Author: "Bao, Y" - Searchworks@Jio Institute Digital Library Search Results

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8,078 results on '"Bao, Y"'

1. 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

2. 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

3. 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

4. Coarse-to-fine Method for Vision-based Pedestrian Traffic Light Detection

Author: WU, X.-H., HU, R., and BAO, Y.-Q.
Subjects: gaussian mixture model, multi-layer neural network, boosting, object detection, computer vision, Electrical engineering. Electronics. Nuclear engineering, TK1-9971, Computer engineering. Computer hardware, TK7885-7895
Abstract: Pedestrian traffic light detection is an important technique of the navigation system for the visually impaired during road crossing. In this paper, a three-stage coarse-to-fine method for pedestrian traffic light detection is proposed. The proposed method is mainly divided into two processes, the training process and the detection process. In the training process, the Gaussian mixture model (GMM) is adopted to determine the parameters of the filter on stage I. The classifier on stage II is trained by a modified convolutional neural network (CNN) to capture features in each channel of the CIELAB color space. The classifier on stage III is trained by the adaptive boosting (AdaBoost) algorithm with Haar features. In the detection process, firstly the board filter is adopted to generate candidate regions of pedestrian traffic lights. Secondly, these candidate regions are detected in multiple scales by the CNN-based classifier with fixed size. Finally the AdaBoost-based classifier is adopted for refinement detection. Testing results verify the effectiveness of the proposed method.
Published: 2020
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5. 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

6. 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

7. 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
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8. 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
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9. The Influence of Thermal Cycle on the Grain Structure of Friction-Stir-Welded Pure Copper Joint

Author: Xu, N., Zhang, B. S., Qiu, Z. H., Ji, X. K., Zhong, C., Song, Q. N., and Bao, Y. F.
Published: 2024
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10. Mendelian Randomization Analysis to Assess Whether Magnetic Resonance Imaging Signs of Cerebral Small Vessel Disease Can Cause Cognitive Decline and Dementia

Author: Liu, L., Shen, Q., Zhang, D., Bao, Y., Xu, F., Huang, H., and Xu, Yanming
Published: 2024
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11. Incidence of tethered cord syndrome in neurofibromatosis types 1 and 2 pediatric patients: a population-level analysis

Author: Bhanja, Debarati, Freedman, Zachary, Sciscent, Bao Y., Moeckel, Camille, Daggubati, Lekhaj, and Rizk, Elias
Published: 2024
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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
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14. The High Expression of SLC7A11 and GPX4 are Significantly Correlated with β-Catenin in Colorectal Cancer

Author: Ou Y, Wu N, Shu L, Zhao Y, Bao Y, and Wu Q
Subjects: colorectal cancer, slc7a11, gpx4, β-catenin, prognosis, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract: Yurong Ou, Ningqi Wu, Lishan Shu, Yang Zhao, Yunfang Bao, Qiong Wu Department of Pathology, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui, People’s Republic of ChinaCorrespondence: Qiong Wu, Department of Pathology, The First Affiliated Hospital of Bengbu Medical University, 287, Changhuai Road, Bengbu, Anhui Province, People’s Republic of China, Tel +86-18607490054, Email wuqiong@bbmc.edu.cnBackground: Existing research shows inducing ferroptosis can improve the effectiveness of tumor treatment. Glutathione peroxidase 4 (GPX4) is a ferroptosis inhibitor. Solute carrier family 7, membrane 11 (SLC7A11) plays a key role in glutathione homeostasis, which is important for protecting cells from oxidative stress. β-catenin is the key protein the Wnt/β-catenin signaling pathway. The purpose of this study was to investigate the expression of SLC7A11 and GPX4 in colorectal cancer (CRC) and their relationship with β-catenin and to analyze the association of these two factors with several clinicopathological features and patient survival.Methods: This study retrospectively collected paraffin-embedded tissue samples from 120 CRC patients, who received surgical resection between 2017 and 2018. We examined the patterns of expression of SLC7A11, GPX4 and β-catenin by using immunohistochemistry. Analyzing the relationships between SLC7A11, GPX4, β-catenin and clinical pathological parameters and their relationships with overall survival (OS).Results: Expression of SLC7A11 and GPX4 were high expression in 60.83% and 64.17% among the patients, respectively, and were higher than those in normal tissue. SLC7A11, GPX4 and β-catenin were positively correlated with each other (P< 0.05). Expression of SLC7A11 and GPX4 significantly correlates with tumor stage and lymph node metastasis (P < 0.05). The β-catenin was related to lymph node metastasis, TNM stage and tumor grade. Kaplan–Meier analysis showed that patient’s OS in the SLC7A11 and GPX4 were reduced (P< 0.05). Univariate and multivariate analyses showed that SLC7A11 and GPX4 were independent risk factors for CRC prognosis.Conclusion: SLC7A11 and GPX4 overexpression is associated with β-catenin and poor prognosis and may be important for predicting CRC invasion, metastasis, and prognosis.Keywords: colorectal cancer, SLC7A11, GPX4, β-catenin, prognosis
Published: 2024

15. Performance of Machine Learning Algorithms in Predicting Prolonged Mechanical Ventilation in Patients with Blunt Chest Trauma

Author: Chen Y, Lu X, Zhang Y, Bao Y, Li Y, and Zhang B
Subjects: mechanical ventilation, blunt chest trauma, machine learning, pulmonary contusion, Therapeutics. Pharmacology, RM1-950
Abstract: Yifei Chen,1,&ast; Xiaoning Lu,2,&ast; Yuefei Zhang,2,&ast; Yang Bao,3 Yong Li,4 Bing Zhang1 1Department of Emergency Medicine, Affiliated Hospital of Yangzhou University, Yangzhou, People’s Republic of China; 2Department of Cardiothoracic Surgery, The affiliated Suqian First People’s Hospital of Nanjing Medical University, Suqian, People’s Republic of China; 3Department of Cardiothoracic Surgery, Affiliated Hospital of Yangzhou University, Yangzhou, People’s Republic of China; 4Trauma Medical Center, Affiliated Hospital of Yangzhou University, Yangzhou, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yong Li, Trauma Medical Center, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu Province, 225000, People’s Republic of China, Email yzliyong0304@163.com Bing Zhang, Department of Emergency Medicine, Affiliated Hospital of Yangzhou University, Yangzhou, Jiangsu Province, 225000, People’s Republic of China, Email 15062791676@163.comPurpose: Mechanical ventilation (MV) is one of the most common treatments for patients with blunt chest trauma (BCT) admitted to the intensive care unit (ICU). Our study aimed to investigate the performance of machine learning algorithms in predicting the prolonged duration of mechanical ventilation (PDMV) in patients with BCT.Methods: In this single-center observational study, patients with BCT who were treated with MV through nasal or oral intubation were selected. PDMV was defined as the duration of mechanical ventilation ≥ 7 days after endotracheal intubation (normal vs prolonged MV; dichotomous outcomes). K-means was used to cluster data from the original cohort by an unsupervised learning method. Multiple machine learning algorithms were used to predict DMV categories. The most significant predictors were identified by feature importance analysis. Finally, a decision tree based on the chi-square automatic interaction detection (CHAID) algorithm was developed to study the cutoff points of predictors in clinical decision-making.Results: A total of 426 patients and 35 characteristics were included. K-means clustering divided the cohort into two clusters (high risk and low risk). The area under the curve (AUC) of the DMV classification algorithms ranged from 0.753 to 0.923. The importance analysis showed that the volume of pulmonary contusion (VPC) was the most important feature to predict DMV. The prediction accuracy of the decision tree based on CHAID reached 86.4%.Conclusion: Machine learning algorithms can predict PDMV in patients with BCT. Therefore, limited medical resources can be more appropriately allocated to BCT patients at risk for PDMV.Keywords: mechanical ventilation, blunt chest trauma, machine learning, pulmonary contusion
Published: 2024

16. Observation of gamma rays up to 320 TeV from the middle-aged TeV pulsar wind nebula HESS J1849$-$000

Author: Amenomori, M., Asano, S., Bao, Y. W., Bi, X. J., Chen, D., Chen, T. L., Chen, W. Y., Chen, Xu, Chen, Y., Cirennima, Cui, S. W., Danzengluobu, Ding, L. K., Fang, J. H., Fang, K., Feng, C. F., Feng, Zhaoyang, Feng, Z. Y., Gao, Qi, Gomi, A., Gou, Q. B., Guo, Y. Q., Guo, Y. Y., Hayashi, Y., He, H. H., He, Z. T., Hibino, K., Hotta, N., Hu, Haibing, Hu, H. B., Hu, K. Y., Huang, J., Jia, H. Y., Jiang, L., Jiang, P., Jin, H. B., Kasahara, K., Katayose, Y., Kato, C., Kato, S., Kawahara, I., Kawashima, T., Kawata, K., Kozai, M., Kurashige, D., Labaciren, Le, G. M., Li, A. F., Li, H. J., Li, W. J., Li, Y., Lin, Y. H., Liu, B., Liu, C., Liu, J. S., Liu, L. Y., Liu, M. Y., Liu, W., Lu, H., Meng, X. R., Meng, Y., Munakata, K., Nagaya, K., Nakamura, Y., Nakazawa, Y., Nanjo, H., Ning, C. C., Nishizawa, M., Noguchi, R., Ohnishi, M., Okukawa, S., Ozawa, S., Qian, X., Qian, X. L., Qu, X. B., Saito, T., Sakakibara, Y., Sakata, M., Sako, T., Sako, T. K., Sasaki, T., Shao, J., Shibata, M., Shiomi, A., Sugimoto, H., Takano, W., Takita, M., Tan, Y. H., Tateyama, N., Torii, S., Tsuchiya, H., Udo, S., Wang, H., Wang, S. F., Wang, Y. P., Wangdui, Wu, H. R., Wu, Q., Xu, J. L., Xue, L., Yang, Z., Yao, Y. Q., Yin, J., Yokoe, Y., Yu, Y. L., Yuan, A. F., Zhai, L. M., Zhang, H. M., Zhang, J. L., Zhang, X., Zhang, X. Y., Zhang, Y., Zhang, Yi, Zhang, Ying, Zhao, S. P., Zhaxisangzhu, Zhou, X. X., and Zou, Y. H.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Gamma rays from HESS J1849$-$000, a middle-aged TeV pulsar wind nebula (PWN), are observed by the Tibet air shower array and the muon detector array. The detection significance of gamma rays reaches $4.0\, \sigma$ and $4.4\, \sigma$ levels above 25 TeV and 100 TeV, respectively, in units of Gaussian standard deviation $\sigma$. The energy spectrum measured between $40\, {\rm TeV} < E < 320\, {\rm TeV}$ for the first time is described with a simple power-law function of ${\rm d}N/{\rm d}E = (2.86 \pm 1.44) \times 10^{-16}(E/40\, {\rm TeV})^{-2.24 \pm 0.41}\, {\rm TeV}^{-1}\, {\rm cm}^{-2}\, {\rm s}^{-1}$. The gamma-ray energy spectrum from the sub-TeV ($E < 1\, {\rm TeV}$) to sub-PeV ($100\, {\rm TeV} < E < 1\, {\rm PeV}$) ranges including the results of previous studies can be modeled with the leptonic scenario, inverse Compton scattering by high-energy electrons accelerated by the PWN of PSR J1849$-$0001. On the other hand, the gamma-ray energy spectrum can also be modeled with the hadronic scenario in which gamma rays are generated from the decay of neutral pions produced by collisions between accelerated cosmic-ray protons and the ambient molecular cloud found in the gamma-ray emitting region. The cutoff energy of cosmic-ray protons $E_{\rm p\, cut}$, cut is estimated at ${\rm log}_{10}(E_{\rm p,\, cut}/{\rm TeV}) = 3.73^{+2.98}_{-0.66}$, suggesting that protons are accelerated up to the PeV energy range. Our study thus proposes that HESS J1849$-$000 should be further investigated as a new candidate for a Galactic PeV cosmic-ray accelerator, PeVatron., Comment: 10 pages, 2 figures, Accepted for publication from the Astrophysical Journal
Published: 2023
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17. Measurement of the Gamma-Ray Energy Spectrum beyond 100 TeV from the HESS J1843$-$033 Region

Author: Amenomori, M., Asano, S., Bao, Y. W., Bi, X. J., Chen, D., Chen, T. L., Chen, W. Y., Chen, Xu, Chen, Y., Cirennima, Cui, S. W., Danzengluobu, Ding, L. K., Fang, J. H., Fang, K., Feng, C. F., Feng, Zhaoyang, Feng, Z. Y., Gao, Qi, Gomi, A., Gou, Q. B., Guo, Y. Q., Guo, Y. Y., He, H. H., He, Z. T., Hibino, K., Hotta, N., Hu, Haibing, Hu, H. B., Hu, K. Y., Huang, J., Jia, H. Y., Jiang, L., Jiang, P., Jin, H. B., Kasahara, K., Katayose, Y., Kato, C., Kato, S., Kawashima, T., Kawata, K., Kozai, M., Kurashige, D., Labaciren, Le, G. M., Li, A. F., Li, H. J., Li, W. J., Li, Y., Lin, Y. H., Liu, B., Liu, C., Liu, J. S., Liu, L. Y., Liu, M. Y., Liu, W., Liu, X. L., Lou, Y. -Q., Lu, H., Meng, X. R., Meng, Y., Munakata, K., Nagaya, K., Nakamura, Y., Nakazawa, Y., Nanjo, H., Ning, C. C., Nishizawa, M., Ohnishi, M., Okukawa, S., Ozawa, S., Qian, L., Qian, X., Qian, X. L., Qu, X. B., Saito, T., Sakakibara, Y., Sakata, M., Sako, T., Sako, T. K., Shao, J., Shibata, M., Shiomi, A., Sugimoto, H., Takano, W., Takita, M., Tan, Y. H., Tateyama, N., Torii, S., Tsuchiya, H., Udo, S., Wang, H., Wang, Y. P., Wangdui, Wu, H. R., Wu, Q., Xu, J. L., Xue, L., Yang, Z., Yao, Y. Q., Yin, J., Yokoe, Y., Yu, N. P., Yuan, A. F., Zhai, L. M., Zhang, C. P., Zhang, H. M., Zhang, J. L., Zhang, X., Zhang, X. Y., Zhang, Y., Zhang, Yi, Zhang, Ying, Zhao, S. P., Zhaxisangzhu, and Zhou, X. X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: HESS J1843$-$033 is a very-high-energy gamma-ray source whose origin remains unidentified. This work presents, for the first time, the energy spectrum of gamma rays beyond $100\, {\rm TeV}$ from the HESS J1843$-$033 region using the data recorded by the Tibet air shower array and its underground muon detector array. A gamma-ray source with an extension of $0.34^{\circ} \pm 0.12^{\circ}$ is successfully detected above $25\, {\rm TeV}$ at $(\alpha,\, \delta) = (281.09^{\circ}\pm 0.10^{\circ},\, -3.76^{\circ}\pm 0.09^{\circ})$ near HESS J1843$-$033 with a statistical significance of $6.2\, \sigma$, and the source is named TASG J1844$-$038. The position of TASG J1844$-$038 is consistent with those of HESS J1843$-$033, eHWC J1842$-$035, and LHAASO J1843$-$0338. The measured gamma-ray energy spectrum in $25\, {\rm TeV} < E < 130\, {\rm TeV}$ is described with ${\rm d}N/{\rm d}E = (9.70\pm 1.89)\times 10^{-16} (E/40\, {\rm TeV})^{-3.26\pm 0.30}\, {\rm TeV}^{-1} {\rm cm}^{-2} {\rm s}^{-1}$, and the spectral fit to the combined spectra of HESS J1843$-$033, LHAASO J1843$-$0338, and TASG J1844$-$038 implies the existence of a cutoff at $49.5\pm 9.0\, {\rm TeV}$. Associations of TASG J1844-038 with SNR G28.6$-$0.1 and PSR J1844-0346 are also discussed in detail for the first time., Comment: 11 pages, 4 figures, 1 table
Published: 2023
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18. A version of Kemer's theorem

Author: Bao, Y. -H., Xu, J. -N., Ye, Y., Zhang, J. J., and Zhang, Y. -F.
Subjects: Mathematics - Rings and Algebras, 16R10, 16R99, 18M60
Abstract: A famous result of Kemer states that every T-ideal is finitely based which answers affirmatively a conjecture of Specht. It turns out that every T-ideal corresponding to an associative PI-algebra with unit is single-element based, namely, is generated by a single element as a T-ideal. Equivalently, every proper operadic ideal of $\ias$ is generated by a single element.
Published: 2023

19. High accuracy, high dynamic range optomechanical accelerometry enabled by dual comb spectroscopy

Author: Long, D. A., Stroud, J. R., Reschovsky, B. J., Bao, Y., Zhou, F., Bresler, S. M., LeBrun, T. W., Plusquellic, D. F., and Gorman, J. J.
Subjects: Physics - Optics, Quantum Physics
Abstract: Cavity optomechanical sensors can offer exceptional sensitivity; however, interrogating the cavity motion with high accuracy and dynamic range has proven to be challenging. Here we employ a dual optical frequency comb spectrometer to readout a microfabricated cavity optomechanical accelerometer, allowing for rapid simultaneous measurements of the cavity's displacement, finesse, and coupling at accelerations up to 24 g (236 m/s$^2$). With this approach, we have achieved a displacement sensitivity of 3 fm/Hz$^{1/2}$, a measurement rate of 100 kHz, and a dynamic range of 3.9 $\times$ 10$^5$ which is the highest we are aware of for a microfabricated cavity optomechanical sensor. In addition, comparisons of our optomechanical sensor coupled directly to a commercial reference accelerometer show agreement at the 0.5% level, a value which is limited by the reference's reported uncertainty. Further, the methods described herein are not limited to accelerometry but rather can be readily applied to nearly any optomechanical sensor where the combination of high speed, dynamic range, and sensitivity is expected to be enabling., Comment: 11 pages, 5 figures
Published: 2023
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20. Early Celecoxib Use in Spontaneous Intracerebral Hemorrhage is Associated with Reduced Mortality

Author: Sciscent, Bao Y., Hallan, David R., Bhanja, Debarati, Staub, Jacob, Crossman, Derek, Rizk, Elias B., Zacko, J. Christopher, Park, Haejoe, and Hazard, Sprague W.
Published: 2024
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21. 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
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22. 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
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23. Exercise Attenuate Diaphragm Atrophy in COPD Mice via Inhibiting the RhoA/ROCK Signaling

Author: Li P, Wang Y, Cao Y, Shi J, Jiang M, Han X, Jiang L, Bao Y, Wu W, and Liu X
Subjects: chronic obstructive pulmonary disease, diaphragmic atrophy, exercise training, inflammation, rhoa/rock signaling, Diseases of the respiratory system, RC705-779
Abstract: Peijun Li,1,&ast; Yingqi Wang,1,&ast; Yuanyuan Cao,2 Jiacheng Shi,1 Meiling Jiang,2 Xiaoyu Han,2 Linhong Jiang,1 Yidie Bao,1 Weibing Wu,2 Xiaodan Liu1,3,4 1School of Rehabilitation Science, Shanghai University of Traditional Chinese Medicine, Shanghai, 201203, People’s Republic of China; 2Department of Sports Rehabilitation, Shanghai University of Sport, Shanghai, 200438, People’s Republic of China; 3Institute of Rehabilitation Medicine, Shanghai Academy of Traditional Chinese Medicine, Shanghai, 201203, People’s Republic of China; 4Engineering Research Center of Traditional Chinese Medicine Intelligent Rehabilitation, Ministry of Education, Shanghai, 201203, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xiaodan Liu; Weibing Wu, Email hzhp403@126.com; wwb75@126.comBackground: Exercise is an indispensable component of pulmonary rehabilitation with strong anti-inflammatory effects. However, the mechanisms by which exercise prevents diaphragmatic atrophy in COPD (chronic obstructive pulmonary disease) remain unclear.Methods: Forty male C57BL/6 mice were assigned to the control (n=16) and smoke (n=24) groups. Mice in the smoke group were exposed to the cigarette smoke (CS) for six months. They were then divided into model and exercise training groups for 2 months. Histological changes were observed in lung and diaphragms. Subsequently, agonist U46639 and antagonist Y27632 of RhoA/ROCK were subjected to mechanical stretching in LPS-treated C2C12 myoblasts. The expression levels of Atrogin-1, MuRF-1, MyoD, Myf5, IL-1β, TNF-α, and RhoA/ROCK were determined by Western blotting.Results: Diaphragmatic atrophy and increased RhoA/ROCK expression were observed in COPD mice. Exercise training attenuated diaphragmatic atrophy, decreased the expression of MuRF-1, and increased MyoD expression in COPD diaphragms. Exercise also affects the upregulation of RhoA/ROCK and inflammation-related proteins. In in vitro experiments with C2C12 myoblasts, LPS remarkably increased the level of inflammation and protein degradation, whereas Y27632 or combined with mechanical stretching prevented this phenomenon considerably.Conclusion: RhoA/ROCK plays an important role in the prevention of diaphragmatic atrophy in COPD.Keywords: chronic obstructive pulmonary disease, diaphragmatic atrophy, exercise training, inflammation, RhoA/ROCK signaling
Published: 2024

24. Integration of Gut Microbiota, Serum Metabolomic, and Network Pharmacology to Reveal the Anti Insomnia Mechanism of Mongolian Medicine Sugemule-4 Decoction on Insomnia Model Rats

Author: Du L, Yang D, Wu L, Mei L, Wu S, Ba Y, Bao Y, Su R, and Song L
Subjects: mongolian medicine sugemule-4 decoction, insomnia, metabolomics, gut microbiota, network pharmacology, molecular mechanism, Therapeutics. Pharmacology, RM1-950
Abstract: Lina Du,1 Dezhi Yang,2 Lan Wu,1 Li Mei,1 Sarula Wu,1 Yasula Ba,1 Yongchang Bao,1 Rigugaqiqige Su,1 Lin Song1 1College of Mongolian Medical, Inner Mongolia Medical University, Hohhot, Inner Mongolia, People’s Republic of China; 2Innovative Mongolian Medical Engineering Research Center, Inner Mongolia International Mongolian Hospital, Hohhot, Inner Mongolia, People’s Republic of ChinaCorrespondence: Lin Song, Inner Mongolia Medical University, Tel +15849163188, Email songlinwpsword@163.comObjective: To explored the potential molecular mechanism of Sugemule-4 decoction (MMS-4D) in treating insomnia.Methods: DL-4-chlorophenylalanine (PCPA) + chronic unpredictable mild stress stimulation (CUMS) was used to induce an insomnia model in rats. After the model was successfully established, MMS-4D was intervened at low, medium, and high doses for 7 days. The open-field test (OFT) was used to preliminarily evaluate the efficacy. The potential mechanism of MMS-4D in treating insomnia was investigated using gut microbiota, serum metabolomics, and network pharmacology (NP). Experimental validation of the main components of the key pathways was carried out using ELISA and Western blot.Results: The weights of the insomnia-model rats were significantly raised (p ≤ 0.05), the total exercise distance in the OFT increased (p ≤ 0.05), the rest time shortened, and the number of standing times increased (p ≤ 0.05), after treatment with MMS-4D. Moreover, there was a substantial recovery in the 5-HT, DA, GABA, and Glu levels in the hypothalamus tissue and the 5-HT and GABA levels in the colon tissue of rats. The expression of DAT and DRD1 proteins in the hippocampus of insomnia rats reduced after drug treatment. MMS-4D may treat insomnia by regulating different crucial pathways including 5-HT -, DA -, GABA -, and Glu-mediated neuroactive light receiver interaction, cAMP signaling pathway, serotonergic, glutamatergic, dopaminergic, and GABAergic synapses.Conclusion: This study revealed that MMS-4D can improve the general state and behavioral changes of insomnia model rats. Its mechanism may be related to the reversal of abnormal pathways mediated by 5-HT, DA, GABA, and Glu, such as Serotonergic synapse, Dopaminergic synapse, Glutamatergic synapse, and GABAergic synapse. Keywords: Mongolian medicine sugemule-4 decoction, insomnia, metabolomics, gut microbiota, network pharmacology, molecular mechanism
Published: 2024

25. Identification of a Prognostic Model Based on NK Cell-Related Genes in Multiple Myeloma Using Single-Cell and Transcriptomic Data Analysis

Author: Mei N, Gong S, Wang L, Wang J, Li J, Bao Y, Zhang H, and Wang H
Subjects: multiple myeloma, nk cell-related genes, nk subtypes, prognostic model, itm2c, Diseases of the blood and blood-forming organs, RC633-647.5
Abstract: Nan Mei,1,&ast; Sha Gong,1,&ast; Lizhao Wang,2 Lu Wang,1 Jincheng Wang,1 Jianpeng Li,3 Yingying Bao,4 Huanming Zhang,1 Huaiyu Wang1 1Department of Hematology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, People’s Republic of China; 2Department of Breast Surgery, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, People’s Republic of China; 3Department of Urology, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, People’s Republic of China; 4Institute of Gene and Cell Therapy, the First Affiliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi Province, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Huaiyu Wang, Department of Hematology, the First Affiliated Hospital of Xi’an Jiaotong University, 277 West Yanta Road, Xi’an, Shannxi, 710061, People’s Republic of China, Email why@xjtufh.edu.cn; whymed@126.comBackground: Multiple myeloma (MM), an incurable plasma cell malignancy. The significance of the relationship between natural killer (NK) cell-related genes and clinical factors in MM remains unclear.Methods: Initially, we extracted NK cell-related genes from peripheral blood mononuclear cells (PBMC) of healthy donors and MM samples by employing single-cell transcriptome data analysis in TISCH2. Subsequently, we screened NK cell-related genes with prognostic significance through univariate Cox regression analysis and protein-protein interaction (PPI) network analysis. Following the initial analyses, we developed potential subtypes and prognostic models for MM using consensus clustering and lasso regression analysis. Additionally, we conducted a correlation analysis to explore the relationship between clinical features and risk scores. Finally, we constructed a weighted gene co-expression network analysis (WGCNA) and identified differentially expressed genes (DEGs) within the MM cohort.Results: We discovered that 153 NK cell-related genes were significantly associated with the prognosisof MM patients (P < 0.05). Patients in NK cluster A exhibited poorer survival outcomes compared to those in cluster B. Furthermore, our NK cell-related genes risk model revealed that patients with a high risk score had significantly worse prognoses (P < 0.05). Patients with a high risk score were more likely to exhibit adverse clinical markers. Additionally, the nomogram based on NK cell-related genes demonstrated strong prognostic performance. The enrichment analysis indicated that immune-related pathways were significantly correlated with both the NK subtypes and the NK cell-related genes risk model. Ultimately, through the combined use of WGCNA and DEGs analysis, and by employing Venn diagrams, we determined that ITM2C is an independent prognostic marker for MM patients.Conclusion: In this study, we developed a novel model based on NK cell-related genes to stratify the prognosis of MM patients. Notably, higher expression levels of ITM2C were associated with more favorable survival outcomes in these patients.Keywords: multiple myeloma, NK cell-related genes, NK subtypes, prognostic model, ITM2C
Published: 2024

26. Muon Collider Forum Report

Author: Black, K. M., Jindariani, S., Li, D., Maltoni, F., Meade, P., Stratakis, D., Acosta, D., Agarwal, R., Agashe, K., Aime, C., Ally, D., Apresyan, A., Apyan, A., Asadi, P., Athanasakos, D., Bao, Y., Barzi, E., Bartosik, N., Bauerdick, L. A. T., Beacham, J., Belomestnykh, S., Berg, J. S., Berryhill, J., Bertolin, A., Bhat, P. C., Biagini, M. E., Bloom, K., Bose, T., Bross, A., Brost, E., Bruhwiler, N., Buonincontri, L., Buttazzo, D., Candelise, V., Canepa, A., Carpenter, L., Casarsa, M., Celiberto, F., Cesarotti, C., Chachamis, G., Chacko, Z., Chang, P., Chekanov, S. V., Chen, T. Y., Chiesa, M., Cohen, T., Costa, M., Craig, N., Crivellin, A., Curatolo, C., Curtin, D., Da Molin, G., Dasu, S., de Gouvea, A., Denisov, D., Dermisek, R., Di Petrillo, K. F., Dorigo, T., Duarte, J. M., Elvira, V. D., Essig, R., Everaerts, P., Fan, J., Felcini, M., Fiore, G., Fiorina, D., Forslund, M., Franceschini, R., Garzelli, M. V., Gerber, C. E., Giambastiani, L., Giove, D., Guiducci, S., Han, T., Hermanek, K., Herwig, C., Hirschauer, J., Holmes, T. R., Homiller, S., Horyn, L. A., Ivanov, A., Jayatilaka, B., Jia, H., Jung, C. K., Kahn, Y., Kaplan, D. M., Kaur, M., Kawale, M., Koppenburg, P., Krintiras, G., Krizka, K., Kuchma, B., Lee, L., Li, L., Li, P., Li, Q., Li, W., Lipton, R., Liu, Z., Lomte, S., Lu, Q., Lucchesi, D., Luo, T., Lyu, K., Ma, Y., Machado, P. A. N., Madrid, C., Mahon, D. J., Mazzacane, A., McGinnis, N., McLean, C., Mele, B., Meloni, F., Middleton, S. C., Mishra, R. K., Mokhov, N., Montella, A., Morandin, M., Nagaitsev, S., Nardi, F., Neubauer, M. S., Neuffer, D. V., Newman, H., Ogaz, R., Ojalvo, I., Oksuzian, I., Orimoto, T., Ozek, B., Pachal, K., Griso, S. Pagan, Panci, P., Papadimitriou, V., Pastrone, N., Pedro, K., Pellemoine, F., Perloff, A., Pinna, D., Piccinini, F., Pleier, Marc-Andre, Posen, S., Potamianos, K., Rappoccio, S., Reece, M., Reina, L., Hall, A. Reinsvold, Riccardi, C., Ristori, L., Robens, T., Ruiz, R., Sala, P., Schulte, D., Sestini, L., Shiltsev, V., Snopok, P., Stark, G., Stupak III, J., Su, S ., Sundrum, R., Swiatlowski, M., Syphers, M. J., Taffard, A., Thompson, W., Torun, Y., Tully, C. G., Vai, I., Valente, M., van Rienen, U., van Weelderen, R., Velev, G., Venkatasubramanian, N., Vittorio, L., Vuosalo, C., Wang, X., Weber, H., Wu, R., Wu, Y., Wulzer, A., Xie, K., Xie, S., Yohay, R., Yonehara, K., Yu, F., Zlobin, A. V., Zuliani, D., and Zurita, J.
Subjects: High Energy Physics - Experiment, High Energy Physics - Phenomenology
Abstract: A multi-TeV muon collider offers a spectacular opportunity in the direct exploration of the energy frontier. Offering a combination of unprecedented energy collisions in a comparatively clean leptonic environment, a high energy muon collider has the unique potential to provide both precision measurements and the highest energy reach in one machine that cannot be paralleled by any currently available technology. The topic generated a lot of excitement in Snowmass meetings and continues to attract a large number of supporters, including many from the early career community. In light of this very strong interest within the US particle physics community, Snowmass Energy, Theory and Accelerator Frontiers created a cross-frontier Muon Collider Forum in November of 2020. The Forum has been meeting on a monthly basis and organized several topical workshops dedicated to physics, accelerator technology, and detector R&D. Findings of the Forum are summarized in this report.
Published: 2022

27. Adaptation and Validation of Learned Helplessness Scale in Chinese Law School Students

Author: Lan C, Zhou T, Bao Y, Wang R, Weng X, Su J, Li Y, Qiao P, and Guo D
Subjects: learned helplessness scale, chinese, adaptation, validation, Psychology, BF1-990, Industrial psychology, HF5548.7-5548.85
Abstract: Cuiyu Lan,1 Tianshu Zhou,2,3 Yuchang Bao,4 Ruizhe Wang,5 Xinying Weng,6 Jie Su,7 Yinlin Li,8 Peijing Qiao,9 Danfeng Guo2 1Faculty of Law, Qingdao University, Qingdao, Shandong, People’s Republic of China; 2Department of Political Science, Suzhou University of Science and Technology, Suzhou, Jiangsu, People’s Republic of China; 3Department of Social and Behavioural Sciences, City University of Hong Kong, Hong Kong, SAR, People’s Republic of China; 4Faculty of Education, University of Macau, Macau, SAR, People’s Republic of China; 5GEC-Advanced Studies Institute, Beijing, People’s Republic of China; 6Ip Ying To Lee Yu Yee School of Humanities and Languages, Caritas Institute of Higher Education, Hong Kong, SAR, People’s Republic of China; 7Department of Public and International Affairs, City University of Hong Kong, Hong Kong, SAR, People’s Republic of China; 8Department of History, Hong Kong Shue Yan University, Hong Kong, SAR, People’s Republic of China; 9Department of Educational Psychology, The Chinese University of Hong Kong, Hong Kong, SAR, People’s Republic of ChinaCorrespondence: Danfeng Guo, Email guodanfeng@usts.edu.cnPurpose: This study aims to translate and validate the Learned Helplessness Scale (LHS) for use in the educational context and specifically among Chinese law school students. Understanding learned helplessness in the context of Chinese law students can provide unique insights into the interaction of legal education, psychological health, and cultural influences, thereby contributing to a more nuanced understanding of learned helplessness.Methods: A total of 711 Chinese college students from two law schools participated in this study. The Learned Helplessness Scale (LHS) was translated into Chinese using forward and backward translation. Exploratory and confirmatory factor analysis, and construct validity were conducted to assess the dimensionality of the Chinese version of the LHS (Chinese LHS).Results: The exploratory factor analysis indicated that the Chinese LHS has a four-factor structure consisting of 14 items, which accounted for 50% of the total variance. The subsequent confirmatory factor analysis further supported this four-factor structure. The internal consistency of the Chinese LHS was found to be medium to high, with Cronbach’s α values ranging from 0.63 to 0.87 for the subfactors, and 0.79 for the total scale. In addition, concurrent validity is also confirmed.Conclusion: The 14-item version of the Chinese LHS is a psychometric sound instrument for assessing learned helplessness among Chinese law school students.Keywords: learned helplessness scale, Chinese, adaptation, validation
Published: 2024

28. Predictors of Inadequate Serum Urate Response to Low-Dose Febuxostat in Male Patients with Gout

Author: Sun W, Zhao X, Dalbeth N, Terkeltaub R, Cui L, Liu Z, Han L, Wang C, Zhang H, Bao Y, Li C, and Lu J
Subjects: gout, febuxostat, urate-lowering therapy, machine learning model, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Wenyan Sun,1,&ast; Xuetong Zhao,2– 4,&ast; Nicola Dalbeth,5,&ast; Robert Terkeltaub,6 Lingling Cui,1 Zhen Liu,1 Lin Han,1 Can Wang,1 Hui Zhang,1,7 Yiming Bao,2– 4 Changgui Li,1,7 Jie Lu1,7 1Shandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, People’s Republic of China; 2National Genomics Data Center, Beijing Institute of Genomics, Chinese Academy of Sciences and China National Center for Bioinformation, Beijing, 100101, People’s Republic of China; 3CAS Key Laboratory of Genome Sciences and Information, Beijing Institute of Genomics, Chinese Academy of Science and China National Center for Bioinformation, Beijing, People’s Republic of China; 4University of Chinese Academy of Sciences, Beijing, 100049, People’s Republic of China; 5Department of Medicine, University of Auckland, Auckland, New Zealand; 6VA San Diego VA Healthcare Center, University of California San Diego, San Diego, CA, USA; 7Institute of Metabolic Diseases, Qingdao University, Qingdao, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Jie Lu, Shandong Provincial Key Laboratory of Metabolic Diseases and Qingdao Key Laboratory of Gout, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, 266003, People’s Republic of China, Email 13127006046@163.com Changgui Li, Shandong Provincial Key Laboratory of Metabolic Diseases, Shandong Provincial Clinical Research Center for Immune Diseases, the Affiliated Hospital of Qingdao University, Qingdao, 266003, People’s Republic of China, Email changguili@vip.163.comObjective: This study aimed to understand predictors of inadequate response (IR) to low-dose febuxostat treatment based on clinical variables.Methods: We pooled data from 340 patients of an observational cohort and two clinical trials who received febuxostat 20 mg/day for at least 3 months. IR was defined as failure to reach the target serum urate level (sUA< 6 mg/dL) at any time point during 3 months treatment. The potential predictors associated with short- or mid-term febuxostat IR after pooling the three cohorts were explored using mixed-effect logistic analysis. Machine learning models were performed to evaluate the predictors for IR using the pooled data as the discovery set and validated in an external test set.Results: Of the 340 patients, 68.9% and 51.8% were non-responders to low-dose febuxostat during short- and mid-term follow-up, respectively. Serum urate and triglyceride (TG) levels were significantly associated with febuxostat IR, but were also selected as significant features by LASSO analysis combined with age, BMI, and C-reactive protein (CRP). These five features in combination, using the best-performing stochastic gradient descent classifier, achieved an area under the receiver operating characteristic curve of 0.873 (95% CI [0.763, 0.942]) and 0.706 (95% CI [0.636, 0.727]) in the internal and external test sets, respectively, to predict febuxostat IR.Conclusion: Response to low-dose febuxostat is associated with early sUA improvement in individual patients, as well as patient age, BMI, and levels of TG and CRP.Keywords: gout, febuxostat, urate-lowering therapy, machine learning model
Published: 2024

29. 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
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30. Spin excitations in the quantum dipolar magnet Yb(BaBO$_3$)$_3$

Author: Jiang, C. Y., Yang, Y. X., Gao, Y. X., Wan, Z. T., Zhu, Z. H., Shiroka, T., Chen, C. S., Wu, Q., Li, X., Jiao, J. C., Chen, K. W., Bao, Y., Tian, Z. M., and Shu, L.
Subjects: Condensed Matter - Strongly Correlated Electrons
Abstract: We report results of magnetization, specific-heat and muon-spin relaxation measurements on single crystals of disorder-free Yb$^{3+}$ triangular lattice Yb(BaBO$_3$)$_3$. The magnetization experiments show anisotropic magnetic properties with Curie-Weiss temperatures $\theta_{\perp}=-1.40$~K ($H \perp c$) and $\theta_{\parallel}=-1.16$~K ($H \parallel c$) determined from low temperature data. The absence of both long-range antiferromagnetic order and spin freezing is confirmed down to 0.27 K at zero field. A two-level Schottky anomaly due to the opening of the ground-state Kramers doublet is observed from the low-temperature specific-heat measurements when the applied magnetic fields $\mu_0H >0.7$~T. At zero field, the increase of both $C_{\rm mag}/T$ and the muon spin relaxation rate $\lambda$ below 1~K is due to the electronic spin excitations, which often exist in quantum magnets where dipole-dipole interaction creates an anisotropy of magnetic properties. The spin excitation is also supported by the unusual maximum of field dependence of $\lambda$ due to the field-induced increase of the density of excitations. We argue that dipolar interaction is dominant and induces the spin dynamics in the quantum magnet Yb(BaBO$_3$)$_3$., Comment: accepted by Phys. Rev. B
Published: 2022
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31. Sclerosing Mucoepidermoid Carcinoma With Eosinophilia: A Diagnostic Challenge

Author: Bao Y. Sciscent, Hanel W. Eberly, Sanica Bhele, and Neerav Goyal
Subjects: mucoepidermoid carcinoma, sclerosing mucoepidermoid carcinoma with eosinophilia, thyroid carcinoma, Otorhinolaryngology, RF1-547, Surgery, RD1-811
Published: 2024
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32. Spherical nanocrystalline ScYSZ thermal barrier material by a novel supersonic plasma spheroidization method: Simple synthesis and excellent performance

Author: Zu, J.H., Liu, X., Liu, D., Feng, Z., Gao, Y., Luo, W.F., Bao, Y., Shang, Q.Y., Fan, W., Wang, Y., and Bai, Y.
Published: 2024
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33. Tislelizumab plus chemotherapy as first-line treatment of locally advanced or metastatic nonsquamous non-small-cell lung cancer (final analysis of RATIONALE-304: a randomized phase III trial)

Author: Lu, S., Wang, J., Yu, Y., Yu, X., Hu, Y., Ma, Z., Li, X., He, W., Bao, Y., and Wang, M.
Published: 2024
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34. Associations of Serum Uric Acid to High-Density Lipoprotein Cholesterol Ratio with Trunk Fat Mass and Visceral Fat Accumulation

Author: Wang Y, Xu Y, Hu T, Xiao Y, Ma X, Yu H, and Bao Y
Subjects: fat distribution, the uhr index, trunk fat mass, visceral fat area, Specialties of internal medicine, RC581-951
Abstract: Yansu Wang,1,&ast; Yiting Xu,1,&ast; Tingting Hu,1 Yunfeng Xiao,2 Yufei Wang,1 Xiaojing Ma,1 Haoyong Yu,1 Yuqian Bao1 1Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai Clinical Center for Diabetes, Shanghai Diabetes Institute, Shanghai Key Laboratory of Diabetes Mellitus, Shanghai, People’s Republic of China; 2Department of Radiology, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, Shanghai, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yuqian Bao, Department of Endocrinology and Metabolism, Shanghai Sixth People’s Hospital Affiliated to Shanghai Jiao Tong University School of Medicine, 600 Yishan Road, Shanghai, 200233, People’s Republic of China, Tel +86-21-64369181, Fax +86-21-64368031, Email yqbao@sjtu.edu.cnBackground: It has been reported recently that the ratio of uric acid to high-density lipoprotein cholesterol (UHR) is correlated with several metabolic disorders. The present study aimed to investigate the associations of UHR with body fat content and distribution.Methods: This study enrolled 300 participants (58 men and 242 women) aged 18 to 65 years. The levels of serum uric acid and high-density lipoprotein cholesterol were measured by standard enzymatic methods. The overall fat content and segmental fat distribution were assessed with an automatic bioelectrical impedance analyzer. In the population with obesity, the visceral fat area (VFA) and subcutaneous fat area (SFA) were measured using magnetic resonance imaging.Results: Among the study population, 219 individuals (73.0%) were with obesity. The median level of UHR in individuals with obesity was 33.7% (26.2% - 45.9%), which was significantly higher than that in those without obesity [22.6% (17.0% - 34.4%), P < 0.01]. UHR was positively associated with overall fat content and segmental fat distribution parameters (all P < 0.01). In multivariate linear regression analysis, compared with body mass index, waist circumference was more closely associated with UHR (standardized β = 0.427, P < 0.001) after adjusting for confounding factors. Additionally, total fat mass (standardized β = 0.225, P = 0.002) and trunk fat mass (standardized β = 0.296, P = 0.036) were more closely linked to UHR than total fat-free mass and leg fat mass, respectively. In the population with obesity, VFA was independently correlated with UHR (P < 0.01), while SFA was not associated with UHR.Conclusion: UHR was significantly associated with overall fat content and trunk fat accumulation. In the population with obesity, UHR was positively associated with VFA. Attention should be paid to the role of excessive trunk fat mass in the relationship between UHR and metabolic disorders.Keywords: fat distribution, the UHR index, trunk fat mass, visceral fat area
Published: 2024

35. A Novel Inflammatory-Nutritional Prognostic Scoring System for Patients with Diffuse Large B Cell Lymphoma

Author: Wang Z, Bao Y, Xu Z, Sun Y, Yan X, Sheng L, and Ouyang G
Subjects: diffuse large b cell lymphoma, lasso cox analysis, inflammatory-nutritional biomarker, prognostic nomograms, overall survival, Pathology, RB1-214, Therapeutics. Pharmacology, RM1-950
Abstract: Zanzan Wang,&ast; Yurong Bao,&ast; Zhijuan Xu, Yongcheng Sun, Xiao Yan, Lixia Sheng, Guifang Ouyang Department of Hematology, The First Affiliated Hospital of Ningbo University, Ningbo, Zhejiang, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Guifang Ouyang, Department of Hematology, The First Affiliated Hospital of Ningbo University, No. 59 Liuting Street, Haishu District, Ningbo, Zhejiang, 315000, People’s Republic of China, Tel/Fax +86 57487085166, Email nbougf@163.comPurpose: This study aimed to examine the predictive ability of inflammatory and nutritional markers and further establish a novel inflammatory nutritional prognostic scoring (INPS) system.Patients and Methods: We collected clinicopathological and baseline laboratory data of 352 patients with DLBCL between April 2010 and January 2023 at the First affiliated hospital of Ningbo University. Eligible patients were randomly divided into training and validation cohorts (n = 281 and 71, respectively) in an 8:2 ratio. We used the least absolute shrinkage and selection operator (LASSO) Cox regression model to determine the most important factors among the eight inflammatory-nutritional variables. The impact of INPS on OS was evaluated using the Kaplan–Meier curve and the Log rank test. A prognostic nomogram was developed based on the multivariate Cox regression method. Then, we used the concordance index (C-index), calibration plot, and time-dependent receiver operating characteristic (ROC) analysis to evaluate the prognostic performance and predictive accuracy of the nomogram.Results: Seven inflammatory-nutritional biomarkers, including neutrophil-lymphocyte ratio (NLR), prognostic nutritional index (PNI), body mass index (BMI), monocyte-lymphocyte ratio (MLR), prealbumin, C reactive protein, and D-dimer were selected using the LASSO Cox analysis to construct INPS, In the multivariate analysis, IPI-High-intermediate group, IPI-High group, high INPS were independently associated with OS, respectively. The prognostic nomogram for overall survival consisting of the above two indicators showed excellent discrimination. The C-index for the nomogram was 0.94 and 0.95 in the training and validation cohorts. The time-dependent ROC curves showed that the predictive accuracy of the nomogram for OS was better than that of the NCCN-IPI system.Conclusion: The INPS based on seven inflammatory-nutritional indexes was a reliable and convenient predictor of outcomes in DLBCL patients.Keywords: diffuse large B cell lymphoma, LASSO Cox analysis, inflammatory-nutritional biomarker, prognostic nomograms, overall survival
Published: 2024

36. Application of Metabolomics and Traditional Chinese Medicine for Type 2 Diabetes Mellitus Treatment

Author: Li J, Zhu N, Wang Y, Bao Y, Xu F, Liu F, and Zhou X
Subjects: metabolomics, traditional chinese medicine, type 2 diabetes mellitus, diabetic complications, diagnosis, Specialties of internal medicine, RC581-951
Abstract: Jing Li,1,&ast; Na Zhu,2,&ast; Yaqiong Wang,2 Yanlei Bao,3 Feng Xu,2 Fengjuan Liu,2 Xuefeng Zhou2 1Jilin Ginseng Academy, Changchun University of Chinese Medicine, Changchun, People’s Republic of China; 2Clinical Trial Research Center, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Central Hospital, Qingdao, People’s Republic of China; 3Department of Pharmacy, Liaoyuan People’s Hospital, Liaoyuan, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Xuefeng Zhou, Clinical Trial Research Center, Affiliated Qingdao Central Hospital of Qingdao University, Qingdao Central Hospital, No. 127, Siliunan Road, Shibei District, Qingdao, Shandong, 266042, People’s Republic of China, Tel +86 18561856181, Email zhouxuefeng5019@163.comAbstract: Diabetes is a major global public health problem with high incidence and case fatality rates. Traditional Chinese medicine (TCM) is used to help manage Type 2 Diabetes Mellitus (T2DM) and has steadily gained international acceptance. Despite being generally accepted in daily practice, the TCM methods and hypotheses for understanding diseases lack applicability in the current scientific characterization systems. To date, there is no systematic evaluation system for TCM in preventing and treating T2DM. Metabonomics is a powerful tool to predict the level of metabolites in vivo, reveal the potential mechanism, and diagnose the physiological state of patients in time to guide the follow-up intervention of T2DM. Notably, metabolomics is also effective in promoting TCM modernization and advancement in personalized medicine. This review provides updated knowledge on applying metabolomics to TCM syndrome differentiation, diagnosis, biomarker discovery, and treatment of T2DM by TCM. Its application in diabetic complications is discussed. The combination of multi-omics and microbiome to fully elucidate the use of TCM to treat T2DM is further envisioned.Keywords: metabolomics, traditional Chinese medicine, type 2 diabetes mellitus, diabetic complications, diagnosis
Published: 2023

37. 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
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38. Potential PeVatron supernova remnant G106.3+2.7 seen in the highest-energy gamma rays

Author: Amenomori, M., Bao, Y. W., Bi, X. J., Chen, D., Chen, T. L., Chen, W. Y., Chen, Xu, Chen, Y., Cirennima, Cui, S. W., Danzengluobu, Ding, L. K., Fang, J. H., Fang, K., Feng, C. F., Feng, Zhaoyang, Feng, Z. Y., Gao, Qi, Gou, Q. B., Guo, Y. Q., Guo, Y. Y., He, H. H., He, Z. T., Hibino, K., Hotta, N., Hu, Haibing, Hu, H. B., Huang, J., Jia, H. Y., Jiang, L., Jin, H. B., Kasahara, K., Katayose, Y., Kato, C., Kato, S., Kawata, K., Kihara, W., Ko, Y., Kozai, M., Labaciren, Le, G. M., Li, A. F., Li, H. J., Li, W. J., Lin, Y. H., Liu, B., Liu, C., Liu, J. S., Liu, M. Y., Liu, W., Lou, Y. -Q., Lu, H., Meng, X. R., Munakata, K., Nakada, H., Nakamura, Y., Nanjo, H., Nishizawa, M., Ohnishi, M., Ohura, T., Ozawa, S., Qian, X. L., Qu, X. B., Saito, T., Sakata, M., Sako, T. K., Shao, J., Shibata, M., Shiomi, A., Sugimoto, H., Takano, W., Takita, M., Tan, Y. H., Tateyama, N., Torii, S., Tsuchiya, H., Udo, S., Wang, H., Wu, H. R., Xue, L., Yamamoto, Y., Yang, Z., Yokoe, Y., Yuan, A. F., Zhai, L. M., Zhang, H. M., Zhang, J. L., Zhang, X., Zhang, X. Y., Zhang, Y., Zhang, Yi, Zhang, Ying, Zhao, S. P., Zhaxisangzhu, and Zhou, X. X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: Cosmic rays (protons and other atomic nuclei) are believed to gain energies of petaelectronvolts (PeV) and beyond at astrophysical particle accelerators called 'PeVatrons' inside our Galaxy. Although a characteristic feature of a PeVatron is expected to be a hard gamma-ray energy spectrum that extends beyond 100 teraelectronvolts (TeV) without a cutoff, none of the currently known sources exhibits such a spectrum due to the low maximum energy of accelerated cosmic rays or insufficient detector sensitivity around 100 TeV. Here we report the observation of gamma-ray emission from the supernova remnant G106.3+2.7 above 10 TeV. This work provides flux data points up to and above 100 TeV and indicates that the very-high-energy gamma-ray emission above 10 TeV is well correlated with a molecular cloud rather than the pulsar PSR J2229+6114. Regarding the gamma-ray emission mechanism of G106.3+2.7, this morphological feature appears to favor a hadronic origin via the {\pi}0 decay caused by accelerated relativistic protons over a leptonic one via the inverse-Compton scattering by relativistic electrons. Furthermore, we point out that an X-ray flux upper limit on the synchrotron spectrum would provide important information to firmly establish the hadronic scenario as the mechanism of particle acceleration at the source., Comment: Published in Nature Astronomy
Published: 2021
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39. Preparation and high-temperature performance of Sc2O3–Y2O3 co-stabilized ZrO2 thermal barrier coatings

Author: Zu, J.H., Gao, Y., Liu, D., Luo, W.F., Feng, Z., Bao, Y., Shang, Q.Y., Bai, Y., Fan, W., Wang, Y., and Yu, F.L.
Published: 2024
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40. Development of μSR apparatuses for a pulsed muon source at China Spallation Neutron Source

Author: Pan, Z.W., Yang, T.Y., Lin, Z.B., Wang, Z., He, Z.Y., Yuan, Y., Ni, X.J., Liu, J.D., Liang, H., Ye, B.J., Li, Q., Bao, Y., Li, Y., Cheng, H., Hu, H.T., Yu, Y.J., Tang, J.Y., Xie, F., Cottrell, S.P., and Pooley, D.E.
Published: 2024
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41. Factors Associated With Inadequate Health Literacy: An Academic Otolaryngology Clinic Population Study

Author: Madison Hearn, Bao Y. Sciscent, Tonya S. King, and Neerav Goyal
Subjects: academic otolaryngology, health literacy, patient demographics, screening, Otorhinolaryngology, RF1-547, Surgery, RD1-811
Abstract: Abstract Objective To characterize the prevalence of inadequate health literacy among otolaryngology patients and assess the association of individual patient factors with inadequate health literacy. Study Design Cross‐sectional study. Setting Tertiary academic medical center otolaryngology clinic. Methods Adult patients presenting to the clinic were recruited from March to June 2022. Participants completed a validated health literacy questionnaire in the waiting room. Data on age, sex, race, insurance, county of residence, and language were extracted from the electronic medical record, linked to the survey responses, and deidentified for analysis. Logistic regression analyses assessed the association between inadequate health literacy and patient factors. Results Of 374 participants, the mean age was 54.8 years (SD = 17.8) and most were white (79%) and native English speakers (95%). The median health literacy score was 14.5 (Q1‐Q3: 12.0‐15.0) and 43 participants (12%) had inadequate health literacy. Bivariate analysis showed the odds of inadequate health literacy were 2.5 times greater for those with public insurance (95% confidence interval [CI]: 1.24‐5.20, P = .011), 3.5 times greater for males (95% CI: 1.75‐6.92, P
Published: 2024
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42. Gamma-ray Observation of the Cygnus Region in the 100 TeV Energy Region

Author: Amenomori, M., Bao, Y. W., Bi, X. J., Chen, D., Chen, T. L., Chen, W. Y., Chen, Xu, Chen, Y., Cirennima, Cui, S. W., Danzengluobu, Ding, L. K., Fang, J. H., Fang, K., Feng, C. F., Feng, Zhaoyang, Feng, Z. Y., Gao, Qi, Gomi, A., Gou, Q. B., Guo, Y. Q., Guo, Y. Y., He, H. H., He, Z. T., Hibino, K., Hotta, N., Hu, Haibing, Hu, H. B., Huang, J., Jia, H. Y., Jiang, L., Jiang, P., Jin, H. B., Kasahara, K., Katayose, Y., Kato, C., Kato, S., Kawata, K., Kozai, M., Kurashige, D., Labaciren, Le, G. M., Li, A. F., Li, H. J., Li, W. J., Li, Y., Lin, Y. H., Liu, B., Liu, C., Liu, J. S., Liu, L. Y., Liu, M. Y., Liu, W., Liu, X. L., Lou, Y. -Q., Lu, H., Meng, X. R., Munakata, K., Nakada, H., Nakamura, Y., Nakazawa, Y., Nanjo, H., Ning, C. C., Nishizawa, M., Ohnishi, M., Ohura, T., Okukawa, S., Ozawa, S., Qian, L., Qian, X., Qian, X. L., Qu, X. B., Saito, T., Sakata, M., Sako, T., Sako, T. K., Shao, J., Shibata, M., Shiomi, A., Sugimoto, H., Takano, W., Takita, M., Tan, Y. H., Tateyama, N., Torii, S., Tsuchiya, H., Udo, S., Wang, H., Wang, Y. P., Wangdui, Wu, H. R., Wu, Q., Xu, J. L., Xue, L., Yamamoto, Y., Yang, Z., Yao, Y. Q., Yin, J., Yokoe, Y., Yu, N. P., Yuan, A. F., Zhai, L. M., Zhang, C. P., Zhang, H. M., Zhang, J. L., Zhang, X., Zhang, X. Y., Zhang, Y., Zhang, Yi, Zhang, Ying, Zhao, S. P., Zhaxisangzhu, and Zhou, X. X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We report observations of gamma-ray emissions with energies in the 100 TeV energy region from the Cygnus region in our Galaxy. Two sources are significantly detected in the directions of the Cygnus OB1 and OB2 associations. Based on their positional coincidences, we associate one with a pulsar PSR J2032+4127 and the other mainly with a pulsar wind nebula PWN G75.2+0.1 with the pulsar moving away from its original birthplace situated around the centroid of the observed gamma-ray emission. This work would stimulate further studies of particle acceleration mechanisms at these gamma-ray sources., Comment: Accepted for publication in the Physical Review Letters
Published: 2021
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43. How National Health Insurance Coverage Policy Affected the Use of Trastuzumab and Rituximab in China: A Bicentric Retrospective Study

Author: Shang L, Lin Y, Fang W, Liu Y, Bao Y, Li X, and Zhang Y
Subjects: national health insurance coverage, trastuzumab, rituximab, breast cancer, non-hodgkin lymphoma, utilization, Public aspects of medicine, RA1-1270
Abstract: Linlin Shang,1,&ast; Yingtao Lin,2,&ast; Wenqing Fang,3 Yanyan Liu,4 Yuwen Bao,4 Xin Li,1,2,4 Yuanyuan Zhang5 1Department of Clinical Pharmacy, School of Pharmacy, Nanjing Medical University, Nanjing, People’s Republic of China; 2Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, People’s Republic of China; 3Department of Medical Ethics Supervision, The First Affiliated Hospital of Soochow University, Suzhou, People’s Republic of China; 4Department of Health Policy, School of Health Policy and Management, Nanjing Medical University, Nanjing, People’s Republic of China; 5Department of Pharmacy, Jiangsu Cancer Hospital, Jiangsu Institute of Cancer Research, The Affiliated Cancer Hospital of Nanjing Medical University, Nanjing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yuanyuan Zhang, Department of Pharmacy, Jiangsu Cancer Hospital, No. 42 Baizi Ting Road, Xuanwu District, Nanjing, Jiangsu, 210009, People’s Republic of China, Email double-z@yeah.net Xin Li, Department of Clinical Pharmacy, School of Pharmacy, Nanjing Medical University, No. 101 Longmian Avenue, Jiangning District, Nanjing, Jiangsu, 211166, People’s Republic of China, Email xinli@njmu.edu.cnBackground: Cancer is a significant health concern and is China’s leading cause of mortality. Targeted therapies, such as trastuzumab and rituximab, have enhanced clinical treatment efficacy. However, their high costs burden patients and healthcare systems considerably. Patient demographic factors further influence the utilization of these expensive drugs. On September 1, 2017, China implemented the National Health Insurance Coverage (NHIC) policy, necessitating additional real-world evidence to assess its impact on patients.Methods: Data on human epidermal growth factor receptor 2-positive breast cancer and CD20-positive non-Hodgkin B-cell lymphoma patients were gathered in Jiangsu Cancer Hospital and Fujian Cancer Hospital from September 2015 to August 2019, including demographic and clinical information. All eligible patients were divided into two groups. Univariate analysis and multivariable logistic regression were used to investigate the differences between subgroups. An interrupted time-series regression was used to examine the change in trastuzumab and rituximab utilization percentages.Results: Before and after the NHIC policy, utilization of trastuzumab increased from 61.13% to 75.10%, and the increase was statistically significant. Rituximab therapy increased statistically significantly from 64.79% to 74.88%. The key factor influencing trastuzumab and rituximab use was the NHIC policy. With policy implementation, medical insurance status, occupations, and cancer disease stage affected trastuzumab and rituximab use.Conclusion: The NHIC policy is essential to the utilization of trastuzumab and rituximab, and the patient’s income level and repayment abilities continue to impact the use of innovative anti-cancer drugs. Appropriate steps, such as reducing the urban-rural gap and broadening medical insurance coverage, would enable more people to access novel anti-cancer drugs.Keywords: National Health Insurance Coverage, trastuzumab, rituximab, breast cancer, non-Hodgkin lymphoma, utilization
Published: 2023

44. Numerical Study of a Rotationally Oscillating Cylinder at Low Reynolds Numbers

Author: Mikhailov, M. S., Bao, Y., Han, Z. L., and Zhu, H. B.
Published: 2023
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45. Calibration of the Air Shower Energy Scale of the Water and Air Cherenkov Techniques in the LHAASO experiment

Author: 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, Z. Cao Z., Chang, J., Chang, J. F., Chang, X. C., Chen, B. M., Chen, J., Chen, L., 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., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Piazzoli, B. DEttorre, Dong, X. J., 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, Q., Gao, W., Ge, M. M., Geng, L. S., Gong, G. H., Gou, Q. B., Gu, M. H., 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, Z. C., Ji, F., Ji, X. L., Jia, H. Y., Jiang, K., Jiang, Z. J., Jin, C., Kuleshov, D., Levochkin, K., Li, B. B., Li, C., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, K., Li, W. L., Li, X., Li, X. R., Li, Y., Li, Y. Z., Li, Z., 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. 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., Mitthumsiri, W., Montaruli, T., Nan, Y. C., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Ruffolo, D., Rulev, V., Saiz, A., Shao, L., Shchegolev, O., Sheng, X. D., Shi, J. R., Song, H. C., Stenkin, Yu. V., Stepanov, V., 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, W., Wang, X. G., Wang, X. J., Wang, X. Y., Wang, Y. D., Wang, Y. J., Wang, Y. P., Wang, Z., Wang, Z. H., Wang, Z. X., Wei, D. M., Wei, J. J., Wei, Y. J., Wen, T., Wu, C. Y., Wu, H. R., Wu, S., Wu, X., Wu, X. F., Xi, S. Q., Xia, J., Xia, J. J., Xiang, G. M., Xiao, G., Xiao, H. B., Xin, G. G., Xin, Y. L., Xing, Y., Xu, D. L., Xu, R. X., Xue, L., Yan, D. H., 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., Zhang, L. X., Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y., Zhang, Y. F., Zhang, Y. L., 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 - Instrumentation and Methods for Astrophysics, Astrophysics - High Energy Astrophysical Phenomena
Abstract: The Wide Field-of-View Cherenkov Telescope Array (WFCTA) and the Water Cherenkov Detector Arrays (WCDA) of LHAASO are designed to work in combination for measuring the energy spectra of various cosmic ray species over a very wide energy range from a few TeV to 10 PeV. The energy calibration of WCDA can be achieved with a proven technique of measuring the westward shift of the Moon shadow of galactic cosmic rays due to the geomagnetic field. This deflection angle $\Delta$ is inversely proportional to the energy of the cosmic rays. The precise measurements of the shifts by WCDA allows us to calibrate its energy scale for energies as high as 35 TeV. The energy scale measured by WCDA can be used to cross calibrate the energy reconstructed by WFCTA, which spans the whole energy range up to 10 PeV. In this work, we will demonstrate the feasibility of the method using the data collected from April 2019 to January 2020 by the WFCTA array and WCDA-1 detector, the first of the three water Cherenkov ponds, already commissioned at LHAASO site.
Published: 2021
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46. First Detection of sub-PeV Diffuse Gamma Rays from the Galactic Disk: Evidence for Ubiquitous Galactic Cosmic Rays beyond PeV Energies

Author: Amenomori, M., Bao, Y. W., Bi, X. J., Chen, D., Chen, T. L., Chen, W. Y., Chen, Xu, Chen, Y., Cirennima, Cui, S. W., Danzengluobu, Ding, L. K., Fang, J. H., Fang, K., Feng, C. F., Feng, Zhaoyang, Feng, Z. Y., Gao, Qi, Gou, Q. B., Guo, Y. Q., Guo, Y. Y., He, H. H., He, Z. T., Hibino, K., Hotta, N., Hu, Haibing, Hu, H. B., Huang, J., Jia, H. Y., Jiang, L., Jin, H. B., Kasahara, K., Katayose, Y., Kato, C., Kato, S., Kawata, K., Kihara, W., Ko, Y., Kozai, M., Labaciren, Le, G. M., Li, A. F., Li, H. J., Li, W. J., Lin, Y. H., Liu, B., Liu, C., Liu, J. S., Liu, M. Y., Liu, W., Lou, Y. -Q., Lu, H., Meng, X. R., Munakata, K., Nakada, H., Nakamura, Y., Nanjo, H., Nishizawa, M., Ohnishi, M., Ohura, T., Ozawa, S., Qian, X. L., Qu, X. B., Saito, T., Sakata, M., Sako, T. K., Shao, J., Shibata, M., Shiomi, A., Sugimoto, H., Takano, W., Takita, M., Tan, Y. H., Tateyama, N., Torii, S., Tsuchiya, H., Udo, S., Wang, H., Wu, H. R., Xue, L., Yamamoto, Y., Yang, Z., Yokoe, Y., Yuan, A. F., Zhai, L. M., Zhang, H. M., Zhang, J. L., Zhang, X., Zhang, X. Y., Zhang, Y., Zhang, Yi, Zhang, Ying, Zhao, S. P., Zhaxisangzhu, and Zhou, X. X.
Subjects: Astrophysics - High Energy Astrophysical Phenomena
Abstract: We report, for the first time, the long-awaited detection of diffuse gamma rays with energies between 100 TeV and 1 PeV in the Galactic disk. Particularly, all gamma rays above 398 TeV are observed apart from known TeV gamma-ray sources and compatible with expectations from the hadronic emission scenario in which gamma rays originate from the decay of $\pi^0$'s produced through the interaction of protons with the interstellar medium in the Galaxy. This is strong evidence that cosmic rays are accelerated beyond PeV energies in our Galaxy and spread over the Galactic disk., Comment: Accepted for publication in the Physical Review Letters
Published: 2021
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47. Construction and On-site Performance of the LHAASO WFCTA Camera

Author: 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, Z., Chang, J., Chang, J. F., Chang, X. C., Chen, B. M., Chen, J., Chen, L., 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., Dai, B. Z., Dai, H. L., Dai, Z. G., Danzengluobu, della Volpe, D., Piazzoli, B. D'Ettorre, Dong, X. J., 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, Q., Gao, W., Ge, M. M., Geng, L. S., Gong, G. H., Gou, Q. B., Gu, M. H., 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, Z. C., Ji, F., Ji, X. L., Jia, H. Y., Jiang, K., Jiang, Z. J., Jin, C., Kuleshov, D., Levochkin, K., Li, B. B., Li, C., Li, F., Li, H. B., Li, H. C., Li, H. Y., Li, J., Li, K., Li, W. L., Li, X., Li, X. R., Li, Y., Li, Y. Z., Li, Z., 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. 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., Mitthumsiri, W., Montaruli, T., Nan, Y. C., Pang, B. Y., Pattarakijwanich, P., Pei, Z. Y., Qi, M. Y., Ruffolo, D., Rulev, V., Sáiz, A., Shao, L., Shchegolev, O., Sheng, X. D., Shi, J. R., Song, H. C., Stenkin, Yu. V., Stepanov, V., 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, W., Wang, X. G., Wang, X. J., Wang, X. Y., Wang, Y. D., Wang, Y. J., Wang, Y. P., Wang, Z., Wang, Z. H., Wang, Z. X., 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, G., Xiao, H. B., Xin, G. G., Xin, Y. L., Xing, Y., Xu, D. L., Xu, R. X., Xue, L., Yan, D. H., 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., Zhang, L. X., Zhang, P. F., Zhang, P. P., Zhang, R., Zhang, S. R., Zhang, S. S., Zhang, X., Zhang, X. P., Zhang, Y., Zhang, Y. F., Zhang, Y. L., 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: Physics - Instrumentation and Detectors, Astrophysics - Instrumentation and Methods for Astrophysics, High Energy Physics - Experiment
Abstract: The focal plane camera is the core component of the Wide Field-of-view Cherenkov/fluorescence Telescope Array (WFCTA) of the Large High-Altitude Air Shower Observatory (LHAASO). Because of the capability of working under moonlight without aging, silicon photomultipliers (SiPM) have been proven to be not only an alternative but also an improvement to conventional photomultiplier tubes (PMT) in this application. Eighteen SiPM-based cameras with square light funnels have been built for WFCTA. The telescopes have collected more than 100 million cosmic ray events and preliminary results indicate that these cameras are capable of working under moonlight. The characteristics of the light funnels and SiPMs pose challenges (e.g. dynamic range, dark count rate, assembly techniques). In this paper, we present the design features, manufacturing techniques and performances of these cameras. Finally, the test facilities, the test methods and results of SiPMs in the cameras are reported here., Comment: 45 pages, 21 figures, article
Published: 2020
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48. Levels of the neck and classification of neck dissection and incisions

Author: Sciscent, Bao Y., Goyal, Neerav, and Lane, Ciaran
Published: 2024
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49. Pregnancy in spina bifida patients: a comparative analysis of peripartum procedures and complications

Author: Sciscent, Bao Y., Bhanja, Debarati, Daggubati, Lekhaj C., Ryan, Casey, Hallan, David R., and Rizk, Elias B.
Published: 2023
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50. Effect of the Online Mindfulness-Based Stress Reduction on Anxiety and Depression Status of COVID-19 Patients Treated in Fangcang Hospitals: A Randomized Controlled Trial

Author: Li H, Chen H, Ye Z, Li X, Yang L, Wang S, Yang S, Liu S, Chen S, Li R, Yang J, Bao Y, Shi Y, Wang J, He Y, and Liu M
Subjects: covid-19 patients, online mindfulness, psychological intervention, anxiety, depression, Psychology, BF1-990, Industrial psychology, HF5548.7-5548.85
Abstract: Haowei Li,1,&ast; Hao Chen,2,3,&ast; Zifeng Ye,4 Xuehang Li,1 Lei Yang,1,5 Shengshu Wang,1 Shanshan Yang,6 Shaohua Liu,1 Shimin Chen,1 Rongrong Li,1 Junhan Yang,1,3 Huaihao Li,1 Yinghui Bao,1 Yueting Shi,1,3 Jianhua Wang,1 Yao He,1,7 Miao Liu3 1Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National Clinical Research Center for Geriatrics Diseases, Second Medical Center of Chinese PLA General Hospital, Beijing, People’s Republic of China; 2Healthcare Office of Medical Service Department, Hainan Hospital, Chinese PLA General Hospital, Sanya, Hainan, People’s Republic of China; 3Department of Statistics and Epidemiology, Graduate School of PLA General Hospital, Beijing, People’s Republic of China; 4Department of Medical Psychology of Hainan Hospital, Chinese PLA General Hospital, Sanya, Hainan, People’s Republic of China; 5Healthcare Office of Chinese PLA General Hospital, Beijing, 100853, People’s Republic of China; 6Department of Disease Prevention and Control, First Medical Center of PLA General Hospital, Beijing, People’s Republic of China; 7State Key Laboratory of Kidney Diseases, Beijing, People’s Republic of China&ast;These authors contributed equally to this workCorrespondence: Yao He, Institute of Geriatrics, Beijing Key Laboratory of Aging and Geriatrics, National clinical research center for geriatrics diseases, State Key Laboratory of Kidney Diseases, Second Medical Center of Chinese PLA General Hospital, 28 Fuxing Road, Beijing, 100853, People’s Republic of China, Tel +86-10-66876411, Email yhe301@x263.net Miao Liu, Department of statistics and epidemiology, Graduate School of PLA General Hospital, 28 Fuxing Road, Beijing, 100853, People’s Republic of China, Tel +86-10-66937715, Email liumiaolmbxb@163.comBackground: As the focal point of epidemic prevention and control, the mental health of COVID-19 patients cannot be ignored. Online Mindfulness-Based Stress Reduction (MBSR) allows for the provision of conveniently accessible, effective and low-cost interventions on a large scale. We aim to evaluate the effectiveness of an online MBSR intervention in alleviating anxiety and depression among asymptomatic/mild patients limited by COVID-19-related restrictions.Methods: Fifty-eight patients treated in Sanya Fangcang hospital were randomly allocated to either to the experimental group (n = 29) following daily, for 5 days, an online-based mindfulness intervention or to the control group (n = 29). Patients from both groups underwent online questionnaires including assessment of anxiety and depression status at pre- and post-tests using Self-rating Anxiety Scale and Self-Rating Depression Scale.Results: After the online-based MBSR program, the anxiety and depression scores of the patients in the MBSR group decreased significantly in comparison to the scores of those in the control group (respectively η2 = 0.175, η2 = 0.215, p < 0.001). And the proportion of severe anxiety and depression patients in the MBSR group decreased to 0% which lower than the control group, and the proportion of light anxiety and depression patients was significantly more than that in the control group after the MBSR intervention.Conclusion: The online-based MBSR intervention appears to be an effective way of alleviating anxiety and depression symptoms among COVID-19 patients with associated quarantine in Fangcang hospital. Given the seriousness of mental health threat that could be posed by this ongoing pandemic, our study provides a new idea and method for cost-effective and time-efficient interventions in the future of epidemic prevention and control.Keywords: COVID-19 patients, online mindfulness, psychological intervention, anxiety, depression
Published: 2023

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