In vivo visualization of tumor-associated macrophages re-education by photoacoustic/fluorescence dual-modal imaging with a metal-organic frames-based caspase-1 nanoreporter

Tumor-associated macrophages (TAMs) are vital in the tumor microenvironment, contributing to immunosuppression and therapy tolerance. Despite their importance, the precise re-education of TAMs in vivo continues to present a formidable challenge. Moreover, the lack of real-time and efficient methods to comprehend the spatiotemporal kinetics of TAMs repolarization remains a significant hurdle, severely hampering the accurate assessment of treatment efficacy and prognosis. Herein, we designed a metal-organic frameworks (MOFs) based Caspase-1 nanoreporter (MCNR) that can deliver a TLR7/8 agonist to the TAMs and track time-sensitive Caspase-1 activity as a direct method to monitor the initiation of immune reprogramming. This nanosystem exhibits excellent TAMs targeting ability, enhanced tumor accumulation, and stimuli-responsive behavior. By inducing the reprogramming of TAMs, they were able to enhance T-cell infiltration in tumor tissue, resulting in inhibited tumor growth and improved survival in mice model. Moreover, MCNR also serves as an activatable photoacoustic and fluorescent dual-mode imaging agent through Caspase-1-mediated specific enzyme digestion. This feature enables non-invasive and real-time antitumor immune activation monitoring. Overall, our findings indicate that MCNR has the potential to be a valuable tool for tumor immune microenvironment remodeling and noninvasive quantitative detection and real-time monitoring of TAMs repolarization to immunotherapy in the early stage.

Observation of D^{+}→K_{S}^{0}a_{0}(980)^{+} in the Amplitude Analysis of D^{+}→K_{S}^{0}π^{+}η

We perform for the first time an amplitude analysis of the decay D^{+}→K_{S}^{0}π^{+}η and report the observation of the decay D^{+}→K_{S}^{0}a_{0}(980)^{+} using 2.93  fb^{-1} of e^{+}e^{-} collision data taken at a center-of-mass energy of 3.773 GeV with the BESIII detector. As the only W-annihilation-free decay among D to a_{0}(980) pseudoscalar, D^{+}→K_{S}^{0}a_{0}(980)^{+} is the ideal decay in extracting the contributions of the W-emission amplitudes involving a_{0}(980) and to study the final-state interactions. The absolute branching fraction of D^{+}→K_{S}^{0}π^{+}η is measured to be (1.27±0.04_{stat}±0.03_{syst})%. The branching fractions of intermediate processes D^{+}→K_{S}^{0}a_{0}(980)^{+} with a_{0}(980)^{+}→π^{+}η and D^{+}→π^{+}K[over ¯]_{0}^{*}(1430)^{0} with K[over ¯]_{0}^{*}(1430)^{0}→K_{S}^{0}η are measured to be (1.33±0.05_{stat}±0.04_{syst})% and (0.14±0.03_{stat}±0.01_{syst})%, respectively.

Observation of Significant Flavor-SU(3) Breaking in the Kaon Wave Function at 12<Q^{2}<25 GeV^{2} and Discovery of the Charmless Decay ψ(3770)→K_{S}^{0}K_{L}^{0}

We present cross sections for the reaction e^{+}e^{-}→K_{S}^{0}K_{L}^{0} at center-of-mass energies ranging from 3.51 to 4.95 GeV using data samples collected in the BESIII experiment, corresponding to a total integrated luminosity of 26.5  fb^{-1}. The ratio of neutral-to-charged kaon form factors at large momentum transfers (12 Collapse

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Affiliation(s)

