are the most precise measurements to date. (C) 2015 The Authors. Published by Elsevier B.V., National Key Basic Research Program of China [2009CB825204, 2015CB856700]; National Natural Science Foundation of China (NSFC) [10935007, 11125525, 11235011, 11305180, 11322544, 11335008, 11425524]; Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program; NSFC [11179007, U1232201, U1332201]; CAS [11179007, U1232201, U1332201, KJCX2-YW-N29, KJCX2-YW-N45]; 100 Talents Program of CAS; CAS Center for Excellence in Particle Physics (CCEPP); INPAC; Shanghai Key Laboratory for Particle Physics and Cosmology; German Research Foundation DFG under Collaborative Research Center [CRC-1044]; Istituto Nazionale di Fisica Nucleare, Italy; Ministry of Development of Turkey [DPT2006K-120470]; Russian Foundation for Basic Research [14-07-91152]; U.S. Department of Energy [DE-FG02-04ER41291, DE-FG02-05ER41374, DE-FG02-94ER40823, DESC0010118]; U.S. National Science Foundation; University of Groningen (RuG); Helmholtzzentrum fur Schwerionenforschung GmbH (GSI), Darmstadt; WCU Program of National Research Foundation of Korea [R32-2008-000-10155-0], The BESIII collaboration thanks the staff of BEPCII and the IHEP computing center for their strong support. This work is supported in part by the National Key Basic Research Program of China under Contract Nos. 2009CB825204 and 2015CB856700; National Natural Science Foundation of China (NSFC) under Contracts Nos. 10935007, 11125525, 11235011, 11305180, 11322544, 11335008, 11425524; the Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program; Joint Large-Scale Scientific Facility Funds of the NSFC and CAS under Contracts Nos. 11179007, U1232201, U1332201; CAS under Contracts Nos. KJCX2-YW-N29, KJCX2-YW-N45; 100 Talents Program of CAS; the CAS Center for Excellence in Particle Physics (CCEPP); INPAC and Shanghai Key Laboratory for Particle Physics and Cosmology; German Research Foundation DFG under Collaborative Research Center Contract No. CRC-1044; Istituto Nazionale di Fisica Nucleare, Italy; Ministry of Development of Turkey under Contract No. DPT2006K-120470; Russian Foundation for Basic Research under Contract No. 14-07-91152; U.S. Department of Energy under Contracts Nos. DE-FG02-04ER41291, DE-FG02-05ER41374, DE-FG02-94ER40823, DESC0010118; U.S. National Science Foundation; University of Groningen (RuG) and the Helmholtzzentrum fur Schwerionenforschung GmbH (GSI), Darmstadt; WCU Program of National Research Foundation of Korea under Contract No. R32-2008-000-10155-0.
Published: 2016

