g-C3N4 as ballistic electron transport "Tunnel" in CsPbBr3-based ternary photocatalyst for gas phase CO2 reduction

Perovskite CsPbBr3 quantum dot shows great potential in artificial photosynthesis, attributed to its outstanding optoelectronic properties. Nevertheless, its photocatalytic activity is hindered by insufficient catalytic active sites and severe charge recombination. In this work, a CsPbBr3@Ag-C3N4 ternary heterojunction photocatalyst is designed and synthesized for high-efficiency CO2 reduction. The CsPbBr3 quantum dots and Ag nanoparticles are chemically anchored on the surface of g-C3N4 sheets, forming an electron transfer tunnel from CsPbBr3 quantum dots to Ag nanoparticles via g-C3N4 sheets. The resulting CsPbBr3@Ag-C3N4 ternary photocatalyst, with spatial separation of photogenerated carriers, achieves a remarkable conversion rate of 19.49 μmol·g-1·h-1 with almost 100 % CO selectivity, a 3.13-fold enhancement in photocatalytic activity as compared to CsPbBr3 quantum dots. Density functional theory calculations reveal the rapid CO2 adsorption/activation and the decreased free energy (0.66 eV) of *COOH formation at the interface of Ag nanoparticles and g-C3N4 in contrast to the g-C3N4, leading to the excellent photocatalytic activity, while the thermodynamically favored CO desorption contributes to the high CO selectivity. This work presents an innovative strategy of constructing perovskite-based photocatalyst by modulating catalyst structure and offers profound insights for efficient CO2 conversion.

Efficient DNA walker guided by ordered cruciform-shaped DNA track for ultrasensitive and rapid electrochemical detection of lead ion

The rational design of DNA tracks is an effective pathway to guide the autonomous movement and high-efficiency recognition in DNA walkers, showing outstanding advantages for the cascade signal amplification of electrochemical biosensors. However, the uncontrolled distance between two adjacent tracks on the electrode could increase the risk of derailment and interruption of the reaction. Hence, a novel four-way balanced cruciform-shaped DNA track (C-DNT) was designed as a structured pathway to improve the effectiveness and stability of the reaction in DNA walkers. In this work, two kinds of cruciform-shaped DNA were interconnected as a robust structure that could avoid the invalid movement of the designed DNA walker on the electrode. When hairpin H2 was introduced onto the electrode, the strand displacement reaction (SDR) effectively triggered movements of the DNA walker along the cruciform-shaped track while leaving ferrocene (Fc) on the electrode, leading to a significant enhancement of the electrochemical signal. This design enabled the walker to move in an excellent organized and controllable manner, thus enhancing the reaction speed and walking efficiency. Compared to other walkers moving on random tracks, the reaction time of the C-DNT-based DNA walker could be reduced to 20 min. Lead ion (Pb2+) was used as a model target to evaluate the analytical performance of this biosensor, which exhibited a low detection limit of 0.033 pM along with a wide detection ranging from 0.1 pM to 500 nM. This strategy presented a novel concept for designing a high-performance DNA walker-based sensing platform for the detection of contaminants.

New-onset or relapse of uveitis after rapid spreading of COVID-19 infection in China and risk factor analysis for relapse

BACKGROUND

The aim of this study was to report the clinical profile of new-onset and relapse of uveitis following rapid spreading of coronavirus disease 2019 (COVID-19) infection due to change of anti-COVID-19 policies in China and investigate potential risk factors for inflammation relapse.

METHODS

In this retrospective case-control study, patients with new-onset or a history of uveitis between December 23, 2022, and February 28, 2023, were included to assess the influence of COVID-19 infection on uveitis. Detailed information on demographic data, clinical characteristics, treatment measures, treatment response, and ocular inflammatory status before and after COVID-19 infection was collected.

RESULTS

This study included 349 patients with a history of uveitis. The uveitis relapse rate was higher (28.8%, n = 288) in those with COVID-19 infection than in patients without COVID-19 infection (14.8%, n = 61) (P = 0.024). Among the relapse cases, 50.8% experienced a relapse of anterior uveitis, while 49.2% had a relapse of uveitis involving the posterior segment. Multivariable regression analysis indicated a positive correlation between disease duration and uveitis relapse, while the last relapse exceeding one year before COVID-19 infection and the use of methotrexate during COVID-19 infection were negatively correlated with relapse of uveitis. Thirteen patients who developed new-onset uveitis following COVID-19 infection were included; among them, three (23.1%) had anterior uveitis and 10 (76.9%) had uveitis affecting the posterior segment. Regarding cases involving the posterior segment, four patients (30.8%) were diagnosed with Vogt-Koyanagi-Harada disease.

CONCLUSIONS

COVID-19 infection increases the rate of uveitis relapse. Long disease duration is a risk factor, while time since the last relapse more than 1 year and methotrexate use are protective factors against uveitis relapse.

Unsupervised spectral reconstruction from RGB images under two lighting conditions

Unsupervised spectral reconstruction (SR) aims to recover the hyperspectral image (HSI) from corresponding RGB images without annotations. Existing SR methods achieve it from a single RGB image, hindered by the significant spectral distortion. Although several deep learning-based methods increase the SR accuracy by adding RGB images, their networks are always designed for other image recovery tasks, leaving huge room for improvement. To overcome this problem, we propose a novel, to our knowledge, approach that reconstructs the HSI from a pair of RGB images captured under two illuminations, significantly improving reconstruction accuracy. Specifically, an SR iterative model based on two illuminations is constructed at first. By unfolding the proximal gradient algorithm solving this SR model, an interpretable unsupervised deep network is proposed. All the modules in the proposed network have precise physical meanings, which enable our network to have superior performance and good generalization capability. Experimental results on two public datasets and our real-world images show the proposed method significantly improves both visually and quantitatively as compared with state-of-the-art methods.

Theory-guided unraveling of the mechanism underlying Cu1.0/Mn1.0-ZnO with dual reaction centers for enhanced peroxymonosulfate activation

Developing efficient catalytic systems for water contamination removal is a topic of great interest. However, the use of heterogeneous catalysts faces challenges due to insufficient active sites and electron cycling. In this study, results from first-principles calculations demonstrate that dual reaction centers (DRCs) are produced around the Cu and Mn sites in Cu1.0/Mn1.0-ZnO due to the electronegativity difference. Experimental results reveal the material with DRCs greatly enhances electron transfer efficiency and significantly impacts the oxidation and reduction of peroxymonosulfate (PMS). In addition, the self-consistent potential correction (SCPC) method was introduced to correct the energy and charge of charged periodic systems simulating a catalytic process, resulting in more precise catalytic results. Specifically, the material exhibits a preference for adsorbing negatively charged PMS anions at electron-deficient Mn sites, facilitating PMS oxidation for the generation of 1O2, and PMS reduction around the electron-rich Cu for the formation of •OH and SO4•-. The major reactive oxygen species is 1O2, showcasing effective performance in various degradation systems. Overall, our work provides novel insights into the persulfate-based heterogeneous catalytic oxidation process, paving the way for the development of high-performance catalytic systems for water purification.

Co-delivery of doxorubicin and STING agonist cGAMP for enhanced antitumor immunity

Many chemotherapeutic agents can induce immunogenic cell death (ICD), which leads to the release of danger-associated molecular patterns (DAMPs) and tumor-associated antigens. This process promotes dendritic cells (DCs) maturation and cytotoxic T lymphocyte (CTL) infiltration. However, cancer cells can employ diverse mechanisms to evade the host immune system. Recent studies have shown that stimulator of interferon genes (STING) agonists, such as cGAMP, can amplify ICD-triggered immune responses and enhance the infiltration of immune cells into the tumor microenvironment (TME). Building upon these findings, we constructed a doxorubicin (DOX) and cGAMP co-delivery system (DOX/cGAMP@NPs) for melanoma and triple-negative breast cancer (TNBC) therapy. The results demonstrated that DOX could effectively destroy tumors and induce the release of DAMPs by ICD. Furthermore, in orthotopic 4T1 tumors mice model and subcutaneous B16 tumor mice model, cGAMP could promote the maturation of DCs and CD8+ T cell activation and infiltration by inducing the secretion of type I interferons and pro-inflammation cytokine, which amplified the antitumor immune response induced by DOX. This strategy also promoted the depletion of immunosuppressive cells, potentially alleviating the immunosuppressive TME. In conclusion, our study highlights the combination of DOX-induced ICD and the immune-enhancing properties of cGAMP holds significant implications for future research and clinical applications.