M Ablikim Institute of High Energy Physics, Beijing 100049, People's Republic of China
M N Achasov Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
P Adlarson Uppsala University, Box 516, SE-75120 Uppsala, Sweden
X C Ai Zhengzhou University, Zhengzhou 450001, People's Republic of China
R Aliberti Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
A Amoroso University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
M R An Liaoning Normal University, Dalian 116029, People's Republic of China
Q An State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y Bai Southeast University, Nanjing 211100, People's Republic of China
O Bakina Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
I Balossino INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
Y Ban Peking University, Beijing 100871, People's Republic of China
H-R Bao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
V Batozskaya Institute of High Energy Physics, Beijing 100049, People's Republic of China National Centre for Nuclear Research, Warsaw 02-093, Poland
K Begzsuren Institute of Physics and Technology, Peace Avenue 54B, Ulaanbaatar 13330, Mongolia
N Berger Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
M Berlowski National Centre for Nuclear Research, Warsaw 02-093, Poland
M Bertani INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
D Bettoni INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
F Bianchi University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
E Bianco University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
A Bortone University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
I Boyko Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
R A Briere Carnegie Mellon University, Pittsburgh, Pennsylvania 15213, USA
A Brueggemann University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
H Cai Wuhan University, Wuhan 430072, People's Republic of China
X Cai Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
A Calcaterra INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
G F Cao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
N Cao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S A Cetin Turkish Accelerator Center Particle Factory Group, Istinye University, 34010 Istanbul, Turkey
J F Chang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
T T Chang Xinyang Normal University, Xinyang 464000, People's Republic of China
W L Chang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
G R Che Nankai University, Tianjin 300071, People's Republic of China
G Chelkov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
C Chen Nankai University, Tianjin 300071, People's Republic of China
Chao Chen Soochow University, Suzhou 215006, People's Republic of China
G Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China
H S Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
M L Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S J Chen Nanjing University, Nanjing 210093, People's Republic of China
S L Chen North China Electric Power University, Beijing 102206, People's Republic of China
S M Chen Tsinghua University, Beijing 100084, People's Republic of China
T Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X R Chen Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X T Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y B Chen Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y Q Chen Jilin University, Changchun 130012, People's Republic of China
Z J Chen Hunan University, Changsha 410082, People's Republic of China
S K Choi Chung-Ang University, Seoul, 06974, Republic of Korea
X Chu Nankai University, Tianjin 300071, People's Republic of China
G Cibinetto INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
S C Coen Bochum Ruhr-University, D-44780 Bochum, Germany
F Cossio INFN, I-10125, Turin, Italy
J J Cui Shandong University, Jinan 250100, People's Republic of China
H L Dai Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J P Dai Yunnan University, Kunming 650500, People's Republic of China
A Dbeyssi Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
R E de Boer Bochum Ruhr-University, D-44780 Bochum, Germany
D Dedovich Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
Z Y Deng Institute of High Energy Physics, Beijing 100049, People's Republic of China
A Denig Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
I Denysenko Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
M Destefanis University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
F De Mori University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
B Ding Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Jinan, Jinan 250022, People's Republic of China
X X Ding Peking University, Beijing 100871, People's Republic of China
Y Ding Liaoning University, Shenyang 110036, People's Republic of China
Y Ding Jilin University, Changchun 130012, People's Republic of China
J Dong Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
L Y Dong Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
M Y Dong Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Dong Wuhan University, Wuhan 430072, People's Republic of China
M C Du Institute of High Energy Physics, Beijing 100049, People's Republic of China
S X Du Zhengzhou University, Zhengzhou 450001, People's Republic of China
Z H Duan Nanjing University, Nanjing 210093, People's Republic of China
P Egorov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
Y H Fan North China Electric Power University, Beijing 102206, People's Republic of China
J Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
S S Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
W X Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Q Fang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
R Farinelli INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
L Fava University of Eastern Piedmont, I-15121 Alessandria, Italy INFN, I-10125, Turin, Italy
F Feldbauer Bochum Ruhr-University, D-44780 Bochum, Germany
G Felici INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
C Q Feng State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J H Feng Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
K Fischer University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
M Fritsch Bochum Ruhr-University, D-44780 Bochum, Germany
C D Fu Institute of High Energy Physics, Beijing 100049, People's Republic of China
J L Fu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y W Fu Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Gao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y N Gao Peking University, Beijing 100871, People's Republic of China
Yang Gao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
S Garbolino INFN, I-10125, Turin, Italy
I Garzia INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy University of Ferrara, I-44122 Ferrara, Italy
P T Ge Wuhan University, Wuhan 430072, People's Republic of China
Z W Ge Nanjing University, Nanjing 210093, People's Republic of China
C Geng Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
E M Gersabeck University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
A Gilman University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
K Goetzen GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
L Gong Liaoning University, Shenyang 110036, People's Republic of China
W X Gong Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
W Gradl Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
S Gramigna INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy University of Ferrara, I-44122 Ferrara, Italy
M Greco University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
M H Gu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y T Gu Guangxi University, Nanning 530004, People's Republic of China
C Y Guan Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z L Guan Henan University of Technology, Zhengzhou 450001, People's Republic of China
A Q Guo Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L B Guo Nanjing Normal University, Nanjing 210023, People's Republic of China
M J Guo Shandong University, Jinan 250100, People's Republic of China
R P Guo Shandong Normal University, Jinan 250014, People's Republic of China
Y P Guo Fudan University, Shanghai 200433, People's Republic of China
A Guskov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
J Gutierrez Indiana University, Bloomington, Indiana 47405, USA
T T Han Institute of High Energy Physics, Beijing 100049, People's Republic of China
W Y Han Liaoning Normal University, Dalian 116029, People's Republic of China
X Q Hao Henan Normal University, Xinxiang 453007, People's Republic of China
F A Harris University of Hawaii, Honolulu, Hawaii 96822, USA
K K He Soochow University, Suzhou 215006, People's Republic of China
K L He Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
F H H Heinsius Bochum Ruhr-University, D-44780 Bochum, Germany
C H Heinz Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
Y K Heng Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C Herold Suranaree University of Technology, University Avenue 111, Nakhon Ratchasima 30000, Thailand
T Holtmann Bochum Ruhr-University, D-44780 Bochum, Germany
P C Hong Fudan University, Shanghai 200433, People's Republic of China
G Y Hou Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X T Hou Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y R Hou University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z L Hou Institute of High Energy Physics, Beijing 100049, People's Republic of China
B Y Hu Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H M Hu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J F Hu South China Normal University, Guangzhou 510006, People's Republic of China
T Hu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y Hu Institute of High Energy Physics, Beijing 100049, People's Republic of China
G S Huang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
K X Huang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
L Q Huang Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X T Huang Shandong University, Jinan 250100, People's Republic of China
Y P Huang Institute of High Energy Physics, Beijing 100049, People's Republic of China
T Hussain University of the Punjab, Lahore-54590, Pakistan
N Hüsken Indiana University, Bloomington, Indiana 47405, USA Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
N In der Wiesche University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
M Irshad State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J Jackson Indiana University, Bloomington, Indiana 47405, USA
S Jaeger Bochum Ruhr-University, D-44780 Bochum, Germany
S Janchiv Institute of Physics and Technology, Peace Avenue 54B, Ulaanbaatar 13330, Mongolia
J H Jeong Chung-Ang University, Seoul, 06974, Republic of Korea
Q Ji Institute of High Energy Physics, Beijing 100049, People's Republic of China
Q P Ji Henan Normal University, Xinxiang 453007, People's Republic of China
X B Ji Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X L Ji Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y Y Ji Shandong University, Jinan 250100, People's Republic of China
X Q Jia Shandong University, Jinan 250100, People's Republic of China
Z K Jia State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
H J Jiang Wuhan University, Wuhan 430072, People's Republic of China
P C Jiang Peking University, Beijing 100871, People's Republic of China
S S Jiang Liaoning Normal University, Dalian 116029, People's Republic of China
T J Jiang Hangzhou Normal University, Hangzhou 310036, People's Republic of China
X S Jiang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y Jiang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J B Jiao Shandong University, Jinan 250100, People's Republic of China
Z Jiao Huangshan College, Huangshan 245000, People's Republic of China
S Jin Nanjing University, Nanjing 210093, People's Republic of China
Y Jin University of Jinan, Jinan 250022, People's Republic of China
M Q Jing Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X M Jing University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
T Johansson Uppsala University, Box 516, SE-75120 Uppsala, Sweden
X Kui Institute of High Energy Physics, Beijing 100049, People's Republic of China
S Kabana Instituto de Alta Investigación, Universidad de Tarapacá, Casilla 7D, Arica 1000000, Chile
N Kalantar-Nayestanaki University of Groningen, NL-9747 AA Groningen, The Netherlands
X L Kang China University of Geosciences, Wuhan 430074, People's Republic of China
X S Kang Liaoning University, Shenyang 110036, People's Republic of China
M Kavatsyuk University of Groningen, NL-9747 AA Groningen, The Netherlands
B C Ke Zhengzhou University, Zhengzhou 450001, People's Republic of China
V Khachatryan Indiana University, Bloomington, Indiana 47405, USA
A Khoukaz University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
R Kiuchi Institute of High Energy Physics, Beijing 100049, People's Republic of China
R Kliemt GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
O B Kolcu Turkish Accelerator Center Particle Factory Group, Istinye University, 34010 Istanbul, Turkey
B Kopf Bochum Ruhr-University, D-44780 Bochum, Germany
M Kuessner Bochum Ruhr-University, D-44780 Bochum, Germany
A Kupsc National Centre for Nuclear Research, Warsaw 02-093, Poland Uppsala University, Box 516, SE-75120 Uppsala, Sweden
W Kühn Justus-Liebig-Universitaet Giessen, II. Physikalisches Institut, Heinrich-Buff-Ring 16, D-35392 Giessen, Germany
J J Lane University of Manchester, Oxford Road, Manchester, M13 9PL, United Kingdom
P Larin Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
A Lavania Indian Institute of Technology Madras, Chennai 600036, India
L Lavezzi University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
T T Lei State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Z H Lei State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
H Leithoff Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
M Lellmann Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
T Lenz Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
C Li Qufu Normal University, Qufu 273165, People's Republic of China
C Li Nankai University, Tianjin 300071, People's Republic of China
C H Li Liaoning Normal University, Dalian 116029, People's Republic of China
Cheng Li State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
D M Li Zhengzhou University, Zhengzhou 450001, People's Republic of China
F Li Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
G Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Li State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
H B Li Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H J Li Henan Normal University, Xinxiang 453007, People's Republic of China
H N Li South China Normal University, Guangzhou 510006, People's Republic of China
Hui Li Nankai University, Tianjin 300071, People's Republic of China
J R Li Tsinghua University, Beijing 100084, People's Republic of China
J S Li Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
J W Li Shandong University, Jinan 250100, People's Republic of China
Ke Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
L J Li Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L K Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
Lei Li Renmin University of China, Beijing 100872, People's Republic of China
M H Li Nankai University, Tianjin 300071, People's Republic of China
P R Li Lanzhou University, Lanzhou 730000, People's Republic of China
Q X Li Shandong University, Jinan 250100, People's Republic of China
S X Li Fudan University, Shanghai 200433, People's Republic of China
T Li Shandong University, Jinan 250100, People's Republic of China
W D Li Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
W G Li Institute of High Energy Physics, Beijing 100049, People's Republic of China
X H Li State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X L Li Shandong University, Jinan 250100, People's Republic of China
Xiaoyu Li Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y G Li Peking University, Beijing 100871, People's Republic of China
Z J Li Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
Z X Li Guangxi University, Nanning 530004, People's Republic of China
C Liang Nanjing University, Nanjing 210093, People's Republic of China
H Liang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H Liang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y F Liang Sichuan University, Chengdu 610064, People's Republic of China
Y T Liang Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
G R Liao Guangxi Normal University, Guilin 541004, People's Republic of China
L Z Liao Shandong University, Jinan 250100, People's Republic of China
Y P Liao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J Libby Indian Institute of Technology Madras, Chennai 600036, India
A Limphirat Suranaree University of Technology, University Avenue 111, Nakhon Ratchasima 30000, Thailand
D X Lin Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
T Lin Institute of High Energy Physics, Beijing 100049, People's Republic of China
B J Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
B X Liu Wuhan University, Wuhan 430072, People's Republic of China
C Liu Jilin University, Changchun 130012, People's Republic of China
C X Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
F H Liu Shanxi University, Taiyuan 030006, People's Republic of China
Fang Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Feng Liu Central China Normal University, Wuhan 430079, People's Republic of China
G M Liu South China Normal University, Guangzhou 510006, People's Republic of China
H Liu Lanzhou University, Lanzhou 730000, People's Republic of China
H B Liu Guangxi University, Nanning 530004, People's Republic of China
H M Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Huanhuan Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Huihui Liu Henan University of Science and Technology, Luoyang 471003, People's Republic of China
J B Liu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J Y Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
K Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
K Y Liu Liaoning University, Shenyang 110036, People's Republic of China
Ke Liu Henan University of Technology, Zhengzhou 450001, People's Republic of China
L Liu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
L C Liu Nankai University, Tianjin 300071, People's Republic of China
Lu Liu Nankai University, Tianjin 300071, People's Republic of China
M H Liu Fudan University, Shanghai 200433, People's Republic of China
P L Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Q Liu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S B Liu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
T Liu Fudan University, Shanghai 200433, People's Republic of China
W K Liu Nankai University, Tianjin 300071, People's Republic of China
W M Liu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X Liu Lanzhou University, Lanzhou 730000, People's Republic of China
Y Liu Lanzhou University, Lanzhou 730000, People's Republic of China
Y Liu Zhengzhou University, Zhengzhou 450001, People's Republic of China
Y B Liu Nankai University, Tianjin 300071, People's Republic of China
Z A Liu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z Q Liu Shandong University, Jinan 250100, People's Republic of China
X C Lou Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
F X Lu Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H J Lu Huangshan College, Huangshan 245000, People's Republic of China
J G Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
X L Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Lu Central South University, Changsha 410083, People's Republic of China
Y P Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Z H Lu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C L Luo Nanjing Normal University, Nanjing 210023, People's Republic of China
M X Luo Zhejiang University, Hangzhou 310027, People's Republic of China
T Luo Fudan University, Shanghai 200433, People's Republic of China
X L Luo Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
X R Lyu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y F Lyu Nankai University, Tianjin 300071, People's Republic of China
F C Ma Liaoning University, Shenyang 110036, People's Republic of China
H Ma Yunnan University, Kunming 650500, People's Republic of China
H L Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China
J L Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L L Ma Shandong University, Jinan 250100, People's Republic of China
M M Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Q M Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China
R Q Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Y Ma Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y Ma Peking University, Beijing 100871, People's Republic of China
Y M Ma Institute of Modern Physics, Lanzhou 730000, People's Republic of China
F E Maas Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
M Maggiora University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
S Malde University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
Q A Malik University of the Punjab, Lahore-54590, Pakistan
A Mangoni INFN Sezione di Perugia, I-06100 Perugia, Italy
Y J Mao Peking University, Beijing 100871, People's Republic of China
Z P Mao Institute of High Energy Physics, Beijing 100049, People's Republic of China
S Marcello University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
Z X Meng University of Jinan, Jinan 250022, People's Republic of China
J G Messchendorp GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany University of Groningen, NL-9747 AA Groningen, The Netherlands
G Mezzadri INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy
H Miao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
T J Min Nanjing University, Nanjing 210093, People's Republic of China
R E Mitchell Indiana University, Bloomington, Indiana 47405, USA
X H Mo Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B Moses Indiana University, Bloomington, Indiana 47405, USA
N Yu Muchnoi Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