5. Measurement of the e(+)e(-) -> pi(+) pi(-) cross section between 600 and 900 MeV using initial state radiation

Author: Ablikim, M, Achasov, MN, Ai, XC, Albayrak, O, Albrecht, M, Ambrose, DJ, Amoroso, A, An, FF, An, Q, Bai, JZ, Ferroli, RB, Ban, Y, Bennett, DW, Bennett, JV, Bertani, M, Bettoni, D, Bian, JM, Bianchi, F, Boger, E, Boyko, I, Briere, RA, Cai, H, Cai, X, Cakir, O, Calcaterra, A, Cao, GF, Cetin, SA, Chang, JF, Chelkov, G, Chen, G, Chen, HS, Chen, HY, Chen, JC, Chen, ML, Chen, SJ, Chen, X, Chen, XR, Chen, YB, Cheng, HP, Chu, XK, Cibinetto, G, Dai, HL, Dai, JP, Dbeyssi, A, Dedovich, D, Deng, ZY, Denig, A, Denysenko, I, Destefanis, M, De Mori, F, Ding, Y, Dong, C, Dong, J, Dong, LY, Dong, MY, Du, SX, Duan, PF, Eren, EE, Fan, JZ, Fang, J, Fang, SS, Fang, X, Fang, Y, Fava, L, Feldbauer, F, Felici, G, Feng, CQ, Fioravanti, E, Fritsch, M, Fu, CD, Gao, Q, Gao, XY, Gao, Y, Gao, Z, Garzia, I, Goetzen, K, Gong, WX, Gradl, W, Greco, M, Gu, MH, Gu, YT, Guan, YH, Guo, AQ, Guo, LB, Guo, Y, Guo, YP, Haddadi, Z, Hafner, A, Han, S, Hao, XQ, Harris, FA, He, KL, He, XQ, Held, T, Heng, YK, Hou, ZL, Hu, C, Hu, HM, Hu, JF, Hu, T, Hu, Y, Huang, GM, Huang, GS, Huang, JS, Huang, XT, Huang, Y, Hussain, T, Ji, Q, Ji, QP, Ji, XB, Ji, XL, Jiang, LW, Jiang, XS, Jiang, XY, Jiao, JB, Jiao, Z, Jin, DP, Jin, S, Johansson, T, Julin, A, Kalantar-Nayestanaki, N, Kang, XL, Kang, XS, Kavatsyuk, M, Ke, BC, Kiese, P, Kliemt, R, Kloss, B, Kolcu, OB, Kopf, B, Kornicer, M, Kuehn, W, Kupsc, A, Lange, JS, Lara, M, Larin, P, Leng, C, Li, C, Li, DM, Li, F, Li, FY, Li, G, Li, HB, Li, JC, Li, J, Li, K, Li, L, Li, PR, Li, T, Li, WD, Li, WG, Li, XL, Li, XM, Li, XN, Li, XQ, Li, ZB, Liang, H, Liang, YF, Liang, YT, Liao, GR, Lin, DX, Liu, BJ, Liu, CX, Liu, FH, Liu, F, Liu, HB, Liu, HH, Liu, HM, Liu, J, Liu, JB, Liu, JP, Liu, JY, Liu, K, Liu, KY, Liu, LD, Liu, PL, Liu, Q, Liu, SB, Liu, X, Liu, YB, Liu, ZA, Liu, Z, Loehner, H, Lou, XC, Lu, HJ, Lu, JG, Lu, Y, Lu, YP, Luo, CL, Luo, MX, Luo, T, Luo, XL, Lyu, XR, Ma, FC, Ma, HL, Ma, LL, Ma, QM, Ma, T, Ma, XN, Ma, XY, Maas, FE, Maggiora, M, Mao, YJ, Mao, ZP, Marcello, S, Messchendorp, JG, Min, J, Mitchell, RE, Mo, XH, Mo, YJ, Morales, CM, Moriya, K, Muchnoi, NY, Muramatsu, H, Nefedov, Y, Nerlingn, F, Nikolaev, IB, Ning, Z, Nisar, S, Niu, SL, Niu, XY, Olsen, SL, Ouyang, Q, Pacetti, S, Patteri, P, Pelizaeus, M, Peng, HP, Peters, K, Pettersson, J, Ping, JL, Ping, RG, Poling, R, Prasad, V, Qi, M, Qian, S, Qiao, CF, Qin, LQ, Qin, N, Qin, XS, Qin, ZH, Qiu, JF, Rashid, KH, Redmer, CF, Ripka, M, Rong, G, Rosner, C, Ruan, XD, Santoro, V, Sarantsev, A, Savrie, M, Schoenning, K, Schumann, S, Shan, W, Shao, M, Shen, CP, Shen, PX, Shen, XY, Sheng, HY, Song, WM, Shepherd, MR, Song, XY, Sosio, S, Spataro, S, Sun, GX, Sun, JF, Sun, SS, Sun, YJ, Sun, YZ, Sun, ZJ, Sun, ZT, Tang, CJ, Tang, X, Tapan, I, Thorndike, EH, Tiemens, M, Ullrich, M, Uman, I, Varner, GS, Wang, B, Wang, D, Wang, DY, Wang, K, Wang, LL, Wang, LS, Wang, M, Wang, P, Wang, PL, Wang, SG, Wang, W, Wang, XF, Wang, YD, Wang, YF, Wang, YQ, Wang, Z, Wang, ZG, Wang, ZH, Wang, ZY, Weber, T, Wei, DH, Wei, JB, Weidenkaff, P, Wen, SP, Wiedner, U, Wolke, M, Wu, LH, Wu, Z, Xia, LG, Xia, Y, Xiao, D, Xiao, H, Xiao, ZJ, Xie, YG, Xiu, QL, Xu, GF, Xu, L, Xu, QJ, Xu, XP, Yan, L, Yan, WB, Yan, WC, Yan, YH, Yang, HJ, Yang, HX, Yang, L, Yang, Y, Yang, YX, Ye, M, Ye, MH, Yin, JH, Yu, BX, Yu, CX, Yu, JS, Yuan, CZ, Yuan, WL, Yuan, Y, Yuncu, A, Zafar, AA, Zallo, A, Zeng, Y, Zhang, BX, Zhang, BY, Zhang, C, Zhang, CC, Zhang, DH, Zhang, HH, Zhang, HY, Zhang, JJ, Zhang, JL, Zhang, JQ, Zhang, JW, Zhang, JY, Zhang, JZ, Zhang, K, Zhang, L, Zhang, XY, Zhang, Y, Zhang, YN, Zhang, YH, Zhang, YT, Zhang, ZH, Zhang, ZP, Zhang, ZY, Zhao, G, Zhao, JW, Zhao, JY, Zhao, JZ, Zhao, L, Zhao, MG, Zhao, Q, Zhao, QW, Zhao, SJ, Zhao, TC, Zhao, YB, Zhao, ZG, Zhemchugov, A, Zheng, B, Zheng, JP, Zheng, WJ, Zheng, YH, Zhong, B, Zhou, L, Zhou, X, Zhou, XK, Zhou, XR, Zhou, XY, Zhu, K, Zhu, KJ, Zhu, S, Zhu, SH, Zhu, XL, Zhu, YC, Zhu, YS, Zhu, ZA, Zhuang, J, Zotti, L, Zou, BS, Zou, JH, Collaboration, BESIII, İstanbul Arel Üniversitesi, [Ablikim, M. -- Ai, X. C. -- An, F. F. -- Bai, J. Z. -- Cai, X. -- Cao, G. F. -- Chang, J. F. -- Chen, G. -- Chen, H. S. -- Chen, J. C. -- Chen, M. L. -- Chen, X. -- Chen, Y. B. -- Dai, H. L. -- Deng, Z. Y. -- Dong, J. -- Dong, L. Y. -- Dong, M. Y. -- Duan, P. F. -- Fang, J. -- Fang, S. S. -- Fang, Y. -- Fu, C. D. -- Gao, Q. -- Gong, W. X. -- Gu, M. H. -- Guan, Y. H. -- Guo, A. Q. -- Guo, Y. -- He, K. L. -- Heng, Y. K. -- Hou, Z. L. -- Hu, H. M. -- Hu, T. -- Hu, Y. -- Ji, Q. -- Ji, X. B. -- Ji, X. L. -- Jiang, X. S. -- Jin, D. P. -- Jin, S. -- Kang, X. L. -- Li, F. -- Li, G. -- Li, H. B. -- Li, J. C. -- Li, W. D. -- Li, W. G. -- Li, X. N. -- Liu, B. J. -- Liu, C. X. -- Liu, Fang -- Liu, H. H. -- Liu, H. M. -- Liu, J. -- Liu, J. Y. -- Liu, P. L. -- Liu, Z. A. -- Lou, X. C. -- Lu, J. G. -- Lu, Y. -- Lu, Y. P. -- Luo, X. L. -- Ma, H. L. -- Ma, Q. M. -- Ma, T. -- Ma, X. Y. -- Mao, Z. P. -- Min, J. -- Mo, X. H. -- Ning, Z. -- Niu, S. L. -- Niu, X. Y. -- Ouyang, Q. -- Ping, R. G. -- Prasad, V. -- Qian, S. -- Qin, X. S. -- Qin, Z. H. -- Qiu, J. F. -- Rong, G. -- Shen, X. Y. -- Sheng, H. Y. -- Song, W. M. -- Song, X. Y. -- Sun, G. X. -- Sun, S. S. -- Sun, Y. Z. -- Sun, Z. J. -- Tang, X. -- Wang, K. -- Wang, L. L. -- Wang, L. S. -- Wang, P. -- Wang, P. L. -- Wang, W. -- Wang, Y. F. -- Wang, Z. -- Wang, Z. G. -- Wang, Z. Y. -- Wen, S. P. -- Wu, L. H. -- Wu, Z. -- Xiao, D. -- Xie, Y. G. -- Xiu, Q. L. -- Xu, G. F. -- Xu, L. -- Yang, H. X. -- Ye, M. -- Yin, J. H. -- Yu, B. X. -- Yuan, C. Z. -- Yuan, Y. -- Zhang, B. X. -- Zhang, B. Y. -- Zhang, C. C. -- Zhang, D. H. -- Zhang, H. Y. -- Zhang, J. J. -- Zhang, J. L. -- Zhang, J. Q. -- Zhang, J. W. -- Zhang, J. Y. -- Zhang, J. Z. -- Zhang, K. -- Zhang, L. -- Zhang, Y. -- Zhang, Y. H. -- Zhao, G. -- Zhao, J. W. -- Zhao, J. Y. -- Zhao, J. Z. -- Zhao, Ling -- Zhao, Q. -- Zhao, Q. W. -- Zhao, T. C. -- Zhao, Y. B. -- Zheng, J. P. -- Zhou, L. -- Zhou, X. Y. -- Zhu, K. -- Zhu, K. J. -- Zhu, S. -- Zhu, Y. S. -- Zhu, Z. A. -- Zhuang, J. -- Zou, B. S. -- Zou, J. H.] Inst High Energy Phys, Beijing 100049, Peoples R China -- [Chen, H. Y. -- Gao, X. Y. -- Shen, C. P.] Beihang Univ, Beijing 100191, Peoples R China -- [Li, Lei] Beijing Inst Petrochem Technol, Beijing 102617, Peoples R China -- [Albrecht, M. -- Held, T. -- Kopf, B. -- Pelizaeus, M. -- Wiedner, U.] Ruhr Univ Bochum, D-44780 Bochum, Germany -- [Albayrak, O. -- Bennett, J. V. -- Briere, R. A. -- Ke, B. C.] Carnegie Mellon Univ, Pittsburgh, PA 15213 USA -- [Huang, G. M. -- Liu, Feng -- Mo, Y. J. -- Yang, Y. -- Zhang, Z. H.] Cent China Normal Univ, Wuhan 430079, Peoples R China -- [Ye, M. H.] China Ctr Adv Sci & Technol, Beijing 100190, Peoples R China -- [Nisar, S.] COMSATS Inst Informat Technol, Def Rd,Off Raiwind Rd, Lahore 54000, Pakistan -- [Achasov, M. N. -- Muchnoi, N. Yu -- Nikolaev, I. B.] GI Budker Inst Nucl Phys SB RAS BINP, Novosibirsk 630090, Russia -- [Goetzen, K. -- Peters, K.] GSI Helmholtzctr Heavy Ion Res GmbH, D-64291 Darmstadt, Germany -- [Liao, G. R. -- Wei, D. H. -- Yang, Y. X.] Guangxi Normal Univ, Guilin 541004, Peoples R China -- [Li, X. M. -- Liu, H. B. -- Ruan, X. D.] Guangxi Univ, Nanning 530004, Peoples R China -- [Li, K. -- Xu, Q. J.] Hangzhou Normal Univ, Hangzhou 310036, Zhejiang, Peoples R China -- [Dbeyssi, A. -- Fritsch, M. -- Kliemt, R. -- Larin, P. -- Lin, D. X. -- Maas, F. E. -- Morales, C. Morales -- Nerlingn, F. -- Rosner, Ch. -- Wang, Y. D.] Helmholtz Inst Mainz, Johann Joachim Becher Weg 45, D-55099 Mainz, Germany -- [Hao, X. Q. -- Huang, J. S. -- Sun, J. F.] Henan Normal Univ, Xinxiang 453007, Peoples R China -- [Liu, H. H.] Henan Univ Sci & Technol, Luoyang 471003, Peoples R China -- [Cheng, H. P. -- Jiao, Z. -- Lu, H. J.] Huangshan Coll, Huangshan 245000, Peoples R China -- [Xia, Y. -- Yan, Y. H. -- Zeng, Y.] Hunan Univ, Changsha 410082, Hunan, Peoples R China -- [Bennett, D. W. -- Lara, M. -- Mitchell, R. E. -- Moriya, K. -- Shepherd, M. R. -- Sun, Z. T.] Indiana Univ, Bloomington, IN 47405 USA -- [Ferroli, R. Baldini -- Bertani, M. -- Calcaterra, A. -- Felici, G. -- Patteri, P. -- Zallo, A.] Ist Nazl Fis Nucl, Lab Nazl Frascati, POB 13, I-00044 Frascati, Italy -- [Pacetti, S.] Ist Nazl Fis Nucl, I-06100 Perugia, Italy -- [Pacetti, S.] Univ Perugia, I-06100 Perugia, Italy -- [Bettoni, D. -- Cibinetto, G. -- Fioravanti, E. -- Garzia, I. -- Santoro, V.] INFN, Sez Ferrara, I-44122 Ferrara, Italy -- [Savrie, M.] Univ Ferrara, I-44122 Ferrara, Italy -- [Denig, A. -- Feldbauer, F. -- Fritsch, M. -- Gradl, W. -- Guo, Y. P. -- Hafner, A. -- Kiese, P. -- Kloss, B. -- Liu, Zhiqing -- Redmer, C. F. -- Ripka, M. -- Schumann, S. -- Wang, Y. Q. -- Weber, T. -- Weidenkaff, P.] Johannes Gutenberg Univ Mainz, Johann Joachim Becher Weg 45, D-55099 Mainz, Germany -- [Boger, E. -- Boyko, I. -- Chelkov, G. -- Dedovich, D. -- Denysenko, I. -- Nefedov, Y. -- Sarantsev, A. -- Zhemchugov, A.] Joint Inst Nucl Res, Dubna 141980, Moscow Region, Russia -- [Kuehn, W. -- Lange, J. S. -- Liang, Y. T. -- Ullrich, M.] Univ Giessen, Inst Phys 2, Heinrich Buff Ring 16, D-35392 Giessen, Germany -- [Haddadi, Z. -- Kalantar-Nayestanaki, N. -- Kavatsyuk, M. -- Loehner, H. -- Messchendorp, J. G. -- Tiemens, M.] Univ Groningen, KVI CART, NL-9747 AA Groningen, Netherlands -- [Chen, X. R. -- Liu, X. -- Yu, J. S.] Lanzhou Univ, Lanzhou 730000, Peoples R China -- [Ding, Y. -- Liu, K. Y. -- Ma, F. C.] Liaoning Univ, Shenyang 110036, Peoples R China -- [Guo, L. B. -- Hu, C. -- Luo, C. L. -- Ping, J. L. -- Xiao, Z. J. -- Zhong, B.] Nanjing Normal Univ, Nanjing 210023, Jiangsu, Peoples R China -- [Chen, S. J. -- Huang, Y. -- Qi, M. -- Yuan, W. L. -- Zhang, C.] Nanjing Univ, Nanjing 210093, Jiangsu, Peoples R China -- [Dong, C. -- Ji, Q. P. -- Jiang, X. Y. -- Kang, X. S. -- Li, X. Q. -- Liu, Y. B. -- Ma, X. N. -- Shen, P. X. -- Wang, B. -- Yu, C. X. -- Zhao, M. G.] Nankai Univ, Tianjin 300071, Peoples R China -- [Ban, Y. -- Chu, X. K. -- Li, F. Y. -- Liu, L. D. -- Mao, Y. J. -- Shan, W. -- Wang, D. -- Wang, D. Y. -- Wang, S. G. -- Wei, J. B.] Peking Univ, Beijing 100871, Peoples R China -- [Li, Jin -- Olsen, S. L.] Seoul Natl Univ, Seoul 151747, South Korea -- [Huang, X. T. -- Jiao, J. B. -- Li, K. -- Li, T. -- Li, X. L. -- Ma, L. L. -- Qin, L. Q. -- Wang, M. -- Zhang, X. Y. -- Zheng, W. J.] Shandong Univ, Jinan 250100, Peoples R China -- [Dai, J. P. -- Yang, H. J.] Shanghai Jiao Tong Univ, Shanghai 200240, Peoples R China -- [Liu, F. H.] Shanxi Univ, Taiyuan 030006, Peoples R China -- [Liang, Y. F. -- Tang, C. J.] Sichuan Univ, Chengdu 610064, Peoples R China -- [Xu, X. P.] Soochow Univ, Suzhou 215006, Peoples R China -- [Li, Z. B. -- Zhang, H. H.] Sun Yat Sen Univ, Guangzhou 510275, Guangdong, Peoples R China -- [Fan, J. Z. -- Gao, Y. -- Liu, K. -- Wang, X. F. -- Xia, L. G. -- Zhu, X. L.] Tsinghua Univ, Beijing 100084, Peoples R China -- [Cakir, O.] Istanbul Aydin Univ, TR-34295 Istanbul, Turkey -- [Cetin, S. A. -- Eren, E. E. -- Kolcu, O. B. -- Uman, I. -- Yuncu, A.] Dogus Univ, TR-34722 Istanbul, Turkey -- [Tapan, I.] Uludag Univ, TR-16059 Bursa, Turkey -- [Li, P. R. -- Liu, Q. -- Lyu, X. R. -- Qiao, C. F. -- Zhang, Y. N. -- Zhang, Yu -- Zheng, Y. H.] Univ Chinese Acad Sci, Beijing 100049, Peoples R China -- [Harris, F. A. -- Kornicer, M. -- Luo, T. -- Varner, G. S.] Univ Hawaii, Honolulu, HI 96822 USA -- [Bian, J. M. -- Julin, A. -- Muramatsu, H. -- Poling, R.] Univ Minnesota, Minneapolis, MN 55455 USA -- [Ambrose, D. J. -- Thorndike, E. H.] Univ Rochester, 601 Elmwood Ave, Rochester, NY 14627 USA -- [He, X. Q. -- Zhu, S. H.] Univ Sci & Technol Liaoning, Anshan 114051, Peoples R China -- [An, Q. -- Fang, X. -- Feng, C. Q. -- Gao, Z. -- Huang, G. S. -- Li, Cheng -- Liang, H. -- Liu, J. B. -- Liu, S. B. -- Peng, H. P. -- Shao, M. -- Sun, Y. J. -- Wang, Z. H. -- Yan, L. -- Yan, W. B. -- Yan, W. C. -- Zhang, Y. T. -- Zhang, Z. P. -- Zhao, Lei -- Zhao, Z. G. -- Zhou, X. K. -- Zhou, X. R. -- Zhu, Y. C.] Univ Sci & Technol China, Hefei 230026, Peoples R China -- [Xiao, H. -- Zheng, B.] Univ South China, Hengyang 421001, Peoples R China -- [Hussain, T. -- Rashid, K. H. -- Zafar, A. A.] Univ Punjab, Lahore 54590, Pakistan -- [Amoroso, A. -- Bianchi, F. -- Destefanis, M. -- De Mori, F. -- Greco, M. -- Hu, J. F. -- Maggiora, M. -- Marcello, S. -- Sosio, S. -- Spataro, S. -- Zotti, L.] Univ Turin, I-10125 Turin, Italy -- [Fava, L.] Univ Piemonte Orientale, I-15121 Alessandria, Italy -- [Amoroso, A. -- Bianchi, F. -- Destefanis, M. -- De Mori, F. -- Fava, L. -- Greco, M. -- Hu, J. F. -- Leng, C. -- Maggiora, M. -- Marcello, S. -- Sosio, S. -- Spataro, S. -- Zotti, L.] Ist Nazl Fis Nucl, I-10125 Turin, Italy -- [Johansson, T. -- Kupsc, A. -- Li, C. -- Pettersson, J. -- Schoenning, K. -- Wolke, M.] Uppsala Univ, Box 516, SE-75120 Uppsala, Sweden -- [Cai, H. -- Han, S. -- Jiang, L. W. -- Liu, J. P. -- Qin, N. -- Yang, L. -- Zhang, Z. Y. -- Zhou, X.] Wuhan Univ, Wuhan 430072, Peoples R China -- [Luo, M. X.] Zhejiang Univ, Hangzhou 310027, Zhejiang, Peoples R China -- [Du, S. X. -- Li, D. M. -- Zhao, S. J.] Zhengzhou Univ, Zhengzhou 450001, Peoples R China -- [An, Q. -- Cai, X. -- Chang, J. F. -- Chen, M. L. -- Chen, X. -- Chen, Y. B. -- Dai, H. L. -- Dong, J. -- Dong, M. Y. -- Fang, J. -- Fang, X. -- Feng, C. Q. -- Gao, Z. -- Gong, W. X. -- Gu, M. H. -- Gu, Y. T. -- Heng, Y. K. -- Hu, T. -- Huang, G. S. -- Ji, X. L. -- Jiang, X. S. -- Jin, D. P. -- Li, Cheng -- Li, F. -- Li, X. N. -- Liang, H. -- Liu, J. B. -- Liu, P. L. -- Liu, S. B. -- Liu, Z. A. -- Lou, X. C. -- Lu, J. G. -- Lu, Y. P. -- Luo, X. L. -- Ma, X. Y. -- Min, J. -- Mo, X. H. -- Ning, Z. -- Niu, S. L. -- Ouyang, Q. -- Peng, H. P. -- Qian, S. -- Qin, Z. H. -- Shao, M. -- Sun, Y. J. -- Sun, Z. J. -- Wang, K. -- Wang, W. -- Wang, Y. F. -- Wang, Z. -- Wang, Z. G. -- Wang, Z. H. -- Wu, Z. -- Xie, Y. G. -- Xiu, Q. L. -- Yan, L. -- Yan, W. B. -- Yan, W. C. -- Ye, M. -- Yu, B. X. -- Zhang, B. Y. -- Zhang, H. Y. -- Zhang, J. W. -- Zhang, Y. H. -- Zhang, Y. T. -- Zhao, J. W. -- Zhao, J. Z. -- Zhao, Lei -- Zhao, Y. B. -- Zhao, Z. G. -- Zheng, J. P. -- Zhou, L. -- Zhou, X. K. -- Zhou, X. R. -- Zhu, K. J. -- Zhu, Y. C. -- Zhuang, J.] State Key Lab Particle Detect & Elect, Beijing 100049, Peoples R China -- [An, Q. -- Cai, X. -- Chang, J. F. -- Chen, M. L. -- Chen, X. -- Chen, Y. B. -- Dai, H. L. -- Dong, J. -- Dong, M. Y. -- Fang, J. -- Fang, X. -- Feng, C. Q. -- Gao, Z. -- Gong, W. X. -- Gu, M. H. -- Gu, Y. T. -- Heng, Y. K. -- Hu, T. -- Huang, G. S. -- Ji, X. L. -- Jiang, X. S. -- Jin, D. P. -- Li, Cheng -- Li, F. -- Li, X. N. -- Liang, H. -- Liu, J. B. -- Liu, P. L. -- Liu, S. B. -- Liu, Z. A. -- Lou, X. C. -- Lu, J. G. -- Lu, Y. P. -- Luo, X. L. -- Ma, X. Y. -- Min, J. -- Mo, X. H. -- Ning, Z. -- Niu, S. L. -- Ouyang, Q. -- Peng, H. P. -- Qian, S. -- Qin, Z. H. -- Shao, M. -- Sun, Y. J. -- Sun, Z. J. -- Wang, K. -- Wang, W. -- Wang, Y. F. -- Wang, Z. -- Wang, Z. G. -- Wang, Z. H. -- Wu, Z. -- Xie, Y. G. -- Xiu, Q. L. -- Yan, L. -- Yan, W. B. -- Yan, W. C. -- Ye, M. -- Yu, B. X. -- Zhang, B. Y. -- Zhang, H. Y. -- Zhang, J. W. -- Zhang, Y. H. -- Zhang, Y. T. -- Zhao, J. W. -- Zhao, J. Z. -- Zhao, Lei -- Zhao, Y. B. -- Zhao, Z. G. -- Zheng, J. P. -- Zhou, L. -- Zhou, X. K. -- Zhou, X. R. -- Zhu, K. J. -- Zhu, Y. C. -- Zhuang, J.] State Key Lab Particle Detect & Elect, Hefei 230026, Peoples R China -- [Cakir, O.] Ankara Univ, TR-06100 Ankara, Turkey -- [Yuncu, A.] Bogazici Univ, TR-34342 Istanbul, Turkey -- [Boger, E. -- Chelkov, G. -- Zhemchugov, A.] Moscow Inst Phys & Technol, Moscow 141700, Russia -- [Chelkov, G.] Tomsk State Univ, Funct Elect Lab, Tomsk 634050, Russia -- [Achasov, M. N. -- Muchnoi, N. Yu -- Nikolaev, I. B.] Novosibirsk State Univ, Novosibirsk 630090, Russia -- [Sarantsev, A.] PNPI, NRC Kurchatov Inst, Gatchina 188300, Russia -- [Lou, X. C.] Univ Texas Dallas, Richardson, TX 75083 USA -- [Kolcu, O. B.] Istanbul Arel Univ, TR-34295 Istanbul, Turkey, Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü., Tapan, İlhan, and Research unit Nuclear & Hadron Physics
Subjects: Nuclear and High Energy Physics, Particle physics, BESIII, детектор, Hadron, KLOE DETECTOR, BESIII, Hadronic cross section, Initial state radiation, Muon anomaly, Pion form factor, 7. Clean energy, 01 natural sciences, NO, law.invention, Subatomär fysik, Pion, law, CMD-2, 0103 physical sciences, Subatomic Physics, Physics, nuclear, Vacuum polarization, 010306 general physics, Collider, Physics, ELECTRON-POSITRON ANNIHILATION, HADRONIC CONTRIBUTIONS, Luminosity (scattering theory), FLAVOR FACTORIES, 010308 nuclear & particles physics, SIGMA(E(+)E(-), Form factor (quantum field theory), G-2, Order (ring theory), Mesons, Detector, Lattice QCD, lcsh:QC1-999, BEPCII, коллайдер, Astronomy & astrophysics, Physics, particles & fields, PI(+)PI(-)), Energy (signal processing), lcsh:Physics
Abstract: WOS: 000368783600087, We extract the e(+) e(-) -> pi(+) pi(-) cross section in the energy range between 600 and 900 MeV, exploiting the method of initial state radiation. A data set with an integrated luminosity of 2.93 fb(-1) taken at a center-of-mass energy of 3.773 GeV with the BESIII detector at the BEPCII collider is used. The cross section is measured with a systematic uncertainty of 0.9%. We extract the pion form factor vertical bar F pi vertical bar(2) as well as the contribution of the measured cross section to the leading-order hadronic vacuum polarization contribution to (g - 2)(mu). We find this value to be a(mu)(pi pi,LO) (600-900 MeV) = (368.2 +/- 2.5(stat)+/- 3.3(sys)).10(-10), which is between the corresponding values using the BaBar or KLOE data. (C) 2015 The Authors. Published by Elsevier B.V., National Key Basic Research Program of China [2015CB856700]; National Natural Science Foundation of China (NSFC) [11125525, 11235011, 11322544, 11335008, 11425524]; Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program; CAS Center for Excellence in Particle Physics (CCEPP); Collaborative Innovation Center for Particles and Interactions (CICPI); Joint Large-Scale Scientific Facility Funds of the NSFC; CAS [11179007, U1232201, U1332201, KJCX2-YW-N29, KJCX2-YW-N45]; 100 Talents Program of CAS; National 1000 Talents Program of China; INPAC; Shanghai Key Laboratory for Particle Physics and Cosmology; German Research Foundation DFG [Collaborative Research Center CRC-1044]; Istituto Nazionale di Fisica Nucleare, Italy; Ministry of Development of Turkey [DPT2006K-120470]; Russian Foundation for Basic Research [14-07-91152]; Swedish Resarch Council; U.S. Department of Energy [DE-FG02-04ER41291, DE-FG02-05ER41374, DE-FG02-94ER40823, DESC0010118]; U.S. National Science Foundation; University of Groningen (RuG); Helmholtzzentrum fur Schwerionenforschung GmbH (GSI), Darmstadt; WCU Program of National Research Foundation of Korea [R32-2008-000-10155-0], The BESIII Collaboration thanks the staff of BEPCII and the IHEP computing center for their strong support. We thank Thomas Teubner for the recalculation of amupi pi,LO (600-900 MeV) and Fedor Ignatov for the useful discussions. This work is supported in part by National Key Basic Research Program of China under Contract No. 2015CB856700; National Natural Science Foundation of China (NSFC) under Contract Nos. 11125525, 11235011, 11322544, 11335008, 11425524; the Chinese Academy of Sciences (CAS) Large-Scale Scientific Facility Program; the CAS Center for Excellence in Particle Physics (CCEPP); the Collaborative Innovation Center for Particles and Interactions (CICPI); Joint Large-Scale Scientific Facility Funds of the NSFC and CAS under Contract Nos. 11179007, U1232201, U1332201; CAS under Contract Nos. KJCX2-YW-N29, KJCX2-YW-N45; 100 Talents Program of CAS; National 1000 Talents Program of China; INPAC and Shanghai Key Laboratory for Particle Physics and Cosmology; German Research Foundation DFG under Contract No. Collaborative Research Center CRC-1044; Istituto Nazionale di Fisica Nucleare, Italy; Ministry of Development of Turkey under Contract No. DPT2006K-120470; Russian Foundation for Basic Research under Contract No. 14-07-91152; The Swedish Resarch Council; U.S. Department of Energy under Contract Nos. DE-FG02-04ER41291, DE-FG02-05ER41374, DE-FG02-94ER40823, DESC0010118; U.S. National Science Foundation; University of Groningen (RuG) and the Helmholtzzentrum fur Schwerionenforschung GmbH (GSI), Darmstadt; WCU Program of National Research Foundation of Korea under Contract No. R32-2008-000-10155-0.
Published: 2016

