Author: "Gao, Q." - Searchworks@Jio Institute Digital Library Search Results

Your search keyword '"Gao, Q."' showing total 13,511 results

Start Over Author "Gao, Q."

13,511 results on '"Gao, Q."'

1. Research on Electric Heating System Plan in Northern Region in Winter Based on Emission Reduction And Existing Heating Costs

Author: Lin, F., Jia, D., Shi, X., and Gao, Q.
Published: 2024

2. Detection of two TeV gamma-ray outbursts from NGC 1275 by LHAASO

Author: Cao, Zhen, Aharonian, F., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo., X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The Water Cherenkov Detector Array (WCDA) is one of the components of Large High Altitude Air Shower Observatory (LHAASO) and can monitor any sources over two-thirds of the sky for up to 7 hours per day with >98\% duty cycle. In this work, we report the detection of two outbursts of the Fanaroff-Riley I radio galaxy NGC 1275 that were detected by LHAASO-WCDA between November 2022 and January 2023 with statistical significance of 5.2~$\sigma$ and 8.3~$\sigma$. The observed spectral energy distribution in the range from 500 GeV to 3 TeV is fitted by a power-law with a best-fit spectral index of $\alpha=-3.37\pm0.52$ and $-3.35\pm0.29$, respectively. The outburst flux above 0.5~TeV was ($4.55\pm 4.21)\times~10^{-11}~\rm cm^{-2}~s^{-1}$ and ($3.45\pm 1.78)\times~10^{-11}~\rm cm^{-2}~s^{-1}$, corresponding to 60\%, 45\% of Crab Nebula flux. Variation analysis reveals the variability time-scale of days at the TeV energy band. A simple test by one-zone synchrotron self-Compton model reproduces the data in the gamma-ray band well., Comment: 11 pages, 8 figures, 3 tables
Published: 2024

3. LHAASO detection of very-high-energy gamma-ray emission surrounding PSR J0248+6021

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, J. H., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We report the detection of an extended very-high-energy (VHE) gamma-ray source coincident with the locations of middle-aged (62.4~\rm kyr) pulsar PSR J0248+6021, by using the LHAASO-WCDA data of live 796 days and LHAASO-KM2A data of live 1216 days. A significant excess of \gray induced showers is observed both by WCDA in energy bands of 1-25~\rm TeV and KM2A in energy bands of $>$ 25~\rm TeV with 7.3 $\sigma$ and 13.5 $\sigma$, respectively. The best-fit position derived through WCDA data is R.A. = 42.06$^\circ \pm$ 0.12$^\circ$ and Dec. = 60.24$^\circ \pm $ 0.13$^\circ$ with an extension of 0.69$^\circ\pm$0.15$^\circ$ and that of the KM2A data is R.A.= 42.29$^\circ \pm $ 0.13$^\circ$ and Dec. = 60.38$^\circ \pm$ 0.07$^\circ$ with an extension of 0.37$^\circ\pm$0.07$^\circ$. No clear extended multiwavelength counterpart of this LHAASO source has been found from the radio band to the GeV band. The most plausible explanation of the VHE \gray emission is the inverse Compton process of highly relativistic electrons and positrons injected by the pulsar. These electrons/positrons are hypothesized to be either confined within the pulsar wind nebula or to have already escaped into the interstellar medium, forming a pulsar halo., Comment: 12 pages, 10 figures, Accepted by Sci. China-Phys. Mech. Astron
Published: 2024

4. Capping effects on spin and charge excitations in parent and superconducting Nd1-xSrxNiO2

Author: Fan, S., LaBollita, H., Gao, Q., Khan, N., Gu, Y., Kim, T., Li, J., Bhartiya, V., Li, Y., Sun, W., Yang, J., Yan, S., Barbour, A., Zhou, X., Cano, A., Bernardini, F., Nie, Y., Zhu, Z., Bisogni, V., Mazzoli, C., Botana, A. S., and Pelliciari, J.
Subjects: Condensed Matter - Superconductivity, Condensed Matter - Strongly Correlated Electrons
Abstract: Superconductivity in infinite layer nickelates Nd1-xSrxNiO2 has so far been achieved only in thin films raising questions on the role of substrates and interfaces. Given the challenges associated with their synthesis it is imperative to identify their intrinsic properties. We use Resonant Inelastic X-ray Scattering (RIXS) to investigate the influence of the SrTiO3 capping layer on the excitations of Nd1-xSrxNiO2 (x = 0 and 0.2). Spin excitations are observed in parent and 20% doped Nd1-xSrxNiO2 regardless of capping, proving that magnetism is intrinsic to infinite-layer nickelates and appears in a significant fraction of their phase diagram. In parent and superconducting Nd1-xSrxNiO2, the spin excitations are slightly hardened in capped samples compared to the non-capped ones. Additionally, a weaker Ni - Nd charge transfer peak at ~ 0.6 eV suggests that the hybridization between Ni 3d and Nd 5d orbitals is reduced in capped samples. From our data, capping induces only minimal differences in Nd1-xSrxNiO2 and we phenomenologically discuss these differences based on the reconstruction of the SrTiO3 - NdNiO2 interface and other mechanisms such as crystalline disorder., Comment: 9 pages, 6 figures
Published: 2024
Full Text: View/download PDF

5. Constraints on Ultra Heavy Dark Matter Properties from Dwarf Spheroidal Galaxies with LHAASO Observations

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Saiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: In this work we try to search for signals generated by ultra-heavy dark matter at the Large High Altitude Air Shower Observatory (LHAASO) data. We look for possible gamma-ray by dark matter annihilation or decay from 16 dwarf spheroidal galaxies in the field of view of LHAASO. Dwarf spheroidal galaxies are among the most promising targets for indirect detection of dark matter which have low fluxes of astrophysical $\gamma$-ray background while large amount of dark matter. By analyzing more than 700 days observational data at LHAASO, no significant dark matter signal from 1 TeV to 1 EeV is detected. Accordingly we derive the most stringent constraints on the ultra-heavy dark matter annihilation cross-section up to EeV. The constraints on the lifetime of dark matter in decay mode are also derived., Comment: 17 pages, 12 figures, accepted by PRL
Published: 2024

6. Data quality control system and long-term performance monitor of the LHAASO-KM2A

Author: Cao, Zhen, Aharonian, F., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Bian, W., Bukevich, A. V., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, H. X., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, J. H., Fang, K., Feng, C. F., Feng, H., Feng, L., Feng, S. H., Feng, X. T., Feng, Y., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., Hasan, M., He, H. H., He, H. N., He, J. Y., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Karpikov, I., Kuleshov, D., Kurinov, K., Li, B. B., Li, C. M., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, Jian, Li, Jie, Li, K., Li, S. D., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, D. B., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Luo, Q., Luo, Y., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, L. J., Pattarakijwanich, P., Pei, Z. Y., Qi, J. C., Qi, M. Y., Qiao, B. Q., Qin, J. J., Raza, A., Ruffolo, D., Sáiz, A., Saeed, M., Semikoz, D., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, D. X., Sun, Q. N., Sun, X. N., Sun, Z. B., Takata, J., Tam, P. H. T., Tang, Q. W., Tang, R., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, Kai, Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, Q. W., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, Y. L., Xing, Y., Xiong, D. R., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, C. Y., Yang, F., Yang, F. F., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, W. X., Yao, Y. H., Yao, Z. G., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zhao, X. H., Zheng, F., Zhong, W. J., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, B. Y., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Experiment, Physics - Instrumentation and Detectors
Abstract: The KM2A is the largest sub-array of the Large High Altitude Air Shower Observatory (LHAASO). It consists of 5216 electromagnetic particle detectors (EDs) and 1188 muon detectors (MDs). The data recorded by the EDs and MDs are used to reconstruct primary information of cosmic ray and gamma-ray showers. This information is used for physical analysis in gamma-ray astronomy and cosmic ray physics. To ensure the reliability of the LHAASO-KM2A data, a three-level quality control system has been established. It is used to monitor the status of detector units, stability of reconstructed parameters and the performance of the array based on observations of the Crab Nebula and Moon shadow. This paper will introduce the control system and its application on the LHAASO-KM2A data collected from August 2021 to July 2023. During this period, the pointing and angular resolution of the array were stable. From the observations of the Moon shadow and Crab Nebula, the results achieved using the two methods are consistent with each other. According to the observation of the Crab Nebula at energies from 25 TeV to 100 TeV, the time averaged pointing errors are estimated to be $-0.003^{\circ} \pm 0.005^{\circ}$ and $0.001^{\circ} \pm 0.006^{\circ}$ in the R.A. and Dec directions, respectively., Comment: 15 pages, 9 figures
Published: 2024

7. Discovery of Very-high-energy Gamma-ray Emissions from the Low Luminosity AGN NGC 4278 by LHAASO

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, J. H., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., Zou, Y. C., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The first source catalog of Large High Altitude Air Shower Observatory reported the detection of a very-high-energy gamma ray source, 1LHAASO J1219+2915. In this paper a further detailed study of the spectral and temporal behavior of this point-like source have been carried. The best-fit position of the TeV source ($\rm{RA}=185.05^{\circ}\pm0.04^{\circ}$, $\rm{Dec}=29.25^{\circ}\pm0.03^{\circ}$) is compatible with NGC 4278 within $\sim0.03$ degree. Variation analysis shows an indication of the variability at a few months level in the TeV band, which is consistent with low frequency observations. Based on these observations, we report the detection of TeV $\gamma$-ray emissions from this low-luminosity AGN NGC 4278. The observations by LHAASO-WCDA during active period has a significance level of 8.8\,$\sigma$ with best-fit photon spectral index $\varGamma=2.56\pm0.14$ and a flux $f_{1-10\,\rm{TeV}}=(7.0\pm1.1_{\rm{sta}}\pm0.35_{\rm{syst}})\times10^{-13}\,\rm{photons\,cm^{-2}\,s^{-1}}$, or approximately $5\%$ of the Crab Nebula. The discovery of VHE from NGC 4278 indicates that the compact, weak radio jet can efficiently accelerate particles and emit TeV photons., Comment: 11 pages, 5 figures
Published: 2024

8. LHAASO-KM2A detector simulation using Geant4

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, J. H., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: KM2A is one of the main sub-arrays of LHAASO, working on gamma ray astronomy and cosmic ray physics at energies above 10 TeV. Detector simulation is the important foundation for estimating detector performance and data analysis. It is a big challenge to simulate the KM2A detector in the framework of Geant4 due to the need to track numerous photons from a large number of detector units (>6000) with large altitude difference (30 m) and huge coverage (1.3 km^2). In this paper, the design of the KM2A simulation code G4KM2A based on Geant4 is introduced. The process of G4KM2A is optimized mainly in memory consumption to avoid memory overffow. Some simpliffcations are used to signiffcantly speed up the execution of G4KM2A. The running time is reduced by at least 30 times compared to full detector simulation. The particle distributions and the core/angle resolution comparison between simulation and experimental data of the full KM2A array are also presented, which show good agreement.
Published: 2024
Full Text: View/download PDF

9. Measurements of All-Particle Energy Spectrum and Mean Logarithmic Mass of Cosmic Rays from 0.3 to 30 PeV with LHAASO-KM2A

Author: The LHAASO Collaboration, Cao, Zhen, Aharonian, F., An, Q., Axikegu, A., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We present the measurements of all-particle energy spectrum and mean logarithmic mass of cosmic rays in the energy range of 0.3-30 PeV using data collected from LHAASO-KM2A between September 2021 and December 2022, which is based on a nearly composition-independent energy reconstruction method, achieving unprecedented accuracy. Our analysis reveals the position of the knee at $3.67 \pm 0.05 \pm 0.15$ PeV. Below the knee, the spectral index is found to be -$2.7413 \pm 0.0004 \pm 0.0050$, while above the knee, it is -$3.128 \pm 0.005 \pm 0.027$, with the sharpness of the transition measured with a statistical error of 2%. The mean logarithmic mass of cosmic rays is almost heavier than helium in the whole measured energy range. It decreases from 1.7 at 0.3 PeV to 1.3 at 3 PeV, representing a 24% decline following a power law with an index of -$0.1200 \pm 0.0003 \pm 0.0341$. This is equivalent to an increase in abundance of light components. Above the knee, the mean logarithmic mass exhibits a power law trend towards heavier components, which is reversal to the behavior observed in the all-particle energy spectrum. Additionally, the knee position and the change in power-law index are approximately the same. These findings suggest that the knee observed in the all-particle spectrum corresponds to the knee of the light component, rather than the medium-heavy components., Comment: 8 pages, 3 figures
Published: 2024
Full Text: View/download PDF

10. Association of serum 25-hydroxyvitamin D with cardiovascular mortality and kidney outcome in patients with early stages of CKD

Author: Lin, Y., Xie, C., Zhang, Y., Luo, F., Gao, Q., Li, Y., Su, L., Xu, R., Zhang, X., Chen, R., Zhou, S., Li, P., Liu, J., Liang, M., and Nie, S.
Published: 2024
Full Text: View/download PDF

11. An accurate and efficient method for calculating surface waves in one-dimensional ideal and defective semi-infinite periodic structures

Author: Yan, B. W., Tang, Z. F., and Gao, Q.
Published: 2024
Full Text: View/download PDF

12. Does or did the supernova remnant Cassiopeia A operate as a PeVatron?

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo., X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: For decades, supernova remnants (SNRs) have been considered the prime sources of Galactic Cosmic rays (CRs). But whether SNRs can accelerate CR protons to PeV energies and thus dominate CR flux up to the knee is currently under intensive theoretical and phenomenological debate. The direct test of the ability of SNRs to operate as CR PeVatrons can be provided by ultrahigh-energy (UHE; $E_\gamma \geq 100$~TeV) $\gamma$-rays. In this context, the historical SNR Cassiopeia A (Cas A) is considered one of the most promising target for UHE observations. This paper presents the observation of Cas A and its vicinity by the LHAASO KM2A detector. The exceptional sensitivity of LHAASO KM2A in the UHE band, combined with the young age of Cas A, enabled us to derive stringent model-independent limits on the energy budget of UHE protons and nuclei accelerated by Cas A at any epoch after the explosion. The results challenge the prevailing paradigm that Cas A-type SNRs are major suppliers of PeV CRs in the Milky Way., Comment: 11 pages, 3 figures, Accepted by the APJL
Published: 2023

13. Very high energy gamma-ray emission beyond 10 TeV from GRB 221009A

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, A., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The highest energy gamma-rays from gamma-ray bursts (GRBs) have important implications for their radiation mechanism. Here we report for the first time the detection of gamma-rays up to 13 TeV from the brightest GRB 221009A by the Large High Altitude Air-shower Observatory (LHAASO). The LHAASO-KM2A detector registered more than 140 gamma-rays with energies above 3 TeV during 230$-$900s after the trigger. The intrinsic energy spectrum of gamma-rays can be described by a power-law after correcting for extragalactic background light (EBL) absorption. Such a hard spectrum challenges the synchrotron self-Compton (SSC) scenario of relativistic electrons for the afterglow emission above several TeV. Observations of gamma-rays up to 13 TeV from a source with a measured redshift of z=0.151 hints more transparency in intergalactic space than previously expected. Alternatively, one may invoke new physics such as Lorentz Invariance Violation (LIV) or an axion origin of very high energy (VHE) signals., Comment: 49pages, 11figures
Published: 2023
Full Text: View/download PDF

14. Postinduction Hypotension and Adverse Outcomes in Older Adults Undergoing Transcatheter Aortic Valve Replacement: A Retrospective Cohort Study

Author: Ni TT, Yao YY, Zhou XX, Lv T, Zou JC, Luo G, Yang JT, Sun DW, Gao Q, Wang TT, Wang RY, Tao XC, and Yan M
Subjects: postinduction hypotension, adverse postoperative outcomes, transcatheter aortic valve replacement, older adults patients, Geriatrics, RC952-954.6
Abstract: Ting-Ting Ni, Yuan-Yuan Yao, Xiao-Xia Zhou, Tao Lv, Jing-Cheng Zou, Ge Luo, Jin-Ting Yang, Da-Wei Sun, Qi Gao, Ting-Ting Wang, Rui-Yu Wang, Xin-Chen Tao, Min Yan Department of Anesthesiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, Hangzhou, Zhejiang, 330100, People’s Republic of ChinaCorrespondence: Min Yan, Department of Anesthesiology, The Second Affiliated Hospital of Zhejiang University School of Medicine, No. 88 Jiefang Road, Hangzhou, Zhejiang, 330100, People’s Republic of China, Email zryanmin@zju.edu.cnPurpose: Postinduction hypotension (PIH), occurring between anaesthesia induction and surgical incision, is particularly concerning in older adults undergoing transcatheter aortic valve replacement (TAVR) due to their multiple comorbidities and age-related cardiovascular changes. This study aimed to assess the relationship between PIH and postoperative adverse events in TAVR patients.Patients and Methods: A total of 777 patients underwent TAVR at The Second Affiliated Hospital of Zhejiang University School of Medicine from January 1, 2020 to February 28, 2023. Four thresholds of MAP were defined, including two absolute thresholds (< 65, < 60 mmHg) and two relative thresholds (20% and 30% lower than baseline). The relationships between PIH and the composite outcome, which included all-cause in-hospital mortality, stroke, acute kidney injury (AKI), and myocardial infarction (MI), were examined using unadjusted analysis, 1:1 propensity score matching(PSM), and inverse probability of treatment weighting (IPTW).Results: A total of 643 older adults were included in the study ultimately. The composite outcome incidence was significantly greater in patients with PIH than in those without PIH (relative risk [RR]: 2.47, 95% CI: 1.66– 3.73 for MAP < 60 mmHg; RR: 1.66, 95% CI: 1.14– 2.46 for a > 30% decrease from baseline). PIH was significantly associated with stroke (RR: 5.22, 95% CI: 1.98– 17.75) and AKI (RR: 2.82, 95% CI: 1.73– 4.79) with a MAP < 60 mmHg.Conclusion: PIH significantly increases the risk of composite outcomes, especially stroke and AKI, in TAVR patients.Keywords: postinduction hypotension, adverse postoperative outcomes, transcatheter aortic valve replacement, older adults
Published: 2024

15. Multi-Omics Exploration of the Role of PTGS2 as a Hub Gene in Ferroptosis Within the Artery of Takayasu Arteritis

Author: Gao Q, Gao S, Li H, Chen Z, Zhang R, Li Y, and Zhang H
Subjects: takayasu arteritis, single-cell rna sequencing, bulk rna sequencing, ferroptosis, ptgs2, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Qing Gao,1,&ast; Shang Gao,2,3,&ast; Haiyang Li,1,&ast; Zuoguan Chen,2 Ran Zhang,2 Yongjun Li,2 Hongjia Zhang1 1Department of Cardiovascular Surgery, Beijing Anzhen Hospital, Capital Medical University, Beijing, People’s Republic of China; 2Department of Vascular Surgery, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China; 3Graduate School of Peking Union Medical College, Chinese Academy of Medical Sciences, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yongjun Li; Hongjia Zhang, Email liyongjun4679@bjhmoh.cn; zhanghongjia722@ccmu.edu.cnIntroduction: Takayasu arteritis (TAK) is an autoimmune disease affecting the aorta and its branches. Despite anti-inflammatory treatments, some patients require surgical vascular reconstruction due to rapid disease progression. The mechanisms behind persistent inflammation are unclear due to a lack of arterial samples. This study explores ferroptosis in TAK using high-throughput and single-cell transcriptomics.Methods: Transcriptomic data were collected from 8 TAK patients (2 for single cell RNA-seq and 6 for bulk RNA-seq) and 8 renal transplant donors, with single-cell data from 3 public carotid artery samples for control. Bioinformatic analysis was performed to identify ferroptosis-related genes in inflamed arteries.Results: We identified 1526 differentially expressed genes and 46 ferroptosis-related genes, with 6 genes including PTGS2 and HIF1A as hub genes. Single-cell analysis of 27,828 cells revealed increased M1-like macrophages, with PTGS2 highly expressed in these cells. Enrichment analysis indicated NF-κB signal pathway involvement.Conclusion: PTGS2 is a core ferroptosis-related gene in TAK vascular inflammation, highly expressed in M1-like macrophages, potentially upregulated via the IL1B-NF-κB pathway.Keywords: Takayasu arteritis, single-cell RNA sequencing, bulk RNA sequencing, ferroptosis, PTGS2
Published: 2024

16. The Expression Levels of Transforming Growth Factor β1 and Tumor Necrosis Factor Receptor Associated Factor 6 in Allergic Rhinitis Patients and Their Potential Relationship with Epithelial - Mesenchymal Transition: A Pilot Prospective Observational Study

Author: Wang K, Gao Q, Bai Y, Yu R, and Luo Q
Subjects: allergic rhinitis, transforming growth factor β1, tumor necrosis factor receptor associated factor 6, epithelial-mesenchymal transition, immunohistochemical, Immunologic diseases. Allergy, RC581-607
Abstract: Kai Wang,1,2,&ast; Qin Gao,3,&ast; Yelong Bai,1 Rong Yu,1 Qing Luo1 1Department of Otorhinolaryngology, FirstAffiliated Hospital of Nanchang University, Nanchang, Jiangxi, People’s Republic of China; 2Department of Otorhinolaryngology, the 908th Hospital of Chinese People’s Liberation Army Joint Logistic Support Force, Nanchang, Jiangxi, People’s Republic of China; 3Department of Otorhinolaryngology, Central Theater Command General Hospital of the People’s Liberation Army of China, Wuhan, Hubei, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qing Luo; Rong Yu, Department of Otorhinolaryngology, FirstAffiliated Hospital of Nanchang University, No. 17 Yongwaizheng Street, Nanchang, Jiangxi, 330006, People’s Republic of China, Tel +86-18170846190, Email luoq@ncu.edu.cn; yurong1982@ncu.eud.cnObjective: To study the role of transforming growth factor beta 1 (TGF-β 1) and tumor necrosis factor receptor related factor 6 (TRAF6) in the progression of epithelial mesenchymal transformation (EMT) in allergic rhinitis (AR).Methods: A total of 30 patients underwent nasal endoscopic surgery at our Hospital were selected for 15 patients in each group based on their allergy status. Inferior turbinate mucosa tissue was obtained and analyzed using immunohistochemical (IHC) tests, real-time quantitative PCR (qRT-PCR) detection, and Western blotting (WB) tests to measure TGF-β 1, TRAF6, E-cadherin, Vimentin, and α-Smooth Muscle Actin (α-SMA) expression levels.Results: The expression levels of TGF-β 1, TRAF6, Vimentin, and α-SMA were significantly higher in the AR group compared to the control group as shown by IHC, qRT-PCR, and WB (P < 0.05). E-cadherin expression was significantly lower group than in the control group (P < 0.05). Protein expression of TGF-β 1 showed significantly positive correlations with TRAF6 (r = 0.8188, P = 0.0002), α-SMA (r = 0.8076, P = 0.0003), and Vimentin (r = 0.6917, P = 0.0043). There was a significantly negative correlation between protein expression of TGF-β 1 and E-cadherin (r = − 0.8032, P = 0.0003). Protein expression of TRAF6 showed a significantly negative correlation with E-cadherin (r = − 0.6405, P = 0.0101) but positive correlations with α-SMA (r = 0.5809, P = 0.0231) and Vimentin (r = 0.555, P = 0.0318).Conclusion: TGF-β 1, TRAF6, and EMT-related markers (Vimentin, α-SMA) were highly expressed in the nasal mucosa of AR patients. TGF-β 1 and TRAF6 may be involved in the epithelial-mesenchymal transition in allergic rhinitis.Keywords: allergic rhinitis, transforming growth factor β 1, tumor necrosis factor receptor associated factor 6, epithelial-mesenchymal transition, immunohistochemical
Published: 2024

17. Inflammatory Memory in Epidermal Stem Cells - A New Strategy for Recurrent Inflammatory Skin Diseases

Author: Gao Q and Hao PS
Subjects: inflammatory memory, recurrent inflammatory skin diseases, epigenetics, skin tissue stem cells., Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Qian Gao,1,2 Ping-Sheng Hao1 1Dermatology, Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China; 2Department of Clinical Medicine, Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of ChinaCorrespondence: Ping-Sheng Hao, Email hpswl@126.comAbstract: The ability of the skin to “remember” has been a potential mechanism for studying recurrent skin diseases. While it has been thought that the ability to retain past encounters is the prerogative of immune cells, it has recently been discovered that skin tissue stem cells can also take on this task. Epithelial stem cells undergoing inflammation retain their “memory” through epigenetic reprogramming and exhibit rapid epithelialization and epidermal proliferation upon secondary stimulation. This is a non-specific memory modality independent of conventional immune memory, in which histone modifications (acetylation and methylation) and specific transcription factors (AP-1 and STAT3) are involved in the establishment of inflammatory memories, and AIM2/Caspase-1/IL-1β mainly performs the rapid effects of memory. This finding is intriguing for addressing recurrent inflammatory skin diseases, which may explain the fixed-site recurrence of inflammatory skin diseases and develop new therapeutic strategies in the future. However, more research is still needed to decipher the mysteries of memory.Keywords: inflammatory memory, recurrent inflammatory skin diseases, epigenetics, skin tissue stem cells
Published: 2024

18. Altered Liver Enzyme Markers in Patients with Asymptomatic, and Mild Omicron Infection: A Retrospective Study

Author: Cao X, Xie YL, Yi JY, Liu ZL, Han M, Duan JH, Gao Q, Mu H, and Zhou CL
Subjects: omicron variant, ‘abnormal liver enzyme markers’, male, inflammatory markers., Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Xi Cao,1 Yong-Li Xie,2,3 Jian-Ying Yi,1 Zhi-Li Liu,4 Meng Han,1 Ji-hui Duan,1 Qiang Gao,1 Hong Mu,1 Chun-lei Zhou1 1Department of Clinical Laboratory, Tianjin First Central Hospital, Tianjin, People’s Republic of China; 2Department of Clinical Laboratory, Tianjin Stomatological Hospital, School of Medicine, Nankai University, Tianjin, People’s Republic of China; 3Tianjin Key Laboratory of Oral and Maxillofacial Function Reconstruction, Tianjin, People’s Republic of China; 4Department of Clinical Laboratory, the Third Central Hospital, Tianjin, People’s Republic of ChinaCorrespondence: Hong Mu; Chun-lei Zhou, Department of Clinical Laboratory, Tianjin First Central Hospital, Tianjin, People’s Republic of China, Email mutjyzxyy@163.com; tj_zcl@hotmail.comPurpose: The emergence of the SARS-CoV-2 Omicron variant has posed a significant global public health challenge. Elucidating the laboratory profiles of individuals infected with this variant is crucial for assessing organ damage. This study aimed to investigate the variations in liver function tests and their correlation with demographic characteristics and inflammatory markers in patients with early Omicron variant infections.Patients and Methods: A retrospective cohort study was conducted on 1133 mild or asymptomatic COVID-19 cases at Tianjin First Central Hospital. Data on age, gender, body mass index (BMI), and serum markers were collected and analyzed. Statistical analyses were performed using SPSS software, version 24.0.Results: Abnormal liver function parameters, including aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), and total bilirubin (TBIL), were observed in 314 (27.71%) patients. “Hepatocellular type” was identified in 56 (4.94%) patients, “cholestatic type” in 185 (16.33%) patients, and “mixed type” in 73 (6.44%) patients. In the mixed group, we observed a pronounced elevation in the levels of ALT, AST, and GGT. Moreover, the hepatocellular group exhibited a statistically significant increase in AST and ALT concentrations relative to both the normal and cholestatic groups. Notably, the cholestatic group demonstrated a substantial increment in ALP levels. Males had a significantly higher prevalence of “abnormal liver enzyme markers” compared to females. Patients with “abnormal liver enzyme markers” exhibited significantly decreased immunoglobulin G (IgG) levels and elevated levels of inflammatory markers, including procalcitonin (PCT), interleukin-6 (IL6), as well as C-reactive protein (CRP) compared to normal group. Logistic regression analysis revealed that male gender and PCT levels were significantly associated with the risk of abnormal liver enzyme markers. Patients in hepatocellular group were likely accompanied with high CRP levels, whereas those in the cholestatic type were associated with high IL6 levels.Conclusion: Early Omicron infection might cause liver stress response. Elevated liver enzyme marker levels were correlated with age, gender, inflammatory factors, and IgG.Keywords: Omicron variant, “abnormal liver enzyme markers”, male, inflammatory markers
Published: 2024

19. Study on Velocity Distribution on Cross-Section Flow of T-Shunt

Author: Fang, L., Gao, Q., Zhou, C., Han, B., and Ge, B.
Published: 2024
Full Text: View/download PDF

20. The First LHAASO Catalog of Gamma-Ray Sources

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo., X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Phenomenology
Abstract: We present the first catalog of very-high energy and ultra-high energy gamma-ray sources detected by the Large High Altitude Air Shower Observatory (LHAASO). The catalog was compiled using 508 days of data collected by the Water Cherenkov Detector Array (WCDA) from March 2021 to September 2022 and 933 days of data recorded by the Kilometer Squared Array (KM2A) from January 2020 to September 2022. This catalog represents the main result from the most sensitive large coverage gamma-ray survey of the sky above 1 TeV, covering declination from $-$20$^{\circ}$ to 80$^{\circ}$. In total, the catalog contains 90 sources with an extended size smaller than $2^\circ$ and a significance of detection at $> 5\sigma$. Based on our source association criteria, 32 new TeV sources are proposed in this study. Among the 90 sources, 43 sources are detected with ultra-high energy ($E > 100$ TeV) emission at $> 4\sigma$ significance level. We provide the position, extension, and spectral characteristics of all the sources in this catalog., Comment: 40 pages, 13 figures, 4 tables
Published: 2023
Full Text: View/download PDF

21. Measurement of ultra-high-energy diffuse gamma-ray emission of the Galactic plane from 10 TeV to 1 PeV with LHAASO-KM2A

Author: Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Q., Cao, W. Y., Cao, Zhe, Chang, J., Chang, J. F., Chen, A. M., Chen, E. S., Chen, Liang, Chen, Lin, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, M. Y., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. Q., Duan, K. K., Fan, J. H., Fan, Y. Z., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gabici, S., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Giacinti, G., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. Y., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, B. W., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S. C., Huang, D. H., Huang, T. Q., Huang, W. J., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, X. W., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Kurinov, K., Li, B. B., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Min, Z., Mitthumsiri, W., Mu, H. J., Nan, Y. C., Neronov, A., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Saiz, A., Semikoz, D., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shu, F. W., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Q. W., Tang, Z. B., Tian, W. W., Wang, C., Wang, C. B., Wang, G. W., Wang, H. G., Wang, H. H., Wang, J. C., Wang, K., Wang, L. P., Wang, L. Y., Wang, P. H., Wang, R., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., Wu, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. F., Xu, R. X., Xu, W. L., Xue, L., Yan, D. H., Yan, J. Z., Yan, T., Yang, C. W., Yang, F., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zha, M., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zhou, B., Zhou, H., Zhou, J. N., Zhou, M., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The diffuse Galactic $\gamma$-ray emission, mainly produced via interactions between cosmic rays and the interstellar medium and/or radiation field, is a very important probe of the distribution, propagation, and interaction of cosmic rays in the Milky Way. In this work we report the measurements of diffuse $\gamma$-rays from the Galactic plane between 10 TeV and 1 PeV energies, with the square kilometer array of the Large High Altitude Air Shower Observatory (LHAASO). Diffuse emissions from the inner ($15^{\circ}10$~TeV). The energy spectrum in the inner Galaxy regions can be described by a power-law function with an index of $-2.99\pm0.04$, which is different from the curved spectrum as expected from hadronic interactions between locally measured cosmic rays and the line-of-sight integrated gas content. Furthermore, the measured flux is higher by a factor of $\sim3$ than the prediction. A similar spectrum with an index of $-2.99\pm0.07$ is found in the outer Galaxy region, and the absolute flux for $10\lesssim E\lesssim60$ TeV is again higher than the prediction for hadronic cosmic ray interactions. The latitude distributions of the diffuse emission are consistent with the gas distribution, while the longitude distributions show clear deviation from the gas distribution. The LHAASO measurements imply that either additional emission sources exist or cosmic ray intensities have spatial variations., Comment: 12 pages, 8 figures, 5 tables; accepted for publication in Physical Review Letters; source mask file provided as ancillary file
Published: 2023
Full Text: View/download PDF

22. Diagnostic Values of Solid Features in Different Sizes Thyroid Nodules Based on C-TIRADS

Author: Huang H, Li HJ, Gao Q, Zhu MJ, and Li WM
Subjects: c-tirads, ultrasound, thyroid nodule, Surgery, RD1-811
Abstract: Hu Huang,1,&ast; Hong-Jian Li,2,&ast; Qi Gao,3 Ming-Jie Zhu,1 Wei-Min Li4 1Department of Thyroid and Breast Surgery, Affiliated Hospital of Jiangnan University, Wuxi, People’s Republic of China; 2Department of Ultrasonography, Huai’an Cancer Hospital, Huai’an, People’s Republic of China; 3Department of Ultrasonography, Zhongda Hospital Southeast University, Nanjing, People’s Republic of China; 4Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Wei-Min Li, Department of Ultrasonography, Affiliated Hospital of Jiangnan University, No. 1000 Hefeng Road, Wuxi, 214000, People’s Republic of China, Email 1005342597@qq.comObjective: As a positive features in C-TIRADS (Chinese Thyroid Imaging Reporting and Data System) guidelines, solid features represent the latest positive indicator, which differs from those in other guidelines. This study was to explore the diagnostic value of the solid features for thyroid nodules of different sizes.Methods: Between January 2022 and October 2023, a total of 1561 patients with 1790 thyroid nodules confirmed by surgical pathology were prospectively included in this study. These nodules were divided into three groups based on their maximum diameter: Group A1 (≤ 10mm), Group A2 (> 10mm, < 20mm), and Group A3 (≥ 20mm). The component characteristics of thyroid nodules in each group were analyzed. Based on the surgical pathology results, Receiver Operating Characteristic (ROC) curves were constructed to evaluate the diagnostic efficiency of C-TIRADS solid features for thyroid nodules of different sizes.Results: As the size of thyroid nodules increased, the incidence of cystic changes in both benign and malignant nodules showed a linear increasing trend (Z-values of 46.251 and 156.586, respectively; P values < 0.001 for both). The occurrence rate of solid malignant nodules was higher than that of benign nodules in all groups, with Area Under Curve (AUC) values of 0.620, 0.723, and 0.767, respectively.Conclusion: With the increase in the size of thyroid nodules, the diagnostic value of solid features for thyroid cancer progressively increases. The specificity of thyroid nodule diagnosis also increases progressively. Which may have certain value in the evaluation process using C-TIRADS, particularly in the management of thyroid nodules in clinical settings.Keywords: C-TIRADS, ultrasound, thyroid nodule
Published: 2024

23. Lumbar Epidural versus Caudal Epidural for Postoperative Analgesia After Lower Extremity Osteotomy Surgery in Pediatric Patients with Osteogenesis Imperfecta: A Propensity-Matched Cohort Analysis in a Single-Center Over 9 Years

Author: Mu J, Xiong S, Yang G, Wang F, Li X, Gao Q, Niu Q, Wong SSC, Xu X, Chan Y, and Li Y
Subjects: lumbar epidural, caudal epidural, osteotomy surgery, osteogenesis imperfecta, cohort analysis, pain management, Medicine (General), R5-920
Abstract: Jingjing Mu,1,2,&ast; Shiyi Xiong,2,&ast; Guixiang Yang,2 Fengfeng Wang,3 Xuanying Li,2 Qiong Gao,2 Qiang Niu,2 Stanley Sau Ching Wong,3 Xuebing Xu,2 Yauwai Chan,2,&ast; Yalan Li1,&ast; 1Department of Anesthesiology, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, People’s Republic of China; 2Department of Anaesthesiology, the University of Hong Kong Shenzhen Hospital, Shenzhen, People’s Republic of China; 3Department of Anaesthesiology, School of Clinical Medicine, Li Ka Shing Faculty of Medicine, the University of Hong Kong, Hong Kong&ast;These authors contributed equally to this workCorrespondence: Yalan Li, Department of Anesthesiology, the First Affiliated Hospital of Jinan University, Guangzhou, Guangdong, 510630, People’s Republic of China, Tel +8613500013993, Email tyalan@jnu.edu.cn Yauwai Chan, Department of Anesthesiology, the University of Hong Kong Shenzhen Hospital, Shenzhen, People’s Republic of China, Tel +86 150 0204 8972, Email cywz01@hku.hkPurpose: Although pediatric epidural analgesia is a well-established technique used perioperatively. It is unclear whether a lumbar or caudal epidural is suitable for osteogenesis imperfecta (OI) patients, which may be associated with brittle bones and spine deformity. We conducted a retrospective study to investigate and compare the efficacy of the two continuous epidural techniques in pediatric patients undergoing lower extremity osteotomy surgery using a propensity score-matched analysis (PSMA).Patients and Methods: A total of 274 patients were included. Patients’ age, weight, and height were adjusted using PSMA. 90 patients were matched for further analysis, with 45 patients in the lumbar epidural group (Group L) and 45 patients in the caudal epidural group (Group C). Pain scores were categorized into three grades: mild (0– 3), moderate (4– 6), and severe (7– 10), and compared between the two groups. Additionally, operation time, operation site, blood loss, scoliosis, oral analgesic medications, and catheter or nerve-related complications were compared.Results: There were no significant differences in operation time, operation site, scoliosis, and blood loss between the two groups. The percentage of moderate to severe pain during movement was significantly higher in Group L than in Group C, with 37.5% versus 17.5% on the second-day post-operation (P=0.039). However, no statistically significant difference was observed on other days. Additionally, there was no significant difference in oral medication consumption and complications between the two groups.Conclusion: Both lumbar and caudal epidural analgesia can be effectively used postoperatively, and a caudal epidural should be considered where performing a lumbar epidural is challenging in OI pediatric patients.Plain Language Summary: Osteogenesis imperfecta (OI) is a rare genetic disorder that affects the body’s connective tissues, particularly the bones and ligaments. It is caused by abnormalities in type I collagen, which leads to skeletal fragility known as “brittle bones”. This fragility can cause various issues, including an increased risk of fractures from minor trauma, limb deformities, and unusual fractures such as vertebral compressions. OI patients may also experience spinal manifestations such as scoliosis and kyphosis.Lumbar epidural analgesia has been found to be effective in providing pain relief for surgeries that involve the lower extremities. Additionally, caudal epidural analgesia has also demonstrated its effectiveness in providing postoperative analgesia for surgeries that affect the lower limbs. However, there is still debate about the safety of epidural analgesia in patients with skeletal dysplasias, especially those with OI. Despite this uncertainty, our center, which was supported by the Rare Diseases Public Welfare Organization, has successfully used epidural analgesia since 2015 in the southern part of China for OI surgeries.We conducted a retrospective study to share our experiences of nine years of practice and compare lumbar epidural with caudal epidural using a propensity score matching to balance basic demographics. We also compared the presence of scoliosis. Our findings suggest that both lumbar and caudal epidural analgesia can be safely used in OI patients. In cases where lumbar punctures may pose challenges due to potential spine deformities, the caudal route can be an alternative.Keywords: Lumbar epidural, caudal epidural, osteotomy surgery, osteogenesis imperfecta, cohort analysis, pain management
Published: 2024

24. Development and Validation of Diagnostic Models for Transcriptomic Signature Genes for Multiple Tissues in Osteoarthritis

Author: Gao Q, Ma Y, Shao T, Tao X, Yang X, Li S, Gu J, and Yu Z
Subjects: osteoarthritis, machine learning, immune cells infiltration, diagnostic model, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Qichang Gao,1 Yiming Ma,1 Tuo Shao,1 Xiaoxuan Tao,2 Xiansheng Yang,1 Song Li,1 Jiaao Gu,1 Zhange Yu1 1Department of Spinal Surgery, The 1st Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People’s Republic of China; 2Department of Radiotherapy, The 3st Affiliated Hospital of Harbin Medical University, Harbin, Heilongjiang Province, People’s Republic of ChinaCorrespondence: Zhange Yu, Email yuzhange1967@163.comBackground: Progress in research on expression profiles in osteoarthritis (OA) has been limited to individual tissues within the joint, such as the synovium, cartilage, or meniscus. This study aimed to comprehensively analyze the common gene expression characteristics of various structures in OA and construct a diagnostic model.Methods: Three datasets were selected: synovium, meniscus, and knee joint cartilage. Modular clustering and differential analysis of genes were used for further functional analyses and the construction of protein networks. Signature genes with the highest diagnostic potential were identified and verified using external gene datasets. The expression of these genes was validated in clinical samples by Real-time (RT)-qPCR and immunohistochemistry (IHC) staining. This study investigated the status of immune cells in OA by examining their infiltration.Results: The merged OA dataset included 438 DEGs clustered into seven modules using WGCNA. The intersection of these DEGs with WGCNA modules identified 190 genes. Using Least Absolute Shrinkage and Selection Operator (LASSO) and Random Forest algorithms, nine signature genes were identified (CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3), each demonstrating substantial diagnostic potential (areas under the curve from 0.701 to 0.925). Furthermore, dysregulation of various immune cells has also been observed.Conclusion: CDADC1, PPFIBP1, ENO2, NOM1, SLC25A14, METTL2A, LINC01089, L3HYPDH, NPHP3 demonstrated significant diagnostic efficacy in OA and are involved in immune cell infiltration.Keywords: osteoarthritis, machine learning, immune cells infiltration, diagnostic model
Published: 2024

25. Uncovering the Role of Anoikis-Related Genes in Modulating Immune Infiltration and Pathogenesis of Diabetic Kidney Disease

Author: Lin J, Lin Y, Li X, He F, Gao Q, Wang Y, Huang Z, and Xiong F
Subjects: diabetic kidney disease, anoikis, immune infiltration, biomarker, pycard, sfn, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Jiaqiong Lin,1,2 Yan Lin,3 Xiaoyong Li,4 Fei He,5 Qinyuan Gao,3 Yuanjun Wang,3 Zena Huang,3 Fu Xiong5,6 1Microbiome Medicine Center, Department of Laboratory Medicine, Zhujiang Hospital, Southern Medical University, Guangzhou, Guangdong, 510282, People’s Republic of China; 2Dongguan Maternal and Child Health Care Hospital, Postdoctoral Innovation Practice Base of Southern Medical University, Dongguan, People’s Republic of China; 3Yunkang School of Medicine and Health, Nanfang College, Guangzhou, People’s Republic of China; 4General Surgery Department; Guangdong Provincial Key Laboratory of Major Obstetric Diseases; Guangdong Provincial Clinical Research Center for Obstetrics and Gynecology; The Third Affiliated Hospital of Guangzhou Medical University, Guangzhou, People’s Republic of China; 5Department of Medical Genetics/Experimental Education/Administration Center, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, People’s Republic of China; 6Department of Fetal Medicine and Prenatal Diagnosis, Zhujiang Hospital, Southern Medical University, Guangzhou, People’s Republic of ChinaCorrespondence: Fu Xiong, Department of Medical Genetics/Experimental Education/Administration Center, School of Basic Medical Sciences, Southern Medical University, Guangzhou, 510515, People’s Republic of China, Email xiongfu@smu.edu.cn Zena Huang, Yunkang School of Medicine and Health, Nanfang College, Guangzhou, 510970, People’s Republic of China, Email 506647168@qq.comBackground: Diabetic kidney disease (DKD) is an intricate complication of diabetes with limited treatment options. Anoikis, a programmed cell death activated by loss of cell anchorage to the extracellular matrix, participated in various physiological and pathological processes. Our study aimed to elucidate the role of anoikis-related genes in DKD pathogenesis.Methods: Differentially expressed genes (DEGs) associated with anoikis in DKD were identified. Weighted gene co-expression network analysis (WGCNA) was conducted to identify DKD-correlated modules and assess their functional implications. A diagnostic model for DKD was developed using LASSO regression and Gene set variation analysis (GSVA) was performed for enrichment analysis. Experimental validation was employed to validate the significance of selected genes in the progression of DKD.Results: We identified 10 anoikis-related DEGs involved in key signaling pathways impacting DKD progression. WGCNA highlighted the yellow module’s significant enrichment in immune response and regulatory pathways. Correlation analysis further revealed the association between immune infiltration and anoikis-related DEGs. Our LASSO regression-based diagnostic model demonstrated a well-predictive efficacy with seven identified genes. GSVA indicated that gene function in the high-risk group was primarily associated with immune regulation. Further experimental validation using diabetic mouse models and data analysis in the single-cell dataset confirmed the significance of PYCARD and SFN in DKD progression. High glucose stimulation in RAW264.7 and TCMK-1 cells showed significantly increased expression levels of both Pycard and Sfn. Co-expression analysis demonstrated distinct functions of PYCARD and SFN, with KEGG pathway analysis showing significant enrichment in immune regulation and cell proliferation pathway.Conclusion: In conclusion, our study provides valuable insights into the molecular mechanisms involved in DKD pathogenesis, specifically highlighting the role of anoikis-related genes in modulating immune infiltration. These findings suggest that targeting these genes may hold promise for future diagnostic and therapeutic approaches in DKD management.Keywords: diabetic kidney disease, anoikis, immune infiltration, biomarker, PYCARD, SFN
Published: 2024

26. Dimensional crossover and symmetry transformation of the charge density waves in VSe2

Author: Chen, P., Chan, Y. -H., Liu, R. -Y., Zhang, H. T., Gao, Q., Fedorov, A. -V., Chou, M. Y., and Chiang, T. -C.
Subjects: Condensed Matter - Mesoscale and Nanoscale Physics
Abstract: Collective phenomena in solids can be sensitive to the dimensionality of the system; a case of special interest is VSe2, which shows a (r7 x r3) charge density wave (CDW) in the single layer with the three-fold symmetry in the normal phase spontaneously broken, in contrast to the (4 x 4) in-plane CDW in the bulk. Angle-resolved photoemission spectroscopy (ARPES) from VSe2 ranging from a single layer to the bulk reveals the evolution of the electronic structure including the Fermi surface contours and the CDW gap. At a thickness of two layers, the ARPES maps are already nearly bulklike, but the transition temperature TC for the (4 x 4) CDW is much higher than the bulk value of 110 K. These results can be understood as a result of dimensional crossover of phonon instability driven by a competition of nesting vectors. Our study provides key insights into the CDW mechanisms and offers a perspective in the search and control of emergent phases in quantum materials.
Published: 2023
Full Text: View/download PDF

27. Alteration Trend and Overlap Analysis of Positive Features in Different-Sized Benign and Malignant Thyroid Nodules: Based on Chinese Thyroid Imaging Reporting and Data System

Author: Qu C, Li HJ, Gao Q, Zhang JC, and Li WM
Subjects: size, thyroid, c-tirads, ultrasound, Medicine (General), R5-920
Abstract: Chen Qu,1,&ast; Hong-jian Li,2,&ast; Qi Gao,3 Jun-chao Zhang,1 Wei-min Li1 1Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People’s Republic of China; 2Department of Ultrasonography, Huai’an Cancer Hospital, Huai’an, Jiangsu, People’s Republic of China; 3Department of Ultrasonography, Zhongda Hospital Southeast University, Nanjing, Jiangsu, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Wei-min Li, Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, 214000, People’s Republic of China, Tel +8613912362309, Email 1005342597@qq.comPurpose: This study aimed to investigate the alteration trends and overlaps of positive features in benign and malignant thyroid nodules of different sizes based on the Chinese Thyroid Imaging Reporting and Data System (C-TIRADS).Patients and Methods: 1337 patients with 1558 thyroid nodules were retrospectively recruited from November 2021 to December 2023. These nodules were divided into three groups according to maximum diameter: A (≤ 10 mm), B (10– 20 mm), and C (≥ 20 mm). C-TIRADS positive features were compared between benign and malignant thyroid nodules of different sizes. In addition, the trends of positive features with changes in nodule size among malignant thyroid nodules were analyzed.Results: The incidence of positive features in malignant thyroid nodules was higher than that in benign. As benign nodules grow, the incidence of all positive features showed a linear decreasing trend (Z values were 72.103, 101.081, 17.344, 33.909, and 129.304, P values < 0.001). With the size of malignant thyroid nodules increased, vertical orientation, solid, marked hypoechogenicity, and ill-defined/irregular margins/extrathyroidal extension showed a linear decreasing trend (Z = 148.854, 135.378, 8.590, and 69.239, respectively; P values < 0.05), while suspicious microcalcifications showed a linear increasing trend (Z = 34.699, P< 0.001). In terms of overlapping characteristics, group A had a significantly higher overlapping rate than the other two groups, and the overlapping rate of solid indicators remained the highest among all three groups (P < 0.05).Conclusion: Differences in positive features were observed between thyroid nodules of different sizes. Except for suspicious microcalcifications, the incidence of other four positive features decreased with increasing nodule size. In addition, a negative correlation was observed between the overlap rate and nodule size. These results may provide a basis for sonographers to upgrade or downgrade thyroid nodules based on their own experience.Keywords: size, thyroid, C-TIRADS, ultrasound
Published: 2024

28. Trends of Drug-Resistant Tuberculosis in an Urban and a Rural Area in China: A 10-Year Population-Based Molecular Epidemiological Study

Author: Xu P, Li M, Jiang Q, Yang C, Liu X, Takiff H, and Gao Q
Subjects: tuberculosis, trends of drug resistance, whole-genome sequencing, urban and rural china, Infectious and parasitic diseases, RC109-216
Abstract: Peng Xu,1 Meng Li,1,2 Qi Jiang,3 Chongguang Yang,4 Xiangxiang Liu,1 Howard Takiff,5 Qian Gao1,2 1National Clinical Research Center for Infectious Diseases, Shenzhen Clinical Research Center for Tuberculosis, Shenzhen Third People’s Hospital, Shenzhen, Guangdong, People’s Republic of China; 2Key Laboratory of Medical Molecular Virology (MOE/NHC/CAMS), School of Basic Medical Science, Shanghai Medical College, Shanghai Institute of Infectious Disease and Biosecurity, Fudan University, Shanghai, People’s Republic of China; 3Department of Epidemiology and Biostatistics, School of Public Health, Wuhan University, Wuhan, People’s Republic of China; 4School of Public Health (Shenzhen), Shenzhen Campus of Sun Yat-Sen University, Shenzhen, People’s Republic of China; 5Laboratorio de Genética Molecular, CMBC, IVIC, Caracas, VenezuelaCorrespondence: Qian Gao, National Clinical Research Center for Infectious Diseases, Shenzhen Clinical Research Center for Tuberculosis, Shenzhen Third People’s Hospital, No. 29 Bulan Road, Longgang District, Shenzhen, Guangdong, People’s Republic of China, Tel +86-21-5423-7195, Fax +86-21-5423-7971, Email qiangao@fudan.edu.cnObjective: Drug resistance is the critical determinant for appropriate tuberculosis (TB) treatment regimens and an important indicator of the local TB burden. We aimed to investigate and compare trends in TB drug resistance in the urban Songjiang District of Shanghai from 2011 to 2020, and the rural Wusheng County of Sichuan Province from 2009 to 2020, to assess the effectiveness of local TB control and treatment programs.Methods: Whole-genome sequencing data of Mycobacterium tuberculosis were used to predict drug-resistance profiles and identify genomic clusters. Clustered, retreated cases of drug-resistant TB with identical resistance mutations, as well as all new resistant cases, were defined as transmitted resistance. The Cochran-Armitage trend test was used to identify trends in the proportions. Differences between groups were tested using the Wilcoxon rank sum or chi-square tests.Results: The annual proportions of rifampicin-resistant (RR), isoniazid-resistant (INH-R) and multidrug-resistant (MDR) TB cases did not change significantly in Songjiang. In Wusheng, however, the percentage of total TB cases that were RR decreased from 13.2% in 2009 to 3.7% in 2020, the INH-R cases decreased from 16.5% to 7.3%, and the MDR cases decreased from 10.7% to 3.7%. In retreated cases, the percentage of drug resistance decreased in both Songjiang and Wusheng, suggesting improved treatment programs. Transmitted resistance accounted for more than two thirds of drug-resistant cases over the entire study periods, and in recent years this proportion has increased significantly in Songjiang.Conclusion: In both urban Songjiang and rural Wusheng, drug-resistant TB is mostly the result of transmission of drug resistant strains and the percentage of transmitted resistance will likely increase with on-going improvements in the TB treatment programs. Reducing the prevalence of drug resistance depends principally upon decreasing transmission through the prompt diagnosis and effective treatment of drug-resistant TB cases.Keywords: tuberculosis, trends of drug resistance, whole-genome sequencing, urban and rural China
Published: 2024

29. Efficacy and Safety of HepaSphere Drug-Eluting Bead Transarterial Chemoembolization Combined with Hepatic Arterial Infusion Chemotherapy as the Second-Line Treatment in Advanced Hepatocellular Carcinoma

Author: Liu B, Gao S, Guo J, Kou F, Liu S, Zhang X, Feng A, Wang X, Cao G, Chen H, Liu P, Xu H, Gao Q, Yang R, Xu L, and Zhu X
Subjects: transarterial chemoembolization, advanced hepatocellular carcinoma, hepatic arterial infusion chemotherapy, propensity score matching, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Baojiang Liu,&ast; Song Gao,&ast; Jianhai Guo, Fuxin Kou, Shaoxing Liu, Xin Zhang, Aiwei Feng, Xiaodong Wang, Guang Cao, Hui Chen, Peng Liu, Haifeng Xu, Qinzong Gao, Renjie Yang, Liang Xu, Xu Zhu Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Interventional Therapy, Peking University Cancer Hospital & Institute, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xu Zhu, Key Laboratory of Carcinogenesis and Translational Research (Ministry of Education/Beijing), Department of Interventional Therapy, Peking University Cancer Hospital & Institute, Beijing, People’s Republic of China, Tel + 86-10-8819-6059, Email drzhuxu@163.comPurpose: Recently, hepatic arterial infusion chemotherapy (HAIC) has also gained popularity for hepatocellular carcinoma (HCC). Several studies have compared HAIC and Transarterial chemoembolization (TACE). However, comparisons between TACE plus HAIC and HAIC are rarely reported. Here, we evaluated the performance of HepaSphere DEB-TACE combined with HAIC (Hepa-HAIC) compared to HAIC in patients with advanced HCC.Patients and Methods: In this retrospective study, we enrolled 167 patients diagnosed with advanced HCC and treated at Peking University Cancer Hospital from May 2018 to May 2022. The cohort comprised 74 patients who received HepaSphere DEB-TACE combined with HAIC-FOLFOX (Hepa-HAIC) and 93 patients who received HAIC-FOLFOX. Over 60% of patients received prior treatments. To avoid selection bias, propensity score matching was applied to the efficacy and safety analyses. The primary endpoints are progression-free survival (PFS) and overall survival (OS); the secondary endpoints include objective response rate (ORR), disease control rate (DCR), and safety.Results: Propensity-matching yielded 48 pairs, and group baselines were almost equal after matching. Median PFS and median OS were both higher in the matched Hepa-HAIC cohort (median PFS: 8.9 vs 5.8 months, p = 0.035; median OS: 22.4 vs 9.5 months, p = 0.027), which was consistent with pre-matching analysis. The ORR in the Hepa-HAIC and HAIC cohorts was 75.0% and 37.5%, respectively; the DCR was 93.8% after Hepa-HAIC and 81.3% after HAIC. There was no treatment-related death. Grade 3– 4 ALT elevation was more frequent in the Hepa-HAIC group (33.3% vs 8.3%, p = 0.003), while vomiting was more frequent in the HAIC group (29.2% vs 12.5%, p = 0.084).Conclusion: The Hepa-HAIC group is superior to the HAIC group in metrics of PFS, OS, ORR, and DCR, which indicates the combination of HepaSphere DEB-TACE and HAIC may lead to improved outcomes with a comparable safety profile in advanced HCC.Keywords: transarterial chemoembolization, advanced hepatocellular carcinoma, hepatic arterial infusion chemotherapy, propensity score matching
Published: 2024

30. Signature of quantum criticality in cuprates by charge density fluctuations

Author: Arpaia, R., Martinelli, L., Sala, M. Moretti, Caprara, S., Nag, A., Brookes, N. B., Camisa, P., Li, Q., Gao, Q., Zhou, X., Garcia-Fernandez, M., Zhou, K. -J., Schierle, E., Bauch, T., Peng, Y. Y., Di Castro, C., Grilli, M., Lombardi, F., Braicovich, L., and Ghiringhelli, G.
Subjects: Condensed Matter - Superconductivity, Condensed Matter - Strongly Correlated Electrons
Abstract: The universality of the strange metal phase in many quantum materials is often attributed to the presence of a quantum critical point (QCP), a zero-temperature phase transition ruled by quantum fluctuations. In cuprates, where superconductivity hinders direct QCP observation, indirect evidence comes from the identification of fluctuations compatible with the strange metal phase. Here we show that the recently discovered charge density fluctuations (CDF) possess the right properties to be associated to a quantum phase transition. Using resonant x-ray scattering, we studied the CDF in two families of cuprate superconductors across a wide doping range (up to $p$=0.22). At $p^*\approx$0.19, the putative QCP, the CDF intensity peaks, and the characteristic energy $\Delta$ is minimum, marking a wedge-shaped region in the phase diagram indicative of a quantum critical behavior, albeit with anomalies. These findings strengthen the role of charge order in explaining strange metal phenomenology and provide insights into high-temperature superconductivity., Comment: 36 pages, 4 figures, 9 supplementary figures
Published: 2022
Full Text: View/download PDF

31. Flux Variations of Cosmic Ray Air Showers Detected by LHAASO-KM2A During a Thunderstorm on 10 June 2021

Author: LHAASO Collaboration, Aharonian, F., An, Q., Axikegu, Bai, L. X., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, J. T., Cao, Zhe, Cao, Zhen, Chang, J., Chang, J. F., Chen, E. S., Chen, Liang, Chen, Long, Chen, M. J., Chen, M. L., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, X. J., Chen, Y., Cheng, H. L., Cheng, N., Cheng, Y. D., Cui, S. W., Cui, X. H., Cui, Y. D., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Duan, K. K., Fan, J. H., Fan, Y. Z., Fan, Z. X., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, X. T., Feng, Y. L., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Gao, W. K., Ge, M. M., Geng, L. S., Gong, G. H., Gou, Q. B., Gu, M. H., Gu, F. L., Guo, J. G., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, S. L., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, C., Hou, X., Hu, H. B., Hu, Q., Hu, S., Hu, S. C., Hu, X. J., Huang, D. H., Huang, W. H., Huang, X. T., Huang, X. Y., Huang, Y., Huang, Z. C., Ji, X. L., Jia, H. Y., Jia, K., Jiang, K., Jiang, Z. J., Jin, M., Kang, M. M., Ke, T., Kuleshov, D., Li, B. B., Li, Cheng, Li, Cong, Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, Jian, Li, Jie, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. S., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Long, W. J., Lu, R., Luo, Q., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Masood, A., Min, Z., Mitthumsiri, W., Nan, Y. C., Ou, Z. W., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Sáiz, A., Shao, C. Y., Shao, L., Shchegolev, O., Sheng, X. D., Shi, J. Y., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Z. B., Tian, W. W., Wang, B. D., Wang, C., Wang, H., Wang, H. G., Wang, J. C., Wang, J. S., Wang, L. P., Wang, L. Y., Wang, R., Wang, R. N., Wang, W., Wang, X. G., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Y. P., Wang, Z. H. Wang. Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X. F., W, Y. S., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xin, G. G., Xin, Y. L., Xing, Y., Xiong, Z., Xu, D. L., Xu, R. X., Xue, L., Yan, D. H., Yan, J. Z., Yang, C. W., Yang, F. F., Yang, H. W., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Yue, H., Zeng, H. D., Zeng, T. X., Zeng, W., Zeng, Z. K., Zha, M., Zhai, X. X., Zhang, B. B., Zhang, F., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, L. X., Zhang, Li, Zhang, Lu, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. B., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Y. L., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, Y., Zhou, B., Zhou, H., Zhou, J. N., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena, High Energy Physics - Experiment
Abstract: The Large High Altitude Air Shower Observatory (LHAASO) has three sub-arrays, KM2A, WCDA and WFCTA. The flux variations of cosmic ray air showers were studied by analyzing the KM2A data during the thunderstorm on 10 June 2021. The number of shower events that meet the trigger conditions increases significantly in atmospheric electric fields, with maximum fractional increase of 20%. The variations of trigger rates (increases or decreases) are found to be strongly dependent on the primary zenith angle. The flux of secondary particles increases significantly, following a similar trend with that of the shower events. To better understand the observed behavior, Monte Carlo simulations are performed with CORSIKA and G4KM2A (a code based on GEANT4). We find that the experimental data (in saturated negative fields) are in good agreement with simulations, assuming the presence of a uniform upward electric field of 700 V/cm with a thickness of 1500 m in the atmosphere above the observation level. Due to the acceleration/deceleration and deflection by the atmospheric electric field, the number of secondary particles with energy above the detector threshold is modified, resulting in the changes in shower detection rate., Comment: 18 pages, 11 figures
Published: 2022
Full Text: View/download PDF

32. Application limit of the photocentre displacement to fundamental stellar parameters of fast rotators -- Illustration on the edge-on fast rotator Regulus

Author: Hadjara, M., Petrov, R. G., Jankov, S., Cruzalèbes, P., Boskri, A., Spang, A., Lagarde, S., He, J., Chen, X., Nitschelm, C., de Almeida, E. S. G., Pereira, G., Michael, E. A., Gao, Q., Wang, W., Reyes, I., Arcos, C., Araya, I., and Curé, M.
Subjects: Astrophysics - Solar and Stellar Astrophysics, Astrophysics - Instrumentation and Methods for Astrophysics
Abstract: Differential Interferometry allows to obtain the differential visibility and phase, in addition to the spectrum. The differential phase contains important information about the structure and motion of stellar photosphere such as stellar spots and non-radial pulsations, and particularly the rotation. Thus, this interferometric observable strongly helps to constrain the stellar fundamental parameters of fast rotators. The spectro-astrometry mainly uses the photocentre displacements, which is a first approximation of the differential phase, and is applicable only for unresolved or marginally objects. We study here the sensitivity of relevant stellar parameters to the simulated photocentres using the SCIROCCO code: a semi-analytical algorithm dedicated to fast rotators, applied to two theoretical modeling stars based on Achernar and Regulus, in order to classify the importance of these parameters and their impact on the modeling. We compare our simulations with published VLTI/AMBER data. This current work sets the limits of application of photocentre displacements to fast rotators, and under which conditions we can use the photocentres and/or the differential phase, through a pre-established physical criterion. To validate our theoretical study, we apply our method of analysis on observed data of the edge-on fast rotator Regulus. For unresolved targets, with a visibility $V\sim 1$, the photocentre can constrain the main stellar fundamental parameters of fast rotators, whereas from marginally resolved objects ($0.8 \leq V < 1$), mainly the rotation axis position angle ($\rm PA_{\rm rot}$) can be directly deduced from the vectorial photocentre displacement, which is very important for young cluster studies.
Published: 2022
Full Text: View/download PDF

33. Evidence for particle acceleration approaching PeV energies in the W51 complex

Author: Cao, Zhen, Aharonian, F., Axikegu, Bai, Y.X., Bao, Y.W., Bastieri, D., Bi, X.J., Bi, Y.J., Bian, W., Bukevich, A.V., Cao, Q., Cao, W.Y., Cao, Zhe, Chang, J., Chang, J.F., Chen, A.M., Chen, E.S., Chen, H.X., Chen, Liang, Chen, Lin, Chen, Long, Chen, M.J., Chen, M.L., Chen, Q.H., Chen, S., Chen, S.H., Chen, S.Z., Chen, T.L., Chen, Y., Cheng, N., Cheng, Y.D., Cui, M.Y., Cui, S.W., Cui, X.H., Cui, Y.D., Dai, B.Z., Dai, H.L., Dai, Z.G., Danzengluobu, Dong, X.Q., Duan, K.K., Fan, J.H., Fan, Y.Z., Fang, J., Fang, J.H., Fang, K., Feng, C.F., Feng, H., Feng, L., Feng, S.H., Feng, X.T., Feng, Y., Feng, Y.L., Gabici, S., Gao, B., Gao, C.D., Gao, Q., Gao, W., Gao, W.K., Ge, M.M., Geng, L.S., Giacinti, G., Gong, G.H., Gou, Q.B., Gu, M.H., Guo, F.L., Guo, X.L., Guo, Y.Q., Guo, Y.Y., Han, Y.A., Hasan, M., He, H.H., He, H.N., He, J.Y., He, Y., Hor, Y.K., Hou, B.W., Hou, C., Hou, X., Hu, H.B., Hu, Q., Hu, S.C., Huang, D.H., Huang, T.Q., Huang, W.J., Huang, X.T., Huang, X.Y., Huang, Y., Ji, X.L., Jia, H.Y., Jia, K., Jiang, K., Jiang, X.W., Jiang, Z.J., Jin, M., Kang, M.M., Karpikov, I., Kuleshov, D., Kurinov, K., Li, B.B., Li, C.M., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H.B., Li, H.C., Li, Jian, Li, Jie, Li, K., Li, S.D., Li, W.L., Li, X.R., Li, Xin, Li, Y.Z., Li, Zhe, Li, Zhuo, Liang, E.W., Liang, Y.F., Lin, S.J., Liu, B., Liu, C., Liu, D., Liu, D.B., Liu, H., Liu, H.D., Liu, J., Liu, J.L., Liu, M.Y., Liu, R.Y., Liu, S.M., Liu, W., Liu, Y., Liu, Y.N., Luo, Q., Luo, Y., Lv, H.K., Ma, B.Q., Ma, L.L., Ma, X.H., Mao, J.R., Min, Z., Mitthumsiri, W., Mu, H.J., Nan, Y.C., Neronov, A., Ou, L.J., Pattarakijwanich, P., Pei, Z.Y., Qi, J.C., Qi, M.Y., Qiao, B.Q., Qin, J.J., Raza, A., Ruffolo, D., Sáiz, A., Saeed, M., Semikoz, D., Shao, L., Shchegolev, O., Sheng, X.D., Shu, F.W., Song, H.C., Stenkin, Yu.V., Stepanov, V., Su, Y., Sun, D.X., Sun, Q.N., Sun, X.N., Sun, Z.B., Takata, J., Tam, P.H.T., Tang, Q.W., Tang, R., Tang, Z.B., Tian, W.W., Wang, C., Wang, C.B., Wang, G.W., Wang, H.G., Wang, H.H., Wang, J.C., Wang, Kai, Wang, L.P., Wang, L.Y., Wang, P.H., Wang, R., Wang, W., Wang, X.G., Wang, X.Y., Wang, Y., Wang, Y.D., Wang, Y.J., Wang, Z.H., Wang, Z.X., Wang, Zhen, Wang, Zheng, Wei, D.M., Wei, J.J., Wei, Y.J., Wen, T., Wu, C.Y., Wu, H.R., Wu, Q.W., Wu, S., Wu, X.F., Wu, Y.S., Xi, S.Q., Xia, J., Xiang, G.M., Xiao, D.X., Xiao, G., Xin, Y.L., Xing, Y., Xiong, D.R., Xiong, Z., Xu, D.L., Xu, R.F., Xu, R.X., Xu, W.L., Xue, L., Yan, D.H., Yan, J.Z., Yan, T., Yang, C.W., Yang, C.Y., Yang, F., Yang, F.F., Yang, L.L., Yang, M.J., Yang, R.Z., Yang, W.X., Yao, Y.H., Yao, Z.G., Yin, L.Q., Yin, N., You, X.H., You, Z.Y., Yu, Y.H., Yuan, Q., Yue, H., Zeng, H.D., Zeng, T.X., Zeng, W., Zha, M., Zhang, B.B., Zhang, F., Zhang, H., Zhang, H.M., Zhang, H.Y., Zhang, J.L., Zhang, Li, Zhang, P.F., Zhang, P.P., Zhang, R., Zhang, S.B., Zhang, S.R., Zhang, S.S., Zhang, X., Zhang, X.P., Zhang, Y.F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L.Z., Zhao, S.P., Zhao, X.H., Zheng, F., Zhong, W.J., Zhou, B., Zhou, H., Zhou, J.N., Zhou, M., Zhou, P., Zhou, R., Zhou, X.X., Zhu, B.Y., Zhu, C.G., Zhu, F.R., Zhu, H., Zhu, K.J., Zou, Y.C., Zuo, X., and Celli, S.
Published: 2024
Full Text: View/download PDF

34. Label-free differentiation of pancreatic pathologies from normal pancreas utilizing end-to-end three-dimensional multimodal networks on CT

Author: Zhang, G., Gao, Q., Zhan, Q., Wang, L., Song, B., Chen, Y., Bian, Y., Ma, C., Lu, J., and Shao, C.
Published: 2024
Full Text: View/download PDF

35. Data quality control system and long-term performance monitor of LHAASO-KM2A

Author: Cao, Zhen, Aharonian, F., Axikegu, Bai, Y.X., Bao, Y.W., Bastieri, D., Bi, X.J., Bi, Y.J., Bian, W., Bukevich, A.V., Cao, Q., Cao, W.Y., Cao, Zhe, Chang, J., Chang, J.F., Chen, A.M., Chen, E.S., Chen, H.X., Chen, Liang, Chen, Lin, Chen, Long, Chen, M.J., Chen, M.L., Chen, Q.H., Chen, S., Chen, S.H., Chen, S.Z., Chen, T.L., Chen, Y., Cheng, N., Cheng, Y.D., Cui, M.Y., Cui, S.W., Cui, X.H., Cui, Y.D., Dai, B.Z., Dai, H.L., Dai, Z.G., Danzengluobu, Dong, X.Q., Duan, K.K., Fan, J.H., Fan, Y.Z., Fang, J., Fang, J.H., Fang, K., Feng, C.F., Feng, H., Feng, L., Feng, S.H., Feng, X.T., Feng, Y., Feng, Y.L., Gabici, S., Gao, B., Gao, C.D., Gao, Q., Gao, W., Gao, W.K., Ge, M.M., Geng, L.S., Giacinti, G., Gong, G.H., Gou, Q.B., Gu, M.H., Guo, F.L., Guo, X.L., Guo, Y.Q., Guo, Y.Y., Han, Y.A., Hasan, M., He, H.H., He, H.N., He, J.Y., He, Y., Hor, Y.K., Hou, B.W., Hou, C., Hou, X., Hu, H.B., Hu, Q., Hu, S.C., Huang, D.H., Huang, T.Q., Huang, W.J., Huang, X.T., Huang, X.Y., Huang, Y., Ji, X.L., Jia, H.Y., Jia, K., Jiang, K., Jiang, X.W., Jiang, Z.J., Jin, M., Kang, M.M., Karpikov, I., Kuleshov, D., Kurinov, K., Li, B.B., Li, C.M., Li, Cheng, Li, Cong, Li, D., Li, F., Li, H.B., Li, H.C., Li, Jian, Li, Jie, Li, K., Li, S.D., Li, W.L., Li, X.R., Li, Xin, Li, Y.Z., Li, Zhe, Li, Zhuo, Liang, E.W., Liang, Y.F., Lin, S.J., Liu, B., Liu, C., Liu, D., Liu, D.B., Liu, H., Liu, H.D., Liu, J., Liu, J.L., Liu, M.Y., Liu, R.Y., Liu, S.M., Liu, W., Liu, Y., Liu, Y.N., Luo, Q., Luo, Y., Lv, H.K., Ma, B.Q., Ma, L.L., Ma, X.H., Mao, J.R., Min, Z., Mitthumsiri, W., Mu, H.J., Nan, Y.C., Neronov, A., Ou, L.J., Pattarakijwanich, P., Pei, Z.Y., Qi, J.C., Qi, M.Y., Qiao, B.Q., Qin, J.J., Raza, A., Ruffolo, D., Sáiz, A., Saeed, M., Semikoz, D., Shao, L., Shchegolev, O., Sheng, X.D., Shu, F.W., Song, H.C., Stenkin, Yu.V., Stepanov, V., Su, Y., Sun, D.X., Sun, Q.N., Sun, X.N., Sun, Z.B., Takata, J., Tam, P.H.T., Tang, Q.W., Tang, R., Tang, Z.B., Tian, W.W., Wang, C., Wang, C.B., Wang, G.W., Wang, H.G., Wang, H.H., Wang, J.C., Wang, Kai, Wang, L.P., Wang, L.Y., Wang, P.H., Wang, R., Wang, W., Wang, X.G., Wang, X.Y., Wang, Y., Wang, Y.D., Wang, Y.J., Wang, Z.H., Wang, Z.X., Wang, Zhen, Wang, Zheng, Wei, D.M., Wei, J.J., Wei, Y.J., Wen, T., Wu, C.Y., Wu, H.R., Wu, Q.W., Wu, S., Wu, X.F., Wu, Y.S., Xi, S.Q., Xia, J., Xiang, G.M., Xiao, D.X., Xiao, G., Xin, Y.L., Xing, Y., Xiong, D.R., Xiong, Z., Xu, D.L., Xu, R.F., Xu, R.X., Xu, W.L., Xue, L., Yan, D.H., Yan, J.Z., Yan, T., Yang, C.W., Yang, C.Y., Yang, F., Yang, F.F., Yang, L.L., Yang, M.J., Yang, R.Z., Yang, W.X., Yao, Y.H., Yao, Z.G., Yin, L.Q., Yin, N., You, X.H., You, Z.Y., Yu, Y.H., Yuan, Q., Yue, H., Zeng, H.D., Zeng, T.X., Zeng, W., Zha, M., Zhang, B.B., Zhang, F., Zhang, H., Zhang, H.M., Zhang, H.Y., Zhang, J.L., Zhang, Li, Zhang, P.F., Zhang, P.P., Zhang, R., Zhang, S.B., Zhang, S.R., Zhang, S.S., Zhang, X., Zhang, X.P., Zhang, Y.F., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L.Z., Zhao, S.P., Zhao, X.H., Zheng, F., Zhong, W.J., Zhou, B., Zhou, H., Zhou, J.N., Zhou, M., Zhou, P., Zhou, R., Zhou, X.X., Zhu, B.Y., Zhu, C.G., Zhu, F.R., Zhu, H., Zhu, K.J., Zou, Y.C., and Zuo, X.
Published: 2025
Full Text: View/download PDF

36. Genomics-Microbiome Based Assessment of Bidirectional Causality Between Gut Microbiota and Psoriasis

Author: Gao Q, Liu JH, Ma WY, Cheng ZL, Hao PS, and Luo NN
Subjects: mendelian randomization, psoriasis, causal relationship, gut microbiota, bidirectional mendelian randomization analysis., Dermatology, RL1-803
Abstract: Qian Gao,1,2 Jing-Hua Liu,1,2 Wen-Yi Ma,1,2 Zi-Lin Cheng,1,2 Ping-Sheng Hao,1 Na-Na Luo1 1Hospital of Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of China; 2Chengdu University of Traditional Chinese Medicine, Chengdu, People’s Republic of ChinaCorrespondence: Ping-Sheng Hao; Na-Na Luo, Email hpswl@126.com; 982820421@qq.comBackground: Traditional observational studies have found a possible risk association of the gut microbiota for psoriasis. Meanwhile, psoriasis may also affect the changes in the gut microbiota. However, the available evidence does not demonstrate a reciprocal relationship between the gut microbiota and psoriasis. This limits our understanding on the role of the gut microbiota in the mechanisms of psoriasis.Methods: To address this question we used Mendelian randomization, a novel epidemiological approach, and acquired the largest current gut microbiota GWAS data from the MiBioGen consortium as well as psoriasis GWAS data from the FinnGen consortium, and performed two-sample bidirectional MR analyses using a multiple MR analysis approach. Finally, the robustness of the results was assessed by sensitivity analysis.Results: Our results indicate that five bacterial genera are causally related to psoriasis and psoriasis is causally related to four bacterial genera.Conclusion: These results suggest a bidirectional causal influence of psoriasis on the gut microbiota. Our results somewhat challenge the causal inferences of previous observational studies. We found that the specific bacterial genera with a risk effect on psoriasis were different from those found to characterize psoriasis in previous observational studies, and that these psoriasis-characterizing genera were inversely associated with psoriasis.Keywords: Mendelian randomization, psoriasis, causal relationship, gut microbiota, bidirectional Mendelian randomization analysis
Published: 2024

37. Adolescent Non-Puerperal Mastitis: Risk Factors, Clinical Characteristics, and Prognosis Analysis

Author: Tang H, Wu X, Feng J, Gao Q, Shao S, Qu W, Xie L, and Sun J
Subjects: adolescents, non-puerperal mastitis, risk factors, clinical characteristics, prognosis, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Huili Tang, Xueqing Wu, Jiamei Feng, Qingqian Gao, Shijun Shao, Wenchao Qu, Lu Xie, Jiaye Sun Department of Breast, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, People’s Republic of ChinaCorrespondence: Xueqing Wu, Department of Breast, Shuguang Hospital, Shanghai University of Traditional Chinese Medicine, 185 Pu ‘an Road, Huangpu District, Shanghai, People’s Republic of China, Tel + 86-13817792022, Fax + 86-200021, Email snow_zi@hotmail.comPurpose: To determine the risk factors, clinical characteristics, and prognosis of adolescent non-puerperal mastitis patients.Patients and methods: A retrospective analysis was conducted on 10 cases of NPM in adolescents who underwent surgical treatment at Shuguang Hospital affiliated to Shanghai University of Traditional Chinese Medicine from August 2021 to August 2023. We analyze the patient’s general information, clinical characteristics, related medical history, laboratory indicators, breast magnetic resonance imaging examination, postoperative pathology, prognosis, etc.Results: The clinical manifestations of NPM in adolescents mainly included redness, swelling and pain in the breasts, congenital nipple retraction, and extensive lesion range. Inflammatory markers and prolactin were elevated. Magnetic resonance imaging showed circular enhancement with abscess formation as the main type. All patients underwent surgical treatment with a fast recovery time after surgery. No recurrence was observed during follow-up and the postoperative breast appearance was satisfactory. Multivariate Logistic regression analysis indicated that congenital nipple retraction, elevated prolactin levels and trauma were independent risk factors for adolescents non-puerperal mastitis.Conclusion: Adolescent non-puerperal mastitis is a rare and unique type. Summarizing its main risk factors, clinical characteristics, and prognosis provides a basis for further research.Keywords: adolescents, non-puerperal mastitis, risk factors, clinical characteristics, prognosis
Published: 2024

38. Peripheral Blood CD8+T Cell as a Prognostic Biomarker for Hospitalised COVID-19 Patients Without Antiviral Treatment

Author: Sun Y, Liu P, Zhang L, Lei S, and Gao Q
Subjects: peripheral blood cd3+cd8+t cell, prognostic biomarker, hospitalised covid-19 patients, Infectious and parasitic diseases, RC109-216
Abstract: Yuming Sun,1– 6,&ast; Peilin Liu,7,&ast; Lifang Zhang,8 Shaorong Lei,1 Qian Gao7 1Department of Plastic and Cosmetic Surgery, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 2National Engineering Research Centre of Personalized Diagnostic and Therapeutic Technology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 3Furong Laboratory, Central South University, Changsha, People’s Republic of China; 4Hunan Key Laboratory of Skin Cancer and Psoriasis, Hunan Engineering Research Centre of Skin Health and Disease, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 5National Clinical Research Centre for Geriatric Disorders, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 6Department of Dermatology, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 7Clinical Laboratory Department, Xiangya Hospital, Central South University, Changsha, People’s Republic of China; 8Department of Plastic and Cosmetic Surgery, Changsha Mylike Cosmetic Hospital, Changsha, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Qian Gao; Shaorong Lei, Email gaoqian@csu.edu.cn; leishaorong@csu.edu.cnBackground: The status of T lymphocyte subset counts in patients with COVID-19 remains uncertain. This study aimed to assess alterations in peripheral blood CD3+CD8+T (CD8+T) cells among hospitalized COVID-19 patients who have not received antiviral treatment and to evaluate their prognostic value within this patient population.Methods: A single-center, retrospective cohort study and a meta-analysis were conducted. The cohort study was performed at Xiangya Hospital from December 5, 2022, to January 31, 2023. We conducted a meta-analysis to explore the association between peripheral blood CD3+CD8+T cells and mortality in COVID-19 patients who did not receive antiviral therapy. All relevant studies in Embase, PubMed, Web of Science databases were systematically searched for meta-analysis.Results: The retrospective cohort study included 201 patients. A significant decrease in peripheral blood CD8+ T cell count was found to be associated with an increased risk of mortality (adjusted odds ratio [aOR]: 13.88; 95% confidence interval [CI]: 3.15– 61.23), after adjusting for gender, age, comorbidities, severity at admission, steroid therapy, and antibiotic therapy. The threshold value for CD8+T cell counts, determined by the receiver operating characteristic (ROC) curve analysis, was 145.5 (area under the curve [AUC]: 0.828, specificity: 90.3%, sensitivity: 72.9%, P< 0.001). Additionally, A total of 7 studies with 2765 participants were included in the meta-analysis. The meta-analysis reveals a significant association between lower CD8+ T cell counts and mortality (odds ratio [OR] = 3.543, 95% CI: 1.726 to 7.272; I2=93%).Conclusion: Peripheral blood CD8+ T cell can serve as a valuable prognostic biomarker for hospitalized patients who do not receive antiviral treatment.Keywords: peripheral blood CD3+CD8+T cell, prognostic biomarker, hospitalised COVID-19 patients
Published: 2024

39. The Challenging Path to Diagnosing Extraintestinal Amoebiasis: A Case Report of an HIV-Infected Patient

Author: Li X, Chen C, Tong L, Gao Q, Chen W, Zhou G, Tong Z, and Wang W
Subjects: amebiasis，amoebic abscess，diagnosis，hiv，metagenomic next-generation sequencing，metronidazole, Infectious and parasitic diseases, RC109-216
Abstract: Xiaofeng Li,1,&ast; Cha Chen,1,&ast; Luyuan Tong,1 Qun Gao,1 Wenxian Chen,2 Guangde Zhou,3 Zhaowei Tong,1 Weihong Wang1,4 1Department of Infectious Diseases, Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, People’s Republic of China; 2Department of Ultrasonography, Huzhou Central Hospital, Affiliated Central Hospital Huzhou University, Huzhou, Zhejiang Province, People’s Republic of China; 3Department of Pathology, Affiliated Beijing You An Hospital, Capital Medical University, Beijing, People’s Republic of China; 4Huzhou Key Laboratory of Precision Medicine Research and Translation for Infectious Diseases, Huzhou, Zhejiang Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Weihong Wang, Email hzwwh0606@163.comBackground: Amoebiasis, an infectious disease caused by the parasitic protozoan E. histolytica, is easily misdiagnosed due to its declining incidence and atypical symptoms.Case Presentation: A 31-year-old male presented to the hospital with dyspnea and inability to lie flat. Imaging studies indicated a large amount of pleural effusion on the right side and multiple huge cysts in the liver. The patient underwent liver tumor resection surgery at another hospital due to suspected malignancy, but no evidence of relevant malignant tumors was found in the pathological examination. Subsequently, we performed metagenomic next-generation sequencing on the liver drainage fluid and obtained liver pathology slides from the hospital where the surgery was performed at that time. Both of them confirmed the diagnosis of amoebic infection. Empirical treatment with metronidazole was initiated before the diagnosis was confirmed, along with symptomatic treatments such as thoracic drainage and liver drainage. Eventually, the patient’s condition improved and he was discharged smoothly.Conclusion: In order to avoid misdiagnosis of amoebiasis, thoroughly inquiring about the patient’s medical history, shifting perspectives and continuing investigating are necessary when one diagnostic approach proves ineffective. Besides, interdisciplinary collaboration and persistent efforts are crucial for accurate diagnosis.Keywords: amebiasis, amoebic abscess, diagnosis, HIV, metagenomic next-generation sequencing, metronidazole
Published: 2023

40. Peta-electron volt gamma-ray emission from the Crab Nebula

Author: The LHAASO Collaboration, Cao, Zhen, Aharonian, F., An, Q., Axikegu, Bai, L. X., Bai, Y. X., Bao, Y. W., Bastieri, D., Bi, X. J., Bi, Y. J., Cai, H., Cai, J. T., Cao, Zhe, Chang, J., Chang, J. F., Chen, B. M., Chen, E. S., Chen, J., Chen, Liang, Chen, Long, Chen, M. J., Chen, M. L., Chen, Q. H., Chen, S. H., Chen, S. Z., Chen, T. L., Chen, X. L., Chen, Y., Cheng, N., Cheng, Y. D., Cui, S. W., Cui, X. H., Cui, Y. D., Piazzoli, B. D'Ettorre, Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Dong, X. J., Duan, K. K., Fan, J. H., Fan, Y. Z., Fan, Z. X., Fang, J., Fang, K., Feng, C. F., Feng, L., Feng, S. H., Feng, Y. L., Gao, B., Gao, C. D., Gao, L. Q., Gao, Q., Gao, W., Ge, M. M., Geng, L. S., Gong, G. H., Gou, Q. B., Gu, M. H., Guo, F. L., Guo, J. G., Guo, X. L., Guo, Y. Q., Guo, Y. Y., Han, Y. A., He, H. H., He, H. N., He, J. C., He, S. L., He, X. B., He, Y., Heller, M., Hor, Y. K., Hou, C., Hou, X., Hu, H. B., Hu, S., Hu, S. C., Hu, X. J., Huang, D. H., Huang, Q. L., Huang, W. H., Huang, X. T., Huang, X. Y., Huang, Z. C., Ji, F., Ji, X. L., Jia, H. Y., Jiang, K., Jiang, Z. J., Jin, C., Ke, T., Kuleshov, D., Levochkin, K., Li, B. B., Li, Cheng, Li, Cong, Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, Jie, Li, Jian, Li, K., Li, W. L., Li, X. R., Li, Xin, Li, Y., Li, Y. Z., Li, Zhe, Li, Zhuo, Liang, E. W., Liang, Y. F., Lin, S. J., Liu, B., Liu, C., Liu, D., Liu, H., Liu, H. D., Liu, J., Liu, J. L., Liu, J. S., Liu, J. Y., Liu, M. Y., Liu, R. Y., Liu, S. M., Liu, W., Liu, Y., Liu, Y. N., Liu, Z. X., Long, W. J., Lu, R., Lv, H. K., Ma, B. Q., Ma, L. L., Ma, X. H., Mao, J. R., Masood, A., Min, Z., Mitthumsiri, W., Montaruli, T., Nan, Y. C., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Qi, Y. Q., Qiao, B. Q., Qin, J. J., Ruffolo, D., Rulev, V., Sáiz, A., Shao, L., Shchegolev, O., Sheng, X. D., Shi, J. Y., Song, H. C., Stenkin, Yu. V., Stepanov, V., Su, Y., Sun, Q. N., Sun, X. N., Sun, Z. B., Tam, P. H. T., Tang, Z. B., Tian, W. W., Wang, B. D., Wang, C., Wang, H., Wang, H. G., Wang, J. C., Wang, J. S., Wang, L. P., Wang, L. Y., Wang, R. N., Wang, Wei, Wang, X. G., Wang, X. J., Wang, X. Y., Wang, Y., Wang, Y. D., Wang, Y. J., Wang, Y. P., Wang, Z. H., Wang, Z. X., Wang, Zhen, Wang, Zheng, Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, W. X., Wu, X. F., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, D. X., Xiao, G., Xiao, H. B., Xin, G. G., Xin, Y. L., Xing, Y., Xu, D. L., Xu, R. X., Xue, L., Yan, D. H., Yan, J. Z., Yang, C. W., Yang, F. F., Yang, J. Y., Yang, L. L., Yang, M. J., Yang, R. Z., Yang, S. B., Yao, Y. H., Yao, Z. G., Ye, Y. M., Yin, L. Q., Yin, N., You, X. H., You, Z. Y., Yu, Y. H., Yuan, Q., Zeng, H. D., Zeng, T. X., Zeng, W., Zeng, Z. K., Zha, M., Zhai, X. X., Zhang, B. B., Zhang, H. M., Zhang, H. Y., Zhang, J. L., Zhang, J. W., Zhang, L. X., Zhang, Li, Zhang, Lu, Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y. F., Zhang, Y. L., Zhang, Yi, Zhang, Yong, Zhao, B., Zhao, J., Zhao, L., Zhao, L. Z., Zhao, S. P., Zheng, F., Zheng, Y., Zhou, B., Zhou, H., Zhou, J. N., Zhou, P., Zhou, R., Zhou, X. X., Zhu, C. G., Zhu, F. R., Zhu, H., Zhu, K. J., and Zuo, X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: The Crab pulsar and the surrounding nebula powered by the pulsar's rotational energy through the formation and termination of a relativistic electron-positron wind is a bright source of gamma-rays carrying crucial information about this complex conglomerate. We report the detection of $\gamma$-rays with a spectrum showing gradual steepening over three energy decades, from $5\times 10^{-4}$ to $1.1$ petaelectronvolt (PeV). The ultra-high-energy photons exhibit the presence of a PeV electron accelerator (a pevatron) with an acceleration rate exceeding 15% of the absolute theoretical limit. Assuming that unpulsed $\gamma$-rays are produced at the termination of the pulsar's wind, we constrain the pevatron's size, between $0.025$ and $0.1$ pc, and the magnetic field $\approx 110 \mu$G. The production rate of PeV electrons, $2.5 \times 10^{36}$ erg $\rm s^{-1}$, constitutes 0.5% of the pulsar's spin-down luminosity, although we do not exclude a non-negligible contribution of PeV protons to the production of the highest energy $\gamma$-rays., Comment: 43 pages, 13 figures, 2 tables; Published in Science
Published: 2021
Full Text: View/download PDF

41. Insight into the influence of frictional heat on the modal characteristics and interface temperature of frictionally damped turbine blades

Author: Gao, Q., Fan, Y., Wu, Y.G., Li, L., and Zhang, D.Y.
Published: 2024
Full Text: View/download PDF

42. Field performance of a geogrid-reinforced expansive soil slope: a case study

Author: Zhang, R., Lan, T., Zheng, J. L., and Gao, Q. F.
Published: 2024
Full Text: View/download PDF

43. Dimensional crossover and symmetry transformation of charge density waves in VSe2

Author: Chen, P, Chan, Y-H, Liu, R-Y, Zhang, HT, Gao, Q, Fedorov, A-V, Chou, MY, and Chiang, T-C
Subjects: Physical Sciences, Chemical Sciences, Engineering, Fluids & Plasmas
Abstract: Collective phenomena in solids can be sensitive to the dimensionality of the system; a case of special interest is VSe2, which shows a (7×3) charge density wave (CDW) in the single layer with threefold symmetry in the normal phase spontaneously broken, in contrast to the (4×4) in-plane CDW in the bulk. Angle-resolved photoemission spectroscopy (ARPES) from VSe2 ranging from a single layer to the bulk reveals the evolution of the electronic structure including the Fermi surface contours and the CDW gap. At a thickness of two layers, the ARPES maps are already nearly bulklike, but the transition temperature TC for the (4×4) CDW is much higher than the bulk value of 110 K. These results can be understood as due to dimensional crossover of phonon instability driven by a competition of nesting vectors. In this letter, we provide key insights into the CDW mechanisms and offer a perspective in the search and control of emergent phases in quantum materials.
Published: 2022

44. Artificial Intelligence in Ophthalmic Surgery: Current Applications and Expectations

Author: Nuliqiman M, Xu M, Sun Y, Cao J, Chen P, Gao Q, Xu P, and Ye J
Subjects: ophthalmic surgery, ai, machine learning, surgery selection, candidate screening, robot-assisted surgery, Ophthalmology, RE1-994
Abstract: Maimaiti Nuliqiman, Mingyu Xu, Yiming Sun, Jing Cao, Pengjie Chen, Qi Gao, Peifang Xu, Juan Ye Eye Center, The Second Affiliated Hospital, School of Medicine, Zhejiang University, Zhejiang Provincial Key Laboratory of Ophthalmology, Zhejiang Provincial Clinical Research Center for Eye Diseases, Zhejiang Provincial Engineering Institute on Eye Diseases, Hangzhou, Zhejiang, People’s Republic of ChinaCorrespondence: Peifang Xu; Juan Ye, Tel +86-571-8778-3897, Fax +86-571-8706-8001, Email xpf1900@zju.edu.cn; yejuan@zju.edu.cnAbstract: Artificial Intelligence (AI) has found rapidly growing applications in ophthalmology, achieving robust recognition and classification in most kind of ocular diseases. Ophthalmic surgery is one of the most delicate microsurgery, requiring high fineness and stability of surgeons. The massive demand of the AI assist ophthalmic surgery will constitute an important factor in boosting accelerate precision medicine. In clinical practice, it is instrumental to update and review the considerable evidence of the current AI technologies utilized in the investigation of ophthalmic surgery involved in both the progression and innovation of precision medicine. Bibliographic databases including PubMed and Google Scholar were searched using keywords such as “ophthalmic surgery”, “surgical selection”, “candidate screening”, and “robot-assisted surgery” to find articles about AI technology published from 2018 to 2023. In addition to the Editorials and letters to the editor, all types of approaches are considered. In this paper, we will provide an up-to-date review of artificial intelligence in eye surgery, with a specific focus on its application to candidate screening, surgery selection, postoperative prediction, and real-time intraoperative guidance.Keywords: ophthalmic surgery, AI, machine learning, surgery selection, candidate screening, robot-assisted surgery
Published: 2023

45. Comparison of Diagnostic Values of ACR TI-RADS versus C-TIRADS Scoring and Classification Systems for the Elderly Thyroid Cancers

Author: Huang H, Zhu MJ, Gao Q, Huang YL, and Li WM
Subjects: thyroid imaging reporting and data system, ultrasound, thyroid nodules, elderly, Medicine (General), R5-920
Abstract: Hu Huang,1 Ming-Jie Zhu,1 Qi Gao,2 Yan-Li Huang,3 Wei-Min Li4 1Department of Thyroid and Breast Surgery, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People’s Republic of China; 2Department of Ultrasonography, Southeast University Affiliated Zhongda Hospital, Nanjing, Jiangsu, People’s Republic of China; 3Department of Special Clinic, General Hospital of Eastern Theater Command, PLA, Nanjing, Jiangsu, People’s Republic of China; 4Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, People’s Republic of ChinaCorrespondence: Wei-Min Li, Department of Ultrasonography, Affiliated Hospital of Jiangnan University, Wuxi, Jiangsu, 214000, People’s Republic of China, Tel +8613912362309, Email 1005342597@qq.comPurpose: To compare the diagnostic value of the Thyroid Imaging Reporting and Data System (TI-RADS) of the American College of Radiology (ACR) versus the Chinese Thyroid Imaging Reporting and Data System (C-TIRADS) scoring and classification system for elderly thyroid cancers.Patients and Methods: A total of 512 nodules from 465 patients aged ≥ 60 with surgical pathology-proven thyroid nodules were enrolled in our study. The ultrasound features of thyroid nodules were independently evaluated by the ACR TI-RADS and C-TIRADS classification systems, and the receiver operating characteristic curve (ROC) was plotted. The optimal cut-off values of the ACR TI-RADS and C-TIRADS scoring and classification systems for diagnosing elderly thyroid nodules were estimated, and the diagnostic efficacy was analyzed.Results: The ACR TI-RADS and C-TIRADS scores and classifications of thyroid cancers were both higher than benign nodules (both P < 0.05). The area under the curve (AUC) of ACR TI-RADS and C-TIRADS scoring and classification systems were 0.861, 0.897, 0.879, and 0.900, respectively, and the AUC of the scoring system was greater than the classification system for both criteria. When the Youden index was the highest, the sensitivity, specificity, positive predictive value (PPV), and negative predictive value (NPV) of the ACR TI-RADS scoring and classification systems were consistent, ie, they were 89.66%, 41.70%, 89.93%, and 59.00%, respectively; the sensitivity, specificity, PPV, and NPV of the C-TIRADS scoring and classification systems were also consistent, ie, they were 88.71%, 44.26%, 90.23%, 59.69%, respectively. The diagnostic efficacy between the two systems was not statistically significant.Conclusion: ACR TI-RADS and C-TIRADS systems had relatively high diagnostic efficacy for elderly thyroid cancer. The diagnostic efficiency of the scoring systems of ACR TI-RADS and C-TIRADS were higher than the classification systems. In addition, the two systems had high clinical practical values, while there is still a significant risk of missed diagnosis.Keywords: thyroid imaging reporting and data system, ultrasound, thyroid nodules, elderly
Published: 2023

46. Development and Validation of a Novel Prognosis Model Based on a Panel of Three Immunogenic Cell Death-Related Genes for Non-Cirrhotic Hepatocellular Carcinoma

Author: Gong J, Yu R, Hu X, Luo H, Gao Q, Li Y, Tan G, and Qin B
Subjects: immunogenic cell death-related genes, non-cirrhotic hepatocellular carcinoma, risk stratification, nomogram, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Jiaojiao Gong,1,2 Renjie Yu,1 Xiaoxia Hu,1 Huating Luo,1 Qingzhu Gao,3 Yadi Li,1 Guili Tan,1 Haiying Luo,1 Bo Qin1 1Department of Infectious Diseases, Chongqing Key Laboratory of Infectious Diseases and Parasitic Diseases, The First Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 2Department of Nephrology, Bishan Hospital of Chongqing Medical University, Chongqing, People’s Republic of China; 3Department of Infectious Diseases, The Second Affiliated Hospital of Chongqing Medical University, Chongqing, People’s Republic of ChinaCorrespondence: Bo Qin, Department of Infectious Diseases, Chongqing Key Laboratory of Infectious Diseases and Parasitic Diseases, The First Affiliated Hospital of Chongqing Medical University, No. 1 Youyi Road, Yuzhong District, Chongqing, 400016, People’s Republic of China, Email cqmuqb@163.comPurpose: The accurate prediction of non-cirrhotic hepatocellular carcinoma (NCHCC) risk facilitates improved surveillance strategy and decreases cancer-related mortality. This study aimed to explore the correlation between immunogenic cell death (ICD) and NCHCC prognosis using The Cancer Genome Atlas (TCGA) datasets, and the potential prognostic value of ICD-related genes in NCHCC.Methods: Clinical and transcriptomic data of patients with NCHCC patients were retrieved from TCGA database. Weighted gene co-expression network analysis was performed to obtain the NCHCC phenotype-related module genes. Consensus clustering analysis was performed to classify the patients into two clusters based on intersection genes among differentially expressed genes (DEGs) between cancer and adjacent tissues, NCHCC phenotype-related genes, and ICD-related genes. NCHCC-derived tissue microarray was used to evaluate the correlation of the expression levels of key genes with NCHCC prognosis using immunohistochemical staining.Results: Cox regression analyses were performed to construct a prognostic risk score model comprising three genes (TMC7, GRAMD1C, and GNPDA1) based on DEGs between two clusters. The model stratified patients with NCHCC into two risk groups. The overall survival (OS) of the high-risk group was significantly lower than that of the low-risk group. Univariable and multivariable Cox regression analyses revealed that these signature genes are independent predictors of OS. Functional analysis revealed differential immune status between the two risk groups. Next, a nomogram was constructed, which demonstrated the potent distinguishing ability of the developed model based on receiver operating characteristic curves. In vitro functional validation revealed that the migration and invasion abilities of HepG2 and Huh7 cells were upregulated upon GRAMD1C knockdown but downregulated upon TMC7 knockdown.Conclusion: This study developed a prognostic model comprising three genes, which can aid in predicting the survival of patients with NCHCC and guide the selection of drugs and molecular markers for NCHCC.Keywords: immunogenic cell death-related genes, non-cirrhotic hepatocellular carcinoma, risk stratification, nomogram
Published: 2023

47. The Treatment of Diffuse Large B-Cell Lymphoma (Triple Expression) Involving the Breast, Spleen, and Bone in a Male Patient with Viral Hepatitis B: A Rare Case Report

Author: Zhou W, Gao Q, Wang L, Li W, He C, Li Y, Feng L, Liu W, Liu L, and Wang Y
Subjects: breast, viral hepatitis b, diffuse large b-cell lymphoma, triple expression, autologous stem cell transplantation., Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Weiling Zhou,1 Qian Gao,1 Lianjing Wang,2 Weijing Li,2 Cuiying He,2 Yanting Li,3 Lei Feng,1 Wei Liu,2 Lihong Liu,2 Yuan Wang1 1Department of Endocrine and Metabolic Diseases, The Fourth Hospital of Hebei Medical University, Shijiazhuang, HeBei Province, 050000, People’s Republic of China; 2Department of Hematology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, HeBei Province, 050000, People’s Republic of China; 3Department of Glandular Surgery, The Second Hospital of Hebei Medical University, Shijiazhuang, HeBei Province, 050000, People’s Republic of ChinaCorrespondence: Lihong Liu, Department of Hematology, The Fourth Hospital of Hebei Medical University, Shijiazhuang, HeBei Province, 050000, People’s Republic of China, Email liulihong111222@163.com Yuan Wang, Department of Endocrine and Metabolic Diseases, The Fourth Hospital of Hebei Medical University, Shijiazhuang, HeBei Province, 050000, People’s Republic of China, Email wyuan1111122222@163.comBackground: Diffuse large B-cell lymphoma (DLBCL) involving the breast, spleen, and bone in a male patient with hepatitis B virus (HBV) infection is extremely rare in clinical practice.Case Presentation: We report a case of DLBCL involving the breast, spleen, and bone (triple expression of Bcl-2+, Bcl-6+, and 70% positive C-mcy) in a male patient with HBV admitted to our hospital. The patient was treated with EPOCH× 4, lenalidomide+EPOCH× 2 chemotherapy, intermittent methotrexate intrathecal injections to prevent central invasion, and autologous stem cell transplantation (ASCT). The patient is currently in complete remission, and the follow-up time was 43 months.Conclusion: A patient with DLBCL involving the breast, spleen, and bone can be treated with a combination of multiple regimens. If the patient’s economic conditions permit it, ASCT can be considered.Keywords: breast, viral hepatitis B, diffuse large B-cell lymphoma, triple expression, autologous stem cell transplantation
Published: 2023

48. Effect of environmental media on the growth rate of fatigue crack in TC4 titanium alloy: Seawater and air

Author: Ren, J.Q., Li, L., Wang, Q., Xin, C., Gao, Q., Li, J.C., Xue, H.T., Lu, X.F., and Tang, F.L.
Published: 2024
Full Text: View/download PDF

49. Coupling effect of loading mode and temperature on the deformation behaviors of TWIP β Ti alloy: From superior tensile strength-ductility synergy to low Charpy impact toughness

Author: Wang, Q., Sang, B., Ren, J.Q., Xin, C., Zhang, Y.H., Gao, Q., Liu, W.F., Ning, Z.L., Yu, J.T., and Lu, X.F.
Published: 2024
Full Text: View/download PDF

50. A novel test apparatus to study the mechanism of harmonic normal force on fretting wear

Author: Gao, Q., Fan, Y., Wu, Y.G., Liu, J.L., Wang, J., and Li, L.
Published: 2024
Full Text: View/download PDF

Catalog

Books, media, physical & digital resources

See catalog results

Searchworks

Select search scope, currently: Articles Catalog books, media & more in Jio Institute collections Articles journal articles & other e-resources

Search

Search Constraints

Refine your results

Search Limiters

Topic

Publication Year Range

Language

Publication Type

Journal

Region

Database

Publisher

13,511 results on '"Gao, Q."'

Search Results

Catalog

Select search scope, currently: Articles

Catalog

books, media & more in Jio Institute collections

Articles

journal articles & other e-resources