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1. Single-cell transcriptome analysis reveals immune microenvironment changes and insights into the transition from DCIS to IDC with associated prognostic genes

Author: Yidi Sun, Zhuoyu Pan, Ziyi Wang, Haofei Wang, Leyi Wei, Feifei Cui, Quan Zou, and Zilong Zhang
Subjects: Single-cell transcriptomics, T cells, DCIS, IDC, Medicine
Abstract: Abstract Background Ductal carcinoma in situ (DCIS) of the breast is an early stage of breast cancer, and preventing its progression to invasive ductal carcinoma (IDC) is crucial for the early detection and treatment of breast cancer. Although single-cell transcriptome analysis technology has been widely used in breast cancer research, the biological mechanisms underlying the transition from DCIS to IDC remain poorly understood. Results We identified eight cell types through cell annotation, finding significant differences in T cell proportions between DCIS and IDC. Using this as a basis, we performed pseudotime analysis on T cell subpopulations, revealing that differentially expressed genes primarily regulate immune cell migration and modulation. By intersecting WGCNA results of T cells highly correlated with the subtypes and the differentially expressed genes, we identified six key genes: FGFBP2, GNLY, KLRD1, TYROBP, PRF1, and NKG7. Excluding PRF1, the other five genes were significantly associated with overall survival in breast cancer, highlighting their potential as prognostic biomarkers. Conclusions We identified immune cells that may play a role in the progression from DCIS to IDC and uncovered five key genes that can serve as prognostic markers for breast cancer. These findings provide insights into the mechanisms underlying the transition from DCIS to IDC, offering valuable perspectives for future research. Additionally, our results contribute to a better understanding of the biological processes involved in breast cancer progression.
Published: 2024
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2. Observation of the semileptonic decays D 0 → K S 0 π − π 0 e + ν e $$ {\textrm{D}}^0\to {\textrm{K}}_{\textrm{S}}^0{\pi}^{-}{\pi}^0{\textrm{e}}^{+}{\nu}_{\textrm{e}} $$ and D + → K S 0 π + π − e + ν e $$ {\textrm{D}}^{+}\to {\textrm{K}}_{\textrm{S}}^0{\pi}^{+}{\pi}^{-}{\textrm{e}}^{+}{\nu}_{\textrm{e}} $$

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, M. R. An, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, T. T. Chang, W. L. Chang, G. R. Che, G. Chelkov, C. Chen, Chao Chen, G. Chen, H. S. Chen, M. L. Chen, S. J. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, W. S. Cheng, S. K. Choi, X. Chu, G. Cibinetto, S. C. Coen, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Z. H. Duan, P. Egorov, Y. L. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, K. Fischer, M. Fritsch, C. Fritzsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, Z. L. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, T. T. Han, W. Y. Han, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, T. Hussain, N. Hüsken, W. Imoehl, J. Jackson, S. Jaeger, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, H. J. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, T. Johansson, X. Kui, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, R. Kappert, M. Kavatsyuk, B. C. Ke, A. Khoukaz, R. Kiuchi, R. Kliemt, O. B. Kolcu, B. Kopf, M. Kuessner, A. Kupsc, W. Kühn, J. J. Lane, P. Larin, A. Lavania, L. Lavezzi, T. T. Lei, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, J. W. Li, K. L. Li, Ke Li, L. J. Li, L. K. Li, Lei Li, M. H. Li, P. R. Li, Q. X. Li, S. X. Li, T. Li, W. D. Li, W. G. Li, X. H. Li, X. L. Li, Xiaoyu Li, Y. G. Li, Z. J. Li, Z. X. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, Y. P. Liao, J. Libby, A. Limphirat, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. H. Liu, Fang Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huanhuan Liu, Huihui Liu, J. B. Liu, J. L. 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. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. L. Ma, J. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, R. T. Ma, X. Y. Ma, Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Malde, Q. A. Malik, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, S. Pogodin, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. J. Qin, L. Q. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, R. S. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, 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. T. Sun, Y. X. Tan, C. J. Tang, G. Y. Tang, J. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, S. J. Wang, B. Wang, B. L. Wang, Bo Wang, C. W. Wang, D. Y. Wang, F. Wang, H. J. Wang, H. P. Wang, J. P. Wang, K. Wang, L. L. Wang, M. Wang, Meng Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, Ziyi Wang, D. Wei, D. H. Wei, F. Weidner, S. P. Wen, C. W. Wenzel, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, X. H. Wu, Y. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, Tao Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Yifan Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, X. D. Yu, C. Z. Yuan, L. Yuan, S. C. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. H. Zhang, H. Q. Zhang, H. Y. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. M. Zhang, L. Q. Zhang, Lei Zhang, P. Zhang, Q. Y. Zhang, Shuihan Zhang, Shulei Zhang, X. D. Zhang, X. M. Zhang, X. Y. Zhang, Xuyan Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Yan Zhang, Yao Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, G. Zhao, J. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, L. P. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. J. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
Subjects: Branching fraction, Charm Physics, e +-e − Experiments, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract By analyzing e + e − annihilation data corresponding to an integrated luminosity of 2.93 fb −1 collected at a center-of-mass energy of 3.773 GeV with the BESIII detector, the first observation of the semileptonic decays D 0 → K S 0 π − π 0 e + ν e $$ {D}^0\to {K}_S^0{\pi}^{-}{\pi}^0{e}^{+}{\nu}_e $$ and D + → K S 0 π + π − e + ν e $$ {D}^{+}\to {K}_S^0{\pi}^{+}{\pi}^{-}{e}^{+}{\nu}_e $$ is reported. In the hypothesis that all events correspond to K 1(1270) decays, the branching fractions are measured to be B D 0 → K 1 1270 − → K S 0 π − π 0 e + ν e = 1.69 − 0.46 + 0.53 ± 0.15 × 10 − 4 $$ \mathcal{B}\left({D}^0\to {K}_1{(1270)}^{-}\left(\to {K}_S^0{\pi}^{-}{\pi}^0\right){e}^{+}{\nu}_e\right)=\left({1.69}_{-0.46}^{+0.53}\pm 0.15\right)\times {10}^{-4} $$ and B D + → K ¯ 1 1270 0 → K S 0 π + π − e + ν e = 1.47 − 0.40 + 0.45 ± 0.14 × 10 − 4 $$ \mathcal{B}\left({D}^{+}\to {\overline{K}}_1{(1270)}^0\left(\to {K}_S^0{\pi}^{+}{\pi}^{-}\right){e}^{+}{\nu}^e\right)=\left({1.47}_{-0.40}^{+0.45}\pm 0.14\right)\times {10}^{-4} $$ with statistical significance of 5.4σ and 5.6σ, respectively. When combined with measurements of the K 1(1270) → K + π − π decays, the absolute branching fractions are determined to be B D 0 → K 1 1270 − e + ν e = 1.08 − 0.13 − 0.10 + 0.14 + 0.08 ± 0.21 × 10 − 3 $$ \mathcal{B}\left({D}^0\to {K}_1{(1270)}^{-}{e}^{+}{\nu}_e\right)=\left({1.08}_{-0.13-0.10}^{+0.14+0.08}\pm 0.21\right)\times {10}^{-3} $$ and B D + → K ¯ 1 1270 0 e + ν e = 1.70 − 0.23 + 0.26 ± 0.13 ± 0.35 × 10 − 3 $$ \mathcal{B}\left({D}^{+}\to {\overline{K}}_1{(1270)}^0{e}^{+}{\nu}_e\right)=\left({1.70}_{-0.23}^{+0.26}\pm 0.13\pm 0.35\right)\times {10}^{-3} $$ . The first and second uncertainties are statistical and systematic, respectively, and the third uncertainties originate from the assumed branching fractions of the K 1(1270) → Kππ decays.
Published: 2024
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3. Attentional templates for target features versus locations

Author: Mikel Jimenez, Ziyi Wang, and Anna Grubert
Subjects: Visual attention, Attentional templates, Spatial attention, Feature-based attention, ERP, N2pc, Medicine, Science
Abstract: Abstract Visual search is guided by visual working memory representations (i.e., attentional templates) that are activated prior to search and contain target-defining features (e.g., color). In the present study, we tested whether attentional templates can also contain spatial target properties (knowing where to look for) and whether attentional selection guided by such feature-specific templates is equally efficient than selection that is based on feature-specific templates (knowing what to look for). In every trial, search displays were either preceded by semantic color or location cues, indicating the upcoming target color or location, respectively. Qualitative differences between feature- and location-based template guidance were substantiated in terms of selection efficiency in low-load (one target color/location) versus high-load trials (two target colors/locations). Behavioral and electrophysiological (N2pc) measures of target selection speed and accuracy were combined for converging evidence. In line with previous studies, we found that color search was highly efficient, even under high-low conditions, when multiple attentional templates were activated to guide attentional selection in a spatially global fashion. Importantly, results in the location task almost perfectly mirrored the findings of the color task, suggesting that multiple templates for different target locations were activated concurrently when two possible target locations were task relevant. Our findings align with accounts that assume a common neuronal network during preparation for location and color search, but regard spatial and feature-based selection mechanisms as independent.
Published: 2024
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4. Measurements of K S 0 $$ {K}_S^0 $$ - K L 0 $$ {K}_L^0 $$ asymmetries in the decays Λ c + → p K L , S 0 $$ {\Lambda}_c^{+}\to p{K}_{L,S}^0 $$ , p K L , S 0 π + π − $$ p{K}_{L,S}^0{\pi}^{+}{\pi}^{-} $$ and p K L , S 0 π 0 $$ p{K}_{L,S}^0{\pi}^0 $$

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, G. R. Che, G. Chelkov, 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. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, J. J. Lane, L. Lavezzi, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, 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. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, T. Lin, 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. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, 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. Ma, F. E. Maas, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, X. K. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, S. S Su, Y. J. Su, 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. Sun, C. J. Tang, G. Y. Tang, J. Tang, M. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, 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 Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, 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. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, 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. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, M. C. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, 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. Zhang, 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. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, 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. Zhou, 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. Zhu, J. H. Zou, and J. Zu
Subjects: Charm Physics, e +-e − Experiments, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract Using e + e − annihilation data sets corresponding to an integrated luminosity of 4.5 fb −1, collected with the BESIII detector at center-of-mass energies between 4.600 and 4.699 GeV, we report the first measurements of the absolute branching fractions B Λ c + → p K L 0 $$ \mathcal{B}\left({\Lambda}_c^{+}\to p{K}_L^0\right) $$ = (1.67 ± 0.06 ± 0.04)%, B Λ c + → p K L 0 π + π − $$ \mathcal{B}\left({\Lambda}_c^{+}\to p{K}_L^0{\pi}^{+}{\pi}^{-}\right) $$ = (1.69 ± 0.10 ± 0.05)%, and B Λ c + → p K L 0 π 0 $$ \mathcal{B}\left({\Lambda}_c^{+}\to p{K}_L^0{\pi}^0\right) $$ = (2.02 ± 0.13 ± 0.05)%, where the first uncertainties are statistical and the second systematic. Combining with the known branching fractions of Λ c + → p K S 0 $$ {\Lambda}_c^{+}\to p{K}_S^0 $$ , Λ c + → p K S 0 π + π − $$ {\Lambda}_c^{+}\to p{K}_S^0{\pi}^{+}{\pi}^{-} $$ , and Λ c + → p K S 0 π 0 $$ {\Lambda}_c^{+}\to p{K}_S^0{\pi}^0 $$ , we present the first measurements of the K S 0 $$ {K}_S^0 $$ - K L 0 $$ {K}_L^0 $$ asymmetries R Λ c + K S , L 0 X = B Λ c + → K S 0 X − B Λ c + → K L 0 X B Λ c + → K S 0 X + B Λ c + → K L 0 X $$ R\left({\Lambda}_c^{+},{K}_{S,L}^0X\right)=\frac{\mathcal{B}\left({\Lambda}_c^{+}\to {K}_S^0X\right)-\mathcal{B}\left({\Lambda}_c^{+}\to {K}_L^0X\right)}{\mathcal{B}\left({\Lambda}_c^{+}\to {K}_S^0X\right)+\mathcal{B}\left({\Lambda}_c^{+}\to {K}_L^0X\right)} $$ in charmed baryon decays: R Λ c + p K S , L 0 = − 0.025 ± 0.031 $$ R\left({\Lambda}_c^{+},p{K}_{S,L}^0\right)=-0.025\pm 0.031 $$ , R Λ c + p K S , L 0 π + π − = − 0.027 ± 0.048 $$ R\left({\Lambda}_c^{+},p{K}_{S,L}^0{\pi}^{+}{\pi}^{-}\right)=-0.027\pm 0.048 $$ and R Λ c + p K S , L 0 π 0 = − 0.015 ± 0.046 $$ R\left({\Lambda}_c^{+},p{K}_{S,L}^0{\pi}^0\right)=-0.015\pm 0.046 $$ . No significant asymmetries with statistical significance are observed.
Published: 2024
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5. Short-term natural gas load forecasting based on EL-VMD-Transformer-ResLSTM

Author: Mingzhi Zhao, Guangrong Guo, Lijun Fan, Long Han, Qiancheng Yu, and Ziyi Wang
Subjects: Medicine, Science
Abstract: Abstract Due to changes in urban residents’ consumption habits and lifestyles, accurately predicting natural gas consumption has become increasingly important. To address this issue, this paper proposes a forecasting model that combines Ensemble Learning (EL), Variational Mode Decomposition (VMD), Transformer, and LSTM. First, XGBoost, CatBoost, and LightGBM are used as base learners in the ensemble learning framework, with the predictions generated by the ensemble model integrated into the original dataset. Next, the VMD method is employed to decompose the natural gas load sequence into several intrinsic mode functions (IMFs), effectively extracting the inherent features of the natural gas load sequence. Finally, the data is input into the Transformer-ResLSTM network for prediction. This network replaces the original Transformer decoder structure with an LSTM network and fully connected layers, creating a new decoder structure. Additionally, a residual connection mechanism is introduced in both the encoder of the Transformer network and the new decoder structure. Experimental results show that, compared to traditional models such as ARIMA, Transformer, GRU, and LSTM, the proposed hybrid model significantly improves prediction accuracy, reducing MSE by 92–98% and MAE by 74–83%. In summary, this method demonstrates significant potential and practical value in enhancing the accuracy of natural gas load forecasting.
Published: 2024
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6. Improved measurement of the branching fraction of h c → γη′/η and search for h c → γπ 0

