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Institute
Using an 𝑒+𝑒− collision data sample of (27.08±0.14)×108 𝜓(3686) events collected by the BESIII detector, we report the first observation of 𝜒𝑐𝐽→Ω−¯Ω+ (𝐽=0, 1, 2) decays with significances of 5.6𝜎, 6.4𝜎, and 18𝜎, respectively, where the 𝜒𝑐𝐽 mesons are produced in the radiative 𝜓(3686) decays. The branching fractions are determined to be ℬ(𝜒𝑐0→Ω−¯Ω+) = (3.51±0.54±0.29)×10−5, ℬ(𝜒𝑐1→Ω−¯Ω+)=(1.49±0.23±0.10)×10−5, and ℬ(𝜒𝑐2→Ω−¯Ω+)=(4.52±0.24±0.18)×10−5, where the first and second uncertainties are statistical and systematic, respectively.
Using a sample of (448.1±2.9)×106 𝜓(3686) decays collected with the BESIII detector at BEPCII, we report an observation of Ξ− transverse polarization with a significance of 7.3𝜎 in the decay 𝜓(3686)→Ξ− ¯Ξ+ (Ξ−→Λ𝜋−, ¯Ξ+→¯Λ𝜋+, Λ→𝑝𝜋−, ¯Λ→¯𝑝𝜋+). The relative phase of the electric and magnetic form factors is determined to be ΔΦ=(0.667±0.111±0.058) rad. This is the first measurement of the relative phase for a 𝜓(3686) decay into a pair of Ξ−¯Ξ+ hyperons. The Ξ− decay parameters (𝛼Ξ−, 𝜙Ξ−) and their conjugates (𝛼¯Ξ+, 𝜙¯Ξ+), the angular-distribution parameter 𝛼𝜓, and the strong-phase difference 𝛿𝑝−𝛿𝑠 for Λ𝜋− scattering are measured to be consistent with previous BESIII results.
We report a measurement of the cross section for the process e+e−→π+π−J/ψ around the X(3872) mass in search for the direct formation of e+e−→X(3872) through the two-photon fusion process. No enhancement of the cross section is observed at the X(3872) peak and an upper limit on the product of electronic width and branching fraction of X(3872)→π+π−J/ψ is determined to be Γee×B(X(3872)→π+π−J/ψ)<7.5×10−3eV at 90% confidence level under an assumption of total width of 1.19±0.21 MeV. This is an improvement of a factor of about 17 compared to the previous limit. Furthermore, using the latest result of B(X(3872)→π+π−J/ψ), an upper limit on the electronic width Γee of X(3872) is obtained to be <0.32eV at the 90% confidence level.
A search for a massless dark photon γ′ is conducted using 4.5 fb−1 of e+e− collision data collected at center-of-mass energies between 4.600 and 4.699 GeV with the BESIII detector at BEPCII. No significant signal is observed, and the upper limit on the branching fraction B(Λ+c→pγ′) is determined to be 8.0×10−5 at 90% confidence level.
Based on (10087±44)×106 𝐽/𝜓 events collected with the BESIII detector at BEPCII, the double Dalitz decay 𝜂′→𝑒+𝑒−𝑒+𝑒− is observed for the first time via the 𝐽/𝜓→𝛾𝜂′ decay process. The significance is found to be 5.7𝜎 with systematic uncertainties taken into consideration. Its branching fraction is determined to be ℬ(𝜂′→𝑒+𝑒−𝑒+𝑒−)=(4.5±1.0(stat)±0.5(sys))×10−6.
Improved measurement of the branching fractions of the inclusive decays D⁺ → Kₛ⁰X and D⁰ → Kₛ⁰X
(2023)
By analyzing 2.93 fb−1 of 𝑒+𝑒− collision data taken at the center-of-mass energy of 3.773 GeV with the BESIII detector, the branching fractions of the inclusive decays 𝐷+→𝐾0 𝑆𝑋 and 𝐷0→𝐾0 𝑆𝑋 are measured to be (33.11±0.13±0.36)% and (20.75±0.12±0.20)%, respectively, where the first uncertainties are statistical and the second are systematic. These results are consistent with the world averages of previous measurements, but with much improved precision.
Using (10.087±0.044)×109 𝐽/𝜓 events collected by the Beijing Spectrum III (BESIII) detector at the Beijing Electron Positron Collider II (BEPCII) collider, we search for the hyperon semileptonic decay Ξ−→Ξ0𝑒−¯𝜈𝑒. No significant signal is observed and the upper limit on the branching fraction ℬ(Ξ−→Ξ0𝑒−¯𝜈𝑒) is set to be 2.59×10−4 at 90% confidence level. This result is one order of magnitude more strict than the previous best limit.
The integrated luminosities of data samples collected in the BESIII experiment in 2016–2017 at center-of-mass energies between 4.19 and 4.28 GeV are measured with a precision better than 1% by analyzing large-angle Bhabha scattering events. The integrated luminosities of old datasets collected in 2010–2014 are updated by considering corrections related to detector performance, offsetting the effect of newly discovered readout errors in the electromagnetic calorimeter, which can haphazardly occur.
Using inclusive decays of the J/ψ, a precise determination of the number of J/ψ events collected with the BESIII detector is performed. For the two data sets taken in 2009 and 2012, the numbers of J/ψ events are recalculated to be (224.0±1.3)×106 and (1088.5±4.4)×106 respectively, which are in good agreement with the previous measurements. For the J/ψ sample taken in 2017--2019, the number of events is determined to be (8774.0±39.4)×106. The total number of J/ψ events collected with the BESIII detector is determined to be (10087±44)×106, where the uncertainty is dominated by systematic effects and the statistical uncertainty is negligible.
Using inclusive decays of the J/ψ, a precise determination of the number of J/ψ events collected with the BESIII detector is performed. For the two data sets taken in 2009 and 2012, the numbers of J/ψ events are recalculated to be (224.0±1.3)×106 and (1088.5±4.4)×106 respectively, which are in good agreement with the previous measurements. For the J/ψ sample taken in 2017--2019, the number of events is determined to be (8774.0±39.4)×106. The total number of J/ψ events collected with the BESIII detector is determined to be (10087±44)×106, where the uncertainty is dominated by systematic effects and the statistical uncertainty is negligible.
A revision of the wild species in the genus Malus Mill. (Rosaceae) is presented based on numerical analyses and specimens from herbaria around the world, while cultivated species such as Malus domestica (Suckow) Borkh. are not included because of their complicated domestication history. Infra- and interspecific morphological variation and species delimitation are clarified based on Principal Component Analyses (PCA) and Cluster Analyses (UPGMA). We found that several morphological characters traditionally used to distinguish species have limited taxonomic value because of high phenotypic variation or plasticity. There is a substantial conflict between traditional morphological and genetic taxonomic concepts, and as a result species lineages are often morphologically indistinguishable. None of the analyses supports the recognition of infraspecific categories in Malus transitoria (Batalin) C.K.Schneid. and interspecific categories between Malus doumeri (Bois) A.Chev. and Malus leiocalyca S.Z.Huang. Based on our analyses, we recognize 26 wild species in the genus, and propose seven new synonymies.
Aims: Carotid intima media thickness (CIMT) predicts cardiovascular (CVD) events, but the predictive value of CIMT change is debated. We assessed the relation between CIMT change and events in individuals at high cardiovascular risk.
Methods and results: From 31 cohorts with two CIMT scans (total n = 89070) on average 3.6 years apart and clinical follow-up, subcohorts were drawn: (A) individuals with at least 3 cardiovascular risk factors without previous CVD events, (B) individuals with carotid plaques without previous CVD events, and (C) individuals with previous CVD events. Cox regression models were fit to estimate the hazard ratio (HR) of the combined endpoint (myocardial infarction, stroke or vascular death) per standard deviation (SD) of CIMT change, adjusted for CVD risk factors. These HRs were pooled across studies.
In groups A, B and C we observed 3483, 2845 and 1165 endpoint events, respectively. Average common CIMT was 0.79mm (SD 0.16mm), and annual common CIMT change was 0.01mm (SD 0.07mm), both in group A. The pooled HR per SD of annual common CIMT change (0.02 to 0.43mm) was 0.99 (95% confidence interval: 0.95–1.02) in group A, 0.98 (0.93–1.04) in group B, and 0.95 (0.89–1.04) in group C. The HR per SD of common CIMT (average of the first and the second CIMT scan, 0.09 to 0.75mm) was 1.15 (1.07–1.23) in group A, 1.13 (1.05–1.22) in group B, and 1.12 (1.05–1.20) in group C.
Conclusions: We confirm that common CIMT is associated with future CVD events in individuals at high risk. CIMT change does not relate to future event risk in high-risk individuals.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A significant roadblock hindering progress in epitranscriptomics is the identification of more than one modification in individual transcript molecules. We address this with CHEUI (CH3 (methylation) Estimation Using Ionic current). CHEUI predicts N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual molecules from the same sample, the stoichiometry at transcript reference sites, and differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals to achieve high single-molecule, transcript-site, and stoichiometry accuracies in multiple tests using synthetic RNA standards and cell line data. CHEUI’s capability to identify two modification types in the same sample reveals a co-occurrence of m6A and m5C in individual mRNAs in cell line and tissue transcriptomes. CHEUI provides new avenues to discover and study the function of the epitranscriptome.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions. CHEUI processes observed and expected nanopore direct RNA sequencing signals with convolutional neural networks to achieve high single-molecule accuracy and outperforms other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The epitranscriptome embodies many new and largely unexplored functions of RNA. A major roadblock in the epitranscriptomics field is the lack of transcriptome-wide methods to detect more than a single RNA modification type at a time, identify RNA modifications in individual molecules, and estimate modification stoichiometry accurately. We address these issues with CHEUI (CH3 (methylation) Estimation Using Ionic current), a new method that concurrently detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) in individual RNA molecules from the same sample, as well as differential methylation between any two conditions, using signals from nanopore direct RNA sequencing. CHEUI processes observed and expected signals with convolutional neural networks to achieve high single-molecule accuracy and outperform other methods in detecting m6A and m5C sites and quantifying their stoichiometry. CHEUI’s unique capability to identify two modification types in the same sample reveals a non-random co-occurrence of m6A and m5C in mRNA transcripts in cell lines and tissues. CHEUI unlocks an unprecedented potential to study RNA modification configurations and discover new epitranscriptome functions.
The expanding field of epitranscriptomics might rival the epigenome in the diversity of the biological processes impacted. However, the identification of modifications in individual RNA molecules remains challenging. We present CHEUI, a new method that detects N6-methyladenosine (m6A) and 5-methylcytidine (m5C) at single-nucleotide and single-molecule resolution from Nanopore signals. CHEUI predicts methylation in Nanopore reads and transcriptomic sites in a single condition, and differential m6A and m5C methylation between any two conditions. Using extensive benchmarking with Nanopore data derived from synthetic and natural RNA, CHEUI showed higher accuracy than other existing methods in detecting m6A and m5C sites and quantifying the site stoichiometry levels, while maintaining a lower proportion of false positives. CHEUI provides a new capability to detect RNA modifications with high accuracy and resolution that can be cost-effectively expanded to other modifications to unveil the full span of the epitranscriptome in normal and disease conditions.