Bipolar membrane electrodialysis integrated with in-situ CO2 absorption for simulated seawater concentrate utilization, carbon storage and production of sodium carbonate

In the context of carbon capture, utilization, and storage, the high-value utilization of carbon storage presents a significant challenge. To address this challenge, this study employed the bipolar membrane electrodialysis integrated with carbon utilization technology to prepare Na2CO3 products using simulated seawater concentrate, achieving simultaneous saline wastewater utilization, carbon storage and high-value production of Na2CO3. The effects of various factors, including concentration of simulated seawater concentrate, current density, CO2 aeration rate, and circulating flow rate of alkali chamber, on the quality of Na2CO3 product, carbon sequestration rate, and energy consumption were investigated. Under the optimal condition, the CO32- concentration in the alkaline chamber reached a maximum of 0.817 mol/L with 98 mol% purity. The resulting carbon fixation rate was 70.50%, with energy consumption for carbon sequestration and product production of 5.7 kWhr/m3 CO2 and 1237.8 kWhr/ton Na2CO3, respectively. This coupling design provides a triple-win outcome promoting waste reduction and efficient utilization of resources.

Quantitative evaluation of urban green exposure and its impact on human health: A case study on the 3-30-300 green space rule

BACKGROUND AND AIMS

Urban green spaces offer various health benefits, yet the impact of comprehensive green exposure criteria on multidimensional health remains unclear. The 3-30-300 green space rule represents the green exposure indicators with specific thresholds. This study aims to quantitatively evaluate urban green exposure in cities and can support investigation of its relationship with human health.

METHODS

We conducted a cross-sectional study based on 902 investigated individuals in 261 residential locations aged 11-95 years from Xiamen City, China. 3-30-300 green exposure was calculated using field surveys, GIS, and Baidu Maps Application Programming Interface (API). Physical health data was based on Occupational Stress Indicator (OSI)-2. Mental health was from the 12-item General Health Questionnaire (GHQ-12). Social health was from a self-constructed evaluation questionnaire. Statistical analyses were conducted using Geographically Weighted Regression and Geographically Weighted Logistic Regression for global and local effects on green exposure and multidimensional health.

RESULT

Among the investigated individuals, only 3.55 % (32/902) fully meet the 3-30-300 rule in Xiamen. Global results show that individuals achieved at least 30 % vegetation coverage (Yes) is associated with better physical (β: 0.76, p < 0.01) and social (β: 0.5, p < 0.01) health. GWLR global results indicate that individuals can "see at least 3 trees from home" meeting one (OR = 0.46, 95%CI: 0.25-0.86, p < 0.05) or two (OR = 0.41, 95%CI: 0.22,0.78, p < 0.01; OR = 0.24, 95%CI: 0.07-0.77, p < 0.05) 3-30-300 rule components are significantly associated with reduced medical visits and hospitalizations refer to not met these criterias. In the GWR local analysis, achieved 30 % vegetation cover is significantly related to improved social health at all locations. Meeting any two indicators also contribute to improved social health (n = 511, β: 0.46-0.51, P < 0.05).

CONCLUSION

Green exposure indicators based on the 3-30-300 rule guiding healthy urban green space development. We observed multidimensional health benefits when 1/3 or 2/3 of the indicators were met.

Semaphorins in tumor microenvironment: Biological mechanisms and therapeutic progress

Hallmark features of the tumor microenvironment include immune cells, stromal cells, blood vessels, and extracellular matrix (ECM), providing a conducive environment for the growth and survival of tumors. Recent advances in the understanding of cancer biology have highlighted the functional role of semaphorins (SEMAs). SEMAs are a large and diverse family of widely expressed secreted and membrane-binding proteins, which were initially implicated in axon guidance and neural development. However, it is now clear that they are widely expressed beyond the nervous system and participate in regulating immune responses and cancer progression. In fact, accumulating evidence disclosed that different SEMAs can either stimulate or restrict tumor progression, some of which act as important regulators of tumor angiogenesis. Conversely, limited information is known about the functional relevance of SEMA signals in TME. In this setting, we systematically elaborate the role SEMAs and their major receptors played in characterized components of TME. Furthermore, we provide a convergent view of current SEMAs pharmacological progress in clinical treatment and also put forward their potential application value and clinical prospects in the future.

Kinetics of chlorine and chloramine reactions in reverse osmosis permeate and their impact on radical formation during UV/chlorine advanced oxidation for potable reuse

Knowledge of the speciation of chlorine and chloramines in reverse osmosis (RO) permeate is needed to estimate the performance (i.e., pollutant log reduction) of subsequent UV/chlorine advanced oxidation processes (AOPs). To accurately predict the speciation, a previously reported breakpoint chlorination kinetic model was experimentally validated for pH 5.5 and reaction times < 3 min and used to predict the kinetics of breakpoint chlorination in RO permeate. The predictions showed that eliminating chloramines by adding chlorine at a dose beyond the chlorine-to-nitrogen (Cl/N) breakpoint ratio is not practical due to the high breakpoint Cl/N ratio for RO permeate (∼3.0 molar ratio) and an estimated > 40 min reaction time. The conversion from monochloramine (NH2Cl) to dichloramine (NHCl2) is the major process involved, and either or both free chlorine and chloramines may be the major species present, depending on the Cl/N ratio. Model simulations showed that increasing the oxidant dose may not always enhance the performance of UV/chlor(am)ine in RO permeate, due to the need for a low free chlorine dose for optimal •OH exposure in RO permeate. Further UV/AOPs modelling showed that it is important to control the NH2Cl concentration to improve the UV/AOP performance in RO permeate, which may be achieved by extending the reaction time after chlorine is added or increasing the applied Cl/N ratio (e.g., increasing chlorine dose). However, these measures only enhance the pollutant percentage removal by about 5 % under the conditions modelled. A simulation tool was developed and is provided to predict the speciation of chlor(am)ine in RO permeate.

On the role of asymmetric molecular geometry in high-performance organic solar cells

Although asymmetric molecular design has been widely demonstrated effective for organic photovoltaics (OPVs), the correlation between asymmetric molecular geometry and their optoelectronic properties is still unclear. To access this issue, we have designed and synthesized several symmetric-asymmetric non-fullerene acceptors (NFAs) pairs with identical physical and optoelectronic properties. Interestingly, we found that the asymmetric NFAs universally exhibited increased open-circuit voltage compared to their symmetric counterparts, due to the reduced non-radiative charge recombination. From our molecular-dynamic simulations, the asymmetric NFA naturally exhibits more diverse molecular interaction patterns at the donor (D):acceptor (A) interface as compared to the symmetric ones, as well as higher D:A interfacial charge-transfer state energy. Moreover, it is observed that the asymmetric structure can effectively suppress triplet state formation. These advantages enable a best efficiency of 18.80%, which is one of the champion results among binary OPVs. Therefore, this work unambiguously demonstrates the unique advantage of asymmetric molecular geometry, unveils the underlying mechanism, and highlights the manipulation of D:A interface as an important consideration for future molecular design.

Kidney function decline mediates the adverse effects of per- and poly-fluoroalkyl substances (PFAS) on uric acid levels and hyperuricemia risk

Previous studies indicated per- and poly-fluoroalkyl substances (PFAS) were related to uric acid and hyperuricemia risk, but evidence for the exposure-response (E-R) curves and combined effect of PFAS mixture is limited. Moreover, the potential mediation effect of kidney function was not assessed. Hence, we conducted a national cross-sectional study involving 13,979 US adults in NHANES 2003-2018 to examine the associations of serum PFAS with uric acid and hyperuricemia risk, and the mediation effects of kidney function. Generalized linear models and E-R curves showed positive associations of individual PFAS with uric acid and hyperuricemia risk, and nearly linear E-R curves indicated no safe threshold for PFAS. Weighted quantile sum regression found positive associations of PFAS mixture with uric acid and hyperuricemia risk, and PFOA was the dominant contributor to the adverse effect of PFAS on uric acid and hyperuricemia risk. Causal mediation analysis indicated significant mediation effects of kidney function decline in the associations of PFAS with uric acid and hyperuricemia risk, with the mediated proportion ranging from 19 % to 57 %. Our findings suggested that PFAS, especially PFOA, may cause increased uric acid and hyperuricemia risk increase even at low levels, and kidney function decline plays a crucial mediation effect.