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H. -R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, G. R. Che, Y. Z. Che, G. Chelkov, 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. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, J. J. Lane, L. Lavezzi, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, 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. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, T. Lin, 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. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, 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. Ma, F. E. Maas, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, X. K. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, M. Q. Ruan, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, S. S Su, Y. J. Su, 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. Sun, C. J. Tang, G. Y. Tang, J. Tang, M. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, 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 Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, 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. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, 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. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, J. H. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Junhao Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, M. C. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, 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. Zhang, 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. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, 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. Zhu, J. H. Zou, and J. Zu
Subjects: Branching fraction, e +-e − Experiments, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract The processes h c → γP (P = η′, η, π 0) are studied with a sample of (27.12 ± 0.14) × 108 ψ(3686) events collected by the BESIII detector at the BEPCII collider. The decay h c → γη is observed for the first time with the significance of 9.0 σ, and the branching fraction is determined to be (3.77 ± 0.55 ± 0.13 ± 0.26) × 10 −4, while B $$ \mathcal{B} $$ (h c → γη′) is measured to be (1.40 ± 0.11 ± 0.04 ± 0.10) × 10 −3, where the first uncertainties are statistical, the second systematic, and the third from the branching fraction of ψ(3686) → π 0 h c . The combination of these results allows for a precise determination of R h c = B h c → γη B h c → γ η ′ , $$ {R}_{h_c}=\frac{\mathcal{B}\left({h}_c\to {\pi}^0\gamma \eta \right)}{\mathcal{B}\left({h}_c\to {\pi}^0\gamma {\eta}^{\prime}\right)}, $$ which is calculated to be (27.0 ± 4.4 ± 1.0)%. The results are valuable for gaining a deeper understanding of η − η′ mixing, and its manifestation within quantum chromodynamics. No significant signal is found for the decay h c → γπ 0, and an upper limit is placed on its branching fraction of B $$ \mathcal{B} $$ (h c → γπ 0) < 5.0 × 10 −5, at the 90% confidence level.
Published: 2024
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7. Understanding and resolving the heterogeneous degradation of anion exchange membrane water electrolysis for large-scale hydrogen production

Author: Jia Lei, Ziyi Wang, Yunze Zhang, Min Ju, Hao Fei, Siyuan Wang, Chengxi Fu, Xinchang Yuan, Qiang Fu, Muhammad Usman Farid, Hui Kong, Alicia Kyoungjin An, Runxu Deng, Feng Liu, and Jian Wang
Subjects: Water electrolysis, Anion exchange membrane, Degradation mechanism, Hydrogen, Energy industries. Energy policy. Fuel trade, HD9502-9502.5, Renewable energy sources, TJ807-830
Abstract: Abstract Anion exchange membrane water electrolysis (AEMWE) has seen rapid advancements over the past decade due to its promising role in green hydrogen production. Ensuring long-term functionality is as crucial as optimizing performance to achieve commercial viability and industrial integration. However, few studies have systematically discussed the degradation issues of this technology. Therefore, a thorough understanding of AEMWE degradation is needed to guide the design, assembly, operation, and maintenance of the device over its lifetime. To address this gap, this review systematically overviewed the heterogeneous degradation of AEMWE across different material and interface levels, focusing on several key components including catalysts, ionomers, membranes, and gas diffusion layers. The influences of these components and their interfaces on the catalytic efficiency, active site density, and mass and electron transfer capabilities were discussed. Moreover, the impacts of operation conditions, including temperature, electrolyte composition, and clamping pressure, on the stable operation of AEMWE were assessed. Accordingly, current mitigation strategies to resolve these degradation phenomena were rigorously evaluated. By offering insights into optimizing operations, designing materials, and improving assessment protocols for AEMWE, this work will contribute to enhancing its stability for large-scale hydrogen production.
Published: 2024
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8. Measurement of the cross sections of e + e − → K − Ξ ¯ + Λ / Σ 0 $$ {e}^{+}{e}^{-}\to {K}^{-}{\overline{\Xi}}^{+}\Lambda /{\Sigma}^0 $$ at center-of-mass energies between 3.510 and 4.914 GeV

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, G. R. Che, G. Chelkov, 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. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, J. J. Lane, P. Larin, L. Lavezzi, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, 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. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, T. Lin, 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. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, X. K. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, 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. Sun, C. J. Tang, G. Y. Tang, J. Tang, M. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, 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 Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, 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. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, 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. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, 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, 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. Zhang, 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. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. D. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
Subjects: e +-e − Experiments, QCD, Particle and Resonance Production, Branching fraction, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract Using e + e − collision data collected with the BESIII detector at the BEPCII collider at center-of-mass energies between 3.510 and 4.914 GeV, corresponding to an integrated luminosity of 25 fb −1, we measure the Born cross sections for the process e + e − → K − Ξ ¯ + Λ / Σ 0 $$ {e}^{+}{e}^{-}\to {K}^{-}{\overline{\Xi}}^{+}\Lambda /{\Sigma}^0 $$ at thirty-five energy points with a partial-reconstruction strategy. By fitting the dressed cross sections of e + e − → K − Ξ ¯ + Λ / Σ 0 $$ {e}^{+}{e}^{-}\to {K}^{-}{\overline{\Xi}}^{+}\Lambda /{\Sigma}^0 $$ , evidence for ψ 4160 → K − Ξ ¯ + Λ $$ \psi (4160)\to {K}^{-}{\overline{\Xi}}^{+}\Lambda $$ is found for the first time with a significance of 4.4σ, including systematic uncertainties. No evidence for other possible resonances is found. In addition, the products of electronic partial width and branching fraction for all assumed resonances decaying into K − Ξ ¯ + Λ / Σ 0 $$ {K}^{-}{\overline{\Xi}}^{+}\Lambda /{\Sigma}^0 $$ are determined.
Published: 2024
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9. Study of η′ → π + π − l + l − decays at BESIII

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, W. L. Chang, G. R. Che, G. Chelkov, C. Chen, C. H. Chen, Chao Chen, G. Chen, H. S. 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. Chen, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, K. Fischer, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, Z. L. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, T. Hussain, F. Hölzken, N Hüsken, N. in der Wiesche, M. Irshad, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, A. Kupsc, W. Kühn, J. J. Lane, P. Larin, L. Lavezzi, T. T. Lei, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, Ke 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, Xiaoyu Li, Y. G. Li, Z. J. Li, Z. X. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, Y. P. Liao, J. Libby, A. Limphirat, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. H. Liu, Fang Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huanhuan 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. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, X. T. Ma, X. Y. Ma, Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Malde, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. J. Qin, L. Q. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, R. S. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, 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. Sun, C. J. Tang, G. Y. Tang, J. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, S. J. Wang, B. Wang, B. L. Wang, Bo Wang, D. Y. Wang, F. Wang, H. J. Wang, J. P. Wang, K. Wang, L. L. Wang, M. Wang, Meng 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 Wang, D. Wei, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, 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. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, Tao Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Yifan Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, X. D. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, 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. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. 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, Shuihan Zhang, Shulei Zhang, X. D. Zhang, X. M. Zhang, X. Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Yan Zhang, Yao Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. J. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
Subjects: e +-e − Experiments, QCD, CP Violation, Dark Matter, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract With a sample of (10087 ± 44) × 106 J/ψ events accumulated with the BESIII detector, we analyze the decays η′ → π + π − l + l − (l = e, μ) via the process J/ψ → γη′. The branching fractions are measured to be B $$ \mathcal{B} $$ (η′ → π + π − e + e − ) = (2.45 ± 0.02(stat.) ± 0.08(syst.)) × 10 −3 and B $$ \mathcal{B} $$ (η′ → π + π − μ + μ − ) = (2.16 ± 0.12(stat.) ± 0.06(syst.)) × 10 −5, and the ratio is B η ′ → π + π − e + e − B η ′ → π + π − μ + μ − = 113.4 ± 0.9 stat . ± 3.7 syst . $$ \frac{\mathcal{B}\left({\eta}^{\prime}\to {\pi}^{+}{\pi}^{-}{e}^{+}{e}^{-}\right)}{\mathcal{B}\left({\eta}^{\prime}\to {\pi}^{+}{\pi}^{-}{\mu}^{+}{\mu}^{-}\right)}=113.4\pm 0.9\left(\textrm{stat}.\right)\pm 3.7\left(\textrm{syst}.\right) $$ . In addition, by combining the η′ → π + π − e + e − and η′ → π + π − μ + μ − decays, the slope parameter of the electromagnetic transition form factor is measured to be b η′ = 1.30 ± 0.19 (GeV/c 2) −2, which is consistent with previous measurements from BESIII and theoretical predictions from the VMD model. The asymmetry in the angle between the π + π − and l + l − decay planes, which has the potential to reveal the CP-violation originating from an unconventional electric dipole transition, is also investigated. The asymmetry parameters are determined to be A CP η ′ → π + π − e + e − = − 0.21 ± 0.73 stat . ± 0.01 syst . % $$ {\mathcal{A}}_{CP}\left({\eta}^{\prime}\to {\pi}^{+}{\pi}^{-}{e}^{+}{e}^{-}\right)=\left(-0.21\pm 0.73\left(\textrm{stat}.\right)\pm 0.01\left(\textrm{syst}.\right)\right)\% $$ and A CP η ′ → π + π − μ + μ − = 0.62 ± 4.71 stat . ± 0.08 syst . % $$ {\mathcal{A}}_{CP}\left({\eta}^{\prime}\to {\pi}^{+}{\pi}^{-}{\mu}^{+}{\mu}^{-}\right)=\left(0.62\pm 4.71\left(\textrm{stat}.\right)\pm 0.08\left(\textrm{syst}.\right)\right)\% $$ , implying that no evidence of CP-violation is observed at the present statistics. Finally, an axion-like particle is searched for via the decay η′ → π + π − a, a → e + e − , and upper limits of the branching fractions are presented for the mass assumptions of the axion-like particle in the range of 0 − 500 MeV/c 2.
Published: 2024
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10. Comprehensive analysis of Alfin-like transcription factors associated with drought and salt stresses in wheat (Triticum aestivum L.)

Author: Hao Liu, Wenyan Liu, Ziyi Wang, Na Li, Yongfeng Xie, and Yanhong Zhao
Subjects: Wheat, Genome-wide identification, Alfin-like transcription factor, Drought and salt stresses, Biotechnology, TP248.13-248.65, Genetics, QH426-470
Abstract: Abstract Background Alfin-like proteins are a kind of plant-specific transcription factors, and play vital roles in plant growth, development and stress responses. Results In this study, a total of 27 Alfin-like transcription factors were identified in wheat. TaAL genes were unevenly distributed on chromosome. Phylogenetic analysis showed TaAL genes were divided into AL-B and AL-C subfamilies, and TaALs with closer evolutionary relationships generally shared more similar exon-intron structures and conserved motifs. The cis-acting element analysis showed MBS, ABRE and CGTCA-motif were the most common in TaAL promoters. The interacting proteins and downstream target genes of TaAL genes were also investigated in wheat. The transcriptome data and real-time PCR results indicated TaAL genes were differentially expressed under drought and salt stresses, and TaAL1-B was significantly up-regulated in response to drought stress. In addition, association analysis revealed that TaAL1-B-Hap-I allelic variation had significantly higher survival rate compared to TaAL1-B-Hap-II under drought stress. Conclusions These results will provide vital information to increase our understanding of the Alfin-like gene family in wheat, and help us in breeding better wheat varieties in the future.
Published: 2024
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11. Measurement of e + e − → ωη′ cross sections at s $$ \sqrt{s} $$ = 2.000 to 3.080 GeV

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, M. R. An, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, T. T. Chang, W. L. Chang, G. R. Che, G. Chelkov, C. Chen, Chao Chen, G. Chen, H. S. Chen, M. L. Chen, S. J. Chen, S. M. Chen, T. Chen, X. R. Chen, X. T. Chen, Y. B. Chen, Y. Q. Chen, Z. J. Chen, W. S. Cheng, S. K. Choi, X. Chu, G. Cibinetto, S. C. Coen, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Z. H. Duan, P. Egorov, Y. H. Y. Fan, Y. L. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, K Fischer, M. Fritsch, C. Fritzsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, C. Y Guan, Z. L. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, T. T. Han, W. Y. Han, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, T. Hussain, N Hüsken, W. Imoehl, J. Jackson, S. Jaeger, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, H. J. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, T. Johansson, X. Kui, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, R. Kappert, M. Kavatsyuk, B. C. Ke, A. Khoukaz, R. Kiuchi, R. Kliemt, O. B. Kolcu, B. Kopf, M. Kuessner, A. Kupsc, W. Kühn, J. J. Lane, P. Larin, A. Lavania, L. Lavezzi, T. T. Lei, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, J. W. Li, K. L. Li, Ke Li, L. J Li, L. K. Li, Lei Li, M. H. Li, P. R. Li, Q. X. Li, S. X. Li, T. Li, W. D. Li, W. G. Li, X. H. Li, X. L. Li, Xiaoyu Li, Y. G. Li, Z. J. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, Y. P. Liao, J. Libby, A. Limphirat, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. H. Liu, Fang Liu, Feng Liu, G. M. Liu, H. Liu, H. M. Liu, Huanhuan Liu, Huihui Liu, J. B. Liu, J. L. 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. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. L. Ma, J. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, R. T. Ma, X. Y. Ma, Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Malde, Q. A. Malik, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, S. Pogodin, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. J. Qin, L. Q. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, V. Rodin, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, R. S. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, 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. T. Sun, Y. X. Tan, C. J. Tang, G. Y. Tang, J. Tang, Y. A. Tang, L. Y Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, S. J. Wang, B. Wang, B. L. Wang, Bo Wang, C. W. Wang, D. Y. Wang, F. Wang, H. J. Wang, H. P. Wang, J. P. Wang, K. Wang, L. L. Wang, M. Wang, Meng Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, Ziyi Wang, D. Wei, D. H. Wei, F. Weidner, S. P. Wen, C. W. Wenzel, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, C. Wu, J. F. Wu, L. H. Wu, L. J. Wu, X. Wu, X. H. Wu, Y. Wu, Y. J. Wu, Z. Wu, L. Xia, X. M. Xian, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, Tao Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Yifan Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, X. D. Yu, C. Z. Yuan, L. Yuan, S. C. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, A. Q. Zhang, B. L. Zhang, B. X. Zhang, D. H. Zhang, G. Y. Zhang, H. Zhang, H. H. Zhang, H. Q. Zhang, H. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. M. Zhang, L. Q. Zhang, Lei Zhang, P. Zhang, Q. Y. Zhang, Shuihan Zhang, Shulei Zhang, X. D. Zhang, X. M. Zhang, X. Y. Zhang, Xuyan Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Yan Zhang, Yao Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, G. Zhao, J. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, L. P. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. J. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
Subjects: e +-e − Experiments, Particle and Resonance Production, Spectroscopy, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract We measured the Born cross sections for the process e + e − → ωη′ at 22 center-of-mass energies from 2.000 to 3.080 GeV with the BESIII detector at the BEPCII collider. We observed a resonant structure with a statistical significance of 9.6σ. A Breit-Wigner fit determines its mass to be M R = (2153 ± 30 ± 31) MeV/c 2 and its width to be Γ R = (167 ± 77 ± 7) MeV, where the first uncertainties are statistical and the second are systematic.
Published: 2024
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12. The Mohawk homeobox gene represents a marker and osteo-inhibitory factor in calvarial suture osteoprogenitor cells

Author: Yiyun Wang, Qizhi Qin, Ziyi Wang, Stefano Negri, Takashi Sono, Robert J. Tower, Zhao Li, Xin Xing, Mary Archer, Neelima Thottappillil, Manyu Zhu, Allister Suarez, Deok-Ho Kim, Tyler Harvey, Chen-Ming Fan, and Aaron W. James
Subjects: Cytology, QH573-671
Abstract: Abstract The regeneration of the mammalian skeleton’s craniofacial bones necessitates the action of intrinsic and extrinsic inductive factors from multiple cell types, which function hierarchically and temporally to control the differentiation of osteogenic progenitors. Single-cell transcriptomics of developing mouse calvarial suture recently identified a suture mesenchymal progenitor population with previously unappreciated tendon- or ligament-associated gene expression profile. Here, we developed a Mohawk homeobox (Mkx CG ; R26R tdT ) reporter mouse and demonstrated that this reporter identifies an adult calvarial suture resident cell population that gives rise to calvarial osteoblasts and osteocytes during homeostatic conditions. Single-cell RNA sequencing (scRNA-Seq) data reveal that Mkx + suture cells display a progenitor-like phenotype with expression of teno-ligamentous genes. Bone injury with Mkx + cell ablation showed delayed bone healing. Remarkably, Mkx gene played a critical role as an osteo-inhibitory factor in calvarial suture cells, as knockdown or knockout resulted in increased osteogenic differentiation. Localized deletion of Mkx in vivo also resulted in robustly increased calvarial defect repair. We further showed that mechanical stretch dynamically regulates Mkx expression, in turn regulating calvarial cell osteogenesis. Together, we define Mkx + cells within the suture mesenchyme as a progenitor population for adult craniofacial bone repair, and Mkx acts as a mechanoresponsive gene to prevent osteogenic differentiation within the stem cell niche.
Published: 2024
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13. Construction of an Interfacial Layer of Aramid Fibers Grafted with Glycidyl POSS Assisted by Heat Treatment and Evaluation of Interfacial Adhesion Properties with Epoxy Resin