6. QTL associated with Fusarium head blight resistance in the soft red winter wheat Ernie

Author: Theresa A. Musket, Shuyu Liu, Lu Hj, Z. A. Abate, Anne L. McKendry, and G. L. Davis
Subjects: Genetic Markers, Fusarium, biology, Quantitative Trait Loci, Chromosome Mapping, food and beverages, General Medicine, Quantitative trait locus, biology.organism_classification, Immunity, Innate, Gibberella zeae, Anthesis, Inbred strain, Agronomy, Genetics, Poaceae, Amplified fragment length polymorphism, Cultivar, Agronomy and Crop Science, Crosses, Genetic, Triticum, Plant Diseases, Biotechnology
Abstract: Fusarium head blight (FHB), mainly caused by Fusarium graminearum Schwabe [telomorph: Gibberella zeae Schw. (Petch)], is an increasingly important disease of wheat (Triticum aestivum L.). Host-plant resistance provides the best hope for reducing economic losses associated with FHB, but new sources of resistance are limited. The moderately resistant winter wheat cultivar, Ernie, may provide a source of resistance that differs from Sumai 3 but these genes have not been mapped. Also hindering resistance breeding may be associations of resistance with agronomic traits such as late maturity that may be undesirable in some production environments. This research was conducted to identify QTL associated with type II FHB resistance (FHB severity, FHBS), and to determine if they are associated with days to anthesis (DTA), number of spikelets (NOS), and the presence/absence of awns. Two hundred and forty-three F(8) recombinant inbred lines from a cross between the resistant cultivar, Ernie and susceptible parent, MO 94-317 were phenotyped for type II FHB resistance using point inoculation in the greenhouse during 2002 and 2003. Genetic linkage maps were constructed using 94 simple sequence repeat (SSR) and 146 amplified fragment length polymorphic (AFLP) markers. Over years four QTL regions on chromosomes 2B, 3B, 4BL and 5A were consistently associated with FHB resistance. These QTL explained 43.3% of the phenotypic variation in FHBS. Major QTL conditioning DTA and NOS were identified on chromosome 2D. Neither the QTL associated with DTA and NOS nor the presence/absence of awns were associated with FHB resistance in Ernie. Our results suggest that the FHB resistance in Ernie appears to differ from that in Sumai 3, thus pyramiding the QTL in Ernie with those from Sumai 3 could result in enhanced levels of FHB resistance in wheat.
Published: 2007

7. Measurement of the branching fractions of D-s(+) -> eta ' X and D-s(+) -> eta 'rho(+) in e(+)e(-) -> Ds+Ds

Author: Ablikim, M, Achasov, MN, Ai, XC, Albayrak, O, Albrecht, M, Ambrose, DJ, Amoroso, A, An, FF, An, Q, Bai, JZ, Ferroli, RB, Ban, Y, Bennett, DW, Bennett, JV, Bertani, M, Bettoni, D, Bian, JM, Bianchi, F, Boger, E, Boyko, I, Briere, RA, Cai, H, Cai, X, Cakir, O, Calcaterra, A, Cao, GF, Cetin, SA, Chang, JF, Chelkov, G, Chen, G, Chen, HS, Chen, HY, Chen, JC, Chen, ML, Chen, SJ, Chen, X, Chen, XR, Chen, YB, Cheng, HP, Chu, XK, Cibinetto, G, Dai, HL, Dai, JP, Dbeyssi, A, Dedovich, D, Deng, ZY, Denig, A, Denysenko, I, Destefanis, M, De Mori, F, Ding, Y, Dong, C, Dong, J, Dong, LY, Dong, MY, Du, SX, Duan, PF, Eren, EE, Fan, JZ, Fang, J, Fang, SS, Fang, X, Fang, Y, Fava, L, Feldbauer, F, Felici, G, Feng, CQ, Fioravanti, E, Fritsch, M, Fu, CD, Gao, Q, Gao, XY, Gao, Y, Gao, Z, Garzia, I, Geng, C, Goetzen, K, Gong, WX, Gradl, W, Greco, M, Gu, MH, Gu, YT, Guan, YH, Guo, AQ, Guo, LB, Guo, Y, Guo, YP, Haddadi, Z, Hafner, A, Han, S, Han, YL, Hao, XQ, Harris, FA, He, KL, He, ZY, Held, T, Heng, YK, Hou, ZL, Hu, C, Hu, HM, Hu, JF, Hu, T, Hu, Y, Huang, GM, Huang, GS, Huang, HP, Huang, JS, Huang, XT, Huang, Y, Hussain, T, Ji, Q, Ji, QP, Ji, XB, Ji, XL, Jiang, LL, Jiang, LW, Jiang, XS, Jiang, XY, Jiao, JB, Jiao, Z, Jin, DP, Jin, S, Johansson, T, Julin, A, Kalantar-Nayestanaki, N, Kang, XL, Kang, XS, Kavatsyuk, M, Ke, BC, Kiese, P, Kliemt, R, Kloss, B, Kolcu, OB, Kopf, B, Kornicer, M, Kuehn, W, Kupsc, A, Lange, JS, Lara, M, Larin, P, Leng, C, Li, C, Li, CH, Li, DM, Li, F, Li, G, Li, HB, Li, JC, Li, J, Li, K, Li, L, Li, PR, Li, T, Li, WD, Li, WG, Li, XL, Li, XM, Li, XN, Li, XQ, Li, ZB, Liang, H, Liang, YF, Liang, YT, Liao, GR, Lin, DX, Liu, BJ, Liu, CX, Liu, FH, Liu, F, Liu, HB, Liu, HH, Liu, HM, Liu, J, Liu, JB, Liu, JP, Liu, JY, Liu, K, Liu, KY, Liu, LD, Liu, PL, Liu, Q, Liu, SB, Liu, X, Liu, XX, Liu, YB, Liu, ZA, Liu, Z, Loehner, H, Lou, XC, Lu, HJ, Lu, JG, Lu, RQ, Lu, Y, Lu, YP, Luo, CL, Luo, MX, Luo, T, Luo, XL, Lv, M, Lyu, XR, Ma, FC, Ma, HL, Ma, LL, Ma, QM, Ma, T, Ma, XN, Ma, XY, Maas, FE, Maggiora, M, Mao, YJ, Mao, ZP, Marcello, S, Messchendorp, JG, Min, J, Min, TJ, Mitchell, RE, Mo, XH, Mo, YJ, Morales, CM, Moriya, K, Muchnoi, NY, Muramatsuau, H, Nefedov, Y, Nerling, F, Nikolaev, IB, Ning, Z, Nisar, S, Niu, SL, Niu, XY, Olsen, SL, Ouyang, Q, Pacetti, S, Patteri, P, Pelizaeus, M, Peng, HP, Peters, K, Pettersson, J, Ping, JL, Ping, RG, Poling, R, Prasad, V, Pu, YN, Qi, M, Qian, S, Qiao, CF, Qin, LQ, Qin, N, Qin, XS, Qin, Y, Qin, ZH, Qiu, JF, Rashid, KH, Redmer, CF, Ren, HL, Ripka, M, Rong, G, Rosner, C, Ruan, XD, Santoro, V, Sarantsev, A, Savrie, M, Schoenning, K, Schumann, S, Shan, W, Shao, M, Shen, CP, Shen, PX, Shen, XY, Sheng, HY, Song, WM, Song, XY, Sosio, S, Spataro, S, Sun, GX, Sun, JF, Sun, SS, Sun, YJ, Sun, YZ, Sun, ZJ, Sun, ZT, Tang, CJ, Tang, X, Tapan, I, Thorndike, EH, Tiemens, M, Ullrich, M, Uman, I, Varner, GS, Wang, B, Wang, BL, Wang, D, Wang, DY, Wang, K, Wang, LL, Wang, LS, Wang, M, Wang, P, Wang, PL, Wang, SG, Wang, W, Wang, XF, Wang, YD, Wang, YF, Wang, YQ, Wang, Z, Wang, ZG, Wang, ZH, Wang, ZY, Weber, T, Wei, DH, Wei, JB, Weidenkaff, P, Wen, SP, Wiedner, U, Wolke, M, Wu, LH, Wu, Z, Xia, LG, Xia, Y, Xiao, D, Xiao, ZJ, Xie, YG, Xiu, QL, Xu, GF, Xu, L, Xu, QJ, Xu, QN, Xu, XP, Yan, L, Yan, WB, Yan, WC, Yan, YH, Yang, HJ, Yang, HX, Yang, L, Yang, Y, Yang, YX, Ye, H, Ye, M, Ye, MH, Yin, JH, Yu, BX, Yu, CX, Yu, HW, Yu, JS, Yuan, CZ, Yuan, WL, Yuan, Y, Yuncu, A, Zafar, AA, Zallo, A, Zeng, Y, Zhang, BX, Zhang, BY, Zhang, C, Zhang, CC, Zhang, DH, Zhang, HH, Zhang, HY, Zhang, JJ, Zhang, JL, Zhang, JQ, Zhang, JW, Zhang, JY, Zhang, JZ, Zhang, K, Zhang, L, Zhang, SH, Zhang, XY, Zhang, Y, Zhang, YN, Zhang, YH, Zhang, YT, Zhang, ZH, Zhang, ZP, Zhang, ZY, Zhao, G, Zhao, JW, Zhao, JY, Zhao, JZ, Zhao, L, Zhao, MG, Zhao, Q, Zhao, QW, Zhao, SJ, Zhao, TC, Zhao, YB, Zhao, ZG, Zhemchugov, A, Zheng, B, Zheng, JP, Zheng, WJ, Zheng, YH, Zhong, B, Zhou, L, Zhou, X, Zhou, XK, Zhou, XR, Zhou, XY, Zhu, K, Zhu, KJ, Zhu, S, Zhu, XL, Zhu, YC, Zhu, YS, Zhu, ZA, Zhuang, J, Zotti, L, Zou, BS, Zou, JH, and Collaboration, BESIII
Subjects: D-s, BESIII, Branching fractions
Published: 2015