Uncertainty Quantification and Interpretability for Clinical Trial Approval Prediction

Background: Clinical trial is a crucial step in the development of a new therapy (e.g., medication) and is remarkably expensive and time-consuming. Forecasting the approval of clinical trials accurately would enable us to circumvent trials destined to fail, thereby allowing us to allocate more resources to therapies with better chances. However, existing approval prediction algorithms did not quantify the uncertainty and provide interpretability, limiting their usage in real-world clinical trial management. Methods: This paper quantifies uncertainty and improves interpretability in clinical trial approval predictions. We devised a selective classification approach and integrated it with the Hierarchical Interaction Network, the state-of-the-art clinical trial prediction model. Selective classification, encompassing a spectrum of methods for uncertainty quantification, empowers the model to withhold decision-making in the face of samples marked by ambiguity or low confidence. This approach not only amplifies the accuracy of predictions for the instances it chooses to classify but also notably enhances the model's interpretability. Results: Comprehensive experiments demonstrate that incorporating uncertainty markedly enhances the model's performance. Specifically, the proposed method achieved 32.37%, 21.43%, and 13.27% relative improvement on area under the precision-recall curve over the base model (Hierarchical Interaction Network) in phase I, II, and III trial approval predictions, respectively. For phase III trials, our method reaches 0.9022 area under the precision-recall curve scores. In addition, we show a case study of interpretability that helps domain experts to understand model's outcome. The code is publicly available at https://github.com/Vincent-1125/Uncertainty-Quantification-on-Clinical-Trial-Outcome-Prediction. Conclusion: Our approach not only measures model uncertainty but also greatly improves interpretability and performance for clinical trial approval prediction.

Study of the f_{0}(980) and f_{0}(500) Scalar Mesons through the Decay D_{s}^{+}→π^{+}π^{-}e^{+}ν_{e}

Using e^{+}e^{-} collision data corresponding to an integrated luminosity of 7.33  fb^{-1} recorded by the BESIII detector at center-of-mass energies between 4.128 and 4.226 GeV, we present an analysis of the decay D_{s}^{+}→π^{+}π^{-}e^{+}ν_{e}, where the D_{s}^{+} is produced via the process e^{+}e^{-}→D_{s}^{*±}D_{s}^{∓}. We observe the f_{0}(980) in the π^{+}π^{-} system and the branching fraction of the decay D_{s}^{+}→f_{0}(980)e^{+}ν_{e} with f_{0}(980)→π^{+}π^{-} measured to be (1.72±0.13_{stat}±0.10_{syst})×10^{-3}, where the uncertainties are statistical and systematic, respectively. The dynamics of the D_{s}^{+}→f_{0}(980)e^{+}ν_{e} decay are studied with the simple pole parametrization of the hadronic form factor and the Flatté formula describing the f_{0}(980) in the differential decay rate, and the product of the form factor f_{+}^{f_{0}}(0) and the c→s Cabibbo-Kobayashi-Maskawa matrix element |V_{cs}| is determined for the first time to be f_{+}^{f_{0}}(0)|V_{cs}|=0.504±0.017_{stat}±0.035_{syst}. Furthermore, the decay D_{s}^{+}→f_{0}(500)e^{+}ν_{e} is searched for the first time but no signal is found. The upper limit on the branching fraction of D_{s}^{+}→f_{0}(500)e^{+}ν_{e}, f_{0}(500)→π^{+}π^{-} decay is set to be 3.3×10^{-4} at 90% confidence level.

A mixed blessing of influent leachate microbes in downstream biotreatment systems of a full-scale landfill leachate treatment plant

In landfill leachate treatment plants (LLTPs), the microbiome plays a pivotal role in the decomposition of organic compounds, reduction in nutrient levels, and elimination of toxins. However, the effects of microbes in landfill leachate influents on downstream treatment systems remain poorly understood. To address this knowledge gap, we collected 23 metagenomic and 12 metatranscriptomic samples from landfill leachate and activated sludge from various treatment units in a full-scale LLTP. We successfully recovered 1,152 non-redundant metagenome-assembled genomes (MAGs), encompassing a wide taxonomic range, including 48 phyla, 95 classes, 166 orders, 247 families, 238 genera, and 1,152 species. More diverse microbes were observed in the influent leachate than in the downstream biotreatment systems, among which, an unprecedented ∼30 % of microbes with transcriptional expression migrated from the influent to the biological treatment units. Network analysis revealed that 399 shared MAGs across the four units exhibited high node centrality and degree, thus supporting enhanced interactions and increased stability of microbial communities. Functional reconstruction and genome characterization of MAGs indicated that these shared MAGs possessed greater capabilities for carbon, nitrogen, sulfur, and arsenic metabolism compared to non-shared MAGs. We further identified a novel species of Zixibacteria in the leachate influent with discrete lineages from those in other environments that accounted for up to 17 % of the abundance of the shared microbial community and exhibited notable metabolic versatility. Meanwhile, we presented groundbreaking evidence of the involvement of Zixibacteria-encoded genes in the production of harmful gas emissions, such as N2O and H2S, at the transcriptional level, thus suggesting that influent microbes may pose safety risks to downstream treatment systems. In summary, this study revealed the complex impact of the influent microbiome on LLTP and emphasizes the need to consider these microbial characteristics when designing treatment technologies and strategies for landfill leachate management.

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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Shallow wet irrigation reduces nitrogen leaching loss rate in paddy fields by microbial regulation and lowers rate of downward migration of leaching water: a 15N-tracer study

China consumes 35% of the world's fertilizer every year; however, most of the nitrogen fertilizers, which are essential for rice cultivation, are not used effectively. In this study, factors affecting the nitrogen leaching loss rate were studied in typical soil and rice varieties in South China. The effects of various irrigation measures on rice growth and nitrogen leaching loss were investigated by conducting experiments with eight groups. These groups included traditional irrigation (TI) and shallow wet irrigation (SWI). The TI is a common irrigation method for farmers in South China, maintaining a water layer of 5-8 cm depth. For SWI, after establishing a shallow water layer usually maintaining at 1-2 cm, paddy is irrigated when the field water level falls to a certain depth, then this process is then repeat as necessary. The nitrogen distribution characteristics were determined using 15N isotope tracing. In addition, the effects of nitrification, denitrification, and microbial composition on soil nitrogen transformation at different depths were studied by microbial functional gene quantification and high-throughput sequencing. The results revealed that in the SWI groups, the total nitrogen leaching loss rate reduced by 0.3-0.8% and the nitrogen use efficiency (NUE) increased by 2.18-4.43% compared with those in the TI groups. After the 15N-labeled nitrogen fertilizer was applied, the main pathways of nitrogen were found to be related to plant absorption and nitrogen residues. Furthermore, paddy soil ammonia-oxidizing archaea were more effective than ammonia-oxidizing bacteria for soil ammonia oxidation by SWI groups. The SWI measures increased the relative abundance of Firmicutes in paddy soil, enhancing the ability of rice to fix nitrogen to produce ammonium nitrogen, thus reducing the dependence of rice on chemical fertilizers. Moreover, SWI enhanced the relative abundance of nirS and nosZ genes within surface soil bacteria, thereby promoting denitrification in the surface soil of paddy fields. SWI also promoted ammonia oxidation and denitrification by increasing the abundance and activity of Proteobacteria, Nitrospirae, and Bacteroidetes. Collectively, SWI effectively reduced the nitrogen leaching loss rate and increase NUE.

