207,724 results on '"Su P"'
Search Results
2. Effectiveness of facial anthropomorphism design for improving multimedia learning outcomes: systematic review and meta-analysis
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Liu Kaifeng and Su Pengbo
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Anthropomorphic ,Multimedia learning ,Meta-analysis ,Retention ,Transfer ,Comprehension ,Special aspects of education ,LC8-6691 - Abstract
Abstract A systematic review and meta-analysis were conducted to examine the effectiveness of facial anthropomorphism of learning material design in improving learning and other relevant outcomes. We searched Web of Science, PsycInfo, and PsycArticle for studies published before February 2023. Learning outcomes included transfer, retention, and comprehension. Other relevant outcomes included affective-motivational, effort, and experience outcomes. Outcomes that were reported in at least five independent experiments were meta-analyzed; otherwise, a narrative synthesis was performed. Subgroup analysis by participants’ age and material type was employed for learning outcomes. A total of 33 independent experiments from 13 research articles were identified and analyzed. For learning outcomes, facial anthropomorphism yielded significant improvements in transfer (standardized mean difference [SMD] 0.28, 95% CI 0.15 to 0.40, p
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- 2024
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3. The GJB3 correlates with the prognosis, immune cell infiltration, and therapeutic responses in lung adenocarcinoma
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Dou Ruigang, Liu Rongfeng, Su Peng, Yu Xiaohui, and Xu Yanzhao
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lung adenocarcinoma ,gjb3 ,transcription factors ,hsa-mir-6511b-5p ,immune ,drug sensitivity ,Medicine - Abstract
Gap junction protein beta 3 (GJB3) has been reported as a tumor suppressor in most tumors. However, its role in lung adenocarcinoma (LUAD) remains unknown. The purpose of this study is to explore the role of GJB3 in the prognosis and tumor microenvironment of LUAD patients. The data used in this study were acquired from The Cancer Genome Atlas, Gene Expression Omnibus, and imvigor210 cohorts. We found that GJB3 expression was increased in LUAD patients and correlated with LUAD stages. LUAD patients with high GJB3 expression exhibited a worse prognosis. A total of 164 pathways were significantly activated in the GJB3 high group. GJB3 expression was positively associated with nine transcription factors and might be negatively regulated by hsa-miR-6511b-5p. Finally, we found that immune cell infiltration and immune checkpoint expression were different between the GJB3 high and GJB3 low groups. In summary. GJB3 demonstrated high expression levels in LUAD patients, and those with elevated GJB3 expression displayed unfavorable prognoses. Additionally, there was a correlation between GJB3 and immune cell infiltration, as well as immune checkpoint expression in LUAD patients
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- 2024
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4. The impact of post-activation potentiation on explosive vertical jump after intermittent time: a meta-analysis and systematic review
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Jiazhe Li, Kim Geok Soh, and Su Peng Loh
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Intermittent time ,Post-activation potentiation ,Explosive vertical jump ,Countermovement jumps ,Meta-analysis ,Medicine ,Science - Abstract
Abstract The optimal intermittent time for post-activation potentiation (PAP) training remains uncertain and contentious. This study employed a meta-analysis to systematically evaluate the effect of different intermittent times on PAP in relation to explosive vertical jump height. Relevant literature was sourced from CNKI, Wanfang, VIP, CBM, PubMed, Web of Science, and Google Scholar databases using keywords such as “postactivation potentiation,” “activation enhancement effect,” “PAP,” “explosive vertical jump,” “explosive vertical high jump,” and “intermittent time.” The search covered publications from the inception of each database until June 2024. Studies involving athletes (regardless of sport type) undergoing PAP training were included, with no restrictions on the methods used to induce PAP. Comparative analysis focused on the heights of countermovement jumps (CMJ) and peak ground reaction force (GRF) before and after interventions. The quality of the included studies was assessed using the Cochrane Risk of Bias Tool, and data were analyzed using RevMan5.3. The study included a total of 21 papers with 327 subjects, primarily using the squat as the method of PAP induction. The meta-analysis revealed that intermittent times of 4 min [MD = − 0.03, 95% CI: − 0.04 ~ − 0.01; Z = 2.71, P = 0.007] and 5–8 min [MD = − 0.03, 95% CI: − 0.04 ~ – 0.01; Z = 3.07, P = 0.002] significantly increased the height of explosive vertical CMJs. However, intermittent times of 1–3 min [MD = –0.00, 95% CI: − 0.01 ~ 0.01; Z = 0.38, P = 0.70] and 10–24 min [MD = − 0.01, 95% CI: − 0.02 ~ 0.00; Z = 1.43, P = 0.15] did not show significant effects on CMJ height. These findings indicate that 4-min and 5–8 min intervals significantly enhance CMJ height, while intervals shorter than 4 min or longer than 8 min do not have a significant impact.
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- 2024
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5. The state of Continuing Professional Development in East and Southeast Asia among the medical practitioners
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Dujeepa D Samarasekera, Shuh Shing Lee, Su Ping Yeo, Julie Chen, Ardi Findyartini, Nadia Greviana, Budi Wiweko, Vishna Devi Nadarajah, Chandramani Thuraisingham, Jen-Hung Yang, and Lawrence Sherman
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medical education ,health profession education ,continuing professional development ,continuing medical education ,accreditation ,Education (General) ,L7-991 ,Medicine (General) ,R5-920 - Abstract
Introduction: Continuing medical education and continuing professional development activities (CME/CPD) improve the practice of medical practitioners and allowing them to deliver quality clinical care. However, the systems that oversee CME/CPD as well as the processes around design, delivery, and accreditation vary widely across countries. This study explores the state of CME/CPD in the East and South East Asian region from the perspective of medical practitioners, and makes recommendations for improvement. Methods: A multi-centre study was conducted across five institutions in Hong Kong, Indonesia, Malaysia, Singapore and Taiwan. The study instrument was a 28-item (27 five-point Likert scale and 1 open-ended items) validated questionnaire that focused on perceptions of the current content, processes and gaps in CME/CPD and further contextualised by educational experts from each participating site. Descriptive analysis was undertaken for quantitative data while the data from open-ended item was categorised into similar categories. Results: A total of 867 medical practitioners participated in the study. For perceptions on current CME/CPD programme, 75.34% to 88.00% of respondents agreed that CME/CPD increased their skills and competence in providing quality clinical care. For the domain on pharmaceutical industry-supported CME/CPD, the issue of commercial influence was apparent with only 30.24%-56.92% of respondents believing that the CME/CPD in their institution was free from commercial bias. Key areas for improvement for future CME/CPD included 1) content and mode of delivery, 2) independence and funding, 3) administration, 4) location and accessibility and 5) policy and collaboration. Conclusion: Accessible, practice-relevant content using diverse learning modalities offered by unbiased content providers and subject to transparent and rigorous accreditation processes with minimal administrative hassle are the main considerations for CME/CPD participants.
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- 2024
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6. Incidence and Impact of Takotsubo Syndrome in Hospitalized Patients With COVID-19
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Pengyang Li, MD, MSc, Ao Shi, PhD, Xiaojia Lu, MD, Chenlin Li, MD, Peng Cai, MSc, Catherine Teng, MD, Lingling Wu, MD, Yuan Shu, Su Pan, MD, Richard A. F. Dixon, PhD, Qi Liu, PhD, and Bin Wang, MD, PhD
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covid-19 ,takotsubo cardiomyopathy ,mortality ,Diseases of the circulatory (Cardiovascular) system ,RC666-701 - Abstract
Background Takotsubo syndrome has been reported in patients with COVID-19, although minimal data are available. This investigation assessed the incidence and impact of takotsubo syndrome on patients hospitalized with COVID-19. Methods A retrospective cohort study was conducted using International Statistical Classification of Diseases, Tenth Revision, codes to identify patients with a primary diagnosis of COVID-19 with or without takotsubo syndrome in the National Inpatient Sample 2020 database. Outcomes between groups were compared after propensity score matching for patient and hospital demographics and comorbidities. Results A total of 211,448 patients with a primary diagnosis of COVID-19 were identified. Of these, 171 (0.08%) had a secondary diagnosis of takotsubo syndrome. Before matching, patients with COVID-19 and takotsubo syndrome, compared with patients without takotsubo syndrome, were older (68.95 vs 64.26 years; P < .001); more likely to be female (64.3% vs 47.2%; P < .001); and more likely to have anxiety (24.6% vs 12.8%; P < .001), depression (17.5% vs 11.4%; P = .02), and chronic obstructive pulmonary disease (24.6% vs 14.7%; P < .001). The takotsubo syndrome group had worse outcomes than the non–takotsubo syndrome group for death (30.4% vs 11.1%), cardiac arrest (7.6% vs 2.1%), cardiogenic shock (12.9% vs 0.4%), length of hospital stay (10.7 vs 7.5 days), and total charges ($152,685 vs $78,468) (all P < .001). After matching and compared with the non–takotsubo syndrome group (n = 508), the takotsubo syndrome group (n = 170) had a higher incidence of inpatient mortality (30% vs 14%; P < .001), cardiac arrest (7.6% vs 2.8%; P = .009), and cardiogenic shock (12.4% vs 0.4%; P < .001); a longer hospital stay (10.7 vs 7.6 days; P < .001); and higher total charges ($152,943 vs $79,523; P < .001). Conclusion Takotsubo syndrome is a rare but severe in-hospital complication in patients with COVID-19.
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- 2024
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7. Maslinic acid alleviates intervertebral disc degeneration by inhibiting the PI3K/AKT and NF-κB signaling pathways
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Que Yichen, Wong Chipiu, Qiu Jincheng, Gao Wenjie, Lin Youxi, Zhou Hang, Gao Bo, Li Pengfei, Deng Zhihuai, Shi Huihong, Hu Wenjun, Liu Song, Peng Yan, Su Peiqiang, Xu Caixia, Liang Anjing, Qiu Xianjian, and Huang Dongsheng
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maslinic acid ,intervertebral disc degeneration ,NF-κB ,PI3K ,senescence ,Biochemistry ,QD415-436 ,Genetics ,QH426-470 - Abstract
Intervertebral disc degeneration (IDD) is the cause of low back pain (LBP), and recent research has suggested that inflammatory cytokines play a significant role in this process. Maslinic acid (MA), a natural compound found in olive plants (Olea europaea), has anti-inflammatory properties, but its potential for treating IDD is unclear. The current study aims to investigate the effects of MA on TNFα-induced IDD in vitro and in other in vivo models. Our findings suggest that MA ameliorates the imbalance of the extracellular matrix (ECM) and mitigates senescence by upregulating aggrecan and collagen II levels as well as downregulating MMP and ADAMTS levels in nucleus pulposus cells (NPCs). It can also impede the progression of IDD in rats. We further find that MA significantly affects the PI3K/AKT and NF-κB pathways in TNFα-induced NPCs determined by RNA-seq and experimental verification, while the AKT agonist Sc-79 eliminates these signaling cascades. Furthermore, molecular docking simulation shows that MA directly binds to PI3K. Dysfunction of the PI3K/AKT pathway and ECM metabolism has also been confirmed in clinical specimens of degenerated nucleus pulposus. This study demonstrates that MA may hold promise as a therapeutic agent for alleviating ECM metabolism disorders and senescence to treat IDD.
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- 2024
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8. A Game-Based Tool for Reducing Jargon Use by Medical Trainees
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Emily Burney, Megha Arora, Mizan Gaillard, Maya Herzig, Leo Lester, Su Park, and Cliff Coleman
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Jargon ,Patient Education ,Communication Skills ,Games ,Health Literacy ,Patient Care ,Medicine (General) ,R5-920 ,Education - Abstract
Introduction Physicians can be unaware that many US adults have intermediate or lower health literacy. Avoiding medical jargon in patient communication can improve poor outcomes associated with lower health literacy, but physicians may struggle to do so as health literacy education is neither standardized nor universal at US allopathic medical schools. As with other skills-based proficiencies in medical education, repeat exposure and active learning help build competency. Medical students developed the Patient Communication Challenge (PCC), an adaptation of the Hasbro game Taboo, to facilitate practice of patient-centered communication skills among medical trainees. Methods Hour-long workshops were held for groups of preclinical medical students. Students watched a communication exemplar video, played the PCC game, and completed a postworkshop survey. To play, two teams competed to earn points by identifying medical concepts as explained by a teammate who described the term without using medical jargon. Results Evaluations indicated that the game was enjoyable and reinforced didactic concepts through active learning, with self-reported participant satisfaction and competency gain. Overall, 59% of participants (53 of 90) completed postworkshop surveys; 91% (48 of 53) agreed they felt more proficient in avoiding jargon, 94% (50 of 53) would recommend the workshop to a classmate, and 100% (53 of 53) would play again. Discussion The PCC can help early medical trainees develop health communication skills through gamification with utilization of adult learning principles and adequate frequency for skill retention. Future applications include longitudinal assessment and expanding to later stages of medical training and other health professions.