8. Study of psi(3686) -> pi(0)h(c), h(c) -> gamma eta(c) via eta(c) exclusive decays

Author: Ablikim, M, Achasov, MN, Albayrak, O, Ambrose, DJ, An, FF, An, Q, Bai, JZ, Ban, Y, Becker, J, Bennett, JV, Bertani, M, Bian, JM, Boger, E, Bondarenko, O, Boyko, I, Briere, RA, Bytev, V, Cai, X, Cakir, O, Calcaterra, A, Cao, GF, Cetin, SA, Chang, JF, Chelkov, G, Chen, G, Chen, HS, Chen, JC, Chen, ML, Chen, SJ, Chen, X, Chen, YB, Cheng, HP, Chu, YP, Cronin-Hennessy, D, Dai, HL, Dai, JP, Dedovich, D, Deng, ZY, Denig, A, Denysenko, I, Destefanis, M, Ding, WM, Ding, Y, Dong, LY, Dong, MY, Du, SX, Fang, J, Fang, SS, Fava, L, Feldbauer, F, Feng, CQ, Ferroli, RB, Fu, CD, Fu, JL, Gao, Y, Geng, C, Goetzen, K, Gong, WX, Gradl, W, Greco, M, Gu, MH, Gu, YT, Guan, YH, Guo, AQ, Guo, LB, Guo, YP, Han, YL, Harris, FA, He, KL, He, M, He, ZY, Held, T, Heng, YK, Hou, ZL, Hu, HM, Hu, T, Huang, GM, Huang, GS, Huang, JS, Huang, XT, Huang, YP, Hussain, T, Ji, CS, Ji, Q, Ji, QP, Ji, XB, Ji, XL, Jiang, LL, Jiang, XS, Jiao, JB, Jiao, Z, Jin, DP, Jin, S, Jing, FF, Kalantar-Nayestanaki, N, Kavatsyuk, M, Kuehn, W, Lai, W, Lange, JS, Li, CH, Li, C, Li, DM, Li, F, Li, G, Li, HB, Li, JC, Li, K, Li, L, Li, QJ, Li, SL, Li, WD, Li, WG, Li, XL, Li, XN, Li, XQ, Li, XR, Li, ZB, Liang, H, Liang, YF, Liang, YT, Liao, GR, Liao, XT, Liu, BJ, Liu, CL, Liu, CX, Liu, CY, Liu, FH, Liu, F, Liu, H, Liu, HH, Liu, HM, Liu, HW, Liu, JP, Liu, KY, Liu, K, Liu, PL, Liu, Q, Liu, SB, Liu, X, Liu, YB, Liu, ZA, Liu, Z, Loehner, H, Lu, GR, Lu, HJ, Lu, JG, Lu, QW, Lu, XR, Lu, YP, Luo, CL, Luo, MX, Luo, T, Luo, XL, Lv, M, Ma, CL, Ma, FC, Ma, HL, Ma, QM, Ma, S, Ma, T, Ma, XY, Ma, Y, Maas, FE, Maggiora, M, Malik, QA, Mao, YJ, Mao, ZP, Messchendorp, JG, Min, J, Min, TJ, Mitchell, RE, Mo, XH, Morales, CM, Motzko, C, Muchnoi, NY, Muramatsu, H, Nefedov, Y, Nicholson, C, Nikolaev, IB, Ning, Z, Olsen, SL, Ouyang, Q, Pacetti, S, Park, JW, Pelizaeus, M, Peng, HP, Peters, K, Ping, JL, Ping, RG, Poling, R, Prencipe, E, Qi, M, Qian, S, Qiao, CF, Qin, XS, Qin, Y, Qin, ZH, Qiu, JF, Rashid, KH, Rong, G, Ruan, XD, Sarantsev, A, Schaefer, BD, Schulze, J, Shao, M, Shen, CP, Shen, XY, Sheng, HY, Shepherd, MR, Song, XY, Spataro, S, Spruck, B, Sun, DH, Sun, GX, Sun, JF, Sun, SS, Sun, YJ, Sun, YZ, Sun, ZJ, Sun, ZT, Tang, CJ, Tang, X, Tapan, I, Thorndike, EH, Toth, D, Ullrich, M, Varner, GS, Wang, B, Wang, BQ, Wang, D, Wang, DY, Wang, K, Wang, LL, Wang, LS, Wang, M, Wang, P, Wang, PL, Wang, Q, Wang, QJ, Wang, SG, Wang, XL, Wang, YD, Wang, YF, Wang, YQ, Wang, Z, Wang, ZG, Wang, ZY, Wei, DH, Wei, JB, Weidenkaff, P, Wen, QG, Wen, SP, Werner, M, Wiedner, U, Wu, LH, Wu, N, Wu, SX, Wu, W, Wu, Z, Xia, LG, Xiao, ZJ, Xie, YG, Xiu, QL, Xu, GF, Xu, GM, Xu, H, Xu, QJ, Xu, XP, Xu, ZR, Xue, F, Xue, Z, Yan, L, Yan, WB, Yan, YH, Yang, HX, Yang, Y, Yang, YX, Ye, H, Ye, M, Ye, MH, Yu, BX, Yu, CX, Yu, HW, Yu, JS, Yu, SP, Yuan, CZ, Yuan, Y, Zafar, AA, Zallo, A, Zeng, Y, Zhang, BX, Zhang, BY, Zhang, C, Zhang, CC, Zhang, DH, Zhang, HH, Zhang, HY, Zhang, JQ, Zhang, JW, Zhang, JY, Zhang, JZ, Zhang, SH, Zhang, XJ, Zhang, XY, Zhang, Y, Zhang, YH, Zhang, YS, Zhang, ZP, Zhang, ZY, Zhao, G, Zhao, HS, Zhao, JW, Zhao, KX, Zhao, L, Zhao, MG, Zhao, Q, Zhao, QZ, Zhao, SJ, Zhao, TC, Zhao, XH, Zhao, YB, Zhao, ZG, Zhemchugov, A, Zheng, B, Zheng, JP, Zheng, YH, Zhong, B, Zhong, J, Zhong, Z, Zhou, L, Zhou, XK, Zhou, XR, Zhu, C, Zhu, K, Zhu, KJ, Zhu, SH, Zhu, XL, Zhu, YC, Zhu, YM, Zhu, YS, Zhu, ZA, Zhuang, J, Zou, BS, Zou, JH, Collaboration, BESIII, Uludağ Üniversitesi/Fen-Edebiyat Fakültesi/Fizik Bölümü., and Tapan, İlhan
Subjects: Physics, Astronomy & astrophysics, Physics, particles & fields, Charmonium, Charm (Particle Physics), Mesons
Abstract: The process psi(3686) -> pi(0)h(c), h(c) -> gamma eta(c) has been studied with a data sample of 106 +/- 4 million Psi(3686) events collected with the BESIII detector at the BEPCII storage ring. The mass and width of the P-wave charmonium spin-singlet state h(c)(P-1(1)) are determined by simultaneously fitting distributions of the pi(0) recoil mass for 16 exclusive eta(c) decay modes. The results, M(h(c)) 3525.31 +/- 0.11(stat) +/- 0.14(syst) MeV/c(2) and Gamma(h(c)) = 0.70 +/- 0.28 +/- 0.22 MeV, are consistent with and more precise than previous measurements. We also determine the branching ratios for the 16 exclusive eta(c) decay modes, five of which have not been measured previously. New measurements of the eta(c) line- shape parameters in the E1 transition h(c) -> gamma eta(c) are made by selecting candidates in the h(c) signal sample and simultaneously fitting the hadronic mass spectra for the 16 eta(c) decay channels. The resulting eta(c) mass and width values are M(eta(c)) = 2984.49 +/- 1.16 +/- 0.52 MeV/c(2) and Gamma(eta(c)) 36.4 +/- 3.2 +/- 1.7 MeV. Ministry of Science and Technology, China - 2009CB825200 National Natural Science Foundation of China (NSFC)- 10745001 / 10625524 / 10821063 / 10825524 / 10835001 / 10935007 / 11125525 / 11079008 / 11179007 / 10979058 Chinese Academy of Sciences - KJCX2-YW-N29 / KJCX2-YW-N45 Istituto Nazionale di Fisica Nucleare (INFN) United States Department of Energy (DOE) - DE-FG02-04ER41291 / DE-FG02-91ER40682 / DE-FG02-94ER40823 National Science Foundation (NSF) University of Groningen (RuG) Helmholtzzentrum fuer Schwerionenforschung GmbH (GSI), Darmstadt WCU Program of National Research Foundation of Korea - R32-008-000-10155-0 Türkiye Cumhuriyeti Kalkınma Bakanlığı - DPT2006K-120470
Published: 2012

9. Newly diagnosed acute lymphoblastic leukemia in China (II): prognosis related to genetic abnormalities in a series of 1091 cases

Author: Xiang Wang, Shunle Chen, Shuhong Shen, Yao Y, Zhou Jf, Bo Jiao, Xiao-Xing Jiang, Zuojia Chen, Shen Zx, Ma Ly, Jingyan Tang, Fei Chen, Long-Jun Gu, Jian Wang, Hao Sg, Shu-Min Xiong, Lu Hj, Bing Chen, Hui Jiang, and Jian-Qing Mi
Subjects: Oncology, Adult, Male, Cancer Research, medicine.medical_specialty, Prognostic factor, China, Adolescent, Lymphoblastic Leukemia, Chromosome Disorders, Newly diagnosed, Immunophenotyping, Young Adult, Internal medicine, medicine, Biomarkers, Tumor, Humans, Young adult, Child, Survival rate, Aged, Chromosome Aberrations, business.industry, Infant, Newborn, Infant, Karyotype, Hematology, Middle Aged, Precursor Cell Lymphoblastic Leukemia-Lymphoma, Prognosis, Survival Rate, Child, Preschool, Karyotyping, Immunology, Female, business, Tyrosine kinase
Abstract: The molecular characterization of cytogenetic abnormalities has not only provided insights into the mechanisms of leukemogenesis but also led to the establishment of new treatment strategies targeting these abnormalities and thereby further improve the prognosis of patients. We analyzed the prognosis of 1091 Chinese patients with newly diagnosed acute lymphoblastic leukemia (ALL) and explored the prognostic impacts of a large number of cytogenetic/molecular abnormalities. It was demonstrated that, in both B- and T-ALL settings, the prognosis was negatively correlated to the age as reported to date. For childhood T-ALL patients, it was also documented that the HOX11 expression represented a favorable prognostic factor as it was in adult ones. We identified CRLF2 overexpression as an intermediate-risk marker and Ik6 variant of IKZF1 gene as a high-risk one when stratifying pediatric B-ALL cases according to cytogenetic/molecular risks. We also found that Ik6 variant and CRLF2 overexpression had an important role in dictating the prognosis of Ph-negative patients, which may be useful markers in guiding the treatment of ALL in the future, with tyrosine kinase inhibitors on the other hand reversing the fate of Ph-positive ALL patients.
Published: 2012

10. Genetics of host-pathogen interactions in the wheat-Stagonospora nodorum pathosystem

Author: Faris JD, Lu HJ, Zhang Z, Reddy L, Liu ZH, Xu SS, Chu CG, Abeysekara N, Fellers JP, Cloutier S, Keller B, Scofield SR, and Friesen TL
Subjects: Wheat breeding, Wheat genetics
Published: 2008

11. Mapping QTL for popping expansion volume in popcorn with simple sequence repeat markers

Author: H. W. Ohm, Lu Hj, and Rex Bernardo
Subjects: Genetic Markers, DNA, Plant, Genetic Linkage, Population, Quantitative Trait Loci, Quantitative trait locus, Biology, Polymerase Chain Reaction, Zea mays, Gene Frequency, Genetics, education, Dent corn, Selection (genetic algorithm), DNA Primers, Repetitive Sequences, Nucleic Acid, education.field_of_study, Models, Genetic, food and beverages, Chromosome Mapping, General Medicine, Heritability, Marker-assisted selection, biology.organism_classification, Microsatellite, Agronomy and Crop Science, Biotechnology, Index selection
Abstract: Popping expansion volume is the most important quality trait in popcorn ( Zea mays L.), but its genetics is not well understood. The objectives of this study were to map quantitative trait loci (QTLs) responsible for popping expansion volume in a popcorn x dent corn cross, and to compare the predicted efficiencies of phenotypic selection, marker-based selection, and marker-assisted selection for popping expansion volume. Of 259 simple sequence repeat (SSR) primer pairs screened, 83 pairs were polymorphic between the H123 (dent corn) and AG19 (popcorn) parental inbreds. Popping test data were obtained for 160 S(1) families developed from the [AG19(H123 x AG19)] BC(1) population. The heritability ( h(2)) for popping expansion volume on an S(1) family mean basis was 0.73. The presence of the gametophyte factor Ga1(s) in popcorn complicates the analysis of popcorn x dent corn crosses. But, from a practical perspective, the linkage between a favorable QTL allele and Ga1(s) in popcorn will lead to selection for the favorable QTL allele. Four QTLs, on chromosomes 1S, 3S, 5S and 5L, jointly explained 45% of the phenotypic variation. Marker-based selection for popping expansion volume would require less time and work than phenotypic selection. But due to the high h(2) of popping expansion volume, marker-based selection was predicted to be only 92% as efficient as phenotypic selection. Marker-assisted selection, which comprises index selection on phenotypic and marker scores, was predicted to be 106% as efficient as phenotypic selection. Overall, our results suggest that phenotypic selection will remain the preferred method for selection in popcorn x dent corn crosses.
Published: 2002

12. Finding centric local outliers in categorical/numerical spaces

Author: Yu, JX, Qian, WN, Lu, HJ, Zhou, AY, Yu, JX, Qian, WN, Lu, HJ, and Zhou, AY
Abstract: Outlier detection techniques are widely used in many applications such as credit-card fraud detection, monitoring criminal activities in electronic commerce, etc. These applications attempt to identify outliers as noises, exceptions, or objects around the border. The existing density-based local outlier detection assigns the degree to which an object is an outlier in a numerical space. In this paper, we propose a novel mutual-reinforcement-based local outlier detection approach. Instead of detecting local outliers as noise, we attempt to identify local outliers in the center, where they are similar to some clusters of objects on one hand, and are unique on the other. Our technique can be used for bank investment to identify a unique body, similar to many good competitors, in which to invest. We attempt to detect local outliers in categorical, ordinal as well as numerical data. In categorical data, the challenge is that there are many similar but different ways to specify relationships among the data items. Our mutual-reinforcement-based approach is stable, with similar but different user-defined relationships. Our technique can reduce the burden for users to determine the relationships among data items, and find the explanations why the outliers are found. We conducted extensive experimental studies using real datasets.
Published: 2006

