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2. Outcomes after perioperative SARS-CoV-2 infection in patients with proximal femoral fractures: an international cohort study
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Athayde Nemésio, MJ Amaral, A Valente da Costa, R Martins, P Guerreiro, A Ruivo, D Breda, JM Oliveira, AL De Oliveira Lopez, M Colino, J De Barros, AP Soares, H Morais, T Revez, MI Manso, JC Domingues, P Henriques, Cardoso N Ribeiro VI, G Martins dos Santos, M Peralta Ferreira, J Ascensão, B Costeira, L Rio Rodrigues, M Sousa Fernandes, P Azevedo, I Lourenço, G Mendinhos, A Nobre Pinto, H Taflin, H Abdou, L O'Meara, Z Cooper, SA Hirji, BU Okafor, V Roxo, CP Raut, JS Jolissaint, DA Mahvi, C Reinke, S Merola, A Ssentongo, P Ssentongo, Oh JS, J Hazelton, J Maines, N Gusani, RCG Martin, N Bhutiani, R Choron, F Soliman, MD E Dauer, E Renza-Stingone, E Gokcen, E Kropf, H Sufrin, J Sewards, J Poggio, K Sanserino, L Rae, M Philp, M Metro, P McNelis, R Petrov, T Pazionis, DB Lumenta, SP Nischwitz, E Richtig, M Pau, P Srekl-Filzmaier, N Eibinger, B Michelitsch, M Fediuk, A Papinutti, TU Cohnert, E Kantor, J Kahiu, S Hosny, A Sultana, M Taggarsi, L Vitone, OP Vaz, I Sarantitis, S Timbrell, A Shugaba, GP Jones, SS Tripathi, MS Greenhalgh, H Emerson, K Vejsbjerg, W McCormick, K Singisetti, Y Aawsaj, R Vanker, M Ghobrial, S Kanthasamy, H Fawi, M Awadallah, J Cheung, S Tingle, F Abbadessa, A Sachdeva, CD Chan, I McPherson, F Mahmoud Ali, S Pandanaboyana, T Grainger, S Nandhra, N Dawe, C McCaffer, J Riches, J Moir, H Elamin Ahmed, C Saleh, RM Koshy, LJ Rogers, PL Labib, N Hope, K Emslie, P Panahi, E Clough, I Enemosah, J Natale, N Raza, JI Webb, M Antar, J Noel, R Nunn, F Eriberto, R Tanna, S Lodhia, C Osório, J Antunes, P Balau, and M Godinho
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Medicine - Abstract
Objectives Studies have demonstrated high rates of mortality in people with proximal femoral fracture and SARS-CoV-2, but there is limited published data on the factors that influence mortality for clinicians to make informed treatment decisions. This study aims to report the 30-day mortality associated with perioperative infection of patients undergoing surgery for proximal femoral fractures and to examine the factors that influence mortality in a multivariate analysis.Setting Prospective, international, multicentre, observational cohort study.Participants Patients undergoing any operation for a proximal femoral fracture from 1 February to 30 April 2020 and with perioperative SARS-CoV-2 infection (either 7 days prior or 30-day postoperative).Primary outcome 30-day mortality. Multivariate modelling was performed to identify factors associated with 30-day mortality.Results This study reports included 1063 patients from 174 hospitals in 19 countries. Overall 30-day mortality was 29.4% (313/1063). In an adjusted model, 30-day mortality was associated with male gender (OR 2.29, 95% CI 1.68 to 3.13, p80 years (OR 1.60, 95% CI 1.1 to 2.31, p=0.013), preoperative diagnosis of dementia (OR 1.57, 95% CI 1.15 to 2.16, p=0.005), kidney disease (OR 1.73, 95% CI 1.18 to 2.55, p=0.005) and congestive heart failure (OR 1.62, 95% CI 1.06 to 2.48, p=0.025). Mortality at 30 days was lower in patients with a preoperative diagnosis of SARS-CoV-2 (OR 0.6, 95% CI 0.6 (0.42 to 0.85), p=0.004). There was no difference in mortality in patients with an increase to delay in surgery (p=0.220) or type of anaesthetic given (p=0.787).Conclusions Patients undergoing surgery for a proximal femoral fracture with a perioperative infection of SARS-CoV-2 have a high rate of mortality. This study would support the need for providing these patients with individualised medical and anaesthetic care, including medical optimisation before theatre. Careful preoperative counselling is needed for those with a proximal femoral fracture and SARS-CoV-2, especially those in the highest risk groups.Trial registration number NCT04323644
- Published
- 2021
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3. Search for lepton number violating decays of D s + $$ {\textrm{D}}_{\textrm{s}}^{+} $$ → h − h0e+e+
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The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, G. Chelkov, C. Chen, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, Z. Y. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, Z. Y. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, L. Lavezzi, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, L. J. Li, L. K. Li, Lei Li, M. H. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. X. Li, T. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. G. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. Y. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, X. Liu, X. Y. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, L. R. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, X. K. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, M. Q. Ruan, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, S. S. Su, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, M. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, S. J. Wang, B. Wang, B. L. Wang, Bo Wang, D. Y. Wang, F. Wang, H. J. Wang, J. J. Wang, J. P. Wang, K. Wang, L. L. Wang, M. Wang, N. Y. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, X. N. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. L. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, Ziyi Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, X. H. Wu, Y. Wu, Y. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, M. C. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, P. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Yan Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, Z. Z. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, Z. C. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
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Charm Physics ,e +-e − Experiments ,Nuclear and particle physics. Atomic energy. Radioactivity ,QC770-798 - Abstract
Abstract Based on 7.33 fb −1 of e + e − collision data collected by the BESIII detector operating at the BEPCII collider at center-of-mass energies from 4.128 to 4.226 GeV, a search for the Majorana neutrino ν m is conducted in the lepton-number-violating decays of D s + $$ {D}_{\textrm{s}}^{+} $$ → h − h 0 e + e +. Here, h − represents a K − or π − , and h 0 represents a π 0, K S 0 $$ {K}_S^0 $$ or ϕ. No significant signal is observed, and the upper limits of their branching fractions at the 90% confidence level are determined to be B $$ \mathcal{B} $$ ( D s + $$ {D}_{\textrm{s}}^{+} $$ → ϕπ − e + e +) < 6.9 × 10 −5, B $$ \mathcal{B} $$ ( D s + $$ {D}_{\textrm{s}}^{+} $$ → ϕK − e + e +) < 9.9 × 10 −5, B $$ \mathcal{B} $$ ( D s + $$ {D}_{\textrm{s}}^{+} $$ → K S 0 $$ {K}_S^0 $$ π − e + e +) < 1.3 × 10 −5, B $$ \mathcal{B} $$ ( D s + $$ {D}_{\textrm{s}}^{+} $$ → K S 0 $$ {K}_S^0 $$ K − e + e +) < 2.9 × 10 −5, B $$ \mathcal{B} $$ ( D s + $$ {D}_{\textrm{s}}^{+} $$ → π − π 0 e + e +) < 2.9 × 10 −5 and B $$ \mathcal{B} $$ ( D s + $$ {D}_{\textrm{s}}^{+} $$ → K − π 0 e + e +) < 3.4 × 10 −5. The Majorana neutrino is searched for with different mass assumptions within the range [0.20, 0.80] GeV/c 2 in the decay of D s + $$ {D}_{\textrm{s}}^{+} $$ → ϕe + ν m with ν m → π − e +, and the upper limits of the branching fractions at the 90% confidence level are at the level of 10 −5–10 −2, depending on the mass of the Majorana neutrino.
