1,328 research outputs found

    Multiplicity dependence of σψ(2S)/σJ/ψ\sigma_{\psi(2S)}/\sigma_{J/\psi} in pppp collisions at s=13\sqrt{s}=13 TeV

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    International audienceThe ratio of production cross-sections of ψ(2S)\psi(2S) over J/ψJ/\psi mesons as a function of charged-particle multiplicity in proton-proton collisions at a centre-of-mass energy s=13\sqrt{s}=13 TeV is measured with a data sample collected by the LHCb detector, corresponding to an integrated luminosity of 658 pb1^{-1}. The ratio is measured for both prompt and non-prompt ψ(2S)\psi(2S) and J/ψJ/\psi mesons. When there is an overlap between the rapidity ranges over which multiplicity and charmonia production are measured, a multiplicity-dependent modification of the ratio is observed for prompt mesons. No significant multiplicity dependence is found when the ranges do not overlap. For non-prompt production, the ψ(2S)toJ/ψ\psi(2S)-to-J/\psi production ratio is roughly independent of multiplicity irrespective of the rapidity range over which the multiplicity is measured. The results are compared to predictions of the co-mover model and agree well except in the low multiplicity region. The ratio of production cross-sections of ψ(2S)\psi(2S) over J/ψJ/\psi mesons are cross-checked with other measurements in di-lepton channels and found to be compatible

    CEPC Technical Design Report -- Accelerator

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    International audienceThe Circular Electron Positron Collider (CEPC) is a large scientific project initiated and hosted by China, fostered through extensive collaboration with international partners. The complex comprises four accelerators: a 30 GeV Linac, a 1.1 GeV Damping Ring, a Booster capable of achieving energies up to 180 GeV, and a Collider operating at varying energy modes (Z, W, H, and ttbar). The Linac and Damping Ring are situated on the surface, while the Booster and Collider are housed in a 100 km circumference underground tunnel, strategically accommodating future expansion with provisions for a Super Proton Proton Collider (SPPC). The CEPC primarily serves as a Higgs factory. In its baseline design with synchrotron radiation (SR) power of 30 MW per beam, it can achieve a luminosity of 5e34 /cm^2/s^1, resulting in an integrated luminosity of 13 /ab for two interaction points over a decade, producing 2.6 million Higgs bosons. Increasing the SR power to 50 MW per beam expands the CEPC's capability to generate 4.3 million Higgs bosons, facilitating precise measurements of Higgs coupling at sub-percent levels, exceeding the precision expected from the HL-LHC by an order of magnitude. This Technical Design Report (TDR) follows the Preliminary Conceptual Design Report (Pre-CDR, 2015) and the Conceptual Design Report (CDR, 2018), comprehensively detailing the machine's layout and performance, physical design and analysis, technical systems design, R&D and prototyping efforts, and associated civil engineering aspects. Additionally, it includes a cost estimate and a preliminary construction timeline, establishing a framework for forthcoming engineering design phase and site selection procedures. Construction is anticipated to begin around 2027-2028, pending government approval, with an estimated duration of 8 years. The commencement of experiments could potentially initiate in the mid-2030s

    DataSheet_2_Differential gene expression and potential regulatory network of fatty acid biosynthesis during fruit and leaf development in yellowhorn (Xanthoceras sorbifolium), an oil-producing tree with significant deployment values.xlsx

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    Xanthoceras sorbifolium (yellowhorn) is a woody oil plant with super stress resistance and excellent oil characteristics. The yellowhorn oil can be used as biofuel and edible oil with high nutritional and medicinal value. However, genetic studies on yellowhorn are just in the beginning, and fundamental biological questions regarding its very long-chain fatty acid (VLCFA) biosynthesis pathway remain largely unknown. In this study, we reconstructed the VLCFA biosynthesis pathway and annotated 137 genes encoding relevant enzymes. We identified four oleosin genes that package triacylglycerols (TAGs) and are specifically expressed in fruits, likely playing key roles in yellowhorn oil production. Especially, by examining time-ordered gene co-expression network (TO-GCN) constructed from fruit and leaf developments, we identified key enzymatic genes and potential regulatory transcription factors involved in VLCFA synthesis. In fruits, we further inferred a hierarchical regulatory network with MYB-related (XS03G0296800) and B3 (XS02G0057600) transcription factors as top-tier regulators, providing clues into factors controlling carbon flux into fatty acids. Our results offer new insights into key genes and transcriptional regulators governing fatty acid production in yellowhorn, laying the foundation for efforts to optimize oil content and fatty acid composition. Moreover, the gene expression patterns and putative regulatory relationships identified here will inform metabolic engineering and molecular breeding approaches tailored to meet biofuel and bioproduct demands.</p

    Image1_Fundus photograph-based cataract evaluation network using deep learning.TIF

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    Background: Our study aims to develop an artificial intelligence-based high-precision cataract classification and grading evaluation network using fundus images.Methods: We utilized 1,340 color fundus photographs from 875 participants (aged 50–91 years at image capture) from the Beijing Eye Study 2011. Four experienced and trained ophthalmologists performed the classification of these cases based on slit-lamp and retro-illuminated images. Cataracts were classified into three types based on the location of the lens opacity: cortical cataract, nuclear cataract, and posterior subcapsular cataract. We developed a Dual-Stream Cataract Evaluation Network (DCEN) that uses color photographs of cataract fundus to achieve simultaneous cataract type classification and severity grading. The accuracy of severity grading was enhanced by incorporating the results of type classification.Results: The DCEN method achieved an accuracy of 0.9762, a sensitivity of 0.9820, an F1 score of 0.9401, and a kappa coefficient of 0.8618 in the cataract classification task. By incorporating type features, the grading of cataract severity can be improved with an accuracy of 0.9703, a sensitivity of 0.9344, an F1 score of 0.9555, and a kappa coefficient of 0.9111. We utilized Grad-CAM visualization technology to analyze and summarize the fundus image features of different types of cataracts, and we verified our conclusions by examining the information entropy of the retinal vascular region.Conclusion: The proposed DCEN provides a reliable ability to comprehensively evaluate the condition of cataracts from fundus images. Applying deep learning to clinical cataract assessment has the advantages of simplicity, speed, and efficiency.</p

    Search for Bc+π+μ+μB_c^+\to\pi^+\mu^+\mu^- decays and measurement of the branching fraction ratio B(Bc+ψ(2S)π+)/B(Bc+J/ψπ+){\cal B}(B_c^+\to\psi(2S)\pi^+)/{\cal B}(B_c^+\to J/\psi \pi^+)