[Diagnostic value of identifying location and amount of free gas in the abdominal cavity by multidetector computed tomography in patients with acute gastrointestinal perforation]

Objective: To evaluate the relationships between the location and extent of diffusion of free intraperitoneal air by multi-slice spiral CT (MSCT) and between the location and size of acute gastrointestinal perforation. Methods: This was a descriptive case series. We examined abdominal CT images of 33 patients who were treated for intraoperatively confirmed gastrointestinal perforation (excluding appendiceal perforation) in the Department of General Surgery, Nanfang Hospital between January and September 2022. We identified five locations of intraperitoneal air: the subphrenic space, hepatic portal space, mid-abdominal wall, mesenteric space, and pelvic cavity. We allocated the 33 patients to an upper gastrointestinal perforation (n=23) and lower gastrointestinal perforation group (n=10) base on intraoperative findings and analyzed the relationships between the locations of free gas and of gastrointestinal perforation. Additionally, we established two models for analyzing the extent of diffusion of free gas in the abdominal cavity and constructed receiver operating characteristic (ROC) curves to analyze the relationships between the two models and the size of the gastrointestinal perforation. Results: In the upper gastrointestinal perforation group, free gas was located around the hepatic portal area in 91.3% (21/23) of patients: this is a significantly greater proportion than that found in the lower gastrointestinal perforation group (5/10) (P=0.016). In contrast, free gas was located in the mesenteric interspace in 8/10 patients in the lower gastrointestinal perforation group; this is a significantly greater proportion than was found in the upper gastrointestinal perforation group (8.7%, 2/23) (P<0.010). The sensitivity of diagnosis of upper gastrointestinal perforation base on the presence of hepatic portal free gas was 84.8% and the specificity 71.4%. Further, the sensitivity of diagnosis of lower gastrointestinal perforation base on the presence of mesenteric interspace free gas was 80.0% and the specificity 91.3%. The rates of presence of free gas in the subdiaphragmatic area, mid-abdominal wall, and pelvic cavity did not differ significantly between the two groups (all P>0.05). Receiver operating characteristic curves showed that when free gas was present in four or more of the studied locations in the abdominal cavity, the optimal cutoff for perforation diameter was 2 cm, the corresponding sensitivity 66.7%, and the specificity 100%, suggesting that abdominal free gas diffuses extensively when the diameter of the perforation is >2 cm. Another model revealed that when free gas is present in three or more of the studied locations, the optimal cutoff for perforation diameter is 1 cm, corresponding to a sensitivity of 91.7% and specificity of 76.2%; suggesting that free gas is relatively confined in the abdominal cavity when the diameter of the perforation is <1 cm. Conclusion: Identifying which of five locations in the abdominal cavity contains free intraperitoneal air by examining MSCT images can be used to assist in the diagnosis of the location and size of acute gastrointestinal perforations.

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.

Interactive design generation and optimization from generative adversarial networks in spatial computing

This paper focuses on exploring the application possibilities and optimization problems of Generative Adversarial Networks (GANs) in spatial computing to improve design efficiency and creativity and achieve a more intelligent design process. A method for icon generation is proposed, and a basic architecture for icon generation is constructed. A system with generation and optimization capabilities is constructed to meet various requirements in spatial design by introducing the concept of interactive design and the characteristics of requirement conditions. Next, the generated icons can effectively maintain diversity and innovation while meeting the conditional features by integrating multi-feature recognition modules into the discriminator and optimizing the structure of conditional features. The experiment uses publicly available icon datasets, including LLD-Icon and Icons-50. The icon shape generated by the model proposed here is more prominent, and the color of colored icons can be more finely controlled. The Inception Score (IS) values under different models are compared, and it is found that the IS value of the proposed model is 7.05, which is higher than that of other GAN models. The multi-feature icon generation model based on Auxiliary Classifier GANs performs well in presenting multiple feature representations of icons. After introducing multi-feature recognition modules into the network model, the peak error of the recognition network is only 2.000 in the initial stage, while the initial error of the ordinary GAN without multi-feature recognition modules is as high as 5.000. It indicates that the improved model effectively helps the discriminative network recognize the core information of icon images more quickly. The research results provide a reference basis for achieving more efficient and innovative interactive space design.

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.

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.

Urinary Microbiome Dysbiosis and Immune Dysregulations as Potential Diagnostic Indicators of Bladder Cancer

There are a total of 82,290 new cases and 16,710 deaths estimated for bladder cancer in the United States in 2023. Currently, urine cytology tests are widely used for bladder cancer diagnosis, though they suffer from variable sensitivity, ranging from 45 to 97%. More recently, the microbiome has become increasingly recognized for its role in human diseases, including cancers. This study attempts to characterize urinary microbiome bladder cancer-specific dysbiosis to explore its diagnostic potential. RNA-sequencing data of urine samples from patients with bladder cancer (n = 18) and matched controls (n = 12) were mapped to bacterial sequences to yield species-level abundance approximations. Urine samples were analyzed at both the population and species level to reveal dysbiosis associated with bladder cancer. A panel of 35 differentially abundant species was discovered, which may be useful as urinary biomarkers for this disease. We further assessed whether these species were of similar significance in a validation dataset (n = 81), revealing that the genera Escherichia, Acinetobacter, and Enterobacter were consistently differentially abundant. We discovered distinct patterns of microbial-associated immune modulation in these samples. Several immune pathways were found to be significantly enriched with respect to the abundance of these species, including antigen processing and presentation, cytosolic DNA sensing, and leukocyte transendothelial migration. Differential cytokine activity was similarly observed, suggesting the urinary microbiome's correlation to immune modulation. The adherens junction and WNT signaling pathways, both implicated in the development and progression of bladder cancer, were also enriched with these species. Our findings indicate that the urinary microbiome may reflect both microbial and immune dysregulations of the tumor microenvironment in bladder cancer. Given the potential biomarker species identified, the urinary microbiome may provide a non-invasive, more sensitive, and more specific diagnostic tool, allowing for the earlier diagnosis of patients with bladder cancer.

Efficacy and Safety of First-line Therapies for Advanced Unresectable Oesophageal Squamous Cell Cancer: a Systematic Review and Network Meta-analysis

AIM

To compare the clinical efficacy and safety of first-line treatments for advanced unresectable oesophageal squamous cell cancer.

MATERIALS AND METHODS

A systematic review and network meta-analysis was carried out by retrieving and retaining relevant literature from databases. The studies were randomised controlled trials comparing first-line treatments for advanced unresectable oesophageal squamous cell cancer. A Bayesian network meta-analysis was used to assess clinical outcomes.

RESULTS

Nine studies including 4499 patients receiving first-line treatments were analysed. For all populations, toripalimab plus chemotherapy tended to provide the best overall survival (hazard ratio 0.58, 95% confidence intervals 0.43-0.78) and sintilimab plus chemotherapy provided the best progression-free survival (0.56, 0.46-0.68). Nivolumab plus chemotherapy presented the best objective response rate (odds ratio 2.45, 1.78-3.42) and camrelizumab plus chemotherapy (0.47, 0.29-0.74) appeared to be the safest. Sintilimab plus chemotherapy (0.55, 0.40-0.75) and nivolumab (0.54, 0.37-0.80) plus chemotherapy had the best overall survival in programmed death ligand 1 (PD-L1) tumour proportion score <1% and ≥1% subgroups. Toripalimab plus chemotherapy (0.61, 0.40-0.93) and pembrolizumab (0.57, 0.43-0.75) were the best in overall survival in combined positive score <10 and ≥10 subgroups, respectively. Toripalimab plus chemotherapy showed the best overall survival in the Asian group; pembrolizumab presented better overall survival in the Asian population than the non-Asian group.

CONCLUSION

Most immunotherapy combined with chemotherapy showed superior clinical benefits and sintilimab plus chemotherapy, toripalimab plus chemotherapy and tislelizumab plus chemotherapy had better comprehensive clinical efficacy. PD-L1 expression detection and ethnicity differences are still of great significance and most suitable regimens varied from each subgroup.

Delayed Crystallization Kinetics Allowing High-Efficiency All-Polymer Photovoltaics with Superior Upscaled Manufacturing

Though encouraging performance is achieved in small-area organic photovoltaics (OPVs), reducing efficiency loss when evoluted to large-area modules is an important but unsolved issue. Considering that polymer materials show benefits in film-forming processability and mechanical robustness, a high-efficiency all-polymer OPV module is demonstrated in this work. First, a ternary blend consisting of two polymer donors, PM6 and PBQx-TCl, and one polymer acceptor, PY-IT, is developed, with which triplet state recombination is suppressed for a reduced energy loss, thus allowing a higher voltage; and donor-acceptor miscibility is compromised for enhanced charge transport, thus resulting in improved photocurrent and fill factor; all these contribute to a champion efficiency of 19% for all-polymer OPVs. Second, the delayed crystallization kinetics from solution to film solidification is achieved that gives a longer operation time window for optimized blend morphology in large-area module, thus relieving the loss of fill factor and allowing a record efficiency of 16.26% on an upscaled module with an area of 19.3 cm2 . Besides, this all-polymer system also shows excellent mechanical stability. This work demonstrates that all-polymer ternary systems are capable of solving the upscaled manufacturing issue, thereby enabling high-efficiency OPV modules.