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- 2024
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9. Prevalence, Transmission and Genetic Diversity of Pyrazinamide Resistance Among Multidrug-Resistant Mycobacterium tuberculosis Isolates in Hunan, China
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Liu B, Su P, Hu P, Yan M, Li W, Yi S, Chen Z, Zhang X, Guo J, Wan X, Wang J, Gong D, Bai H, Wan K, Liu H, Li G, and Tan Y
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tuberculosis ,pyrazinamide ,transmission ,pnca mutations ,rpsa mutations ,pand mutations ,china ,Infectious and parasitic diseases ,RC109-216 - Abstract
Binbin Liu,1,* Pan Su,1,* Peilei Hu,1,* Mi Yan,1 Wenbin Li,1 Songlin Yi,1 Zhenhua Chen,1 Xiaoping Zhang,1 Jingwei Guo,1 Xiaojie Wan,1 Jue Wang,1 Daofang Gong,1 Hua Bai,1 Kanglin Wan,2 Haican Liu,2 Guilian Li,2 Yunhong Tan1 1Clinical Laboratory, Hunan Chest Hospital, Changsha, People’s Republic of China; 2National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of China*These authors contributed equally to this workCorrespondence: Yunhong Tan, Clinical Laboratory, Hunan Chest Hospital, Changsha, People’s Republic of China, Tel +86-13874947876, Email Tanyunhong@163.com Guilian Li, National Key Laboratory of Intelligent Tracking and Forecasting for Infectious Diseases, National Institute for Communicable Disease Control and Prevention, Chinese Center for Disease Control and Prevention, Beijing, People’s Republic of China, Tel +86-18519886112, Email liguilian@icdc.cnBackground: China is a country with a burden of high rates of both TB and multidrug-resistant TB (MDR-TB). However, published data on pyrazinamide (PZA) resistance are still limited in Hunan province, China. This study investigated the prevalence, transmission, and genetic diversity of PZA resistance among multidrug-resistant Mycobacterium tuberculosis isolates in Hunan province.Methods: Drug susceptibility testing (DST) with the Bactec MGIT 960 PZA kit and pyrazinamidase (PZase) testing were conducted on all 298 MDR clinical isolates. Moreover, 24-locus MIRU-VNTR and DNA sequencing of pncA, rpsA, and panD genes were conducted on 180 PZA-resistant (PZA-R) isolates.Results: The prevalence of PZA resistance among MDR-TB strains reached 60.4%. Newly diagnosed PZA-R TB patients and clustered isolates with identical pncA, rpsA, and panD mutations showed that transmission of PZA-R isolates played a significant role in the formation of PZA-R TB. Ninety-eight mutation patterns were observed in the pncA among 180 PZA-R isolates, and seventy-one (72.4%) were point mutations. Twenty-four of these mutations are new, including 2 base substitutions (V93G and T153S) and 22 nucleotide deletions or insertions. The W119C was found in PZA-S isolates, on the other hand, F94L and V155A mutations were found in both PZA resistant and susceptible isolates with positive PZase activity, indicating that they were not associated with PZA resistance. This is not entirely in line with the WHO catalogue. Ten novel rpsA mutations were found in 10 PZA-R isolates, which all combined with mutations in pncA. Thus, it is unpredictable whether these mutations in rpsA can impact PZA resistance. No panD mutation was found in all PZA-R isolates.Conclusion: DNA sequencing of pncA and PZase activity testing have great potential in predicting PZA resistance.Keywords: tuberculosis, pyrazinamide, transmission, pncA mutations, rpsA mutations, panD mutations, China
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- 2024
10. Treatment and utilization of coal mine water based on 'deep ground-underground-surface ground' linkage system
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ZHANG Chunhui, ZHAO Guifeng, SU Peidong, XIAO Nan, ZHANG Yizhen, and SHEN Zhelin
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coal mine ,mine water ,recycling ,reinjection ,Engineering geology. Rock mechanics. Soil mechanics. Underground construction ,TA703-712 ,Mining engineering. Metallurgy ,TN1-997 - Abstract
The highly-efficient treatment of coal mine water with low energy consumption contributes to much safer and more efficient coal mining. At present, mine water processing at underground is still limited for its small treatment water yield, large land coverage and high operation cost in ground treatment, high drainage costs when mine water is discharged into the surface water system. In this light, this study puts forward the "deep ground-underground-surface ground" linkage system for mine water treatment and utilization. The treated mine water is partially used for mining production and residential usage, while the rest is subject to deep reinjection or drainage. This paper first introduces the operational mechanism behind this system, and then evaluates its validity in terms of water quality, water quantity, hydrogeology, underground structure, and feasibility of recharge engineering. This study then reviews existing mine water treatment methods for varying water qualities and the "underground-surface" joint treatment system; Also, a summary is drawn regarding the selection of aquifer for reinjection purposes, reinjection construction operations, reinjection tests, simulation of reinjection water quality, and analysis of reinjection safety.
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- 2024
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11. An Imperfect Monitoring Game-Theoretic Approach for Crowdsourcing Reliable Wireless Data
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Zunera Javed, Zaheer Khan, Janne J. Lehtomaki, Hamed Ahmadi, and Su P. Sone
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6G ,unlicensed spectrum ,game theory ,crowdsourcing ,shared spectrum ,real data ,Telecommunication ,TK5101-6720 ,Transportation and communications ,HE1-9990 - Abstract
Gathering useful and trustworthy wireless data using crowdsourcing to train/validate machine learning (ML) algorithms can be difficult due to two factors: 1) correctness and reliability of the gathered data from various independent wireless access points (APs) can be unknown, and 2) designing a metric that can capture the value of the collected data for the considered model can be a challenge. To address the challenge of reliable data collection, we propose a game-theoretic crowdsourcing-based wireless data collection mechanism that can be used to reliably collect the data. We also propose a metric that can capture the value of the collected data for the considered model. Our proposed method provides protection against selfish deviations and unlike other game-theoretic works does not make an assumption that the actions of the crowdsourcing agent APs are known to the crowdsourcing entity. We consider a technique that can be used to infer the actions of agent APs and propose that the participants utilize the n-round Win-stay lose shift (WSLS) strategy. In our work, we compare the performance of the proposed strategy against various other game-theoretic strategies, such as always high, always low, WSLS with probability p, and tit for tat. In our performance evaluation, we utilize both real and synthetic wireless channel utilization data. Our results show that the n-round WSLS strategy outperforms the other game-theoretic strategies.
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- 2024
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12. Effect of Fermented Mulberry Leaves on Gut Health of Finishing Pigs
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Su Peng, Yiyan Cui, Miao Yu, Min Song, Zhimei Tian, Dun Deng, Zhichang Liu, and Xianyong Ma
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fattening pigs ,fermented mulberry leaves ,gut health ,nutrient digestibility ,Veterinary medicine ,SF600-1100 ,Zoology ,QL1-991 - Abstract
This study was conducted to investigate the effects of supplementing fermented mulberry leaves (FML) on intestinal morphology, antioxidant capacity, and immune function in the gut of finishing pigs. Eighteen 132-day-old healthy crossbred (Duroc × Landrace × Yorkshire) male castrated pigs were randomly divided into two treatment groups with nine replicates per group. The control (CON) group was fed the basal diet, and the FML group was fed the basal diet supplemented with 10% FML. The experiment lasted 69 days. The results showed that 10% FML improved gut health. The apparent total tract digestibility in dry matter, crude protein, crude fiber, neutral detergent fiber, acidic detergent fiber, ether extract, and crude ash increased in the 10% FML group of finishing pigs compared to the CON group (p < 0.05). Duodenal, jejunal, and ileal intestinal morphology, such as villus height and villus-height-to-crypt-depth ratio, increased in the 10% FML group compared to the CON group, whereas crypt depth decreased in the duodenum, jejunum, and ileum (p < 0.05). Total antioxidant capacity increased in the ileum of the 10% FML group compared with the CON group (p < 0.05). The FML supplementation improved the contents of duodenal immunoglobulin A, jejunal interleukin-1β, interleukin-8, ileal interleukin-1β, interleukin-6, interferon-γ, and immunoglobulins A and M compared to the control group (p < 0.05). Moreover, FML downregulated the mRNA expression levels of tumor necrosis factor-α in the duodenum, Toll-like receptor 4, nuclear factor-κ B-P65, and myeloid differentiation factor 88 in the jejunum, and Toll-like receptor 4 and nuclear factor-κ B-P65 in the ileum (p < 0.05). The FML also upregulated Montrose uniting church 1 in the duodenum and claudin 2 in the ileum (p < 0.05). In conclusion, dietary supplementation with 10% FML improved the gut health of finishing pigs and FML is a potential feed ingredient for pig breeding.
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- 2024
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13. Immune checkpoint inhibitors combined with concurrent chemoradiotherapy in locally advanced esophageal squamous cell carcinoma
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Jiang-Qiong Huang, Huan-Wei Liang, Yang Liu, Long Chen, Su Pei, Bin-Bin Yu, and Xin-Bin Pan
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esophageal squamous cell carcinoma ,immune checkpoint inhibitors ,concurrent chemoradiotherapy ,survival ,immunotherapy ,Immunologic diseases. Allergy ,RC581-607 - Abstract
PurposeThis study aims to evaluate the efficacy of immune checkpoint inhibitors (ICIs) combined with concurrent chemoradiotherapy (CCRT) versus CCRT alone in patients with locally advanced esophageal squamous cell carcinoma.Materials and methodsThis retrospective cohort study included patients diagnosed with locally advanced esophageal squamous cell carcinoma who received either CCRT alone or CCRT combined with ICIs from April 2019 to February 2023. The primary endpoint was progression-free survival (PFS), and the secondary endpoint was overall survival (OS).ResultsA total of 101 patients were enrolled, with 58 undergoing CCRT alone and 43 receiving CCRT+ICI. The CCRT+ICI group demonstrated a higher complete response rate compared to the CCRT alone group (11.6% vs. 1.7%, P = 0.037). However, no significant difference was observed in 1-year PFS (58.9% vs. 55.2%; hazard ratio [HR] = 1.26, 95% confidence interval [CI]: 0.70-2.26; P = 0.445) or 1-year OS (70.8% vs. 75.9%; HR = 1.21, 95% CI: 0.58-2.53; P = 0.613) between CCRT+ICI and CCRT alone groups. The CCRT alone group experienced a higher incidence of leukopenia of any grade (93.1% vs. 76.7%, P = 0.039) but a lower incidence of pneumonitis of any grade (36.2% vs. 65.1%, P = 0.008).ConclusionCCRT+ICI may not lead to improved survival outcomes compared to CCRT alone in patients with locally advanced esophageal squamous cell carcinoma. These findings indicate the need for further investigation into this treatment approach.
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- 2024
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14. Impact of Thrombolysis Time Metrics When Participating in National Stroke Center Construction Project
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Cheng W, Mofatteh M, Baizabal-Carvallo JF, Lu S, Su P, Chen Y, Li L, Qin L, Zuo X, Lan Y, Huang Y, Yu Z, Luo Z, and Chen G
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acute ischemic stroke ,intravenous thrombolysis ,door-to-needle time ,onset-to-needle time. ,Medicine (General) ,R5-920 - Abstract
Wanchun Cheng,1 Mohammad Mofatteh,2 José Fidel Baizabal-Carvallo,3,4 Shaohuan Lu,1 Ping Su,1 Yimin Chen,5,6 Luoming Li,1 Lizhi Qin,1 Xingmei Zuo,1 Yifeng Lan,7 Yue Huang,8 Zhihui Yu,9 Zirui Luo,10,* Gang Chen10,* 1Department of Neurology and National Stroke Center, The 5th People’s Hospital of Foshan City, Foshan, People’s Republic of China; 2School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast, UK; 3Parkinson’s Disease Center and Movement Disorders Clinic, Department of Neurology, Baylor College of Medicine, Houston, Texas, USA; 4Department of Sciences and Engineering, University of Guanajuato, León, Mexico; 5Department of Neurology and Advanced National Stroke Center, Foshan Sanshui District People’s Hospital, Foshan, People’s Republic of China; 6Neuro International Collaboration (NIC), Foshan, People’s Republic of China; 7Department of Radiology, the 5th People’s Hospital of Foshan City, Foshan, People’s Republic of China; 8Department of Emergency, the 5th People’s Hospital of Foshan City, Foshan, People’s Republic of China; 9Department of Laboratory Medicine, the 5th People’s Hospital of Foshan City, Foshan, People’s Republic of China; 10Dean’s Office, the 5th People’s Hospital of Foshan City, Foshan, People’s Republic of China*These authors contributed equally to this workCorrespondence: Zirui Luo; Gang Chen, Dean’s Office, the 5th People’s Hospital of Foshan City, Foshan, Guangdong Province, 528211, People’s Republic of China, Email ziruiluo@126.com; vikingsaga@163.comPurpose: Intravenous thrombolysis has emerged as an effective approach to improve the long-term survival and functional status of patients with ischemic stroke. The aim of this study was to assess the impact of a national stroke project on the door-to-needle-time (DNT).Patients and Methods: The patients were divided into pre-construction and construction periods. Construction Measures were performed during the construction period. The DNT and onset-to-needle time (ONT) were compared in two period groups.Results: After participating in the National Stroke Center Project and effective measurements, the thrombolysis treatment metrics were improved significantly. The DNT (IQR) was shortened from 65.0 (54.5,85.0) minutes in the Pre-Construction period to 40.0 (33.0,53.0) minutes in the Construction period (p < 0.001). Similarly, the ONT was reduced from 157.0 (IQR) (115.0,184.0) minutes to 116.0 (87.8,170.0) minutes (p = 0.035).Conclusion: The DNT time and ONT time can be shortened by National Stroke Center Construction projects. More suitable hospitals should be encouraged to participate as the National Stroke Center.Keywords: acute ischemic stroke, intravenous thrombolysis, door-to-needle time, onset-to-needle time
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- 2023
15. Combination immunotherapy of glioblastoma with dendritic cell cancer vaccines, anti-PD-1 and poly I:C
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Ping Zhu, Shi-You Li, Jin Ding, Zhou Fei, Sheng-Nan Sun, Zhao-Hui Zheng, Ding Wei, Jun Jiang, Jin-Lin Miao, San-Zhong Li, Xing Luo, Kui Zhang, Bin Wang, Kun Zhang, Su Pu, Qian-Ting Wang, Xin-Yue Zhang, Gao-Liu Wen, Jun O. Liu, John Thomas August, Huijie Bian, Zhi-Nan Chen, and You-Wen He
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Glioblastoma multiforme ,DC vaccine ,Tumor-associated antigens ,Neoantigens ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Glioblastoma (GBM) is a lethal cancer with limited therapeutic options. Dendritic cell (DC)-based cancer vaccines provide a promising approach for GBM treatment. Clinical studies suggest that other immunotherapeutic agents may be combined with DC vaccines to further enhance antitumor activity. Here, we report a GBM case with combination immunotherapy consisting of DC vaccines, anti-programmed death-1 (anti-PD-1) and poly I:C as well as the chemotherapeutic agent cyclophosphamide that was integrated with standard chemoradiation therapy, and the patient remained disease-free for 69 months. The patient received DC vaccines loaded with multiple forms of tumor antigens, including mRNA-tumor associated antigens (TAA), mRNA-neoantigens, and hypochlorous acid (HOCl)-oxidized tumor lysates. Furthermore, mRNA-TAAs were modified with a novel TriVac technology that fuses TAAs with a destabilization domain and inserts TAAs into full-length lysosomal associated membrane protein-1 to enhance major histocompatibility complex (MHC) class I and II antigen presentation. The treatment consisted of 42 DC cancer vaccine infusions, 26 anti-PD-1 antibody nivolumab administrations and 126 poly I:C injections for DC infusions. The patient also received 28 doses of cyclophosphamide for depletion of regulatory T cells. No immunotherapy-related adverse events were observed during the treatment. Robust antitumor CD4+ and CD8+ T-cell responses were detected. The patient remains free of disease progression. This is the first case report on the combination of the above three agents to treat glioblastoma patients. Our results suggest that integrated combination immunotherapy is safe and feasible for long-term treatment in this patient. A large-scale trial to validate these findings is warranted.