13. A false negative approach to mining frequent itemsets from high speed transactional data streams

Author: Yu, JX, Chong, ZH, Lu, HJ, Zhang, ZJ, Zhou, AY, Yu, JX, Chong, ZH, Lu, HJ, Zhang, ZJ, and Zhou, AY
Abstract: Mining frequent itemsets from transactional data streams is challenging due to the nature of the exponential explosion of itemsets and the limit memory space required for mining frequent itemsets. Given a domain of I unique items, the possible number of itemsets can be up to 2(I) - 1. When the length of data streams approaches to a very large number N, the possibility of an itemset to be frequent becomes larger and difficult to track with limited memory. The existing studies on finding frequent items from high speed data streams are false-positive oriented. That is, they control memory consumption in the counting processes by an error parameter epsilon, and allow items with support below the specified minimum supports but above s - epsilon counted as frequent ones. However, such false-positive oriented approaches cannot be effectively applied to frequent itemsets mining for two reasons. First, false-positive items found increase the number of false-positive frequent itemsets exponentially. Second, minimization of the number of false-positive items found, by using a small 6, will make memory consumption large. Therefore, such approaches may make the problem computationally intractable with bounded memory consumption. In this paper, we developed algorithms that call effectively mine frequent item(set)s from high speed transactional data streams with a bound of memory consumption. Our algorithms are based on Chernoff bound in which we use a running error parameter to prune itein(set)s and use a reliability parameter to control memory. While our algorithms are false-negative oriented, that is, certain frequent itemsets may not appear in the results, the number of false-negative itemsets can be controlled by a predefined parameter so that desired recall rate of frequent itemsets can be guaranteed. Our extensive experimental studies show that the proposed algorithms have high accuracy, require less memory, and consume less CPU time. They significantly outperform the exist
Published: 2006

14. False-negative frequent items mining from data streams with bursting

Author: Chong, ZH, Yu, JX, Lu, HJ, Zhang, ZJ, Zhou, AY, Chong, ZH, Yu, JX, Lu, HJ, Zhang, ZJ, and Zhou, AY
Abstract: False-negative frequent items mining from a high speed transactional data stream is to find an approximate set of frequent items with respect to a minimum support threshold, s. It controls the possibility of missing frequent items using a reliability parameter delta. The importance of false-negative frequent items mining is that it can exclude false-positives and therefore significantly reduce the memory consumption for frequent itemsets mining. The key issue of false-negative frequent items mining is how to minimize the possibility of missing frequent items. In this paper, we propose a new false-negative frequent items mining algorithm, called Loss-Negative, for handling bursting in data streams. The new algorithm consumes the smallest memory in comparison with other false-negative and false-positive frequent items algorithms. We present theoretical bound of the new algorithm, and analyze the possibility of minimization of missing frequent items, in terms of two possibilities, namely, in-possibility and out-possibility. The former is about how a frequent item can possibly pass the first pruning. The latter is about how long a frequent item can stay in memory while no occurrences of the item comes in the following data stream for a certain period. The new proposed algorithm is superior to the existing false-negative frequent items mining algorithms in terms of the two possibilities. We demonstrate the effectiveness of the new algorithm in this paper.
Published: 2005

15. Dynamically updating XML data: Numbering scheme revisited

Author: Yu, JX, Luo, DF, Meng, XF, Lu, HJ, Yu, JX, Luo, DF, Meng, XF, and Lu, HJ
Abstract: Almost all existing approaches use certain numbering scheme to encode XML elements to facilitate query processing when XML data is stored in databases. For example, under the most popular region-based numbering scheme, the starting and ending positions of an element in a document are used as the code to identify the element so that the ancestor/descendant relationship between two elements can be determined by merely examining their codes. While such numbering scheme can greatly improve query performance, renumbering large amount of elements caused by updates becomes a performance bottleneck if XML documents are frequently updated. Unfortunately, no satisfactory work has been reported for efficient update of XML data. In this paper, we first formalize the XML data update problem by defining the basic operators to support most XML update queries. We then present a new numbering scheme that not only requires minimal code-length in comparison with existing numbering schema but also improves update performance when XML data is frequently updated at arbitrary positions. The fundamental difference between our new scheme and existing ones is that, instead of maintaining the explicit codes for elements, we only store the necessary information and generate the codes when they are needed in query processing. In addition to present the basic scheme, we also discuss some optimization techniques to further reduce the update cost. Results of a comprehensive performance study are provided to show the advantages of the new scheme.
Published: 2005

16. Locating motifs in time-series data

Author: Liu, Z., Yu, JX, Lin, XM, Lu, HJ, Wang, W., Liu, Z., Yu, JX, Lin, XM, Lu, HJ, and Wang, W.
Abstract: Finding motifs in time-series is proposed to make clustering of time-series subsequences meaningful, because most existing algorithms of clustering time-series subsequences are reported meaningless in recent studies. The existing motif finding algorithms emphasize the efficiency at the expense of quality, in terms of the number of time-series subsequences in a motif and the total number of motifs found. In this paper, we formalize the problem as a continuous top-k motif balls problem in an m-dimensional space, and propose heuristic approaches that can significantly improve the quality of motifs with reasonable overhead, as shown in our experimental studies.
Published: 2005

17. Constructing suffix tree for gigabyte sequences with megabyte memory

Author: Cheung, CF, Yu, JX, Lu, HJ, Cheung, CF, Yu, JX, and Lu, HJ
Abstract: Mammalian genomes are typically 3Gbps (gibabase pairs) in size. The largest public database NCBI (National Center for Biotechnology Information (http://www.ncbi.nlm.nih.gov)) of DNA contains more than 20 Gbps. Suffix trees are widely acknowledged as a data structure to support exact/approximate sequence matching queries as well as repetitive structure finding efficiently when they can reside in main memory. But, it has been shown as difficult to handle long DNA sequences using suffix trees due to the so-called memory bottleneck problems. The most space efficient main-memory suffix tree construction algorithm takes nine hours and 45 GB memory space to index the human genome [ 19]. In this paper, we show that suffix trees for long DNA sequences can be efficiently constructed on disk using small bounded main memory space and, therefore, all existing algorithms based on suffix trees can be used to handle long DNA sequences that cannot be held in main memory. We adopt a two-phase strategy to construct a suffix tree on disk: 1) to construct a diskbase suffix-tree without suffix links and 2) rebuild suffix links upon the suffix-tree being constructed on disk, if needed. We propose a new disk-based suffix tree construction algorithm, called DynaCluster, which shows O(n log n) experimental behavior regarding CPU cost and linearity for I/O cost. DynaCluster needs 16 MB main memory only to construct more than 200 Mbps DNA sequences and significantly outperforms the existing disk-based suffix-tree construction algorithms using prepartitioning techniques in terms of both construction cost and query processing cost. We conducted extensive performance studies and report our findings in this paper.
Published: 2005

18. On new fuzzy morphological associative memories

Author: Wang, ST, Lu, HJ, Wang, ST, and Lu, HJ
Abstract: In this paper, the new fuzzy morphological associative memories (FMAMs) based on fuzzy operations (Lambda, .) and (V, .) are presented. FMAM with (V, .) is extremely robust for dilative noise and FMAM with (Lambda, .) is extremely robust for erosive noise. Autoassociative FMAM has the unlimited storage capability and can converge in one step. The convex autoassociative FMAM can be used to achieve a reasonable tradeoff for the mixed noise. Finally, comparisons between autoassociative FMAM and the famous FAM are discussed. FMAM, as another new encoding way of fuzzy rules, still has a multitude of open problems worthy to explore in the future.
Published: 2004

19. A simple but effective dynamic materialized view caching

Author: Choi, CH, Yu, JX, Lu, HJ, Choi, CH, Yu, JX, and Lu, HJ
Abstract: Dynamic materialized view management problem has recently received considerable attention. The problem is how to dynamically maintain some results of online analytical processing (OLAP) queries for the purpose of maximizing the possibility to answer subsequence ad-hoc OLAP queries as well as minimizing the total query processing cost. However, the existing dynamic view management approaches have their limit to support OLAP queries. In this paper, we propose a dynamic materialized view management approach with a new hit prediction, which can predict the users' interest in an ongoing fashion and adapt to changes if necessary. Extensive performance studies show that our approach achieves a high cache hit ratio and speeds up the response time or throughout for different kinds of CLAP queries.
Published: 2004

20. Classifying text streams in the presence of concept drifts

Author: Fung, GPC, Yu, JX, Lu, HJ, Fung, GPC, Yu, JX, and Lu, HJ
Abstract: Concept drifting is always an interesting problem. For instance, a user is interested in a set of topics, X, for a period, may switches to a different set of topics, Y, in the next period. In this paper, we focus on two issues of concept drifts, namely, concept drifts detection and model adaptation in a text stream context. We use statistical control to detect concept drifts, and propose a new multi-classifier strategy for model adaptation. We conducted extensive experiments and reported our findings in this paper.
Published: 2004

21. Data mining proxy: Serving large number of users for efficient frequent itemset mining

Author: Li, ZH, Yu, JX, Lu, HJ, Xu, YB, Liu, GM, Li, ZH, Yu, JX, Lu, HJ, Xu, YB, and Liu, GM
Abstract: Data mining has attracted a lot of research efforts during the past decade. However, little work has been reported on supporting a large number of users with similar data mining tasks. In this paper, we present a data mining proxy approach that provides basic services so that the overall performance of the system can be maximized for frequent itemset mining. Our data mining proxy is designed to fast respond a user's request by constructing the required tree using both trees in the proxy that are previously built for other users' requests and trees stored on disk that are pre-computed from transactional databases. We define a set of basic operations on pattern trees including tree projection and tree merge. Our performance study indicated that the data mining proxy significantly reduces the I/O cost and CPU cost to construct trees. The frequent pattern mining costs with the trees constructed can be minimized.
Published: 2004

22. Selecting views with maintenance cost constraints: Issues, heuristics and performance

Author: Yu, JX, Choi, CH, Gou, G., Lu, HJ, Yu, JX, Choi, CH, Gou, G., and Lu, HJ
Abstract: In order to efficiently support a large number of on-line analytical processing (OLAP) queries, a data warehouse needs to precompute or materialise some of such OLAP queries. One of the important issues is how to select such a set of materialised views in order to minimise the total processing cost,for OLAP queries. The maintenance-cost view-selection problem is to select a set of materialised views under a maintenance cost constraint (such as maintenance time), in order to minimise the total query processing cost for a given set of queries. This problem is more difficult than the view selection problem under a disk-space constraint, because a selected view may make the previously selected views less beneficial, due to the fact that the total maintenance cost for a set of views may decrease when more views are materialised while the maintenance cost always increases under disk-space constraint. The problem has recently received significant attention. Several greedy/heuristic algorithms were proposed. However the quality of the greedy/heuristic algorithms has not been well analysed. In this paper, in a multidimensional data warehouse environment, we re-examine the greedy/heuristic algorithms in various settings, and provide users with insights on the quality of these heuristic algorithms.
Published: 2004

23. Efficient mining of frequent patterns using ascending frequency ordered prefix-tree

Author: Liu, GM, Lu, HJ, Lou, WW, Liu, GM, Lu, HJ, and Lou, WW
Abstract: Mining frequent patterns, including mining frequent closed patterns or maximal patterns, is a fundamental and important problem in data mining area. Many algorithms adopt the pattern growth approach, which is shown to be superior to the candidate generate-and-test approach, especially when long patterns exist in the datasets. In this paper, we identify the key factors that influence the performance of the pattern growth approach, and optimize them to further improve the performance. Our algorithm uses a simple while compact data structure-ascending frequency ordered prefix-tree (AFOPT) to store the conditional databases, in which we use arrays to store single branches to further save space. The AFOPT structure is traversed in top-down depth-first order. Our analysis and experiment results show that the combination of the top-down traversal strategy and the ascending frequency order achieves significant performance improvement over previous works.
Published: 2004

24. Classifying high-speed text streams

Author: Fung, GPC, Yu, JX, Lu, HJ, Fung, GPC, Yu, JX, and Lu, HJ
Abstract: Recently, a new class of data-intensive application becomes widely recognized where data is modeled best as transient open-end streams rather than persistent tables on disk. It leads to a new surge of research interest called data streams. However, most of the reported works are concentrated on structural data, such as bit-sequences, and seldom focus on unstructural data, such as textual documents. In this paper, we propose an efficient classification approach for classifying highspeed text streams. The proposed approach is based on sketches such that it is able to classify the streams efficiently by scanning them only once, meanwhile consuming a small bounded of memory in both model maintenance and operation. Extensive experiments using benchmarks and a real-life news article collection are conducted. The encouraging results indicated that our proposed approach is highly feasible.
Published: 2003

25. Dynamic materialized view management based on predicates

Author: Choi, CH, Yu, JX, Lu, HJ, Choi, CH, Yu, JX, and Lu, HJ
Abstract: For the purpose of satisfying different users' profiles and accelerating the subsequence OLAP (Online Analytical Processing) queries in a large data warehouse, dynamic materialized OLAP view management is highly desirable. Previous work caches data as either chunks or multidimensional range fragments. In this paper, we focus on ROLAP (Relational OLAP) in an existing relational database system. We propose a dynamic predicate-based partitioning approach, which can support a wide range of OLAP queries. We conducted extensive performance studies using TPCH benchmark data on IBM DB2 and encouraging results are obtained which indicate that our approach is highly feasible.
Published: 2003

26. Ascending frequency ordered prefix-tree: Efficient mining of frequent patterns

Author: Liu, GM, Lu, HJ, Xu, YB, Yu, JX, Liu, GM, Lu, HJ, Xu, YB, and Yu, JX
Abstract: Mining frequent patterns is a fundamental and important problem in many data mining applications. Many of the algorithms adopt the pattern growth approach, which is shown to be superior to the candidate generate-and-test approach significantly. In this paper we identify the key factors that influence the performance of the pattern growth approach, and optimize them to further improve the performance. Our algorithm uses a simple while compact data structure-ascending frequency ordered prefix-tree(AFOPT) to organize the conditional databases, in which we use arrays to store single branches to further save space. We traverse our prefix-tree structure using a top-down strategy. Our experiment results show that the combination of the top-down traversal strategy and the ascending frequency item ordering method achieves significant performance improvement over previous works.
Published: 2003

27. Managing very large document collections using semantics

Author: Wang, GR, Lu, HJ, Yu, G., Bao, YB, Wang, GR, Lu, HJ, Yu, G., and Bao, YB
Abstract: In this paper, a system is presented where documents are no longer identified by their file names. Instead, a document is represented by its semantics in terms of descriptor and content vector. The descriptor of a document consists of a set of attributes, such as date of creation, its type, its size, annotations, etc. The content vector of a document consists of a set of terms extracted from the document. In this paper, a semantic document management system XBASE is designed and implemented based on the semantics and the functions of three main modules, X-Loader, X-Explorer and X-Query.
Published: 2003

