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75 results on '"Hou, C"'

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

Author

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

Published
2025
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2. Evidence for particle acceleration approaching PeV energies in the W51 complex

Author

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

Published
2024
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3. Method to measure muon content of extensive air showers with LHAASO KM2A-WCDA synergy

Author

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, J.T., Cao, Q., Cao, W.Y., Cao, Zhe, Chang, J., Chang, J.F., 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, 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., 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., 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., 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.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., 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., 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, C., Wang, C.B., Wang, G.W., Wang, H.G., Wang, H.H., Wang, J.C., Wang, J.S., 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., 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., Zeng, Z.K., 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, 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.

Published
2024
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4. Pointing calibration of LHAASO-WFCTA telescopes using bright stars

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.

Published
2023
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5. Absolute calibration of LHAASO WFCTA camera based on LED

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, Zhen, 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., Dai, B.Z., Dai, H.L., Dai, Z.G., Danzengluobu, della Volpe, D., Piazzoli, B. D’Ettorre, 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., Fu, Y.T., Gan, H.Y., 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., 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, Cong, Li, Cheng, Li, F., Li, H.B., Li, H.C., Li, H.Y., Li, J., Li, K., Li, W.L., Li, Xin, Li, X.R., 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, W., 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, Lu, Zhang, Li, Zhang, L.X., 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, Yong, Zhang, Yi, 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.

Published
2022
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6. Performance test of the electromagnetic particle detectors for 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., 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., D’Ettorre Piazzoli, B., 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., Jia, K., Jiang, K., Jiang, Z.J., Jin, C., Kuleshov, D., Levochkin, K., Li, B., Li, B.B., Li, C., Li, F., Li, H., 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., Zhao, X., 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.

Published
2021
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8. The performance of a prototype array of water Cherenkov detectors for the LHAASO project

Author

An, Q., Bai, Y.X., Bi, X.J., Cao, Z., Chang, J.F., Chen, G., Chen, M.J., Chen, S.M., Chen, S.Z., Chen, T.L., Chen, X., Chen, Y.T., Cui, S.W., Dai, B.Z., Du, Q., Danzengluobu, Feng, C.F., Feng, S.H., Gao, B., Gao, S.Q., Ge, M.M., Gu, M.H., Hao, X.J., He, H.H., Hou, C., Hu, H.B., Hu, X.B., Huang, J., Huang, W.P., Jia, H.Y., Jiang, K., Liu, J., Liu, J.L., Liu, J.S., Liu, S.B., Liu, Y., Liu, Y.N., Li, Q.J., Li, C., Li, F., Li, H.C., Li, X.R., Lu, H., Lv, H.K., Mao, Y.J., Ma, L.L., Ma, X.H., Shao, J., Shao, M., Sheng, X.D., Sun, G.X., Sun, Z.B., Tang, Z.B., Wu, C.Y., Wu, H.R., Wu, Q., Xiao, G., Xu, Y., Yang, Q.Y., Yang, R., Yao, Z.G., You, X.H., Yuan, A.F., Zhang, B.K., Zhang, H.M., Zhang, S.R., Zhang, S.S., Zhang, X.Y., Zhang, Y., Zhang, L., Zhai, L.M., Zhao, J., Zhao, L., Zhao, Z.G., Zha, M., Zhou, B., Zhu, F.R., Zhu, K.J., Zhuang, J., and Zuo, X.

Published
2013
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9. Corrigendum to “Performance test of the electromagnetic particle detectors for the LHAASO experiment” [Nucl. Instrum. Methods Phys. Res. A 1001 (2021) 165193]

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., Jia, K., Jiang, K., Jiang, Z.J., Jin, C., Kuleshov, D., Levochkin, K., Li, B., Li, B.B., Li, C., Li, F., Li, H., 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., Zhao, X., 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.

Published
2022
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10. 568P Unraveling interferon-related autophagic signatures in immune myopathies.

Author

Authier, F., Hou, C., Periou, B., Souvannanorath, S., Malfatti, E., and Martin, L.

Subjects
*INCLUSION body myositis, *HUMAN genome, *MUSCLE diseases, *AUTOIMMUNE diseases, *AUTOPHAGY
Abstract

Among the immune myopathies, inclusion body myositis (IBM) is the most common acquired muscle disease in adults over 45, characterized by immune-related inflammation, a type-II interferon (IFN) signature, and degenerative features that include tau and p62 aggregates, two proteins associated with the autophagy. Autophagy regulates immune responses, and its dysfunction has been linked to autoimmune and inflammatory disorders, including not only IBM but dematomyositis (DM, type-I IFN) and antisynthetase syndrome (ASS, type-II IFN)) as well. We hypothesize that autophagy is IFN subtypes-based differentially regulated in immune myopathies and could offer specific therapeutic strategies according to the IFN subtype. Total RNA was extracted from muscle sample using Qiagen RNeasy Kit. RNAseq of muscle was performed by the IMRB genomic platform. Sequencing was carried out on single-end 75 bp of Illumina NextSeq500 with number of reads 16 millions/sample. For RNAseq analysis, the samples were quality-checked using the software FastQC v.0.11.8. Trimmomatic v.0.39 was used to filter reads using a quality 20 (sliding window of 5 reads) and minimal length of 50pb, which led to more than 96% of surviving reads. Filtered reads were aligned to the human genome GRCh38 using STAR v.2.6.1d. The mapping of the reads for the different regions of the genome and the level of probsets expression was calculated using RSEM v.1.3.2. The level of probsets expression was normalized and differentially expressed probsets were determined using edgeR package. A difference was considered significant at p<0.05. Heatmaps were generated online using Morpheus software. Through transcriptomic analysis of muscular biopsies, we identified 51 dysregulated autophagy-related genes in DM, 50 in ASS, and 86 in IBM compared to CTL. Among these, 19 genes showed abnormal regulation in an interferon-related signature regardless of their subtypes, and 19 in a signature specifically linked to interferon type-II. Only 6 or 7 genes were commonly expressed between muscular type-I (DM) and type-II (ASS or IBM) interferonopathies. We noticed an autophagy-related signatures which are differentially expressed in immune myopathies, with distinct dysregulations observed: 19 in DM, 6 in ASS, and 41 in IBM. These findings improve our understanding and identification of therapeutic targets for managing muscular interferonopathies, involving autophagy regulation in tandem with their interferon subtypes or independently. [ABSTRACT FROM AUTHOR]

Published
2024
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11. Physical activity patterns, genetic susceptibility, and risk of hip/knee osteoarthritis: a prospective cohort study based on the UK Biobank.

Author

Zhu, J., Chen, W., Hu, Y., Qu, Y., Yang, H., Zeng, Y., Hou, C., Ge, F., Zhou, Z., and Song, H.

Abstract

Objectives: The effect of physical activity on hip/knee osteoarthritis (OA) and how it varies by genetic susceptibility to OA remains inconclusive.Methods: In a cohort study of UK Biobank, 436,166 OA-free participants were recruited in 2006-2010 and followed for knee/hip OA until the end of 2020. 28 physical activity-related items were collected at baseline. Cox regression was used to estimate associations between physical activity behaviors, as well as major activity patterns (i.e., significant principal components[PCs] identified by principal component analysis), and risk of OA, adjusting for multiple confounders. We further stratified the analyses by polygenic risk score (PRS) for OA to examine the impact of genetic susceptibility to OA on the studied association.Results: During a mean follow-up of 11.15 years, 13,227 hip and 21,119 knee OA cases were identified. 19, out of 28, studied items showed associations with increased OA risk. Compared with low adherence group(<1st tertile of PC score for each pattern), individuals with high adherence to five identified patterns were associated with increased risk of OA. The moderate adherence to "strenuous sports"(HR = 0.93, 95%CI: 0.89-0.97) and "walking for pleasure"(HR = 0.93, 95%CI: 0.89-0.98) patterns was associated with reduced OA. Similar risk patterns were obtained in the stratified analysis by PRS levels for OA.Conclusion: High intensity of most activity patterns were associated with increased OA. However, a protective effect was suggested for moderate adherence to patterns of "strenuous sports" and "walking for pleasure" that consistent across different genetic susceptibilities, underscoring the potential benefits of moderate-intensity physical activity on OA. [ABSTRACT FROM AUTHOR]

Published
2022
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12. Tumor–stroma ratio is a crucial histological predictor of occult cervical lymph node metastasis and survival in early-stage (cT1/2N0) oral squamous cell carcinoma.

Author

Huang, S., Cai, H., Song, F., Zhu, Y., Hou, C., and Hou, J.

Subjects
LYMPHATIC metastasis, SQUAMOUS cell carcinoma, PROGRESSION-free survival, PROGNOSIS, OVERALL survival
Abstract

Occult cervical lymph node metastasis is a significant prognostic factor in patients with early-stage (cT1/2N0) oral squamous cell carcinoma (OSCC). The aim of this study was to investigate the potential value of the tumor–stroma ratio (TSR) as a histological predictor of occult cervical metastasis and survival in early-stage OSCC. This retrospective study included 151 patients who underwent excision of the primary lesion and elective neck dissection from 2013 to 2017. The clinicopathological features of the tumor, risk factors associated with occult neck metastasis, and prognostic factors for overall survival (OS) and disease-free survival (DFS) were studied. A significant correlation of TSR (P = 0.009) was found with occult neck metastasis in the multivariate logistic regression model. Multivariate Cox proportional hazards regression analysis showed that the TSR (P = 0.002) and perineural invasion (P = 0.011) were associated with OS. Occult neck metastasis (P = 0.032) was associated with DFS. These findings indicate that assessment of the TSR might be useful in prognostication for early-stage OSCC patients. Moreover, the TSR is effective in allowing an accurate evaluation of the risk of occult neck metastasis, and this may be easily applicable in the routine pathological diagnosis and clinical decision-making for elective neck dissection. [ABSTRACT FROM AUTHOR]

Published
2022
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18. Spatio-temporal tuning of coherent motion evoked responses in 4-6 month old infants and adults.

Author

Hou C, Gilmore RO, Pettet MW, Norcia AM, Hou, C, Gilmore, R O, Pettet, M W, and Norcia, A M

Abstract

Motion cues provide a rich source of information about translations of the observer through the environment as well as the movements of objects and surfaces. While the direction of motion can be extracted locally these local measurements are, in general, insufficient for determining object and surface motions. To study the development of local and global motion processing mechanisms, we recorded Visual Evoked Potentials (VEPs) in response to dynamic random dot displays that alternated between coherent rotational motion and random motion at 0.8 Hz. We compared the spatio-temporal tuning of the evoked response in 4-6 months old infants to that of adults by recording over a range of dot displacements and temporal update rates. Responses recorded at the frequency of the coherent motion modulation were tuned for displacement at the occipital midline in both adults in infants. Responses at lateral electrodes were tuned for speed in adults, but not in infants. Infant responses were maximal at a larger range of spatial displacement than that of adults. In contrast, responses recorded at the dot-update rate showed a more similar parametric displacement tuning and scalp topography in infants and adults. Taken together, our results suggest that while local motion processing is relatively mature at 4-6 months, global integration mechanisms exhibit significant immaturities at this age. [ABSTRACT FROM AUTHOR]

Published
2009
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19. MicroRNA-320 regulates matrix metalloproteinase-13 expression in chondrogenesis and interleukin-1β-induced chondrocyte responses.

Author

Meng, F., Zhang, Z., Chen, W., Huang, G., He, A., Hou, C., Long, Y., Yang, Z., and Liao, W.

Abstract

Objective: Metalloproteinases (MMPs) are key regulators of osteoarthritis (OA) and collagen degradation and have been shown to participate in endochondral ossification. The aim of this study was to determine whether microRNA-320 (miR-320) regulates the expression of MMP-13 in chondrogenesis and inflammation.Experimental Design: miR-320 expression was assessed in vitro, in the ATDC5 cell model of chondrogenesis and in interleukin-1β (IL-1β)-treated primary mouse chondrocytes (PMCs), and in vivo, in normal and OA human cartilage by in situ hybridization. ATDC5 and PMCs were transfected with miR-320 or its antisense inhibitor (anti-miR-320), respectively. The roles of activated MAP kinases (MAPK) and NF-κB were evaluated by using specific inhibitors. Direct interaction between miR-320 and its putative binding site in the 3'-untranslated region (3'-UTR) of Mmp-13 mRNA was confirmed by the luciferase reporter assay.Results: miR-320 expression was elevated in chondrogenic and hypertrophic ATDC5, while significantly reduced in OA cartilage compared with normal cartilage. Stimulation with IL-1β led to a significant reduction in miR-320 expression in PMCs. Upregulation of MMP-13 expression was correlated with downregulation of miR-320 expression in both PMCs and ATDC5. Overexpression of miR-320 suppressed the activity of a reporter construct containing the 3'-UTR and inhibited MMP-13 expression in both ATDC5 and IL-1β-treated PMCs, while treatment with anti-miR-320 enhanced MMP-13 expression. NF-κB and MAPK activation downregulated miR-320 expression.Conclusion: Cartilage development and homeostasis are influenced by miR-320, which directly targets MMP-13 and regulates chondrogenesis and the IL-1β-stimulated catabolic effect in mouse chondrocytes. [ABSTRACT FROM AUTHOR]

Published
2016
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24. Unilateral lesion of the nigrostriatal pathway decreases the response of GABA interneurons in the dorsal raphe nucleus to 5-HT1A receptor stimulation in the rat

Author

Hou, C., Xue, L., Feng, J., Zhang, L., Wang, Y., Chen, L., Wang, T., Zhang, Q.J., and Liu, J.

Subjects
*INTERNEURONS, *SEROTONIN receptors, *GABA, *RAPHE nuclei, *6-Hydroxydopamine, *SUBSTANTIA nigra, *LABORATORY rats, *ELECTROPHYSIOLOGY
Abstract

Abstract: This study examined the firing rate and pattern of electrophysiologically and chemically identified GABA interneurons in the dorsal raphe nucleus (DRN), and role of 5-HT1A receptor agonist 8-OH-DPAT and the medial prefrontal cortex (mPFC) in the firing activity in rats with 6-hydroxydopamine lesions of the substantia nigra pars compacta (SNc). The interneurons in rats with lesions of the SNc showed a more burst-firing, while having no change in the firing rate; the mPFC and combined mPFC and SNc lesions in rats decreased the firing rate of the interneurons and firing pattern shifted towards a more burst-firing compared to rats with sham lesions of the SNc, respectively. In rats with sham lesions of the SNc, administration of 8-OH-DPAT (1–243μg/kg, i.v.) produced excitatory–inhibitory, excitatory and inhibitory effects in the firing rate of individual interneurons. However, when these effects were averaged over the group, 8-OH-DPAT had no significant effect on firing rate. In rats with lesions of the SNc, mPFC and the paired lesions, 8-OH-DPAT, at the same doses, inhibited all interneurons tested, respectively. Cumulative doses producing inhibition in rats with the paired lesions were higher than that of rats with lesions of the mPFC. In contrast to rats with sham lesions of the SNc, SNc lesion reduced expression of 5-HT1A receptor on parvalbumin positive neurons in the DRN, a subpopulation of GABA interneurons. Our results indicate that the SNc and mPFC regulate the firing activity of GABA interneurons in the DRN. Furthermore, response of likely GABA interneurons to systemic administration of 8-OH-DPAT is altered by lesion of the SNc and mPFC. [Copyright &y& Elsevier]

Published
2012
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25. Insulin promotes bone formation in augmented maxillary sinus in diabetic rabbits.

Author

Hou, C.-J., Liu, J.-L., Li, X., and Bi, L.-J.

Subjects
BONE growth, MAXILLARY sinus, PEOPLE with diabetes, NEOVASCULARIZATION, OSTEOBLASTS, HYDROXYAPATITE in medicine, LABORATORY rabbits
Abstract

Abstract: The role of insulin during the formation of bone in the augmented space of the maxillary sinus in patients with diabetes is unclear. The authors compared the differences in bone formation after maxillary sinus floor elevation in diabetic and healthy animals and evaluated the effects of insulin on osteogenesis and the differentiation and activities of the osteoblasts. 10 male Japanese white rabbits were divided into two groups after diabetic induction by a single injection of monohydrated alloxan and having maintained steady blood glucose levels. The groups included the diabetes mellitus group (DM; n =5) and the DM+insulin group (n =5); another five healthy rabbits comprised the control group. Maxillary sinus floor elevation was performed by grafting hydroxyapatite particles. Compared with the control group, the newly formed bone area, number of blood vessels and osteoblasts, collagen I content and serum osteocalcin levels were significantly decreased in DM rabbits (P <0.01). Insulin treatment reversed the decrease in bone formation, blood vessels, osteoblasts, collagen I and serum osteocalcin (P <0.01). Insulin treatment also promoted osteogenesis in the augmented space of the diabetic rabbits, which might have resulted from promotion of osteoblast differentiation and upregulation of neovascularization. [Copyright &y& Elsevier]

Published
2012
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26. Experimental determination of propagation constant using end-etched fiber

Author

Hou, C.-H.H., Grossman, B.G., Murshid, S.H., and Sokol, M.

Subjects
*FIBER optics, *RADIO wave propagation
Abstract

We introduce the end-etched fiber technique to separate all guided modes into individual mode rings. A direct measurement of the propagation constant as a function of propagation angle and fiber parameters is demonstrated, for a single-mode fiber. We have favorable experimental results when compared to the theoretical values. [Copyright &y& Elsevier]

Published
2003
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27. Cardiac function evaluation of bone marrow mesenchymal stromal cells intracoronary transplantation in acute myocardial infarction Lee-Sung mini-PIG model.

Author

Hou, C., Lin, C., Wu, C., Tsung, L., Lin, M., Chen, Y., Chang, S., Hsu, C., Liao, C., Shih, Y., Li, Y., Hsu, L., Li, P., Yu, W., Chen, K., Wang, M., Yang, C., Lin, P., Shen, H., and Chen, W.

Subjects
*INTRA-aortic balloon counterpulsation, *MESENCHYMAL stem cells, *CELL transplantation, *MYOCARDIAL infarction, *TRANSLUMINAL angioplasty, *CORONARY vasospasm, *MYOCARDIAL reperfusion, *TROPONIN I
Abstract

Acute myocardial infarction (AMI) is the single most common cause of death worldwide. Mesenchymal stromal cells (MSC) exert strong immunomodulatory effects and angiogenesis property. MSC cell therapy in AMI therefore showed some beneficial effects in several studies and clinical trials. Here we isolated, expanded and characterized human bone marrow MSC in serum free condition under GTP regulation and evaluated the efficacy of these MSC in porcine AMI model after intracoronary injection. These MSC also showed strong immunosuppression, T cell suppression, immunomodulation and anti-inflammatory activity by MLR, T cell proliferation, IDO activity and INF-r TNF-a expression assay. AMI was induced in Lee-Sung mini pigs by inflating an angioplasty balloon for 90 mins in the mid-left anterior descending artery. After reperfusion for 90min, MSC or buffer were intracoronary injected through the central lumen of the balloon catheter. Fifteen animals were randomly divided into three groups: shame control; AMI-M (MSCs injection); AMI-B (Buffer injection). After 90 min balloon occlusion, the left ventricular ejection fraction (LVEF) by echocardiography of three groups were reduced from 62.94±4.28% (sham), 62.46±2.10% (AMI-B), 61.01±3.21% (AMI-M) to 63.08±5.69 % (sham), 37.44±4.27% (AMI-B), 39.92±2.67% (AMI-M), respectively. ST-segment elevation were observed by electrocardiography. Three months after AMI, LEVF of sham group was 60.98±3.82 %. LVEF of AMI-B group remained low at 36.68±3.12 % while AMI-M group rose to 53.71±4.46 %. The concentration of cardiac troponin I (cTnI) and cardiac troponin T (cTnT) of three groups were 4.6, 19.5 pg/mL (sham); 149.2, 388.6 pg/mL (AMI-B) and 21.9, 46.1 pg/mL (AMI-M) after 24 hours of AMI, respectively. Those biomarker of AMI-B was higher than ULRF while AMI-M group was reduced to lower than the MI reference value. It suggested that the MSC transplantation may suppress the myocardial necrosis caused by AMI. LEVF and MRI images as well as blood concentration of cTnT I/T were analyzed during five-month follow-up. After 5 months of observation, the status of tissue necrosis, fibrosis or angiogenesis were assayed by tissue analysis including TCC staining; hematoxylin-eosin staining; Masson Trichrome staining; immunohistochemistry staining with cTnT, cTnI, vWF and isolectin-B4. In conclusion, our bone marrow MSCs showed promising potential for cardiac function improvement after intracoronary transplantation into AMI Lee-Sung mini-pig. [ABSTRACT FROM AUTHOR]

Published
2020
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28. Investigation into cooling-rate dependent residual stresses in ZrB2–SiC composites using improved Raman spectroscopy method.

Author

Jin, X.C., Sun, Y.L., Hou, C., Wang, X.B., He, G.Y., and Fan, X.L.

Subjects
*RESIDUAL stresses, *RAMAN spectroscopy, *SINTERING, *THERMAL stresses, *GRAIN size
Abstract

Thermal residual stresses in spark-plasma sintered ZrB 2 –20 vol% SiC composites were investigated for different cooling rates employed after sintering. A new method using barycenter shift of Raman emission band was developed to determine the surface residual stress in the SiC phase, with improved accuracy compared to measurements using the traditional peak shift method. Effect of cooling rate on thermal residual stress was quantitatively identified and analyzed. The results showed that the Raman spectroscopy method based on the barycenter shift can reduce the measurement scatter caused by small grain sizes and thus has better accuracy for ZrB 2 –SiC composites with nano-sized SiC particles. It was found that a lower cooling rate reduced the average residual stress, which was attributed to the smaller thermal gradient, lower stain rate and more diffusional creep with reduction in cooling rate. In addition, decreasing grain size could also reduce the residual stress. This study provides an improved measurement method and a better understanding of the development and distribution of thermal residual stresses in ZrB 2 –SiC composites. [ABSTRACT FROM AUTHOR]

Published
2019
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45. Improving impact toughness of high-C-Cr bearing steel by Si-Mo alloying and low-temperature austempering.

Author

Zhao, J., Zhao, T., Hou, C. S., Zhang, F. C., and Wang, T. S.

Subjects
*BEARING steel, *SILICON alloys, *AUSTEMPERING (Heat treatment), *LOW temperatures, *BAINITE, *NANOSTRUCTURED materials, *CARBIDES
Abstract

Nanostructured bainite and dispersed carbide particles were formed in Si-Mo-alloyed high-C-Cr bearing steels by low-temperature austempering after partial austenitizing in the intercritical gamma + carbide region. Comparing with conventional quenched and tempered high-C-Cr bearing steel, the impact toughness is remarkably enhanced; the hardness is still adequate for the bearings even though it is slightly decreased. [ABSTRACT FROM AUTHOR]

Published
2015
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46. Dense and smooth amorphous films of multicomponent FeCoNiCuVZrAl high-entropy alloy deposited by direct current magnetron sputtering

Author

Liu, L., Zhu, J.B., Hou, C., Li, J.C., and Jiang, Q.

Subjects
*AMORPHOUS substances, *THIN films, *IRON alloys, *DIRECT currents, *MAGNETRON sputtering, *MICROSTRUCTURE, *MECHANICAL properties of metals
Abstract

Abstract: The multicomponent amorphous nitride films of FeCoNiCuVZrAl high-entropy alloy were deposited by direct current magnetron sputtering in the mixture atmosphere of Ar and N2. The systematical investigations demonstrate that the chemical composition, microstructure, and mechanical properties of the amorphous films intimately rely on the concentration of N2 in the atmosphere mixture. When N2 flow ratio increases from 0% to 50%, the thickness of the films decreases, whereas the roughness firstly decreases and then increases. At the N2 flow ratio of 30%, a perfect dense and smooth amorphous nitride film could be achieved. While the hardness and Young’s modulus of the film reach the maximum values of 12 and 166GPa, respectively. [Copyright &y& Elsevier]

Published
2013
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47. Abnormal DNA methylation in CD4(+) T cells from people with latent autoimmune diabetes in adults.

Author

Li Y, Zhao M, Hou C, Liang G, Yang L, Tan Y, Wang Z, Yin H, Zhou Z, and Lu Q

Abstract

Aberrant DNA methylation in T cells has been linked to pathogenesis of autoimmune diseases. To investigate genomic and gene-specific DNA methylation levels in CD4(+) T cells from patients with latent autoimmune diabetes in adults (LADA), and to investigate changes in the expression of genes that regulate methylation as well as the autoimmune-related gene FOXP3 in these patients. Global CD4(+) T cell DNA methylation was measured in 15 LADA patients and 11 healthy controls using a methylation quantification kit. mRNA levels of DNA methytransferases (DNMTs), methyl-DNA binding domain proteins (MBDs) and FOXP3 were measured by real time PCR. Methylation of a FOXP3 regulatory element region was determined by bisulphite genomic sequencing. Genomic DNA methylation in CD4(+) T cells from LADA patients was significantly increased compared to controls. DNMT3b mRNA levels were higher in CD4(+) T cells from LADA patients than in controls. DNMT3b expression positively correlated with global DNA methylation in LADA CD4(+) T cells. FOXP3 expression was decreased, and the FOXP3 promoter region was hypermethylated in CD4(+) T cells from LADA patients compared with controls. DNA methylation levels are altered in CD4(+) T cells from LADA patients, which may contribute to disease onset and progression by affecting the expression of autoimmune-related genes. [ABSTRACT FROM AUTHOR]

Published
2011

48. Anti-inflammatory properties of phenolic compounds and crude extract from Porphyra dentata.

Author

Kazlowska K, Hsu T, Hou C, Yang W, and Tsai G

Abstract

ETHNOPHARMACOLOGICAL RELEVANCE: Porphyra dentata, a red edible seaweed, has long been used worldwide in folk medicine for the treatment of inflammatory diseases such as hypersensitivity, lymphadenitis, bronchitis. AIMS OF STUDY: To clarify the anti-inflammatory role of Porphyra dentata crude extract and its identified phenolic compounds by investigating their effect on the nitric oxide (NO)/inducible nitric oxide synthase (iNOS) transcription pathway in macrophage RAW 264.7 cells. MATERIALS AND METHODS: Porphyra dentata crude extract was prepared with methanol. High performance liquid chromatography (HPLC) hyphenated to electrospray ionization mass spectrometry (ESI-MS) and UV detection were utilized to analyze the extract fingerprints. Nitrite measurement, iNOS promoter activity and nuclear factor-kappaB (NF-kappaB) enhancer activity were used to assess the anti-inflammatory effect in lipopolysaccharide (LPS) challenged mouse RAW 264.7 cell line. RESULTS: Phenolic compounds (catechol, rutin and hesperidin) were identified in the crude extract of Porphyra dentata. The crude extract and the phenolic compounds inhibited the production of NO in LPS-stimulated RAW 264.7 cells. Catechol was a more potent suppressor of the up-regulation of iNOS promoter and NF-kappaB enhancer than rutin and yet, hesperidin alone failed to inhibit either activity. CONCLUSION: Our results indicate that catechol and rutin, but not hesperidin, are primary bioactive phenolic compounds in the crude extract to suppress NO production in LPS-stimulated macrophages via NF-kappaB-dependent iNOS gene transcription. The data also explain the anti-inflammatory use and possible mechanism of Porphyra dentata in iNOS implicated diseases. [ABSTRACT FROM AUTHOR]

Published
2010
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49. Prostacyclin protects renal tubular cells from gentamicin-induced apoptosis via a PPARα-dependent pathway.

Author

Hsu, Y.-H., Chen, C.-H., Hou, C.-C., Sue, Y.-M., Cheng, C.-Y., Cheng, T.-H., Lin, H., Tsai, W.-L., Chan, P., and Chen, T.-H.

Subjects
*PROSTACYCLIN, *ADENOVIRUSES, *CYCLOOXYGENASES, *GENTAMICIN, *DOCOSAHEXAENOIC acid, *APOPTOSIS
Abstract

To study the protective effect of prostacyclin (PGI2) we increased PGI2 production by infected NRK-52E cells with an adenovirus carrying cyclooxygenase-1 and prostacyclin synthase. PGI2 overexpression protected these cells from gentamicin-induced apoptosis by reducing cleaved caspase-3 and caspase-9, cytochrome c, and decreasing generation of reactive oxygen species. Expression of the nuclear receptor of PGI2, peroxisome proliferator-activated receptor-α (PPARα), was reduced during gentamicin treatment of the cells, while its overexpression significantly inhibited gentamicin-induced apoptosis and the amount of cleaved caspase-3. Transformation with PPARα short interfering RNA abolished the protective effect of PGI2 overproduction in gentamicin-treated cells. The PPARα activator docosahexaenoic acid given to gentamicin-treated mice significantly reduced the number of apoptotic cells in renal cortex, but this protective effect was not seen in PPARα knockout mice. Our study suggests that increased endogenous PGI2 production protects renal tubular cells from gentamicin-induced apoptosis through a PPARα-signaling pathway.Kidney International (2008) 73, 578–587; doi:10.1038/sj.ki.5002704; published online 21 November 2007 [ABSTRACT FROM AUTHOR]

Published
2008
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50. Comparison of Bacillus monooxygenase genes for unique fatty acid production

Author

Hilker, B.L., Fukushige, H., Hou, C., and Hildebrand, D.

Subjects
*FATTY acids, *BACILLUS (Bacteria), *GENETIC code, *ENZYMOLOGY
Abstract

Abstract: This paper reviews Bacillus genes encoding monooxygenase enzymes producing unique fatty acid metabolites. Specifically, it examines standard monooxygenase electron transfer schemes and related domain structures of these fused domain enzymes on route to understanding the observed oxygenase activities. A few crystallographic analyses of the standard bearer enzyme P450BM-3 are discussed to try to rationalize the common chemistries of this important enzyme family. Detailed P450BM-3 enzyme activities toward different substrates and the unique substrate-specific primary oxidation products are examined. A few orthologs to the recurring P450BM-3 enzyme as well as related small single-to-triple nucleotides changed mutants are also discussed. Finally, preliminary data characterizing unique in vivo-based primary and secondary products of a novel ortholog, the ALA2 strain, are presented. This later strain synthesizes several unique multi-oxidized reaction products that require additional study to further understand. It is hoped that a better understanding of these oxygenase reactions, particularly the ALA2 strain, will allow for realistically priced production of target multiple-oxygenated compounds with potential uses as specialty chemicals or as therapeutic agents. [Copyright &y& Elsevier]

Published
2008
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