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- 2025
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4. Measurement of the branching fraction of D + → τ + ν τ
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The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, G. Chelkov, C. Chen, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, Z. Y. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, Q. P. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, L. Lavezzi, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, L. J. Li, L. K. Li, Lei Li, M. H. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. X. Li, T. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. G. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, C. X. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. Y. Liu, K. 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Wang, K. Wang, L. L. Wang, M. Wang, N. Y. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, X. N. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. L. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, Ziyi Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, X. H. Wu, Y. Wu, Y. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, M. C. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, P. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y Zhang, X. Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Yan Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, Z. Z. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, Z. C. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
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Branching fraction ,Charm Physics ,e +-e − Experiments ,Flavour Physics ,Nuclear and particle physics. Atomic energy. Radioactivity ,QC770-798 - Abstract
Abstract By analyzing e + e − collision data with an integrated luminosity of 7.9 fb −1 collected with the BESIII detector at the center-of-mass energy of 3.773 GeV, the branching fraction of D + → τ + ν τ is determined as B $$ \mathcal{B} $$ = (9.9 ± 1.1stat ± 0.5syst) × 10 −4. Using the most precise result B $$ \mathcal{B} $$ (D + → μ + ν μ ) = (3.981 ± 0.079stat ± 0.040syst) × 10 −4 [1], we determine R τ/μ = Γ(D + → τ + ν τ )/Γ(D + → μ + ν μ ) = 2.49 ± 0.31, achieving a factor of two improvement in precision compared to the previous BESIII result. This measurement is in agreement with the standard model prediction of lepton flavor universality within one standard deviation.
- Published
- 2025
- Full Text
- View/download PDF
5. Search for the radiative decays D + → γρ + and D + → γK *+
- Author
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The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, G. Chelkov, C. Chen, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, Z. Y. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, J. J. Lane, L. Lavezzi, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, L. J. Li, L. K. Li, Lei Li, M. H. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. X. Li, T. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. G. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. Y. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, X. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, L. R. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, X. K. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, M. Q. Ruan, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, S. S Su, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, M. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, S. J. Wang, B. Wang, B. L. Wang, Bo Wang, D. Y. Wang, F. Wang, H. J. Wang, J. J. Wang, J. P. Wang, K. Wang, L. L. Wang, M. Wang, N. Y. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, X. N. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. L. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, Ziyi Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, X. H. Wu, Y. Wu, Y. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, M. C. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, P. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y Zhang, X. Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Yan Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, Z. Z. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, Z. C. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
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Charm Physics ,e +-e − Experiments ,Nuclear and particle physics. Atomic energy. Radioactivity ,QC770-798 - Abstract
Abstract We search for the radiative decays D + → γρ + and D + → γK *+ using 20.3 fb −1 of e + e − annihilation data collected at the center-of-mass energy s $$ \sqrt{s} $$ = 3.773 GeV by the BESIII detector operating at the BEPCII collider. No significant signals are observed, and the upper limits on the branching fractions of D + → γρ + and D + → γK *+ at 90% confidence level are set to be 1.3 × 10 −5 and 1.8 × 10 −5, respectively.
- Published
- 2024
- Full Text
- View/download PDF
6. Erratum to: Helicity amplitude analysis of χ cJ → ϕϕ
- Author
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The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, M. Albrecht, R. Aliberti, A. Amoroso, M. R. An, Q. An, X. H. Bai, Y. Bai, O. Bakina, R. Baldini Ferroli, I. Balossino, Y. Ban, V. Batozskaya, D. Becker, K. Begzsuren, N. Berger, M. Bertani, D. Bettoni, F. Bianchi, J. Bloms, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, W. L. Chang, G. Chelkov, C. Chen, Chao Chen, G. Chen, H. S. Chen, M. L. Chen, S. J. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Z. J. Chen, W. S. Cheng, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, S. X. Du, P. Egorov, Y. L. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, K Fischer, M. Fritsch, C. Fritzsch, C. D. Fu, H. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, M. Greco, L. M. Gu, M. H. Gu, Y. T. Gu, C. Y Guan, A. Q. Guo, L. B. Guo, R. P. Guo, Y. P. Guo, A. Guskov, T. T. Han, W. Y. Han, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, M. Himmelreich, G. Y. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Z. Huang, T. Hussain, N Hüsken, W. Imoehl, M. Irshad, J. Jackson, S. Jaeger, S. Janchiv, E. Jang, J. H. Jeong, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, Z. K. Jia, H. B. Jiang, S. S. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, T. Johansson, N. Kalantar-Nayestanaki, X. S. Kang, R. Kappert, M. Kavatsyuk, B. C. Ke, I. K. Keshk, A. Khoukaz, R. Kiuchi, R. Kliemt, L. Koch, O. B. Kolcu, B. Kopf, M. Kuemmel, M. Kuessner, A. Kupsc, W. Kühn, J. J. Lane, J. S. Lange, P. Larin, A. Lavania, L. Lavezzi, T. T. Lei, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. Li, H. B. Li, H. J. Li, H. N. Li, J. Q. Li, J. S. Li, J. W. Li, Ke Li, L. J Li, L. K. Li, Lei Li, M. H. Li, P. R. Li, S. X. Li, S. Y. Li, T. Li, W. D. Li, W. G. Li, X. H. Li, X. L. Li, Xiaoyu Li, Z. X. Li, Z. Y. Li, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, J. Libby, A. Limphirat, C. X. Lin, D. X. Lin, T. Lin, B. J. Liu, C. X. Liu, D. Liu, F. H. Liu, Fang Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huanhuan Liu, Huihui Liu, J. B. Liu, J. L. Liu, J. Y. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, X. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, R. T. Ma, X. Y. Ma, Y. Ma, F. E. Maas, M. Maggiora, S. Maldaner, S. Malde, Q. A. Malik, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, N. Yu. Muchnoi, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, S. Pogodin, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, Z. Qian, C. F. Qiao, J. J. Qin, L. Q. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, K. H. Rashid, C. F. Redmer, K. J. Ren, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, A. Sarantsev, Y. Schelhaas, C. Schnier, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, X. Y. Shen, B. A. Shi, H. C. Shi, J. Y. Shi, q. q. Shi, R. S. Shi, X. Shi, X. D Shi, J. J. Song, W. M. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, K. X. Su, P. P. Su, Y. J. Su, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, X Sun, Y. J. Sun, Y. Z. Sun, Z. T. Sun, Y. H. Tan, Y. X. Tan, C. J. Tang, G. Y. Tang, J. Tang, L. Y Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, I. Uman, B. Wang, B. L. Wang, C. W. Wang, D. Y. Wang, F. Wang, H. J. Wang, H. P. Wang, K. Wang, L. L. Wang, M. Wang, M. Z. Wang, Meng Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. H. Wang, W. P. Wang, X. Wang, X. F. Wang, X. L. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. Q. Wang, Yaqian Wang, Z. Wang, Z. Y. Wang, Ziyi Wang, D. H. Wei, F. Weidner, S. P. Wen, D. J. White, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, X. H. Wu, Y. Wu, Y. J Wu, Z. Wu, L. Xia, T. Xiang, D. Xiao, G. Y. Xiao, H. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, Q. J. Xu, X. P. Xu, Y. C. Xu, Z. P. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, H. J. Yang, H. L. Yang, H. X. Yang, L. Yang, S. L. Yang, Tao Yang, Y. F. Yang, Y. X. Yang, Yifan Yang, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, T. Yu, X. D. Yu, C. Z. Yuan, L. Yuan, S. C. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, X. Zeng, Y. Zeng, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. H. Zhang, H. Y. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. M. Zhang, L. Q. Zhang, Lei Zhang, P. Zhang, Q. Y. Zhang, Shuihan Zhang, Shulei Zhang, X. D. Zhang, X. M. Zhang, X. Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Yan Zhang, Yao Zhang, Z. H. Zhang, Z. Y. Zhang, G. Zhao, J. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, Q. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, Y. H. Zheng, B. Zhong, C. Zhong, X. Zhong, H. Zhou, L. P. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. J. Zhu, Y. C. Zhu, Z. A. Zhu, B. S. Zou, J. H. Zou, and J. Zu
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Nuclear and particle physics. Atomic energy. Radioactivity ,QC770-798 - Published
- 2024
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7. Search for the radiative decay D s + → γρ 770 + $$ {D}_s^{+}\to \gamma \rho {(770)}^{+} $$
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The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, X. Y. Chai, J. F. Chang, G. R. Che, Y. Z. Che, G. Chelkov, C. Chen, C. H. Chen, Chao Chen, G. Chen, H. S. Chen, H. Y. Chen, M. L. Chen, S. J. Chen, S. L. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, Z. Y. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, J. J. Lane, L. Lavezzi, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, K. Li, K. L. Li, L. J. Li, L. K. Li, Lei Li, M. H. Li, P. R. Li, Q. M. Li, Q. X. Li, R. Li, S. X. Li, T. Li, W. D. Li, W. G. Li, X. Li, X. H. Li, X. L. Li, X. Y. Li, X. Z. Li, Y. G. Li, Z. J. Li, Z. Y. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. Liu, F. H. Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. H. Liu, H. M. Liu, Huihui Liu, J. B. Liu, J. Y. Liu, K. Liu, K. Y. Liu, Ke Liu, L. Liu, L. C. Liu, Lu Liu, M. H. Liu, P. L. Liu, Q. Liu, S. B. Liu, T. Liu, W. K. Liu, W. M. Liu, X. Liu, Y. Liu, Y. B. Liu, Z. A. Liu, Z. D. Liu, Z. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, L. R. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. M. Ma, F. E. Maas, I. MacKay, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, X. K. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, M. Q. Ruan, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, S. S Su, Y. J. Su, G. B. Sun, G. X. Sun, H. Sun, H. K. Sun, J. F. Sun, K. Sun, L. Sun, S. S. Sun, T. Sun, W. Y. Sun, Y. Sun, Y. J. Sun, Y. Z. Sun, Z. Q. Sun, Z. T. Sun, C. J. Tang, G. Y. Tang, J. Tang, M. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, S. J. Wang, B. Wang, B. L. Wang, Bo Wang, D. Y. Wang, F. Wang, H. J. Wang, J. J. Wang, J. P. Wang, K. Wang, L. L. Wang, M. Wang, N. Y. Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, X. N. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. L. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, Ziyi Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, X. H. Wu, Y. Wu, Y. H. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, M. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. Xu, Y. C. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, M. C. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. C. Zhang, H. H. Zhang, H. Q. Zhang, H. R. Zhang, H. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. S. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, N Zhang, P. Zhang, Q. Y. Zhang, R. Y. Zhang, S. H. Zhang, Shulei Zhang, X. M. Zhang, X. Y Zhang, X. Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Yan Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, Z. Z. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, L. Zhao, Lei Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, Z. C. Zhou, A. N. Zhu, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, T. J. Zhu, W. D. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
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Branching fraction ,Charm Physics ,e +-e − Experiments ,Rare Decay ,Nuclear and particle physics. Atomic energy. Radioactivity ,QC770-798 - Abstract
Abstract Using 7.33 fb −1 of e + e − collision data samples collected with the BESIII detector at center-of-mass energies between 4.128 and 4.226 GeV, we search for the radiative decay D s + → γρ 770 + $$ {D}_s^{+}\to \gamma \rho {(770)}^{+} $$ for the first time. A hint of D s + → γρ 770 + $$ {D}_s^{+}\to \gamma \rho {(770)}^{+} $$ is observed with a statistical significance of 2.5σ. The branching fraction of D s + → γρ 770 + $$ {D}_s^{+}\to \gamma \rho {(770)}^{+} $$ is measured to be (2.2 ± 0.9stat. ± 0.2syst. ) × 10 −4, corresponding to an upper limit of 6.1 × 10 −4 at the 90% confidence level.
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- 2024
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8. Physics-Informed Machine Learning for Microscale Drying of Plant-Based Foods: A Systematic Review of Computational Models and Experimental Insights.
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C. P. Batuwatta-Gamage, H. Jeong, HCP Karunasena, M. A. Karim, C. M. Rathnayaka, and Y. T. Gu
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- 2025
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9. Comprehensive study on prediction of endurance properties from breakdown voltage in high-reliable STT-MRAM.
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Hideo Sato, H. M. Shin, H. Jung, S. W. Lee, H. Bae, H. Kwon, K. H. Ryu, W. C. Lim, Y. S. Han, J. H. Jeong, J. M. Lee, D. S. Kim, K. Lee, J. H. Lee, J. H. Park, Y. J. Song, Y. Ji, B. I. Seo, J. W. Kim, and H. H. Kim
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- 2023
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10. Highly Reliable and Manufacturable MRAM embedded in 14nm FinFET node.
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S. Ko, J. H. Park, J. H. Bak, H. Jung, J. Shim, D. S. Kim, W. Lim, D.-E. Jeong, J. H. Lee, K. Lee, J.-H. Park, Y. Kim, C. Kim, J. H. Jeong, C. Y. Lee, S. H. Han, Y. Ji, S. H. Hwang, Hye Ji Shin, Y. J. Song, Yu-Gyun Shin, and J. H. Song
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- 2023
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11. The state of science on severe air pollution episodes: Quantitative and qualitative analysis
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Lidia Morawska, Tong Zhu, Nairui Liu, Mehdi Amouei Torkmahalleh, Maria de Fatima Andrade, Benjamin Barratt, Parya Broomandi, Giorgio Buonanno, Luis Carlos Belalcazar Ceron, Jianmin Chen, Yan Cheng, Greg Evans, Mario Gavidia, Hai Guo, Ivan Hanigan, Min Hu, Cheol H. Jeong, Frank Kelly, Laura Gallardo, Prashant Kumar, Xiaopu Lyu, Benjamin J. Mullins, Claus Nordstrøm, Gavin Pereira, Xavier Querol, Nestor Yezid Rojas Roa, Armistead Russell, Helen Thompson, Hao Wang, Lina Wang, Tao Wang, Aneta Wierzbicka, Tao Xue, and Celine Ye
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Severe air pollution events ,Pollution episodes ,Urban air pollution ,Pollution emissions ,Formation of secondary pollutants ,Mitigating air pollutants ,Environmental sciences ,GE1-350 - Abstract
Severe episodic air pollution blankets entire cities and regions and have a profound impact on humans and their activities. We compiled daily fine particle (PM2.5) data from 100 cities in five continents, investigated the trends of number, frequency, and duration of pollution episodes, and compared these with the baseline trend in air pollution. We showed that the factors contributing to these events are complex; however, long-term measures to abate emissions from all anthropogenic sources at all times is also the most efficient way to reduce the occurrence of severe air pollution events. In the short term, accurate forecasting systems of such events based on the meteorological conditions favouring their occurrence, together with effective emergency mitigation of anthropogenic sources, may lessen their magnitude and/or duration. However, there is no clear way of preventing events caused by natural sources affected by climate change, such as wildfires and desert dust outbreaks.
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- 2021
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12. Traffic-related air pollution near roadways: discerning local impacts from background
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N. Hilker, J. M. Wang, C.-H. Jeong, R. M. Healy, U. Sofowote, J. Debosz, Y. Su, M. Noble, A. Munoz, G. Doerksen, L. White, C. Audette, D. Herod, J. R. Brook, and G. J. Evans
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Environmental engineering ,TA170-171 ,Earthwork. Foundations ,TA715-787 - Abstract
Adverse health outcomes related to exposure to air pollution have gained much attention in recent years, with a particular emphasis on traffic-related pollutants near roadways, where concentrations tend to be most severe. As such, many projects around the world are being initiated to routinely monitor pollution near major roads. Understanding the extent to which local on-road traffic directly affects these measurements, however, is a challenging problem, and a more thorough comprehension of it is necessary to properly assess its impact on near-road air quality. In this study, a set of commonly measured air pollutants (black carbon; carbon dioxide; carbon monoxide; fine particulate matter, PM2.5; nitrogen oxides; ozone; and ultrafine particle concentrations) were monitored continuously between 1 June 2015 and 31 March 2017 at six stations in Canada: two near-road and two urban background stations in Toronto, Ontario, and one near-road and one urban background station in Vancouver, British Columbia. Three methods of differentiating between local and background concentrations at near-road locations were tested: (1) differences in average pollutant concentrations between near-road and urban background station pairs, (2) differences in downwind and upwind pollutant averages, and (3) interpolation of rolling minima to infer background concentrations. The last two methods use near-road data only, and were compared with method 1, where an explicit difference was measured, to assess accuracy and robustness. It was found that method 2 produced average local concentrations that were biased high by a factor of between 1.4 and 1.7 when compared with method 1 and was not universally feasible, whereas method 3 produced concentrations that were in good agreement with method 1 for all pollutants except ozone and PM2.5, which are generally secondary and regional in nature. The results of this comparison are intended to aid researchers in the analysis of data procured in future near-road monitoring studies. Lastly, upon determining these local pollutant concentrations as a function of time, their variability with respect to wind speed (WS) and wind direction (WD) was assessed relative to the mean values measured at the specific sites. This normalization allowed generalization across the pollutants and made the values from different sites more comparable. With the exception of ozone and PM2.5, local pollutant concentrations at these near-road locations were enhanced by a factor of 2 relative to their mean in the case of stagnant winds and were shown to be proportional to WS−0.6. Downwind conditions enhanced local concentrations by a factor of ∼2 relative to their mean, while upwind conditions suppressed them by a factor of ∼4. Site-specific factors such as distance from roadway and local meteorology should be taken into consideration when generalizing these factors. The methods used to determine these local concentrations, however, have been shown to be applicable across pollutants and different near-road monitoring environments.