J Muskalla Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
Y Nefedov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
F Nerling Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
I B Nikolaev Budker Institute of Nuclear Physics SB RAS (BINP), Novosibirsk 630090, Russia
Z Ning Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
S Nisar COMSATS University Islamabad, Lahore Campus, Defence Road, Off Raiwind Road, 54000 Lahore, Pakistan
Q L Niu Lanzhou University, Lanzhou 730000, People's Republic of China
W D Niu Soochow University, Suzhou 215006, People's Republic of China
Y Niu Shandong University, Jinan 250100, People's Republic of China
S L Olsen University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Q Ouyang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S Pacetti INFN Sezione di Perugia, I-06100 Perugia, Italy University of Perugia, I-06100 Perugia, Italy
X Pan Soochow University, Suzhou 215006, People's Republic of China
Y Pan Southeast University, Nanjing 211100, People's Republic of China
A Pathak Jilin University, Changchun 130012, People's Republic of China
P Patteri INFN Laboratori Nazionali di Frascati, INFN Laboratori Nazionali di Frascati, I-00044 Frascati, Italy
Y P Pei State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
M Pelizaeus Bochum Ruhr-University, D-44780 Bochum, Germany
H P Peng State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y Y Peng Lanzhou University, Lanzhou 730000, People's Republic of China
K Peters GSI Helmholtzcentre for Heavy Ion Research GmbH, D-64291 Darmstadt, Germany
J L Ping Nanjing Normal University, Nanjing 210023, People's Republic of China
R G Ping Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S Plura Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
V Prasad Instituto de Alta Investigación, Universidad de Tarapacá, Casilla 7D, Arica 1000000, Chile
F Z Qi Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Qi State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
H R Qi Tsinghua University, Beijing 100084, People's Republic of China
M Qi Nanjing University, Nanjing 210093, People's Republic of China
T Y Qi Fudan University, Shanghai 200433, People's Republic of China
S Qian Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
W B Qian University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C F Qiao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J J Qin University of South China, Hengyang 421001, People's Republic of China
L Q Qin Guangxi Normal University, Guilin 541004, People's Republic of China
X S Qin Shandong University, Jinan 250100, People's Republic of China
Z H Qin Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J F Qiu Institute of High Energy Physics, Beijing 100049, People's Republic of China
S Q Qu Tsinghua University, Beijing 100084, People's Republic of China
C F Redmer Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
K J Ren Liaoning Normal University, Dalian 116029, People's Republic of China
A Rivetti INFN, I-10125, Turin, Italy
M Rolo INFN, I-10125, Turin, Italy
G Rong Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Ch Rosner Helmholtz Institute Mainz, Staudinger Weg 18, D-55099 Mainz, Germany
S N Ruan Nankai University, Tianjin 300071, People's Republic of China
N Salone National Centre for Nuclear Research, Warsaw 02-093, Poland
A Sarantsev Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
Y Schelhaas Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
K Schoenning Uppsala University, Box 516, SE-75120 Uppsala, Sweden
M Scodeggio INFN Sezione di Ferrara, INFN Sezione di Ferrara, I-44122 Ferrara, Italy University of Ferrara, I-44122 Ferrara, Italy
K Y Shan Fudan University, Shanghai 200433, People's Republic of China
W Shan Hunan Normal University, Changsha 410081, People's Republic of China
X Y Shan State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J F Shangguan Soochow University, Suzhou 215006, People's Republic of China
L G Shao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
M Shao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
C P Shen Fudan University, Shanghai 200433, People's Republic of China
H F Shen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
W H Shen University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Y Shen Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B A Shi University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H C Shi State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
J L Shi Fudan University, Shanghai 200433, People's Republic of China
J Y Shi Institute of High Energy Physics, Beijing 100049, People's Republic of China
Q Q Shi Soochow University, Suzhou 215006, People's Republic of China
R S Shi Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Shi Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J J Song Henan Normal University, Xinxiang 453007, People's Republic of China
T Z Song Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
W M Song Institute of High Energy Physics, Beijing 100049, People's Republic of China Jilin University, Changchun 130012, People's Republic of China
Y J Song Fudan University, Shanghai 200433, People's Republic of China
Y X Song Peking University, Beijing 100871, People's Republic of China
S Sosio University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
S Spataro University of Turin and INFN, University of Turin, I-10125 Turin, Italy INFN, I-10125, Turin, Italy
F Stieler Johannes Gutenberg University of Mainz, Johann-Joachim-Becher-Weg 45, D-55099 Mainz, Germany
Y J Su University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
G B Sun Wuhan University, Wuhan 430072, People's Republic of China
G X Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Sun University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H K Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China
J F Sun Henan Normal University, Xinxiang 453007, People's Republic of China
K Sun Tsinghua University, Beijing 100084, People's Republic of China
L Sun Wuhan University, Wuhan 430072, People's Republic of China
S S Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
T Sun Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
W Y Sun Jilin University, Changchun 130012, People's Republic of China
Y Sun China University of Geosciences, Wuhan 430074, People's Republic of China
Y J Sun State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Y Z Sun Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z T Sun Shandong University, Jinan 250100, People's Republic of China
Y X Tan State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
C J Tang Sichuan University, Chengdu 610064, People's Republic of China
G Y Tang Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Tang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
Y A Tang Wuhan University, Wuhan 430072, People's Republic of China
L Y Tao University of South China, Hengyang 421001, People's Republic of China
Q T Tao Hunan University, Changsha 410082, People's Republic of China
M Tat University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
J X Teng State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
V Thoren Uppsala University, Box 516, SE-75120 Uppsala, Sweden
W H Tian Shanxi Normal University, Linfen 041004, People's Republic of China
W H Tian Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
Y Tian Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z F Tian Wuhan University, Wuhan 430072, People's Republic of China
I Uman Near East University, Nicosia, North Cyprus, 99138, Mersin 10, Turkey
Y Wan Soochow University, Suzhou 215006, People's Republic of China
S J Wang Shandong University, Jinan 250100, People's Republic of China
B Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China
B L Wang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Bo Wang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
C W Wang Nanjing University, Nanjing 210093, People's Republic of China
D Y Wang Peking University, Beijing 100871, People's Republic of China
F Wang University of South China, Hengyang 421001, People's Republic of China
H J Wang Lanzhou University, Lanzhou 730000, People's Republic of China
J P Wang Shandong University, Jinan 250100, People's Republic of China
K Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
L L Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China
M Wang Shandong University, Jinan 250100, People's Republic of China
Meng Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
N Y Wang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S Wang Fudan University, Shanghai 200433, People's Republic of China
S Wang Lanzhou University, Lanzhou 730000, People's Republic of China
T Wang Fudan University, Shanghai 200433, People's Republic of China
T J Wang Nankai University, Tianjin 300071, People's Republic of China
W Wang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
W Wang University of South China, Hengyang 421001, People's Republic of China
W P Wang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X Wang Peking University, Beijing 100871, People's Republic of China
X F Wang Lanzhou University, Lanzhou 730000, People's Republic of China
X J Wang Liaoning Normal University, Dalian 116029, People's Republic of China
X L Wang Fudan University, Shanghai 200433, People's Republic of China
Y Wang Tsinghua University, Beijing 100084, People's Republic of China
Y D Wang North China Electric Power University, Beijing 102206, People's Republic of China
Y F Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y L Wang Henan Normal University, Xinxiang 453007, People's Republic of China
Y N Wang North China Electric Power University, Beijing 102206, People's Republic of China
Y Q Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Yaqian Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China Hebei University, Baoding 071002, People's Republic of China
Yi Wang Tsinghua University, Beijing 100084, People's Republic of China
Z Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Z L Wang University of South China, Hengyang 421001, People's Republic of China
Z Y Wang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Ziyi Wang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
D Wei University of Science and Technology Liaoning, Anshan 114051, People's Republic of China
D H Wei Guangxi Normal University, Guilin 541004, People's Republic of China
F Weidner University of Muenster, Wilhelm-Klemm-Strasse 9, 48149 Muenster, Germany
S P Wen Institute of High Energy Physics, Beijing 100049, People's Republic of China
C W Wenzel Bochum Ruhr-University, D-44780 Bochum, Germany
U Wiedner Bochum Ruhr-University, D-44780 Bochum, Germany
G Wilkinson University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
M Wolke Uppsala University, Box 516, SE-75120 Uppsala, Sweden
L Wollenberg Bochum Ruhr-University, D-44780 Bochum, Germany
C Wu Liaoning Normal University, Dalian 116029, People's Republic of China
J F Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China China Center of Advanced Science and Technology, Beijing 100190, People's Republic of China
L H Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China
L J Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Wu Fudan University, Shanghai 200433, People's Republic of China
X H Wu Jilin University, Changchun 130012, People's Republic of China
Y Wu University of Science and Technology of China, Hefei 230026, People's Republic of China
Y H Wu Soochow University, Suzhou 215006, People's Republic of China
Y J Wu Institute of Modern Physics, Lanzhou 730000, People's Republic of China
Z Wu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
L Xia State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X M Xian Liaoning Normal University, Dalian 116029, People's Republic of China
T Xiang Peking University, Beijing 100871, People's Republic of China
D Xiao Lanzhou University, Lanzhou 730000, People's Republic of China
G Y Xiao Nanjing University, Nanjing 210093, People's Republic of China
S Y Xiao Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y L Xiao Fudan University, Shanghai 200433, People's Republic of China
Z J Xiao Nanjing Normal University, Nanjing 210023, People's Republic of China
C Xie Nanjing University, Nanjing 210093, People's Republic of China
X H Xie Peking University, Beijing 100871, People's Republic of China
Y Xie Shandong University, Jinan 250100, People's Republic of China
Y G Xie Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y H Xie Central China Normal University, Wuhan 430079, People's Republic of China
Z P Xie State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
T Y Xing Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C F Xu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C J Xu Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
G F Xu Institute of High Energy Physics, Beijing 100049, People's Republic of China
H Y Xu University of Jinan, Jinan 250022, People's Republic of China
Q J Xu Hangzhou Normal University, Hangzhou 310036, People's Republic of China
Q N Xu Inner Mongolia University, Hohhot 010021, People's Republic of China
W Xu Institute of High Energy Physics, Beijing 100049, People's Republic of China
W L Xu University of Jinan, Jinan 250022, People's Republic of China
X P Xu Soochow University, Suzhou 215006, People's Republic of China
Y C Xu Yantai University, Yantai 264005, People's Republic of China
Z P Xu Nanjing University, Nanjing 210093, People's Republic of China
Z S Xu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
F Yan Fudan University, Shanghai 200433, People's Republic of China
L Yan Fudan University, Shanghai 200433, People's Republic of China
W B Yan State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
W C Yan Zhengzhou University, Zhengzhou 450001, People's Republic of China
X Q Yan Institute of High Energy Physics, Beijing 100049, People's Republic of China
H J Yang Shanghai Jiao Tong University, Shanghai 200240, People's Republic of China
H L Yang Jilin University, Changchun 130012, People's Republic of China
H X Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Tao Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Yang Fudan University, Shanghai 200433, People's Republic of China
Y F Yang Nankai University, Tianjin 300071, People's Republic of China
Y X Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Yifan Yang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z W Yang Lanzhou University, Lanzhou 730000, People's Republic of China
Z P Yao Shandong University, Jinan 250100, People's Republic of China
M Ye Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
M H Ye China Center of Advanced Science and Technology, Beijing 100190, People's Republic of China
J H Yin Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z Y You Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
B X Yu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
C X Yu Nankai University, Tianjin 300071, People's Republic of China
G Yu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J S Yu Hunan University, Changsha 410082, People's Republic of China
T Yu University of South China, Hengyang 421001, People's Republic of China
X D Yu Peking University, Beijing 100871, People's Republic of China
C Z Yuan Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L Yuan Beihang University, Beijing 100191, People's Republic of China
S C Yuan Institute of High Energy Physics, Beijing 100049, People's Republic of China
Y Yuan Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z Y Yuan Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
C X Yue Liaoning Normal University, Dalian 116029, People's Republic of China
A A Zafar University of the Punjab, Lahore-54590, Pakistan
F R Zeng Shandong University, Jinan 250100, People's Republic of China
S H Zeng University of South China, Hengyang 421001, People's Republic of China
X Zeng Fudan University, Shanghai 200433, People's Republic of China
Y Zeng Hunan University, Changsha 410082, People's Republic of China
Y J Zeng Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Y Zhai Jilin University, Changchun 130012, People's Republic of China
Y C Zhai Shandong University, Jinan 250100, People's Republic of China
Y H Zhan Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
A Q Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B L Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B X Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
D H Zhang Nankai University, Tianjin 300071, People's Republic of China
G Y Zhang Henan Normal University, Xinxiang 453007, People's Republic of China
H Zhang University of Science and Technology of China, Hefei 230026, People's Republic of China
H C Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H H Zhang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H H Zhang Jilin University, Changchun 130012, People's Republic of China
H Q Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
H Y Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
J Zhang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
J Zhang Zhengzhou University, Zhengzhou 450001, People's Republic of China
J J Zhang Shanxi Normal University, Linfen 041004, People's Republic of China
J L Zhang Henan University, Kaifeng 475004, People's Republic of China
J Q Zhang Nanjing Normal University, Nanjing 210023, People's Republic of China
J W Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J X Zhang Lanzhou University, Lanzhou 730000, People's Republic of China
J Y Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Z Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Jianyu Zhang University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L M Zhang Tsinghua University, Beijing 100084, People's Republic of China
L Q Zhang Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
Lei Zhang Nanjing University, Nanjing 210093, People's Republic of China
P Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Q Y Zhang Liaoning Normal University, Dalian 116029, People's Republic of China Zhengzhou University, Zhengzhou 450001, People's Republic of China
Shuihan Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Shulei Zhang Hunan University, Changsha 410082, People's Republic of China
X D Zhang North China Electric Power University, Beijing 102206, People's Republic of China
X M Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
X Y Zhang Shandong University, Jinan 250100, People's Republic of China
Y Zhang University of South China, Hengyang 421001, People's Republic of China
Y Zhang University of Oxford, Keble Road, Oxford OX13RH, United Kingdom
Y T Zhang Zhengzhou University, Zhengzhou 450001, People's Republic of China
Y H Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Yan Zhang State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Yao Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z D Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z H Zhang Institute of High Energy Physics, Beijing 100049, People's Republic of China
Z L Zhang Jilin University, Changchun 130012, People's Republic of China
Z Y Zhang Nankai University, Tianjin 300071, People's Republic of China
Z Y Zhang Wuhan University, Wuhan 430072, People's Republic of China
G Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Y Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J Z Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Lei Zhao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Ling Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China
M G Zhao Nankai University, Tianjin 300071, People's Republic of China
R P Zhao University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S J Zhao Zhengzhou University, Zhengzhou 450001, People's Republic of China
Y B Zhao Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
Y X Zhao Institute of Modern Physics, Lanzhou 730000, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Z G Zhao State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
A Zhemchugov Joint Institute for Nuclear Research, 141980 Dubna, Moscow region, Russia
B Zheng University of South China, Hengyang 421001, People's Republic of China
J P Zheng Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China
W J Zheng Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
Y H Zheng University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
B Zhong Nanjing Normal University, Nanjing 210023, People's Republic of China
X Zhong Sun Yat-Sen University, Guangzhou 510275, People's Republic of China
H Zhou Shandong University, Jinan 250100, People's Republic of China
L P Zhou Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
X Zhou Wuhan University, Wuhan 430072, People's Republic of China
X K Zhou Central China Normal University, Wuhan 430079, People's Republic of China
X R Zhou State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
X Y Zhou Liaoning Normal University, Dalian 116029, People's Republic of China
Y Z Zhou Fudan University, Shanghai 200433, People's Republic of China
J Zhu Nankai University, Tianjin 300071, People's Republic of China
K Zhu Institute of High Energy Physics, Beijing 100049, People's Republic of China
K J Zhu Institute of High Energy Physics, Beijing 100049, People's Republic of China State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
L Zhu Jilin University, Changchun 130012, People's Republic of China
L X Zhu University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
S H Zhu University of Science and Technology Liaoning, Anshan 114051, People's Republic of China
S Q Zhu Nanjing University, Nanjing 210093, People's Republic of China
T J Zhu Fudan University, Shanghai 200433, People's Republic of China
W J Zhu Fudan University, Shanghai 200433, People's Republic of China
Y C Zhu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China
Z A Zhu Institute of High Energy Physics, Beijing 100049, People's Republic of China University of Chinese Academy of Sciences, Beijing 100049, People's Republic of China
J H Zou Institute of High Energy Physics, Beijing 100049, People's Republic of China
J Zu State Key Laboratory of Particle Detection and Electronics, Beijing 100049, Hefei 230026, People's Republic of China University of Science and Technology of China, Hefei 230026, People's Republic of China