Author: Yang Li, Caiwen Shi, Xiaoli Pan, Ziyi Wang, and Le Yang
Subjects: Chemistry, QD1-999
Published: 2024
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14. Measurement of absolute branching fractions of D s + $$ {\textrm{D}}_{\textrm{s}}^{+} $$ hadronic decays

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, G. R. Che, G. Chelkov, 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. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, Z. L. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, J. J. Lane, P. Larin, L. Lavezzi, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, 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, Ke 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. Z. Li, Xiaoyu Li, Y. G. Li, Z. J. Li, Z. X. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. H. Liu, Fang Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huanhuan 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. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, X. T. Ma, X. Y. Ma, Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, X. K. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, 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. Sun, C. J. Tang, G. Y. Tang, J. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, 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, Meng 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 Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, 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. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, 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. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, Tao Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Yifan Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Y. J. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, 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, Shuihan Zhang, Shulei Zhang, X. D. Zhang, X. M. Zhang, X. Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Yan Zhang, Yao Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, Z. Z. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. D. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
Subjects: Branching fraction, Charm Physics, CP Violation, e +-e − Experiments, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract Using e + e − collision data collected at the BESIII detector at center-of-mass energies between 4.128 and 4.226 GeV, corresponding to an integrated luminosity of 7.33 fb −1, we determine the absolute branching fractions of fifteen hadronic D s + $$ {D}_s^{+} $$ decays with a double-tag technique. In particular, we make precise measurements of the branching fractions B D s + → K + K − π + = 5.49 ± 0.04 ± 0.07 % $$ \mathcal{B}\left({D}_s^{+}\to {K}^{+}{K}^{-}{\pi}^{+}\right)=\left(5.49\pm 0.04\pm 0.07\right)\% $$ , B D s + → K S 0 K + = 1.50 ± 0.01 ± 0.01 % $$ \mathcal{B}\left({D}_s^{+}\to {K}_S^0{K}^{+}\right)=\left(1.50\pm 0.01\pm 0.01\right)\% $$ and B D s + → K + K − π + π 0 = 5.50 ± 0.05 ± 0.11 % $$ \mathcal{B}\left({D}_s^{+}\to {K}^{+}{K}^{-}{\pi}^{+}{\pi}^0\right)=\left(5.50\pm 0.05\pm 0.11\right)\% $$ , where the first uncertainties are statistical and the second ones are systematic. The CP asymmetries in these decays are also measured and all are found to be compatible with zero.
Published: 2024
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15. Research on safety condition assessment methodology for single tower steel box girder suspension bridges over the sea based on improved AHP-fuzzy comprehensive evaluation

Author: Huifeng Su, Cheng Guo, Ziyi Wang, Tao Han, David Bonfils Kamanda, Fengzhao Su, and Liuhong Shang
Subjects: Single tower steel box girder suspension bridges over sea, Health monitoring, Improved Analytic Hierarchy Process (AHP), Fuzzy comprehensive evaluation method, Safety assessment, Medicine, Science
Abstract: Abstract In order to propose a reliable method for assessing the safety condition for single-tower steel box girder Suspension bridges over the sea, a condition monitoring system is established by installing sensors on the bridge structure. The system is capable of gathering monitoring data that influence the safety status of the bridge. These include cable tension, load on the main tower and pylon, bearing displacement, wind direction, wind speed, and ambient temperature and humidity. Furthermore, an improved Analytic Hierarchy Process (AHP) algorithm is developed by integrating a hybrid triangular fuzzy number logic structure. This improvement, coupled with comprehensive fuzzy evaluation methods, improves the consistency, weight determination, and security evaluation capabilities of the AHP algorithm. Finally, taking the No.2 Channel Bridge as an example and based on the data collected by the health monitoring system, the application of the safety assessment method proposed in this paper provides favorable results in evaluating the overall safety status of the bridge in practical engineering applications. This provides a basis for management decisions by bridge maintenance departments. This project confirms that the research results can provide a reliable method for assessing the security status of relevant areas.
Published: 2024
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16. Uncertainty estimation- and attention-based semi-supervised models for automatically delineate clinical target volume in CBCT images of breast cancer

Author: Ziyi Wang, Nannan Cao, Jiawei Sun, Heng Zhang, Sai Zhang, Jiangyi Ding, Kai Xie, Liugang Gao, and Xinye Ni
Subjects: Breast cancer, Automatic delineation, Cone-beam computed tomography, clinical target volume, Semi-supervised learning, Uncertainty estimation, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Abstract Objectives Accurate segmentation of the clinical target volume (CTV) of CBCT images can observe the changes of CTV during patients' radiotherapy, and lay a foundation for the subsequent implementation of adaptive radiotherapy (ART). However, segmentation is challenging due to the poor quality of CBCT images and difficulty in obtaining target volumes. An uncertainty estimation- and attention-based semi-supervised model called residual convolutional block attention-uncertainty aware mean teacher (RCBA-UAMT) was proposed to delineate the CTV in cone-beam computed tomography (CBCT) images of breast cancer automatically. Methods A total of 60 patients who undergone radiotherapy after breast-conserving surgery were enrolled in this study, which involved 60 planning CTs and 380 CBCTs. RCBA-UAMT was proposed by integrating residual and attention modules in the backbone network 3D UNet. The attention module can adjust channel and spatial weights of the extracted image features. The proposed design can train the model and segment CBCT images with a small amount of labeled data (5%, 10%, and 20%) and a large amount of unlabeled data. Four types of evaluation metrics, namely, dice similarity coefficient (DSC), Jaccard, average surface distance (ASD), and 95% Hausdorff distance (95HD), are used to assess the model segmentation performance quantitatively. Results The proposed method achieved average DSC, Jaccard, 95HD, and ASD of 82%, 70%, 8.93, and 1.49 mm for CTV delineation on CBCT images of breast cancer, respectively. Compared with the three classical methods of mean teacher, uncertainty-aware mean-teacher and uncertainty rectified pyramid consistency, DSC and Jaccard increased by 7.89–9.33% and 14.75–16.67%, respectively, while 95HD and ASD decreased by 33.16–67.81% and 36.05–75.57%, respectively. The comparative experiment results of the labeled data with different proportions (5%, 10% and 20%) showed significant differences in the DSC, Jaccard, and 95HD evaluation indexes in the labeled data with 5% versus 10% and 5% versus 20%. Moreover, no significant differences were observed in the labeled data with 10% versus 20% among all evaluation indexes. Therefore, we can use only 10% labeled data to achieve the experimental objective. Conclusions Using the proposed RCBA-UAMT, the CTV of breast cancer CBCT images can be delineated reliably with a small amount of labeled data. These delineated images can be used to observe the changes in CTV and lay the foundation for the follow-up implementation of ART.
Published: 2024
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17. Search for ∆S = 2 nonleptonic hyperon decays Ω − → Σ0 π − and Ω − → nK −

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, G. R. Che, G. Chelkov, 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. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, F. Hanisch, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, Y. S. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, J. J. Lane, P. Larin, L. Lavezzi, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, 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. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, Y. P. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, T. Lin, 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. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, J. R. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, T. Ma, X. T. Ma, X. Y. Ma, Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, X. K. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, 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. Sun, C. J. Tang, G. Y. Tang, J. Tang, M. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, 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 Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, 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. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, 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. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, T. Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, 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, 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. Zhang, 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. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. D. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
Subjects: Beyond Standard Model, e +-e − Experiments, Rare Decay, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract Using (27.12 ± 0.14) × 108 ψ(3686) events collected by the BESIII detector at the center-of-mass energy of s $$ \sqrt{s} $$ = 3.686 GeV, we search for the first time for two nonleptonic hyperon decays that change strangeness by two units, Ω − → Σ0 π − and Ω − → nK − . No significant signal is observed. The upper limits on their decay branching fractions are determined to be B $$ \mathcal{B} $$ (Ω − → Σ0 π − ) < 5.4 × 10 −4 and B $$ \mathcal{B} $$ (Ω − → nK − ) < 2.4 × 10 −4 at the 90% confidence level.
Published: 2024
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18. Measurement of Born cross section of e + e − → Σ + Σ ¯ − $$ {e}^{+}{e}^{-}\to {\Sigma}^{+}{\overline{\Sigma}}^{-} $$ at center-of-mass energies between 3.510 and 4.951 GeV

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, G. R. Che, G. Chelkov, 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. Chen, S. K. Choi, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, Z. L. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, S. L. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, T. Hussain, F. Hölzken, N Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, J. J. Lane, P. Larin, L. Lavezzi, T. T. Lei, Z. H. Lei, M. Lellmann, T. Lenz, 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, Ke 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. Z. Li, Xiaoyu Li, Y. G. Li, Z. J. Li, Z. X. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. H. Liu, Fang Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huanhuan 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. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, X. T. Ma, X. Y. Ma, Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Malde, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, X. K. Qiao, J. J. Qin, L. Q. Qin, L. Y. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, 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. Sun, C. J. Tang, G. Y. Tang, J. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, 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, Meng 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 Wang, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, 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. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, 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. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, Tao Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Yifan Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Y. J. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, 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, Shuihan Zhang, Shulei Zhang, X. D. Zhang, X. M. Zhang, X. Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Yan Zhang, Yao Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, Z. Z. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, B. M. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, K. S. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. D. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
Subjects: e +-e − Experiments, QCD, Branching fraction, Electroweak Interaction, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract Using 24.1 fb −1 of e + e − collision data collected with the BESIII detector at the BEPCII collider, the Born cross sections and effective form factors of the e + e − → Σ + Σ ¯ − $$ {e}^{+}{e}^{-}\to {\Sigma}^{+}{\overline{\Sigma}}^{-} $$ reaction are measured. The measurements are performed at center-of-mass energies ranging from 3.510 to 4.951 GeV. No significant evidence for the decay of the charmonium(-like) states, ψ(3770), ψ(4040), ψ(4160), Y(4230), Y(4360), ψ(4415), and Y(4660), into a Σ + Σ ¯ − $$ {\Sigma}^{+}{\overline{\Sigma}}^{-} $$ final state is observed. Consequently, upper limits for the products of the branching fractions and the electronic partial widths at the 90% confidence level are reported for these decays.
Published: 2024
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19. Laboratory channel widening quantification using deep learning

Author: Ziyi Wang, Haifei Liu, Chao Qin, Robert R. Wells, Liekai Cao, Ximeng Xu, Henrique G. Momm, and Fenli Zheng
Subjects: Linear erosion channel, Channel expansion, Failure block, Sediment discharge, Channel edge detection, Science
Abstract: Linear erosion channel (LEC) devastates arable land and significantly contributes to soil loss in agricultural watersheds. In the presence of a less- or non-erodible layer, channel widening governs the erosion process once the channel bed incises to this layer, accompanied by failure block generation and transport. Current knowledge on channel widening, however, is limited due to the lack of robust and efficient methods to capture the rapid sidewall expansion process. Laboratory experiments were designed to simulate the channel widening process with an initial channel width of 10 cm. Two packed soil beds with a non-erodible layer and two slope gradients (5 % and 11 %) were subjected to the inflow rate of 0.67 L/s. Images were captured by mounted digital cameras and automatically transformed into orthophotos. Channel edges and failure blocks were automatically detected by deep learning algorithm in a newly developed Channel-DeepLab network model based upon DeepLabv3+ platform. The procedure includes learning samples labelling, data augmentation, model construction, training, and validation. Sediment discharge and changes in channel width, geometry of channel edges, and failure blocks were measured. The results indicate that initial period is critical for erosion prediction and remediation due to its small sidewall failure interval, high channel expansion rate and sediment discharge. Channel surface area has great potential on accumulated sediment discharge prediction. The slope section that witnessed the fastest channel widening rate migrated downwards when slope gradient increased from 5 % to 11 %. The total number and area of the failure blocks increased with time, while the collapse frequency of the sidewalls decreased. Upstream reach experienced the highest sidewall collapse frequency and rate of disaggregation and transport, while the downstream reach experienced the highest total number of failure blocks. A time lag was found between sidewall collapse and sediment discharge, which increased as time progressed, attributing to decreased runoff erosivity as the flow velocity decreased. Results of this study will provide methodological support for channel sidewall and streambank retreat monitoring, realizing the automatic detection of channel edges and efficient output of rapid sidewall expansion process with high temporal and spatial precision. Future work can be focused on broadening the applicability of the Channel-DeepLab network model and quantifying the delayed response process between sidewall failure and sediment discharge.
Published: 2024
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20. Bibliometric analysis of research trends on factors affecting older adults with mild cognitive impairment

Author: Lei Yang, Rushi Yang, Bo Wang, Tiantian Liu, and Ziyi Wang
Subjects: mild cognitive impairment, elderly, influencing factors, bibliometrics, visual analysis, Neurology. Diseases of the nervous system, RC346-429
Abstract: BackgroundIn recent years, the problem of cognitive impairment in the elderly has become increasingly prominent. Understanding the research trend of influencing factors of mild cognitive impairment, and provide reference for medical staff to early screening of the elderly with mild cognitive impairment.ObjectiveThrough the visual analysis of the influence factors of the elderly with mild cognitive impairment, the current research status was discussed.MethodsThe relevant literature in the field of influencing factors of mild cognitive impairment in the elderly included in the Web of Science core collection database from 2013 to 2022 was searched. Using software such as Cite Space and VOS viewer to visually analyze literature citations, country, keywords, and development trends.ResultsA total of 547 relevant literatures were included, and the number of publications showed an increasing trend in the past ten years. The United States ranked first in both the number of published papers (157) and centrality (0.34), and the United States and China had a greater influence on the influencing factors of mild cognitive impairment. Alzheimer’s disease, cognitive decline, the elderly, risk factors, are the research hotspot in this field.ConclusionCognitive decline will affect the autonomy of the elderly. Cognitive frailty, MRI is the forefront of MCI research, to understand the research hotspots and frontiers in this field, to conduct early screening and intervention guidance for people with mild cognitive impairment, so as to delay the occurrence of Alzheimer’s disease, and reduce the pressure on family caregivers and society.
Published: 2024
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21. Astaxanthin attenuates doxorubicin-induced liver injury via suppression of ferroptosis in rats

Author: Bowen Yin, Jingyi Ren, Xuanyi Liu, Miaomiao Lu, Dan Huang, Yadong Zhang, Jinshi Zuo, Rui Wen, Huanting Pei, Siqi Zhu, Zhenao Zhang, Ziyi Wang, and Yuxia Ma
Subjects: Astaxanthin, Doxorubicin, Hepatotoxicity, Iron accumulation, Ferroptosis, Nutrition. Foods and food supply, TX341-641
Abstract: Hepatotoxicity is an significant side effect of doxorubicin (DOX), while astaxanthin (ASX) has the anti-liver injury biological functions. In this study, we utilized in vivo and in vitro methods to investigate the protective effect of ASX against DOX-induced hepatotoxicity and elucidate its potential mechanism. Our researchers measured liver injury indicators and the expression of ferroptosis-related protein (transferrin receptor 1 (TFRC), ferroportin 1 (FPN1), ferritin light chain (FTL), ferritin heavy chain-1 (FTH1), glutathione peroxidase 4 (GPX4), and solute carrier family 7 member 11 (SLC7A11)) in rats and HepG2 cells. We found that ASX could effectively reduce ferroptosis level and relieve a range of DOX-caused manifestations of liver injury, including inflammation and oxidative damage. ASX may play the same role as ferroptosis inhibitor, Fer-1 and DFP, in this process. The findings demonstrated that the intervention with ASX ameliorated the DOX-induced liver injury by mitigating the DOX-induced ferroptosis through inhibiting iron accumulation.
Published: 2024
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22. circRUNX2.2, highly expressed in Marek's disease tumor tissues, functions in cis to regulate parental gene RUNX2 expression

Author: Lulu Wang, Gang Zheng, Yiming Yuan, Ziyi Wang, Qinyuan Wang, Meng Sun, Junfeng Wu, Changjun Liu, Yongzhen Liu, Bo Zhang, Hao Zhang, Ning Yang, and Ling Lian
Subjects: chicken, Marek's disease, RUNX2, circRUNX2.2, transcriptional regulation, Animal culture, SF1-1100
Abstract: ABSTRACT: Marek's disease (MD), an immunosuppression disease induced by Marek's disease virus (MDV), is one of the significant diseases affecting the health and productive performance of poultry. The roles of circular RNAs (circRNAs) in MD development were poorly understood. In this study, we found a circRNA derived from exon 6 of RUNX family transcription factor 2 (RUNX2) gene, named circRUNX2.2, was highly expressed in chicken tumorous spleens (TS) induced by MDV. Through fluorescence in situ hybridization and nuclear-cytoplasmic separation assay, we determined circRUNX2.2 was mainly located in the nucleus. Knockout experiments confirmed that the flanking complementary sequences (RCMs) mediated its circularization. Gain of function assay and dual luciferase reporter gene assay revealed that circRUNX2.2 could promote the expression of RUNX2 via binding with its promoter region. RNA antisense purification assay and mass spectrometry assay showed circRUNX2.2 could recruit proteins such as CHD9 protein. Knocking down CHD9 expression decreased the expression of RUNX2 gene, which confirmed the positive regulation that circRUNX2.2 on RUNX2 expression was probably facilitated via recruiting CHD9 protein. Functional experiments showed that circRUNX2.2 promoted the proliferation of the MD lymphoma-derived chicken cell line, MDCC-MSB1, which confirmed the potential oncogenic role of circRNX2.2 in tumor development. In conclusion, we found that the RUNX2-derived circRUNX2.2 can positively regulate the transcription of the parental gene RUNX2 in a cis-acting manner. The high expression of circRUNX2.2 in MD tumor tissues indicated that it might mediate MD lymphoma progression.
Published: 2024
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23. Causal relationship between plasma lipidome and four types of pancreatitis: a bidirectional Mendelian randomization study

Author: Runzhou Ma, Chengming Chen, Ziyi Wang, Huaibin Guo, and Wanxing Zhang
Subjects: lipidome, pancreatitis, causal inference, bidirectional Mendelian randomization, sensitivity, Diseases of the endocrine glands. Clinical endocrinology, RC648-665
Abstract: BackgroundPancreatitis is a serious and complex inflammatory disease that imposes a severe effect on quality of life. Links between plasma lipidome and pancreatitis have been reported, some of which have not yet been clearly elucidated.MethodsTherefore, our study aimed to investigate the causal relationships between plasma lipidome and four types of pancreatitis by conducting a bidirectional, two-sample Mendelian randomization (MR) analysis. We obtained genetic variants associated with 179 lipid species from a Genome-wide association analysis of plasma lipidome. The aggregated statistical data of acute pancreatitis (AP), alcohol-induced acute pancreatitis (AAP), chronic pancreatitis (CP), and alcohol-induced chronic pancreatitis (ACP) from the FinnGen consortium were exploited as the outcome. The inverse variance weighted (IVW) technique as the main method was used for MR analysis and sensitivity analyses were used to evaluate heterogeneity and pleiotropy.ResultsAfter FDR correction, SE (27:1/20:4) (OR = 0.938, 95%CI = 0.906-0.972, P = 4.38 × 10-4, PFDR = 0.039) was identified to be significantly associated with AP risk. Eight lipid species were identified to be significantly associated with CP risk: SE (27:1/20:4) (OR = 0.911, 95%CI = 0.869-0.954, P = 8.89 × 10-5, PFDR = 0.016), LPC (20:4) (OR = 0.892, 95%CI = 0.843-0.945, P = 9.74 × 10-5, PFDR = 0.009), PC (16:0_22:5) (OR = 0.880, 95%CI = 0.818-0.947, P = 6.29 × 10-4, PFDR = 0.028), PC (17:0_20:4) (OR = 0.893, 95%CI = 0.842-0.948, P = 1.76 × 10-4, PFDR = 0.010), PC (18:0_20:4) (OR = 0.920, 95%CI = 0.874-0.969, P = 1.70 × 10-3, PFDR = 0.038), PC (O-16:0/20:4) (OR = 0.871, 95%CI = 0.804-0.943, P = 6.95 × 10-4, PFDR = 0.025), PC (O-16:1/20:4) (OR = 0.890, 95%CI = 0.832-0.953, P = 7.85 × 10-4, PFDR = 0.023), and PE (O-18:1/20:4) (OR = 0.866, 95%CI = 0.791-0.947, P = 1.61 × 10-3, PFDR = 0.041). Furthermore, genetically predicted increased LPC (20:4) (OR = 0.862, 95%CI = 0.796-0.934, P = 3.00 × 10-4, PFDR = 0.027) and SM (34:2;O2) (OR = 0.753, 95%CI = 0.659-0.860, P = 2.97 × 10-5, PFDR = 0.005) levels were associated with decreased risk of ACP.ConclusionsOur findings provide evidence of causal associations between the specific types of lipidome and pancreatitis, offering new insights into future clinical research.
Published: 2024
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24. Co‐Regulating Solvation Structure and Hydrogen Bond Network via Bio‐Inspired Additive for Highly Reversible Zinc Anode

Author: Sida Zhang, Qianzhi Gou, Weigen Chen, Haoran Luo, Ruduan Yuan, Kaixin Wang, Kaida Hu, Ziyi Wang, Changding Wang, Ruiqi Liu, Zhixian Zhang, Yu Lei, Yujie Zheng, Lei Wang, Fu Wan, Baoyu Li, and Meng Li
Subjects: aqueous zinc‐ion batteries, electrode interface, electrolyte additive, zinc anode, Science
Abstract: Abstract The feasibility of aqueous zinc‐ion batteries for large‐scale energy storage is hindered by the inherent challenges of Zn anode. Drawing inspiration from cellular mechanisms governing metal ion and nutrient transport, erythritol is introduced, a zincophilic additive, into the ZnSO4 electrolyte. This innovation stabilizes the Zn anode via chelation interactions between polysaccharides and Zn2+. Experimental tests in conjunction with theoretical calculation results verified that the erythritol additive can simultaneously regulate the solvation structure of hydrated Zn2+ and reconstruct the hydrogen bond network within the solution environment. Additionally, erythritol molecules preferentially adsorb onto the Zn anode, forming a dynamic protective layer. These modifications significantly mitigate undesirable side reactions, thus enhancing the Zn2+ transport and deposition behavior. Consequently, there is a notable increase in cumulative capacity, reaching 6000 mA h cm⁻2 at a current density of 5 mA cm−2. Specifically, a high average coulombic efficiency of 99.72% and long cycling stability of >500 cycles are obtained at 2 mA cm−2 and 1 mA h cm−2. Furthermore, full batteries comprised of MnO2 cathode and Zn anode in an erythritol‐containing electrolyte deliver superior capacity retention. This work provides a strategy to promote the performance of Zn anodes toward practical applications.
Published: 2024
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25. Efferocytosis assay to quantify the engulfment and acidification of apoptotic cells by macrophages using flow cytometry

Author: Xun Wu, Ziyi Wang, Tyler Shern, and Hanrui Zhang
Subjects: Cell-based Assays, Flow Cytometry, Immunology, Science (General), Q1-390
Abstract: Summary: Efficient macrophage efferocytosis maintains homeostasis and resolves inflammation. Here, we provide a protocol to assess the engulfment and acidification of apoptotic cells (ACs) by macrophages. We describe steps for preparing bone marrow-derived macrophages (BMDMs) and peritoneal macrophages (PMs), fluorescent labeling of ACs using both a pH-sensitive dye, pHrodo-Red succinimidyl ester, and a pH-insensitive dye, Hoechst, and subsequent incubation with macrophages for efferocytosis. We then detail procedures for flow cytometry-based quantification of engulfment and acidification.For complete details on the use and execution of this protocol, please refer to Shi and Wu et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.
Published: 2024
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26. Reconstructing Redox Landscape With Coupled Nitrogen‐Sulfur Isotopes: A Case Study From Middle‐Late Triassic Chang 7 Member of the Yanchang Formation in the Ordos Basin (North China)

Author: Xubin Wang, Lin Dong, Tong Li, Kun Ling, Changhu Zhang, Yijie Bin, Ziyi Wang, Zhijun Jin, and Jinhua Fu
Subjects: nitrogen isotopes, pyrite sulfur isotopes, benthic euxinia, Ordos Basin, Geophysics. Cosmic physics, QC801-809, Geology, QE1-996.5
Abstract: Abstract Euxinia, a crucial geological condition, usually signifies more severe extinction events attributed to deoxygenation in Earth's history. Despite extensive exploration of various proxies in paleoredox studies, most are primarily utilized to reconstruct atmospheric pO2, the proportion of anoxic water relative to the entire basin, and broader trends in redox states. Few, however, hold the capacity to precisely delineate local euxinia within confined areas. To address this gap and gain insights into the temporal and spatial extent of benthic euxinia, we propose leveraging the synergistic analysis of total nitrogen isotopes (δ15NTN) and pyrite sulfur isotopes (δ34Spy). Our study focuses on the Triassic Chang 7 Member from the Yanchang Formation, Ordos Basin, North China. Through coupling the δ15NTN and δ34Spy systematics on 11 drill cores within the Ordos Basin, we reconstruct the temporal and spatial distribution of the benthic euxinia zone during the Chang‐7 period. Our results suggest strong spatial heterogeneity of benthic redox conditions, with the euxinia boundary shifting from the central lake to the southwestern sections. Moreover, we identify redox‐controlling factors, including organic carbon loading, water depth, and potential water circulation, and evaluate their interplay with benthic euxinia. Furthermore, the discernment of water circulation patterns may provide an innovative approach to restore the paleowind direction. These findings highlight the effectiveness of coupling δ15NTN and δ34Spy in reconstructing the local benthic redox landscape of benthic environments, and enrich our understanding of biogeochemical processes.
Published: 2024
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27. Danzhou chicken: a unique genetic resource revealed by genome-wide resequencing data

Author: Xinfeng Xie, Liguang Shi, Ziqi Zhong, Ziyi Wang, Deyou Pan, Guanyu Hou, and Qian Xiao
Subjects: Danzhou chicken, whole-genome sequencing, single nucleotide polymorphism, genetic diversity, population structure, Animal culture, SF1-1100
Abstract: ABSTRACT: Danzhou chicken (DZ) is a local breed in China noted for its strong adaptability, roughage resistance, strong wildness, and delicious taste, thus containing important genetic resources. In this study, genome re-sequencing data was generated from 200 DZ chickens. Combined with previously generated data from 72 additional chickens across six other exotic and local breeds, these data were used to systematically evaluate the germplasm characteristics of DZ chickens from a genomic perspective. Unlike exotic breeds, both DZ and southern local chicken varieties exhibited high genetic diversity, and the genetic distance between DZ and southern local chickens was smaller than the genetic distance between DZ and exotic chickens. A reconstructed Neighbor-Joining phylogenetic tree indicated that all sampled populations clustered into single independent populations, with DZ chickens showing clear evidence of intra-population differentiation, forming 2 subpopulations. Principal component analysis and ADMIXTURE analysis showed that DZ was significantly different from other breeds. These results indicate that DZ is a unique genetic resource that is different from other southern native and exotic chickens. The results of the study will improve our understanding of the genetic structure and current status of the DZ breed, which is of great significance in promoting the conservation of genetic resources of DZ chickens and fostering breed innovations and genetic improvement.
Published: 2024
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28. Evolutionary characteristics and influencing factors of carbon emissions in China: An examination of a 43-year urban scale

Author: Zhongyin Wei, Tinghao Hu, Zhongcheng Wei, Ziyi Wang, and Dongqi Sun
Subjects: Carbon emissions, Carbon distribution, Urbanization, Evolutionary characteristics, Heterogeneous effect, Reform and opening up, Science (General), Q1-390, Social sciences (General), H1-99
Abstract: Since 1978, China's rapid urbanization and industrialization have significantly increased carbon emissions. This study employs spatial autocorrelation, kernel density estimation, and spatiotemporal geographically weighted regression (GTWR) methods to analyze the spatiotemporal evolution characteristics of carbon emissions across 336 Chinese cities from 1978 to 2020. It also explores the dominant influencing factors for different cities at various stages of development. The findings reveal that carbon emissions in Chinese cities exhibit a stepwise growth pattern: “slow growth (1978–1995) - low-level stability (1996–2000) - rapid growth (2001–2012) - high-level stability (2013–2020).'' The gap between cities has widened rapidly, and spatially, the distribution follows a “core-periphery” pattern. The increase in carbon emissions in core cities has transformed the urban hierarchy from a “generally low-carbon” structure to a “pyramid” structure. Compared to 1995, the influence of population size on carbon emissions decreased in 2020 (0.54–0.38), while the impact of infrastructure development and technological advances increased (0.02–0.25, 0.09 to 0.19). Due to the varying stages of urban development across regions, the influencing factors of carbon emissions exhibit spatial heterogeneity. Specifically, population size has a stronger positive impact on carbon emissions in the Southeast, technological advances in East and North China, and industrial structure in the Yangtze River Basin region. Infrastructure construction and investment levels show a dampening effect on carbon emissions in the Yangtze River Basin. Finally, the study proposes policy recommendations focusing on implementing regional “gradient” carbon reduction and promoting regional collaborative carbon reduction driven by core cities.
Published: 2024
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29. Wheat kinase TaSnRK2.4 forms a functional module with phosphatase TaPP2C01 and transcription factor TaABF2 to regulate drought response

Author: Yanyang Zhang, Xiaoyang Hou, Tianjiao Li, Ziyi Wang, Jiaqi Zhang, Chunlin Zhang, Xianchang Liu, Xinxin Shi, Wanrong Duan, and Kai Xiao
Subjects: Triticum aestivum, SnRK2.4 kinase, Gene expression, Protein interaction, Transgene analysis, Transcriptional activation, Agriculture, Agriculture (General), S1-972
Abstract: SNF1-related protein kinase 2 (SnRK2) family members are essential components of the plant abscisic acid (ABA) signaling pathway initiated by osmotic stress and triggering a drought stress response. This study characterized the molecular properties of TaSnRK2.4 and its function in mediating adaptation to drought in Triticum aestivum. Transcripts of TaSnRK2.4 were upregulated upon drought and ABA signaling and associated with drought- and ABA-responsive cis-elements ABRE and DRE, and MYB and MYC binding sites in the promoter as indicated by reporter GUS protein staining and activity driven by truncations of the promoter. Yeast two-hybrid, BiFC, and Co-IP assays indicated that TaSnRK2.4 protein interacts with TaPP2C01 and an ABF transcription factor (TF) TaABF2. The results suggested that TaSnRK2.4 forms a functional TaPP2C01-TaSnRK2.4-TaABF2 module with its upstream and downstream partners. Transgene analysis revealed that TaSnRK2.4 and TaABF2 positively regulate drought tolerance whereas TaPP2C01 acts negatively by modulating stomatal movement, osmotic adjustment, reactive oxygen species (ROS) homeostasis, and root morphology. Expression analysis, yeast one-hybrid, and transcriptional activation assays indicated that several osmotic stress-responsive genes, including TaSLAC1-4, TaP5CS3, TaSOD5, TaCAT1, and TaPIN4, are regulated by TaABF2. Transgene analysis verified their functions in positively regulating stomatal movement (TaSLAC1-4), proline accumulation (TaP5CS3), SOD activity (TaSOD5), CAT activity (TaCAT1), and root morphology (TaPIN4). There were high correlations between plant biomass and yield with module transcripts in a wheat variety panel cultivated under drought conditions in the field. Our findings provide insights into understanding plant drought response underlying the SnRK2 signaling pathway in common wheat.
Published: 2024
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30. Observation of ψ(3686) → Ω − K + Ξ ¯ 0 $$ {\varOmega}^{-}{K}^{+}{\overline{\Xi}}^0 $$ + c.c.

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, O. Afedulidis, X. C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, G. R. Che, G. Chelkov, 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. Chen, S. K. Choi, X. Chu, G. Cibinetto, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, C. Q. Deng, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Y. Y. Duan, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, Y. Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, Y. T. Feng, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, X. B. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y. Guan, Z. L. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, J. Gutierrez, K. L. Han, T. T. Han, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, B. Y. Hu, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, W. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, D. Jiang, H. B. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, J. K. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, X. M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O. B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, J. J. Lane, P. Larin, L. Lavezzi, T. T. Lei, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, Ke 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. Z. Li, Xiaoyu Li, Y. G. Li, Z. J. Li, Z. X. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, J. Libby, A. Limphirat, C. C. Lin, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. H. Liu, Fang Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huanhuan 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. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. Ma, H. L. Ma, J. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, X. T. Ma, X. Y. Ma, Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Malde, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, B. Moses, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L. S. Nie, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, X. K. Qiao, J. J. Qin, L. Q. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, Z. H. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, Z. J. Shang, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, S. Y. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, 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. Sun, C. J. Tang, G. Y. Tang, J. Tang, Y. A. Tang, L. Y. Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, Y. Wan, 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, Meng 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 Wang, D. Wei, D. H. Wei, F. Weidner, S. P. Wen, Y. R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, 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. Wu, L. Xia, X. M. Xian, B. H. Xiang, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, Tao Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Yifan Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, X. D. Yu, Y. C. Yu, C. Z. Yuan, J. Yuan, L. Yuan, S. C. Yuan, Y. Yuan, Y. J. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, S. H. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, 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. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, L. M. Zhang, Lei Zhang, P. Zhang, R. Y. Zhang, Shuihan Zhang, Shulei Zhang, X. D. Zhang, X. M. Zhang, X. Y. Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Y. M. Zhang, Yan Zhang, Yao Zhang, Z. D. Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, Z. Z. Zhang, G. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, N. Zhao, R. P. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, J. Y. Zhou, L. P. Zhou, S. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. D. Zhu, W. J. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
Subjects: Branching fraction, e +-e − Experiments, QCD, Quarkonium, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract Using (27.12 ± 0.14) × 108 ψ(3686) events collected with the BESIII detector at BEPCII, the decay of ψ(3686) → Ω − K + Ξ ¯ 0 $$ {\varOmega}^{-}{K}^{+}{\overline{\Xi}}^0 $$ + c.c. is observed for the first time. The branching fraction of this decay is measured to be B ψ 3686 → Ω − K + Ξ ¯ 0 + c . c . $$ {\mathcal{B}}_{\psi (3686)\to {\varOmega}^{-}{K}^{+}{\overline{\Xi}}^0+\textrm{c}.\textrm{c}.} $$ = (2.78 ± 0.40 ± 0.18) × 10 −6, where the first uncertainty is statistical and the second is systematic. Possible baryon excited states are searched for in this decay, but no evident intermediate state is observed with the current sample size.
Published: 2024
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31. KLF7 regulates super-enhancer-driven IGF2BP2 overexpression to promote the progression of head and neck squamous cell carcinoma

Author: Hongshi Cai, Jianfeng Liang, Yaoqi Jiang, Ziyi Wang, Hongyu Li, Wenjin Wang, Cheng Wang, and Jinsong Hou
Subjects: Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Abstract Background Head and neck squamous carcinoma (HNSCC) is known for its high aggressiveness and susceptibility to cervical lymph node metastasis, which greatly contributes to its poor prognosis. During tumorigenesis, many types of cancer cells acquire oncogenic super-enhancers (SEs) that drive the overexpression of oncogenes, thereby maintaining malignant progression. This study aimed to identify and validate the role of oncogenic SE-associated genes in the malignant progression of HNSCC. Methods We identified HNSCC cell-specific SE-associated genes through H3K27Ac ChIP-seq and overlapped them with HNSCC-associated genes obtained from The Cancer Genome Atlas (TCGA) dataset and Gene Expression Omnibus (GEO) datasets using weighted gene coexpression network analysis (WGCNA) to identify hub genes. The expression of IGF2BP2 and KLF7 in HNSCC was detected using clinical samples. To determine the biological role of IGF2BP2, we performed CCK-8, colony formation assay, Transwell migration assay, invasion assay, and orthotopic xenograft model experiments. Furthermore, we utilized a CRISPR/Cas9 gene-editing system, small-molecule inhibitors, ChIP-qPCR, and dual-luciferase reporter assays to investigate the molecular mechanisms of IGF2BP2 and its upstream transcription factors. Results Our study identified IGF2BP2 as a hub SE-associated gene that exhibited aberrant expression in HNSCC tissues. Increased expression of IGF2BP2 was observed to be linked with malignant progression and unfavorable prognosis in HNSCC patients. Both in vitro and in vivo experiments confirmed that IGF2BP2 promotes the tumorigenicity and metastasis of HNSCC by promoting cell proliferation, migration, and invasion. Mechanistically, the IGF2BP2-SE region displayed enrichment for H3K27Ac, BRD4, and MED1, which led to the inhibition of IGF2BP2 transcription and expression through deactivation of the SE-associated transcriptional program. Additionally, KLF7 was found to induce the transcription of IGF2BP2 and directly bind to its promoter and SE regions. Moreover, the abundance of KLF7 exhibited a positive correlation with the abundance of IGF2BP2 in HNSCC. Patients with high expression of both KLF7 and IGF2BP2 showed poorer prognosis. Lastly, we demonstrated that the small molecule inhibitor JQ1, targeting BRD4, attenuated the proliferation and metastatic abilities of HNSCC cells. Conclusions Our study reveals the critical role of IGF2BP2 overexpression mediated by SE and KLF7 in promoting HNSCC progression. Targeting SE-associated transcriptional programs may represent a potential therapeutic strategy in managing HNSCC.
Published: 2024
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32. High Dynamic Range Probing of Single-Molecule Mechanical Force Transitions at Cell–Matrix Adhesion Bonds by a Plasmonic Tension Nanosensor

Author: Shaojun Wu, Wenjing Tang, Ziyi Wang, Zhuodong Tang, Peng Zheng, Zixuan Chen, and Jun-Jie Zhu
Subjects: Chemistry, QD1-999
Published: 2024
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33. Ubiquitous protein lactylation in health and diseases

Author: Junyong Wang, Ziyi Wang, Qixu Wang, Xiao Li, and Yaping Guo
Subjects: Glycolysis, Lactate, Lactylation, Pathological process, Cytology, QH573-671
Abstract: Abstract For decades, lactate has been considered a byproduct of glycolysis. The lactate shuttle hypothesis shifted the lactate paradigm, demonstrating that lactate not only plays important roles in cellular metabolism but also cellular communications, which can transcend compartment barriers and can occur within and among different cells, tissues and organs. Recently, the discovery that lactate can induce a novel post-translational modification, named lysine lactylation (Kla), brings forth a new avenue to study nonmetabolic functions for lactate, which has inspired a ‘gold rush’ of academic and commercial interest. Zhang et al. first showed that Kla is manifested in histones as epigenetic marks, and then mounting evidences demonstrated that Kla also occurs in diverse non-histone proteins. The widespread Kla faithfully orchestrates numerous biological processes, such as transcription, metabolism and inflammatory responses. Notably, dysregulation of Kla touches a myriad of pathological processes. In this review, we comprehensively reviewed and curated the existing literature to retrieve the new identified Kla sites on both histones and non-histone proteins and summarized recent major advances toward its regulatory mechanism. We also thoroughly investigated the function and underlying signaling pathway of Kla and comprehensively summarize how Kla regulates various biological processes in normal physiological states. In addition, we also further highlight the effects of Kla in the development of human diseases including inflammation response, tumorigenesis, cardiovascular and nervous system diseases and other complex diseases, which might potentially contribute to deeply understanding and interpreting the mechanism of its pathogenicity. Graphical Abstract
Published: 2024
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34. A 4D-CBCT correction network based on contrastive learning for dose calculation in lung cancer

Author: Nannan Cao, Ziyi Wang, Jiangyi Ding, Heng Zhang, Sai Zhang, Liugang Gao, Jiawei Sun, Kai Xie, and Xinye Ni
Subjects: 4D-CBCT, Deep learning, Image quality correction, Lung cancer, Medical physics. Medical radiology. Nuclear medicine, R895-920, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Abstract Objective This study aimed to present a deep-learning network called contrastive learning-based cycle generative adversarial networks (CLCGAN) to mitigate streak artifacts and correct the CT value in four-dimensional cone beam computed tomography (4D-CBCT) for dose calculation in lung cancer patients. Methods 4D-CBCT and 4D computed tomography (CT) of 20 patients with locally advanced non-small cell lung cancer were used to paired train the deep-learning model. The lung tumors were located in the right upper lobe, right lower lobe, left upper lobe, and left lower lobe, or in the mediastinum. Additionally, five patients to create 4D synthetic computed tomography (sCT) for test. Using the 4D-CT as the ground truth, the quality of the 4D-sCT images was evaluated by quantitative and qualitative assessment methods. The correction of CT values was evaluated holistically and locally. To further validate the accuracy of the dose calculations, we compared the dose distributions and calculations of 4D-CBCT and 4D-sCT with those of 4D-CT. Results The structural similarity index measure (SSIM) and peak signal-to-noise ratio (PSNR) of the 4D-sCT increased from 87% and 22.31 dB to 98% and 29.15 dB, respectively. Compared with cycle consistent generative adversarial networks, CLCGAN enhanced SSIM and PSNR by 1.1% (p
Published: 2024
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35. Genome wide detection of CNV and their association with body size in Danzhou chickens

Author: Xinfeng Xie, Liguang Shi, Guanyu Hou, Ziqi Zhong, Ziyi Wang, Deyou Pan, Wei Na, and Qian Xiao
Subjects: Danzhou chicken, whole-genome sequencing, copy number variation, genome-wide association studie, Animal culture, SF1-1100
Abstract: ABSTRACT: Copy number variation (CNV) is a crucial component of genetic diversity in the genome, serving as the foundation for the genetic architecture and phenotypic variability of complex traits. In this study, we examined CNVs in the Danzhou (DZ) chicken, an indigenous breed exclusive to Hainan Province, China. By employing whole-genome resequencing data from 200 DZ chickens, we conducted a comprehensive genome-wide analysis of CNVs using CNVpytor and performed CNV-based genome-wide association studies (GWAS) on 6 body size traits, including body slope length (BSL), keel length (KeL), tibial length (TiL), tibial circumference (TiC), chest width (ChW), and chest depth (ChD) utilizing linear mixed model methods considering a genomic relationship matrix. We identified a total of 144,265 autosomal CNVs among the 200 individuals, comprising 67,818 deletions and 76,447 duplications. After merging these variants together, we obtained 4,824 distinct copy number variant regions, which accounted for approximately 20% of the chicken autosomal genome. Furthermore, we discovered several significantly associated CNV segments with body size traits located proximal to genes such as IHH, WNT6, WNT10A, LPR4, FZD2, WNT7B, and GNAS that have been extensively implicated in skeletal development and growth processes. These findings enhance our understanding of CNVs in chickens and their potential impact on body size traits by revealing candidate genes involved in the regulation of these traits. This establishes a solid framework for future studies and may prove particularly beneficial for exploring genetic structural variation in chickens.
Published: 2024
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36. Ebselen improves fungal keratitis through exerting anti-inflammation, anti-oxidative stress, and antifungal effects

Author: Bing Yu, Qian Wang, Lina Zhang, Jing Lin, Zhuhui Feng, Ziyi Wang, Lingwen Gu, Xue Tian, Songying Luan, Cui Li, and Guiqiu Zhao
Subjects: Fungal keratitis, Ebselen, Thioredoxin, Inflammation, Anti-oxidative, Medicine (General), R5-920, Biology (General), QH301-705.5
Abstract: Fungal keratitis is a severely vision-threatening corneal infection, where the prognosis depends on both fungal virulence and host immune defense. Inappropriate host responses can induce substantial inflammatory damage to the cornea. Therefore, in the treatment of fungal keratitis, it is important to concurrently regulate the immune response while efforts are made to eliminate the pathogen. Ebselen is a widely studied organo-selenium compound and has been demonstrated to have antifungal, antibacterial, anti-inflammatory, and oxidative stress-regulatory properties. The effectiveness of ebselen for the treatment of fungal keratitis remains unknown. In this study, ebselen was demonstrated to produce a marked inhibitory effect on Aspergillus fumigatus (A. fumigatus), including spore germination inhibition, mycelial growth reduction, and fungal biofilm disruption. The antifungal activity of ebselen was related to the cell membrane damage caused by thioredoxin (Trx) system inhibition-mediated oxidative stress. On the contrary, ebselen enhanced the antioxidation of Trx system in mammalian cells. Further, ebselen was proven to suppress the expressions of inflammatory mediators (IL-1β, IL-6, TNF-α, COX-2, iNOS, and CCL2) and reduce the production of oxidative stress-associated indicators (ROS, NO, and MDA) in fungi-stimulated RAW264.7 cells. In addition, ebselen regulated PI3K/Akt/Nrf2 and p38 MAPK signaling pathways, which contributed to the improvement of inflammation and oxidative stress. Finally, we verified the therapeutic effect of ebselen on mouse fungal keratitis. Ebselen improved the prognosis and reduced the fungal burden in mouse corneas. Expressions of inflammatory mediators, as well as the infiltration of macrophages and neutrophils in the cornea were also obviously decreased by ebselen. In summary, ebselen exerted therapeutic effects by reducing fungal load and protecting host tissues in fungal keratitis, making it a promising treatment for fungal infections.
Published: 2024
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37. Efficient degradation and detoxification of structurally different dyes and mixed dyes by LAC-4 laccase purified from white-rot fungi Ganoderma lucidum