Toward safer highway work zones: An empirical analysis of crash risks using improved safety potential field and machine learning techniques

Due to complex traffic conditions, transition areas in highway work zones are associated with a higher crash risk than other highway areas. Understanding risk-contributing features in transition areas is essential for ensuring traffic safety on highways. However, conventional surrogate safety measures (SSMs) are quite limited in identifying the crash risk in transition areas due to the complex traffic environment. To this end, this study proposes an improved safety potential field, named the Work-Zone Crash Risk Field (WCRF). The WCRF force can be used to measure the crash risk of individual vehicles that enter a work zone considering the influence of multiple features, upon which the overall crash risk of the road segment in a specific time window can be estimated. With the overall crash risk used as a label, the time-window-based traffic data are used to train and validate an eXtreme Gradient Boosting (XGBoost) classifier, and the Shapley Additive Explanations (SHAP) method is integrated with the XGBoost classifier to identify the key risk-contributing traffic features. To assess the proposed approach, a case study is conducted using real-time vehicle trajectory data collected in two work zones along a highway in China. The results demonstrate that the WCRF-based SSM outperforms conventional SSMs in identifying crash risks in work zone transition areas on highways. In addition, we perform lane-based analysis regarding the impact of setting up work zones on highway safety and investigate the heterogeneity in risk-contributing features across different work zones. Several interesting findings from the analysis are reported in this paper. Compared to existing SSMs, the WCRF-based SSM offers a more practical and comprehensive way to describe the crash risk in work zones. The approach using the developed WCRF technique offers improved capabilities in identifying key risk-contributing features, which is expected to facilitate the development of safety management strategies for work zones.

Thorough Optimization for Intrinsically Stretchable Organic Photovoltaics

The development of intrinsically stretchable organic photovoltaics (is-OPVs) with a high efficiency is of significance for practical application. However, their efficiencies lag far behind those of rigid or even flexible counterparts. To address this issue, an advanced top-illuminated OPV is designed and fabricated, which is intrinsically stretchable and has a high performance, through systematic optimizations from material to device. First, the stretchability of the active layer is largely increased by adding a low-elastic-modulus elastomer of styrene-ethylene-propylene-styrene tri-block copolymer (SEPS). Second, the stretchability and conductivity of the opaque electrode are enhanced by a conductive polymer/metal (denoted as M-PH1000@Ag) composite electrode strategy. Third, the optical and electrical properties of a sliver nanowire transparent electrode are improved by a solvent vapor annealing strategy. High-performance is-OPVs are successfully fabricated with a top-illuminated structure, which provides a record-high efficiency of 16.23%. Additionally, by incorporating 5-10% elastomer, a balance between the efficiency and stretchability of the is-OPVs is achieved. This study provides valuable insights into material and device optimizations for high-efficiency is-OPVs, with a low-cost production and excellent stretchability, which indicates a high potential for future applications of OPVs.

Impact analysis of operating conditions on carbon dioxide reduction in microbial electrosynthesis: Insight into the substance utilization and microbial response

Microbial electrosynthesis (MES) is facing a series of problems including low energy utilization and production efficiency of high value-added products, which seriously hinder its practical application. In this study, a more practical direct current power source was used and the anaerobic activated sludge from wastewater treatment plants was inoculated to construct the acetic acid-producing MES. The operating conditions of acetic acid production were further optimized and the specific mechanisms involving the substance utilization and microbial response were revealed. The optimum conditions were the potential of 3.0 V and pH 6.0. Under these conditions, highly electroactive biofilms formed and all kinds of substances were effectively utilized. In addition, dominant bacteria (Acetobacterium, Desulfovibrio, Sulfuricurvum, Sulfurospirillum, and Fusibacter) had high abundances. Under optimal conditions, acetic acid-forming characteristic genera (Acetobacterium) had the highest relative abundance (Biocathode-25.82 % and Suspension-17.24 %). This study provided references for the optimal operating conditions of MES and revealed the corresponding mechanisms.

Indoor metabolites and chemicals outperform microbiome in classifying childhood asthma and allergic rhinitis

Indoor microorganisms impact asthma and allergic rhinitis (AR), but the associated microbial taxa often vary extensively due to climate and geographical variations. To provide more consistent environmental assessments, new perspectives on microbial exposure for asthma and AR are needed. Home dust from 97 cases (32 asthma alone, 37 AR alone, 28 comorbidity) and 52 age- and gender-matched controls in Shanghai, China, were analyzed using high-throughput shotgun metagenomic sequencing and liquid chromatography-mass spectrometry. Homes of healthy children were enriched with environmental microbes, including Paracoccus, Pseudomonas, and Psychrobacter, and metabolites like keto acids, indoles, pyridines, and flavonoids (astragalin, hesperidin) (False Discovery Rate < 0.05). A neural network co-occurrence probability analysis revealed that environmental microorganisms were involved in producing these keto acids, indoles, and pyridines. Conversely, homes of diseased children were enriched with mycotoxins and synthetic chemicals, including herbicides, insecticides, and food/cosmetic additives. Using a random forest model, characteristic metabolites and microorganisms in Shanghai homes were used to classify high and low prevalence of asthma/AR in an independent dataset in Malaysian schools (N = 1290). Indoor metabolites achieved an average accuracy of 74.9% and 77.1% in differentiating schools with high and low prevalence of asthma and AR, respectively, whereas indoor microorganisms only achieved 51.0% and 59.5%, respectively. These results suggest that indoor metabolites and chemicals rather than indoor microbiome are potentially superior environmental indicators for childhood asthma and AR. This study extends the traditional risk assessment focusing on allergens or air pollutants in childhood asthma and AR, thereby revealing potential novel intervention strategies for these diseases.

DJ-1 inhibits ferroptosis in cerebral ischemia-reperfusion via ATF4/HSPA5 pathway

DJ-1 has been confirmed to have neuroprotective effects. Ferroptosis is an iron-dependent programmed cell death mode associated with ischemic stroke. The ATF4/HSPA5 pathway has been shown to play an important role in the regulation of ferroptosis. To explore the role and possible mechanism of DJ-1 in regulating ferroptosis in cerebral ischemia-reperfusion injury. In this study, Middle cerebral artery occlusion/reperfusion (MCAO/R) was used to simulate cerebral ischemia-reperfusion injury in vivo. Detected ferroptosis-related indicators and observed mitochondrial morphology in brain tissue using transmission electron microscopy. ATF4 was subsequently interfered to observe the effect of DJ-1 on ferroptosis. The results suggest that after interfering with DJ-1, the iron content and malondialdehyde (MDA) content of ferroptosis-related indicators increased, the GSH content decreased, and the mitochondrial structure was severely damaged. We then found that DJ-1 attenuated ferroptosis following ATF4 reduction. In this study, we found that the neuroprotective effect of DJ-1 is related to the inhibition of ferroptosis, and its molecular mechanism is closely related to the ATF4/HSPA5 pathway, which may play a key role in inhibiting brain ischemia-reperfusion (I/R) ferroptosis.

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.

Q-TWiST analysis of survival benefits with brigatinib versus crizotinib in patients with anaplastic lymphoma kinase-positive non-small cell lung cancer based on results of the ALTA-1L trial

OBJECTIVES

The ALTA-1L phase 3 open-label trial demonstrated increased progression-free survival (PFS) with brigatinib versus crizotinib in patients with anaplastic lymphoma kinase-positive (ALK-positive) locally advanced or metastatic non-small cell lung cancer (NSCLC) previously untreated with ALK-targeted therapy. This post-hoc analysis of data from the ALTA-1L trial used the quality-adjusted (QA) time without symptoms of disease or toxicity (Q-TWiST) methodology to compare the QA survival benefit of brigatinib versus crizotinib in this patient population.

PATIENTS AND METHODS

The Q-TWiST analysis was performed using final (January 29, 2021) individual patient-level blinded independent review committee (BIRC)- and investigator-assessed survival data for brigatinib (n = 137) and crizotinib (n = 138) in adult patients (N = 275) with ALK-positive locally advanced or metastatic NSCLC previously untreated with ALK-targeted therapy. Q-TWiST was compared between the two treatments. Subgroup analyses were performed in patients stratified by various clinicopathological characteristics, including presence or absence of brain metastases at baseline.

RESULTS

Brigatinib was associated with significantly longer time without symptoms of disease or toxicity (P < 0.001) than crizotinib, with significantly greater Q-TWiST (mean [SE] months: BIRC-assessed, 28.2 [1.2] versus 25.1 [1.1], P = 0.045; investigator-assessed, 28.5 [1.2] versus 24.8 [1.1], P = 0.018). Relative gains in Q-TWiST with brigatinib compared to crizotinib were clinically meaningful (BIRC-assessed, 10.4%; investigator-assessed, 12.3%). Patients with brain metastases at baseline receiving brigatinib had significantly greater Q-TWiST (mean [SE] months: BIRC-assessed, 29.0 [1.9] versus 19.0 [1.9], P = 0.0001) than those receiving crizotinib.

CONCLUSION

First-line brigatinib treatment was associated with significant and clinically meaningful gains in Q-TWiST compared to crizotinib in patients with ALK-positive locally advanced or metastatic NSCLC, supporting the results of the ALTA-1L trial and brigatinib as a safe and effective first-line treatment for ALK-positive NSCLC.

PlzD modifies Vibrio vulnificus foraging behavior and virulence in response to elevated c-di-GMP

IMPORTANCE

Many free-swimming bacteria propel themselves through liquid using rotary flagella, and mounting evidence suggests that the inhibition of flagellar rotation initiates biofilm formation, a sessile lifestyle that is a nearly universal surface colonization paradigm in bacteria. In general, motility and biofilm formation are inversely regulated by the intracellular second messenger bis-(3´-5´)-cyclic dimeric guanosine monophosphate (c-di-GMP). Here, we identify a protein, PlzD, bearing a conserved c-di-GMP binding PilZ domain that localizes to the flagellar pole in a c-di-GMP-dependent manner and alters the foraging behavior, biofilm, and virulence characteristics of the opportunistic human pathogen, Vibrio vulnificus. Our data suggest that PlzD interacts with components of the flagellar stator to decrease bacterial swimming speed and changes in swimming direction, and these activities are enhanced when cellular c-di-GMP levels are elevated. These results reveal a physical link between a second messenger (c-di-GMP) and an effector (PlzD) that promotes transition from a motile to a sessile state in V. vulnificus.

Metagenomic analysis unveils the underexplored roles of prokaryotic viruses in a full-scale landfill leachate treatment plant

Enormous viral populations have been identified in activated sludge systems, but their ecological and biochemical roles in landfill leachate treatment plants remain poorly understood. To address this knowledge gap, we conducted an in-depth analysis using 36 metagenomic datasets that we collected and sequenced during a half-year time-series sampling campaign at six sites in a full-scale landfill leachate treatment plant (LLTP), elucidating viral distribution, virus‒host dynamics, virus-encoded auxiliary metabolic genes (AMGs), and viral contributions to the spread of virulence and antibiotic resistance genes. Our findings demonstrated that viral and prokaryotic communities differed widely among different treatment units, with stability over time. LLTP viruses were linked to various prokaryotic hosts, spanning 35 bacterial phyla and one archaeal phylum, which included the core microbes involved in biological treatments, as well as some of the less well-characterized microbial dark matter phyla. By encoding 2364 auxiliary metabolic genes (AMGs), viruses harbored the potential to regulate microbial nucleotide metabolism, facilitate the biodegradation of complex organic matter, and enhance flocculation and settling in biological treatment plants. The abundance distribution of AMGs varied considerably across treatment units and showed a lifestyle-dependent pattern with temperate virus-associated AMGs exhibiting a higher average abundance in downstream biological treatment units and effluent water. Meanwhile, temperate viruses tended to carry a higher load of virulence factor genes (VFGs), antibiotic resistance genes (ARGs), and biotic and metal resistance genes (BMRGs), and engaged in more frequent gene exchanges with prokaryotes than lytic viruses, thus acting as a pivotal contributor to the dissemination of pathogenicity and resistance genes in downstream LLTP units. This study provided a comprehensive profile of viral and prokaryotic communities in the LLTP and unveiled the varying roles of different-lifestyle viruses in biochemical processes and water quality safety.

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.

Targeting senescent hepatocytes using the thrombomodulin-PAR1 inhibitor vorapaxar ameliorates NAFLD progression

BACKGROUND AND AIMS

Senescent hepatocytes accumulate in parallel with fibrosis progression during NASH. The mechanisms that enable progressive expansion of nonreplicating cell populations and the significance of that process in determining NASH outcomes are unclear. Senescing cells upregulate thrombomodulin-protease-activated receptor-1 (THBD-PAR1) signaling to remain viable. Vorapaxar blocks the activity of that pathway. We used vorapaxar to determine if and how THBD-PAR1 signaling promotes fibrosis progression in NASH.

APPROACH AND RESULTS

We evaluated the THBD-PAR1 pathway in liver biopsies from patients with NAFLD. Chow-fed mice were treated with viral vectors to overexpress p16 in hepatocytes and induce replicative senescence. Effects on the THBD-PAR1 axis and regenerative capacity were assessed; the transcriptome of p16-overexpressing hepatocytes was characterized, and we examined how conditioned medium from senescent but viable (dubbed "undead") hepatocytes reprograms HSCs. Mouse models of NASH caused by genetic obesity or Western diet/CCl 4 were treated with vorapaxar to determine effects on hepatocyte senescence and liver damage. Inducing senescence upregulates the THBD-PAR1 signaling axis in hepatocytes and induces their expression of fibrogenic factors, including hedgehog ligands. Hepatocyte THBD-PAR1 signaling increases in NAFLD and supports sustained hepatocyte senescence that limits effective liver regeneration and promotes maladaptive repair. Inhibiting PAR1 signaling with vorapaxar interrupts this process, reduces the burden of 'undead' senescent cells, and safely improves NASH and fibrosis despite ongoing lipotoxic stress.

CONCLUSION

The THBD-PAR1 signaling axis is a novel therapeutic target for NASH because blocking this pathway prevents accumulation of senescing but viable hepatocytes that generate factors that promote maladaptive liver repair.

The Incidence and Prognosis Value of Perineural Invasion in Rectal Carcinoma: From Meta-Analyses and Real-World Clinical Pathological Features

AIMS

Perineural invasion (PNI) is a special type of metastasis of several cancers and has been reported as being a factor for poor prognosis in colorectal carcinoma. However, investigations of PNI in only rectal cancer and a comprehensive analysis combining meta-analyses with real-world case studies remain lacking.

MATERIALS AND METHODS

First, articles from 2000 to 2020 concerning the relationship between PNI and rectal cancer prognoses and clinical features were meta-analysed. Subsequently, we carried out a retrospective analysis of 312 rectal cancer cases that underwent radical surgery in the real world. The incidence of PNI and the relationship between PNI and prognosis, as well as clinicopathological factors, were investigated.

RESULTS

The incidence of PNI was 23.09% and 33.01% in the meta-analysis and clinical cases, respectively. PNI occurred as early as stage I (2.94%). Moreover, neoadjuvant therapy significantly reduced the PNI-positive rate (20.34% versus 26.54%). Both meta-analysis and real-world clinical case studies suggested that PNI-positive patients had poorer prognoses than PNI-negative patients. We established an effective risk model consisting of T stage, differentiation and lymphovascular invasion to predict PNI in rectal cancer.

CONCLUSION

PNI is a poor prognostic factor for rectal cancer and could occur even in stage I. Additionally, neoadjuvant therapy could sufficiently reduce the PNI-positive rate. T stage, lymphovascular invasion and differentiation grade were independent risk factors for PNI and the risk model that included these factors could predict the probability of PNI.

Virus-pathogen interactions improve water quality along the Middle Route of the South-to-North Water Diversion Canal

Bacterial pathogens and viruses are the leading causes of global waterborne diseases. Here, we discovered an interesting natural paradigm of water "self-purification" through virus-pathogen interactions over a 1432 km continuum along the Middle Route of the South-to-North Water Diversion Canal (MR-SNWDC) in China, the largest water transfer project in the world. Due to the extremely low total phosphorus (TP) content (ND-0.02 mg/L) in the MR-SNWDC, the whole canal has experienced long-lasting phosphorus (P) limitation since its operation in 2015. Based on 4443 metagenome-assembled genomes (MAGs) and 40,261 nonredundant viral operational taxonomic units (vOTUs) derived from our recent monitoring campaign, we found that residential viruses experiencing extreme P constraints had to adopt special adaptive strategies by harboring smaller genomes to minimize nucleotide replication, DNA repair, and posttranslational modification costs. With the decreasing P supply downstream, bacterial pathogens showed repressed environmental fitness and growth potential, and a weakened capacity to maintain P acquisition, membrane formation, and ribonucleotide biosynthesis. Consequently, the unique viral predation effects under P limitation, characterized by enhanced viral lytic infections and an increased abundance of ribonucleotide reductase (RNR) genes linked to viral nuclear DNA replication cycles, led to unexpectedly lower health risks from waterborne bacterial pathogens in the downstream water-receiving areas. These findings highlighted the great potential of water self-purification associated with virus-pathogen dynamics for water-quality improvement and sustainable water resource management.

Circ-RAPGEF5 promotes intrahepatic cholangiocarcinoma progression by stabilizing SAE1 to facilitate SUMOylation

BACKGROUND

Intrahepatic cholangiocarcinoma (ICC) is an aggressive malignancy with a poor prognosis. The underlying functions and mechanisms of circular RNA and SUMOylation in the development of ICC remain poorly understood.

METHODS

Circular RNA hsa_circ_0001681 (termed Circ-RAPGEF5 hereafter) was identified by circular RNA sequencing from 19 pairs of ICC and adjacent tissue samples. The biological function of Circ-RAPGEF5 in tumor proliferation and metastasis was examined by a series of in vitro assays. A preclinical model was used to validate the therapeutic effect of targeting Circ-RAPGEF5. RNA pull-down and dual-luciferase reporter assays were used to access the RNA interactions. Western blot and Co-IP assays were used to detect SUMOylation levels.

RESULTS

Circ-RAPGEF5, which is generated from exons 2 to 6 of the host gene RAPGEF5, was upregulated in ICC. In vitro and in vivo assays showed that Circ-RAPGEF5 promoted ICC tumor proliferation and metastasis, and inhibited apoptosis. Additionally, high Circ-RAPGEF5 expression was significantly correlated with a poor prognosis. Further investigation showed that SAE1, a potential target of Circ-RAPGEF5, was also associated with poor oncological outcomes. RNA pull-down and dual-luciferase reporter assays showed an interaction of miR-3185 with Circ-RAPGEF5 and SAE1. Co-IP and western blot assays showed that Circ-RAPGEF5 is capable of regulating SUMOylation.

CONCLUSION

Circ-RAPGEF5 promotes ICC tumor progression and SUMOylation by acting as a sponge for miR-3185 to stabilize SAE1. Targeting Circ-RAPGEF5 or SAE1 might be a novel diagnostic and therapeutic strategy in ICC.

Predicting chlorine demand by peracetic acid in drinking water treatment

Peracetic acid (PAA) may be used in drinking water treatment for pre-oxidation and mussel control at the intake. PAA may exert a downstream chlorine demand, but full details of this reaction have not been reported. There are three possible mechanisms of this demand: (1) PAA may react directly with chlorine; (2) PAA exists in equilibrium with hydrogen peroxide, which is known to react with chlorine; and (3) as H2O2 reacts with chlorine, PAA will hydrolyze to form more H2O2 to re-establish PAA/H2O2 equilibrium, thereby serving as an indirect reservoir of chlorine demand. While the H2O2 reaction with chlorine is well known, the other mechanisms of possible PAA-induced chlorine demand have not previously been investigated. The observed molar stoichiometric ratio of PAA to free chlorine (n) for the presumed direct PAA + free chlorine reaction was determined to be approximately 2, and the corresponding observed reaction rate coefficients at pH 6, 7, 8, and 9 were 2.76, 3.14, 1.61, 10.1 M-n·s-1, respectively (at 25 °C). With these estimated values, a kinetic model was built to predict the chlorine demand by PAA. The results suggest that chlorine demand from PAA is likely to be negligible over the course of several days (e.g., < 20% chlorine loss) for most conditions except for high pH (e.g., >8) and high PAA:Cl2 molar ratios (e.g., >2:1).

The Impact of Subjective Poverty on the Mental Health of the Elderly in China: The Mediating Role of Social Capital

Reducing subjective poverty among the elderly is an important aspect of poverty governance and is a necessary part of implementing the healthy aging strategy in China. In both China and the majority of low- and middle-income countries, systematic research on the relationship between subjective poverty and the mental health of the elderly needs to be expanded. In this study, we aimed to examine how social capital, including bonding and bridging social capital, mediate the relationship between subjective poverty and mental health among the elderly in China. Relying on the 2018 Chinese Longitudinal Healthy Longevity Survey (CLHLS) data, we used ordered probit (oprobit) regression, propensity score matching (PSM), and instrumental variable (IV) regression to estimate the effects of subjective poverty on mental health. The results indicate that subjective poverty has a significant negative impact on the mental health of the elderly in China. More importantly, social capital, including bonding and bridging social capital, partially mediates the relationship between subjective poverty and mental health. We believe that, in the process of implementing the government's healthy aging strategy in China, society and government should recognize the importance and value of subjective poverty governance for the elderly. In particular, the construction of a social relationship network that centers on bonding and bridging social capital could be instrumental in dealing with subjective poverty among the elderly and safeguarding their mental health and wellbeing.

Developmental toxicity, immunotoxicity and cardiotoxicity induced by methidathion in early life stages of zebrafish

Methidathion is a highly effective organophosphorus pesticide and is extensively utilized for the control of insects in agricultural production. However, there is little information on the adverse effects and underlying mechanisms of methidathion on aquatic organisms. In this work, embryonic zebrafish were exposed to methidathion at concentrations of 4, 10, and 25 mg/L for 96 h, and morphological changes and activities of antioxidant indicators alterations were detected. In addition, the locomotor behavioral abilities of zebrafish exposed to methidathion were also measured. To further explore the mechanism of the toxic effects of methidathion, gene expression levels associated with cardiac development, cell apoptosis, and the immune system were tested through qPCR assays. The findings revealed that methidathion exposure could induce a decrease in survival rate, hatchability, length of body, and increase in abnormality of zebrafish, as well as cardiac developmental toxicity. The LC50 value of methidathion in zebrafish embryos was determined to be about 30.72 mg/L at 96 hpf. Additionally, methidathion exposure triggered oxidative stress in zebrafish by increasing SOD activity, ROS, and MDA content. Acridine orange (AO) staining indicated that methidathion exposure led to apoptosis, which was mainly distributed in the pericardial region. Furthermore, significant impairments of locomotor activity in zebrafish larvae were induced by methidathion exposure. Lastly, the expression of pro-inflammatory factors including IFN-γ, IL-6, IL-8, CXCL-clc, TLR4, and MYD88 significantly up-regulated in exposed zebrafish. Taken together, the results in this work illustrated that methidathion caused developmental toxicity, cardiotoxicity, and immunotoxicity in embryogenetic zebrafish.