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- 2023
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16. Patient preference and acceptability of self-sampling for cervical screening in colposcopy clinic attenders: A cross-sectional semi-structured survey.
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Sophie Webb, Nafeesa Mat Ali, Amy Sawyer, David J Clark, Megan A Brown, Yolanda Augustin, Yin Ling Woo, Su Pei Khoo, Sally Hargreaves, Henry M Staines, Sanjeev Krishna, and Kevin Hayes
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Public aspects of medicine ,RA1-1270 - Abstract
Low vaginal self-sampling has been pioneered as an important development to improve uptake of cervical screening globally. Limited research is available in specific patient groups in the UK exploring views around self-sampling to detect high-risk human papillomavirus (hrHPV) DNA. Therefore, we explored patient views to support development of a novel point-of-care self-sampling cervical cancer screening device, by undertaking a cross-sectional semi-structured questionnaire survey to explore preferences, acceptability, barriers and facilitators around self-sampling. Patients attending a colposcopy clinic, 25-64 years old, were invited to participate after having carried out a low vaginal self-sample using a regular flocked swab. Participants self-completed an anonymous 12-point questionnaire. Quantitative data were analysed in MS Excel and Graphpad Prism, and qualitative data with Nvivo. We recruited 274 patients with a questionnaire response rate of 76%. Acceptability of self-sampling was high (95%, n = 187/197; Cronbachs-α = 0.778). Participants were asked their choice of future screening method: a) low vaginal self-sampling, b) healthcare professional collected vaginal swab, c) cervical brush sample with healthcare professional speculum examination, or d) no preference. Preferences were: a) 37% (n = 74/198), b) 19% (n = 37/198); c) 9% (n = 17/198), and d) 35% (n = 70/198), showing no single option as a strong preference. Key motivators were: Test simplicity (90%, n = 170/190), speed (81%, n = 153/190) and less pain (65%, n = 123/190). Barriers included lack of confidence taking the sample (53%, n = 10/19), resulting in preference for a healthcare professional sample (47%, n = 9/19). Whilst self-sampling showed high acceptability, lack of strong preference for screening method may reflect that respondents attending colposcopy are already engaged with screening and have differing perception of cervical cancer risk. This group appear less likely to 'switch' to self-sampling, and it may be better targeted within primary and community care, focusing on under-screened populations. Any shift in this paradigm in the UK requires comprehensive education and support for patients and providers.
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- 2024
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17. Analysis of using big data to achieve precise employment of college students
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Su Peng
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collaborative filtering ,employment recommendation platform ,clustering technology ,precise employment ,97q70 ,Mathematics ,QA1-939 - Abstract
In order to solve the social phenomenon of difficult employment of college students, this paper constructs a college employment recommendation platform based on an improved collaborative filtering algorithm. Firstly, the framework of the employment recommendation system is designed for the user use case, system architecture, system function module, database and user portrait of the platform, respectively. The functional modules are divided into six module parts: campus recruitment management, internship management, employment information management, employment affairs management, employment analysis management, and employment guidance management. Next, the platform is analyzed to combine the recommendation algorithm of collaborative filtering algorithm and clustering technology based on users and items to realize the recommendation of employment information for college students. Then the employment platform is used to get the high stickiness of users in the employment of college students, which can retain about 62% of the number of people, and even 5% of the number of people are particularly fond of the platform. With the increase in recommendation times, the recommendation accuracy increased from 22% to 83%, and the accuracy increased by about 61%, and the comparison with the traditional employment form is much higher than the traditional method in both accuracy and recall rate by about 30%. It is concluded that using the college employment recommendation platform can realize the precise employment of college students.
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- 2024
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18. Spatial effect of digital inclusive finance on agricultural carbon emission intensity and mechanism
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SU Peitian, WANG Lei
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digital inclusive finance ,agricultural carbon emission intensity ,spatial spillover effect ,impact mechanism ,dynamic spatial durbin model ,china ,Environmental sciences ,GE1-350 ,Biology (General) ,QH301-705.5 - Abstract
[Objective] With the deepening of the digital economy, digital inclusive finance has become an important path for reducing the carbon emission intensity of agriculture. This study explored the relationship between digital inclusive finance and the carbon emission intensity of agriculture. [Methods] Starting from the spatial effects and influencing mechanisms, this study used digital inclusive finance and agricultural carbon emission intensity data from 31 provinces in China from 2011 to 2020 and kernel density estimation and spatial autocorrelation analysis of the characteristics of agricultural carbon emission intensity, the dynamic spatial Durbin model, and mediation effect models to analyze the impact of digital inclusive finance on agricultural carbon emission intensity. [Results] The results show that: (1) The level of agricultural carbon emission intensity was continuously decreasing, with strong spatial heterogeneity and clear agglomeration characteristics, while the spatial pattern of digital inclusive finance and agricultural carbon emission intensity exhibited certain mismatch in their development. (2) Digital inclusive finance had a significant negative effect on agricultural carbon emission intensity, and the conclusion is robust based on endogeneity tests, sample exclusions, and policy effect tests. Broader coverage and higher level of digitalization of digital inclusive finance can significantly inhibit agricultural carbon emission intensity. (3) Digital inclusive finance affects the level of agricultural carbon emissions by influencing the level of agricultural technology innovation and entrepreneurial activities, and urbanization and marketization levels have significant moderating effects that can strengthen the carbon reduction effect of digital finance. [Conclusion] It is recommended to further optimize the organizational model of digital inclusive finance to support low-carbon agricultural development, strengthen the construction of digital infrastructure to stimulate agricultural innovation and entrepreneurship, and thus promote the development of low-carbon agriculture.
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- 2023
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19. A Roadmap of CAR-T-Cell Therapy in Glioblastoma: Challenges and Future Perspectives
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Megan Montoya, Marco Gallus, Su Phyu, Jeffrey Haegelin, John de Groot, and Hideho Okada
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glioblastoma ,CAR-T-cell therapy ,tumor immune microenvironment ,Cytology ,QH573-671 - Abstract
Glioblastoma (GBM) is the most common primary malignant brain tumor, with a median overall survival of less than 2 years and a nearly 100% mortality rate under standard therapy that consists of surgery followed by combined radiochemotherapy. Therefore, new therapeutic strategies are urgently needed. The success of chimeric antigen receptor (CAR) T cells in hematological cancers has prompted preclinical and clinical investigations into CAR-T-cell treatment for GBM. However, recent trials have not demonstrated any major success. Here, we delineate existing challenges impeding the effectiveness of CAR-T-cell therapy for GBM, encompassing the cold (immunosuppressive) microenvironment, tumor heterogeneity, T-cell exhaustion, local and systemic immunosuppression, and the immune privilege inherent to the central nervous system (CNS) parenchyma. Additionally, we deliberate on the progress made in developing next-generation CAR-T cells and novel innovative approaches, such as low-intensity pulsed focused ultrasound, aimed at surmounting current roadblocks in GBM CAR-T-cell therapy.
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- 2024
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20. Tumor Suppressor MicroRNAs in Clinical and Preclinical Trials for Neurological Disorders
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Austin Lui, Timothy Do, Omar Alzayat, Nina Yu, Su Phyu, Hillary Joy Santuya, Benjamin Liang, Vidur Kailash, Dewey Liu, Sabra S. Inslicht, Kiarash Shahlaie, and DaZhi Liu
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tumor suppressor microRNA ,cancers ,neurological disorders ,Medicine ,Pharmacy and materia medica ,RS1-441 - Abstract
Cancers and neurological disorders are two major types of diseases in humans. We developed the concept called the “Aberrant Cell Cycle Disease (ACCD)” due to the accumulating evidence that shows that two different diseases share the common mechanism of aberrant cell cycle re-entry. The aberrant cell cycle re-entry is manifested as kinase/oncoprotein activation and tumor suppressor (TS) inactivation, which are associated with both tumor growth in cancers and neuronal death in neurological disorders. Therefore, some cancer therapies (e.g., kinase/oncogene inhibition and TS elevation) can be leveraged for neurological treatments. MicroRNA (miR/miRNA) provides a new style of drug-target binding. For example, a single tumor suppressor miRNA (TS-miR/miRNA) can bind to and decrease tens of target kinases/oncogenes, producing much more robust efficacy to block cell cycle re-entry than inhibiting a single kinase/oncogene. In this review, we summarize the miRNAs that are altered in both cancers and neurological disorders, with an emphasis on miRNA drugs that have entered into clinical trials for neurological treatment.
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- 2024
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21. 956 Glioma-neuronal circuit remodeling induces regional immunosuppression
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Hideho Okada, Tiffany Chen, Takahide Nejo, Akane Yamamichi, Shawn L Hervey-Jumper, Su Phyu, Saritha Krishna, Christian Jimenez, Jacob S Young, Senthilnath Lakshmanachetty, Payal Watchmaker, Abrar Choudhury, and David R Raleigh
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Published
- 2023
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22. 277 SynNotch-CAR T cells demonstrate potent anti-tumor efficacy in a preclinical immunocompetent mouse model for glioblastoma
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Yuan Wang, Hideho Okada, Wendell Lim, Tiffany Chen, Takahide Nejo, Akane Yamamichi, Su Phyu, Senthilnath Lakshmanachetty, Payal Watchmaker, Milos Simic, Chanelle Shepherd, Jason Duecker, Olga Troyanskaya, Lia Cardarelli, and Sachdev Sidhu
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Published
- 2023
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23. 843 Intra-cerebral spinal fluid vaccination to facilitate antigen-specific T-cell responses in the CNS tumors
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Hideho Okada, Tiffany Chen, Akane Yamamichi, Naozumi Harada, Su Phyu, and Akinari Kazuyoshi
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Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Published
- 2023
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24. Reusing Effluent Water in Drainage Ditches for Irrigation in Hilly Regions
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SHAO Peiyin, LI Yalong, XIONG Yujiang, YUAN Niannian, and SU Peilan
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hilly irrigated area ,circular irrigation ,nitrogen and phosphorus load ,rice ,water saving and pollutant reduction ,Agriculture (General) ,S1-972 ,Irrigation engineering. Reclamation of wasteland. Drainage ,TC801-978 - Abstract
【Objective】 Most hilly regions in China are short of freshwater resources and recycling the effluent water in their drainage ditches is a way to relieve this pressure and improve water use efficiency. This paper investigates how reusing the effluent water for irrigation affects leaching of nitrogen (N) and phosphorus (P) from soils. 【Method】 In-situ experiment was set up in a field to measure the change in water flow and N and P concentrations in the ditches and the ditch buckets. We calculated the ratio of recycled water volume to the volume of water pumped for irrigation (i.e., regression rate), as well as the change in N and P pollutant loads and their determinants. 【Result】 The water had been drained and reused for irrigations for 24 cycles during the growing season, and the total regression rate reached 89.93%. The loads of total P, total N, nitrate nitrogen and ammonia nitrogen during the growing season were 0.28, 3.27, 2.35 and 2.35 kg/hm2, respectively. The load reductions of P and N were correlated with the ratio of their concentrations in the effluent and in the irrigation water. The reduction in total P and ammonia was significantly correlated with the regression rate. The reduction in total N and nitrate was significantly correlated with irrigation and rainfall in the second day after the irrigation. Nitrate reduction rate was also significantly correlated with temperature. 【Conclusion】 The cycles of drainage and its reuse for irrigation not only saves water but also improves utilization of water and fertilizers, thereby reducing the risk of N and P pollution to the downstream.
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- 2023
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25. Therapeutic angiogenesis and tissue revascularization in ischemic vascular disease
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Xinyue Chen, Wenlu Yu, Jing Zhang, Xiao Fan, Xiao Liu, Qi Liu, Su Pan, Richard A. F. Dixon, Pengyang Li, Peng Yu, and Ao Shi
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Ischemic vascular disease ,Therapeutic angiogenesis ,Tissue revascularization ,Biological therapy ,Biology (General) ,QH301-705.5 - Abstract
Abstract Ischemic vascular disease is a major healthcare problem. The keys to treatment lie in vascular regeneration and restoration of perfusion. However, current treatments cannot satisfy the need for vascular regeneration to restore blood circulation. As biomedical research has evolved rapidly, a variety of potential alternative therapeutics has been explored widely, such as growth factor-based therapy, cell-based therapy, and material-based therapy including nanomedicine and biomaterials. This review will comprehensively describe the main pathogenesis of vascular injury in ischemic vascular disease, the therapeutic function of the above three treatment strategies, the corresponding potential challenges, and future research directions.
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- 2023
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26. Visual Analysis of Psychological Resilience Research Based on Web of Science Database
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Su P, Yi J, Chen X, and Xiao Y
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psychological resilience ,citespace ,hot spots ,visualization ,Psychology ,BF1-990 ,Industrial psychology ,HF5548.7-5548.85 - Abstract
Pan Su,1 Jindong Yi,1 Xiuwen Chen,2 Yao Xiao3– 5 1Teaching and Research Section of Clinical Nursing, Emergency Department, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 2Teaching and Research Section of Clinical Nursing, Department of Operating Room, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 3Department of General Surgery, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 4International Joint Research Center of Minimally Invasive Endoscopic Technology Equipment & Standards, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 5National Clinical Research Center for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People’s Republic of ChinaCorrespondence: Yao Xiao; Xiuwen Chen, Email yaoxiao@csu.edu.cn; chenxiuwen2020@163.comBackground: The importance of psychological resilience that people show in coping with stress and adversity is prominent, but few studies have used rigorous bibliometric tools to analyze the knowledge structure and distribution of psychological resilience research.Objective: The purpose of this study was to sort out and summarize the previous studies on psychological resilience by using bibliometrics. Specifically, the time distribution of psychological resilience research was determined by publication trend, the power distribution was determined by the distribution of countries, authors, institutions and journals, the hot research spots were analyzed according to the results of keyword cluster analysis, and the research frontier was explored according to the results of burst keywords.Methods: CiteSpace5.8.R3 was used to analyze the literatures on psychological resilience collected in Web of Science core Collection database from January 1, 2010, to June 16, 2022.Results: A total of 8462 literatures were included after screening. Research on psychological resilience has been on the rise in recent years. The United States had made a high contribution in this field. Robert H Pietrzak, George A Bonanno, Connor KM and others were highly influential. J Pers Soc Psychol has the highest citation frequency and centrality. The research hot spots focus on five aspects: study on psychological resilience related to COVID-19 pandemic, influencing factors of psychological resilience, psychological resilience related to PTSD, study on psychological resilience of special population, and the molecular biology and genetic basis of psychological resilience. Psychological resilience related to COVID-19 pandemic was the most cutting-edge research aspect.Conclusion: The current situation and trend of psychological resilience research were found in this study, which may be used to identify more hot issues and explore new research directions in this field.Keywords: psychological resilience, CiteSpace, hot spots, visualization
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- 2023
27. Hippo pathway inhibition promotes metabolic adaptability and antioxidant response in myoblasts
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Qi Liu, Su Pan, Pengyang Li, and Richard A. F. Dixon
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Medicine ,Science - Abstract
Abstract Metabolic plasticity in a hostile environment ensures cell survival. We investigated whether Hippo pathway inhibition contributed to cell adaptations under challenging conditions. We examined metabolic profiles and fuel substrate choices and preferences in C2C12 myoblasts after Hippo pathway inhibition via Salvador knockdown (SAV1 KD). SAV1 KD induced higher ATP production and a more energetic phenotype. Bioenergetic profiling showed enhanced key mitochondrial parameters including spare respiratory capacity. SAV1 KD cells showed markedly elevated glycolysis and glycolytic reserves; blocking other fuel-oxidation pathways enhanced mitochondrial flexibility of glucose oxidation. Under limited glucose, endogenous fatty acid oxidation increased to cope with bioenergetic stress. Gene expression patterns after SAV1 KD suggested transcriptional upregulation of key metabolic network regulators to promote energy production and free radical scavenging that may prevent impaired lipid and glucose metabolism. In SAV1 KD cells, sirtuin signaling was the top enriched canonical pathway linked with enhanced mitochondrial ATP production. Collectively, we demonstrated that Hippo pathway inhibition in SAV1 KD cells induces multiple metabolic properties, including enhancing mitochondrial spare respiratory capacity or glycolytic reserve to cope with stress and upregulating metabolic pathways supporting elevated ATP demand, bioenergetics, and glycolysis and counteracting oxidative stress. In response to metabolic challenges, SAV1 KD cells can increase fatty acid oxidation or glucose-coupled oxidative phosphorylation capacity to compensate for substrate limitations or alternative fuel oxidation pathway inhibition.
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- 2023
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28. Integrating nutriepigenomics in Parkinson’s disease management: New promising strategy in the omics era
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Khairiah Razali, Khaled Algantri, Su Peng Loh, Shi-Hui Cheng, and Wael Mohamed
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Parkinson’s disease ,Omics ,Nutriepigenomics ,Nutrigenetics ,Nutrigenomics ,Epigenetic mechanisms ,Neurosciences. Biological psychiatry. Neuropsychiatry ,RC321-571 - Abstract
Parkinson’s disease (PD) is the most prevalent brain motor disorder and is frequently regarded as an idiopathic and sporadic disease due to its unclear etiology. Although the pathological mechanisms of PD have already been investigated at various omics levels, no disease-modifying drugs are currently available. At the moment, treatments can only provide symptomatic relief to control or improve motor symptoms. Parkinson’s disease is a multifactorial disease, the development and progression of which are influenced by multiple factors, including the genetic markups and the environment. As an indispensable component of our daily life, nutrition is considered one of the most robust environmental factors affecting our health. Consequently, depending on our dietary habits, nutrition can either induce or reduce our susceptibility to PD. Epigenetic mechanisms regulate gene expression through DNA methylation, histone modifications, and non-coding RNAs (ncRNAs) activity. Accumulating evidence from nutriepigenomics studies has reported altered epigenetic mechanisms in clinical and pre-clinical PD models, and the potential role of nutrition in modifying the changes. In addition, through nutrigenetics and nutrigenomics studies, the diet-gene, and gene-diet interactions concerning PD development and progression have been investigated. Herein, current findings on the roles of nutrition in epigenetic mechanisms underpinning PD development and progression are discussed. Recent advancements in the multi-omics approach in PD nutrition research are also underlined. The ability of nutrients to influence epigenetic mechanisms and the availability of multi-omics applications compel the immediate use of personalized nutrition as adjuvant therapy for PD.
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- 2022
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29. Applications of carbon dots and its modified carbon dots in bone defect repair
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Longchuan Zhu, Weijian Kong, Jijun Ma, Renfeng Zhang, Cheng Qin, Hao Liu, and Su Pan
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Bone defect repair ,Carbon dots ,Antibacterial ,Anti-infective ,Good biocompatibility ,Alkaline phosphatase ,Biology (General) ,QH301-705.5 - Abstract
Abstract Bone defect repair is a continual and complicated process driven by a variety of variables. Because of its bright multicolor luminescence, superior biocompatibility, water dispersibility, and simplicity of synthesis from diverse carbon sources, carbon dots (CDs) have received a lot of interest. It has a broad variety of potential biological uses, including bone defect repair, spinal cord injury, and wound healing. Materials including CDs as the matrix or major component have shown considerable benefits in enabling bone defect healing in recent years. By altering the carbon dots or mixing them with other wound healing-promoting agents or materials, the repair effect may be boosted even further. The report also shows and discusses the use of CDs to heal bone abnormalities. The study first presents the fundamental features of CDs in bone defect healing, then provides CDs manufacturing techniques that should be employed in bone defect repair, and lastly examines their development in the area of bioengineering, particularly in bone defect repair. In this work, we look at how carbon dots and their alteration products may help with bone defect healing by being antibacterial, anti-infective, osteogenic differentiation-promoting, and gene-regulating.
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- 2022
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30. Peripheral Blood Lymphocyte Subsets as a Risk Predictor of Patients with Endometrioid Endometrial Cancer
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Su P, An J, Yu L, Lei H, Huang L, Mao X, and Sun P
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cd4+ t lymphocyte ,nk cells ,endometrioid endometrial cancer ,lymphocyte subsets ,immune ,Pathology ,RB1-214 ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Pingping Su,1 Jian An,1,2 Lirui Yu,1 Huifang Lei,1 Lixiang Huang,1 Xiaodan Mao,1,3 Pengming Sun1,3 1Department of Gynecology, Laboratory of Gynecologic Oncology, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, Fujian, 350001, People’s Republic of China; 2Department of Gynecology, Women and Children’s Hospital, School of Medicine, Xiamen University, Xiamen, People’s Republic of China; 3Fujian Key Laboratory of Women and Children’s Critical Diseases Research, Fujian Maternity and Child Health Hospital, College of Clinical Medicine for Obstetrics & Gynecology and Pediatrics, Fujian Medical University, Fuzhou, Fujian, 350001, People’s Republic of ChinaCorrespondence: Pengming Sun; Xiaodan Mao, Fujian Maternity and Child Health Hospital, Affiliated Hospital of Fujian Medical University, Fuzhou, Fujian, 350001, People’s Republic of China, Tel +86-591-87558732, Fax +86-591-87551247, Email fmsun1975@fjmu.edu.cn; maodan1985@yeah.netPurpose: This study aimed to explore lymphocyte subsets for the personalized prediction of endometrioid endometrial cancer (EEC) risk and evaluated the correlation between immune cells and International Federation of Gynecology and Obstetrics (FIGO) staging in patients with EEC.Patients and Methods: A case-control study was conducted to analyze the clinical data of 421 patients admitted to Fujian Maternity and Child Health Hospital from October 2017 to December 2021. t-tests or Mann–Whitney U-tests were used to analyze the percentages and absolute counts of peripheral blood lymphocyte subsets in patients with EEC and patients without cancer. The independent risk factors for ECC and FIGO stage were analyzed via multivariate binary logistic regression. The receiver operating characteristic curve was calculated to evaluate the prediction efficacy of risk factors on ECC.Results: The CD4+ T% in the 121 patients with EEC was lower than in the 300 patients without cancer (P = 0.013). The absolute counts of peripheral CD4+ T (P = 0.002) and T cells (P = 0.007) in 37 patients with EEC were lower than in 51 patients without cancer. Multivariate binary logistic regression analysis showed that CD4+ T% and natural killer cell (NK)% were independent risk factors for FIGO staging in patients with EEC. NK% was significantly higher in patients with advanced stage (FIGO III and IV) than those with early EEC (FIGO I and II) (P = 0.004). To determine the early and advance FIGO stage of EEC, the cutoff value of NK% was calculated as 19.94%.Conclusion: With the decrease of CD4+ T counts, the immune status of patients with EEC is impaired. NK cells may help in the evaluation of the prognosis of patients with EEC and are likely to be an independent risk factor for FIGO staging in patients with EEC.Keywords: CD4+ T lymphocyte, NK cells, endometrioid endometrial cancer, lymphocyte subsets, immune
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- 2022
31. Multidisciplinary Treatment Strategy for Early Colon Cancer: A Review-An English Version
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Gyung Son, Su Park, Tae Kim, Byung-Soo Park, In Lee, Joo-Young Na, Dong Shin, Sang Oh, Sung Cho, Hyun Kim, and Hyung Kim
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colonic neoplasms ,lymph nodes ,endoscopic mucosal resection ,minimally invasive surgical procedures ,laparoscopy ,Diseases of the digestive system. Gastroenterology ,RC799-869 - Abstract
Treatment for early colon cancer has progressed rapidly, with endoscopic resection and minimally invasive surgery. It is important to select patients without risk of lymph node metastasis before deciding on endoscopic resection for early colon cancer treatment. Pathological risk factors include histologic grade of cancer cell differentiation, lymphovascular invasion, perineural invasion, tumor budding, and deep submucosal invasion. These risk factors for predicting lymph node metastasis are crucial for determining the treatment strategy of endoscopic excision and radical resection for early colon cancer. A multidisciplinary approach is emphasized to establish a treatment strategy for early colon cancer to minimize the risk of complications and obtain excellent oncologic outcomes by selecting an appropriate treatment optimized for the patient's stage and condition. Therefore, we aimed to review the optimal multidisciplinary treatment strategies, including endoscopy and surgery, for early colon cancer.
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- 2022
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32. The Implementation of a Primary HPV Self-Testing Cervical Screening Program in Malaysia through Program ROSE—Lessons Learnt and Moving Forward
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Yin Ling Woo, Su Pei Khoo, Patti Gravitt, David Hawkes, Reena Rajasuriar, and Marion Saville
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self-sampling ,cervical screening ,HPV testing ,cervical cancer elimination ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Program ROSE (removing obstacles to cervical screening) is a primary HPV-based cervical screening program that incorporates self-sampling and digital technology, ensuring that women are linked to care. It was developed based on the principles of design thinking in the context of Malaysia. The program illustrates the importance of collaborative partnerships and addressing the multi-faceted barriers from policy changes, and infrastructure readiness to the implementation of a radically new cervical screening program in communities. The paradigm shift in cervical cancer requires a monumental and concerted effort in educating both the healthcare providers and the general public. In this short review, we highlight how Pilot Project ROSE incorporated evidence-based tools that rapidly scaled up to Program ROSE. These ideas and solutions can be adapted and adopted by other countries. Notwithstanding the impact of COVID-19, it is incumbent on countries to pave the road towards the elimination of cervical cancer with pre-existing footpaths.
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- 2022
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33. Cross‐domain extendable gesture recognition system using WiFi signals
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Yuxi Qin, Su Pan, and Zibo Li
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gesture recognition ,sensors ,wireless channels ,Electrical engineering. Electronics. Nuclear engineering ,TK1-9971 - Abstract
Abstract This letter proposes a cross‐domain WiFi‐based gesture recognition system (WiCross) based on a dynamically weighted multi‐label generative adversarial network. Most existing WiFi‐based gesture recognition systems are user, orientation, and environment sensitive, which limits the application of WiFi sensing. Compared with the influence of users and environments on WiFi sensing systems, the influence of orientation on WiFi sensing systems is more difficult to remove. To alleviate the confusion caused by the orientation more effectively, we arrange the transmitting and receiving antennas according to the characteristics of the Fresnel region. It is proposed to dynamically weight different links according to users' orientations and use a multi‐label generative adversarial network to obtain domain‐independent features. More importantly, WiCross can use domain‐independent features to classify some unknown gestures without modifying any code or dataset. Lightweight computing resource consumption allows WiCross to respond in real time. The experimental results show that WiCross can achieve an in‐domain recognition accuracy of 93.54% and a cross‐domain recognition accuracy of 93.11%.