Author: Wei Deng, Mingrui Ge, Ziyi Wang, Chenwen Weng, and Yang Yang
Subjects: White-rot fungi Ganoderma lucidum, LAC-4 laccase, Dye degradation and detoxification, Single dye, Mixed dyes, Environmental pollution, TD172-193.5, Environmental sciences, GE1-350
Abstract: The purpose of this study is to evaluate the decolorization ability and detoxification effect of LAC-4 laccase on various types of single and mixed dyes, and lay a good foundation for better application of laccase in the efficient treatment of dye pollutants. The reaction system of the LAC-4 decolorizing single dyes (azo, anthraquinone, triphenylmethane, and indigo dyes, 17 dyes in total) were established. To explore the decolorization effect of the dye mixture by LAC-4, two dyes of the same type or different types were mixed at the same concentration (100 mg/L) in the reaction system containing 0.5 U laccase, and time-course decolorization were performed on the dye mixture. The combined dye mixtures consisted of azo + azo, azo + anthraquinone, azo + indigo, azo + triphenylmethane, indigo + triphenylmethane, and triphenylmethane + triphenylmethane. The results obtained in this study were as follows. Under optimal conditions of 30 °C and pH 5.0, LAC-4 (0.5 U) can efficiently decolorize four different types of dyes. The 24-hour decolorization efficiencies of LAC-4 for 800 mg/L Orange G and Acid Orange 7 (azo), Remazol Brilliant Blue R (anthraquinone), Bromophenol Blue and Methyl Green (triphenylmethane), and Indigo Carmine (indigo) were 75.94%, 93.30%, 96.56%, 99.94%, 96.37%, and 37.23%, respectively. LAC-4 could also efficiently decolorize mixed dyes with different structures. LAC-4 can achieve a decolorization efficiency of over 80% for various dye mixtures such as Orange G + Indigo Carmine (100 mg/L+100 mg/L), Reactive Orange 16 + Methyl Green (100 mg/L+100 mg/L), and Remazol Brilliant Blue R + Methyl Green (100 mg/L+100 mg/L). During the decolorization process of the mixed dyes by laccase, four different interaction relationships were observed between the dyes. Decolorization efficiencies and rates of the dyes that were difficult to be degraded by laccase could be greatly improved when mixed with other dyes. Degradable dyes could greatly enhance the ability of LAC-4 to decolorize extremely difficult-to-degrade dyes. It was also found that the decolorization efficiencies of the two dyes significantly increased after mixing. The possible mechanisms underlying the different interaction relationships were further discussed. Free, but not immobilized, LAC-4 showed a strong continuous batch decolorization ability for single dyes, two-dye mixtures, and four-dye mixtures with different structures. LAC-4 exhibited high stability, sustainable degradability, and good reusability in the continuous batch decolorization. The LAC-4-catalyzed decolorization markedly reduced or fully abolished the toxic effects of single dyes (azo, anthraquinone, and indigo dye) and mix dyes (nine dye mixtures containing four structural types of dyes) on plants. Our findings indicated that LAC-4 laccase had significant potential for use in bioremediation due to its efficient degradation and detoxification of single and mixed dyes with different structural types.
Published: 2024
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38. Exosomal circ50547 as a potential marker and promotor of gastric cancer progression via miR-217/HNF1B axis

Author: Xueyan Zang, Rongrong Wang, Ziyi Wang, Shuangyang Qiu, Fan Zhang, Le Zhou, Ye Shen, Hui Qian, Wenrong Xu, and Jiajia Jiang
Subjects: Exosomes, CircRNAs, Gastric cancer, miRNA, Stem，Resistance, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Background: Exosomes, one of small extracellular vesicles, play a vital role in cell to cell communication and contribute to the advancement of tumors through their cargo molecules. Exosomal circRNAs have emerged as significant players in various types of tumors. Thus, this study aimed to investigate how exosomal circRNAs are involved in the diagnosis and progression of gastric cancer (GC). Methods: Serum exosomes were characterized using transmission electron microscopy, nanoparticle tracking analysis and Western blot. CCK-8, colony formation and transwell assays were conducted to study the function of hsa_circ_0050547 (named as circ50547). qRT-PCR was used to quantify the expression of circ50547 in GC tissues and serum exosomes. Fluorescence in situ hybridization was applied to detect the cellular distribution of circ50547. Stemness and drug-resistance were detected by sphere formation, WB, flow cytometry and half-maximal inhibitory concentration analyses. Bioinformatic analyses, luciferase experiments, qRT-PCR and WB were used to investigate molecular mechanisms. Results: We discovered for the first time a new type of GC-derived exosomal circRNA, circ50547. We found that circ50547 is highly expressed in both GC tissues and serum exosomes. Interestingly, we observed that the diagnostic value of exosomal circ50547 is superior to that of serum circ50547. Circ50547 overexpression enhanced the proliferation, migration, invasion, stemness and drug resistance of GC cells, while knockdown of circ50547 showed the opposite effect. Mechanistically, circ50547 acted as a sponge for miR-217 to regulate the expression of HNF1B, which promoted gastric cancer progression. Conclusion: Exosomal circ50547 may be a promising marker for the diagnosis and prognosis prediction of GC. These findings suggest that it plays an oncogenic role through miR-217/HNF1B signaling pathway in GC.
Published: 2024
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39. Bioinformatics and systems biology approach to identify the pathogenetic link of neurological pain and major depressive disorder

Author: Jinjing Hu, Jia Fu, Yuxin Cai, Shuping Chen, Mengjian Qu, Lisha Zhang, Weichao Fan, Ziyi Wang, Qing Zeng, and Jihua Zou
Subjects: neurological pain, gene expression omnibus (GEO), major depressive disorder (MDD), bioinformatics, receiver operating characteristic (ROC), Biology (General), QH301-705.5, Medicine
Abstract: Neurological pain (NP) is always accompanied by symptoms of depression, which seriously affects physical and mental health. In this study, we identified the common hub genes (Co-hub genes) and related immune cells of NP and major depressive disorder (MDD) to determine whether they have common pathological and molecular mechanisms. NP and MDD expression data was downloaded from the Gene Expression Omnibus (GEO) database. Common differentially expressed genes (Co-DEGs) for NP and MDD were extracted and the hub genes and hub nodes were mined. Co-DEGs, hub genes, and hub nodes were analyzed for Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment. Finally, the hub nodes, and genes were analyzed to obtain Co-hub genes. We plotted Receiver operating characteristic (ROC) curves to evaluate the diagnostic impact of the Co-hub genes on MDD and NP. We also identified the immune-infiltrating cell component by ssGSEA and analyzed the relationship. For the GO and KEGG enrichment analyses, 93 Co-DEGs were associated with biological processes (BP), such as fibrinolysis, cell composition (CC), such as tertiary granules, and pathways, such as complement, and coagulation cascades. A differential gene expression analysis revealed significant differences between the Co-hub genes ANGPT2, MMP9, PLAU, and TIMP2. There was some accuracy in the diagnosis of NP based on the expression of ANGPT2 and MMP9. Analysis of differences in the immune cell components indicated an abundance of activated dendritic cells, effector memory CD8+ T cells, memory B cells, and regulatory T cells in both groups, which were statistically significant. In summary, we identified 6 Co-hub genes and 4 immune cell types related to NP and MDD. Further studies are needed to determine the role of these genes and immune cells as potential diagnostic markers or therapeutic targets in NP and MDD.
Published: 2024
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40. Metabolic reprogramming in esophageal squamous cell carcinoma

Author: Ziyi Wang, Xiangyu Sun, Zehui Li, Huidong Yu, Wenya Li, and Yan Xu
Subjects: metabolic reprogramming, glucose, fatty acid, amino acid, esophageal cancer, Therapeutics. Pharmacology, RM1-950
Abstract: Esophageal squamous cell carcinoma (ESCC) is a malignancy with high incidence in China. Due to the lack of effective molecular targets, the prognosis of ESCC patients is poor. It is urgent to explore the pathogenesis of ESCC to identify promising therapeutic targets. Metabolic reprogramming is an emerging hallmark of ESCC, providing a novel perspective for revealing the biological features of ESCC. In the hypoxic and nutrient-limited tumor microenvironment, ESCC cells have to reprogram their metabolic phenotypes to fulfill the demands of bioenergetics, biosynthesis and redox homostasis of ESCC cells. In this review, we summarized the metabolic reprogramming of ESCC cells that involves glucose metabolism, lipid metabolism, and amino acid metabolism and explore how reprogrammed metabolism provokes novel opportunities for biomarkers and potential therapeutic targets of ESCC.
Published: 2024
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41. Cardiovascular protection of YiyiFuzi powder and the potential mechanisms through modulating mitochondria-endoplasmic reticulum interactions

Author: Jingyi Ding, Ran Ji, Ziyi Wang, Yuzhi Jia, Tiantian Meng, Xinbin Song, Jing Gao, and Qingyong He
Subjects: cardiovascular disease, mitochondria, endoplasmic reticulum, Chinese herbal medicine, YiYiFuZi powder, Therapeutics. Pharmacology, RM1-950
Abstract: Cardiovascular diseases (CVD) remain the leading cause of death worldwide and represent a major public health challenge. YiyiFuzi Powder (YYFZ), composed of Coicis semen and Fuzi, is a classical traditional Chinese medicine prescription from the Synopsis of Golden Chamber dating back to the Han Dynasty. Historically, YYFZ has been used to treat various CVD, rooted in Chinese therapeutic principles. Network pharmacology analysis indicated that YYFZ may exhibit direct or indirect effects on mitochondria-endoplasmic reticulum (ER) interactions. This review, focusing on the cardiovascular protective effects of Coicis semen and Fuzi, summarizes the potential mechanisms by which YYFZ acts on mitochondria and the ER. The underlying mechanisms are associated with regulating cardiovascular risk factors (such as blood lipids and glucose), impacting mitochondrial structure and function, modulating ER stress, inhibiting oxidative stress, suppressing inflammatory responses, regulating cellular apoptosis, and maintaining calcium ion balance. The involved pathways include, but were not limited to, upregulating the IGF-1/PI3K/AKT, cAMP/PKA, eNOS/NO/cGMP/SIRT1, SIRT1/PGC-1α, Klotho/SIRT1, OXPHOS/ATP, PPARα/PGC-1α/SIRT3, AMPK/JNK, PTEN/PI3K/AKT, β2-AR/PI3K/AKT, and modified Q cycle signaling pathways. Meanwhile, the MCU, NF-κB, and JAK/STAT signaling pathways were downregulated. The PERK/eIF2α/ATF4/CHOP, PERK/SREBP-1c/FAS, IRE1, PINK1-dependent mitophagy, and AMPK/mTOR signaling pathways were bidirectionally regulated. High-quality experimental studies are needed to further elucidate the underlying mechanisms of YYFZ in CVD treatment.
Published: 2024
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42. Causal relations between ischemic stroke and epilepsy: A bidirectional Mendelian randomization study

Author: Zongzhi Jiang, Yining Sun, Ziyi Wang, and Songyan Liu
Subjects: Ischemic stroke, Generalized epilepsy, Focal epilepsy, Mendelian randomization, Casual analysis, Science (General), Q1-390, Social sciences (General), H1-99
Abstract: Background: Although previous studies have reported a bidirectional relationship between ischemic stroke (IS) and epilepsy, the existence of a causal nexus and its directionality remains a topic of controversy. Methods: The single nucleotide polymorphisms (SNPs) associated with IS were extracted from the Genome-Wide Association Study (GWAS) database. Pooled genetic data encompassing all epilepsy cases, as well as generalized and focal epilepsy subtypes, were acquired from the International League Against Epilepsy's GWAS study. In this study, the primary analysis approach utilized the inverse variance weighting (IVW) method as the main analytical technique. To enhance the robustness of the findings against potential pleiotropy, additional sensitivity analyses were conducted. Results: In the forward analysis, the IVW method demonstrated that IS was associated with an increased risk of all epilepsy (odds ratio (OR) = 1.127, 95 % confidence interval (CI) = 1.038–1.224, P = 0.004) and generalized epilepsy (IVW: OR = 1.340, 95 % CI = 1.162–1.546, P = 5.70 × 10–5). There was no substantial causal relationship observed between IS and focal epilepsy (P > 0.05). Furthermore, generalized epilepsy, focal epilepsy, and all epilepsy did not show a causal relationship with IS. Conclusion: This Mendelian randomization (MR) analysis demonstrates that IS increases the risk of developing epilepsy, especially generalized epilepsy. Conversely, no clear causal association was found between epilepsy and the onset of stroke. Therefore, the possible mechanisms of the effect of epilepsy on the pathogenesis of IS still need to be further investigated.
Published: 2024
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43. Biochar addition can negatively affect plant community performance when altering soil properties in saline-alkali wetlands

Author: Ziyi Wang, Mengxuan He, Xueqiang Lu, Zirui Meng, Jie Liu, and Xunqiang Mo
Subjects: biochar, coastal saline-alkali soils, soil properties, plant community performance, community stability, Plant culture, SB1-1110
Abstract: Biochar is a widely proposed solution for improving degraded soil in coastal wetland ecosystems. However, the impacts of biochar addition on the soil and plant communities in the wetland remains largely unknown. In this study, we conducted a greenhouse experiment using soil seed bank from a coastal saline-alkaline wetland. Three types of biochar, including Juglans regia biochar (JBC), Spartina alterniflora biochar (SBC) and Flaveria bidentis biochar (FBC), were added to the saline-alkaline soil at ratios of 1%, 3% and 5% (w/w). Our findings revealed that biochar addition significantly increased soil pH, and increased available potassium (AK) by 3.74% - 170.91%, while reduced soil salinity (expect for 3% SBC and 5%SBC) by 28.08% - 46.93%. Among the different biochar types, the application of 5% FBC was found to be the most effective in increasing nutrients and reducing salinity. Furthermore, biochar addition generally resulted in a decrease of 7.27% - 90.94% in species abundance, 17.26% - 61.21% in community height, 12.28% - 56.42% in stem diameter, 55.34% - 90.11% in total biomass and 29.22% - 78.55% in root tissue density (RTD). In particular, such negative effects was the worst in the SBC samples. However, 3% and 5% SBC increased specific root length (SRL) by 177.89% and 265.65%, and specific root surface area (SRSA) by 477.02% and 286.57%, respectively. The findings suggested that the plant community performance was primarily affected by soil pH, salinity and nutrients levels. Furthermore, biochar addition also influenced species diversity and functional diversity, ultimately affecting ecosystem stability. Therefore, it is important to consider the negative findings indirectly indicate the ecological risks associated with biochar addition in coastal salt-alkaline soils. Furthermore, Spartina alterniflora was needed to desalt before carbonization to prevent soil salinization when using S. alterniflora biochar, as it is a halophyte.
Published: 2024
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44. Search for a massless particle beyond the Standard Model in the Σ+ → p + invisible decay

Author: M. Ablikim, M.N. Achasov, P. Adlarson, O. Afedulidis, X.C. Ai, R. Aliberti, A. Amoroso, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, H.-R. Bao, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R.A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G.F. Cao, N. Cao, S.A. Cetin, J.F. Chang, G.R. Che, G. Chelkov, 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. Chen, S.K. Choi, G. Cibinetto, F. Cossio, J.J. Cui, H.L. Dai, J.P. Dai, A. Dbeyssi, R.E. de Boer, D. Dedovich, C.Q. Deng, Z.Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X.X. Ding, Y. Ding, J. Dong, L.Y. Dong, M.Y. Dong, X. Dong, M.C. Du, S.X. Du, Z.H. Duan, P. Egorov, Y.H. Fan, J. Fang, S.S. Fang, W.X. Fang, Y. Fang, Y.Q. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C.Q. Feng, J.H. Feng, Y.T. Feng, M. Fritsch, C.D. Fu, J.L. Fu, Y.W. Fu, H. Gao, X.B. Gao, Y.N. Gao, Yang Gao, S. Garbolino, I. Garzia, L. Ge, P.T. Ge, Z.W. Ge, C. Geng, E.M. Gersabeck, A. Gilman, K. Goetzen, L. Gong, W.X. Gong, W. Gradl, S. Gramigna, M. Greco, M.H. Gu, Y.T. Gu, C.Y. Guan, Z.L. Guan, A.Q. Guo, L.B. Guo, M.J. Guo, R.P. Guo, Y.P. Guo, A. Guskov, J. Gutierrez, K.L. Han, T.T. Han, X.Q. Hao, F.A. Harris, K.K. He, K.L. He, F.H. Heinsius, C.H. Heinz, Y.K. Heng, C. Herold, T. Holtmann, P.C. Hong, G.Y. Hou, X.T. Hou, Y.R. Hou, Z.L. Hou, B.Y. Hu, H.M. Hu, J.F. Hu, S.L. Hu, T. Hu, Y. Hu, G.S. Huang, K.X. Huang, L.Q. Huang, X.T. Huang, Y.P. Huang, T. Hussain, F. Hölzken, N. Hüsken, N. in der Wiesche, J. Jackson, S. Janchiv, J.H. Jeong, Q. Ji, Q.P. Ji, W. Ji, X.B. Ji, X.L. Ji, Y.Y. Ji, X.Q. Jia, Z.K. Jia, D. Jiang, H.B. Jiang, P.C. Jiang, S.S. Jiang, T.J. Jiang, X.S. Jiang, Y. Jiang, J.B. Jiao, J.K. Jiao, Z. Jiao, S. Jin, Y. Jin, M.Q. Jing, X.M. Jing, T. Johansson, S. Kabana, N. Kalantar-Nayestanaki, X.L. Kang, X.S. Kang, M. Kavatsyuk, B.C. Ke, V. Khachatryan, A. Khoukaz, R. Kiuchi, O.B. Kolcu, B. Kopf, M. Kuessner, X. Kui, N. Kumar, A. Kupsc, W. Kühn, J.J. Lane, P. Larin, L. Lavezzi, T.T. Lei, Z.H. Lei, M. Lellmann, T. Lenz, 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, Ke 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.Z. Li, Xiaoyu Li, Y.G. Li, Z.J. Li, Z.X. Li, C. Liang, H. Liang, Y.F. Liang, Y.T. Liang, G.R. Liao, L.Z. Liao, J. Libby, A. Limphirat, C.C. Lin, D.X. Lin, T. Lin, B.J. Liu, B.X. Liu, C. Liu, C.X. Liu, F.H. Liu, Fang Liu, Feng Liu, G.M. Liu, H. Liu, H.B. Liu, H.M. Liu, Huanhuan 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. Liu, X.C. Lou, F.X. Lu, H.J. Lu, J.G. Lu, X.L. Lu, Y. Lu, Y.P. Lu, Z.H. Lu, C.L. Luo, M.X. Luo, T. Luo, X.L. Luo, X.R. Lyu, Y.F. Lyu, F.C. Ma, H. Ma, H.L. Ma, J.L. Ma, L.L. Ma, M.M. Ma, Q.M. Ma, R.Q. Ma, T. Ma, X.T. Ma, X.Y. Ma, Y. Ma, Y.M. Ma, F.E. Maas, M. Maggiora, S. Malde, Y.J. Mao, Z.P. Mao, S. Marcello, Z.X. Meng, J.G. Messchendorp, G. Mezzadri, H. Miao, T.J. Min, R.E. Mitchell, X.H. Mo, B. Moses, N.Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, L.S. Nie, I.B. Nikolaev, Z. Ning, S. Nisar, Q.L. Niu, W.D. Niu, Y. Niu, S.L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y.P. Pei, M. Pelizaeus, H.P. Peng, Y.Y. Peng, K. Peters, J.L. Ping, R.G. Ping, S. Plura, V. Prasad, F.Z. Qi, H. Qi, H.R. Qi, M. Qi, T.Y. Qi, S. Qian, W.B. Qian, C.F. Qiao, X.K. Qiao, J.J. Qin, L.Q. Qin, L.Y. Qin, X.S. Qin, Z.H. Qin, J.F. Qiu, Z.H. Qu, C.F. Redmer, K.J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, S.N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K.Y. Shan, W. Shan, X.Y. Shan, Z.J. Shang, J.F. Shangguan, L.G. Shao, M. Shao, C.P. Shen, H.F. Shen, W.H. Shen, X.Y. Shen, B.A. Shi, H. Shi, H.C. Shi, J.L. Shi, J.Y. Shi, Q.Q. Shi, S.Y. Shi, X. Shi, J.J. Song, T.Z. Song, W.M. Song, Y.J. Song, Y.X. Song, S. Sosio, S. Spataro, F. Stieler, Y.J. Su, 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. Sun, C.J. Tang, G.Y. Tang, J. Tang, M. Tang, Y.A. Tang, L.Y. Tao, Q.T. Tao, M. Tat, J.X. Teng, V. Thoren, W.H. Tian, Y. Tian, Z.F. Tian, I. Uman, Y. Wan, 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, Meng 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 Wang, D.H. Wei, F. Weidner, S.P. Wen, Y.R. Wen, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, 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. Wu, L. Xia, X.M. Xian, B.H. Xiang, T. Xiang, D. Xiao, G.Y. Xiao, S.Y. Xiao, Y.L. Xiao, Z.J. Xiao, C. Xie, X.H. Xie, Y. Xie, Y.G. Xie, Y.H. Xie, Z.P. Xie, T.Y. Xing, 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.C. Xu, Z.P. Xu, Z.S. Xu, F. Yan, L. Yan, W.B. Yan, W.C. Yan, X.Q. Yan, H.J. Yang, H.L. Yang, H.X. Yang, Tao Yang, Y. Yang, Y.F. Yang, Y.X. Yang, Yifan Yang, Z.W. Yang, Z.P. Yao, M. Ye, M.H. Ye, J.H. Yin, Z.Y. You, B.X. Yu, C.X. Yu, G. Yu, J.S. Yu, T. Yu, X.D. Yu, Y.C. Yu, C.Z. Yuan, J. Yuan, L. Yuan, S.C. Yuan, Y. Yuan, Y.J. Yuan, Z.Y. Yuan, C.X. Yue, A.A. Zafar, F.R. Zeng, S.H. Zeng, X. Zeng, Y. Zeng, Y.J. Zeng, X.Y. Zhai, Y.C. Zhai, Y.H. Zhan, 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, Shuihan Zhang, Shulei Zhang, X.D. Zhang, X.M. Zhang, X.Y. Zhang, Y. Zhang, Y.T. Zhang, Y.H. Zhang, Y.M. Zhang, Yan Zhang, Yao Zhang, Z.D. Zhang, Z.H. Zhang, Z.L. Zhang, Z.Y. Zhang, Z.Z. Zhang, G. Zhao, J.Y. Zhao, J.Z. Zhao, Lei Zhao, Ling Zhao, M.G. Zhao, N. Zhao, R.P. Zhao, S.J. Zhao, Y.B. Zhao, Y.X. Zhao, Z.G. Zhao, A. Zhemchugov, B. Zheng, B.M. Zheng, J.P. Zheng, W.J. Zheng, Y.H. Zheng, B. Zhong, X. Zhong, H. Zhou, J.Y. Zhou, L.P. Zhou, S. Zhou, X. Zhou, X.K. Zhou, X.R. Zhou, X.Y. Zhou, Y.Z. Zhou, J. Zhu, K. Zhu, K.J. Zhu, K.S. Zhu, L. Zhu, L.X. Zhu, S.H. Zhu, S.Q. Zhu, T.J. Zhu, W.D. Zhu, Y.C. Zhu, Z.A. Zhu, J.H. Zou, and J. Zu
Subjects: BESIII, FCNC process, Hyperon decay, BSM particle, Physics, QC1-999
Abstract: A massless particle beyond the Standard Model is searched for in the two-body decay Σ+→p+invisible using (1.0087±0.0044)×1010 J/ψ events collected at a center-of-mass energy of s=3.097GeV with the BESIII detector at the BEPCII collider. No significant signal is observed, and the upper limit on the branching fraction B(Σ+→p+invisible) is determined to be 3.2×10−5 at the 90% confidence level. This is the first search for a flavor-changing neutral current process with missing energy in hyperon decays which plays an important role in constraining new physics models.
Published: 2024
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45. Land Remote Sensing Applications Using Spaceborne GNSS Reflectometry: A Comprehensive Overview

Author: Jinwei Bu, Qiulan Wang, Ziyi Wang, Shaoqiang Fan, Xinyu Liu, and Xiaoqing Zuo
Subjects: Disaster warning, ecological protection, environmental monitoring, land remote sensing (RS), resource management, spaceborne GNSS reflectometry (GNSS-R), Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract: The refracted, reflected, and scattered signals from global navigation satellite systems (GNSSs) have been successfully used for remote sensing (RS) of the Earth's surface and atmosphere, demonstrating their potential for sensing the atmosphere, oceans, land surface and cryosphere. As an emerging RS technology, spaceborne GNSS reflectometry (GNSS-R) has been widely used in the field of land RS, with unique advantages such as high spatial and temporal resolution, low observation cost, wide coverage, and all-weather operation. In recent years, the successful launch of several spaceborne GNSS-R satellites has opened new opportunities in this field. The aim of this article is to provide a comprehensive overview of the breakthrough applications of spaceborne GNSS-R technology in the key areas of soil moisture, soil salinity, vegetation monitoring, soil freezing and thawing, flood detection, inland water bodies, wetland dynamics, forest fire disturbances, and land topography measurements, as well as possible future research directions. In addition, this article also emphasizes the contribution and role of China's recently launched Tianmu-1 constellation (as of 5 January 2024, 23 satellites have been in stable operation in orbit) in the development of spaceborne GNSS-R technology and explores the future development direction and prospects of this technology. By comprehensively analyzing the latest progress and innovative achievements of relevant research, the significant impact of this technology on land RS applications in environmental monitoring, resource management, and disaster warning has been highlighted. Through a comprehensive summary of these fields, the enormous potential of spaceborne GNSS-R technology in promoting sustainable development and ecological protection has been revealed.
Published: 2024
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46. Intrusion Detection System for In-Vehicle CAN-FD Bus ID Based on GAN Model

Author: Xu Wang, Yihu Xu, Yinan Xu, Ziyi Wang, and Yujing Wu
Subjects: Binary image coding, CAN-FD, dual discriminator, generative adversarial network (GAN), ID image feature extractor, network security, Electrical engineering. Electronics. Nuclear engineering, TK1-9971
Abstract: The growing abundance of electronic control units and peripheral devices loaded and connected to smart connected cars has resulted in a constant stream of cyber-attacks at various levels and dimensions. The CAN-FD bus plays a crucial role in smart connected cars. Currently, the majority of research efforts remain centered around the traditional CAN bus, with fewer studies addressing intrusion detection for the CAN-FD bus in smart connected vehicles. CAN-FD boasts a notable improvement in transmission speed, capable of reaching up to 8 Mbps compared to the 1 Mbps of the standard CAN bus. Utilizing intrusion detection systems designed for the CAN bus in high-speed CAN-FD applications could potentially hinder normal transmission and detection efficiency. Hence, we focus on the attack and intrusion detection of CAN-FD bus ID nodes to prevent unauthorized access and potential malicious attacks. We propose an ID intrusion detection system based on an improved Generative Adversarial Network (GAN) model, which consists of two parts: a data pre-processing module and a detection module. To apply the GAN model to the vehicle bus, we perform pre-processing of the bus data. We introduce the concept of dual discriminator to improve the detection rate and enable the handling of unknown attacks. With the output of dual discriminator, we can determine whether there are any anomalies in the detection data. First, we use a data pre-processing module to convert the ID segments of the automobile CAN-FD into binary image encoding to form ID images. Subsequently, these ID images are fed into an ID image feature extractor in the detection module to extract various auxiliary features. The discriminator receives these auxiliary features and calculates the probability of whether the received image is a normal ID image or not to determine the authenticity of the ID image. The experimental results show that the proposed intrusion detection system is able to detect a message within 0.15 ms, which fully meets the real-time detection requirements while the vehicle is in motion. The average detection rate of the proposed system for different types of attacks is 99.93%, which is an average of 1.2% improvement of the detection rate over the GIDS algorithm. The proposed system not only ensures the normal communication of CAN-FD bus but also realizes real-time and accurate intrusion detection.
Published: 2024
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47. Measurement of the e + e − → K S 0 K L 0 $$ {K}_S^0{K}_L^0 $$ π 0 cross sections from s $$ \sqrt{s} $$ = 2.000 to 3.080 GeV

Author: The BESIII collaboration, M. Ablikim, M. N. Achasov, P. Adlarson, X. C. Ai, R. Aliberti, A. Amoroso, M. R. An, Q. An, Y. Bai, O. Bakina, I. Balossino, Y. Ban, V. Batozskaya, K. Begzsuren, N. Berger, M. Berlowski, M. Bertani, D. Bettoni, F. Bianchi, E. Bianco, A. Bortone, I. Boyko, R. A. Briere, A. Brueggemann, H. Cai, X. Cai, A. Calcaterra, G. F. Cao, N. Cao, S. A. Cetin, J. F. Chang, T. T. Chang, W. L. Chang, G. R. Che, G. Chelkov, C. Chen, Chao Chen, G. Chen, H. S. 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, W. S. Cheng, S. K. Choi, X. Chu, G. Cibinetto, S. C. Coen, F. Cossio, J. J. Cui, H. L. Dai, J. P. Dai, A. Dbeyssi, R. E. de Boer, D. Dedovich, Z. Y. Deng, A. Denig, I. Denysenko, M. Destefanis, F. De Mori, B. Ding, X. X. Ding, Y. Ding, J. Dong, L. Y. Dong, M. Y. Dong, X. Dong, M. C. Du, S. X. Du, Z. H. Duan, P. Egorov, Y. H. Fan, J. Fang, S. S. Fang, W. X. Fang, Y. Fang, R. Farinelli, L. Fava, F. Feldbauer, G. Felici, C. Q. Feng, J. H. Feng, K Fischer, M. Fritsch, C. D. Fu, J. L. Fu, Y. W. Fu, H. Gao, Y. N. Gao, Yang Gao, S. Garbolino, I. Garzia, P. T. Ge, Z. W. Ge, C. Geng, E. M. Gersabeck, A Gilman, K. Goetzen, L. Gong, W. X. Gong, W. Gradl, S. Gramigna, M. Greco, M. H. Gu, Y. T. Gu, C. Y Guan, Z. L. Guan, A. Q. Guo, L. B. Guo, M. J. Guo, R. P. Guo, Y. P. Guo, A. Guskov, T. T. Han, W. Y. Han, X. Q. Hao, F. A. Harris, K. K. He, K. L. He, F. H. H. Heinsius, C. H. Heinz, Y. K. Heng, C. Herold, T. Holtmann, P. C. Hong, G. Y. Hou, X. T. Hou, Y. R. Hou, Z. L. Hou, H. M. Hu, J. F. Hu, T. Hu, Y. Hu, G. S. Huang, K. X. Huang, L. Q. Huang, X. T. Huang, Y. P. Huang, T. Hussain, N Hüsken, N. in der Wiesche, M. Irshad, J. Jackson, S. Jaeger, S. Janchiv, J. H. Jeong, Q. Ji, Q. P. Ji, X. B. Ji, X. L. Ji, Y. Y. Ji, X. Q. Jia, Z. K. Jia, H. J. Jiang, P. C. Jiang, S. S. Jiang, T. J. Jiang, X. S. Jiang, Y. Jiang, J. B. Jiao, Z. Jiao, S. Jin, Y. Jin, M. Q. Jing, T. Johansson, X. Kui, S. Kabana, N. Kalantar-Nayestanaki, X. L. Kang, X. S. Kang, M. Kavatsyuk, B. C. Ke, A. Khoukaz, R. Kiuchi, R. Kliemt, O. B. Kolcu, B. Kopf, M. Kuessner, A. Kupsc, W. Kühn, J. J. Lane, P. Larin, A. Lavania, L. Lavezzi, T. T. Lei, Z. H. Lei, H. Leithoff, M. Lellmann, T. Lenz, C. Li, C. H. Li, Cheng Li, D. M. Li, F. Li, G. Li, H. Li, H. B. Li, H. J. Li, H. N. Li, Hui Li, J. R. Li, J. S. Li, J. W. Li, K. L. Li, Ke Li, L. J Li, L. K. Li, Lei Li, M. H. Li, P. R. Li, Q. X. Li, S. X. Li, T. Li, W. D. Li, W. G. Li, X. H. Li, X. L. Li, Xiaoyu Li, Y. G. Li, Z. J. Li, Z. X. Li, C. Liang, H. Liang, Y. F. Liang, Y. T. Liang, G. R. Liao, L. Z. Liao, Y. P. Liao, J. Libby, A. Limphirat, D. X. Lin, T. Lin, B. J. Liu, B. X. Liu, C. Liu, C. X. Liu, F. H. Liu, Fang Liu, Feng Liu, G. M. Liu, H. Liu, H. B. Liu, H. M. Liu, Huanhuan Liu, Huihui Liu, J. B. Liu, J. L. 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. Q. Liu, X. C. Lou, F. X. Lu, H. J. Lu, J. G. Lu, X. L. Lu, Y. Lu, Y. P. Lu, Z. H. Lu, C. L. Luo, M. X. Luo, T. Luo, X. L. Luo, X. R. Lyu, Y. F. Lyu, F. C. Ma, H. L. Ma, J. L. Ma, L. L. Ma, M. M. Ma, Q. M. Ma, R. Q. Ma, R. T. Ma, X. Y. Ma, Y. Ma, Y. M. Ma, F. E. Maas, M. Maggiora, S. Malde, Q. A. Malik, A. Mangoni, Y. J. Mao, Z. P. Mao, S. Marcello, Z. X. Meng, J. G. Messchendorp, G. Mezzadri, H. Miao, T. J. Min, R. E. Mitchell, X. H. Mo, N. Yu. Muchnoi, J. Muskalla, Y. Nefedov, F. Nerling, I. B. Nikolaev, Z. Ning, S. Nisar, Q. L. Niu, W. D. Niu, Y. Niu, S. L. Olsen, Q. Ouyang, S. Pacetti, X. Pan, Y. Pan, A. Pathak, P. Patteri, Y. P. Pei, M. Pelizaeus, H. P. Peng, Y. Y. Peng, K. Peters, J. L. Ping, R. G. Ping, S. Plura, V. Prasad, F. Z. Qi, H. Qi, H. R. Qi, M. Qi, T. Y. Qi, S. Qian, W. B. Qian, C. F. Qiao, J. J. Qin, L. Q. Qin, X. P. Qin, X. S. Qin, Z. H. Qin, J. F. Qiu, S. Q. Qu, C. F. Redmer, K. J. Ren, A. Rivetti, M. Rolo, G. Rong, Ch. Rosner, S. N. Ruan, N. Salone, A. Sarantsev, Y. Schelhaas, K. Schoenning, M. Scodeggio, K. Y. Shan, W. Shan, X. Y. Shan, J. F. Shangguan, L. G. Shao, M. Shao, C. P. Shen, H. F. Shen, W. H. Shen, X. Y. Shen, B. A. Shi, H. C. Shi, J. L. Shi, J. Y. Shi, Q. Q. Shi, R. S. Shi, X. Shi, J. J. Song, T. Z. Song, W. M. Song, Y. J. Song, Y. X. Song, S. Sosio, S. Spataro, F. Stieler, Y. J. Su, 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. T. Sun, Y. X. Tan, C. J. Tang, G. Y. Tang, J. Tang, Y. A. Tang, L. Y Tao, Q. T. Tao, M. Tat, J. X. Teng, V. Thoren, W. H. Tian, Y. Tian, Z. F. Tian, I. Uman, S. J. Wang, B. Wang, B. L. Wang, Bo Wang, C. W. Wang, D. Y. Wang, F. Wang, H. J. Wang, H. P. Wang, J. P. Wang, K. Wang, L. L. Wang, M. Wang, Meng Wang, S. Wang, T. Wang, T. J. Wang, W. Wang, W. P. Wang, X. Wang, X. F. Wang, X. J. Wang, X. L. Wang, Y. Wang, Y. D. Wang, Y. F. Wang, Y. H. Wang, Y. N. Wang, Y. Q. Wang, Yaqian Wang, Yi Wang, Z. Wang, Z. L. Wang, Z. Y. Wang, Ziyi Wang, D. Wei, D. H. Wei, F. Weidner, S. P. Wen, C. W. Wenzel, U. Wiedner, G. Wilkinson, M. Wolke, L. Wollenberg, 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. Wu, L. Xia, X. M. Xian, T. Xiang, D. Xiao, G. Y. Xiao, S. Y. Xiao, Y. L. Xiao, Z. J. Xiao, C. Xie, X. H. Xie, Y. Xie, Y. G. Xie, Y. H. Xie, Z. P. Xie, T. Y. Xing, C. F. Xu, C. J. Xu, G. F. Xu, H. Y. Xu, Q. J. Xu, Q. N. Xu, W. Xu, W. L. Xu, X. P. Xu, Y. C. Xu, Z. P. Xu, Z. S. Xu, F. Yan, L. Yan, W. B. Yan, W. C. Yan, X. Q. Yan, H. J. Yang, H. L. Yang, H. X. Yang, Tao Yang, Y. Yang, Y. F. Yang, Y. X. Yang, Yifan Yang, Z. W. Yang, Z. P. Yao, M. Ye, M. H. Ye, J. H. Yin, Z. Y. You, B. X. Yu, C. X. Yu, G. Yu, J. S. Yu, T. Yu, X. D. Yu, C. Z. Yuan, L. Yuan, S. C. Yuan, X. Q. Yuan, Y. Yuan, Z. Y. Yuan, C. X. Yue, A. A. Zafar, F. R. Zeng, X. Zeng, Y. Zeng, Y. J. Zeng, X. Y. Zhai, Y. C. Zhai, Y. H. Zhan, 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. Y. Zhang, J. Zhang, J. J. Zhang, J. L. Zhang, J. Q. Zhang, J. W. Zhang, J. X. Zhang, J. Y. Zhang, J. Z. Zhang, Jianyu Zhang, Jiawei Zhang, L. M. Zhang, L. Q. Zhang, Lei Zhang, P. Zhang, Q. Y. Zhang, Shuihan Zhang, Shulei Zhang, X. D. Zhang, X. M. Zhang, X. Y. Zhang, Xuyan Zhang, Y. Zhang, Y. T. Zhang, Y. H. Zhang, Yan Zhang, Yao Zhang, Z. H. Zhang, Z. L. Zhang, Z. Y. Zhang, G. Zhao, J. Zhao, J. Y. Zhao, J. Z. Zhao, Lei Zhao, Ling Zhao, M. G. Zhao, S. J. Zhao, Y. B. Zhao, Y. X. Zhao, Z. G. Zhao, A. Zhemchugov, B. Zheng, J. P. Zheng, W. J. Zheng, Y. H. Zheng, B. Zhong, X. Zhong, H. Zhou, L. P. Zhou, X. Zhou, X. K. Zhou, X. R. Zhou, X. Y. Zhou, Y. Z. Zhou, J. Zhu, K. Zhu, K. J. Zhu, L. Zhu, L. X. Zhu, S. H. Zhu, S. Q. Zhu, T. J. Zhu, W. J. Zhu, Y. C. Zhu, Z. A. Zhu, J. H. Zou, and J. Zu
Subjects: e +-e − Experiments, Particle and Resonance Production, Spectroscopy, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract: Abstract Based on e + e − collision data collected at center-of-mass energies from 2.000 to 3.080 GeV by the BESIII detector at the BEPCII collider, a partial wave analysis is performed for the process e + e − → K S 0 K L 0 $$ {K}_S^0{K}_L^0 $$ π 0. The results allow the Born cross sections of the process e + e − → K S 0 K L 0 $$ {K}_S^0{K}_L^0 $$ π 0, as well as its subprocesses e + e − → K ∗(892)0 K ¯ $$ \overline{K} $$ 0 and K 2 ∗ $$ {K}_2^{\ast } $$ (1430)0 K ¯ $$ \overline{K} $$ 0 to be measured. The Born cross sections for e + e − → K S 0 K L 0 $$ {K}_S^0{K}_L^0 $$ π 0 are consistent with previous measurements by BaBar, but with substantially improved precision. The Born cross section lineshape of the process e + e − K ∗(892)0 K ¯ $$ \overline{K} $$ 0 is consistent with a vector meson state around 2.2 GeV with a significance of 3.2σ. A Breit-Wigner fit determines its mass as M Y = (2164.7 ± 9.1 ± 3.1) MeV/c 2 and its width as Γ Y = (32.4 ± 21.0 ± 1.8) MeV.
Published: 2024
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48. Association between suicide attempts and anemia in late-life depression inpatients

Author: Jie Li, Lan Wang, Ziyi Wang, Fengxue Zhao, Yadi Sun, Ying Lu, and Lei Yang
Subjects: Late-life depression, Anemia, Suicide attempt, Serum ferritin, Folate, Vitamin B12, Geriatrics, RC952-954.6
Abstract: Abstract Background Anemia is strongly associated with late-life depression (LLD), however, few studies have investigated the relationship between anemia and suicide attempts in LLD patients. It is still challenging to predict suicide risk in patients with depression. Therefore, there is growing interest in potential biomarkers of depressive disorders and suicidal behavior, which may play a significant role in the early diagnosis and treatment of depression. This study aimed to compare serum ferritin, folate, vitamin B12, and erythrocyte parameter levels in patients with LLD with those in healthy older adults, and the relationship between serum ferritin, folate, vitamin B12, and suicide attempts in patients. Methods Serum ferritin, folate, vitamin B12, and erythrocyte parameter levels were measured in 66 hospitalized LLD patients (30 without suicide attempt, 36 with suicide attempt) and 47 healthy individuals. All participants were surveyed for basic conditions and suicide attempts, and depression was assessed in LLD patients. Results Serum ferritin, folate, vitamin B12, red blood cell count, hemoglobin, hematocrit, mean platelet volume and plateletcrit levels were significantly lower in LLD patients compared with healthy older adults (P
Published: 2024
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49. Stability of Regional Ecological Supply–Demand Is Enhanced by Complex Network Modeling: Evidence From the Xuzhou Metropolitan Area, China

Author: Zixuan Li, Jiang Chang, Ziyi Wang, Yedong Chen, and Cheng Li
Subjects: Complex network models, ecological networks, ecological supply and demand, sustainable development, Xuzhou metropolitan area (XMA), Ocean engineering, TC1501-1800, Geophysics. Cosmic physics, QC801-809
Abstract: In the era of urbanization, the imperative to safeguard ecological security and navigate toward sustainable development is paramount. The emergence of remote sensing technologies has unveiled a nuanced approach to understanding and refining regional ecological networks, which are crucial for achieving these objectives. However, in constructing regional ecological networks, the spatial integration of ecological supply and demand frequently goes unaddressed. The balance of supply and demand for ecosystem services is a complex challenge. In addition, there is an absence of comprehensive effectiveness assessment for regional ecological networks constructed based on element evaluation. To address these challenges, this study focuses on the highly urbanized Xuzhou metropolitan area (XMA) in eastern China, utilizing remote sensing data to develop an ecological network focused on balancing ecological supply and demand. The study adeptly applies complex network theory to model the ecological network, analyzing its structure and function to propose thorough and specific optimization strategies. The results indicate that the ecological network in the XMA encompasses 20 ecological supply sources, 21 demand sources, and various corridors and nodes, displaying a “dense in the center, sparse on the periphery” spider-web-like distribution. After postoptimization, the network integrates further stepping stones and corridors, evolving into a denser and more intricate structure. The study emphasizes the significance of constructing regional ecological networks based on ecological supply and demand, aiding the balance of ecosystem services in urbanized areas. It highlights the transformative potential of complex network models in ecological network optimization, facilitating effective ecosystem management and spatial planning.
Published: 2024
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50. Blockade of Hepatocyte PCSK9 Ameliorates Hepatic Ischemia-Reperfusion Injury by Promoting Pink1-Parkin–Mediated MitophagySummary

Author: Yu Zhang, Ziyi Wang, Chenyang Jia, Wenjie Yu, Xiangdong Li, Nan Xia, Huiling Nie, Likalamu Pascalia Wikana, Minhao Chen, Yong Ni, Sheng Han, and Liyong Pu
Subjects: Hepatic Ischemia-Reperfusion, PCSK9, PINK1, Mitophagy, STING, Diseases of the digestive system. Gastroenterology, RC799-869
Abstract: Background & Aims: Hepatic ischemia-reperfusion injury is a significant complication of partial hepatic resection and liver transplantation, impacting the prognosis of patients undergoing liver surgery. The protein proprotein convertase subtilisin/kexin type 9 (PCSK9) is primarily synthesized by hepatocytes and has been implicated in myocardial ischemic diseases. However, the role of PCSK9 in hepatic ischemia-reperfusion injury remains unclear. This study aims to investigate the role and mechanism of PCSK9 in hepatic ischemia-reperfusion injury. Methods: We first examined the expression of PCSK9 in mouse warm ischemia-reperfusion models and AML12 cells subjected to hypoxia/reoxygenation. Subsequently, we explored the impact of PCSK9 on liver ischemia-reperfusion injury by assessing mitochondrial damage and the resulting inflammatory response. Results: Our findings reveal that PCSK9 is up-regulated in response to ischemia-reperfusion injury and exacerbates hepatic ischemia-reperfusion injury. Blocking PCSK9 can alleviate hepatocyte mitochondrial damage and the consequent inflammatory response mediated by ischemia-reperfusion. Mechanistically, this protective effect is dependent on mitophagy. Conclusions: Inhibiting PCSK9 in hepatocytes attenuates the inflammatory responses triggered by reactive oxygen species and mitochondrial DNA by promoting PINK1-Parkin–mediated mitophagy. This, in turn, ameliorates hepatic ischemia-reperfusion injury.
Published: 2024
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