[Effects of enhancing the expression of aryl hydrocarbon receptor in post-traumatic mice macrophages on the inflammatory cytokine level and bactericidal ability]

Objective: To explore the expression pattern of aryl hydrocarbon receptor (AhR) in mice peritoneal macrophages (PMs) after major trauma and analyze the effects of enhanced AhR expression on the inflammatory cytokine level and bactericidal ability after trauma. Methods: The experimental study method was used. Forty 6-8-week-old male C57BL/6J mice (the same mouse age, sex, and strain below) were divided into control group, post trauma hour (PTH) 2 group, PTH 6 group, and PTH 12 group according to the random number table (the same grouping method below), with 10 mice in each group. Mice in the latter 3 groups were constructed as severe trauma model with fracture+blood loss, while mice in control group were left untreated. The primary PMs (the same cells below) were extracted from the mice in control group, PTH 2 group, PTH 6 group, and PTH 12 group when uninjured or at PTH 2, 6, and 12, respectively. Then the protein and mRNA expressions of AhR were detected by Western blotting and real-time fluorescence quantitative reverse transcription polymerase chain reaction, respectively, and the gene expressions of AhR signaling pathway related molecules were analyzed by transcriptome sequencing. Twenty mice were divided into control group and PTH 6 group, with 10 mice in each group, and the PMs were extracted. The level of ubiquitin of AhR was detected by immunoprecipitation. Twelve mice were divided into dimethyl sulfoxide (DMSO) alone group, PTH 6+DMSO group, MG-132 alone group, and PTH 6+MG-132 group, with 3 mice in each group. After the corresponding treatment, PMs were extracted, and the protein expression of AhR was detected by Western blotting. Twenty mice were constructed as PTH 6 model. Then, the PMs were extracted and divided into empty negative control adenovirus (Ad-NC) group and AhR overexpression adenovirus (Ad-AhR) group. The protein expression of AhR was detected by Western blotting at 36 h after some PMs were transfected with the corresponding adenovirus. The rest cells in Ad-NC group were divided into Ad-NC alone group and Ad-NC+endotoxin/lipopolysaccharide (LPS) group, and the rest cells in Ad-AhR group were divided into Ad-AhR alone group and Ad-AhR+LPS group. The expressions of interleukin-6 (IL-6) and tumor necrosis factor α (TNF-α) in the cell supernatant were detected by enzyme-linked immunosorbent assay at 12 h after the corresponding treatment (n=6). Twenty mice were obtained to extract PMs. The cells were divided into control+Ad-NC group, PTH 6+Ad-NC group, control+Ad-AhR group, and PTH 6+Ad-AhR group, and the intracellular bacterial load was detected by plate spread method after the corresponding treatment (n=6). Data were statistically analyzed with one-way analysis of variance, least significant difference test, analysis of variance for factorial design, and independent sample t test. Results: Compared with 1.16±0.28 of control group, the protein expressions of AhR in PMs in PTH 2 group (0.59±0.14), PTH 6 group (0.72±0.16), and PTH 12 group (0.71±0.17) were all significantly decreased (P<0.05). The overall comparison of the difference of AhR mRNA expression in PMs among control group, PTH 2 group, PTH 6 group, and PTH 12 group showed no statistical significance (P>0.05). The AhR signaling pathway related molecules included AhR, AhR inhibitor, cytochrome P450 family member 1b1, cytochrome P450 family member 11a1, heat shock protein 90, aryl hydrocarbon receptor-interaction protein, and heat shock protein 70 interaction protein. The heat shock protein 90 expression of PMs in PTH 2 group was higher than that in control group, while the expressions of other molecules did not change significantly after trauma. Compared with that in control group, the level of ubiquitin of AhR in PMs in PTH 6 group was increased. Compared with that in DMSO alone group, the protein expression of AhR in PMs in PTH 6+DMSO group was decreased, while that in PMs in MG-132 alone group had no significant change. Compared with that in PTH 6+DMSO group, the protein expression of AhR in PMs in PTH 6+MG-132 group was up-regulated. At transfection hour 36, compared with that in Ad-NC group, the protein expression of AhR in PMs in Ad-AhR group was increased. At treatment hour 12, compared with those in Ad-NC+LPS group, the expressions of IL-6 and TNF-α in PM supernatant of Ad-AhR+LPS group were significantly decreased (with t values of 4.80 and 3.82, respectively, P<0.05). The number of intracellular bacteria of 1×106 PMs in control+Ad-NC group, PTH 6+Ad-NC group, control+Ad-AhR group, and PTH 6+Ad-AhR group was (3.0±1.8), (41.8±10.2), (1.8±1.2), and (24.2±6.3) colony forming unit, respectively. Compared with that in PTH 6+Ad-NC group, the number of intracellular bacteria of PMs in PTH 6+Ad-AhR group was significantly decreased (t=3.61, P<0.05). Conclusions: Ubiquitin degradation of AhR in PMs of mice after major trauma results in decreased protein expression of AhR. Increasing the expression of AhR in post-traumatic macrophages can reduce the expressions of LPS-induced inflammatory cytokines IL-6 and TNF-α, and improve the bactericidal ability of macrophages after trauma.

[Lactate-induced up-regulation of PLEKHA4 promotes proliferation and apoptosis of human glioma cells]

OBJECTIVE

To investigate the effect of lactic acid-induced upregulation of PLEKHA4 expression on biological behaviors of glioma cells and the possible molecular mechanism.

METHODS

GEO database and GEPIA2 website were used to analyze the relationship between PLEKHA4 expression level and the pathological grade of glioma. A specific PLEKHA4 siRNA was transfected in glioma U251 and T98G cells, and the changes in cell proliferation ability were assessed by real-time cell analysis technology and Edu experiment. The colony-forming ability of the cells was evaluated using plate cloning assay, and cell cycle changes and cell apoptosis were analyzed with flow cytometry. The mRNA expression of PLEKHA4 was detected by PCR in glioma samples and controls and in glioma cells treated with lactic acid and glucose. Xenograft mice in vivo was used to detect tumor formation in nude mice; Western blotting was used to detect the expressions of cyclinD1, CDK2, Bcl2, β-catenin and phosphorylation of the key proteins in the MAPK signaling pathway.

RESULTS

The results of GEO database and online website analysis showed that PLEKHA4 was highly expressed in glioma tissues and was associated with poor prognosis; PLEKHA4 knockdown obviously inhibited the proliferation and attenuated the clone-forming ability of the glioma cells (P < 0.05). Flow cytometry showed that PLEKHA4 knockdown caused cell cycle arrest in G1 phase and promoted apoptosis of the cells (P < 0.01). PLEKHA4 gene mRNA expression was increased in glioma samples and glioma cells after lactate and glucose treatment (P < 0.01). PLEKHA4 knockdown, tumor formation ability of nude mice decreased; PLEKHA4 knockdown obviously lowered the expression of cyclinD1, CDK2, Bcl2 and other functional proteins, inhibited the phosphorylation of ERK and p38 and reduced the expression of β-catenin protein (P < 0.01).

CONCLUSION

PLEKHA4 knockdown inhibited the proliferation of glioma cells and promoted apoptosis by inhibiting the activation of the MAPK signaling pathway and expression of β-catenin. Lactic acid produced by glycolysis upregulates the expression of PLEKHA4 in glioma cells.

Improved ambient air quality is associated with decreased prevalence of childhood asthma and infancy shortly after weaning is a sensitive exposure window

BACKGROUND

The urban ambient air quality has been largely improved in the past decade. It is unknown whether childhood asthma prevalence is still increasing in ever top-ranking city of Shanghai, whether the improved air quality is beneficial for children's asthma and what time window of exposure plays critical roles.

METHODS

Using a repeat cross-sectional design, we analyzed the association between early life exposure to particles and wheezing/asthma in each individual and combined surveys in 2011 and 2019, respectively, in 11,825 preschool children in Shanghai.

RESULTS

A significantly lower prevalence of doctor-diagnosed asthma (DDA) (6.6% vs. 10.5%, p < 0.001) and wheezing (10.5% vs. 23.2%, p < 0.001) was observed in 2019 compared to 2011. Exposure to fine particulate matter (PM_2.5 ), coarse particles (PM_2.5-10 ) and inhalable particles (PM₁₀ ) was decreased in 2019 by 6.3%, 35.4%, and 44.7% in uterus and 24.3%, 20.2%, and 31.8% in infancy, respectively. Multilevel log-binomial regression analysis showed exposure in infancy had independent association with wheezing/DDA adjusting for exposure in uterus. For each interquartile range (IQR) increase of infancy PM_2.5 , PM_2.5-10 and PM₁₀ exposure, the odds ratios were 1.39 (95% confidence interval (CI): 1.24-1.56), 1.51 (95% CI:1.15-1.98) and 1.53 (95% CI:1.27-1.85) for DDA, respectively. The distributed lag non-linear model showed the sensitive exposure window (SEW) was 5.5-11 months after birth. Stratified analysis showed the SEWs were at or shortly after weaning, but only in those with <6 months of exclusive breastfeeding.

CONCLUSIONS

Improved ambient PM benefits in decreasing childhood asthma prevalence. We firstly reported the finding of SEW to PM at or closely after weaning on childhood asthma.

Serum extracellular vesicles derived hsa-miR-320d as an indicator for progression of clear cell renal cell carcinoma

BACKGROUND

Renal cell carcinoma (RCC) is a prevalent malignancy with a rising incidence in developing countries. Clear cell renal cell carcinoma (ccRCC) constitutes 70% of RCC cases and is prone to metastasis and recurrence, yet lacks a liquid biomarker for surveillance. Extracellular vesicles (EVs) have shown promise as biomarkers in various malignancies. In this study, we investigated the potential of serum EV-derived miRNAs as a biomarker for ccRCC metastasis and recurrence.

MATERIALS AND METHODS

Patients diagnosed with ccRCC between 2017 and 2020 were recruited in this study. In the discovery phase, high throughput small RNA sequencing was used to analyze RNA extracted from serum EVs derived from localized ccRCC (LccRCC) and advanced ccRCC (AccRCC). In the validation phase, qPCR was employed for quantitative detection of candidate biomarkers. Migration and invasion assays were performed on ccRCC cell line OSRC2.

RESULTS

Serum EVs derived hsa-miR-320d was significantly up-regulated in patients with AccRCC than in patients with LccRCC (p < 0.01). In addition, Serum EVs derived hsa-miR-320d was also significantly up-regulated in patients who experienced recurrence or metastasis (p < 0.01). Besides, hsa-miR-320d enhances the pro-metastatic phenotype of ccRCC cells in vitro.

CONCLUSIONS

Serum EVs derived hsa-miR-320d as a liquid biomarker exhibits significant potential for identifying the recurrence or metastasis of ccRCC, as well as hsa-miR-320d promotes ccRCC cells migration and invasion.

Prevalence, risk factors, impact and management of pneumonia among preschool children in Chinese seven cities: a cross-sectional study with interrupted time series analysis

BACKGROUND

Pneumonia is a common disease worldwide in preschool children. Despite its large population size, China has had no comprehensive study of the national prevalence, risk factors, and management of pneumonia among preschool children. We therefore investigated the prevalence of pneumonia among preschool children in Chinese seven representative cities, and explore the possible risk factors of pneumonia on children, with a view to calling the world's attention to childhood pneumonia to reduce the prevalence of childhood pneumonia.

METHODS

Two group samples of 63,663 and 52,812 preschool children were recruited from 2011 and 2019 surveys, respectively. Which were derived from the cross-sectional China, Children, Homes, Health (CCHH) study using a multi-stage stratified sampling method. This survey was conducted in kindergartens in seven representative cities. Exclusion criteria were younger than 2 years old or older than 8 years old, non-permanent population, basic information such as gender, date of birth and breast feeding is incomplete. Pneumonia was determined on the basis of parents reported history of clearly diagnosed by the physician. All participants were assessed with a standard questionnaire. Risk factors for pneumonia, and association between pneumonia and other respiratory diseases were examined by multivariable-adjusted analyses done in all participants for whom data on the variables of interest were available. Disease management was evaluated by the parents' reported history of physician diagnosis, longitudinal comparison of risk factors in 2011 and 2019.

RESULTS

In 2011 and 2019, 31,277 (16,152 boys and 15,125 girls) and 32,016 (16,621 boys and 15,395 girls) preschool children aged at 2-8 of permanent population completed the questionnaire, respectively, and were thus included in the final analysis. The findings showed that the age-adjusted prevalence of pneumonia in children was 32.7% in 2011 and 26.4% in 2019. In 2011, girls (odds ratio [OR] 0.91, 95%CI [confidence interval]0.87-0.96; p = 0.0002), rural (0.85, 0.73-0.99; p = 0.0387), duration of breastfeeding ≥ 6 months(0.83, 0.79-0.88; p < 0.0001), birth weight (g) ≥ 4000 (0.88, 0.80-0.97; p = 0.0125), frequency of putting bedding to sunshine (Often) (0.82, 0.71-0.94; p = 0.0049), cooking fuel type (electricity) (0.87, 0.80-0.94; p = 0.0005), indoor use air-conditioning (0.85, 0.80-0.90; p < 0.0001) were associated with a reduced risk of childhood pneumonia. Age (4-6) (1.11, 1.03-1.20; p = 0.0052), parental smoking (one) (1.12, 1.07-1.18; p < 0.0001), used antibiotics (2.71, 2.52-2.90; p < 0.0001), history of parental allergy (one and two) (1.21, 1.12-1.32; p < 0.0001 and 1.33, 1.04-1.69; p = 0.0203), indoor dampness (1.24, 1.15-1.33; p < 0.0001), home interior decoration (1.11, 1.04-1.19; p = 0.0013), Wall painting materials (Paint) (1.16, 1.04-1.29; p = 0.0084), flooring materials (Laminate / Composite wood) (1.08, 1.02-1.16; p = 0.0126), indoor heating mode(Central heating)(1.18, 1.07-1.30, p = 0.0090), asthma (2.38, 2.17-2.61; p < 0.0001), allergic rhinitis (1.36, 1.25-1.47; p < 0.0001), wheezing (1.64, 1.55-1.74; p < 0.0001) were associated with an elevated risk of childhood pneumonia; pneumonia was associated with an elevated risk of childhood asthma (2.53, 2.31-2.78; p < 0.0001), allergic rhinitis (1.41, 1.29-1.53; p < 0.0001) and wheezing (1.64, 1.55-1.74; p < 0.0001). In 2019, girls (0.92, 0.87-0.97; p = 0.0019), duration of breastfeeding ≥ 6 months (0.92, 0.87-0.97; p = 0.0031), used antibiotics (0.22, 0.21-0.24; p < 0.0001), cooking fuel type (Other) (0.40, 0.23-0.63; p = 0.0003), indoor use air-conditioning (0.89, 0.83-0.95; p = 0.0009) were associated with a reduced risk of childhood pneumonia. Urbanisation (Suburb) (1.10, 1.02-1.18; p = 0.0093), premature birth (1.29, 1.08-1.55; p = 0.0051), birth weight (g) < 2500 (1.17, 1.02-1.35; p = 0.0284), parental smoking (1.30, 1.23-1.38; p < 0.0001), history of parental asthma (One) (1.23, 1.03-1.46; p = 0.0202), history of parental allergy (one and two) (1.20, 1.13-1.27; p < 0.0001 and 1.22, 1.08-1.37; p = 0.0014), cooking fuel type (Coal) (1.58, 1.02-2.52; p = 0.0356), indoor dampness (1.16, 1.08-1.24; p < 0.0001), asthma (1.88, 1.64-2.15; p < 0.0001), allergic rhinitis (1.57, 1.45-1.69; p < 0.0001), wheezing (2.43, 2.20-2.68; p < 0.0001) were associated with an elevated risk of childhood pneumonia; pneumonia was associated with an elevated risk of childhood asthma (1.96, 1.72-2.25; p < 0.0001), allergic rhinitis (1.60, 1.48-1.73; p < 0.0001) and wheezing (2.49, 2.25-2.75; p < 0.0001).

CONCLUSIONS

Pneumonia is prevalent among preschool children in China, and it affects other childhood respiratory diseases. Although the prevalence of pneumonia in Chinese children shows a decreasing trend in 2019 compared to 2011, a well-established management system is still needed to further reduce the prevalence of pneumonia and reduce the burden of disease in children.

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.