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FedDUS: Lung tumor segmentation on CT images through federated semi-supervised with dynamic update strategy

BACKGROUND AND OBJECTIVE

Lung tumor annotation is a key upstream task for further diagnosis and prognosis. Although deep learning techniques have promoted automation of lung tumor segmentation, there remain challenges impeding its application in clinical practice, such as a lack of prior annotation for model training and data-sharing among centers.

METHODS

In this paper, we use data from six centers to design a novel federated semi-supervised learning (FSSL) framework with dynamic model aggregation and improve segmentation performance for lung tumors. To be specific, we propose a dynamically updated algorithm to deal with model parameter aggregation in FSSL, which takes advantage of both the quality and quantity of client data. Moreover, to increase the accessibility of data in the federated learning (FL) network, we explore the FAIR data principle while the previous federated methods never involve.

RESULT

The experimental results show that the segmentation performance of our model in six centers is 0.9348, 0.8436, 0.8328, 0.7776, 0.8870 and 0.8460 respectively, which is superior to traditional deep learning methods and recent federated semi-supervised learning methods.

CONCLUSION

The experimental results demonstrate that our method is superior to the existing FSSL methods. In addition, our proposed dynamic update strategy effectively utilizes the quality and quantity information of client data and shows efficiency in lung tumor segmentation. The source code is released on (https://github.com/GDPHMediaLab/FedDUS).

Investigation of the ΔI=1/2 Rule and Test of CP Symmetry through the Measurement of Decay Asymmetry Parameters in Ξ^{-} Decays

Using (10087±44)×10^{6}  J/ψ events collected with the BESIII detector, numerous Ξ^{-} and Λ decay asymmetry parameters are simultaneously determined from the process J/ψ→Ξ^{-}Ξ[over ¯]^{+}→Λ(pπ^{-})π^{-}Λ[over ¯](n[over ¯]π^{0})π^{+} and its charge-conjugate channel. The precisions of α_{Λ0} for Λ→nπ^{0} and α[over ¯]_{Λ0} for Λ[over ¯]→n[over ¯]π^{0} compared to world averages are improved by factors of 4 and 1.7, respectively. The ratio of decay asymmetry parameters of Λ→nπ^{0} to that of Λ→pπ^{-}, ⟨α_{Λ0}⟩/⟨α_{Λ-}⟩, is determined to be 0.873±0.012_{-0.010}^{+0.011}, where the first and the second uncertainties are statistical and systematic, respectively. The ratio is smaller than unity more than 5σ, which signifies the existence of the ΔI=3/2 transition in Λ for the first time. Besides, we test for CP symmetry in Ξ^{-}→Λπ^{-} and in Λ→nπ^{0} with the best precision to date.

Observation of D_{s}^{+}→η^{'}μ^{+}ν_{μ}, Precision Test of Lepton Flavor Universality with D_{s}^{+}→η^{(')}l^{+}ν_{l}, and First Measurements of D_{s}^{+}→η^{(')}μ^{+}ν_{μ} Decay Dynamics

By analyzing 7.33  fb^{-1} of e^{+}e^{-} annihilation data collected at center-of-mass energies between 4.128 and 4.226 GeV with the BESIII detector, we report the observation of the semileptonic decay D_{s}^{+}→η^{'}μ^{+}ν_{μ}, with a statistical significance larger than 10σ, and the measurements of the D_{s}^{+}→ημ^{+}ν_{μ} and D_{s}^{+}→η^{'}μ^{+}ν_{μ} decay dynamics for the first time. The branching fractions of D_{s}^{+}→ημ^{+}ν_{μ} and D_{s}^{+}→η^{'}μ^{+}ν_{μ} are determined to be (2.235±0.051_{stat}±0.052_{syst})% and (0.801±0.055_{stat}±0.028_{syst})%, respectively, with precision improved by factors of 6.0 and 6.6 compared to the previous best measurements. Combined with the results for the decays D_{s}^{+}→ηe^{+}ν_{e} and D_{s}^{+}→η^{'}e^{+}ν_{e}, the ratios of the decay widths are examined both inclusively and in several ℓ^{+}ν_{ℓ} four-momentum transfer ranges. No evidence for lepton flavor universality violation is found within the current statistics. The products of the hadronic form factors f_{+,0}^{η^{(')}}(0) and the c→s Cabibbo-Kobayashi-Maskawa matrix element |V_{cs}| are determined. The results based on the two-parameter series expansion are f_{+,0}^{η}(0)|V_{cs}|=0.452±0.010_{stat}±0.007_{syst} and f_{+,0}^{η^{'}}(0)|V_{cs}|=0.504±0.037_{stat}±0.012_{syst}, which help to constrain present models on f_{+,0}^{η^{(')}}(0). The forward-backward asymmetries are determined to be ⟨A_{FB}^{η}⟩=-0.059±0.031_{stat}±0.005_{syst} and ⟨A_{FB}^{η^{'}}⟩=-0.064±0.079_{stat}±0.006_{syst} for the first time, which are consistent with the theoretical calculation.

Determination of the Σ^{+} Timelike Electromagnetic Form Factors

Based on data samples collected with the BESIII detector at the BEPCII collider, the process e^{+}e^{-}→Σ^{+}Σ[over ¯]^{-} is studied at center-of-mass energies sqrt[s]=2.3960, 2.6454, and 2.9000 GeV. Using a fully differential angular description of the final state particles, both the relative magnitude and phase information of the Σ^{+} electromagnetic form factors in the timelike region are extracted. The relative phase between the electric and magnetic form factors is determined to be sinΔΦ=-0.67±0.29(stat)±0.18(syst) at sqrt[s]=2.3960  GeV, ΔΦ=55°±19°(stat)±14°(syst) at sqrt[s]=2.6454  GeV, and 78°±22°(stat)±9°(syst) at sqrt[s]=2.9000  GeV. For the first time, the phase of the hyperon electromagnetic form factors is explored in a wide range of four-momentum transfer. The evolution of the phase along with four-momentum transfer is an important input for understanding its asymptotic behavior and the dynamics of baryons.

A novel stratification scheme combined with internal arteries in CT imaging for guiding postoperative adjuvant transarterial chemoembolization in hepatocellular carcinoma: a retrospective cohort study

BACKGROUND

Although postoperative adjuvant transarterial chemoembolization (PA-TACE) improves survival outcomes in a subset of patients with resected hepatocellular carcinoma (HCC), the lack of reliable biomarkers for patient selection remains a significant challenge. The present study aimed to evaluate whether computed tomography (CT) imaging can provide more value for predicting benefits from PA-TACE and to establish a new scheme for guiding PA-TACE benefits.

METHODS

In this retrospective study, patients with HCC who had undergone preoperative contrast-enhanced CT and curative hepatectomy were evaluated. Inverse probability of treatment weight was performed to balance the difference of baseline characteristics. Cox models were used to test the interaction among PA-TACE, imaging features and pathological indicators. An HCC imaging and pathological classification (HIPC) scheme incorporating these imaging and pathological indicators was established.

RESULTS

This study included 1,488 patients (median age, 52 y [IQR, 45-61 y]; 1,309 male). Microvascular invasion (MVI) positive, and diameter>5 cm tumors achieved a higher recurrence-free survival (RFS), and overall survival (OS) benefit, respectively, from PA-TACE than MVI negative, and diameter ≤5 cm tumors. Patients with internal arteries (IA) positive benefited more than those with IA negative in terms of RFS (P=0.016) and OS (P=0.018). PA-TACE achieved significant RFS and OS improvements in HIPC3 (IA present and diameter, or two or three tumors) patients but not in HIPC1 (diameter ≤5 cm, MVI negative) and HIPC2 (other single tumor) patients. Our scheme may decrease the number of patients receiving PA-TACE by approximately 36.5% compared to the previous suggestion.

CONCLUSIONS

IA can provide more value for predicting the benefit of PA-TACE treatment. The proposed HIPC scheme can be used to stratify patients with and without survival benefits from PA-TACE.

Predictive value of hematological parameters in cirrhotic patients with open umbilical hernia repair

PURPOSE

Patients with liver cirrhosis sometimes suffer from high recurrence rates and postoperative complications. We previously reported that platelet-related hematological parameters are associated with the outcomes after incisional herniorrhaphy, and aim to evaluate the predictive value of these criteria in cirrhotic patients undergoing open umbilical herniorrhaphy.

METHODS

This is a retrospective study. The data of 95 cirrhotic patients undergoing open umbilical herniorrhaphy were analyzed. Patients were grouped based on the recurrence and defined hematological values. Platelet-multiple-lymphocyte index (PLM), neutrophil-leukocyte ratio, lymphocyte-monocyte ratio, platelet-neutrophil ratio, systemic immune-inflammation index, and aspartate aminotransferase-leukocyte ratio values were calculated based on preoperative blood analyses. The outcomes were obtained from hospital records and follow-up calls to patients.

RESULTS

Using cutoff values acquired by the Youden Index, we found a PLM value < 27.9, and the history of inguinal herniorrhaphy were revealed to be statistically significant in the recurrence based on univariant and multivariant analyses (p < 0.05). We further divided patients into two groups based on the cutoff value of PLM and found that a PLM value < 27.9 was significantly associated with the recurrence of incisional hernias (p = 0.018) and the occurrence of postoperative foreign sensation (p = 0.044), and tended to result in other postoperative complications such as cardiopathy, respiratory infection, hypoproteinemia, and hepatic diseases (p = 0.089).

CONCLUSION

The preoperative hematological values, especially PLM, may indicate the outcomes in cirrhotic patients after open umbilical herniorrhaphy. Accurate identification of risks may alert the intraoperative and postoperative care for patients.

SwinHR: Hemodynamic-powered hierarchical vision transformer for breast tumor segmentation

Accurate and automated segmentation of breast tumors in dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) plays a critical role in computer-aided diagnosis and treatment of breast cancer. However, this task is challenging, due to random variation in tumor sizes, shapes, appearances, and blurred boundaries of tumors caused by inherent heterogeneity of breast cancer. Moreover, the presence of ill-posed artifacts in DCE-MRI further complicate the process of tumor region annotation. To address the challenges above, we propose a scheme (named SwinHR) integrating prior DCE-MRI knowledge and temporal-spatial information of breast tumors. The prior DCE-MRI knowledge refers to hemodynamic information extracted from multiple DCE-MRI phases, which can provide pharmacokinetics information to describe metabolic changes of the tumor cells over the scanning time. The Swin Transformer with hierarchical re-parameterization large kernel architecture (H-RLK) can capture long-range dependencies within DCE-MRI while maintaining computational efficiency by a shifted window-based self-attention mechanism. The use of H-RLK can extract high-level features with a wider receptive field, which can make the model capture contextual information at different levels of abstraction. Extensive experiments are conducted in large-scale datasets to validate the effectiveness of our proposed SwinHR scheme, demonstrating its superiority over recent state-of-the-art segmentation methods. Also, a subgroup analysis split by MRI scanners, field strength, and tumor size is conducted to verify its generalization. The source code is released on (https://github.com/GDPHMediaLab/SwinHR).

Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response

BACKGROUNDS

Since breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research was to construct and independently validate a biomarker of tumor microenvironment (TME) phenotypes via a machine learning-based radiomics way. The interrelationship between the biomarker, TME phenotypes and recipients' clinical response was also revealed.

METHODS

In this retrospective multi-cohort investigation, five separate cohorts of breast cancer patients were recruited to measure breast cancer TME phenotypes via a radiomics signature, which was constructed and validated by integrating RNA-seq data with DCE-MRI images for predicting immunotherapy response. Initially, we constructed TME phenotypes using RNA-seq of 1089 breast cancer patients in the TCGA database. Then, parallel DCE-MRI images and RNA-seq of 94 breast cancer patients obtained from TCIA were applied to develop a radiomics-based TME phenotypes signature using random forest in machine learning. The repeatability of the radiomics signature was then validated in an internal validation set. Two additional independent external validation sets were analyzed to reassess this signature. The Immune phenotype cohort (n = 158) was divided based on CD8 cell infiltration into immune-inflamed and immune-desert phenotypes; these data were utilized to examine the relationship between the immune phenotypes and this signature. Finally, we utilized an Immunotherapy-treated cohort with 77 cases who received anti-PD-1/PD-L1 treatment to evaluate the predictive efficiency of this signature in terms of clinical outcomes.

RESULTS

The TME phenotypes of breast cancer were separated into two heterogeneous clusters: Cluster A, an "immune-inflamed" cluster, containing substantial innate and adaptive immune cell infiltration, and Cluster B, an "immune-desert" cluster, with modest TME cell infiltration. We constructed a radiomics signature for the TME phenotypes ([AUC] = 0.855; 95% CI 0.777-0.932; p < 0.05) and verified it in an internal validation set (0.844; 0.606-1; p < 0.05). In the known immune phenotypes cohort, the signature can identify either immune-inflamed or immune-desert tumor (0.814; 0.717-0.911; p < 0.05). In the Immunotherapy-treated cohort, patients with objective response had higher baseline radiomics scores than those with stable or progressing disease (p < 0.05); moreover, the radiomics signature achieved an AUC of 0.784 (0.643-0.926; p < 0.05) for predicting immunotherapy response.

CONCLUSIONS

Our imaging biomarker, a practicable radiomics signature, is beneficial for predicting the TME phenotypes and clinical response in anti-PD-1/PD-L1-treated breast cancer patients. It is particularly effective in identifying the "immune-desert" phenotype and may aid in its transformation into an "immune-inflamed" phenotype.

First Measurement of the Decay Asymmetry in the Pure W-Boson-Exchange Decay Λ_{c}^{+}→Ξ^{0}K^{+}

Based on 4.4  fb^{-1} of e^{+}e^{-} annihilation data collected at the center-of-mass energies between 4.60 and 4.70 GeV with the BESIII detector at the BEPCII collider, the pure W-boson-exchange decay Λ_{c}^{+}→Ξ^{0}K^{+} is studied with a full angular analysis. The corresponding decay asymmetry is measured for the first time to be α_{Ξ^{0}K^{+}}=0.01±0.16(stat)±0.03(syst). This result reflects the noninterference effect between the S- and P-wave amplitudes. The phase shift between S- and P-wave amplitudes has two solutions, which are δ_{p}-δ_{s}=-1.55±0.25(stat)±0.05(syst)  rad or 1.59±0.25(stat)±0.05(syst)  rad.

Sonographic and clinicopathologic features of metaplastic breast carcinoma and infiltrating ductal carcinoma: a comparative single-center cohort study

Background

The rarity of metaplastic breast carcinoma (MBC) has resulted in limited sonographic data. Given the inferior prognosis of MBC compared to invasive ductal carcinoma (IDC), accurate preoperative differentiation between the two is imperative for effective treatment planning and prognostic prediction. The objective of this study was to assess the diagnostic accuracy of MBC and differentiate it from IDC by analyzing sonographic and clinicopathologic features.

Methods

In this retrospective cohort study, 197 women comprising 200 IDC lesions were enrolled between January 2012 and December 2021 and 20 women comprising 20 pure MBC lesions were enrolled between January 2019 and December 2019. A comparison was made between the sonographic and clinicopathologic characteristics of MBC and IDC.

Results

The results indicated that patients with MBC had a higher proportion of tumor grade 3 (95.0% vs. 32.5%; P<0.001), high Ki-67 expression (100.0% vs. 75.0%; P<0.001), and the triple-negative subtype (90.0% vs. 13.0%; P<0.001) as compared to those with IDC. On ultrasound (US) findings, MBC lesions tended to have a larger size (≥5 cm: 45.0% vs. 1.5%; P<0.001), regular shape (45.0% vs. 1.5%, P<0.001), circumscribed margin (40.0% vs. 0.5%, P<0.001), a complex cystic and solid echo pattern (50.0% vs. 3.5%; P<0.001), and posterior acoustic enhancement (95.0% vs. 14.5%; P<0.001). Additionally, MBC was more likely to be misinterpreted as a benign lesion by sonographers than was IDC (30.0% vs. 4.5%; P<0.001). Multilayer perceptron analysis revealed posterior acoustic enhancement, circumscribed margins, and size as distinguishing factors between these two tumor types. The estimated rates of local recurrence, distant metastasis, and 5-year overall survival in 19 cases with MBC were found to be 10.5%, 31.6%, and 65.0%, respectively.

Conclusions

MBC typically presents as a large breast mass with more benign US features in older women, findings which may facilitate its accurate diagnosis and differentiation from other breast masses.

Comparison of the efficacy of febuxostat vs. benzbromarone in the treatment of gout: a meta-analysis in Chinese gout patients

OBJECTIVE

Febuxostat and benzbromarone are two common drugs for the treatment of gout, but the clinical efficacy of these two drugs is controversial. This meta-analysis aimed to compare the efficacy of febuxostat and benzbromarone in the treatment of gout.

MATERIALS AND METHODS

PubMed, Embase, and the Cochrane Library were searched for articles related to febuxostat and benzbromarone in the treatment of gout from inception to January 7, 2023. Titles and abstracts were reviewed in accordance with predesigned inclusion and exclusion criteria, and data were extracted independently. The Newcastle-Ottawa Scale (NOS) was used to evaluate the quality of the studies, and the continuous variables were expressed as the standard mean square error (SMD) by STATA 16 (Stata Corp., College Station, TX, USA). The sensitivity analysis was conducted by randomly removing a study, and the heterogeneity was analyzed by funnel plots and Egger's test.

RESULTS

According to the search strategy, a total of 1,043 publications were retrieved from the three aforementioned databases, of which 45 publications were excluded due to duplication. Fourteen studies remained after screening titles and abstracts, and a total of 7 studies met the inclusion criteria after a comprehensive evaluation of the 14 studies. Meta-analysis showed that the uric acid (UA)-reducing effect of febuxostat is better than that of benzbromarone, while febuxostat showed a better ability to improve the estimated glomerular filtration rate (eGFR) and reduce Cr and blood urea nitrogen (BUN). In terms of hepatotoxicity, benzbromarone was not as potent as febuxostat in increasing alanine transaminase (ALT) and aspartate transaminase (AST), suggesting that benzbromarone has less hepatotoxicity. Moreover, there was no significant difference in the effect on blood lipid levels between the two drugs.

CONCLUSIONS

The beneficial effect of febuxostat on renal function-related indexes such as the eGFR, Cr and BUN is significant, while benzbromarone is more effective in reducing UA and has relatively less hepatotoxicity. The specific efficacy of the two drugs needs to be confirmed by further research.

Artificial intelligence-assisted analysis for tumor-immune interaction within the invasive margin of colorectal cancer

BACKGROUND

In colorectal cancer (CRC), both tumor invasion and immunological analysis at the tumor invasive margin (IM) are significantly associated with patient prognosis, but have traditionally been reported independently. We propose a new scoring system, the TGP-I score, to assess the association and interactions between tumor growth pattern (TGP) and tumor infiltrating lymphocytes at the IM and to predict its prognostic validity for CRC patient stratification.

MATERIALS AND METHODS

The types of TGP were assessed in hematoxylin and eosin-stained whole-slide images. The CD3⁺ T-cells density at the IM was automatically quantified on immunohistochemical-stained slides using a deep learning method. A discovery (N = 347) and a validation (N = 132) cohorts were used to evaluate the prognostic value of the TGP-I score for overall survival.

RESULTS

The TGP-I score₃ (trichotomy) was an independent prognostic factor, with higher TGP-I score₃ associated with worse prognosis in the discovery (unadjusted hazard ratio [HR] for high vs. low 3.62, 95% confidence interval [CI] 2.22-5.90; p < 0.001) and validation cohort (unadjusted HR for high vs. low 5.79, 95% CI 1.84-18.20; p = 0.003). The relative contribution of each parameter to predicting survival was analyzed. The TGP-I score₃ had similar importance compared to tumor-node-metastasis staging (31.2% vs. 32.9%) and was stronger than other clinical parameters.

CONCLUSIONS

This automated workflow and the proposed TGP-I score could further provide accurate prognostic stratification and have potential value for supporting the clinical decision-making of stage I-III CRC patients.Key messagesA new scoring system, the TGP-I score, was proposed to assess the association and interactions of TGP and TILs at the tumor invasive margin.TGP-I score could be an independent predictor of prognosis for CRC patients, with higher scores being associated with worse survival.TGP-I score had similar importance compared to tumor-node-metastasis staging and was stronger than other clinical parameters.

[Analysis of the relationship between PI-RADS scores and the pathological results of targeted biopsy based on MRI]

Objective: To analyze the relationship between Prostate Imaging Reporting and Data System (PI-RADS) scores and the pathological results of transperineal magnetic resonance-ultrasound fusion guided biopsy. Methods: The clinical data, magnetic resonance imaging (MRI) results and prostate puncture biopsies of 517 patients who were assigned to PI-RADS score of 4 or 5 and underwent transperineal magnetic resonance-ultrasound fusion guided biopsy at The First Affiliated Hospital of Nanjing Medical University from June 2019 to March 2022 were retrospectively analyzed. Patients were divided into the PI-RADS 4 and PI-RADS 5 groups according to their PI-RADS scores and were stratified by their prostate specific antigen (PSA) values (PSA<10 ng/ml vs. PSA 10-20 ng/ml). The pathological negative rates from the biopsy, the distribution of the grade groups according to the grading system by World Health Organization/International Society of Urological Pathology (WHO/ISUP), the detection rates of prostate cancer (PCa) and clinically significant prostate cancer (CsPCa)between the groups were compared. Results: 369 patients with a PI-RADS score of 4 and 148 patients with a PI-RADS score of 5 were included in our research. The overall detection rates of PCa and CsPCa were 77.8% (402/517) and 66.7% (345/517), respectively. In the PI-RADS 4 group, patients with prostate negative biopsies or in WHO/ISUP 1, 2, 3, 4, or 5 grade groups accounted for 28.2%, 12.7%, 20.1%, 17.1%, 18.4% and 3.5%, respectively, whereas in the PI-RADS 5 group the rates were 7.4%, 6.8%, 22.3%, 22.3%, 26.4%, and 14.9%, respectively. The difference was statistically significant (P<0.001). The detection rates of PCa and CsPCa in the PI-RADS 4 group [71.8% (265/369) vs. 59.1% (218/369), P<0.001] were lower than those of the PI-RADS 5 group [92.6% (137/148) vs. 85.8% (127/148), P<0.001]. In the PI-RADS 4 group, the proportion of patients classified into WHO/ISUP 4-5 grade groups was lower than that of patients in the PI-RADS 5 group [22.0% (81/369) vs 41.2% (61/148) (P<0.001)]. The detection rates of PCa and CsPCa in the PSA<10 ng/ml stratification were less than that in the PSA 10-20 ng/ml stratification[74.1% (281/379) vs. 87.7% (121/138), P=0.001], and [60.9% (231/379) vs. 82.6% (114/138), P<0.001]. For patients with PSA<10 ng/ml, the detection rates of PCa and CsPCa in the PI-RADS 4 group were less than those in the PI-RADS5 group [70.9% (217/306) vs. 87.7% (64/73), P=0.003], and [56.2% (172/306) vs. 80.8% (59/73), P<0.001]. For those with a PSA value of 10-20 ng/ml, the detection rates of PCa and CsPCa in the PI-RADS 4 group were less than those in the PI-RADS 5 group [76.2% (48/63) vs. 97.3% (73/75), P<0.001], and [73.0% (46/63) vs. 90.7% (68/75), P=0.006]. There were statistically significant differences in the proportions of patients with prostate negative biopsy and those falling into WHO/ISUP grade groups 1, 2, 3, 4, or 5 (P<0.001) between the PI-RADS 4 group and the PI-RADS 5 group in both stratifications. Conclusions: In this study, the detection rates of CsPCa and PCa in the PI-RADS 4 group were less than those in the PI-RADS 5 group. With the increase of PI-RADS scores, the detection rate of high-grade PCa increased. The same results held for patients with PSA<10 ng/ml or with PSA 10-20 ng/ml.

[Epidemiological characteristics of familiar adult inherited metabolic liver disease]

Inherited metabolic liver diseases can occur in multi-age groups such as children, adolescents, adults, and others. With the improvement of diagnosis and treatment levels, more and more patients with childhood-onset diseases are surviving into adulthood. Some diseases originally faced by pediatric hepatologists also appear in adult hepatology clinics. This raises new challenges for adult hepatologists, requiring them to master more professional knowledge. However, specific data on the incidence rate of most inherited metabolic liver diseases is still lacking in our country. This article reviews the research progress of hereditary metabolic liver diseases and summarizes the epidemiological characteristics of familiar hereditary metabolic liver diseases in China.