28. Automata match: a new XML query processing approach

Author: Yu, JX, Wang, GR, Lu, HJ, Yu, G., Lv, JH, Sun, B., Yu, JX, Wang, GR, Lu, HJ, Yu, G., Lv, JH, and Sun, B.
Abstract: With the widespread use of Well technologies and the growing number of applications adopting XML as the standard language for representing and exchanging structured data and documents oil the Internet, XML query processing becomes important as well as XML data management. In this paper, we propose a new novel XML path query evaluating approach, called Automata Match (AM). In our AM algorithm, all XML path expression queries are first converted into query automata as finite state automata. If the path schema of path instances in the XML data tree is accepted by the query automata, the corresponding path instances will be included in the result Set. Our AM algorithm supports all basic operators used in regular path expressions (RPE) Such as "{*}", "+", "?" and "\$\textbackslash{}\$". In addition, We also support both predicate filtering and "//" (for containment-joins). It is worth noting that "//" is not an operator in regular path expressions. In order to support "//", schema automata are proposed in this paper with two optimization strategies. We describe the implementation details Of Our approach, and conducted extensive performance Studies using XML benchmarks. The AM approach Outperforms existing approaches even for containment joins. We wil report Our findings in this paper.
Published: 2003

29. XParent: An efficient RDBMS-Based XML database system

Author: Jiang, HF, Lu, HJ, Wei, W., Yu, JX, Jiang, HF, Lu, HJ, Wei, W., and Yu, JX
Abstract: An efficient relational database management system (RDMS)-based extensible markup language (XML) database system, XParent, was discussed. XParent is based on an efficient, model-mapping-based approach, which uses a fixed database schema to store any XML documents without help of data type descriptors (DTD). The visual query interface of XParent provides both expressive power for professionals and user friendliness for naive users.
Published: 2002

30. DIRECT: a system for mining data value conversion rules from disparate data sources

Author: Fan, WG, Lu, HJ, Madnick, SE, Cheung, D., Fan, WG, Lu, HJ, Madnick, SE, and Cheung, D.
Abstract: The successful integration of data from autonomous and heterogeneous systems calls for the resolution of semantic conflicts that may be present. Such conflicts are often reflected by discrepancies in attribute values of the same data object. In this paper, we describe a recently developed prototype system, Discovering and REconciling ConflicTs (DIRECT). The system mines data value conversion rules in the process of integrating business data from multiple sources. The system architecture and functional modules are described. The process of discovering conversion rules from sales data of a trading company is presented as an illustrative example. (C) 2002 Elsevier Science B.V. All rights reserved.
Published: 2002

31. A template model for multidimensional inter-transactional association rules

Author: Feng, L., Yu, JX, Lu, HJ, Han, JW, Feng, L., Yu, JX, Lu, HJ, and Han, JW
Abstract: Multidimensional inter-transactional association rules extend the traditional association rules to describe more general associations among items with multiple properties across transactions. "After McDonald and Burger King open branches, KFC will open a branch two months later and one mile away" is an example of such rules. Since the number of potential inter-transactional association rules tends to be extremely large, mining inter-transactional associations poses more challenges on efficient processing than mining traditional intra-transactional associations. In order to make such association rule mining truly practical and computationally tractable, in this study we present a template model to help users declare the interesting multidimensional inter-transactional associations to be mined. With the guidance of templates, several optimization techniques, i.e., joining, converging, and speeding, are devised to speed up the discovery of inter-transactional association rules. We show, through a series of experiments on both synthetic and real-life data sets, that these optimization techniques can yield significant performance benefits.
Published: 2002

32. A new integrated clustering algorithm GFC and switching regressions

Author: Wang, ST, Jiang, HF, Lu, HJ, Wang, ST, Jiang, HF, and Lu, HJ
Abstract: The switching regression problems are attracting more and more attention in a variety of disciplines such as pattern recognition, economics and databases. To solve switching regression problems, many approaches have been investigated. In this paper, we present a new integrated clustering algorithm GFC that combines gravity-based clustering algorithm GC with fuzzy clustering. GC, as a new hard clustering algorithm presented here, is based on the well-known Newton's Gravity Law. Our theoretic analysis shows that GFC can converge to a local minimum of the object function. Our experimental results illustrate that GFC for switching regression problems has better performance than standard fuzzy clustering algorithms, especially in terms of convergence speed. Hence GFC is a new more efficient algorithm for switching regression problems.
Published: 2002

33. Data extraction from the Web based on pre-defined schema

Author: Meng, XF, Lu, HJ, Wang, HY, Gu, MZ, Meng, XF, Lu, HJ, Wang, HY, and Gu, MZ
Abstract: With the development of the Internet, the World Wide Web has become an invaluable information source for most organizations. However, most documents available from the Web are in HTML form which is originally designed for document formatting with little consideration of its contents. Effectively extracting data from such documents remains a non-trivial task. In this paper, we present a schema-guided approach to extracting data from HTML pages. Under the approach, the user defines a schema specifying what to be extracted and provides sample mappings between the schema and the HTML page. The system will induce the mapping rules and generate a wrapper that takes the HTML page as input and produces the required data in the form of XML conforming to the user-defined schema. A prototype system implementing the approach has been developed. The preliminary experiments indicate that the proposed semi-automatic approach is not only easy to use but also able to produce a wrapper that extracts required data from inputted pages with high accuracy.
Published: 2002

34. A fast scalable classifier tightly integrated with RDBMS

Author: Liu, HY, Lu, HJ, Chen, J., Liu, HY, Lu, HJ, and Chen, J.
Abstract: In this paper, we report our success in building efficient scalable classifiers by exploring the capabilities of modern relational database management systems (RDBMS). In addition to high classification accuracy, the unique features of the approach include its high training speed, linear scalability, and simplicity in implementation. More importantly, the major computation required in the approach can be implemented using standard functions provided by the modern relational DBMS. Besides, with the effective rule pruning strategy, the algorithm proposed in this paper can produce a compact set of classification rules. The results of experiments conducted for performance evaluation and analysis are presented.
Published: 2002

35. Toward multidatabase mining: Identifying relevant databases

Author: Liu, H., Lu, HJ, Yao, J., Liu, H., Lu, HJ, and Yao, J.
Abstract: Various tools and systems for knowledge discovery and data mining are developed and available for applications. However, when we are immersed in heaps of databases, an immediate question is where we should start mining. It is not true that the more databases, the better for data mining. It is only true when the databases involved are relevant to a task at hand. In this paper, breaking away from the conventional data mining assumption that many databases be joined into one, we argue that the first step for multidatabase mining is to identify databases that are most likely relevant to an application; without doing so, the mining process can be lengthy, aimless, and ineffective. A measure of relevance is thus proposed for mining tasks with an objective of finding patterns or regularities about certain attributes. An efficient algorithm for identifying relevant databases is described. Experiments are conducted to verify the measure's performance and to exemplify its application.
Published: 2001

36. Discovering and reconciling value conflicts for numerical data integration

Author: Fan, WG, Lu, HJ, Madnick, SE, Cheung, D., Fan, WG, Lu, HJ, Madnick, SE, and Cheung, D.
Abstract: The built-up in Information Technology capital fueled by the Internet and cost-effectiveness of new telecommunications technologies has led to a proliferation of information systems that are in dire need to exchange information but incapable of doing so due to the lack of semantic interoperability. It is now evident that physical connectivity (the ability to exchange bits and bytes) is no longer adequate: the integration of data from autonomous and heterogeneous systems calls for the prior identification and resolution of semantic conflicts that may be present. Unfortunately, this requires the system integrator to sift through the data from disparate systems in a painstaking manner. We suggest that this process can be partially automated by presenting a methodology and technique for the discovery of potential semantic conflicts as well as the underlying data transformation needed to resolve the conflicts. Our methodology begins by classifying data value conflicts into two categories: context independent and context dependent. While context independent conflicts are usually caused by unexpected errors, the context dependent conflicts are primarily a result of the heterogeneity of underlying data sources. To facilitate data integration, data value conversion rules are proposed to describe the quantitative relationships among data values involving context dependent conflicts. A general approach is proposed to discover data value conversion rules from the data. The approach consists of the five major steps: relevant attribute analysis, candidate model selection, conversion function generation, conversion function selection and conversion rule formation. It is being implemented in a prototype system, DIRECT, for business data using statistics based techniques. Preliminary study using both synthetic and real world data indicated that the proposed approach is promising. (C) 2001 Elsevier Science Ltd. All rights reserved.
Published: 2001

37. Attribute value extraction and standardization in data integration

Author: Lu, HJ, Tian, ZP, Lu, HJ, and Tian, ZP
Abstract: Most database systems and data analysis tools work with relational or well-structured data. When data is collected from various sources for warehousing or analysis, extracting and formatting the input data into required form is never a trivial task, as it seems. We present in this paper a pattern-matching based approach for extracting and standardizing attribute values from input data entries in the form of character strings. The core component of the approach is a powerful pattern language, which provides a simple way for specifying the semantic features, length limitations, external references, element extraction and restructure of attributes. Attribute values can then be extracted from input strings by pattern matching. Constraints on attributes can be enforced so that the attribute values are standardized even the input data is from different sources and in different formats. The pattern language and matching algorithms are presented. A prototype system based on the proposed approach is also described.
Published: 2001

38. FACT: A learning based web query processing system

Author: Chen, ST, Diao, YL, Lu, HJ, Tian, ZP, Chen, ST, Diao, YL, Lu, HJ, and Tian, ZP
Published: 2000

39. ISOSCALAR MONOPOLE AND DIPOLE STRENGTH BETWEEN 10 MEV AND 20 MEV IN MG-24, FROM INELASTIC ALPHA-SCATTERING AT AND AROUND 0-DEGREES

Author: LU, HJ, BRANDENBURG, S, DELEO, R, HARAKEH, MN, POELHEKKEN, TD, VANDERWOUDE, A, KVI - Center for Advanced Radiation Technology, and Damage and Repair in Cancer Development and Cancer Treatment (DARE)
Published: 1986

40. Studies on the Synthesis, Properties and Biological Activities of (9-Carbazoly)-carboxylic Acids

Author: Lao, Wj, Jing You, Lu, Hj, Chen, Sl, Shen, Xj, and Ou, Qy

41. Effects of oceanographic conditions on fishery distribution: A case study of chub mackerel (Scomber japonicus) in northeastern Taiwan.

Author: Mondal S, Muller-Karger F, Ray A, Ito SI, Lee MA, and Lu HJ
Subjects: Animals, Taiwan, Perciformes physiology, Ecosystem, Oceanography, Salinity, Temperature, Environmental Monitoring, Fisheries, Climate Change
Abstract: Changes in oceanographic conditions can affect species distribution in marine habitats. Global climate change may negatively influence the oceanographic factor-species distribution relationship. Here, we assessed the influence of oceanographic conditions on chub mackerel (Scomber japonicus) distribution in northeastern Taiwan by constructing and using a habitat ensemble model incorporating chub mackerel fishery, climatic oscillation, and oceanography data. Our results indicated that the chub mackerel catch was mainly influenced by the Western Pacific Oscillation. Moreover, sea-surface height and mixed-layer depth exerted the most and least significant effects on chub mackerel distribution, respectively. The chub mackerel catch rate peaked in the study area with a sea-surface temperature of 29 °C, sea-surface chlorophyll of 0.25 mg/m 3 , sea-surface salinity of 33.7 psμ, and SSH of 0.575 m. Chub mackerel was the most widely distributed in the area between 25°N, 120.5°E and 26.2°N, 121.5°E. Our findings can be used to develop critical adaptation plans for managing chub mackerel fisheries in the northeastern waters of Taiwan. Considering changing climate conditions globally, the incorporation of this knowledge into managerial strategies may aid decision-makers in protecting not only other ocean fisheries but also individuals dependent on them., 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
Full Text: View/download PDF

42. Relevance and mechanism of STAT3/miR-221-3p/Fascin-1 axis in EGFR TKI resistance of triple-negative breast cancer.

Author: Jin LL, Lu HJ, Shao JK, Wang Y, Lu SP, Huang BF, Hu GN, Jin HC, and Wang CQ
Subjects: Humans, Female, Cell Line, Tumor, Gene Expression Regulation, Neoplastic drug effects, Neoplasm Proteins metabolism, Neoplasm Proteins genetics, Neoplasm Proteins biosynthesis, RNA, Neoplasm genetics, RNA, Neoplasm metabolism, RNA, Neoplasm biosynthesis, MicroRNAs genetics, MicroRNAs metabolism, Triple Negative Breast Neoplasms drug therapy, Triple Negative Breast Neoplasms genetics, Triple Negative Breast Neoplasms pathology, Triple Negative Breast Neoplasms metabolism, STAT3 Transcription Factor metabolism, STAT3 Transcription Factor genetics, Drug Resistance, Neoplasm drug effects, ErbB Receptors metabolism, ErbB Receptors genetics, Gefitinib pharmacology, Microfilament Proteins metabolism, Microfilament Proteins genetics, Carrier Proteins metabolism, Carrier Proteins genetics, Protein Kinase Inhibitors pharmacology
Abstract: The epidermal growth factor receptor 1 (EGFR) plays a crucial role in the progression of various malignant tumors and is considered a potential target for treating triple-negative breast cancer (TNBC). However, the effectiveness of representative tyrosine kinase inhibitors (TKIs) used in EGFR-targeted therapy is limited in TNBC patients. In our study, we observed that the TNBC cell lines MDA-MB-231 and MDA-MB-468 exhibited resistance to Gefitinib. Treatment with Gefitinib caused an upregulation of Fascin-1 (FSCN1) protein expression and a downregulation of miR-221-3p in these cell lines. However, sensitivity to Gefitinib was significantly improved in both cell lines with either inhibition of FSCN1 expression or overexpression of miR-221-3p. Our luciferase reporter assay confirmed that FSCN1 is a target of miR-221-3p. Moreover, Gefitinib treatment resulted in an upregulation of phosphorylated signal transducer and activator of transcription 3 (p-STAT3) in MDA-MB-231 cells. Using Stattic, a small-molecule inhibitor of STAT3, we observed a significant enhancement in the inhibitory effect of Gefitinib on the growth, migration, and invasion of MDA-MB-231 cells. Additionally, Stattic treatment upregulated miR-221-3p expression and downregulated FSCN1 mRNA and protein expression. A strong positive correlation was noted between the expression of STAT3 and FSCN1 in breast cancer tissues. Furthermore, patients with high expression levels of both STAT3 and FSCN1 had a worse prognosis. Our findings suggest that elevated FSCN1 expression is linked to primary resistance to EGFR TKIs in TNBC. Moreover, we propose that STAT3 regulates the expression of miR-221-3p/FSCN1 and therefore modulates resistance to EGFR TKI therapy in TNBC. Combining EGFR TKI therapy with inhibition of FSCN1 or STAT3 may offer a promising new therapeutic option for TNBC., (© 2023. The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.)
Published: 2024
Full Text: View/download PDF

43. Study of the Decay and Production Properties of D_{s1}(2536) and D_{s2}^{*}(2573).

Author: Ablikim M, Achasov MN, Adlarson P, Afedulidis O, Ai XC, Aliberti R, Amoroso A, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Bao HR, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chai XY, Chang JF, Che GR, Che YZ, Chelkov G, Chen C, Chen CH, Chen C, Chen G, Chen HS, Chen HY, Chen ML, Chen SJ, Chen SL, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Chen ZY, Choi SK, Cibinetto G, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng CQ, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du MC, Du SX, Duan YY, Duan ZH, Egorov P, Fan YH, Fang J, Fang J, Fang SS, Fang WX, Fang Y, Fang YQ, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Feng YT, Fritsch M, Fu CD, Fu JL, Fu YW, Gao H, Gao XB, Gao YN, Gao Y, Garbolino S, Garzia I, Ge L, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guo AQ, Guo LB, Guo MJ, Guo RP, Guo YP, Guskov A, Gutierrez J, Han KL, Han TT, Hanisch F, Hao XQ, Harris FA, He KK, He KL, Heinsius FH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu BY, Hu HM, Hu JF, Hu SL, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Huang YS, Hussain T, Hölzken F, Hüsken N, In der Wiesche N, Jackson J, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji W, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang D, Jiang HB, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao JK, Jiao Z, Jin S, Jin Y, Jing MQ, Jing XM, Johansson T, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kavatsyuk M, Ke BC, Khachatryan V, Khoukaz A, Kiuchi R, Kolcu OB, Kopf B, Kuessner M, Kui X, Kumar N, Kupsc A, Kühn W, Lane JJ, Lavezzi L, Lei TT, Lei ZH, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li K, Li KL, Li LJ, Li LK, Li L, Li MH, Li PR, Li QM, Li QX, Li R, Li SX, Li T, Li WD, Li WG, Li X, Li XH, Li XL, Li XY, Li XZ, Li YG, Li ZJ, Li ZY, Liang C, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao YP, Libby J, Limphirat A, Lin CC, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu F, Liu FH, Liu F, Liu GM, Liu H, Liu HB, Liu HH, Liu HM, Liu H, Liu JB, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu X, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZD, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo JR, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma H, Ma HL, Ma JL, Ma LL, Ma LR, Ma MM, Ma QM, Ma RQ, Ma T, Ma XT, Ma XY, Ma YM, Maas FE, MacKay I, Maggiora M, Malde S, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Moses B, Muchnoi NY, Muskalla J, Nefedov Y, Nerling F, Nie LS, Nikolaev IB, Ning Z, Nisar S, Niu QL, Niu WD, Niu Y, Olsen SL, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Pei YP, Pelizaeus M, Peng HP, Peng YY, Peters K, Ping JL, Ping RG, Plura S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qiao XK, Qin JJ, Qin LQ, Qin LY, Qin XP, Qin XS, Qin ZH, Qiu JF, Qu ZH, Redmer CF, Ren KJ, Rivetti A, Rolo M, Rong G, Rosner C, Ruan MQ, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shang ZJ, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi H, Shi HC, Shi JL, Shi JY, Shi QQ, Shi SY, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Song YX, Sosio S, Spataro S, Stieler F, Su SS, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZQ, Sun ZT, Tang CJ, Tang GY, Tang J, Tang M, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian Y, Tian ZF, Uman I, Wan Y, Wang SJ, Wang B, Wang BL, Wang B, Wang DY, Wang F, Wang HJ, Wang HP, Wang JJ, Wang JP, Wang K, Wang LL, Wang M, Wang NY, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang XN, Wang Y, Wang YD, Wang YF, Wang YL, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei DH, Weidner F, Wen SP, Wen YR, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YH, Wu YJ, Wu Z, Xia L, Xian XM, Xiang BH, Xiang T, Xiao D, Xiao GY, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu M, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu Y, Xu YC, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YF, Yang YX, Yang ZW, Yao ZP, Ye M, Ye MH, Yin JH, Yin J, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu MC, Yu T, Yu XD, Yu YC, Yuan CZ, Yuan J, Yuan J, Yuan L, Yuan SC, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng SH, Zeng X, Zeng Y, Zeng YJ, Zeng YJ, Zhai XY, Zhai YC, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang H, Zhang HC, Zhang HH, Zhang HH, Zhang HQ, Zhang HR, Zhang HY, Zhang J, Zhang J, Zhang JJ, Zhang JL, Zhang JQ, Zhang JS, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang LM, Zhang L, Zhang P, Zhang QY, Zhang RY, Zhang SH, Zhang S, Zhang XM, Zhang XY, Zhang XY, Zhang Y, Zhang Y, Zhang YT, Zhang YH, Zhang YM, Zhang Y, Zhang ZD, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhang ZZ, Zhao G, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao N, Zhao RP, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng BM, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou JY, Zhou LP, Zhou S, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhou ZC, Zhu AN, Zhu J, Zhu K, Zhu KJ, Zhu KS, Zhu L, Zhu LX, Zhu SH, Zhu TJ, Zhu WD, Zhu YC, Zhu ZA, Zou JH, and Zu J
Abstract: The e^{+}e^{-}→D_{s}^{+}D_{s1}(2536)^{-} and e^{+}e^{-}→D_{s}^{+}D_{s2}^{*}(2573)^{-} processes are studied using data samples collected with the BESIII detector at center-of-mass energies from 4.530 to 4.946 GeV. The absolute branching fractions of D_{s1}(2536)^{-}→D[over ¯]^{*0}K^{-} and D_{s2}^{*}(2573)^{-}→D[over ¯]^{0}K^{-} are measured for the first time to be (35.9±4.8±3.5)% and (37.4±3.1±4.6)%, respectively. The e^{+}e^{-}→D_{s}^{+}D_{s1}(2536)^{-} and e^{+}e^{-}→D_{s}^{+}D_{s2}^{*}(2573)^{-} cross sections are measured, and a resonant structure at around 4.6 GeV with a width of 50 MeV is observed in both processes with a statistical significance of 7.2σ and 15σ, respectively. The state is observed for the first time in e^{+}e^{-}→D_{s}^{+}D_{s2}^{*}(2573)^{-} and could be the Y(4626) found by the Belle oration in the D_{s}^{+}D_{s1}(2536)^{-} final state, since they have similar masses and widths. There is also evidence for a structure at around 4.75 GeV in both processes.
Published: 2024
Full Text: View/download PDF

44. Treatment of a femoral neck fracture combined with ipsilateral femoral head and intertrochanteric fractures: A case report.

Author: Yu X, Li YZ, Lu HJ, and Liu BL
Abstract: Background: This article presents a rare case of a complex hip fracture involving the ipsilateral femoral neck, trochanter, and femoral head, that was accompanied by hip dislocation. Currently, there is no established standard treatment method for this specific type of fracture. Therefore, it is crucial to comprehensively consider factors such as patient age, fracture type, and degree of displacement to achieve a successful outcome., Case Summary: A 38-year-old man sustained a comminuted fracture of his right hip as a result of a car accident. The injuries included a fracture of the femoral head, a fracture of the femoral neck, an intertrochanteric fracture of the femur, and a posterior dislocation of the hip on the same side. We opted for a treatment approach combining the use of a proximal femoral locking plate, cannulated screws, and Kirschner wires. Following the surgery, we developed an individualized rehabilitation program to restore patient limb function., Conclusion: For this complex fracture, we selected appropriate internal fixation and formulated individualized rehabilitation, which ultimately achieved good results., Competing Interests: Conflict-of-interest statement: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as potential conflicts of interest., (©The Author(s) 2024. Published by Baishideng Publishing Group Inc. All rights reserved.)
Published: 2024
Full Text: View/download PDF

45. Prognostic factors and risk-stratification model of recurrent or metastatic head and neck squamous cell carcinoma treated with cetuximab containing regimen.

Author: Yang MH, Chen TH, Wang HM, Hsieh JC, Huang HC, Hsieh MC, Yen CJ, Wu SY, Hua CH, Lien MY, Chang YF, Wang HC, Chien CY, Huang TL, Lu HJ, Lin JC, Wang CC, Liu YC, Chen JP, Lu WC, Yiu CY, Lin CL, Lou PJ, and Chu PY
Subjects: Humans, Female, Male, Middle Aged, Prognosis, Aged, Risk Assessment methods, Taiwan epidemiology, Antineoplastic Combined Chemotherapy Protocols therapeutic use, Retrospective Studies, Adult, Risk Factors, Cetuximab therapeutic use, Cetuximab administration & dosage, Squamous Cell Carcinoma of Head and Neck drug therapy, Squamous Cell Carcinoma of Head and Neck mortality, Squamous Cell Carcinoma of Head and Neck pathology, Head and Neck Neoplasms drug therapy, Head and Neck Neoplasms pathology, Head and Neck Neoplasms mortality, Neoplasm Recurrence, Local
Abstract: Background: In recent years, the addition of cetuximab to chemotherapy has improved treatment outcomes for patients with recurrent/metastatic head and neck squamous cell carcinoma (R/M HNSCC). In this study, we present the real-world survival data of R/M HNSCC patients who received cetuximab-containing regimens from thirteen medical centers in Taiwan, as well as a three-level risk stratification model for this patient population., Methods: This study enrolled R/M HNSCC patients from thirteen medical centers in Taiwan who received cetuximab-containing regimens from January 1, 2017 to June 6, 2022. The cases were divided into a training cohort and a validation cohort based on the start of treatment. Overall survival (OS) was evaluated in both cohorts and exploratory analysis was performed to identify associated adverse clinical and laboratory factors. The results of the exploratory analysis were used to construct a three-level risk stratification prediction model for OS., Results: A total of 1434 patients with R/M HNSCC were enrolled in this study and received cetuximab-containing regimens. The overall population had a median OS of 8.57 months (95% CI: 8.07 - 9.08). Multivariate analysis of the training cohort identified poor ECOG performance status, heavy alcohol consumption, and prior adjuvant CCRT or lack of prior RT as adverse prognostic factors. Comparison of laboratory data between patients with OS≦6 and OS > 6 also revealed unfavorable factors, including increased white blood cell count, decreased hemoglobin level, increased platelet count, increased absolute neutrophil count, decreased absolute lymphocyte count, and increased neutrophil-to-lymphocyte ratio. Using forward prediction, a three-level risk stratification prediction model was constructed using the variables of ECOG performance status, alcohol consumption, skin metastasis, modality of radiation therapy, hemoglobin level, and neutrophil-to-lymphocyte ratio. The median OS in the low-risk, intermediate-risk, and high-risk groups were 12.02 months (95% CI 10.44-13.61), 7.5 months (95% CI 7.33-8.17), and 4.01 months (95% CI 3.94-4.08), respectively, with a log-rank test p-value < 0.001., Conclusion: This study presents a three-level risk stratification model with strong prediction ability for OS in R/M HNSCC patients who received cetuximab-containing regimens. The results are based on real-world data and may provide valuable information for clinicians in treatment planning and future drug development., (© 2024. The Author(s).)
Published: 2024
Full Text: View/download PDF

46. Association of PCSK9 inhibitors with mortality: insights from a retrospective cohort analysis.

Author: Huang CH, Wang SI, Fan FS, Lu HJ, and Wei JC
Subjects: Humans, Retrospective Studies, Male, Female, Aged, Middle Aged, Treatment Outcome, Risk Factors, Time Factors, Risk Assessment, Hyperlipidemias drug therapy, Hyperlipidemias mortality, Databases, Factual, Cause of Death trends, Serine Proteinase Inhibitors therapeutic use, Serine Proteinase Inhibitors adverse effects, Biomarkers blood, Dyslipidemias drug therapy, Dyslipidemias mortality, Dyslipidemias blood, Dyslipidemias diagnosis, Antibodies, Monoclonal, Humanized therapeutic use, Proprotein Convertase 9, PCSK9 Inhibitors, Hydroxymethylglutaryl-CoA Reductase Inhibitors therapeutic use
Abstract: Aims: Proprotein convertase subtilisin/kexin type 9 (PCSK9) inhibitors are effective in reducing cardiovascular events, but their impact on all-cause mortality and medical utilization compared to statins is unclear. This study investigated PCSK9 inhibitor use and its impact on mortality and medical utilization vs. statins, using TriNetX database data with up to 9 years of follow-up., Methods and Results: This retrospective cohort study analysed TriNetX data spanning 1 July 2015, to 31 December 2023, including 79 194 PCSK9 inhibitor users (alirocumab, evolocumab, inclisiran) and 5 437 513 statin users with hyperlipidaemia. The primary outcomes were all-cause mortality and medical utilization, including hospital inpatient services, emergency department visits, critical care, and mechanical ventilation. Propensity score matching showed that PCSK9 inhibitor use was associated with a 28.3% lower risk of all-cause mortality [adjusted hazard ratio (aHR) 0.717, 95% confidence interval (CI): 0.673-0.763] and significant reductions in medical utilization (hospital inpatient services usage: aHR 0.692, 95% CI: 0.664-0.721; emergency department services: aHR 0.756, 95% CI: 0.726-0.788; critical care services: aHR 0.619, 95% CI: 0.578-0.664; and mechanical ventilation: aHR 0.537, 95% CI: 0.484-0.596) compared to statins. These findings were consistent across various demographics and clinical subgroups. The sensitivity analyses supported the robustness of the findings., Conclusion: PCSK9 inhibitors significantly reduced all-cause mortality and medical utilization compared to statins, suggesting their important role in dyslipidaemia management, particularly for statin-naïve or intolerant patients. Further research, including randomized controlled trials, is needed to confirm these findings and explore the underlying mechanisms., (© The Author(s) 2024. Published by Oxford University Press on behalf of the European Society of Cardiology.)
Published: 2024
Full Text: View/download PDF

47. Dielectrophoretic capture of Escherichia coli and boar sperms using ULSI-fabricated three-dimensional protruding TiN nano-electrode arrays.