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- 2023
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34. Multimodal molecular landscape of response to Y90-resin microsphere radioembolization followed by nivolumab for advanced hepatocellular carcinoma
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David Tai, Fei Yao, Joycelyn Lee, Su Pin Choo, Joe Yeong, Jeffrey Chun Tatt Lim, Han Chong Toh, Xinru Lim, Jiangfeng Ye, Denise Goh, Tony Kiat-Hon Lim, Mai Chan Lau, Timothy Wai Ho Shuen, Weiwei Zhai, Jia Qi Lim, Lawrence Cheung, Neslihan Arife Kaya, Cheryl Zi Jin Phua, Felicia Yu Ting Wee, Craig Ryan Joseph, Zhen Wei Neo, Kelvin S H Loke, Apoorva Gogna, May Yin Lee, Axel Hilmer, Yun Shen Chan, and Wai Leong Tam
- Subjects
Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Background Combination therapy with radioembolization (yttrium-90)-resin microspheres) followed by nivolumab has shown a promising response rate of 30.6% in a Phase II trial (CA209-678) for advanced hepatocellular carcinoma (HCC); however, the response mechanisms and relevant biomarkers remain unknown.Methods By collecting both pretreatment and on-treatment samples, we performed multimodal profiling of tissue and blood samples and investigated molecular changes associated with favorable responses in 33 patients from the trial.Results We found that higher tumor mutation burden, NCOR1 mutations and higher expression of interferon gamma pathways occurred more frequently in responders. Meanwhile, non-responders tended to be enriched for a novel Asian-specific transcriptomic subtype (Kaya_P2) with a high frequency of chromosome 16 deletions and upregulated cell cycle pathways. Strikingly, unlike other cancer types, we did not observe any association between T-cell populations and treatment response, but tumors from responders had a higher proportion of CXCL9+/CXCR3+ macrophages. Moreover, biomarkers discovered in previous immunotherapy trials were not predictive in the current cohort, suggesting a distinctive molecular landscape associated with differential responses to the combination therapy.Conclusions This study unraveled extensive molecular changes underlying distinctive responses to the novel treatment and pinpointed new directions for harnessing combination therapy in patients with advanced HCC.
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- 2023
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35. Immunohistochemical scoring of LAG-3 in conjunction with CD8 in the tumor microenvironment predicts response to immunotherapy in hepatocellular carcinoma
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Chun Chau Lawrence Cheung, Yong Hock Justin Seah, Juntao Fang, Nicole Hyacinth Calpatura Orpilla, Mai Chan Lau, Chun Jye Lim, Xinru Lim, Justina Nadia Li Wen Lee, Jeffrey Chun Tatt Lim, Sherlly Lim, Qing Cheng, Han Chong Toh, Su Pin Choo, Suat Ying Lee, Joycelyn Jie Xin Lee, Jin Liu, Tony Kiat Hon Lim, David Tai, and Joe Yeong
- Subjects
LAG-3 ,CD8 ,immunotherapy ,immune checkpoint blockade ,biomarkers ,hepatocellular carcinoma ,Immunologic diseases. Allergy ,RC581-607 - Abstract
IntroductionImmune checkpoint blockade (ICB) is a systemic therapeutic option for advanced hepatocellular carcinoma (HCC). However, low patient response rates necessitate the development of robust predictive biomarkers that identify individuals who will benefit from ICB. A 4-gene inflammatory signature, comprising CD8, PD-L1, LAG-3, and STAT1, was recently shown to be associated with a better overall response to ICB in various cancer types. Here, we examined whether tissue protein expression of CD8, PD-L1, LAG-3, and STAT1 predicts response to ICB in HCC.MethodsHCC samples from 191 Asian patients, comprising resection specimens from 124 patients (ICB-naïve) and pre-treatment specimens from 67 advanced HCC patients treated with ICB (ICB-treated), were analyzed for CD8, PD-L1, LAG-3, and STAT1 tissue expression using multiplex immunohistochemistry followed by statistical and survival analyses.ResultsImmunohistochemical and survival analyses of ICB-naïve samples showed that high LAG-3 expression was associated with shorter median progression-free survival (mPFS) and overall survival (mOS). Analysis of ICB-treated samples revealed that high proportions of LAG-3+ and LAG-3+CD8+ cells pre-treatment were most closely associated with longer mPFS and mOS. Using a log-likelihood model, adding the total LAG-3+ cell proportion to the total CD8+ cell proportion significantly increased the predictive values for mPFS and mOS, compared with the total CD8+ cell proportion alone. Moreover, levels of CD8 and STAT1, but not PD-L1, were significantly correlated with better responses to ICB. After analyzing viral-related and non-viral HCC samples separately, only the LAG3+CD8+ cell proportion was significantly associated with responses to ICB regardless of viral status.ConclusionImmunohistochemical scoring of pre-treatment levels of LAG-3 and CD8 in the tumor microenvironment may help predict ICB benefits in HCC patients. Furthermore, immunohistochemistry-based techniques offer the advantage of being readily translatable in the clinical setting.
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- 2023
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36. The Role of Neuro-Immune Interactions in Chronic Pain: Implications for Clinical Practice
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Su PP, Zhang L, He L, Zhao N, and Guan Z
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neuroimmune ,chronic pain ,glial cells ,macrophage ,t-cells ,Medicine (General) ,R5-920 - Abstract
Po-Yi Paul Su,1 Lingyi Zhang,1,2 Liangliang He,1,3 Na Zhao,1 Zhonghui Guan1 1Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA; 2Department of Anesthesiology, the First Affiliated Hospital, Sun Yat-Sen University, Guangzhou, People’s Republic of China; 3Department of Pain Management, Xuanwu Hospital, Capital Medical University, Beijing, People’s Republic of ChinaCorrespondence: Zhonghui Guan, Department of Anesthesia and Perioperative Care, University of California San Francisco, San Francisco, CA, USA, Tel +415.885.7246, Fax +415.885.7575, Email zhonghui.guan@ucsf.eduAbstract: Chronic pain remains a public health problem and contributes to the ongoing opioid epidemic. Current pain management therapies still leave many patients with poorly controlled pain, thus new or improved treatments are desperately needed. One major challenge in pain research is the translation of preclinical findings into effective clinical practice. The local neuroimmune interface plays an important role in the initiation and maintenance of chronic pain and is therefore a promising target for novel therapeutic development. Neurons interface with immune and immunocompetent cells in many distinct microenvironments along the nociceptive circuitry. The local neuroimmune interface can modulate the activity and property of the neurons to affect peripheral and central sensitization. In this review, we highlight a specific subset of many neuroimmune interfaces. In the central nervous system, we examine the interface between neurons and microglia, astrocytes, and T lymphocytes. In the periphery, we profile the interface between neurons in the dorsal root ganglion with T lymphocytes, satellite glial cells, and macrophages. To bridge the gap between preclinical research and clinical practice, we review the preclinical studies of each neuroimmune interface, discuss current clinical treatments in pain medicine that may exert its action at the neuroimmune interface, and highlight opportunities for future clinical research efforts.Keywords: neuroimmune, chronic pain, glial cells, macrophage, T-cells
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- 2022
37. PDA-Based Drug Delivery Nanosystems: A Potential Approach for Glioma Treatment
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Wu H, Wei M, Xu Y, Li Y, Zhai X, Su P, Ma Q, and Zhang H
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glioma ,polydopamine ,polymeric nanoparticles ,photothermal therapy ,chemotherapy ,synergistic therapy ,Medicine (General) ,R5-920 - Abstract
Hao Wu,1 Min Wei,1 Yu Xu,2 Yuping Li,3 Xue Zhai,4 Peng Su,4 Qiang Ma,3 Hengzhu Zhang3 1Neurosurgery, Graduate School of Dalian Medical University, Dalian, People’s Republic of China; 2Nanotechnology, Jinling Institute of Technology, Nanjing, People’s Republic of China; 3Department of Neurosurgery, Clinical Medical College, Yangzhou University, Yangzhou, People’s Republic of China; 4Department of Advanced Materials, Nanjing University of Posts & Telecommunications, Nanjing, People’s Republic of ChinaCorrespondence: Hengzhu Zhang, 98 Nantong Xi Lu, Yangzhou, Jiangsu Province, People’s Republic of China, Tel +86 18051061558, Fax +86-0514-87373562, Email zhanghengzhu@sina.comAbstract: Glioma is characterized by high mortality and low postoperative survival. Despite the availability of various therapeutic approaches and molecular typing, the treatment failure rate and the recurrence rate of glioma remain high. Given the limitations of existing therapeutic tools, nanotechnology has emerged as an alternative treatment option. Nanoparticles, such as polydopamine (PDA)-based nanoparticles, are embodied with reliable biodegradability, efficient drug loading rate, relatively low toxicity, considerable biocompatibility, excellent adhesion properties, precisely targeted delivery, and strong photothermal conversion properties. Therefore, they can further enhance the therapeutic effects in patients with glioma. Moreover, polydopamine contains pyrocatechol, amino and carboxyl groups, active double bonds, catechol, and other reactive groups that can react with biofunctional molecules containing amino, aldehyde, or sulfhydryl groups (main including, self-polymerization, non-covalent self-assembly, π-π stacking, electrostatic attraction interaction, chelation, coating and covalent co-assembly), which form a reversible dynamic covalent Schiff base bond that is extremely sensitive to pH values. Meanwhile, PDA has excellent adhesion capability that can be further functionally modified. Consequently, the aim of this review is to summarize the application of PDA-based NPs in glioma and to acquire insight into the therapeutic effect of the drug-loaded PDA-based nanocarriers (PDA NPs). A wealthy understanding and argument of these sides is anticipated to afford a better approach to develop more reasonable and valid PDA-based cancer nano-drug delivery systems. Finally, we discuss the expectation for the prospective application of PDA in this sphere and some individual viewpoints.Keywords: glioma, polydopamine, polymeric nanoparticles, photothermal therapy, chemotherapy, synergistic therapy
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- 2022
38. A first-in-human phase 1/2 study of FGF401 and combination of FGF401 with spartalizumab in patients with hepatocellular carcinoma or biomarker-selected solid tumors
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Stephen L. Chan, Martin Schuler, Yoon-Koo Kang, Chia-Jui Yen, Julien Edeline, Su Pin Choo, Chia-Chi Lin, Takuji Okusaka, Karl-Heinz Weiss, Teresa Macarulla, Stéphane Cattan, Jean-Frederic Blanc, Kyung-Hun Lee, Michela Maur, Shubham Pant, Masatoshi Kudo, Eric Assenat, Andrew X. Zhu, Thomas Yau, Ho Yeong Lim, Jordi Bruix, Andreas Geier, Carmen Guillén-Ponce, Angelica Fasolo, Richard S. Finn, Jia Fan, Arndt Vogel, Shukui Qin, Markus Riester, Vasiliki Katsanou, Monica Chaudhari, Tomoyuki Kakizume, Yi Gu, Diana Graus Porta, Andrea Myers, and Jean-Pierre Delord
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Hepatocellular carcinoma ,FGFR4 ,PD-1 ,PD-L1 ,Immune checkpoint inhibitors ,Phase 1 ,Neoplasms. Tumors. Oncology. Including cancer and carcinogens ,RC254-282 - Abstract
Abstract Background Deregulation of FGF19-FGFR4 signaling is found in several cancers, including hepatocellular carcinoma (HCC), nominating it for therapeutic targeting. FGF401 is a potent, selective FGFR4 inhibitor with antitumor activity in preclinical models. This study was designed to determine the recommended phase 2 dose (RP2D), characterize PK/PD, and evaluate the safety and efficacy of FGF401 alone and combined with the anti-PD-1 antibody, spartalizumab. Methods Patients with HCC or other FGFR4/KLB expressing tumors were enrolled. Dose-escalation was guided by a Bayesian model. Phase 2 dose-expansion enrolled patients with HCC from Asian countries (group1), non-Asian countries (group2), and patients with other solid tumors expressing FGFR4 and KLB (group3). FGF401 and spartalizumab combination was evaluated in patients with HCC. Results Seventy-four patients were treated in the phase I with single-agent FGF401 at 50 to 150 mg. FGF401 displayed favorable PK characteristics and no food effect when dosed with low-fat meals. The RP2D was established as 120 mg qd. Six of 70 patients experienced grade 3 dose-limiting toxicities: increase in transaminases (n = 4) or blood bilirubin (n = 2). In phase 2, 30 patients in group 1, 36 in group 2, and 20 in group 3 received FGF401. In total, 8 patients experienced objective responses (1 CR, 7 PR; 4 each in phase I and phase II, respectively). Frequent adverse events (AEs) were diarrhea (73.8%), increased AST (47.5%), and ALT (43.8%). Increase in levels of C4, total bile acid, and circulating FGF19, confirmed effective FGFR4 inhibition. Twelve patients received FGF401 plus spartalizumab. RP2D was established as FGF401 120 mg qd and spartalizumab 300 mg Q3W; 2 patients reported PR. Conclusions At biologically active doses, FGF401 alone or combined with spartalizumab was safe in patients with FGFR4/KLB-positive tumors including HCC. Preliminary clinical efficacy was observed. Further clinical evaluation of FGF401 using a refined biomarker strategy is warranted. Trial registration NCT02325739 .
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- 2022
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39. The Component and Functional Pathways of Gut Microbiota Are Altered in Populations with Poor Sleep Quality – A Preliminary Report
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Zhang Jianghui, Zhang Xueqing, Zhang Kexin, Lu Xiaoyan, Yuan Guojing, Yang Huayu, Guo Haiyun, Zhu Zhihui, Wang Tianli, Hao Jiahu, Sun Ying, Su Puyu, and Zhang Zhihua
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sleep disorder ,poor sleep quality ,gut microbiota ,16s rrna ,college students ,Genetics ,QH426-470 ,Microbiology ,QR1-502 - Abstract
With the development of genome sequencing, many researchers have investigated the mechanism by which the intestinal microbiota influences sleep across the brain-gut axis. However, the relationship between gut microbiota and sleep disorder remains unclear. Thus, we studied the difference in gut microbiota composition between poor sleep quality- and normal populations, which helps set the ground for future research. The recruited college students provided baseline information and stool samples and completed the Pittsburgh Sleep Quality Index (PSQI). We compared the two groups’ gut microbiota composition and functional differentiation by using the 16S rRNA gene sequencing analysis. The main bacterial difference and the most critical effect were mainly concentrated within Tenericutes and Elusimicrobia. Compared with the healthy control group, some functions of the gut microbiota were impaired in the poor sleep quality group, such as butanoate metabolism and propanoate metabolism. Bacterial taxa with significant differences raised the possibility for future diagnosis and treatment of sleep problems.
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- 2022
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40. Study of the light scalar $a_{0}(980)$ through the decay $D^{0} \to a_{0}(980)^-e^{+} \nu_{e}$ with $a_{0}(980)^- \to \eta \pi^-$
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
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High Energy Physics - Experiment - Abstract
Using 7.93 ${\rm fb^{-1}}$ of $e^+e^-$ collision data collected at a center-of-mass energy of 3.773 ${\rm GeV}$ with the BESIII detector, we present an analysis of the decay $D^{0} \to \eta \pi^- e^+ \nu_{e}$. The branching fraction of the decay $D^{0} \to a_{0}(980)^{-} e^+ \nu_{e}$ with $a_{0}(980)^{-} \to \eta \pi^{-}$ is measured to be $(0.86\pm0.17_{\text{stat}}\pm0.05_{\text{syst}})\times 10^{-4}$. The decay dynamics of this process is studied with a single-pole parameterization of the hadronic form factor and the Flatt\'e formula describing the $a_0(980)$ line shape in the differential decay rate. The product of the form factor $f^{ a_0}_{+}(0)$ and the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cd}|$ is determined for the first time with the result $f^{ a_0}_+(0)|V_{cd}|=0.126\pm0.013_{\rm stat}\pm0.003_{\rm syst}$.
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- 2024
41. Golden Touchstone: A Comprehensive Bilingual Benchmark for Evaluating Financial Large Language Models
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Wu, Xiaojun, Liu, Junxi, Su, Huanyi, Lin, Zhouchi, Qi, Yiyan, Xu, Chengjin, Su, Jiajun, Zhong, Jiajie, Wang, Fuwei, Wang, Saizhuo, Hua, Fengrui, Li, Jia, and Guo, Jian
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Computer Science - Computation and Language ,Computer Science - Computational Engineering, Finance, and Science - Abstract
As large language models become increasingly prevalent in the financial sector, there is a pressing need for a standardized method to comprehensively assess their performance. However, existing finance benchmarks often suffer from limited language and task coverage, as well as challenges such as low-quality datasets and inadequate adaptability for LLM evaluation. To address these limitations, we propose "Golden Touchstone", the first comprehensive bilingual benchmark for financial LLMs, which incorporates representative datasets from both Chinese and English across eight core financial NLP tasks. Developed from extensive open source data collection and industry-specific demands, this benchmark includes a variety of financial tasks aimed at thoroughly assessing models' language understanding and generation capabilities. Through comparative analysis of major models on the benchmark, such as GPT-4o Llama3, FinGPT and FinMA, we reveal their strengths and limitations in processing complex financial information. Additionally, we open-sourced Touchstone-GPT, a financial LLM trained through continual pre-training and financial instruction tuning, which demonstrates strong performance on the bilingual benchmark but still has limitations in specific tasks.This research not only provides the financial large language models with a practical evaluation tool but also guides the development and optimization of future research. The source code for Golden Touchstone and model weight of Touchstone-GPT have been made publicly available at \url{https://github.com/IDEA-FinAI/Golden-Touchstone}, contributing to the ongoing evolution of FinLLMs and fostering further research in this critical area., Comment: 26 pages, 9 tables, 3 figures
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- 2024
42. Model Predictive Control Enabled UAV Trajectory Optimization and Secure Resource Allocation
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Li, Zhendong, Su, Chang, Su, Zhou, Peng, Haixia, Wang, Yuntao, Chen, Wen, and Wu, Qingqing
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Electrical Engineering and Systems Science - Signal Processing - Abstract
In this paper, we investigate a secure communication architecture based on unmanned aerial vehicle (UAV), which enhances the security performance of the communication system through UAV trajectory optimization. We formulate a control problem of minimizing the UAV flight path and power consumption while maximizing secure communication rate over infinite horizon by jointly optimizing UAV trajectory, transmit beamforming vector, and artificial noise (AN) vector. Given the non-uniqueness of optimization objective and significant coupling of the optimization variables, the problem is a non-convex optimization problem which is difficult to solve directly. To address this complex issue, an alternating-iteration technique is employed to decouple the optimization variables. Specifically, the problem is divided into three subproblems, i.e., UAV trajectory, transmit beamforming vector, and AN vector, which are solved alternately. Additionally, considering the susceptibility of UAV trajectory to disturbances, the model predictive control (MPC) approach is applied to obtain UAV trajectory and enhance the system robustness. Numerical results demonstrate the superiority of the proposed optimization algorithm in maintaining accurate UAV trajectory and high secure communication rate compared with other benchmark schemes.
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- 2024
43. MuCol Milestone Report No. 5: Preliminary Parameters
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Accettura, Carlotta, Adrian, Simon, Agarwal, Rohit, Ahdida, Claudia, Aimé, Chiara, Aksoy, Avni, Alberghi, Gian Luigi, Alden, Siobhan, Alfonso, Luca, Amapane, Nicola, Amorim, David, Andreetto, Paolo, Anulli, Fabio, Appleby, Rob, Apresyan, Artur, Asadi, Pouya, Mahmoud, Mohammed Attia, Auchmann, Bernhard, Back, John, Badea, Anthony, Bae, Kyu Jung, Bahng, E. J., Balconi, Lorenzo, Balli, Fabrice, Bandiera, Laura, Barbagallo, Carmelo, Barlow, Roger, Bartoli, Camilla, Bartosik, Nazar, Barzi, Emanuela, Batsch, Fabian, Bauce, Matteo, Begel, Michael, Berg, J. Scott, Bersani, Andrea, Bertarelli, Alessandro, Bertinelli, Francesco, Bertolin, Alessandro, Bhat, Pushpalatha, Bianchi, Clarissa, Bianco, Michele, Bishop, William, Black, Kevin, Boattini, Fulvio, Bogacz, Alex, Bonesini, Maurizio, Bordini, Bernardo, de Sousa, Patricia Borges, Bottaro, Salvatore, Bottura, Luca, Boyd, Steven, Breschi, Marco, Broggi, Francesco, Brunoldi, Matteo, Buffat, Xavier, Buonincontri, Laura, Burrows, Philip Nicholas, Burt, Graeme Campbell, Buttazzo, Dario, Caiffi, Barbara, Calatroni, Sergio, Calviani, Marco, Calzaferri, Simone, Calzolari, Daniele, Cantone, Claudio, Capdevilla, Rodolfo, Carli, Christian, Carrelli, Carlo, Casaburo, Fausto, Casarsa, Massimo, Castelli, Luca, Catanesi, Maria Gabriella, Cavallucci, Lorenzo, Cavoto, Gianluca, Celiberto, Francesco Giovanni, Celona, Luigi, Cemmi, Alessia, Ceravolo, Sergio, Cerri, Alessandro, Cerutti, Francesco, Cesarini, Gianmario, Cesarotti, Cari, Chancé, Antoine, Charitonidis, Nikolaos, Chiesa, Mauro, Chiggiato, Paolo, Ciccarella, Vittoria Ludovica, Puviani, Pietro Cioli, Colaleo, Anna, Colao, Francesco, Collamati, Francesco, Costa, Marco, Craig, Nathaniel, Curtin, David, Damerau, Heiko, Da Molin, Giacomo, D'Angelo, Laura, Dasu, Sridhara, de Blas, Jorge, De Curtis, Stefania, De Gersem, Herbert, Delahaye, Jean-Pierre, Del Moro, Tommaso, Denisov, Dmitri, Denizli, Haluk, Dermisek, Radovan, Valdor, Paula Desiré, Desponds, Charlotte, Di Luzio, Luca, Di Meco, Elisa, Diociaiuti, Eleonora, Di Petrillo, Karri Folan, Di Sarcina, Ilaria, Dorigo, Tommaso, Dreimanis, Karlis, Pree, Tristan du, Yildiz, Hatice Duran, Edgecock, Thomas, Fabbri, Siara, Fabbrichesi, Marco, Farinon, Stefania, Ferrand, Guillaume, Somoza, Jose Antonio Ferreira, Fieg, Max, Filthaut, Frank, Fox, Patrick, Franceschini, Roberto, Ximenes, Rui Franqueira, Gallinaro, Michele, Garcia-Sciveres, Maurice, Garcia-Tabares, Luis, Gargiulo, Ruben, Garion, Cedric, Garzelli, Maria Vittoria, Gast, Marco, Generoso, Lisa, Gerber, Cecilia E., Giambastiani, Luca, Gianelle, Alessio, Gianfelice-Wendt, Eliana, Gibson, Stephen, Gilardoni, Simone, Giove, Dario Augusto, Giovinco, Valentina, Giraldin, Carlo, Glioti, Alfredo, Gorzawski, Arkadiusz, Greco, Mario, Grojean, Christophe, Grudiev, Alexej, Gschwendtner, Edda, Gueli, Emanuele, Guilhaudin, Nicolas, Han, Chengcheng, Han, Tao, Hauptman, John Michael, Herndon, Matthew, Hillier, Adrian D, Hillman, Micah, Holmes, Tova Ray, Homiller, Samuel, Jana, Sudip, Jindariani, Sergo, Johannesson, Sofia, Johnson, Benjamin, Jones, Owain Rhodri, Jurj, Paul-Bogdan, Kahn, Yonatan, Kamath, Rohan, Kario, Anna, Karpov, Ivan, Kelliher, David, Kilian, Wolfgang, Kitano, Ryuichiro, Kling, Felix, Kolehmainen, Antti, Kong, K. C., Kosse, Jaap, Krintiras, Georgios, Krizka, Karol, Kumar, Nilanjana, Kvikne, Erik, Kyle, Robert, Laface, Emanuele, Lane, Kenneth, Latina, Andrea, Lechner, Anton, Lee, Junghyun, Lee, Lawrence, Lee, Seh Wook, Lefevre, Thibaut, Leonardi, Emanuele, Lerner, Giuseppe, Li, Peiran, Li, Qiang, Li, Tong, Li, Wei, Lindroos, Mats, Lipton, Ronald, Liu, Da, Liu, Miaoyuan, Liu, Zhen, Voti, Roberto Li, Lombardi, Alessandra, Lomte, Shivani, Long, Kenneth, Longo, Luigi, Lorenzo, José, Losito, Roberto, Low, Ian, Lu, Xianguo, Lucchesi, Donatella, Luo, Tianhuan, Lupato, Anna, Ma, Yang, Machida, Shinji, Madlener, Thomas, Magaletti, Lorenzo, Maggi, Marcello, Durand, Helene Mainaud, Maltoni, Fabio, Manczak, Jerzy Mikolaj, Mandurrino, Marco, Marchand, Claude, Mariani, Francesco, Marin, Stefano, Mariotto, Samuele, Martin-Haugh, Stewart, Masullo, Maria Rosaria, Mauro, Giorgio Sebastiano, Mazzolari, Andrea, Mękała, Krzysztof, Mele, Barbara, Meloni, Federico, Meng, Xiangwei, Mentink, Matthias, Métral, Elias, Miceli, Rebecca, Milas, Natalia, Mohammadi, Abdollah, Moll, Dominik, Montella, Alessandro, Morandin, Mauro, Morrone, Marco, Mulder, Tim, Musenich, Riccardo, Nardecchia, Marco, Nardi, Federico, Nenna, Felice, Neuffer, David, Newbold, David, Novelli, Daniel, Olvegård, Maja, Onel, Yasar, Orestano, Domizia, Osborne, John, Otten, Simon, Torres, Yohan Mauricio Oviedo, Paesani, Daniele, Griso, Simone Pagan, Pagani, Davide, Pal, Kincso, Palmer, Mark, Pampaloni, Alessandra, Panci, Paolo, Pani, Priscilla, Papaphilippou, Yannis, Paparella, Rocco, Paradisi, Paride, Passeri, Antonio, Pasternak, Jaroslaw, Pastrone, Nadia, Pellecchia, Antonello, Piccinini, Fulvio, Piekarz, Henryk, Pieloni, Tatiana, Plouin, Juliette, Portone, Alfredo, Potamianos, Karolos, Potdevin, Joséphine, Prestemon, Soren, Puig, Teresa, Qiang, Ji, Quettier, Lionel, Rabemananjara, Tanjona Radonirina, Radicioni, Emilio, Radogna, Raffaella, Rago, Ilaria Carmela, Ratkus, Andris, Resseguie, Elodie, Reuter, Juergen, Ribani, Pier Luigi, Riccardi, Cristina, Ricciardi, Stefania, Robens, Tania, Robert, Youri, Rogers, Chris, Rojo, Juan, Romagnoni, Marco, Ronald, Kevin, Rosser, Benjamin, Rossi, Carlo, Rossi, Lucio, Rozanov, Leo, Ruhdorfer, Maximilian, Ruiz, Richard, Saini, Saurabh, Sala, Filippo, Salierno, Claudia, Salmi, Tiina, Salvini, Paola, Salvioni, Ennio, Sammut, Nicholas, Santini, Carlo, Saputi, Alessandro, Sarra, Ivano, Scarantino, Giuseppe, Schneider-Muntau, Hans, Schulte, Daniel, Scifo, Jessica, Sen, Tanaji, Senatore, Carmine, Senol, Abdulkadir, Sertore, Daniele, Sestini, Lorenzo, Rêgo, Ricardo César Silva, Simone, Federica Maria, Skoufaris, Kyriacos, Sorbello, Gino, Sorbi, Massimo, Sorti, Stefano, Soubirou, Lisa, Spataro, David, Queiroz, Farinaldo S., Stamerra, Anna, Stapnes, Steinar, Stark, Giordon, Statera, Marco, Stechauner, Bernd Michael, Su, Shufang, Su, Wei, Sun, Xiaohu, Sytov, Alexei, Tang, Jian, Tang, Jingyu, Taylor, Rebecca, Kate, Herman Ten, Testoni, Pietro, Thiele, Leonard Sebastian, Garcia, Rogelio Tomas, Topp-Mugglestone, Max, Torims, Toms, Torre, Riccardo, Tortora, Luca, Tortora, Ludovico, Trifinopoulos, Sokratis, Udongwo, Sosoho-Abasi, Vai, Ilaria, Valente, Riccardo Umberto, van Rienen, Ursula, Van Weelderen, Rob, Vanwelde, Marion, Velev, Gueorgui, Venditti, Rosamaria, Vendrasco, Adam, Verna, Adriano, Vernassa, Gianluca, Verweij, Arjan, Verwilligen, Piet, Villamizar, Yoxara, Vittorio, Ludovico, Vitulo, Paolo, Vojskovic, Isabella, Wang, Dayong, Wang, Lian-Tao, Wang, Xing, Wendt, Manfred, Widorski, Markus, Wozniak, Mariusz, Wu, Yongcheng, Wulzer, Andrea, Xie, Keping, Yang, Yifeng, Yap, Yee Chinn, Yonehara, Katsuya, Yoo, Hwi Dong, You, Zhengyun, Zanetti, Marco, Zaza, Angela, Zhang, Liang, Zhu, Ruihu, Zlobin, Alexander, Zuliani, Davide, and Zurita, José Francisco
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Physics - Accelerator Physics - Abstract
This document is comprised of a collection of updated preliminary parameters for the key parts of the muon collider. The updated preliminary parameters follow on from the October 2023 Tentative Parameters Report. Particular attention has been given to regions of the facility that are believed to hold greater technical uncertainty in their design and that have a strong impact on the cost and power consumption of the facility. The data is collected from a collaborative spreadsheet and transferred to overleaf.
- Published
- 2024
- Full Text
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44. Measurement of the branching fraction of $D^+ \to \tau^+\nu_{\tau}$
- Author
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Tian, J. Y., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
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High Energy Physics - Experiment - Abstract
By analyzing $e^{+}e^{-}$ collision data with an integrated luminosity of 7.9~fb$^{-1}$ collected with the BESIII detector at the center-of-mass energy of 3.773~GeV, the branching fraction of $D^+\to\tau^+\nu_{\tau}$ is determined as $\mathcal{B}=(9.9\pm 1.1_\mathrm{stat}\pm 0.5_\mathrm{syst})\times10^{-4}$. Taking the most precise result $\mathcal{B}(D^+\to\mu^+\nu_{\mu})=(3.981\pm 0.079_\mathrm{stat}\pm0.040_\mathrm{syst})\times10^{-4}$, we determine $R_{\tau/\mu} = \Gamma(D^+\to\tau^+\nu_{\tau})/\Gamma(D^+\to\mu^+\nu_{\mu})= 2.49\pm0.31$, achieving a factor of two improvement in precision compared to the previous BESIII result. This measurement is in agreement with the standard model prediction of lepton flavor universality within one standard deviation.
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- 2024
45. Search for $\eta_c(2S)\to p\bar{p}$ and branching fraction measurements of $\chi_{cJ} \to p\bar{p}$ via $\psi(2S)$ radiative decays
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De~Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, G. F., Fan, J. J., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, P., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lan, W. N., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, Lei, Li, M. H., Li, P. L., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, Q. M., Ma, R. Q., Ma, R. Y., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Y. H., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H. R., Qi, M., Qian, S., Qian, W. B., Qiao, C. F., Qiao, J. H., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, Bo, Wang, C., Wang, D. Y., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, L. W., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, Lianjie, Wu, X., Wu, X. H., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, H., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, W. P., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, R. J., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Y. Z., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Yue, Ying, Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. X., Zhang, Z. Y., Zhang, Z. Z., Zhang, Zh. Zh., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, X. R., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, W. Z., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
- Subjects
High Energy Physics - Experiment - Abstract
Using $(27.12\pm0.14) \times 10^{8}$ $\psi(2S)$ events collected by the BESIII detector operating at BEPCII, we search for the decay $\eta_c(2S)\to p\bar{p}$ via the process $\psi(2S)\to \gamma\eta_c(2S)$, and only find a signal with a significance of $1.7\,\sigma$. The upper limit of the product branching fraction at the 90% confidence level is determined to be $\mathcal{B}(\psi(2S)\to \gamma\eta_c(2S))\times \mathcal{B}(\eta_c(2S)\to p\bar{p})<2.4\times 10^{-7}$. The branching fractions of $\chi_{cJ}\to p\bar{p}~(J=0,1,2)$ are also measured to be $\mathcal{B}(\chi_{c0}\to p\bar{p})=(2.51\pm0.02\pm0.08)\times 10^{-4}$, $\mathcal{B}(\chi_{c1}\to p\bar{p})=(8.16\pm0.09\pm0.25)\times 10^{-4}$, and $\mathcal{B}(\chi_{c2}\to p\bar{p})=(8.33\pm0.09\pm0.22)\times 10^{-4}$, where the first uncertainty is statistical and the second systematic.
- Published
- 2024
46. Search for $e^{+}e^{-} \to \phi \chi_{c0}$ and $\phi\eta_{c2}(1D)$ at center-of-mass energies from 4.47 to 4.95 GeV
- Author
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D., Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
- Subjects
High Energy Physics - Experiment - Abstract
Utilizing a data set of $6.7$ fb$^{-1}$ from electron-positron collisions recorded by the BESIII detector at the BEPCII storage ring, a search is conducted for the processes $e^{+}e^{-} \to \phi \chi_{c0}$ and $\phi\eta_{c2}(1D)$ across center-of-mass energies from 4.47 to 4.95 GeV. In the absence of any significant signals, upper limits are set. These include limits on the Born cross sections for $e^{+}e^{-} \to \phi \chi_{c0}$, as well as the product of the Born cross section for $e^{+}e^{-} \to \phi\eta_{c2}(1D)$ and a sum of five branching fractions. Furthermore, the product of the electronic width of $Y(4660)$ and the branching fraction of the $Y(4660) \to \phi\chi_{c0}$, denoted as $\Gamma^{Y(4660)}_{e^{+}e^{-}} \mathcal{B}_{Y(4660) \to \phi\chi_{c0}}$, is determined to be $< 0.40$ eV at the 90\% confidence level., Comment: 14 pages, 6 figures
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- 2024
47. Observation of $\chi_{cJ}\to p \bar p K^0_S K^- \pi^+ + c.c.$
- Author
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chai, X. Y., Chang, J. F., Che, G. R., Che, Y. Z., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, Q. P., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, T. Y., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, C. X., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y. M., Maas, F. E., MacKay, I., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, M. Q., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. H., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, J. H., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
- Subjects
High Energy Physics - Experiment - Abstract
By analyzing $(27.12\pm0.14)\times10^8$ $\psi(3686)$ events collected with the BESIII detector operating at the BEPCII collider, the decays of $\chi_{cJ} \to p \bar{p} K^0_S K^- \pi^+ +c.c.(J=0, 1, 2)$ are observed for the first time with statistical significances greater than $10\sigma$. The branching fractions of these decays are determined to be $\mathcal{B}(\chi_{c0}\to p \bar p K^{0}_{S} K^- \pi^+ + c.c.)=(2.61\pm0.27\pm0.32)\times10^{-5},$ $\mathcal{B}(\chi_{c1}\to p \bar p K^{0}_{S} K^- \pi^+ + c.c.)=(4.16\pm0.24\pm0.46)\times10^{-5},$ and $\mathcal{B}(\chi_{c2}\to p \bar p K^{0}_{S} K^- \pi^+ + c.c.)=(5.63\pm0.28\pm0.46)\times10^{-5}$, respectively. The processes $\chi_{c1,2} \to \bar{p} \Lambda(1520) K^0_S \pi^{+} + c.c.$ are also observed, with statistical significances of 5.7$\sigma$ and 7.0$\sigma$, respectively. Evidence for $\chi_{c0} \to\bar{p} \Lambda(1520) K^0_S \pi^{+} + c.c.$ is found with statistical significances of 3.3$\sigma$ each. The corresponding branching fractions are determined to be $\mathcal{B}(\chi_{c0}\to \bar{p} \Lambda(1520) K^0_S \pi^{+} + c.c.) =(1.61^{+0.68}_{-0.64}\pm0.23)\times10^{-5}$, $\mathcal{B}(\chi_{c1}\to \bar{p} \Lambda(1520) K^0_S \pi^{+} + c.c.)=(4.06^{+0.80}_{-0.76}\pm0.52)\times10^{-5}$, and $\mathcal{B}(\chi_{c2}\to \bar{p} \Lambda(1520) K^0_S \pi^{+} + c.c.)=(4.09^{+0.87}_{-0.84}\pm0.42)\times10^{-5}$. Here, the first uncertainties are statistical and the second ones are systematic., Comment: 12 pages, 5 figures
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- 2024
48. The 2020 United States Decennial Census Is More Private Than You (Might) Think
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Su, Buxin, Su, Weijie J., and Wang, Chendi
- Subjects
Computer Science - Cryptography and Security ,Computer Science - Data Structures and Algorithms ,Statistics - Applications ,Statistics - Machine Learning - Abstract
The U.S. Decennial Census serves as the foundation for many high-profile policy decision-making processes, including federal funding allocation and redistricting. In 2020, the Census Bureau adopted differential privacy to protect the confidentiality of individual responses through a disclosure avoidance system that injects noise into census data tabulations. The Bureau subsequently posed an open question: Could sharper privacy guarantees be obtained for the 2020 U.S. Census compared to their published guarantees, or equivalently, had the nominal privacy budgets been fully utilized? In this paper, we affirmatively address this open problem by demonstrating that between 8.50% and 13.76% of the privacy budget for the 2020 U.S. Census remains unused for each of the eight geographical levels, from the national level down to the block level. This finding is made possible through our precise tracking of privacy losses using $f$-differential privacy, applied to the composition of private queries across various geographical levels. Our analysis indicates that the Census Bureau introduced unnecessarily high levels of injected noise to achieve the claimed privacy guarantee for the 2020 U.S. Census. Consequently, our results enable the Bureau to reduce noise variances by 15.08% to 24.82% while maintaining the same privacy budget for each geographical level, thereby enhancing the accuracy of privatized census statistics. We empirically demonstrate that reducing noise injection into census statistics mitigates distortion caused by privacy constraints in downstream applications of private census data, illustrated through a study examining the relationship between earnings and education.
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- 2024
49. Observation of $D^+\to\eta^\prime\mu^+\nu_\mu$ and First Study of $D^+\to \eta^\prime \ell^+\nu_\ell$ Decay Dynamics
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
- Subjects
High Energy Physics - Experiment - Abstract
Using $20.3\,\rm fb^{-1}$ of $e^+e^-$ collision data collected at the center-of-mass energy 3.773\,GeV with the BESIII detector, we report the first observation of the semileptonic decay $D^+\to \eta^\prime \mu^+\nu_\mu$ with significance of $8.6\sigma$ including systematic uncertainties, and an improved measurement of $D^+\to \eta^\prime e^+\nu_e$. The branching fractions of $D^+\to \eta^\prime \mu^+\nu_\mu$ and $D^+\to \eta^\prime e^+\nu_e$ are determined to be $(1.92\pm0.28_{\rm stat}\pm 0.08_{\rm syst})\times 10^{-4}$ and $(1.79\pm0.19_{\rm stat}\pm 0.07_{\rm syst})\times 10^{-4}$, respectively. From an analysis of the $D^+\to \eta^\prime \ell^+\nu_\ell$ decay dynamics, the product of the hadronic form factor $f_+^{\eta^{\prime}}(0)$ and the CKM matrix element $|V_{cd}|$ is measured for the first time, giving $f^{\eta^\prime}_+(0)|V_{cd}| = (5.92\pm0.56_{\rm stat}\pm0.13_{\rm syst})\times 10^{-2}$. No evidence for violation of $\mu-e$ lepton-flavor universality is found in both the full range and several bins of $\ell^+\nu_\ell$ four-momentum transfer. The $\eta-\eta^\prime$ mixing angle in the quark flavor basis is determined to be $\phi_{\rm P} =(39.8\pm0.8_{\rm stat}\pm0.3_{\rm syst})^\circ$.
- Published
- 2024
50. Precision Measurement of the Branching Fraction of $D^{+}\to \mu^{+}\nu_{\mu}$
- Author
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BESIII Collaboration, Ablikim, M., Achasov, M. N., Adlarson, P., Afedulidis, O., Ai, X. C., Aliberti, R., Amoroso, A., An, Q., Bai, Y., Bakina, O., Balossino, I., Ban, Y., Bao, H. -R., Batozskaya, V., Begzsuren, K., Berger, N., Berlowski, M., Bertani, M., Bettoni, D., Bianchi, F., Bianco, E., Bortone, A., Boyko, I., Briere, R. A., Brueggemann, A., Cai, H., Cai, X., Calcaterra, A., Cao, G. F., Cao, N., Cetin, S. A., Chang, J. F., Che, G. R., Chelkov, G., Chen, C., Chen, C. H., Chen, Chao, Chen, G., Chen, H. S., Chen, H. Y., Chen, M. L., Chen, S. J., Chen, S. L., Chen, S. M., Chen, T., Chen, X. R., Chen, X. T., Chen, Y. B., Chen, Y. Q., Chen, Z. J., Chen, Z. Y., Choi, S. K., Cibinetto, G., Cossio, F., Cui, J. J., Dai, H. L., Dai, J. P., Dbeyssi, A., de Boer, R. E., Dedovich, D., Deng, C. Q., Deng, Z. Y., Denig, A., Denysenko, I., Destefanis, M., De Mori, F., Ding, B., Ding, X. X., Ding, Y., Dong, J., Dong, L. Y., Dong, M. Y., Dong, X., Du, M. C., Du, S. X., Duan, Y. Y., Duan, Z. H., Egorov, P., Fan, Y. H., Fang, J., Fang, S. S., Fang, W. X., Fang, Y., Fang, Y. Q., Farinelli, R., Fava, L., Feldbauer, F., Felici, G., Feng, C. Q., Feng, J. H., Feng, Y. T., Fritsch, M., Fu, C. D., Fu, J. L., Fu, Y. W., Gao, H., Gao, X. B., Gao, Y. N., Gao, Yang, Garbolino, S., Garzia, I., Ge, L., Ge, P. T., Ge, Z. W., Geng, C., Gersabeck, E. M., Gilman, A., Goetzen, K., Gong, L., Gong, W. X., Gradl, W., Gramigna, S., Greco, M., Gu, M. H., Gu, Y. T., Guan, C. Y., Guo, A. Q., Guo, L. B., Guo, M. J., Guo, R. P., Guo, Y. P., Guskov, A., Gutierrez, J., Han, K. L., Han, T. T., Hanisch, F., Hao, X. Q., Harris, F. A., He, K. K., He, K. L., Heinsius, F. H., Heinz, C. H., Heng, Y. K., Herold, C., Holtmann, T., Hong, P. C., Hou, G. Y., Hou, X. T., Hou, Y. R., Hou, Z. L., Hu, B. Y., Hu, H. M., Hu, J. F., Hu, S. L., Hu, T., Hu, Y., Huang, G. S., Huang, K. X., Huang, L. Q., Huang, X. T., Huang, Y. P., Huang, Y. S., Hussain, T., Hölzken, F., Hüsken, N., der Wiesche, N. in, Jackson, J., Janchiv, S., Jeong, J. H., Ji, Q., Ji, Q. P., Ji, W., Ji, X. B., Ji, X. L., Ji, Y. Y., Jia, X. Q., Jia, Z. K., Jiang, D., Jiang, H. B., Jiang, P. C., Jiang, S. S., Jiang, T. J., Jiang, X. S., Jiang, Y., Jiao, J. B., Jiao, J. K., Jiao, Z., Jin, S., Jin, Y., Jing, M. Q., Jing, X. M., Johansson, T., Kabana, S., Kalantar-Nayestanaki, N., Kang, X. L., Kang, X. S., Kavatsyuk, M., Ke, B. C., Khachatryan, V., Khoukaz, A., Kiuchi, R., Kolcu, O. B., Kopf, B., Kuessner, M., Kui, X., Kumar, N., Kupsc, A., Kühn, W., Lane, J. J., Lavezzi, L., Lei, T. T., Lei, Z. H., Lellmann, M., Lenz, T., Li, C., Li, C. H., Li, Cheng, Li, D. M., Li, F., Li, G., Li, H. B., Li, H. J., Li, H. N., Li, Hui, Li, J. R., Li, J. S., Li, K., Li, K. L., Li, L. J., Li, L. K., Li, Lei, Li, M. H., Li, P. R., Li, Q. M., Li, Q. X., Li, R., Li, S. X., Li, T., Li, W. D., Li, W. G., Li, X., Li, X. H., Li, X. L., Li, X. Y., Li, X. Z., Li, Y. G., Li, Z. J., Li, Z. Y., Liang, C., Liang, H., Liang, Y. F., Liang, Y. T., Liao, G. R., Liao, Y. P., Libby, J., Limphirat, A., Lin, C. C., Lin, D. X., Lin, T., Liu, B. J., Liu, B. X., Liu, C., Liu, C. X., Liu, F., Liu, F. H., Liu, Feng, Liu, G. M., Liu, H., Liu, H. B., Liu, H. H., Liu, H. M., Liu, Huihui, Liu, J. B., Liu, J. Y., Liu, K., Liu, K. Y., Liu, Ke, Liu, L., Liu, L. C., Liu, Lu, Liu, M. H., Liu, P. L., Liu, Q., Liu, S. B., Liu, T., Liu, W. K., Liu, W. M., Liu, X., Liu, Y., Liu, Y. B., Liu, Z. A., Liu, Z. D., Liu, Z. Q., Lou, X. C., Lu, F. X., Lu, H. J., Lu, J. G., Lu, X. L., Lu, Y., Lu, Y. P., Lu, Z. H., Luo, C. L., Luo, J. R., Luo, M. X., Luo, T., Luo, X. L., Lyu, X. R., Lyu, Y. F., Ma, F. C., Ma, H., Ma, H. L., Ma, J. L., Ma, L. L., Ma, L. R., Ma, M. M., Ma, Q. M., Ma, R. Q., Ma, T., Ma, X. T., Ma, X. Y., Ma, Y., Ma, Y. M., Maas, F. E., Maggiora, M., Malde, S., Mao, Y. J., Mao, Z. P., Marcello, S., Meng, Z. X., Messchendorp, J. G., Mezzadri, G., Miao, H., Min, T. J., Mitchell, R. E., Mo, X. H., Moses, B., Muchnoi, N. Yu., Muskalla, J., Nefedov, Y., Nerling, F., Nie, L. S., Nikolaev, I. B., Ning, Z., Nisar, S., Niu, Q. L., Niu, W. D., Niu, Y., Olsen, S. L., Ouyang, Q., Pacetti, S., Pan, X., Pan, Y., Pathak, A., Pei, Y. P., Pelizaeus, M., Peng, H. P., Peng, Y. Y., Peters, K., Ping, J. L., Ping, R. G., Plura, S., Prasad, V., Qi, F. Z., Qi, H., Qi, H. R., Qi, M., Qi, T. Y., Qian, S., Qian, W. B., Qiao, C. F., Qiao, X. K., Qin, J. J., Qin, L. Q., Qin, L. Y., Qin, X. P., Qin, X. S., Qin, Z. H., Qiu, J. F., Qu, Z. H., Redmer, C. F., Ren, K. J., Rivetti, A., Rolo, M., Rong, G., Rosner, Ch., Ruan, S. N., Salone, N., Sarantsev, A., Schelhaas, Y., Schoenning, K., Scodeggio, M., Shan, K. Y., Shan, W., Shan, X. Y., Shang, Z. J., Shangguan, J. F., Shao, L. G., Shao, M., Shen, C. P., Shen, H. F., Shen, W. H., Shen, X. Y., Shi, B. A., Shi, H., Shi, H. C., Shi, J. L., Shi, J. Y., Shi, Q. Q., Shi, S. Y., Shi, X., Song, J. J., Song, T. Z., Song, W. M., Song, Y. J., Song, Y. X., Sosio, S., Spataro, S., Stieler, F., Su, S. S, Su, Y. J., Sun, G. B., Sun, G. X., Sun, H., Sun, H. K., Sun, J. F., Sun, K., Sun, L., Sun, S. S., Sun, T., Sun, W. Y., Sun, Y., Sun, Y. J., Sun, Y. Z., Sun, Z. Q., Sun, Z. T., Tang, C. J., Tang, G. Y., Tang, J., Tang, M., Tang, Y. A., Tao, L. Y., Tao, Q. T., Tat, M., Teng, J. X., Thoren, V., Tian, W. H., Tian, Y., Tian, Z. F., Uman, I., Wan, Y., Wang, S. J., Wang, B., Wang, B. L., Wang, Bo, Wang, D. Y., Wang, F., Wang, H. J., Wang, J. J., Wang, J. P., Wang, K., Wang, L. L., Wang, M., Wang, N. Y., Wang, S., Wang, T., Wang, T. J., Wang, W., Wang, W. P., Wang, X., Wang, X. F., Wang, X. J., Wang, X. L., Wang, X. N., Wang, Y., Wang, Y. D., Wang, Y. F., Wang, Y. L., Wang, Y. N., Wang, Y. Q., Wang, Yaqian, Wang, Yi, Wang, Z., Wang, Z. L., Wang, Z. Y., Wang, Ziyi, Wei, D. H., Weidner, F., Wen, S. P., Wen, Y. R., Wiedner, U., Wilkinson, G., Wolke, M., Wollenberg, L., Wu, C., Wu, J. F., Wu, L. H., Wu, L. J., Wu, X., Wu, X. H., Wu, Y., Wu, Y. H., Wu, Y. J., Wu, Z., Xia, L., Xian, X. M., Xiang, B. H., Xiang, T., Xiao, D., Xiao, G. Y., Xiao, S. Y., Xiao, Y. L., Xiao, Z. J., Xie, C., Xie, X. H., Xie, Y., Xie, Y. G., Xie, Y. H., Xie, Z. P., Xing, T. Y., Xu, C. F., Xu, C. J., Xu, G. F., Xu, H. Y., Xu, M., Xu, Q. J., Xu, Q. N., Xu, W., Xu, W. L., Xu, X. P., Xu, Y., Xu, Y. C., Xu, Z. S., Yan, F., Yan, L., Yan, W. B., Yan, W. C., Yan, X. Q., Yang, H. J., Yang, H. L., Yang, H. X., Yang, T., Yang, Y., Yang, Y. F., Yang, Y. X., Yang, Z. W., Yao, Z. P., Ye, M., Ye, M. H., Yin, J. H., Yin, Junhao, You, Z. Y., Yu, B. X., Yu, C. X., Yu, G., Yu, J. S., Yu, M. C., Yu, T., Yu, X. D., Yu, Y. C., Yuan, C. Z., Yuan, J., Yuan, L., Yuan, S. C., Yuan, Y., Yuan, Z. Y., Yue, C. X., Zafar, A. A., Zeng, F. R., Zeng, S. H., Zeng, X., Zeng, Y., Zeng, Y. J., Zhai, X. Y., Zhai, Y. C., Zhan, Y. H., Zhang, A. Q., Zhang, B. L., Zhang, B. X., Zhang, D. H., Zhang, G. Y., Zhang, H., Zhang, H. C., Zhang, H. H., Zhang, H. Q., Zhang, H. R., Zhang, H. Y., Zhang, J., Zhang, J. J., Zhang, J. L., Zhang, J. Q., Zhang, J. S., Zhang, J. W., Zhang, J. X., Zhang, J. Y., Zhang, J. Z., Zhang, Jianyu, Zhang, L. M., Zhang, Lei, Zhang, P., Zhang, Q. Y., Zhang, R. Y., Zhang, S. H., Zhang, Shulei, Zhang, X. D., Zhang, X. M., Zhang, X. Y, Zhang, X. Y., Zhang, Y., Zhang, Y. T., Zhang, Y. H., Zhang, Y. M., Zhang, Yan, Zhang, Z. D., Zhang, Z. H., Zhang, Z. L., Zhang, Z. Y., Zhang, Z. Z., Zhao, G., Zhao, J. Y., Zhao, J. Z., Zhao, L., Zhao, Lei, Zhao, M. G., Zhao, N., Zhao, R. P., Zhao, S. J., Zhao, Y. B., Zhao, Y. X., Zhao, Z. G., Zhemchugov, A., Zheng, B., Zheng, B. M., Zheng, J. P., Zheng, W. J., Zheng, Y. H., Zhong, B., Zhong, X., Zhou, H., Zhou, J. Y., Zhou, L. P., Zhou, S., Zhou, X., Zhou, X. K., Zhou, X. R., Zhou, X. Y., Zhou, Y. Z., Zhou, Z. C., Zhu, A. N., Zhu, J., Zhu, K., Zhu, K. J., Zhu, K. S., Zhu, L., Zhu, L. X., Zhu, S. H., Zhu, T. J., Zhu, W. D., Zhu, Y. C., Zhu, Z. A., Zou, J. H., and Zu, J.
- Subjects
High Energy Physics - Experiment - Abstract
Using $20.3~\mathrm{fb}^{-1}$ of $e^+e^-$ collision data collected at a center-of-mass energy of $E_{\rm cm}=3.773$ GeV with the BESIII detector operating at the BEPCII collider, we determine the branching fraction of the leptonic decay $D^+\to\mu^+\nu_\mu$ to be $(3.981\pm0.079_{\rm stat}\pm0.040_{\rm syst})\times10^{-4}$. Interpreting our measurement with knowledge of the Fermi coupling constant $G_F$, the masses of the $D^+$ and $\mu^+$ as well as the lifetime of the $D^+$, we determine $f_{D^+}|V_{cd}|=(47.53\pm0.48_{\rm stat}\pm0.24_{\rm syst}\pm0.12_{\rm input})~\mathrm{MeV}$. This result is a factor of 2.3 more precise than the previous best measurement. Using the value of the magnitude of the Cabibbo-Kobayashi-Maskawa matrix element $|V_{cd}|$ given by the global standard model fit, we obtain the $D^+$ decay constant $f_{D^+}=(211.5\pm2.3_{\rm stat}\pm1.1_{\rm syst}\pm0.8_{\rm input})$ MeV. Alternatively, using the value of $f_{D^+}$ from a precise lattice quantum chromodynamics calculation, we extract $|V_{cd}|=0.2242\pm0.0023_{\rm stat}\pm0.0011_{\rm syst}\pm0.0009_{\rm input}$., Comment: 9 pages, 2 figures
- Published
- 2024
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