Test of CP Symmetry in Hyperon to Neutron Decays

The quantum entangled J/ψ→Σ^{+}Σ[over ¯]^{-} pairs from (1.0087±0.0044)×10^{10}  J/ψ events taken by the BESIII detector are used to study the nonleptonic two-body weak decays Σ^{+}→nπ^{+} and Σ[over ¯]^{-}→n[over ¯]π^{-}. The CP-odd weak decay parameters of the decays Σ^{+}→nπ^{+} (α_{+}) and Σ[over ¯]^{-}→n[over ¯]π^{-} (α[over ¯]_{-}) are determined to be 0.0481±0.0031_{stat}±0.0019_{syst} and -0.0565±0.0047_{stat}±0.0022_{syst}, respectively. The decay parameter α[over ¯]_{-} is measured for the first time, and the accuracy of α_{+} is improved by a factor of 4 compared to the previous results. The simultaneously determined decay parameters allow the first precision CP symmetry test for any hyperon decay with a neutron in the final state with the measurement of A_{CP}=(α_{+}+α[over ¯]_{-})/(α_{+}-α[over ¯]_{-})=-0.080±0.052_{stat}±0.028_{syst}. Assuming CP conservation, the average decay parameter is determined as ⟨α_{+}⟩=(α_{+}-α[over ¯]_{-})/2=-0.0506±0.0026_{stat}±0.0019_{syst}, while the ratios α_{+}/α_{0} and α[over ¯]_{-}/α[over ¯]_{0} are -0.0490±0.0032_{stat}±0.0021_{syst} and -0.0571±0.0053_{stat}±0.0032_{syst}, where α_{0} and α[over ¯]_{0} are the decay parameters of the decays Σ^{+}→pπ^{0} and Σ[over ¯]^{-}→p[over ¯]π^{0}, respectively.

Measurement of Energy-Dependent Pair-Production Cross Section and Electromagnetic Form Factors of a Charmed Baryon

We study the process e^{+}e^{-}→Λ_{c}^{+}Λ[over ¯]_{c}^{-} at twelve center-of-mass energies from 4.6119 to 4.9509 GeV using data samples collected by the BESIII detector at the BEPCII collider. The Born cross sections and effective form factors (|G_{eff}|) are determined with unprecedented precision after combining the single and double-tag methods based on the decay process Λ_{c}^{+}→pK^{-}π^{+}. Flat cross sections around 4.63 GeV are obtained and no indication of the resonant structure Y(4630), as reported by Belle, is found. In addition, no oscillatory behavior is discerned in the |G_{eff}| energy dependence of Λ_{c}^{+}, in contrast to what is seen for the proton and neutron cases. Analyzing the cross section together with the polar-angle distribution of the Λ_{c}^{+} baryon at each energy point, the moduli of electric and magnetic form factors (|G_{E}| and |G_{M}|) are extracted and separated. For the first time, the energy dependence of the form factor ratio |G_{E}/G_{M}| is observed, which can be well described by an oscillatory function.

Using the non-distortion IVIM to reduce the need for contrast agents in nasopharyngeal MRI

BACKGROUND

Patients with nasopharyngeal carcinoma (NPC) who undergo longitudinal follow-up contrast-enhanced MRI are at risk of developing gadolinium deposition in their neural tissue, which may potentially harm them. Therefore, for these patients, a non-contrast-enhanced method is potentially beneficial as an alternative approach to predict enhancement in T1-weighted imaging (CE-T1WI). The traditional intravoxel incoherent motion (IVIM) is one of the non-contrast-enhanced methods; however, the severe distortion and signal loss limit its application in patients with NPC. The present study aimed to investigate whether non-distortion IVIM could reduce the need of CE-T1WI in the follow-up of patients with NPC.

METHODS

The patients with NPC underwent Turbo Spin-echo MVXD diffusion-weighted imaging-based IVIM (non-distortion IVIM) from November 2021 to May 2022. Firstly, thirty patients with NPC were underwent both non-distortion IVIM and traditional IVIM. The distortion rate (DR) of the non-distortion IVIM was compared with the traditional IVIM. Then, twenty-one NPC patients with tumors (areas >50mm2) were included and correlation coefficient analysis was used to assess the relationship between their non-distortion IVIM and CE-T1WI. Linear regression analysis was performed to determine whether non-distortion IVIM predictors could predict CE-T1WI.

RESULTS

The correlation was observed between the parameter f of the non-distortion IVIM and the enhancement ratio of CE-T1WI (r = 0.543, P = 0.011). Moreover, the linear regression analysis revealed that f was an independent IVIM predictor of CE-T1WI in patients with NPC (P = 0.011). The DR of the non-distortion IVIM was significantly smaller than that of the traditional IVIM (0.12 ± 0.05 vs 0.48 ± 0.16, P < 0.001).

CONCLUSIONS

In patients with NPC, non-distortion IVIM showed potential clinical benefits to reduce the need for contrast agents, and it can independently predict the enhancement ratio.

Determination of Spin-Parity Quantum Numbers for the Narrow Structure near the pΛ[over ¯] Threshold in e^{+}e^{-}→pK^{-}Λ[over ¯]+c.c

A narrow structure in the pΛ[over ¯] system near the mass threshold, named as X(2085), is observed in the process e^{+}e^{-}→pK^{-}Λ[over ¯] with a statistical significance greater than 20σ. Its spin and parity are determined for the first time to be J^{P}=1^{+} in an amplitude analysis, with a statistical significance greater than 5σ over other quantum numbers (0^{-},1^{-} and 2^{+}). The pole positions of X(2085) are measured to be M_{pole}=(2084_{-2}^{+4}±9)  MeV and Γ_{pole}=(58_{-3}^{+4}±25)  MeV, where the first uncertainties are statistical and the second ones are systematic. The analysis is based on the study of the process e^{+}e^{-}→pK^{-}Λ[over ¯] and uses the data samples collected with the BESIII detector at the center-of-mass energies sqrt[s]=4.008, 4.178, 4.226, 4.258, 4.416, and 4.682 GeV with a total integrated luminosity of 8.35  fb^{-1}.

Precise Measurement of the e^{+}e^{-}→D_{s}^{*+}D_{s}^{*-} Cross Sections at Center-of-Mass Energies from Threshold to 4.95 GeV

The process e^{+}e^{-}→D_{s}^{*+}D_{s}^{*-} is studied with a semi-inclusive method using data samples at center-of-mass energies from threshold to 4.95 GeV collected with the BESIII detector operating at the Beijing Electron Positron Collider. The Born cross sections of the process are measured for the first time with high precision in this energy region. Two resonance structures are observed in the energy-dependent cross sections around 4.2 and 4.4 GeV. By fitting the cross sections with a coherent sum of three Breit-Wigner amplitudes and one phase-space amplitude, the two significant structures are assigned masses of (4186.8±8.7±30) and (4414.6±3.4±6.1)  MeV/c^{2}, widths of (55±15±53) and (122.5±7.5±8.1)  MeV, where the first errors are statistical and the second ones are systematic. The inclusion of a third Breit-Wigner amplitude is necessary to describe a structure around 4.79 GeV.

First Experimental Study of the Purely Leptonic Decay D_{s}^{*+}→e^{+}ν_{e}

Using 7.33  fb^{-1} of e^{+}e^{-} collision data taken with the BESIII detector at the BEPCII collider, we report the first experimental study of the purely leptonic decay D_{s}^{*+}→e^{+}ν_{e}. Our data contain a signal of this decay with a statistical significance of 2.9σ. The branching fraction of D_{s}^{*+}→e^{+}ν_{e} is measured to be (2.1_{-0.9_{stat}}^{+1.2}±0.2_{syst})×10^{-5}, corresponding to an upper limit of 4.0×10^{-5} at the 90% confidence level. Taking the total width of the D_{s}^{*+} [(0.070±0.028)  keV] predicted with the radiative D_{s}^{*+} decay from the lattice QCD calculation as input, the decay constant of the D_{s}^{*+} is determined to be f_{D_{s}^{*+}}=(214_{-46_{stat}}^{+61}±44_{syst})  MeV, corresponding to an upper limit of 354 MeV at the 90% confidence level.

Effect of artesunate on cardiovascular complications in periodontitis in a type I diabetes rat model and related mechanisms

PURPOSE

Both cardiovascular disease and periodontitis are complications of diabetes that have a great impact on human life and health. Our previous research found that artesunate can effectively improve cardiovascular disease in diabetes and has an inhibitory effect on periodontal disease. Therefore, the present study aimed to explore the potential therapeutic possibility of artesunate in the protection against cardiovascular complications in periodontitis with type I diabetes rats and to elucidate the possible underlying mechanisms.

METHODS

Sprague‒Dawley rats were randomly divided into the healthy, diabetic, periodontitis, diabetic with periodontitis, and artesunate treatment groups (10, 30, and 60 mg/kg, i.g.). After artesunate treatment, oral swabs were collected and used to determine changes in the oral flora. Micro-CT was performed to observe changes in alveolar bone. Blood samples were processed to measure various parameters, while cardiovascular tissues were evaluated by haematoxylin-eosin, Masson, Sirius red, and TUNEL staining to observe fibrosis and apoptosis. The protein and mRNA expression levels in the alveolar bone and cardiovascular tissues were detected using immunohistochemistry and RT‒PCR.

RESULTS

Diabetic rats with periodontitis and cardiovascular complications maintained heart and body weight but exhibited reduced blood glucose levels, and they were able to regulate blood lipid indicators at normal levels after artesunate treatment. The staining assays suggested that treatment with 60 mg/kg artesunate has a significant therapeutic effect on myocardial apoptotic fibrosis. The high expression of NF-κB, TLR4, VEGF, ICAM-1, p38 MAPK, TGF-β, Smad2, and MMP9 in the alveolar bone and cardiovascular tissue in the type I diabetes and type I diabetes with periodontitis rat models was reduced after treatment with artesunate in a concentration-dependent manner. Micro-CT showed that treatment with 60 mg/kg artesunate effectively alleviated alveolar bone resorption and density reduction. The sequencing results suggested that each model group of rats had vascular and oral flora dysbiosis, but artesunate treatment could correct the dysbacteriosis.

CONCLUSIONS

Periodontitis-related pathogenic bacteria cause dysbiosis of the oral and intravascular flora in type I diabetes and aggravate cardiovascular complications. The mechanism by which periodontitis aggravates cardiovascular complications involves the NF-κB pathway, which induces myocardial apoptosis, fibrosis, and vascular inflammation.

Guidelines for the Diagnosis and Treatment of Primary Liver Cancer (2022 Edition)

Background

Primary liver cancer, of which around 75-85% is hepatocellular carcinoma in China, is the fourth most common malignancy and the second leading cause of tumor-related death, thereby posing a significant threat to the life and health of the Chinese people.

Summary

Since the publication of Guidelines for Diagnosis and Treatment of Primary Liver Cancer in China in June 2017, which were updated by the National Health Commission in December 2019, additional high-quality evidence has emerged from researchers worldwide regarding the diagnosis, staging, and treatment of liver cancer, that requires the guidelines to be updated again. The new edition (2022 Edition) was written by more than 100 experts in the field of liver cancer in China, which not only reflects the real-world situation in China but also may reshape the nationwide diagnosis and treatment of liver cancer.

Key Messages

The new guideline aims to encourage the implementation of evidence-based practice and improve the national average 5-year survival rate for patients with liver cancer, as proposed in the "Health China 2030 Blueprint."

Value of mammographic microcalcifications and MRI-enhanced lesions in the evaluation of residual disease after neoadjuvant therapy for breast cancer

Background

Microcalcifications persist even if a patient with breast cancer achieves pathologic complete response (pCR) as confirmed by surgery after neoadjuvant treatment (NAT). In practice, surgeons tend to remove all the microcalcifications. This study aimed to explore the correlation between changes in the extent of microcalcification after NAT and pathological tumor response and compare the accuracy of mammography (MG) and magnetic resonance imaging (MRI) in predicting the size of residual tumors.

Methods

This was a retrospective study which included a consecutive series of patients in Guangdong Provincial People's Hospital. Between January 2010 and January 2020, 127 patients with breast cancer and Breast Imaging Reporting and Data System (BI-RADS) 4-5 microcalcifications were included in this study. The maximum diameter of the microcalcifications on MG and lesion enhancement on MRI pre- and post-NAT were measured. The correlations between the changes in residual microcalcifications on MG and pCR were analyzed. Intraclass correlation coefficients (ICCs) were computed between the extent of the residual microcalcifications, residual enhancement, and residual tumor size.

Results

There were no statistically significant differences in the changes in microcalcifications after NAT according to the RECIST criteria on MRI (P=0.09) and Miller-Payne grade (P=0.14). MRI showed a higher agreement than did residual microcalcifications on MG in predicting residual tumor size (ICC: 0.771 vs. 0.097).

Conclusions

MRI is more accurate for evaluating residual tumor size in breast cancer. In our study, the extent of microcalcifications on MG after NAT had nearly no correlation with the pathological size of the residual tumor. Therefore, residual tumors with microcalcifications may not necessarily be a contraindication to breast-conserving surgery.

Use of Pretreatment Multiparametric MRI to Predict Tumor Regression Pattern to Neoadjuvant Chemotherapy in Breast Cancer

RATIONALE AND OBJECTIVES

To develop an easy-to-use model by combining pretreatment MRI and clinicopathologic features for early prediction of tumor regression pattern to neoadjuvant chemotherapy (NAC) in breast cancer.

MATERIALS AND METHODS

We retrospectively analyzed 420 patients who received NAC and underwent definitive surgery in our hospital from February 2012 to August 2020. Pathologic findings of surgical specimens were used as the gold standard to classify tumor regression patterns into concentric and non-concentric shrinkage. Morphologic and kinetic MRI features were both analyzed. Univariable and multivariable analyses were performed to select the key clinicopathologic and MRI features for pretreatment prediction of regression pattern. Logistic regression and six machine learning methods were used to construct prediction models, and their performance were evaluated with receiver operating characteristic curve.

RESULTS

Two clinicopathologic variables and three MRI features were selected as independent predictors to construct prediction models. The apparent area under the curve (AUC) of seven prediction models were in the range of 0.669-0.740. The logistic regression model yielded an AUC of 0.708 (95% confidence interval [CI]: 0.658-0.759), and the decision tree model achieved the highest AUC of 0.740 (95% CI: 0.691-0.787). For internal validation, the optimism-corrected AUCs of seven models were in the range of 0.592-0.684. There was no significant difference between the AUCs of the logistic regression model and that of each machine learning model.

CONCLUSION

Prediction models combining pretreatment MRI and clinicopathologic features are useful for predicting tumor regression pattern in breast cancer, which can assist to select patients who can benefit from NAC for de-escalation of breast surgery and modify treatment strategy.

SMILE: Cost-sensitive multi-task learning for nuclear segmentation and classification with imbalanced annotations

High throughput nuclear segmentation and classification of whole slide images (WSIs) is crucial to biological analysis, clinical diagnosis and precision medicine. With the advances of CNN algorithms and the continuously growing datasets, considerable progress has been made in nuclear segmentation and classification. However, few works consider how to reasonably deal with nuclear heterogeneity in the following two aspects: imbalanced data distribution and diversified morphology characteristics. The minority classes might be dominated by the majority classes due to the imbalanced data distribution and the diversified morphology characteristics may lead to fragile segmentation results. In this study, a cost-Sensitive MultI-task LEarning (SMILE) framework is conducted to tackle the data heterogeneity problem. Based on the most popular multi-task learning backbone in nuclei segmentation and classification, we propose a multi-task correlation attention (MTCA) to perform feature interaction of multiple high relevant tasks to learn better feature representation. A cost-sensitive learning strategy is proposed to solve the imbalanced data distribution by increasing the penalization for the error classification of the minority classes. Furthermore, we propose a novel post-processing step based on the coarse-to-fine marker-controlled watershed scheme to alleviate fragile segmentation when nuclei are with large size and unclear contour. Extensive experiments show that the proposed method achieves state-of-the-art performances on CoNSeP and MoNuSAC 2020 datasets. The code is available at: https://github.com/panxipeng/nuclear_segandcls.

Development and validation of a scoring system incorporating tumor growth pattern and perineural invasion for risk stratification in colorectal cancer

Tumor growth pattern (TGP) and perineural invasion (PNI) at the invasive margin have been recognized as indicators of tumor invasiveness and prognostic events in colorectal cancer (CRC). This study aims to develop a scoring system incorporating TGP and PNI, and further investigate its prognostic significance for CRC risk stratification. A scoring system, termed tumor-invasion score, was established by summing TGP and PNI scores. The discovery cohort (N = 444) and the validation cohort (N = 339) were used to explore the prognostic significance of the tumor-invasion score. The endpoints of the event were disease-free survival (DFS) and overall survival (OS) which were analyzed by the Cox proportional hazard model. In the discovery cohort, Cox regression analysis showed that DFS and OS were inferior for score 4 group compared with score 1 group (DFS, hazard ratio (HR) 4.44, 95% confidence interval (CI) 2.49-7.92, p < 0.001; OS, 4.41, 2.37-8.19,p < 0.001). The validation cohort showed similar results (DFS, 4.73, 2.39-9.37, p < 0.001; OS, 5.52, 2.55-12.0, p < 0.001). The model combining tumor-invasion score and clinicopathologic information showed good discrimination performance than single predictors. TGP and PNI were associated with tumor invasiveness and survival in CRC. The tumor-invasion score generated by TGP and PNI scores served as an independent prognostic parameter of DFS and OS for CRC patients.

MRI-based Quantification of Intratumoral Heterogeneity for Predicting Treatment Response to Neoadjuvant Chemotherapy in Breast Cancer

Background Breast cancer is highly heterogeneous, resulting in different treatment responses to neoadjuvant chemotherapy (NAC) among patients. A noninvasive quantitative measure of intratumoral heterogeneity (ITH) may be valuable for predicting treatment response. Purpose To develop a quantitative measure of ITH on pretreatment MRI scans and test its performance for predicting pathologic complete response (pCR) after NAC in patients with breast cancer. Materials and Methods Pretreatment MRI scans were retrospectively acquired in patients with breast cancer who received NAC followed by surgery at multiple centers from January 2000 to September 2020. Conventional radiomics (hereafter, C-radiomics) and intratumoral ecological diversity features were extracted from the MRI scans, and output probabilities of imaging-based decision tree models were used to generate a C-radiomics score and ITH index. Multivariable logistic regression analysis was used to identify variables associated with pCR, and significant variables, including clinicopathologic variables, C-radiomics score, and ITH index, were combined into a predictive model for which performance was assessed using the area under the receiver operating characteristic curve (AUC). Results The training data set was comprised of 335 patients (median age, 48 years [IQR, 42-54 years]) from centers A and B, and 590, 280, and 384 patients (median age, 48 years [IQR, 41-55 years]) were included in the three external test data sets. Molecular subtype (odds ratio [OR] range, 4.76-8.39 [95% CI: 1.79, 24.21]; all P < .01), ITH index (OR, 30.05 [95% CI: 8.43, 122.64]; P < .001), and C-radiomics score (OR, 29.90 [95% CI: 12.04, 81.70]; P < .001) were independently associated with the odds of achieving pCR. The combined model showed good performance for predicting pCR to NAC in the training data set (AUC, 0.90) and external test data sets (AUC range, 0.83-0.87). Conclusion A model that combined an index created from pretreatment MRI-based imaging features quantitating ITH, C-radiomics score, and clinicopathologic variables showed good performance for predicting pCR to NAC in patients with breast cancer. © RSNA, 2023 Supplemental material is available for this article. See also the editorial by Rauch in this issue.

First Study of Reaction Ξ^{0}n→Ξ^{-}p Using Ξ^{0}-Nucleus Scattering at an Electron-Positron Collider

Using (1.0087±0.0044)×10^{10}  J/ψ events collected with the BESIII detector at the BEPCII storage ring, the process Ξ^{0}n→Ξ^{-}p is studied, where the Ξ^{0} baryon is produced in the process J/ψ→Ξ^{0}Ξ[over ¯]^{0} and the neutron is a component of the ^{9}Be, ^{12}C, and ^{197}Au nuclei in the beam pipe. A clear signal is observed with a statistical significance of 7.1σ. The cross section of the reaction Ξ^{0}+^{9}Be→Ξ^{-}+p+^{8}Be is determined to be σ(Ξ^{0}+^{9}Be→Ξ^{-}+p+^{8}Be)=(22.1±5.3_{stat}±4.5_{sys})  mb at the Ξ^{0} momentum of 0.818  GeV/c, where the first uncertainty is statistical and the second is systematic. No significant H-dibaryon signal is observed in the Ξ^{-}p final state. This is the first study of hyperon-nucleon interactions in electron-positron collisions and opens up a new direction for such research.

Measurements of Normalized Differential Cross Sections of Inclusive π^{0} and K_{S}^{0} Production in e^{+}e^{-} Annihilation at Energies from 2.2324 to 3.6710 GeV

Based on electron positron collision data collected with the BESIII detector operating at the BEPCII storage rings, the differential cross sections of inclusive π^{0} and K_{S}^{0} production as a function of hadron momentum, normalized by the total cross section of the e^{+}e^{-}→hadrons process, are measured at six center-of-mass energies from 2.2324 to 3.6710 GeV. Our results, which cover a relative hadron energy range from 0.1 to 0.9, significantly deviate from several theoretical calculations based on existing fragmentation functions.

Homogeneous-resolution photoacoustic microscopy for ultrawide field-of-view neurovascular imaging in Alzheimer's disease

Neurovascular imaging is essential for investigating neurodegenerative diseases. However, the existing neurovascular imaging technology suffers from a trade-off between a field of view (FOV) and resolution in the whole brain, resulting in an inhomogeneous resolution and lack of information. Here, homogeneous-resolution arched-scanning photoacoustic microscopy (AS-PAM), which has an ultrawide FOV to cover the entire mouse cerebral cortex, was developed. Imaging of the neurovasculature was performed with a homogenous resolution of 6.9 µm from the superior sagittal sinus to the middle cerebral artery and caudal rhinal vein in an FOV of 12 × 12 mm². Moreover, using AS-PAM, vascular features of the meninges and cortex were quantified in early Alzheimer's disease (AD) and wild-type (WT) mice. The results demonstrated high sensitivity to the pathological progression of AD on tortuosity and branch index. The high-fidelity imaging capability in large FOV enables AS-PAM to be a promising tool for precise brain neurovascular visualization and quantification.

HoVer-Trans: Anatomy-Aware HoVer-Transformer for ROI-Free Breast Cancer Diagnosis in Ultrasound Images

Ultrasonography is an important routine examination for breast cancer diagnosis, due to its non-invasive, radiation-free and low-cost properties. However, the diagnostic accuracy of breast cancer is still limited due to its inherent limitations. Then, a precise diagnose using breast ultrasound (BUS) image would be significant useful. Many learning-based computer-aided diagnostic methods have been proposed to achieve breast cancer diagnosis/lesion classification. However, most of them require a pre-define region of interest (ROI) and then classify the lesion inside the ROI. Conventional classification backbones, such as VGG16 and ResNet50, can achieve promising classification results with no ROI requirement. But these models lack interpretability, thus restricting their use in clinical practice. In this study, we propose a novel ROI-free model for breast cancer diagnosis in ultrasound images with interpretable feature representations. We leverage the anatomical prior knowledge that malignant and benign tumors have different spatial relationships between different tissue layers, and propose a HoVer-Transformer to formulate this prior knowledge. The proposed HoVer-Trans block extracts the inter- and intra-layer spatial information horizontally and vertically. We conduct and release an open dataset GDPH&SYSUCC for breast cancer diagnosis in BUS. The proposed model is evaluated in three datasets by comparing with four CNN-based models and three vision transformer models via five-fold cross validation. It achieves state-of-the-art classification performance (GDPH&SYSUCC AUC: 0.924, ACC: 0.893, Spec: 0.836, Sens: 0.926) with the best model interpretability. In the meanwhile, our proposed model outperforms two senior sonographers on the breast cancer diagnosis when only one BUS image is given (GDPH&SYSUCC-AUC ours: 0.924 vs. reader1: 0.825 vs. reader2: 0.820).

Precision Measurement of the Decay Σ^{+}→pγ in the Process J/ψ→Σ^{+}Σ[over ¯]^{-}

Using (10 087±44)×10^{6} J/ψ events collected with the BESIII detector, the radiative hyperon decay Σ^{+}→pγ is studied at an electron-positron collider experiment for the first time. The absolute branching fraction is measured to be (0.996±0.021_{stat}±0.018_{syst})×10^{-3}, which is lower than its world average value by 4.2 standard deviations. Its decay asymmetry parameter is determined to be -0.652±0.056_{stat}±0.020_{syst}. The branching fraction and decay asymmetry parameter are the most precise to date, and the accuracies are improved by 78% and 34%, respectively.

Necrosis score as a prognostic factor in stage I-III colorectal cancer: a retrospective multicenter study

BACKGROUND

Tumor necrosis results from failure to meet the requirement for rapid proliferation of tumor, related to unfavorable prognosis in colorectal cancer (CRC). However, previous studies used traditional microscopes to evaluate necrosis on slides, lacking a simultaneous phase and panoramic view for assessment. Therefore, we proposed a whole-slide images (WSIs)-based method to develop a necrosis score and validated its prognostic value in multicenter cohorts.

METHODS

Necrosis score was defined as the proportion of necrosis in the tumor area, semi-quantitatively classified into 3-level score groups by the cut-off of 10% and 30% on HE-stained WSIs. 768 patients from two centers were enrolled in this study, divided into a discovery (N = 445) and a validation (N = 323) cohort. The prognostic value of necrosis score was evaluated by Kaplan-Meier curves and the Cox model.

RESULT

Necrosis score was associated with overall survival, with hazard ratio for high vs. low in discovery and validation cohorts being 2.62 (95% confidence interval 1.59-4.32) and 2.51 (1.39-4.52), respectively. The 3-year disease free survival rates of necrosis-low, middle, and high were 83.6%, 80.2%, and 59.8% in discovery cohort, and 86.5%, 84.2%, and 66.5% in validation cohort. In necrosis middle plus high subgroup, there was a trend but no significant difference in overall survival between surgery alone and adjuvant chemotherapy group in stage II CRC (P = .075).

CONCLUSION

As a stable prognostic factor, high-level necrosis evaluated by the proposed method on WSIs was associated with unfavorable outcomes. Additionally, adjuvant chemotherapy provide survival benefits for patients with high necrosis in stage II CRC.

Measurements of the Electric and Magnetic Form Factors of the Neutron for Timelike Momentum Transfer

We present the first measurements of the electric and magnetic form factors of the neutron in the timelike (positive q^{2}) region as function of four-momentum transfer. We explored the differential cross sections of the reaction e^{+}e^{-}→n[over ¯]n with data collected with the BESIII detector at the BEPCII accelerator, corresponding to an integrated luminosity of 354.6  pb^{-1} in total at twelve center-of-mass energies between sqrt[s]=2.0-2.95  GeV. A relative uncertainty of 18% and 12% for the electric and magnetic form factors, respectively, is achieved at sqrt[s]=2.3935  GeV. Our results are comparable in accuracy to those from electron scattering in the comparable spacelike region of four-momentum transfer. The electromagnetic form factor ratio R_{em}≡|G_{E}|/|G_{M}| is within the uncertainties close to unity. We compare our result on |G_{E}| and |G_{M}| to recent model predictions, and the measurements in the spacelike region to test the analyticity of electromagnetic form factors.

Observation of a New X(3872) Production Process e^{+}e^{-}→ωX(3872)

Using 4.7  fb^{-1} of e^{+}e^{-} collision data at center-of-mass energies from 4.661 to 4.951 GeV collected by the BESIII detector at the BEPCII collider, we observe the X(3872) production process e^{+}e^{-}→ωX(3872) for the first time. The significance is 7.8σ, including both the statistical and systematic uncertainties. The e^{+}e^{-}→ωX(3872) Born cross section and the corresponding upper limit at 90% confidence level at each energy point are reported. The line shape of the cross section indicates that the ωX(3872) signals may be from the decays of some nontrivial structures.

Observation of Three Charmoniumlike States with J^{PC}=1^{--} in e^{+}e^{-}→D^{*0}D^{*-}π^{+}

The Born cross sections of the process e^{+}e^{-}→D^{*0}D^{*-}π^{+} at center-of-mass energies from 4.189 to 4.951 GeV are measured for the first time. The data samples used correspond to an integrated luminosity of 17.9  fb^{-1} and were collected by the BESIII detector operating at the BEPCII storage ring. Three enhancements around 4.20, 4.47, and 4.67 GeV are visible. The resonances have masses of 4209.6±4.7±5.9  MeV/c^{2}, 4469.1±26.2±3.6  MeV/c^{2}, and 4675.3±29.5±3.5  MeV/c^{2} and widths of 81.6±17.8±9.0  MeV, 246.3±36.7±9.4  MeV, and 218.3±72.9±9.3  MeV, respectively, where the first uncertainties are statistical and the second systematic. The first and third resonances are consistent with the ψ(4230) and ψ(4660) states, respectively, while the second one is compatible with the ψ(4500) observed in the e^{+}e^{-}→K^{+}K^{-}J/ψ process. These three charmoniumlike ψ states are observed in the e^{+}e^{-}→D^{*0}D^{*-}π^{+} process for the first time.

Genome-wide identification and expression analysis of SlRR genes in response to abiotic stress in tomato

The cytokinin two-component signal transduction system (TCS) is involved in many biological processes, including hormone signal transduction and plant growth regulation. Although cytokinin TCS has been well characterized in Arabidopsis thaliana, its role in tomato remains elusive. In this study, we characterized the diversity and function of response regulator (RR) genes, a critical component of TCS, in tomato. In total, we identified 31 RR genes in the tomato genome. These SlRR genes were classified into three subgroups (type-A, type-B and type-C). Various stress-responsive cis-elements were present in the tomato RR gene promoters. Their expression responses under pesticide treatment were evaluated by transcriptome analysis. Their expression under heat, cold, ABA, salinity and NaHCO₃ treatments was further investigated by qRT-PCR and complemented with the available transcription data under these treatments. Specifically, SlRR13 expression was significantly upregulated under salinity, drought, cold and pesticide stress and was downregulated under ABA treatment. SlRR23 expression was induced under salt treatment, while the transcription level of SlRR1 was increased under cold and decreased under salt stress. We also found that GATA transcription factors played a significant role in the regulation of SlRR genes. Based on our results, tomato SlRR genes are involved in responses to abiotic stress in tomato and could be implemented in molecular breeding approaches to increase resistance of tomato to environmental stresses.

L-distance ratio: a new distance ratio-based evaluation method for the diagnosis of cirrhosis using enhanced computed tomography

Background

Early detection of liver cirrhosis is of great significance to the formulation of treatment plans and improving prognosis. Computed tomography (CT) is commonly used in the assessment of patients with chronic liver disease. In this study, we proposed a new distance ratio method for accurate diagnosis of cirrhosis using CT images.

Methods

This was a retrospective study of a consecutive series of patients in Guangdong Provincial People's Hospital. Sixty-two patients with pathologically diagnosed cirrhosis but whose morphologic changes were insufficient to diagnose cirrhosis were included in the cirrhosis group. Those who were pathologically confirmed to be free of cirrhosis and fibrosis and without a history of chronic hepatic were classified as the control group. A total of 124 patients underwent abdominal dynamic enhanced CT. Both the L-distance ratio-the ratio of the distance from the right portal vein bifurcation point to the anterior and posterior edges of the liver-and the caudate-right lobe ratio were measured by two independent radiologists. Intraclass correlation coefficients (ICCs) were used to assess the agreement between the radiologists. Binary logistic regression was performed for univariate analysis, and the odds ratio (OR) was also calculated. The discrimination ability of the two methods was evaluated by the area under the receiver operating characteristic curve (AUC).

Results

For both the L-distance ratio and the caudate-right lobe ratio, high agreement was observed between the two radiologists, although the ICC value of the L-distance ratio was slightly higher than that of the caudate-right lobe ratio (0.916 vs. 0.907). Binary logistic regression suggested that higher ratios were correlated with cirrhosis [the L-distance ratio, high vs. low OR =4.41, 95% confidence interval (CI): 2.08-9.36, P<0.001; the caudate-right lobe ratio, high vs. low OR =2.19, 95% CI: 1.07-4.49, P=0.031]. The AUCs of the L-distance ratio and the caudate-right lobe ratio were 0.823 (95% CI: 0.752-0.894) and 0.663 (95% CI: 0.569-0.757), respectively.

Conclusions

The L-distance ratio method proposed in this paper is more simple, accurate, and reliable than the caudate-right lobe ratio method in the diagnosis of cirrhosis.

CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation

Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain tumor diagnosis, cancer management and research purposes. With the great success of the ten-year BraTS challenges as well as the advances of CNN and Transformer algorithms, a lot of outstanding BTS models have been proposed to tackle the difficulties of BTS in different technical aspects. However, existing studies hardly consider how to fuse the multi-modality images in a reasonable manner. In this paper, we leverage the clinical knowledge of how radiologists diagnose brain tumors from multiple MRI modalities and propose a clinical knowledge-driven brain tumor segmentation model, called CKD-TransBTS. Instead of directly concatenating all the modalities, we re-organize the input modalities by separating them into two groups according to the imaging principle of MRI. A dual-branch hybrid encoder with the proposed modality-correlated cross-attention block (MCCA) is designed to extract the multi-modality image features. The proposed model inherits the strengths from both Transformer and CNN with the local feature representation ability for precise lesion boundaries and long-range feature extraction for 3D volumetric images. To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder. We compare the proposed model with six CNN-based models and six transformer-based models on the BraTS 2021 challenge dataset. Extensive experiments demonstrate that the proposed model achieves state-of-the-art brain tumor segmentation performance compared with all the competitors.

Reef islands have continually adjusted to environmental change over the past two millennia

Global environmental change is identified as a driver of physical transformation of coral reef islands over the past half-century, and next 100 years, posing major adaptation challenges to island nations. Here we resolve whether these recent documented changes in islands are unprecedented compared with the pre-industrial era. We utilise radiometric dating, geological, and remote sensing techniques to document the dynamics of a Maldivian reef island at millennial to decadal timescales. Results show the magnitude of island change over the past half-century (±40 m movement) is not unprecedented compared with paleo-dynamic evidence that reveals large-scale changes in island dimension, shape, beach levels, as well as positional changes of ±200 m since island formation ~1,500 years ago. Results highlight the value of a multi-temporal methodological approach to gain a deeper understanding of the dynamic trajectories of reef islands, to support development of adaptation strategies at timeframes relevant to human security.

MRI characteristics of breast edema for assessing axillary lymph node burden in early-stage breast cancer: a retrospective bicentric study

OBJECTIVES

To investigate whether breast edema characteristics at preoperative T2-weighted imaging (T2WI) could help evaluate axillary lymph node (ALN) burden in patients with early-stage breast cancer.

METHODS

This retrospective study included women with clinical T1 and T2 stage breast cancer and preoperative MRI examination in two independent cohorts from May 2014 to December 2020. Low (< 3 LNs+) and high (≥ 3 LNs+) pathological ALN (pALN) burden were recorded as endpoint. Breast edema score (BES) was evaluated at T2WI. Univariable and multivariable analyses were performed by the logistic regression model. The added predictive value of BES was examined utilizing the area under the curve (AUC), net reclassification improvement (NRI), and integrated discrimination improvement (IDI).

RESULTS

A total of 1092 patients were included in this study. BES was identified as the independent predictor of pALN burden in primary (n = 677) and validation (n = 415) cohorts. The analysis using MRI-ALN status showed that BES significantly improved the predictive performance of pALN burden (AUC: 0.65 vs 0.71, p < 0.001; IDI = 0.045, p < 0.001; continuous NRI = 0.159, p = 0.050). These results were confirmed in the validation cohort (AUC: 0.64 vs 0.69, p = 0.009; IDI = 0.050, p < 0.001; continuous NRI = 0.213, p = 0.047). Furthermore, BES was positively correlated with biologically invasive clinicopathological factors (p < 0.05).

CONCLUSIONS

In individuals with early-stage breast cancer, preoperative MRI characteristics of breast edema could be a promising predictor for pALN burden, which may aid in treatment planning.

KEY POINTS

• In this retrospective study of 1092 patients with early-stage breast cancer from two cohorts, the MRI characteristic of breast edema has independent and additive predictive value for assessing axillary lymph node burden. • Breast edema characteristics at T2WI positively correlated with biologically invasive clinicopathological factors, which may be useful for preoperative diagnosis and treatment planning for individual patients with breast cancer.