Author: Lu HJ, Liao IH, Lien CL, Shiau JH, Shen CF, Chou KR, and Cheng CM
Abstract: Object: In recent years, dielectrophoresis has become widely recognized as a highly suitable method for creating good tools for particle separation, with significant successes achieved in a variety of areas., Method: Expanding upon this, we adopted a semiconductor CMOS process, instead of a MEMS process, which allowed for the following: 1) wire insulation to mitigate Joule heat and prevent thermal fluctuation interference with the dielectrophoretic force; 2) isolation of harmful materials from biological samples, making the chip biocompatible; and, 3) the ability to employ nano-electrodes capable of generating a stronger electric field than conventional electrodes, thus allowing chip capture at lower voltages. Additionally, our chip is scalable, enabling multiplied throughput based on sample processing requirements., Results and Dissusion: These features make our chip more widely applicable and suitable for capturing bacteria and sperm. In this study, we focused on optimizing the parameters of dielectrophoresis and employed 3-D protruding TiN nano-electrode arrays to facilitate the capture of Escherichia coli and boar sperms. The experimental data demonstrates that the capture efficiency of this chip for E. coli was approximately 79.25% ± 2.66%, and the highest capture efficiency for sperms was approximately 39.2% ± 3.9%., Competing Interests: Authors J-HS, K-RC, and C-LL are employed by NEAT Biotech Inc. in Taiwan. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Lu, Liao, Lien, Shiau, Shen, Chou and Cheng.)
Published: 2024
Full Text: View/download PDF

48. Radiomic biomarkers for platinum-refractory head and neck cancer in the era of immunotherapy.

Author: Lu HJ, Shen CY, Chiu YW, Lin WL, Peng CY, Tseng HC, Hsin CH, Chuang CY, Chen CC, Wu MF, Huang WS, and Shen WC
Subjects: Humans, Male, Female, Middle Aged, Aged, Retrospective Studies, Biomarkers, Tumor, Prognosis, Immunotherapy, Drug Resistance, Neoplasm, Adult, Aged, 80 and over, B7-H1 Antigen antagonists & inhibitors, Radiomics, Squamous Cell Carcinoma of Head and Neck diagnostic imaging, Squamous Cell Carcinoma of Head and Neck drug therapy, Squamous Cell Carcinoma of Head and Neck therapy, Immune Checkpoint Inhibitors therapeutic use, Head and Neck Neoplasms diagnostic imaging, Head and Neck Neoplasms drug therapy, Head and Neck Neoplasms therapy
Abstract: Objective: Immune checkpoint inhibitors (ICI) are recommended as the first-line therapy for platinum-refractory head and neck squamous cell carcinoma (HNSCC), a disease with a poor prognosis. However, biomarkers in this situation are rare. The objective was to identify radiomic features-associated biomarkers to guide the prognosis and treatment opinions in the era of ICI., Methods: A total of 31 platinum-refractory HNSCC patients were retrospectively enrolled. Of these, 65.5% (20/31) received ICI-based therapy and 35.5% (11/31) did not. Radiomic features of the primary site at the onset of recurrent metastatic (R/M) status were extracted. Prognostic and predictive radiomic biomarkers were analysed., Results: The median overall survival from R/M status (R/M OS) was 9.6 months. Grey-level co-occurrence matrix-associated texture features were the most important in identifying the patients with or without 9-month R/M death. A radiomic risk-stratification model was established and equally separated the patients into high-, intermittent- and lower-risk groups (1-year R/M death rate, 100.0% vs. 70.8% vs. 27.1%, p = 0.001). Short-run high grey-level emphasis (SRHGE) was more suitable than programmed death ligand 1 (PD-L1) expression in selecting whether patients received ICI-based therapy., Conclusions: Radiomic features were effective prognostic and predictive biomarkers. Future studies are warranted., (© 2024 Wiley Periodicals LLC.)
Published: 2024
Full Text: View/download PDF

49. Attachment security, environmental adversity, and fast life history behavioral profiles in human adolescents.

Author: Lu HJ, Lansford JE, Liu YY, Chen BB, Bornstein MH, Skinner AT, Dodge KA, Steinberg L, Deater-Deckard K, Rothenberg WA, Bacchini D, Pastorelli C, Alampay LP, Sorbring E, Gurdal S, Al-Hassan SM, Oburu P, Yotanyamaneewong S, Tapanya S, Di Giunta L, Uribe Tirado LM, and Chang L
Abstract: One species-general life history (LH) principle posits that challenging childhood environments are coupled with a fast or faster LH strategy and associated behaviors, while secure and stable childhood environments foster behaviors conducive to a slow or slower LH strategy. This coupling between environments and LH strategies is based on the assumption that individuals' internal traits and states are independent of their external surroundings. In reality, individuals respond to external environmental conditions in alignment with their intrinsic vitality, encompassing both physical and mental states. The present study investigated attachment as an internal mental state, examining its role in mediating and moderating the association between external environmental adversity and fast LH strategies. A sample of 1169 adolescents (51% girls) from 9 countries was tracked over 10 years, starting from age 8. The results confirm both mediation and moderation and, for moderation, secure attachment nullified and insecure attachment maintained the environment-LH coupling. These findings suggest that attachment could act as an internal regulator, disrupting the contingent coupling between environmental adversity and a faster pace of life, consequently decelerating human LH.
Published: 2024
Full Text: View/download PDF

50. Search for Rare Decays of D_{s}^{+} to Final States π^{+}e^{+}e^{-}, ρ^{+}e^{+}e^{-}, π^{+}π^{0}e^{+}e^{-}, K^{+}π^{0}e^{+}e^{-}, and K_{S}^{0}π^{+}e^{+}e^{-}.

Author: Ablikim M, Achasov MN, Adlarson P, Afedulidis O, Ai XC, Aliberti R, Amoroso A, An Q, Bai Y, Bakina O, Balossino I, Ban Y, Bao HR, Batozskaya V, Begzsuren K, Berger N, Berlowski M, Bertani M, Bettoni D, Bianchi F, Bianco E, Bortone A, Boyko I, Briere RA, Brueggemann A, Cai H, Cai X, Calcaterra A, Cao GF, Cao N, Cetin SA, Chang JF, Chang WL, Che GR, Chelkov G, Chen C, Chen CH, Chen C, Chen G, Chen HS, Chen ML, Chen SJ, Chen SL, Chen SM, Chen T, Chen XR, Chen XT, Chen YB, Chen YQ, Chen ZJ, Chen ZY, Choi SK, Chu X, Cibinetto G, Cossio F, Cui JJ, Dai HL, Dai JP, Dbeyssi A, de Boer RE, Dedovich D, Deng CQ, Deng ZY, Denig A, Denysenko I, Destefanis M, De Mori F, Ding B, Ding XX, Ding Y, Ding Y, Dong J, Dong LY, Dong MY, Dong X, Du MC, Du SX, Duan ZH, Egorov P, Fan YH, Fang J, Fang J, Fang SS, Fang WX, Fang Y, Fang YQ, Farinelli R, Fava L, Feldbauer F, Felici G, Feng CQ, Feng JH, Feng YT, Fischer K, Fritsch M, Fu CD, Fu JL, Fu YW, Gao H, Gao YN, Gao Y, Garbolino S, Garzia I, Ge PT, Ge ZW, Geng C, Gersabeck EM, Gilman A, Goetzen K, Gong L, Gong WX, Gradl W, Gramigna S, Greco M, Gu MH, Gu YT, Guan CY, Guan ZL, Guo AQ, Guo LB, Guo MJ, Guo RP, Guo YP, Guskov A, Gutierrez J, Han KL, Han TT, Hao XQ, Harris FA, He KK, He KL, Heinsius FH, Heinz CH, Heng YK, Herold C, Holtmann T, Hong PC, Hou GY, Hou XT, Hou YR, Hou ZL, Hu BY, Hu HM, Hu JF, Hu T, Hu Y, Huang GS, Huang KX, Huang LQ, Huang XT, Huang YP, Huang ZY, Hussain T, Hölzken F, Hüsken N, In der Wiesche N, Irshad M, Jackson J, Janchiv S, Jeong JH, Ji Q, Ji QP, Ji W, Ji XB, Ji XL, Ji YY, Jia XQ, Jia ZK, Jiang D, Jiang HB, Jiang PC, Jiang SS, Jiang TJ, Jiang XS, Jiang Y, Jiao JB, Jiao JK, Jiao Z, Jin S, Jin Y, Jing MQ, Jing XM, Johansson T, Kabana S, Kalantar-Nayestanaki N, Kang XL, Kang XS, Kavatsyuk M, Ke BC, Khachatryan V, Khoukaz A, Kiuchi R, Kolcu OB, Kopf B, Kuessner M, Kui X, Kupsc A, Kühn W, Lane JJ, Larin P, Lavezzi L, Lei TT, Lei ZH, Leithoff H, Lellmann M, Lenz T, Li C, Li C, Li CH, Li C, Li DM, Li F, Li G, Li H, Li HB, Li HJ, Li HN, Li H, Li JR, Li JS, Li K, Li LJ, Li LK, Li L, Li MH, Li PR, Li QM, Li QX, Li R, Li SX, Li T, Li WD, Li WG, Li X, Li XH, Li XL, Li X, Li YG, Li ZJ, Li ZX, Liang C, Liang H, Liang H, Liang YF, Liang YT, Liao GR, Liao LZ, Liao YP, Libby J, Limphirat A, Lin DX, Lin T, Liu BJ, Liu BX, Liu C, Liu CX, Liu FH, Liu F, Liu F, Liu GM, Liu H, Liu HB, Liu HM, Liu H, Liu H, Liu JB, Liu JY, Liu K, Liu KY, Liu K, Liu L, Liu LC, Liu L, Liu MH, Liu PL, Liu Q, Liu SB, Liu T, Liu WK, Liu WM, Liu X, Liu X, Liu XY, Liu Y, Liu Y, Liu YB, Liu ZA, Liu ZD, Liu ZQ, Lou XC, Lu FX, Lu HJ, Lu JG, Lu XL, Lu Y, Lu YP, Lu ZH, Luo CL, Luo MX, Luo T, Luo XL, Lyu XR, Lyu YF, Ma FC, Ma H, Ma HL, Ma JL, Ma LL, Ma MM, Ma QM, Ma RQ, Ma XT, Ma XY, Ma Y, Ma YM, Maas FE, Maggiora M, Malde S, Mangoni A, Mao YJ, Mao ZP, Marcello S, Meng ZX, Messchendorp JG, Mezzadri G, Miao H, Min TJ, Mitchell RE, Mo XH, Moses B, Muchnoi NY, Muskalla J, Nefedov Y, Nerling F, Nikolaev IB, Ning Z, Nisar S, Niu QL, Niu WD, Niu Y, Olsen SL, Ouyang Q, Pacetti S, Pan X, Pan Y, Pathak A, Patteri P, Pei YP, Pelizaeus M, Peng HP, Peng YY, Peters K, Ping JL, Ping RG, Plura S, Prasad V, Qi FZ, Qi H, Qi HR, Qi M, Qi TY, Qian S, Qian WB, Qiao CF, Qin JJ, Qin LQ, Qin XS, Qin ZH, Qiu JF, Qu SQ, Qu ZH, Redmer CF, Ren KJ, Rivetti A, Rolo M, Rong G, Rosner C, Ruan SN, Salone N, Sarantsev A, Schelhaas Y, Schoenning K, Scodeggio M, Shan KY, Shan W, Shan XY, Shangguan JF, Shao LG, Shao M, Shen CP, Shen HF, Shen WH, Shen XY, Shi BA, Shi HC, Shi JL, Shi JY, Shi QQ, Shi RS, Shi SY, Shi X, Song JJ, Song TZ, Song WM, Song YJ, Sosio S, Spataro S, Stieler F, Su YJ, Sun GB, Sun GX, Sun H, Sun HK, Sun JF, Sun K, Sun L, Sun SS, Sun T, Sun WY, Sun Y, Sun YJ, Sun YZ, Sun ZQ, Sun ZT, Tang CJ, Tang GY, Tang J, Tang YA, Tao LY, Tao QT, Tat M, Teng JX, Thoren V, Tian WH, Tian Y, Tian ZF, Uman I, Wan Y, Wang SJ, Wang B, Wang BL, Wang B, Wang DY, Wang F, Wang HJ, Wang JP, Wang K, Wang LL, Wang M, Wang M, Wang NY, Wang S, Wang S, Wang T, Wang TJ, Wang W, Wang W, Wang WP, Wang X, Wang XF, Wang XJ, Wang XL, Wang XN, Wang Y, Wang YD, Wang YF, Wang YL, Wang YN, Wang YQ, Wang Y, Wang Y, Wang Z, Wang ZL, Wang ZY, Wang Z, Wei D, Wei DH, Weidner F, Wen SP, Wen YR, Wiedner U, Wilkinson G, Wolke M, Wollenberg L, Wu C, Wu JF, Wu LH, Wu LJ, Wu X, Wu XH, Wu Y, Wu YH, Wu YJ, Wu Z, Xia L, Xian XM, Xiang BH, Xiang T, Xiao D, Xiao GY, Xiao SY, Xiao YL, Xiao ZJ, Xie C, Xie XH, Xie Y, Xie YG, Xie YH, Xie ZP, Xing TY, Xu CF, Xu CJ, Xu GF, Xu HY, Xu QJ, Xu QN, Xu W, Xu WL, Xu XP, Xu YC, Xu ZP, Xu ZS, Yan F, Yan L, Yan WB, Yan WC, Yan XQ, Yang HJ, Yang HL, Yang HX, Yang T, Yang Y, Yang YF, Yang YX, Yang Y, Yang ZW, Yao ZP, Ye M, Ye MH, Yin JH, You ZY, Yu BX, Yu CX, Yu G, Yu JS, Yu T, Yu XD, Yuan CZ, Yuan J, Yuan L, Yuan SC, Yuan Y, Yuan ZY, Yue CX, Zafar AA, Zeng FR, Zeng SH, Zeng X, Zeng Y, Zeng YJ, Zeng YJ, Zhai XY, Zhai YC, Zhan YH, Zhang AQ, Zhang BL, Zhang BX, Zhang DH, Zhang GY, Zhang H, Zhang HC, Zhang HH, Zhang HH, Zhang HQ, Zhang HY, Zhang J, Zhang J, Zhang JJ, Zhang JL, Zhang JQ, Zhang JW, Zhang JX, Zhang JY, Zhang JZ, Zhang J, Zhang LM, Zhang L, Zhang P, Zhang QY, Zhang S, Zhang S, Zhang XD, Zhang XM, Zhang XY, Zhang Y, Zhang YT, Zhang YH, Zhang YM, Zhang Y, Zhang Y, Zhang ZD, Zhang ZH, Zhang ZL, Zhang ZY, Zhang ZY, Zhao G, Zhao JY, Zhao JZ, Zhao L, Zhao L, Zhao MG, Zhao RP, Zhao SJ, Zhao YB, Zhao YX, Zhao ZG, Zhemchugov A, Zheng B, Zheng JP, Zheng WJ, Zheng YH, Zhong B, Zhong X, Zhou H, Zhou JY, Zhou LP, Zhou X, Zhou XK, Zhou XR, Zhou XY, Zhou YZ, Zhu J, Zhu K, Zhu KJ, Zhu L, Zhu LX, Zhu SH, Zhu SQ, Zhu TJ, Zhu WJ, Zhu YC, Zhu ZA, Zou JH, and Zu J
Abstract: Using 7.33 fb^{-1} of e^{+}e^{-} collision data collected by the BESIII detector at center-of-mass energies in the range of sqrt[s]=4.128-4.226 GeV, we search for the rare decays D_{s}^{+}→h^{+}(h^{0})e^{+}e^{-}, where h represents a kaon or pion. By requiring the e^{+}e^{-} invariant mass to be consistent with a ϕ(1020), 0.98
Published: 2024
Full Text: View/download PDF

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Region

Database

Publisher

610 results on '"Lu, HJ"'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources