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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.
By analyzing e+e− annihilation data corresponding to an integrated luminosity of 2.93fb−1 collected at the center-of-mass energy of 3.773\,GeV with the BESIII detector, we report the first observations of the doubly Cabibbo-suppressed decays D+→K+π0π0 and D+→K+π0η. The branching fractions of D+→K+π0π0 and D+→K+π0η are measured to be (2.1±0.4stat±0.1syst)×10−4 and (2.1±0.5stat±0.1syst)×10−4 with statistical significances of 8.8σ and 5.5σ, respectively. In addition, we search for the subprocesses D+→K∗(892)+π0 and D+→K∗(892)+η with K∗(892)+→K+π0. The branching fraction of D+→K∗(892)+η is determined to be (4.4+1.8−1.5stat±0.2syst)×10−4, with a statistical significance of 3.2σ. No significant signal for D+→K∗(892)+π0 is found and we set an upper limit on the branching fraction of this decay at the 90\% confidence level to be 5.4×10−4.
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.
Based on (10087±44)×106 J/ψ events collected with the BESIII detector, the process J/ψ→γπ+π−η′ is studied using two dominant decay channels of the η′ meson, η′→γπ+π− and η′→ηπ+π−,η→γγ. The X(2600) is observed with a statistical significance larger than 20σ in the π+π−η′ invariant mass spectrum, and it has a strong correlation to a structure around 1.5 GeV/{\it c}2 in the π+π− invariant mass spectrum. A simultaneous fit on the π+π−η′ and π+π− invariant mass spectra with the two η′ decay modes indicates that the mass and width of the X(2600) state are 2617.8±2.1+18.2−1.9 MeV/{\it c}2 and 200±8+20−17 MeV, respectively. The corresponding branching fractions are measured to be B(J/ψ→γX(2600))⋅B(X(2600)→f0(1500)η′)⋅B(f0(1500)→π+π−) = (3.39±0.18+0.91−0.66)×10−5 and B(J/ψ→γX(2600))⋅B(X(2600)→f′2(1525)η′)⋅B(f′2(1525)→π+π−) = (2.43±0.13+0.31−1.11)×10−5, where the first uncertainties are statistical, and the second systematic.
Analyzing (448.1±2.9)×106 ψ(3686) events collected with the BESIII detector at the BEPCII collider, the ψ(3686)→ωK0SK0S decay is observed for the first time. The branching fraction for this decay is determined to be Bψ(3686)→ωK0SK0S=(7.04±0.39±0.36)×10−5, where the first uncertainty is statistical and the second is systematic.
Based on a sample of (10.09±0.04)×109 J/ψ events collected with the BESIII detector operating at the BEPCII storage ring, a partial wave analysis of the decay J/ψ→γηη′ is performed. An isoscalar state with exotic quantum numbers JPC=1−+, denoted as η1(1855), has been observed for the first time with statistical significance larger than 19σ. Its mass and width are measured to be (1855±9+6−1)~MeV/c2 and (188±18+3−8)~MeV, respectively. The product branching fraction B(J/ψ→γη1(1855)→γηη′) is measured to be (2.70±0.41+0.16−0.35)×10−6. The first uncertainties are statistical and the second are systematic. In addition, an upper limit on the branching ratio B(f0(1710)→ηη′)/B(f0(1710)→ππ) is determined to be 1.61×10−3 at 90\% confidence level, which lends support to the hypothesis that the f0(1710) has a large glueball component.
Using J/ψ radiative decays from 9.0 billion J/ψ events collected by the BESIII detector, we search for di-muon decays of a CP-odd light Higgs boson (A0), predicted by many new physics models beyond the Standard Model, including the Next-to-Minimal Supersymmetric Standard Model. No evidence for the CP-odd light Higgs production is found, and we set 90% confidence level upper limits on the product branching fraction B(J/ψ→γA0)×B(A0→μ+μ−) in the range of (1.2−778.0)×10−9 for 0.212≤mA0≤3.0 GeV/c2. The new measurement is a 6-7 times improvement over our previous measurement, and is also slightly better than the BaBar measurement in the low-mass region for tanβ=1.
Using J/ψ radiative decays from 9.0 billion J/ψ events collected by the BESIII detector, we search for di-muon decays of a CP-odd light Higgs boson (A0), predicted by many new physics models beyond the Standard Model, including the Next-to-Minimal Supersymmetric Standard Model. No evidence for the CP-odd light Higgs production is found, and we set 90% confidence level upper limits on the product branching fraction B(J/ψ→γA0)×B(A0→μ+μ−) in the range of (1.2−778.0)×10−9 for 0.212≤mA0≤3.0 GeV/c2. The new measurement is a 6-7 times improvement over our previous measurement, and is also slightly better than the BaBar measurement in the low-mass region for tanβ=1.
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 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.
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 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.