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- 2019
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13. Prognostic value of novel neutrophil-to-hemoglobin and lymphocyte score in patients with acute myocardial infarction
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Hyeon Jeong Kim, Jang Hoon Lee, Se Yong Jang, Myung Hwan Bae, Dong Heon Yang, Hun Sik Park, Yongkeun Cho, Myung H Jeong, Jong-Seon Park, Hyo-Soo Kim, Seung-Ho Hur, In-Whan Seong, Myeong-Chan Cho, and Shung Chull Chae
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Medicine - Abstract
We developed and assessed whether a novel neutrophil-to-hemoglobin and lymphocyte (NHL) score would improve the ability to predict clinical outcome compared with neutrophil-to-lymphocyte ratio (NLR) and systemic immune-inflammation index (SII) in acute myocardial infarction (AMI). We examined 13,072 AMI patients from the Korean AMI Registry–National Institute of Health database. NHL score was calculated as follows: NHL score (U) = N/(Hb × L), where N, Hb, and L are baseline blood neutrophil, hemoglobin, and lymphocyte count. The primary outcome was the occurrence of major adverse cerebrocardiovascular events (MACCEs) at 2 years. The NLR, SII, and NHL score were independent predictors of 2-year MACCEs. The area under the curve of the NHL score (0.637) for predicting 2-year MACCEs was significantly higher compared with those of SII (0.589) and NLR (0.607). The NHL score significantly improved the reclassification and integrated discrimination compared with NLR ( p < 0.0001) and SII ( p < 0.0001). A high NHL score (≥ 0.35 U) was an independent predictor of 2-year MACCEs (adjusted hazard ratio, 1.41; 95% confidence interval, 1.29–1.55; p < 0.001). The NHL score could be a novel model for predicting long-term MACCEs in patients with AMI.
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- 2021
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14. INFACTORY: A RESTFUL API SERVER FOR EASILY CREATING INDOORGML
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H. Jeong, H. Ryoo, and K.-J. Li
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Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Applied optics. Photonics ,TA1501-1820 - Abstract
Recently, services and systems that deal with indoor spatial information are increasing. Each service or system adopts a data model that can store necessary indoor space data according to its purpose. However, since the content of indoor spatial information that can be expressed by each data model is differ and limited, it is necessary to exchange information between the systems in order to use rich indoor spatial data. OGC has published IndoorGML as the standard for exchange of indoor spatial information data between systems. To use IndoorGML as an exchange format, the software which supports IndoorGML construction is fundamental. But there are several limitations in the previous IndoorGML data editing tools. There is no editing tool that can generate all the features which are defined by IndoorGML. If users want to generate IndoorGML data, they need to consider the requirements of the IndoorGML. In this study, we implemented InFactory, which is a IndoorGML generation tool based on RESTful API supporting users to easily construct IndoorGML data. Users can easily create IndoorGML without knowledge on the schema and requirements of IndoorGML using InFactory. In addition, developers on IndoorGML data construction tools such as GUI editors do not have to implement duplicated IndoorGML generation program for their systems. Using Java API that supports CRUD on IndoorGML data, users can also deal with IndoorGML data in their applications.
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- 2018
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15. Four‐kilowatt homogeneous microwave heating system using a power‐controlled phase‐shifting mode for improved heating uniformity
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C.‐H. Jeong, S.‐H. Ahn, and W.‐S. Lee
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power‐controlled phase‐shifting mode ,multimode microwave cavity ,phase‐shifting WR‐340 waveguides ,microwave power ,system controller ,magnetron output power controls ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
For homogeneous microwave heating in a microwave cavity with multiple 2.45 GHz microwave generators, power‐controlled phase‐shifting mode is proposed in this Letter. The proposed microwave heating system consists of a multimode microwave cavity in which dielectric materials are placed to be heated, four phase‐shifting WR‐340 waveguides for different phase excitation, four 1 kW magnetrons that produce microwave power at 2.45 GHz, and a power supply with a system controller for the magnetron output power controls. Using the proposed power‐controlled phase‐shifting mode, uniform heating distribution is achieved by reducing the hot and cold spots in the microwave cavity. The proposed experiments demonstrated that in comparison to a conventional mode with simultaneous multiple inputs, the proposed mode can achieve an improved heating uniformity of ∼64.4%.
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- 2019
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16. Effects of UBE3A on Cell and Liver Metabolism through the Ubiquitination of PDHA1 and ACAT1
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Kangli Peng, Shirong Wang, Ruochuan Liu, Li Zhou, Geon H. Jeong, In Ho Jeong, Xianpeng Liu, Hiroaki Kiyokawa, Bingzhong Xue, Bo Zhao, Hang Shi, and Jun Yin
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Biochemistry - Published
- 2023
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17. Temporally delineated sources of major chemical species in high Arctic snow
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K. M. Macdonald, S. Sharma, D. Toom, A. Chivulescu, A. Platt, M. Elsasser, L. Huang, R. Leaitch, N. Chellman, J. R. McConnell, H. Bozem, D. Kunkel, Y. D. Lei, C.-H. Jeong, J. P. D. Abbatt, and G. J. Evans
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Physics ,QC1-999 ,Chemistry ,QD1-999 - Abstract
Long-range transport of aerosol from lower latitudes to the high Arctic may be a significant contributor to climate forcing in the Arctic. To identify the sources of key contaminants entering the Canadian High Arctic an intensive campaign of snow sampling was completed at Alert, Nunavut, from September 2014 to June 2015. Fresh snow samples collected every few days were analyzed for black carbon, major ions, and metals, and this rich data set provided an opportunity for a temporally refined source apportionment of snow composition via positive matrix factorization (PMF) in conjunction with FLEXPART (FLEXible PARTicle dispersion model) potential emission sensitivity analysis. Seven source factors were identified: sea salt, crustal metals, black carbon, carboxylic acids, nitrate, non-crustal metals, and sulfate. The sea salt and crustal factors showed good agreement with expected composition and primarily northern sources. High loadings of V and Se onto Factor 2, crustal metals, was consistent with expected elemental ratios, implying these metals were not primarily anthropogenic in origin. Factor 3, black carbon, was an acidic factor dominated by black carbon but with some sulfate contribution over the winter-haze season. The lack of K+ associated with this factor, a Eurasian source, and limited known forest fire events coincident with this factor's peak suggested a predominantly anthropogenic combustion source. Factor 4, carboxylic acids, was dominated by formate and acetate with a moderate correlation to available sunlight and an oceanic and North American source. A robust identification of this factor was not possible; however, atmospheric photochemical reactions, ocean microlayer reaction, and biomass burning were explored as potential contributors. Factor 5, nitrate, was an acidic factor dominated by NO3−, with a likely Eurasian source and mid-winter peak. The isolation of NO3− on a separate factor may reflect its complex atmospheric processing, though the associated source region suggests possibly anthropogenic precursors. Factor 6, non-crustal metals, showed heightened loadings of Sb, Pb, and As, and correlation with other metals traditionally associated with industrial activities. Similar to Factor 3 and 5, this factor appeared to be largely Eurasian in origin. Factor 7, sulfate, was dominated by SO42− and MS with a fall peak and high acidity. Coincident volcanic activity and northern source regions may suggest a processed SO2 source of this factor.
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- 2018
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18. Effect of rapeseed meal supplementation to gestation diet on reproductive performance, blood profiles and milk composition of sows
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H. B. Choi, J. S. Hong, S. S. Jin, S. W. Jung, J. C. Jang, J. H. Jeong, and Y. Y. Kim
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Rapeseed Meal ,Reproductive Performance ,Sow ,Thyroid Hormone ,Animal culture ,SF1-1100 ,Animal biochemistry ,QP501-801 - Abstract
Objective This experiment evaluated the effect of dietary supplementation levels of rapeseed meal (RSM) in gestation diets on reproductive performance, blood profiles, milk composition of sows, and growth of their progeny. Methods A total of 55 mixed-parity sows (Yorkshire×Landrace; average parity = 3.82) with an initial body weight (BW) of 193.0 kg were used in this experiment. Sows were allotted to one of 5 treatments at breeding based on BW and backfat thickness in a completely randomized design. Treatments consisted of dietary RSM supplementation levels (0%, 3%, 6%, 9%, and 12%) in gestation diets. During lactation all sows were fed a common lactation diet with no RSM supplementation. Results Body weight, backfat thickness, litter size, lactation feed intake, and milk composition of sows, and growth of their progeny were not different among dietary treatments. In blood profiles, a quadratic increase (Quadratic, p
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- 2018
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19. Scalable whole-exome sequencing of cell-free DNA reveals high concordance with metastatic tumors
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Viktor A. Adalsteinsson, Gavin Ha, Samuel S. Freeman, Atish D. Choudhury, Daniel G. Stover, Heather A. Parsons, Gregory Gydush, Sarah C. Reed, Denisse Rotem, Justin Rhoades, Denis Loginov, Dimitri Livitz, Daniel Rosebrock, Ignaty Leshchiner, Jaegil Kim, Chip Stewart, Mara Rosenberg, Joshua M. Francis, Cheng-Zhong Zhang, Ofir Cohen, Coyin Oh, Huiming Ding, Paz Polak, Max Lloyd, Sairah Mahmud, Karla Helvie, Margaret S. Merrill, Rebecca A. Santiago, Edward P. O’Connor, Seong H. Jeong, Rachel Leeson, Rachel M. Barry, Joseph F. Kramkowski, Zhenwei Zhang, Laura Polacek, Jens G. Lohr, Molly Schleicher, Emily Lipscomb, Andrea Saltzman, Nelly M. Oliver, Lori Marini, Adrienne G. Waks, Lauren C. Harshman, Sara M. Tolaney, Eliezer M. Van Allen, Eric P. Winer, Nancy U. Lin, Mari Nakabayashi, Mary-Ellen Taplin, Cory M. Johannessen, Levi A. Garraway, Todd R. Golub, Jesse S. Boehm, Nikhil Wagle, Gad Getz, J. Christopher Love, and Matthew Meyerson
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Science - Abstract
Identifying the mutational landscape of tumours from cell-free DNA in the blood could help diagnostics in cancer. Here, the authors present ichorCNA, software that quantifies tumour content in cell free DNA, and they demonstrate that cell-free DNA whole-exome sequencing is concordant with metastatic tumour whole-exome sequencing.
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- 2017
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20. Various levels of copra meal supplementation with β-Mannanase on growth performance, blood profile, nutrient digestibility, pork quality and economical analysis in growing-finishing pigs
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H. J. Kim, S. O. Nam, J. H. Jeong, L. H. Fang, H. B. Yoo, S. H. Yoo, J. S. Hong, S. W. Son, S. H. Ha, and Y. Y. Kim
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Copra meal ,β-mannanase ,Growth performance ,Economical analysis ,Growing-finishing pigs ,Animal culture ,SF1-1100 - Abstract
Abstract Background To reduce use of main feed ingredient like corn, soy bean meal (SBM) and wheat, alternative ingredients has been studied like copra meal (CM). Production amount of CM which has been high makes CM to be an alternative feed stuff. However, low digestibility on AA and low energy content by high fiber content can be an obstacle for using CM. This experiment was conducted to evaluate the effects of CM supplementation with β-mannanase on growth performance, blood profile, nutrient digestibility, pork quality and economic analysis in growing-finishing pigs. Methods A total of 100 growing pigs ([Yorkshire × Landrace] × Duroc) averaging 31.22 ± 2.04 kg body weight were allotted to 5 different treatments by weight and sex in a randomized complete block (RCB) design in 5 replicate with 4 pigs per pen. Treatments were 1) Control (corn-SBM based diet + 0.1% of β-mannanase (800 IU)), 2) CM10 (10% copra meal + 0.1% β-mannanase (800 IU)), 3) CM15 (15% copra meal + 0.1% β-mannanase (800 IU)), 4) CM20 (20% copra meal + 0.1% β-mannanase (800 IU)) and 5) CM25 (25% copra meal + 0.1% β-mannanase (800 IU)). Four phase feeding program was used: growing I (week 1–3), growing II (week 4–6), finishing I (week 7–9) and finishing II (week 10–12). Results In growth performance, there was no significant difference among treatments during whole experimental period. In growingI phase, G:F ratio tended to increase when CM was increased (P = 0.05), but ADG and ADFI tended to decrease in finishingII phase (linear, P = 0.08). Also, increasing CM reduced ADG (linear, P = 0.02) and feed efficiency (linear, P = 0.08) during the whole finishing period. In blood profiles, BUN was linearly increased as CM increased (linear, P = 0.02) at growingII period. In digestibility trial, there was no significant difference in dry matter, crude fat, crude ash and nitrogen digestibility. However, crude protein digestibility was decreased linearly (linear, P = 0.02). In economic analysis, feed cost per weight gain and total feed cost per pig were reduced in overall period when CM was provided by 25% (linear, P = 0.02). Conclusion CM with 0.1% of β-mannanase (800 IU) could be supplemented instead of corn and SBM up to 25% without detrimental effects on growth performance and pork quality of growing-finishing pigs.
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- 2017
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21. Assessment of Speckle-Pattern Quality using Deep-Learning-Based CNN
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T.-H. Kwon, J. Park, H. Jeong, and K. Park
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Mechanics of Materials ,Mechanical Engineering ,Aerospace Engineering - Published
- 2022
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22. Quantitative evaluation of bending reliability for a flexible near-field communication tag.
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J.-H. Jeong, J.-H. Kim, and Chung-Seog Oh
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- 2017
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23. COMPUTATIONAL ANALYSIS OF THE AERODYNAMIC EFFECTS OF ICING ON MEDIUM-SIZED TRANSPORT AIRCRAFT
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D.I. Jang, H.J. Lee, H. Jeong, H. Lee, and R.S. Myong
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General Medicine - Published
- 2022
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24. Linear Magnetodielectric Effect and Huge Negative Magnetization in a Mixed-Valent Cobalt Manganite Spinel Oxide
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K. Meera, R. Muralidharan, and Y. H. Jeong
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Condensed Matter Physics ,Electronic, Optical and Magnetic Materials - Published
- 2022
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25. Stachydrine alleviates lipid-induced skeletal muscle insulin resistance via AMPK/HO-1-mediated suppression of inflammation and endoplasmic reticulum stress
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T. W. Jung, H. Kim, S. Y. Park, W. Cho, H. Oh, H. J. Lee, A. M. Abd El-Aty, A. Hacimuftuoglu, and J. H. Jeong
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Endocrinology ,Endocrinology, Diabetes and Metabolism - Published
- 2022
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26. Relationships between Single Nucleotide Polymorphism Markers and Meat Quality Traits of Duroc Breeding Stocks in Korea
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J. S. Choi, S. K. Jin, Y. H. Jeong, Y. C. Jung, J. H. Jung, K. S. Shim, and Y. I. Choi
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Duroc ,Single Nucleotide Polymorphism Markers ,Carcass Trait ,Meat Quality ,Fatty Acid Composition ,Animal culture ,SF1-1100 ,Animal biochemistry ,QP501-801 - Abstract
This study was conducted to determine the relationships of five intragenic single nucleotide polymorphism (SNP) markers (protein kinase adenosine monophosphate-activated γ3 subunit [PRKAG3], fatty acid synthase [FASN], calpastatin [CAST], high mobility group AT-hook 1 [HMGA1], and melanocortin-4 receptor [MC4R]) and meat quality traits of Duroc breeding stocks in Korea. A total of 200 purebred Duroc gilts from 8 sires and 40 dams at 4 pig breeding farms from 2010 to 2011 reaching market weight (110 kg) were slaughtered and their carcasses were chilled overnight. Longissimus dorsi muscles were removed from the carcass after 24 h of slaughter and used to determine pork properties including carcass weight, backfat thickness, moisture, intramuscular fat, pH24h, shear force, redness, texture, and fatty acid composition. The PRKAG3, FASN, CAST, and MC4R gene SNPs were significantly associated with the meat quality traits (p
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- 2016
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27. Effects of Dietary Energy Levels on the Physiological Parameters and Reproductive Performance of Gestating Gilts
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S. S. Jin, S. W. Jung, J. C. Jang, W. L. Chung, J. H. Jeong, and Y. Y. Kim
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Energy Level ,Gilts ,Body Weight ,Backfat Thickness ,Reproductive Performance ,Animal culture ,SF1-1100 ,Animal biochemistry ,QP501-801 - Abstract
This experiment was conducted to investigate the effects of dietary energy levels on the physiological parameters and reproductive performance of gestating first parity sows. A total of 52 F1 gilts (Yorkshire×Landrace) were allocated to 4 dietary treatments using a completely randomized design. Each treatment contained diets with 3,100, 3,200, 3,300, or 3,400 kcal of metabolizable energy (ME)/kg, and the daily energy intake of the gestating gilts in each treatment were 6,200, 6,400, 6,600, and 6,800 kcal of ME, respectively. During gestation, the body weight (p = 0.04) and weight gain (p = 0.01) of gilts linearly increased with increasing dietary energy levels. Backfat thickness was not affected at d110 of gestation by dietary treatments, but increased linearly (p = 0.05) from breeding to d 110 of gestation. There were no significant differences on the litter size or litter birth weight. During lactation, the voluntary feed intake of sows tended to decrease when the dietary energy levels increased (p = 0.08). No difference was observed in backfat thickness of the sows within treatments; increasing energy levels linearly decreased the body weight of sows (p
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- 2016
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28. Design of 12-phase, 2-stage Harmonic Rejection Mixer for TV Tuners
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D. Lee, H. Jeong, and M. Lee
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Harmonic rejection mixer ,multiphase ,TV tuner ,direct conversion ,active mixer ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
A two-stage 12-phase harmonic rejection mixer (HRM) for TV tuners is proposed in order to reject the local oscillator (LO) harmonics up to the ninth order. The proposed weighing scheme for 12-phase, 2-stage harmonic mixing can reduce the harmonic rejection (HR) sensitivity to the amplitude error caused by irrational numbers such as . To verify this HR, the 2-stage HR circuit is designed with baseband gm weighting in order to save power and improve the HR ratios without calibration. The proposed HRM achieves the third to ninth worst HR ratios, more than 55 dB, according to Monte Carlo simulations. It consumes 6.5 mA under a 2.5 V supply voltage.
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- 2016
29. Development of Open source-based automatic shooting and processing UAV imagery for Orthoimage Using Smart Camera UAV
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J. W. Park, H. H. Jeong, J. S. Kim, and C. U. Choi
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Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Applied optics. Photonics ,TA1501-1820 - Abstract
Recently, aerial photography with unmanned aerial vehicle (UAV) system uses UAV and remote controls through connections of ground control system using bandwidth of about 430 MHz radio Frequency (RF) modem. However, as mentioned earlier, existing method of using RF modem has limitations in long distance communication. The Smart Camera equipments’s LTE (long-term evolution), Bluetooth, and Wi-Fi to implement UAV that uses developed UAV communication module system carried out the close aerial photogrammetry with the automatic shooting. Automatic shooting system is an image capturing device for the drones in the area’s that needs image capturing and software for loading a smart camera and managing it. This system is composed of automatic shooting using the sensor of smart camera and shooting catalog management which manages filmed images and information. Processing UAV imagery module used Open Drone Map. This study examined the feasibility of using the Smart Camera as the payload for a photogrammetric UAV system. The open soure tools used for generating Android, OpenCV (Open Computer Vision), RTKLIB, Open Drone Map.
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- 2016
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30. Assessing the Accuracy of Ortho-image using Photogrammetric Unmanned Aerial System
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H. H. Jeong, J. W. Park, J. S. Kim, and C. U. Choi
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Technology ,Engineering (General). Civil engineering (General) ,TA1-2040 ,Applied optics. Photonics ,TA1501-1820 - Abstract
Smart-camera can not only be operated under network environment anytime and any place but also cost less than the existing photogrammetric UAV since it provides high-resolution image, 3D location and attitude data on a real-time basis from a variety of built-in sensors. This study’s proposed UAV photogrammetric method, low-cost UAV and smart camera were used. The elements of interior orientation were acquired through camera calibration. The image triangulation was conducted in accordance with presence or absence of consideration of the interior orientation (IO) parameters determined by camera calibration, The Digital Elevation Model (DEM) was constructed using the image data photographed at the target area and the results of the ground control point survey. This study also analyzes the proposed method’s application possibility by comparing a Ortho-image the results of the ground control point survey. Considering these study findings, it is suggested that smartphone is very feasible as a payload for UAV system. It is also expected that smartphone may be loaded onto existing UAV playing direct or indirect roles significantly.
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- 2016
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31. COMPUTATIONAL ANALYSIS OF CALIBRATION ERROR CHARACTERISTICS OF A FIVE-HOLE PROBE ACCORDING TO ALTITUDE CHANGE
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H. Jeong, M.J. Kang, H.J. Lee, H. Jo, J.H. Jo, and R.S. Myong
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- 2022
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32. A Study on the Distribution of Air Pollutants in Petrochemical Industrial Complex On Summer
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J. H. Jeong, I. S. Kim, E. H. Oh, J. R. Lee, and H. S. Kim
- Abstract
Objectives : Using a mobile laboratory equipped with Proton Transfer Reaction-Time of Flight/Mass Spectrometry (PTR-ToF-MS), we investigate the degree of air pollution level in the petrochemical industrial complex by investigating the distribution characteristics of air pollutants and tracking emission sources.Methods : The research area within the petrochemical industrial complex was measured three times each at dawn, day, night, and the distribution of VOCs at each time was investigated and presented as a pollution map. In the emission source tracking, hot spot (high-concentration) wes found by mobile monitoring, and the emission source was closely tracked by stationary monitoring.Results and Discussion : Acetaldehyde measured in the petrochemical industrial complex was confirmed to have the highest 3 to 5 degrees by direct olfactory method. In most instances the highest concentration was at dawn, potentially due to stagnant pollutants caused by temperature reversal. It was observed that the probability of finding an hot spot was higher during dawn and that it was significantly affected by the wind direction and speed at the time of measurement. When tracking the emission sources, appropriate areas of concern for contamination were selected on the basis of the stationary monitoring results, which were obtained from areas showing high concentrations after mobile monitoring.Conclusion : As a result of the investigation of the distribution characteristics and emission sources of air pollutants in the petrochemical industrial complex, we found that acetaldehyde was the major pollutant and presented an efficient method to search for areas of concern for air pollution. Through these results, it is expected that emission sources can be managed more efficiently.
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- 2022
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33. Coarray Fortran Parallel Implementation of a Finite Volume Method-Based Aircraft Ice Accretion Simulation Code
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L. Prince Raj, E. Esmaeilifar, B. Sengupta, H. Jeong, and R. S. Myong
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Control and Systems Engineering ,Aerospace Engineering ,General Materials Science ,Electrical and Electronic Engineering - Published
- 2023
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34. Theory of transition from brittle to ductile fracture
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K. C. Le, H. Jeong, and T. M. Tran
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- 2023
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35. Incidence, relevant patient factors, and clinical outcomes of the misdiagnosis of ST-elevation myocardial infarction: Results from the Korea Acute Myocardial Infraction Registry
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K Cho, M H Shin, M C Kim, D S Sim, Y J Hong, J H Kim, Y Ahn, H S Kim, S H Hur, S R Lee, J Y Hwang, S K Oh, K S Cha, and M H Jeong
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General Medicine ,Cardiology and Cardiovascular Medicine ,Critical Care and Intensive Care Medicine - Abstract
Funding Acknowledgements Type of funding sources: Public grant(s) – National budget only. Main funding source(s): Korea Centre for Disease Control and Prevention. Background/Introduction Data regarding the incidence, relevant patient factors, and clinical outcomes of the misdiagnosis of ST-elevation myocardial infarction (STEMI) in the modern era of percutaneous coronary intervention are limited. Purpose We investigated the incidence, relevant patient factors, and clinical outcomes of the misdiagnosis of STEMI from a nationwide, prospective Korean registry of acute myocardial infarction. Methods Out of 28,470 patients with acute myocardial infarction between November 2011 and June 2020, 11,796 were eventually diagnosed with STEMI following a coronary angiogram. They were classified into two groups: patients with an initial working diagnosis of STEMI before starting the initial treatment and patients with an initial working diagnosis of non-STEMI (misdiagnosed group). Results Out of 11,796 patients with a final diagnosis of STEMI, 165 (1.4%) were misdiagnosed. The door-to-angiography time in the misdiagnosed group was five times longer than that in the timely diagnosed group (median 220 [interquartile range 66–1177] vs. 43 [31–58] minutes, P Conclusion Misdiagnosis of STEMI is not uncommon and is associated with a significant delay in coronary angiography, resulting in an increased one-year mortality for patients with culprit lesions in the left anterior descending artery.
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- 2023
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36. A Comparison Between Sensitivity Analysis of Oxygenation Severity and Acute Respiratory Distress Syndrome (ARDS) of a Machine Learning Algorithm to Predict Hypoxic Respiratory Failure by Utilizing Features Derived From Electrocardiogram (ECG) and Routine Clinical Data
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C.E. Marshall, S. Narendrula, J. Wang, J.G. De Souza Vale, H. Jeong, P. Krishnan, P. Yang, N.J. Austin, J.S. McLean, A.L. Holder, A.M. Esper, and R. Kamaleswaran
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- 2023
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37. Supplementary Figures 1 - 9 from CAF-Secreted Annexin A1 Induces Prostate Cancer Cells to Gain Stem Cell–like Features
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Pradip Roy-Burman, Ebrahim Zandi, Joseph H. Jeong, Chun-Peng Liao, Mengmeng Liang, Helty Adisetiyo, Kevin A. Nash, and Lauren A. Geary
- Abstract
PDF file - 2259KB, Supplementary Figure S1. Phenotypic changes consistent with EMT in cE1 culture with CAF CM. A, The greatest observed amount of EMT-like morphology was seen in SCmed cE1 cells grown with CAF CM treatment after 14 days in 3-D Matrigel assays. Representative bright field images of the observed morphologies are shown. B, CAF CM treatment did not lead to increased ability to form spheroids or undergo morphologic changes akin to EMT in SCnone, compared to SCmed. CAFs 1 and 3 were used in independent repeats. Bar 100 μm. Supplementary Figure S2. Ammonium sulfate precipitation of conditioned media proteins. A, Potential mediators of EMT-CSC lineage present in the CAF CM have been enriched using several rounds of ammonium sulfate precipitation followed by in vitro biological assays for protein activity affecting the EMT16 sensitive fraction of the cE1 cell line, SCmed, using the three-dimensional Matrigel culture system. The 40% AS-enriched fraction from the CAF CM had the most extensive EMT-like morphology after 14 days of culture, compared to vehicle control and NPF control. Bright field images were taken at 40x magnification. Bar 100 μm. B, Biological activity for each CAF CM fraction was assayed using the in vitro spheroid formation assay with cE1 SCmed. In agreement with the data in A, SCmed cells treated with CAF CM 40% AS-fraction displayed the highest propensity toward EMT based on quantitative real time PCR analysis of EMT transcription factors (Snail, Slug and Twist), stem cell transcription factors (Oct4, Sox2 and Nanog) and decrease in E-Cadherin. Ratios are expressed as fold change normalized to β-actin expression. CAFs 1 and 3 were used in independent repeats. Supplementary Figure S3. A, Dose response to AnxA1 nAb in the presence of AnxA1 enriched CAF CM AS fraction. CAFs 1 and 3 were used in independent repeats. B, Fpr-rs1 is found in greatest abundance in cE1 cells. ?, P < 0.05; *, P
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- 2023
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38. Figure S3 from AHNAK Loss in Mice Promotes Type II Pneumocyte Hyperplasia and Lung Tumor Development
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Je Kyung Seong, Yun Soo Bae, Cheolju Lee, Joseph H. Jeong, Jong Kyu Woo, Mira Sohn, Yeri Son, Seo Hyun Lee, Yo Na Kim, Jae Hoon Shin, Hee Jung Lim, Ji Won Choi, Il Yong Kim, and Jun Won Park
- Abstract
Representative H&E images of Ahnak-/- and WT lungs in embryonic day 18.5 and postnatal day 1.
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- 2023
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39. Supplementary Tables 1 - 2 from CAF-Secreted Annexin A1 Induces Prostate Cancer Cells to Gain Stem Cell–like Features
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Pradip Roy-Burman, Ebrahim Zandi, Joseph H. Jeong, Chun-Peng Liao, Mengmeng Liang, Helty Adisetiyo, Kevin A. Nash, and Lauren A. Geary
- Abstract
PDF file - 107KB, Supplementary Table S1. Antibodies and their dilutions for immunohistochemistry (IHC), immunofluorescence (IF), western blot (WB) and cell culture neutralizing antibody assays are listed. Supplementary Table S2. Real Time Quantitative Reverse Transcription PCR Primer List.
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- 2023
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40. Data from CAF-Secreted Annexin A1 Induces Prostate Cancer Cells to Gain Stem Cell–like Features
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Pradip Roy-Burman, Ebrahim Zandi, Joseph H. Jeong, Chun-Peng Liao, Mengmeng Liang, Helty Adisetiyo, Kevin A. Nash, and Lauren A. Geary
- Abstract
Annexin A1 (AnxA1), a phospholipid-binding protein and regulator of glucocorticoid-induced inflammatory signaling, has implications in cancer. Here, a role for AnxA1 in prostate adenocarcinoma was determined using primary cultures and a tumor cell line (cE1), all derived from the conditional Pten deletion mouse model of prostate cancer. AnxA1 secretion by prostate-derived cancer-associated fibroblasts (CAF) was significantly higher than by normal prostate fibroblasts (NPF). Prostate tumor cells were sorted to enrich for epithelial subpopulations based on nonhematopoietic lineage, high SCA-1, and high or medium levels of CD49f. Compared with controls, AnxA1 enhanced stem cell–like properties in high- and medium-expression subpopulations of sorted cE1 and primary cells, in vitro, through formation of greater number of spheroids with increased complexity, and in vivo, through generation of more, larger, and histologically complex glandular structures, along with increased expression of p63, a basal/progenitor marker. The differentiated medium-expression subpopulations from cE1 and primary cells were most susceptible to gain stem cell–like properties as shown by increased spheroid and glandular formation. Further supporting this increased plasticity, AnxA1 was shown to regulate epithelial-to-mesenchymal transition in cE1 cells. These results suggest that CAF-secreted AnxA1 contributes to tumor stem cell dynamics via two separate but complementary pathways: induction of a dedifferentiation process leading to generation of stem-like cells from a subpopulation of cancer epithelial cells and stimulation of proliferation and differentiation of the cancer stem-like cells.Implications: AnxA1 participates in a paradigm in which malignant prostate epithelial cells that are not cancer stem cells are induced to gain cancer stem cell–like properties. Mol Cancer Res; 12(4); 607–21. ©2014 AACR.
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- 2023
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41. Supplementary Figure Legends from CAF-Secreted Annexin A1 Induces Prostate Cancer Cells to Gain Stem Cell–like Features
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Pradip Roy-Burman, Ebrahim Zandi, Joseph H. Jeong, Chun-Peng Liao, Mengmeng Liang, Helty Adisetiyo, Kevin A. Nash, and Lauren A. Geary
- Abstract
PDF file - 90KB
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- 2023
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42. Data from AHNAK Loss in Mice Promotes Type II Pneumocyte Hyperplasia and Lung Tumor Development
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Je Kyung Seong, Yun Soo Bae, Cheolju Lee, Joseph H. Jeong, Jong Kyu Woo, Mira Sohn, Yeri Son, Seo Hyun Lee, Yo Na Kim, Jae Hoon Shin, Hee Jung Lim, Ji Won Choi, Il Yong Kim, and Jun Won Park
- Abstract
AHNAK is known to be a tumor suppressor in breast cancer due to its ability to activate the TGFβ signaling pathway. However, the role of AHNAK in lung tumor development and progression remains unknown. Here, the Ahnak gene was disrupted to determine its effect on lung tumorigenesis and the mechanism by which it triggers lung tumor development was investigated. First, AHNAK protein expression was determined to be decreased in human lung adenocarcinomas compared with matched nonneoplastic lung tissues. Then, Ahnak−/− mice were used to investigate the role of AHNAK in pulmonary tumorigenesis. Ahnak−/− mice showed increased lung volume and thicker alveolar walls with type II pneumocyte hyperplasia. Most importantly, approximately 20% of aged Ahnak−/− mice developed lung tumors, and Ahnak−/− mice were more susceptible to urethane-induced pulmonary carcinogenesis than wild-type mice. Mechanistically, Ahnak deficiency promotes the cell growth of lung epithelial cells by suppressing the TGFβ signaling pathway. In addition, increased numbers of M2-like alveolar macrophages (AM) were observed in Ahnak−/− lungs, and the depletion of AMs in Ahnak−/− lungs alleviated lung hyperplastic lesions, suggesting that M2-like AMs promoted the progression of lung hyperplastic lesions in Ahnak-null mice. Collectively, AHNAK suppresses type II pneumocyte proliferation and inhibits tumor-promoting M2 alternative activation of macrophages in mouse lung tissue. These results suggest that AHNAK functions as a novel tumor suppressor in lung cancer.Implications: The tumor suppressor function of AHNAK, in murine lungs, occurs by suppressing alveolar epithelial cell proliferation and modulating lung microenvironment. Mol Cancer Res; 16(8); 1287–98. ©2018 AACR.
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- 2023
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43. Supplementary Materials and Methods from CAF-Secreted Annexin A1 Induces Prostate Cancer Cells to Gain Stem Cell–like Features
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Pradip Roy-Burman, Ebrahim Zandi, Joseph H. Jeong, Chun-Peng Liao, Mengmeng Liang, Helty Adisetiyo, Kevin A. Nash, and Lauren A. Geary
- Abstract
PDF file - 131KB
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- 2023
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44. Supplementary Table 3 from CAF-Secreted Annexin A1 Induces Prostate Cancer Cells to Gain Stem Cell–like Features
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Pradip Roy-Burman, Ebrahim Zandi, Joseph H. Jeong, Chun-Peng Liao, Mengmeng Liang, Helty Adisetiyo, Kevin A. Nash, and Lauren A. Geary
- Abstract
XLS file - 23KB, Supplementary Table S3: Proteins.
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- 2023
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45. Supplementary figure legends from AHNAK Loss in Mice Promotes Type II Pneumocyte Hyperplasia and Lung Tumor Development
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Je Kyung Seong, Yun Soo Bae, Cheolju Lee, Joseph H. Jeong, Jong Kyu Woo, Mira Sohn, Yeri Son, Seo Hyun Lee, Yo Na Kim, Jae Hoon Shin, Hee Jung Lim, Ji Won Choi, Il Yong Kim, and Jun Won Park
- Abstract
Supplementary figure legends
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- 2023
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46. Bilateral teleoperation control of a quadrotor system with a haptic device: Experimental studies.
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S. H. Jeong and S. Jung
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- 2014
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47. Compressively Certifying Quantum Measurements
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I. Gianani, Y.S. Teo, V. Cimini, H. Jeong, G. Leuchs, M. Barbieri, and L.L. Sánchez-Soto
- Subjects
Physics ,QC1-999 ,Computer software ,QA76.75-76.765 - Abstract
We introduce a reliable compressive procedure to uniquely characterize any given low-rank quantum measurement using a minimal set of probe states that is based solely on data collected from the unknown measurement itself. The procedure is most compressive when the measurement constitutes pure detection outcomes, requiring only an informationally complete number of probe states that scales linearly with the system dimension. We argue and provide numerical evidence showing that the minimal number of probe states needed is even generally below the numbers known in the closely related classical phase-retrieval problem because of the quantum constraint. We also present affirmative results with polarization experiments that illustrate significant compressive behaviors for both two- and four-qubit detectors just by using random product probe states.
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- 2020
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48. Top 100 cited studies in periacetabular osteotomy for acetabular dysplasia: do lower levels of evidence guide clinical practice?
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Stacy H. Jeong, Linsen T. Samuel, Elaine Lu, Robert J. Burkhart, Alexander J. Acuña, and Atul F. Kamath
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Treatment Outcome ,Pediatrics, Perinatology and Child Health ,Hip Dislocation ,Humans ,Acetabulum ,Orthopedics and Sports Medicine ,Prospective Studies ,Hip Dislocation, Congenital ,Osteotomy ,Retrospective Studies - Abstract
As no prior study has examined the citations profile of key articles related to periacetabular osteotomy (PAO), our analysis utilized the Web of Science database to (1) identify the most-cited clinical studies relating to PAO in the management of acetabular dysplasia and (2) assess any trends over time with respect to the quality of literature. The top 100 highest-cited studies related to PAO had a mean of 49 citations (range, 6-666 per study). With respect to the level of evidence, most studies had level IV evidence (58%); 1% level I, 16% level II, 28% level III and 2% level V. Most studies were retrospective ( n = 86); there were 14 prospective studies (including one randomized study). The most common study designs were case series ( n = 58) and cohort ( n = 16), followed by matched-cohort ( n = 13) and case-control ( n = 6). The mean ± SD Newcastle-Ottawa Scale score was 6.48 ± 1.31. A total of 59 and 41 of the included articles were classified as high risk and high quality, respectively. No studies were classified as very high risk. As a whole, our analysis demonstrated that currently available PAO literature is still of low quality and of low level of evidence. While PAO has been well-documented as a durable procedure for addressing acetabular dysplasia, future research must focus on higher quality, randomized and prospective data to answer key clinical or technique-related topics.
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- 2022
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49. Supplemental Figures from TPL2 Is an Oncogenic Driver in Keratocanthoma and Squamous Cell Carcinoma
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Joseph H. Jeong, Jae U. Jung, Jonathan Melamed, Fang-Ming Deng, Jeong-Sang Lee, Kyung-Soo Inn, Ola Forslund, Sung-Dae Cho, Sung-Im Do, Sang Hyuk Lee, Joo-Hyung Lee, and Jun-Han Lee
- Abstract
Supplementary Fig. S1 - Generation of inducible TPL2 transgenic (iTPL2 TG) mice. Supplementary Fig. S2 - Percentage of tumor-free for iTPL2 TG mice ON DOX Supplementary Fig. S3 - Detection of transgene TPL2 expression using an antibody against HA epitope tag (A) and three different antibodies against TPL2 (B) in an iTPL2 wt (#100) TG-driven KA-like cSCC ON DOX. Supplementary Fig. S4 - A, effects on TPL2-related signaling pathways with the treatments of indicated inhibitors in stable cell lines, overexpressing either control vector (Vector), TPL2-WT, or TPL2-IN, by western blot. Supplementary Fig. S5 - Western blot analyses of normal mouse skin samples Supplementary Fig. S6 - For In vitro kinase assay Supplementary Fig. S7 -Co-immunofluorescence of TPL2 Supplementary Fig. S8 - Quantification of each staining with iTPL2 TG-driven cSCC
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- 2023
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50. Supplemental data from TPL2 Is an Oncogenic Driver in Keratocanthoma and Squamous Cell Carcinoma
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Joseph H. Jeong, Jae U. Jung, Jonathan Melamed, Fang-Ming Deng, Jeong-Sang Lee, Kyung-Soo Inn, Ola Forslund, Sung-Dae Cho, Sung-Im Do, Sang Hyuk Lee, Joo-Hyung Lee, and Jun-Han Lee
- Abstract
Supplementary Materials and Methods (supplementary information on establsihing cell lines and generating inducible TPL2 transgenic (iTPL2 TG) mice) & Supplementary Figure Legends
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- 2023
- Full Text
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