[The efficacy and safety of immunotherapy combined with chemotherapy neoadjuvant in locally advanced resectable hypopharyngeal squamous cell carcinoma]

Objective: To explore the efficacy and safety of immunoneoadjuvant therapy with pembrolizumab combined with chemotherapy in locally advanced resectable hypopharyngeal squamous cell carcinoma patients. Methods: This study was a prospective, single arm, single center clinical study that was opened for enrollment in April 2021. Patients who met the inclusion criteria at the Cancer Hospital of the Chinese Academy of Medical Sciences were treated with neoadjuvant therapy of pembrolizumab combined with cisplatin and paclitaxel, and after treatments, received surgery and postoperative adjuvant therapy. The main endpoint of this study was postoperative pathological complete response (pCR), and other observations included adverse reactions and long-term prognoses of patients after neoadjuvant therapy. Results: By September 2023, a total of 23 patients who underwent neoadjuvant therapy and surgery were enrolled in the study and all patients were males aged 49-74 years. All patients were locally advanced stage, including 3 patients in stage Ⅲ and 20 patients in stage Ⅳ. There were 12 cases of primary lesions with posterior ring involvement accompanied by fixation of one vocal cord and 20 cases of regional lymph node metastases classified as N2. Eighteen cases received a two cycle regimen and 5 cases received a three cycle regimen for neoadjuvant therapy. The postoperative pCR rate was 26.1% (6/23), with no surgical delay caused by adverse drug reactions. The laryngeal preservation rate was 87.0% (20/23). Pharyngeal fistula was the main surgical complication, with an incidence of 21.7% (5/23). The median follow-up time was 15 months, and 3 patients experienced local recurrence. Conclusions: The immunoneoadjuvant therapy of pembrolizumab combined with chemotherapy has a high pCR rate in locally advanced resectable hypopharyngeal squamous cell carcinoma, with increased laryngeal preservation rate and no significant impact on surgical safety.

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.

[The predictive value of dynamic changes of neutrophil/lymphocyte ratio (NLR) combined with the model of end-stage liver disease (MELD) score in patients with acute-on-chronic hepatitis B liver failure]

Objective: To determine the predictive value of dynamic changes of neutrophil/lymphocyte ratio (NLR) combined with the model of end-stage liver disease (MELD) score in patients with acute-on-chronic hepatitis B liver failure. Methods: Patients with acute-on-chronic hepatitis B liver failure who were hospitalized in the Department of Hepatology of Qilu Hospital of Shandong University from January 2010 to July 2023 were retrospectively enrolled. According to the clinical outcomes of patients within 30 days of admission, they were divided into the survival group and the death group. The dynamic changes in NLR and initial values on day 3, 5, 8, and 12 in two groups were analyzed for the diagnostic value of 30-day prognosis in patients with acute-on-chronic hepatitis B liver failure. Logistic regression analysis and machine learning XGBoost algorithm were used to evaluate the risk factors influencing the prognosis of patients at 30 days. Receiver operating characteristic(ROC) curve was used to evaluate the diagnostic value of NLR and initial value change combined with MELD score on day 12 of admission in patients with chronic acute hepatitis B liver failure. Results: A total of 243 patients were enrolled in the study, including 145 patients in the survival group [115 males, 30 females, aged 25-74 (47±11)] and 98 patients in the death group [80 males, 18 females, aged 22-80 (49±13) ]. The median initial NLR of survival group and death group were 3.5 (2.1, 5.3) and 4.9 (2.9, 8.3), respectively, and the difference was statistically significant (P=0.003). The variation of NLR from the initial value on day 3, 5, 8, and 12 in the survival group [1.6 (0, 4.3), 1.9 (-0.2, 4.1), 2.0 (-0.1, 4.3) and 2.9 (0.3, 7.0), respectively] were lower than that in the death group [3.2 (0.9, 7.5), 5.1 (1.8, 7.6), 5.8 (2.0, 10.6) and 9.6 (3.5, 16.4), respectively] (all P<0.001). Logistic regression multivariate analysis showed that the changes in NLR on the 12th day and initial value (OR=1.07,95%CI:1.01-1.14, P=0.014), the changes in NLR on the 3rd day and initial value (OR=2.71, 95%CI: 1.32-5.55, P=0.007), the initial value of NLR (OR=1.18,95%CI:1.01-1.37,P=0.035) and fibrinogen (OR=0.21,95%CI:0.05-0.96,P=0.044) were related factors for death within 30 days. Machine learning XGBoost algorithm showed that the weight of the change between the NLR on the 12th day and the initial value was the highest. The area under the ROC curve of the combined MELD score was 0.812 (95%CI: 0.728-0.895), the specificity was 67.78%, and the sensitivity was 82.35%. Conclusion: Dynamic change of NLR combined with MELD score has high predictive value for the short-term prognosis of patients with acute-on-chronic hepatitis B liver failure.

[Surgical treatment and prognosis analysis of hilar cholangiocarcinoma]

Objective: To investigate the surgical treatment effect and prognostic factors of hilar cholangiocarcinoma. Methods: This is an ambispective cohort study. From August 2005 to December 2022,data of 510 patients who diagnosed with hilar cholangiocarcinoma and underwent surgical resection at the Hepatobiliary Center of the First Affiliated Hospital of Nanjing Medical University were retrospectively collected. In the cohort,there were 324 males and 186 females,with an age of (M (IQR)) 63(13)years (range:25 to 85 years). The liver function at admission was Child-Pugh A (343 cases,67.3%) and Child-Pugh B (167 cases,32.7%). Three hundred and seventy-two(72.9%) patients had jaundice symptoms and the median total bilirubin was 126.3(197.6) μmol/L(range: 5.4 to 722.8 μmol/L) at admission. Two hundred and fourty-seven cases (48.4%) were treated with percutaneous transhepatic cholangial drainage or endoscopic nasobiliary drainage before operation. The median bilirubin level in the drainage group decreased from 186.4 μmol/L to 85.5 μmol/L before operation. Multivariate Logistic regression was used to identify the influencing factors for R0 resection,and Cox regression was used to construct multivariate prediction models for overall survival(OS) and disease-free survival(DFS). Results: Among 510 patients who underwent surgical resection,Bismuth-Corlett type Ⅲ-Ⅳ patients accounted for 71.8%,among which 86.1% (315/366) underwent hemi-hepatectomy,while 81.9% (118/144) underwent extrahepatic biliary duct resection alone in Bismuch-Corlett type Ⅰ-Ⅱ patients. The median OS time was 22.8 months, and the OS rates at 1-,3-,5-and 10-year were 72.2%,35.6%,24.8% and 11.0%,respectively. The median DFS time was 15.2 months,and the DFS rates was 66.0%,32.4%,20.9% and 11.0%,respectively. The R0 resection rate was 64.5% (329/510), and the OS rates of patients with R0 resection at 1-,3-,5-and 10-year were 82.5%, 48.6%, 34.4%, 15.2%,respectively. The morbidity of Clavien-Dindo grade Ⅲ-Ⅴ complications was 26.1%(133/510) and the 30-day mortality was 4.3% (22/510). Multivariate Logistic regression indicated that Bismuth-Corlett type Ⅰ-Ⅲ (P=0.009), hemi-hepatectomy and extended resection (P=0.001),T1 and T2 patients without vascular invasion (T2 vs. T1:OR=1.43 (0.61-3.35),P=0.413;T3 vs. T1:OR=2.57 (1.03-6.41), P=0.010;T4 vs. T1, OR=3.77 (1.37-10.38), P<0.01) were more likely to obtain R0 resection. Preoperative bilirubin,Child-Pugh grade,tumor size,surgical margin,T stage,N stage,nerve infiltration and Edmondson grade were independent prognostic factors for OS and DFS of hilar cholangiocarcinoma patients without distant metastasis. Conclusions: Radical surgical resection is necessary to prolong the long-term survival of hilar cholangiocarcinoma patients. Hemi-hepatectomy and extended resection,regional lymph node dissection and combined vascular resection if necessary,can improve R0 resection rate.

Cognitive style of field dependence-independence modulates the working memory storage of biological motion

The biological motion refers to the continuous configuration movement of live agents in space. The perceptual processing of biological motion has the specificity of the dissociation between body form and body motion. However, there is limited evidence for whether such specificity continues when holding biological motion in working memory. We explored this question from the perspective of field dependence (FD) and field independence (FI) cognitive styles in the current study. Three categories of biological motion have been developed: intact movement, motion feature, and form feature. We examined the working memory capacity of motion features, form features, intact movements (Experiments 1-3), and the recognition of three categories of biological motion when remembering intact movements (Experiment 4). The results showed that for the motion features, FI individuals had better memory performance when remembering five items and showed greater working memory capacity and recognition compared with FD individuals, whereas the opposite pattern was observed between FI and FD individuals for the form features. The cognitive style could modulate the working memory storage of biological motion when the task becomes demanding, suggesting that body form and body motion are dissociable in working memory. Our study provided additional evidence for the specificity of biological motion processing in working memory, extending the hierarchical neural model. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Insights into structural and functional regulation of chalcopyrite and enhanced mechanism of reactive oxygen species (ROS) generation in advanced oxidation process (AOP): A review

Chalcopyrite, renowned for its distinctive mixed redox-couple characteristics, exhibits excellent electron transfer properties both on its surface and within its crystal structure. This unique characteristic has attracted significant attention in various fields, including optics, electronics, and magnetism, as well as demonstrated remarkable catalytic efficacy in the environmental field. The rapid and effective electron transfer capability of a catalyst is crucial for advanced oxidation processes (AOPs). However, the performance of CuFeS2 in AOPs is hindered by its low electron transfer efficacy. This review aims to summarize the key steps and mechanisms of chalcopyrite-induced AOPs and provide strategies for enhancing effective electron transfer efficacies by controlling the structure and function of synthetic/natural chalcopyrite. These strategies include enhancing the catalytic performance of chalcopyrite and constructing composites to enhance catalytic activity (e.g., chelating agents, heterojunctions). Additionally, the factors influencing the generation of reactive oxygen species in chalcopyrite-induced AOPs are investigated, such as the types and properties of oxidants (e.g., H2O2, peroxymonocarbonate), the microstructure of catalysts, and reaction conditions in catalytic systems (e.g., pH values, dosage, temperature). Future perspectives on the applications of chalcopyrite are presented at the end of this paper. Overall, this review assists in narrowing the scope of chalcopyrite studies in AOPs and aids researchers in optimizing synthetic/natural catalysts for contaminant treatment.

Exhalomics as a noninvasive method for assessing rumen fermentation in dairy cows: Can exhaled-breath metabolomics replace rumen sampling?

Previously, we used secondary electrospray ionization-mass spectrometry (SESI-MS) to investigate the diurnal patterns and signal intensities of exhaled (EX) volatile fatty acids (VFA) of dairy cows. The current study aimed to validate the potential of an exhalomics approach for evaluating rumen fermentation. The experiment was conducted in a switchback design, with 3 periods of 9 d each, including 7 d for adaptation and 2 d for sampling. Four rumen-cannulated original Swiss Brown (Braunvieh) cows were randomly assigned to 1 of 2 diet sequences (ABA or BAB): (A) low starch (LS; 6.31% starch on a dry matter basis) and (B) high starch (HS; 16.2% starch on a dry matter basis). Feeding was once per day at 0830 h. Exhalome (with the GreenFeed System), and rumen samples were collected 8 times to represent every 3 h of a day, and EX-VFA and ruminal (RM)-VFA were analyzed using SESI-MS and HPLC, respectively. Furthermore, the VFA concentration in the gas phase (HR-VFA) was predicted based on RM-VFA and Henry's Law (HR) constants. No interactions were identified between the types of diets (HS vs. LS) and the measurement methods on daily average VFA profiles (RM vs. EX or HR vs. EX), suggesting a consistent performance among the methods. Additionally, when the 3-h interval VFA data from HS and LS diets were analyzed separately, no interactions were observed between methods and time of day, indicating that the relative daily pattern of VFA molar proportions was similar regardless of the VFA measurement method used. The results revealed that the levels of acetate sharply increased immediately after feeding, trailed by an increase in the acetate:propionate ratio and a steady increase for propionate (2 h after feeding the HS diet, 4 h for LS), and butyrate. This change was more pronounced for the HS diet than the LS diet. However, there was no overall diet effect on the VFA molar proportions, although the measurement methods affected the molar proportions. Furthermore, we observed a strong positive correlation between the levels of RM and EX acetate for both diets (HS: r = 0.84; LS: r = 0.85), RM and EX propionate (r = 0.74), and RM and EX acetate:propionate ratio (r = 0.80). Both EX-VFA and RM-VFA exhibited similar responses to feeding and dietary treatments, suggesting that EX-VFA could serve as a useful proxy for characterizing RM-VFA molar proportions to evaluate rumen fermentation. Similar relationships were observed between RM-VFA and HR-VFA. In conclusion, this study underscores the potential of exhalomics as a reliable approach for assessing rumen fermentation. Moving forward, research should further explore the depth of exhalomics in ruminant studies to provide a comprehensive insight into rumen fermentation metabolites, especially across diverse dietary conditions.

C-type lectin 2D (CLEC2D) is upregulated in clear cell renal cell carcinoma (ccRCC) tissues and predicts poor prognosis

Clear cell renal cell carcinoma (ccRCC) is known as the most common type of renal cancer. Recently, a series of advances have been made in targeted therapy for ccRCC. To combat this highly metastatic tumor, novel therapeutic targets still need to be developed. C-type lectins (CLECs) contain a characteristic C-type lectin-like domain and affect several physiological functions. The effects of C-type lectin 2D (CLEC2D) on cancer progression have been revealed in several types of cancers; however, its expression in ccRCC tissues, and the possible effects on the progression and metastasis of ccRCC, are still unclear. Herein, we found the high mRNA and protein levels of CLEC2D in ccRCC tissues. We further found that CLEC2D expression was correlated with the prognosis of ccRCC patients and correlated with the tumor size (p = 0.019*) of patients. In addition, CLEC2D affected tumor immune infiltration, confirmed by the further analysis. CLEC2D knockdown suppressed the proliferation of ccRCC cells in vitro and restrained ccRCC tumor growth and immune infiltration in mice. Therefore, we believe that CLEC2D has the potential to serve as a promising ccRCC therapeutic target.

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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Dual-Emission Single Sensing Element-Assembled Fluorescent Sensor Arrays for the Rapid Discrimination of Multiple Surfactants in Environments

Surfactants are considered as typical emerging pollutants, their extensive use of in disinfectants has hugely threatened the ecosystem and human health, particularly during the pandemic of coronavirus disease-19 (COVID-19), whereas the rapid discrimination of multiple surfactants in environments is still a great challenge. Herein, we designed a fluorescent sensor array based on luminescent metal-organic frameworks (UiO-66-NH2@Au NCs) for the specific discrimination of six surfactants (AOS, SDS, SDSO, MES, SDBS, and Tween-20). Wherein, UiO-66-NH2@Au NCs were fabricated by integrating UiO-66-NH2 (2-aminoterephthalic acid-anchored-MOFs based on zirconium ions) with gold nanoclusters (Au NCs), which exhibited a dual-emission features, showing good luminescence. Interestingly, due to the interactions of surfactants and UiO-66-NH2@Au NCs, the surfactants can differentially regulate the fluorescence property of UiO-66-NH2@Au NCs, producing diverse fluorescent "fingerprints", which were further identified by pattern recognition methods. The proposed fluorescence sensor array achieved 100% accuracy in identifying various surfactants and multicomponent mixtures, with the detection limit in the range of 0.0032 to 0.0315 mM for six pollutants, which was successfully employed in the discrimination of surfactants in real environmental waters. More importantly, our findings provided a new avenue in rapid detection of surfactants, rendering a promising technique for environmental monitoring against trace multicontaminants.

Protocol for fast clonal family inference and analysis from large-scale B cell receptor repertoire sequencing data

The expeditious identification and comprehensive analysis of clonal families from extensive B cell receptor (BCR) repertoire sequencing data are imperative for elucidating the intricacies of B cell immune responses. Here, we introduce a computational pipeline designed to swiftly deduce clonal families from bulk BCR heavy-chain sequencing data, accompanied by a suite of functional modules tailored to streamline post-clustering analysis. The outlined methodology encompasses guidelines for software installation, meticulous data preparation, and the systematic inference and analysis of clonal families. For complete details on the use and execution of this protocol, please refer to Wang et al.1.

Genome-Wide Association Studies on Chinese Wheat Cultivars Reveal a Novel Fusarium Crown Rot Resistance Quantitative Trait Locus on Chromosome 3BL

Fusarium crown rot (FCR), primarily caused by Fusarium pseudograminearum, has emerged as a new threat to wheat production and quality in North China. Genetic enhancement of wheat resistance to FCR remains the most effective approach for disease control. In this study, we phenotyped 435 Chinese wheat cultivars through FCR inoculation at the seedling stage in a greenhouse. Our findings revealed that only approximately 10.8% of the wheat germplasms displayed moderate or high resistance to FCR. A genome-wide association study (GWAS) using high-density 660K SNP led to the discovery of a novel quantitative trait locus on the long arm of chromosome 3B, designated as Qfcr.hebau-3BL. A total of 12 significantly associated SNPs were closely clustered within a 1.05 Mb physical interval. SNP-based molecular markers were developed to facilitate the practical application of Qfcr.hebau-3BL. Among the five candidate FCR resistance genes within the Qfcr.hebau-3BL, we focused on TraesCS3B02G307700, which encodes a protein kinase, due to its expression pattern. Functional validation revealed two transcripts, TaSTK1.1 and TaSTK1.2, with opposing roles in plant resistance to fungal disease. These findings provide insights into the genetic basis of FCR resistance in wheat and offer valuable resources for breeding resistant varieties.

Chiral Covalent Organic Cages: Structural Isomerism and Enantioselective Catalysis

Covalent organic cages are a prominent class of discrete porous architectures; however, their structural isomerism remains relatively unexplored. Here, we demonstrate the structural isomerism of chiral covalent organic cages that renders distinct enantioselective catalytic properties. Imine condensations of tetra-topic 5,10-di(3,5-diformylphenyl)-5,10-dihydrophenazine and ditopic 1,2-cyclohexanediamine produce two chiral [4 + 8] organic cage isomers with totally different topologies and geometries that depend on the orientations of four tetraaldehyde units with respect to each other. One isomer (PN-1) has an unprecedented Johnson-type J26 structure, whereas another (PN-2) adopts a tetragonal prismatic structure. After the reduction of the imine linkages, the cages are transformed into two amine bond-linked isomers PN-1R and PN-2R. After binding to Ni(II) ions, both can serve as efficient catalysts for asymmetric Michael additions, whereas PN-2R affords obviously higher enantioselectivity and reactivity than PN-1R presumably because of its large cavity and open windows that can concentrate reactants for the reactions. Density-functional theory (DFT) calculations further confirm that the enantioselective catalytic performance varies depending on the isomer.

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

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

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

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

UPLC-quadrupole time-of-flight-tandem mass spectrometry combined with chemometrics and network pharmacology to differentiate Coreopsis tinctoria Nutt

Coreopsis tinctoria Nutt. (C. tinctoria) is a traditional medicinal plant, primarily found in plateau areas with altitudes exceeding 3000 m. The efficacy of C. tinctoria appears to be intricately tied to its quality. However, there is a scarcity of studies focused on evaluating the quality of C. tinctoria from diverse geographical locations. In this study, we used ultra-performance liquid chromatography-quadrupole time-of-flight-tandem mass spectrometry to analyze and identify the prevalent chemical components in 12 batches of C. tinctoria sourced from Xinjiang, Qinghai, Tibet, and Yunnan provinces in China. By using cluster analysis and discriminant analysis of partial least squares, we assessed the similarity and identified varying components in the 12 batches of C. tinctoria. Subsequently, their quality was further evaluated. Utilizing network pharmacology, we identified potential active components for the treatment of diabetes mellitus. The findings revealed the presence of 16 flavonoids, 3 phenylpropanes, 2 sugars, 2 amino acids, and 7 hydrocarbons in the analyzed samples. Through variable importance screening, 17 constituents were identified as quality difference markers. Marein and flavanomarein emerged as pivotal markers, crucial for distinguishing variations in C. tinctoria. In addition, network pharmacology predicted 187 targets for 9 common active components, including marein and flavanomarein. Simultaneously, 1747 targets related to diabetes mellitus were identified. The drug-component-disease target network comprised 91 nodes and 179 edges, encompassing 1 drug node, 9 component nodes, and 81 target nodes. In summary, marein and flavanomarein stand out as key biomarkers for assessing the quality of C. tinctoria, offering a scientific foundation for the quality evaluation of C. tinctoria Nutt.

Dietary supplementation of vitamin D3 and calcium partially recover the compromised time budget and circadian rhythm of lying behavior in lactating cows under heat stress

Heat stress (HS) impedes cattle behavior and performance and is an animal comfort and welfare issue. The objective of this study was to characterize the time budget and circadian rhythm of lying behavior in dairy cows during HS and to assess the effect of dietary supplementation of vitamin D3 and Ca. Twelve multiparous Holstein cows (42.2 ± 5.6 kg milk/d; 83 ± 27 d in milk) housed in tiestalls were used in a split-plot design with the concentration of dietary vitamin E and Se as main plots (LESe: 11.1 IU/kg and 0.55 mg/kg, and HESe: 223 IU/kg and 1.8 mg/kg, respectively). Within each plot cows were randomly assigned to (1) HS with low concentrations of vitamin D3 and Ca (HS, 1,012 IU/kg and 0.73%, respectively), (2) HS with high concentrations of vitamin D3 and Ca (HS+D3/Ca; 3,764 IU/kg and 0.97%, respectively), or (3) thermoneutral pair-fed (TNPF) with low concentrations of vitamin D3 and Ca (1,012 IU/kg and 0.73%, respectively) in a Latin square design with 14-d periods and 7-d washouts. Lying behavior was measured with HOBO Loggers in 15-min intervals. Overall, cows in HS spent less time lying per day relative to TNPF from d 7 to 14. Daily lying time was positively correlated with milk yield, energy-corrected milk yield, and feed efficiency, and was negatively correlated with rectal temperature, respiratory rate, fecal calprotectin, tumor necrosis factor-α, and C-reactive protein. A treatment by time interaction was observed for lying behavior: the time spent lying was lesser for cows in HS than in TNPF in the early morning (0000-0600 h) and in the night (1800-2400 h). The circadian rhythm of lying behavior was characterized by fitting a cosine function of time into linear mixed model. Daily rhythmicity of lying was detected for cows in TNPF and HS+D3/Ca, whereas only a tendency in HS cows was observed. Cows in TNPF had the highest mesor (the average level of diurnal fluctuations; 34.2 min/h) and amplitude (the distance between the peak and mesor; 17.9 min/h). Both the mesor and amplitude were higher in HS+D3/Ca relative to HS (26.6 vs. 25.2 min/h and 3.91 min/h vs. 2.18 min/h, respectively). The acrophase (time of the peak) of lying time in TNPF, HS, and HS+D3/Ca were 0028, 0152, and 0054 h, respectively. Lastly, a continuous increase in daily lying time in TNPF was observed during the first 4 d of the experimental period in which DMI was gradually restricted, suggesting that intake restrictions may shift feeding behavior and introduce biases in the behavior of animals. In conclusion, lying behavior was compromised in dairy cows under HS, characterizing reduced daily lying time and disrupted circadian rhythms, and the compromised lying behavior can be partially restored by supplementation of vitamin D3 and Ca. Further research may be required for a more suitable model to study behavior of cows under HS.

Vagococcus fluvialis isolation from the urine of a bladder cancer patient: a case report

Vagococcus fluvialis infection is rare in humans, and there is limited research on the clinical manifestations and antimicrobial susceptibility testing of Vagococcus fluvialis infection. Here, We isolated Vagococcus fluvialis from the urine samples of bladder cancer patients at Hunan Provincial People's Hospital, and it is the first reported case of Vagococcus fluvialis isolated from the urine. The fully automated microbial identification system and matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS) identified the bacterium as Vagococcus fluvialis with a confidence level of 99.9%. The VITEK-2Compact fully automated microbial susceptibility analysis system indicated that it was most sensitive to tigecycline, vancomycin, quinupristin/dalfopristin, linezolid, and showed moderate sensitivity to erythromycin, levofloxacin, ciprofloxacin, ampicillin/sulbactam, and tetracycline. Additionally, it exhibited synergy when combined with high-level gentamicin and vancomycin, showing sensitivity. However, it displayed poor activity against penicillin and furanth. According to our knowledge, this is the first study to isolate and identify Vagococcus fluvialis from the urine of bladder cancer patients and the systematically reviewed other reported Vagococcus infections on human, which provide an experimental basis for guiding the rational use of drugs in the clinical treatment and diagnose of Vagococcus fluvialis infection and related pathogenic mechanism research. Meanwhile, we have systematically reviewed other reported.

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.

Self-reporting and Biodegradable Thermosetting Solid Polymer Electrolyte

To date, significant efforts have been dedicated to improve their ionic conductivity, thermal stability, and mechanical strength of solid polymer electrolytes (SPEs). However, direct monitoring of physical and chemical changes in SPEs is still lacking. Moreover, existing thermosetting SPEs are hardly degradable. Herein, by overcoming the limitation predicted by Flory theory, self-reporting and biodegradable thermosetting hyperbranched poly(β-amino ester)-based SPEs (HPAE-SPEs) are reported. HPAE is successfully synthesized through a well-controlled "A2+B4" Michael addition strategy and then crosslinked it in situ to produce HPAE-SPEs. The multiple tertiary aliphatic amines at the branching sites confer multicolour luminescence to HPAE-SPEs, enabling direct observation of its physical and chemical damage. After use, HPAE-SPEs can be rapidly hydrolysed into non-hazardous β-amino acids and polyols via self-catalysis. Optimized HPAE-SPE exhibits an ionic conductivity of 1.3×10-4  S/cm at 60 °C, a Na+ transference number (t N a + ${{t}_{Na}^{+}}$ ) of 0.67, a highly stable sodium plating-stripping behaviour and a low overpotential of ≈190 mV. This study establishes a new paradigm for developing SPEs by engineering multifunctional polymers. The self-reporting and biodegradable properties would greatly expand the scope of applications for SPEs.

A Novel Piecewise Cubic Hermite Interpolating Polynomial-Enhanced Convolutional Gated Recurrent Method under Multiple Sensor Feature Fusion for Tool Wear Prediction

The monitoring of the lifetime of cutting tools often faces problems such as life data loss, drift, and distortion. The prediction of the lifetime in this situation is greatly compromised with respect to the accuracy. The recent rise of deep learning, such as Gated Recurrent Unit Units (GRUs), Hidden Markov Models (HMMs), Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), Attention networks, and Transformers, has dramatically improved the data problems in tool lifetime prediction, substantially enhancing the accuracy of tool wear prediction. In this paper, we introduce a novel approach known as PCHIP-Enhanced ConvGRU (PECG), which leverages multiple-feature fusion for tool wear prediction. When compared to traditional models such as CNNs, the CNN Block, and GRUs, our method consistently outperformed them across all key performance metrics, with a primary focus on the accuracy. PECG addresses the challenge of missing tool wear measurement data in relation to sensor data. By employing PCHIP interpolation to fill in the gaps in the wear values, we have developed a model that combines the strengths of both CNNs and GRUs with data augmentation. The experimental results demonstrate that our proposed method achieved an exceptional relative accuracy of 0.8522, while also exhibiting a Pearson's Correlation Coefficient (PCC) exceeding 0.95. This innovative approach not only predicts tool wear with remarkable precision, but also offers enhanced stability.

Multicolor Fluorescent Inks Based on Lanthanide Hybrid Organogels for Anticounterfeiting and Logic Circuit Design

With the rapid development of information technology, the encrypted storage of information is becoming increasingly important for human life. The luminescent materials with a color-changed response under physical or chemical stimuli are crucial for information coding and anticounterfeiting. However, traditional fluorescent materials usually face problems such as a lack of tunable fluorescence, insufficient surface-adaptive adhesion, and strict synthesis conditions, hindering their practical applications. Herein, a series of luminescent lanthanide hybrid organogels (Ln-MOGs) were rapidly synthesized using a simple method at room temperature through the coordination between lanthanide ions and 2,6-pyridinedicarboxylic acid and 5-aminoisophthalic acid. And the multicolor fluorescent inks were also prepared based on the Ln-MOG and hyaluronic acid, with the advantages of being easy to write, color-adjustable, and water-responsive discoloration, which has been applied to paper-based anticounterfeiting technology. Inspired by the responsiveness of the fluorescent inks to water, we designed a logic system that can realize single-input logic operations (NOT and PASS1) and double-input logic operations (NAND, AND, OR, NOR, XOR). The encryption of a binary code can be actualized utilizing different luminescent response modes based on the logic circuit system. By adjusting the energy sensitization and luminescence mechanism of lanthanide ions in the gel structure, the information reading and writing ability of the fluorescent inks were verified, which has great potential in the field of multicolor pattern anticounterfeiting and information encryption.

Targeting glycogen synthase kinase-3β for Alzheimer's disease: Recent advances and future Prospects

Senile plaques induced by β-amyloid (Aβ) abnormal aggregation and neurofibrillary tangles (NFT) caused by tau hyperphosphorylation are important pathological manifestations of Alzheimer's disease (AD). Glycogen synthase kinase-3 (GSK-3) is a conserved kinase; one member GSK-3β is highly expressed in the AD brain and involved in the formation of NFT. Hence, pharmacologically inhibiting GSK-3β activity and expression is a good approach to treat AD. As summarized in this article, multiple GSK-3β inhibitors has been comprehensively summarized over recent five years. However, only lithium carbonate and Tideglusib have been studied in clinical trials of AD. Besides ATP-competitive and non-ATP-competitive inhibitors, peptide inhibitors, allosteric inhibitors and other types of inhibitors have gradually attracted more interest. Moreover, considering the close relationship between GSK-3β and other targets involved in cholinergic hypothesis, Aβ aggregation hypothesis, tau hyperphosphorylation hypothesis, oxidative stress hypothesis, neuro-inflammation hypothesis, etc., diverse multifunctional molecules and multi-target directed ligands (MTDLs) have also been disclosed. We hope that these recent advances and critical perspectives will facilitate the discovery of safe and effective GSK-3β inhibitors for AD treatment.

Development of an ARMS multiplex real-time PCR assay for the detection of HLA-B*13:01 genotype by detecting highly specific SNPs

OBJECTIVES

HLA-B*13:01 was strongly associated with Dapsone Hypersensitivity Syndrome (DHS). This study aimed to develop and validate a rapid and economical method for HLA-B*13:01 genotyping.

METHODS

Two tubes multiplex real-time PCR detection system comprising amplification refractory mutation system primers and TaqMan probes was established for HLA-B*13:01 genotyping. Sequence-based typing was applied to validate the accuracy of the assay.

RESULTS

The accuracy of the assay was 100% for HLA-B*13:01 genotyping. The detection limit of the new method was 0.025 ng DNA. The positive rate of HLA-B*13:01 in the Bouyei (20%, n = 50) populations was significantly higher than that in the Uighur population (4%, n = 100), Han (4.5%, n = 200), and Tibetan (1%, n = 100) ( P  < 0.05).

CONCLUSION

The proposed method is rapid and reliable for HLA-B*13:01 screening in a clinical setting.

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.

Single-cell transcriptomics reveals ferrimagnetic vortex iron oxide nanoring-mediated mild magnetic hyperthermia exerts antitumor effects by alleviating macrophage suppression in breast cancer

The potential of Ferrimagnetic vortex iron oxide nanoring-mediated mild magnetic hyperthermia (FVIO-MHT) in solid tumor therapy has been demonstrated. However, the impact of FVIO-MHT on the tumor microenvironment (TME) remains unclear. This study utilized single-cell transcriptome sequencing to examine the alterations in the TME in response to FVIO-MHT in breast cancer. The results revealed the cellular composition within the tumor microenvironment (TME) was primarily modified due to a decrease in tumor cells and an increased infiltration of myeloid cells. Subsequently, an enhancement in active oxygen (ROS) metabolism was observed, indicating oxidative damage to tumor cells. Interestingly, FVIO-MHT reprogrammed the macrophages' phenotypes, as evidenced by alterations in the transcriptome characteristics associated with both classic and alternative activated phenotypes. And an elevated level of ROS generation and oxidative phosphorylation suggested that activated phagocytosis and inflammation occurred in macrophages. Additionally, cell-cell communication analysis revealed that FVIO-MHT attenuated the suppression between tumor cells and macrophages by inhibiting phagocytic checkpoint and macrophage migration inhibitory factor signaling pathways. Inhibition of B2m, an anti-phagocytosis checkpoint, could promote macrophage-mediated phagocytosis and significantly inhibit tumor growth. These data emphasize FVIO-MHT may promote the antitumor capabilities of macrophages by alleviating the suppression between tumor cells and macrophages.

DSMT-Net: Dual Self-Supervised Multi-Operator Transformation for Multi-Source Endoscopic Ultrasound Diagnosis

Pancreatic cancer has the worst prognosis of all cancers. The clinical application of endoscopic ultrasound (EUS) for the assessment of pancreatic cancer risk and of deep learning for the classification of EUS images have been hindered by inter-grader variability and labeling capability. One of the key reasons for these difficulties is that EUS images are obtained from multiple sources with varying resolutions, effective regions, and interference signals, making the distribution of the data highly variable and negatively impacting the performance of deep learning models. Additionally, manual labeling of images is time-consuming and requires significant effort, leading to the desire to effectively utilize a large amount of unlabeled data for network training. To address these challenges, this study proposes the Dual Self-supervised Multi-Operator Transformation Network (DSMT-Net) for multi-source EUS diagnosis. The DSMT-Net includes a multi-operator transformation approach to standardize the extraction of regions of interest in EUS images and eliminate irrelevant pixels. Furthermore, a transformer-based dual self-supervised network is designed to integrate unlabeled EUS images for pre-training the representation model, which can be transferred to supervised tasks such as classification, detection, and segmentation. A large-scale EUS-based pancreas image dataset (LEPset) has been collected, including 3,500 pathologically proven labeled EUS images (from pancreatic and non-pancreatic cancers) and 8,000 unlabeled EUS images for model development. The self-supervised method has also been applied to breast cancer diagnosis and was compared to state-of-the-art deep learning models on both datasets. The results demonstrate that the DSMT-Net significantly improves the accuracy of pancreatic and breast cancer diagnosis.

Diagnosis by Endoscopic Ultrasonography-Guided Sampling through the Lower Gastrointestinal Tract

Endoscopic ultrasound-guided fine-needle aspiration/biopsy (EUS-FNA/FNB) is very safe and has a high diagnostic rate for upper gastrointestinal lesions, especially pancreatic lesions, but its application in the lower gastrointestinal tract has rarely been reported. Due to the tortuous course of the colorectum, with the sigmoid colon particularly prone to perforation, most endoscopists are reluctant to perform lateral-sector endoscopic ultrasound scanning without a water-bag protection for the puncture. The ultrasonic endoscopy and flexible puncture needle techniques recently introduced into clinical practice have made ultrasound-guided puncture safer and more convenient. In addition, endoscopists have carefully tested various protective measures to improve the safety of the lower gastrointestinal puncture, substantially increasing its clinical feasibility. In this article, we review the iterations of endoscopic ultrasound equipment introduced in recent years and the many ingenious ideas proposed by endoscopists regarding lower gastrointestinal puncture.

Poly-adenine-mediated tetrahedral DNA nanostructure with multiple target-recognition sites for ultrasensitive and rapid electrochemical detection of Aflatoxin B1

Tetrahedral DNA nanostructures (TDNs) are widely used in the development of electrochemical biosensors due to their structural stability, programmability, and strong interfacial orderliness. However, the complex modifications on the electrode and the single vertex target recognition of the TDNs limit their applications in electrochemical biosensing. Herein, we developed a universal detection system based on a novel polyadenine-based tetrahedral DNA nanostructure (ATDN) using Aflatoxin B1 (AFB1) as the model target for analysis. In the absence of target AFB1, the signal probes (SP) modified with ferrocene would be anchored by five aptamers on ATDN. The target capture by aptamers led to a release of SP from the electrode surface, resulting in a significant reduction of the electrochemical signal. This new nanostructure was not only dispensed with multi-step electrode modifications and strong mechanical rigidity but also had five modification sites which enhanced the detection sensitivity for the target. As a result, this biosensor shows good analytical performance in the linear range of 1 fg mL-1 to 1 ng mL-1, exhibiting a low detection limit of 0.33 fg mL-1. Satisfactory accuracy has also been demonstrated through good recoveries (95.2%-98.9%). The proposed new tetrahedral DNA nanostructure can provide a more rapid and sensitive alternative to previous electrochemical sensors based on the conventional TDN. Since DNA sequences can be designed flexibly, the sensing platform in this strategy can be extended to detect various targets in different fields.

Genomic epidemiological investigation of an outbreak of Serratia marcescens neurosurgical site infections associated with contaminated haircutting toolkits in a hospital barber shop

BACKGROUND

Nine surgical site infections caused by Serratia marcescens were diagnosed in neurosurgical patients in a 3500-bed hospital between 2nd February and 6th April 2022.

OBJECTIVE

To trace the source of infections caused by S. marcescens to expedite termination of the outbreak and prevent future epidemics.

METHODS

A review of all surgical procedures and cultures yielding S. marcescens since February 2022 was conducted. Samples were collected from patients and environmental sources. S. marcescens isolates were characterized by antibiotic susceptibility testing. Whole-genome sequencing (WGS) was used to investigate genetic relationships. Resistance genes, virulence genes and plasmid replicons were identified.

RESULTS

S. marcescens was isolated from patients' puncture fluid, cerebrospinal fluid and other secretions, and was also cultured from the barbers' haircutting tools, including leather knives, slicker scrapers and razors. In total, 15 isolates were obtained from patients and eight isolates were obtained from haircutting tools. All isolates exhibited identical antibiotic resistance patterns. WGS revealed close clustering among the 23 isolates which differed significantly from previous strains. Three resistance genes and nine virulence-associated genes were detected in all isolates, and 19 of 23 isolates harboured an MOBP-type plasmid. The results confirmed an outbreak of S. marcescens, which was traced to contaminated haircutting tools in the hospital barber shop. The outbreak ended after extensive reinforcement of infection control procedures and re-education of the barbers.

CONCLUSIONS

These results highlight the risk of postoperative infections related to pre-operative skin preparation, and demonstrate the value of next-generation sequencing tools to expedite outbreak investigations.

Induced mechanism of phosphatase hormesis by Cd ions and rhizosphere metabolites of Trifolium repens L

Rhizosphere phosphatases can exhibit hormetic effects in response to cadmium (Cd) ion stimulation. However, understanding the mechanisms underlying hormesis effects on soil ecosystems is challenging as studies on hormesis are usually specific to an organism, cell, or organ. To comprehensively investigate the mechanism of phosphatase hormesis, this study utilized in situ zymography and metabolomics to analyze the rhizosphere of Trifolium repens L. (white clover). Zymograms showed that rhizosphere phosphatase displayed a hormetic effect in 10 mg kg-1 Cd contaminated soil, with a hotspot area 1.8 times larger than non-Cd contaminated soil and a slight increase in enzyme activity. Nevertheless, the phosphatase activity was substantially suppressed upon elevating the Cd concentration in the soil to 50 mg kg-1. Differential metabolite identification and KEEG pathway enrichment analysis revealed that both rhizosphere organic acids and amino acid compounds positively affected phosphatase activity, and both were able to stabilize complexation with Cd ions via carboxyl groups. Besides, molecular docking models suggested that Cd ions act as cofactors to induce the formation of hydrogen bonds between amino acids/organic acids and phosphatase residues to form a triplet complex with a more stable structure, thereby improving phosphatase activity. The results indicated that amino acids and organic acids are heavily enriched in the rhizosphere of white clover and form a particular structure with soil Cd ions and phosphatase, which is essential for inducing the phosphatase hormesis as a detoxification mechanism in the rhizosphere micro-ecosystem.

An integrated hinged dual-probe for co-target fast switching imaging

The diversity of functional applications of atomic force microscopes is the key to the development of nanotechnology. However, the single probe configuration of the traditional atomic force microscope restricts the realization of different application requirements for the same target area of a single sample, and the replacement of the working probe will lead to the loss of the target area. Here, the design, simulation, fabrication, and application of a unique atomic force microscope dual-probe are presented, which consists of a pair of parallel cantilevers with a narrow gap and a U-shaped hinged probe base. The Integrated Hinged Dual-Probe (IHDP) is developed specifically for fast switching of probes working in limited space and independent and precise manipulation of each probe. The deflection signal sensing of two cantilevers is achieved simultaneously by a single laser beam, and the decoupled independent cantilever deflection signals do not interfere with each other. The switching of the working probe is achieved by a piezoelectric ceramic with a 2 µm stroke and U-shaped hinge structure, which is fast and does not require tedious and repetitive spatial position calibration. By measuring standard grid samples, IHDP exhibits excellent measurement and characterization capabilities. Finally, a working probe switching imaging experiment was conducted on solidified rat cardiomyocytes, and the experimental process and imaging results demonstrated the superiority of IHDP in switching probe scanning imaging of the same target area of a single sample. The two probes of IHDP can undergo arbitrary functionalization modifications, which helps achieve multidimensional information acquisition for a single target.

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.

Clinical utility of repeat fetal echocardiography in congenital heart disease

OBJECTIVES

To investigate the utility of repeat fetal echocardiography (FE) following a diagnosis of structural congenital heart disease (CHD) on the initial FE. We evaluated how often changes in management and counseling occurred based on subsequent FE findings and sought to determine which types of CHD were more likely to have changes in management and/or counseling based on repeat FE.

METHODS

This was a retrospective review of all patients who presented to our center between January 2012 and January 2019 and who had more than one FE performed for structural CHD. We reviewed consultation notes to determine whether management or counseling had changed based on FE findings at follow-up visits. Management variables included a change in location or mode of delivery, plan for atrial septostomy, initiation of prostaglandin infusion, umbilical line placement and planned admission location (nursery vs neonatal intensive care unit). We defined a counseling change as any of the above changes in management as well as any meaningful refinements in the cardiac diagnosis that led to a change in the overall prognosis or future management. Initial diagnoses were grouped into anatomically/hemodynamically relevant subgroups. Fisher's exact test was used to assess the relationship between the initial diagnosis and changes in management. Post-hoc pairwise comparisons were performed using Dunnett's test.

RESULTS

Between January 2012 and January 2019, 267 patients underwent 534 follow-up FE assessments performed for structural CHD. Management change based on repeat FE occurred in 41/267 (15.4%) cases. A change in management was associated with the diagnosis made at the initial visit (P < 0.001). The proportion of cases with a management change was highest among those with an initial diagnosis of pulmonary valve abnormality/non-critical pulmonary stenosis (4/11 (36.4%)), followed by balanced atrioventricular canal (AVC) defect (5/17 (29.4%)) and left ventricular outflow tract obstruction/aortic valve abnormality or coarctation/interrupted aortic arch (19/68 (27.9%)). No management change occurred in fetuses diagnosed with isolated ventricular septal defect (VSD), truncus arteriosus, pulmonary vein anomaly or isolated arch sidedness/branching abnormality. Compared to those with a VSD, management was significantly more likely to be changed in fetuses with a balanced AVC defect (P = 0.025) and left heart lesions (P = 0.002). Right heart lesions showed a trend towards an increased incidence of management change (P = 0.05). A counseling change based on repeat FE occurred in 108/267 (40.4%) cases. The proportion of cases with a counseling change was highest among those with an initial diagnosis of pulmonary valve abnormality/non-critical pulmonary stenosis (8/11 (72.7%)) and hypoplastic left heart syndrome/critical aortic stenosis (5/9 (55.6%)).

CONCLUSIONS

The clinical utility of follow-up FE is associated with the type of CHD diagnosed. Follow-up FE led to changes in management in several types of CHD, most commonly in cases with an initial diagnosis of right and left outflow obstructive lesions and balanced AVC defect. When developing programmatic protocols for the frequency of FE assessments, the type of CHD should be a major determinant, but additional studies are required to reach a consensus on how often serial FE should be performed for each type of CHD. © 2023 International Society of Ultrasound in Obstetrics and Gynecology.

MRI-based radiomics analysis of bladder cancer: prediction of pathological grade and histological variant

AIM

To develop magnetic resonance imaging (MRI)-based radiomics models for the prediction of the pathological grade and histological variant of bladder cancer.

MATERIALS AND METHODS

A total of 227 patients who underwent bladder MRI and had histopathologically confirmed grades and variants were included retrospectively from January 2017 to March 2022. They were assigned to a training set (n=131) and a testing set (n=96) based on the MRI system. MRI-based radiomics features were extracted from manually segmented volumes of interest from high-b-value DWI images and ADC maps. The radiomics models were trained with all possible pipelines in the training set. One optimal model was selected using the fivefold cross-validation method and verified by the testing set according to the pathological results. Univariate and multivariate analyses were performed to identify the significant clinical and imaging factors for developing clinical-radiomics models.

RESULTS

The radiomics model for grade prediction had area under the curve (AUC) values of 0.784, 0.786, and 0.733 in the training, cross-validation, and testing sets, respectively. The radiomics model for variant prediction had AUC values of 0.748, 0.757, and 0.789 in the training, cross-validation, and testing sets, respectively. The performance of the clinical-radiomics model was significantly improved compared with the radiomics models alone for the total dataset (AUC for grade: 0.846 versus 0.756; AUC for variant: 0.810 versus 0.757, p<0.05).

CONCLUSION

MRI-based radiomics models could be used to predict the pathological grade and histological variants of bladder cancer with relatively good performance.

[Safety of the strategy of minimizing intestinal resection during surgery for pelvic radiation- induced terminal small intestinal stenosis]

Objective: To investigate the efficacy of strategies for minimizing small bowel resection during surgery for pelvic radiation-induced terminal small intestinal stenosis in preventing postoperative complications such as anastomotic leakage and short bowel syndrome. Methods: This was a retrospective cohort study. There are two subtypes of chronic radiation enteritis (CRE) with combined intestinal stenosis and intestinal obstruction: (1) Type I: terminal ileal lesions with a normal ileal segment of 2-20 cm between the ileal lesion and ileocecal junction; and (2) Type II: the lesion is located in the small bowel at a distance from the ileocecal region, usually accompanied by extensive damage to the bowel segments outside the lesion. The indications for minimal bowel resection are as follows: (1) diagnosis of Type I small bowel CRE; (2) absence of radiological evidence of rectosigmoid damage; and (3) absence of colonic obstruction. The contraindications are: (1) stenotic, penetrating lesions of the distal cecum; (2) emergency surgery; (3) recurrence of malignant tumor or history of radiotherapy for recurrent malignant tumor; (4) interval between radiotherapy and surgery <6 months; and (5) history of preoperative small bowel resection or abdominal chemotherapy. Case data of 40 patients with Type I CRE who met the above criteria and had undergone minimal bowel resection between April 2017 and December 2019 were retrospectively analyzed (minimal bowel resection group; including 13 patients from Jinling Hospital, 16 from the Ninth People's Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, and 11 from the Affiliated Hospital of Xuzhou Medical University). Forty patients with Type I CRE who had undergone resection of intestinal stenosis lesions and the ileocecal region between October 2015 and March 2017 were included as historical controls (conventional resection group; all from Jinling Hospital). The specific strategy for minimal bowel resection was one-stage partial ileal resection+ileo anastomosis+protective small bowel stoma. In contrast, conventional resection comprised ileocecal resection+ileocecal-ascending colon anastomosis. Postoperative complications, intraoperative and postoperative recovery, and changes in postoperative quality of life were analyzed in both groups. The severity of postoperative complications was assessed by Clavien-Dindo and the Comprehensive Complication Index (CCI). Karnofsky performance scores (KPS) were used to evaluate the quality of life of patients in the two groups preoperatively and postoperatively. The higher the KPS score, the better the quality of life. Results: Baseline patient characteristics did not differ significantly between the two groups (P>0.05). Compared with the conventional resection group, the length of small bowel resected in the minimal bowel resection group (51 [20-200] cm vs. 91 [60-200] cm, Z=5.653, P<0.001), duration of postoperative total enteral nutrition [9 (3-18) days vs. 12 (4-50) days, Z=2.172, P=0.030], and duration of postoperative hospital stay [17 (9-24) days vs 29 (13-57) days, Z=6.424, P<0.001] were shorter; all of these differences are statistically significant. The overall incidence of postoperative complications was lower in the minimal bowel resection group than in the conventional resection group [20.0% (8/40) vs. 70.0% (28/40), χ2=19.967, P<0.001], These comprised short bowel syndrome [5.0% (2/40) vs. 25.0% (10/40), χ2=6.274, P=0.012], anastomotic leakage or fistula [2.5% (1/40) vs. 22.5% (9/40), χ2=7.314, P=0.014], and pleural effusion [7.5% (3/40) vs. 25.0% (10/40), χ2=4.500, P=0.034], all of which occurred less often in the minimal bowel resection than conventional resection group. The CCI index was also lower in the minimal bowel resection group than in the conventional resection group [CCI>40: 2.5% (1/40) vs. 12.5% (5/40), Z=18.451, P<0.001]. KPS scores were higher in the minimal bowel resection group 1 and 3 months postoperatively than they had been 1 day preoperatively (79.9±4.7 vs. 75.3±4.1, 86.2±4.8 vs. 75.3±4.1, both P<0.05). In the minimal bowel resection group, seven patients were satisfied with their current quality of life and refused to undergo stoma reduction at follow-up and one deferred stoma reduction because of rectal bleeding. The remaining 32 patients underwent stoma reduction 3 to 12 months after surgery, 26 of whom underwent ileo-cecal anastomosis. The remaining six underwent resection of the stoma and anastomosis of the ileum to the ascending colon. Conclusions: The strategy of minimal small bowel resection in patients with radiation-induced bowel injuries reduces the length of resected small bowel, decreases the risk and severity of postoperative complications, and is associated with a better prognosis and quality of life than conventional resection.

Fast clonal family inference from large-scale B cell repertoire sequencing data

Advances in high-throughput sequencing technologies have facilitated the large-scale characterization of B cell receptor (BCR) repertoires. However, the vast amount and high diversity of the BCR sequences pose challenges for efficient and biologically meaningful analysis. Here, we introduce fastBCR, an efficient computational approach for inferring B cell clonal families from massive BCR heavy chain sequences. We demonstrate that fastBCR substantially reduces the running time while ensuring high accuracy on simulated datasets with diverse numbers of B cell lineages and varying mutation rates. We apply fastBCR to real BCR sequencing data from peripheral blood samples of COVID-19 patients, showing that the inferred clonal families display disease-associated features, as well as corresponding antigen-binding specificity and affinity. Overall, our results demonstrate the advantages of fastBCR for analyzing BCR repertoire data, which will facilitate the identification of disease-associated antibodies and improve our understanding of the B cell immune response.

CEP55-associated lethal fetal syndrome: a case report of a Chinese family

Background: Research on fetal loss related to germline mutations in single genes remains limited. Disruption of CEP55 has recently been established in association with perinatal deaths characterized by hydranencephaly, renal dysplasia, oligohydramnios, and characteristic dysmorphisms. We herein present a Chinese family with recurrent fetal losses due to compound heterozygous nonsense CEP55 variants. Case presentations: The Chinese couple had a history of five pregnancies, with four of them proceeding abnormally. Two stillbirths (II:3 and II:4) sequentially occurred in the third and fourth pregnancy. Prenatal ultrasound scans revealed phenotypic similarities between fetuses II:3 and II:4, including oligohydramnios, bilateral renal dysplasia and hydrocephalus/hydranencephaly. Clubfoot and syndactyly were also present in both stillborn babies. Fetus II:3 presented with endocardial cushion defects while fetus II:4 did not. With the product of conception in the fourth pregnancy, whole exome sequencing (WES) on fetus II:4 identified compound heterozygous nonsense CEP55 variants comprised of c.190C>T(p.Arg64*) and c.208A>T(p.Lys70*). Both variants were expected to result in lack of the TSG101 and ALIX binding domain. Sanger sequencing confirmed the presence and cosegregation of both variants. Conclusion: This is the fifth reported family wherein biallelic CEP55 variants lead to multiple perinatal deaths. Our findings, taken together with previously described phenotypically similar cases and even those with a milder and viable phenotype, broaden the genotypic and phenotypic spectrum of CEP55-associated lethal fetal syndrome, highlighting the vital biomolecular function of CEP55.

Molecular-scale characterization of groundwater treatment sludge from around the world: Implications for potential arsenic recovery

Iron (Fe)-based treatment methods are widely applied to remove carcinogenic arsenic (As) from drinking water, but generate toxic As-laden Fe (oxyhydr)oxide waste that has traditionally been ignored for resource recovery by the water sector. However, the European Commission recently classified As as a Critical Raw Material (CRM), thus providing new incentives to re-think As-laden groundwater treatment sludge. Before As recovery techniques can be developed for groundwater treatment waste, detailed information on its structure and composition is essential. To this end, we comprehensively characterized sludge generated from a variety of As-rich groundwater treatment plants in different geographic regions by combining a suite of macroscopic measurements, such as total digestions, leaching tests and BET surface area with molecular-scale solid-phase analysis by Fe and As K-edge X-ray absorption spectroscopy (XAS). We found that the As mass fraction of all samples ranged from ∼200-1200 mg As/kg (dry weight) and the phosphorous (P) content reached ∼0.5-2 mass%. Notably, our results indicated that the influent As level was a poor predictor of the As sludge content, with the highest As mass fractions (940-1200 mg As/kg) measured in sludge generated from treating low groundwater As levels (1.1-22 µg/L). The Fe K-edge XAS data revealed that all samples consisted of nanoscale Fe(III) precipitates with less structural order than ferrihydrite, which is consistent with their high BET surface area (up to >250 m2/g) and large As and P mass fractions. The As K-edge XAS data indicated As was present in all samples predominantly as As(V) bound to Fe(III) precipitates in the binuclear-corner sharing (2C) geometry. Overall, the similar structure and composition of all samples implies that As recovery methods optimized for one type of Fe-based treatment sludge can be applied to many groundwater treatment sludges. Our work provides a critical foundation for further research to develop resource recovery methods for As-rich waste.

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}.

Measurement of Ultra-High-Energy Diffuse Gamma-Ray Emission of the Galactic Plane from 10 TeV to 1 PeV with LHAASO-KM2A

The diffuse Galactic γ-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this Letter, we report the measurements of diffuse γ rays from the Galactic plane between 10 TeV and 1 PeV energies, with the square kilometer array of the Large High Altitude Air Shower Observatory (LHAASO). Diffuse emissions from the inner (15°10  TeV). The energy spectrum in the inner Galaxy regions can be described by a power-law function with an index of -2.99±0.04, which is different from the curved spectrum as expected from hadronic interactions between locally measured cosmic rays and the line-of-sight integrated gas content. Furthermore, the measured flux is higher by a factor of ∼3 than the prediction. A similar spectrum with an index of -2.99±0.07 is found in the outer Galaxy region, and the absolute flux for 10≲E≲60  TeV is again higher than the prediction for hadronic cosmic ray interactions. The latitude distributions of the diffuse emission are consistent with the gas distribution, while the longitude distributions show clear deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations.