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1. Correction: CEPC Technical Design Report: Accelerator

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Abdallah, Waleed, de Freitas, Tiago CarlosAdorno, Afanaciev, Konstantin, Ahmad, Shakeel, Ahmed, Ijaz, Ai, Xiaocong, Aleem, Abid, Altmannshofer, Wolfgang, Alves, Fabio, An, Weiming, An, Rui, Anderle, Daniele Paolo, Antusch, Stefan, Arai, Yasuo, Arbuzov, Andrej, Arhrib, Abdesslam, Ashry, Mustafa, Bai, Sha, Bai, Yu, Bai, Yang, Bairathi, Vipul, Balazs, Csaba, Bambade, Philip, Ban, Yong, Bandyopadhyay, Triparno, Bao, Shou-Shan, Barber, Desmond P., Bat, Ays¸e, Batozskaya, Varvara, Behera, Subash Chandra, Belyaev, Alexander, Bertucci, Michele, Bi, Xiao-Jun, Bi, Yuanjie, Bian, Tianjian, Bianchi, Fabrizio, Bieko¨tter, Thomas, Biglietti, Michela, Bilanishvili, Shalva, Binglin, Deng, Bodrov, Denis, Bogomyagkov, Anton, Bondarenko, Serge, Boogert, Stewart, Boonekamp, Maarten, Borri, Marcello, Bosotti, Angelo, Boudry, Vincent, Boukidi, Mohammed, Boyko, Igor, Bozovic, Ivanka, Bozzi, Giuseppe, Brient, Jean-Claude, Budzinskaya, Anastasiia, Bukhari, Masroor, Bytev, Vladimir, Cacciapaglia, Giacomo, Cai, Hua, Cai, Wenyong, Cai, Wujun, Cai, Yijian, Cai, Yizhou, Cai, Yuchen, Cai, Haiying, Cai, Huacheng, Calibbi, Lorenzo, Cang, Junsong, Cao, Guofu, Cao, Jianshe, Chance, Antoine, Chang, Xuejun, Chang, Yue, Chang, Zhe, Chang, Xinyuan, Chao, Wei, Chatrabhuti, Auttakit, Che, Yimin, Che, Yuzhi, Chen, Bin, Chen, Danping, Chen, Fuqing, Chen, Fusan, Chen, Gang, Chen, Guoming, Chen, Hua-Xing, Chen, Huirun, Chen, Jinhui, Chen, Ji-Yuan, Chen, Kai, Chen, Mali, Chen, Mingjun, Chen, Mingshui, Chen, Ning, Chen, Shanhong, Chen, Shanzhen, Chen, Shao-Long, Chen, Shaomin, Chen, Shiqiang, Chen, Tianlu, Chen, Wei, Chen, Xiang, Chen, Xiaoyu, Chen, Xin, Chen, Xun, Chen, Xurong, Chen, Ye, Chen, Ying, Chen, Yukai, Chen, Zelin, Chen, Zilin, Chen, Gang, Chen, Boping, Chen, Chunhui, Cheng, Hok Chuen, Cheng, Huajie, Cheng, Shan, Cheng, Tongguang, Chi, Yunlong, Chimenti, Pietro, Chiu, Wen Han, Cho, Guk, Chu, Ming-Chung, Chu, Xiaotong, Chu, Ziliang, Coloretti, Guglielmo, Crivellin, Andreas, Cui, Hanhua, Cui, Xiaohao, Cui, Zhaoyuan, D’Anzi, Brunella, Dai, Ling-Yun, Dai, Xinchen, Dai, Xuwen, De Maria, Antonio, De Filippis, Nicola, De La Taille, Christophe, De Mori, Francesca, De Sio, Chiara, Del Core, Elisa, Deng, Shuangxue, Deng, Wei-Tian, Deng, Zhi, Deng, Ziyan, Dev, Bhupal, Dewen, Tang, Di Micco, Biagio, Ding, Ran, Ding, Siqin, Ding, Yadong, Dong, Haiyi, Dong, Jianing, Dong, Jing, Dong, Lan, Dong, Mingyi, Dong, Xu, Dong, Yipei, Dong, Yubing, Dordevic, Milos, Drewes, Marco, Du, Mingxuan, Du, Mingxuan, Du, Qianqian, Du, Xiaokang, Du, Yanyan, Du, Yong, Du, Yunfei, Duan, Chun-Gui, Duan, Zhe, Dydyshka, Yahor, Egede, Ulrik, Elmetenawee, Walaa, Eo, Yun, Fan, Ka Yan, Fan, Kuanjun, Fan, Yunyun, Fang, Bo, Fang, Shuangshi, Fang, Yuquan, Farilla, Ada, Farinelli, Riccardo, Farooq, Muhammad, Golfe, Angeles Faus, Fazliakhmetov, Almaz, Fei, Rujun, Feng, Bo, Feng, Chong, Feng, Junhua, Feng, Xu, Feng, Zhuoran, ZhuoranFeng, Castillo, Luis Roberto Flores, Forest, Etienne, Fowlie, Andrew, Fox, Harald, Fu, Hai-Bing, Fu, Jinyu, Fuks, Benjamin, Funakoshi, Yoshihiro, Gabrielli, Emidio, Gan, Nan, Gang, Li, Gao, Jie, Gao, Meisen, Gao, Wenbin, Gao, Wenchun, Gao, Yu, Gao, Yuanning, Gao, Zhanxiang, Gao, Yanyan, Ge, Kun, Ge, Shao-Feng, Ge, Zhenwu, Geng, Li-Sheng, Geng, Qinglin, Geng, Chao-Qiang, Ghosh, Swagata, Gioiosa, Antonio, Gladilin, Leonid, Gong, Ti, Gori, Stefania, Gou, Quanbu, Grinstein, Sebastian, Gu, Chenxi, Guillermo, Gerardo, da Costa, Joao Guimaraes, Guo, Dizhou, Guo, Fangyi, Guo, Jiacheng, Guo, Jun, Guo, Lei, Guo, Lei, Guo, Xia, Guo, Xin-Heng, Guo, Xinyang, Guo, Yun, Guo, Yunqiang, Guo, Yuping, Guo, Zhi-Hui, Gutie´rrez-Rodríguez, Alejandro, Ha, Seungkyu, Habib, Noman, Hajer, Jan, Hammer, Francois, Han, Chengcheng, Han, Huayong, Han, Jifeng, Han, Liang, Han, Liangliang, Han, Ruixiong, Han, Yang, Han, Yezi, Han, Yuanying, Han, Tao, Hao, Jiankui, Hao, Xiqing, XiqingHao, He, Chuanqi, He, Dayong, He, Dongbing, He, Guangyuan, He, Hong-Jian, He, Jibo, He, Jun, He, Longyan, He, Xiang, He, Xiao-Gang, He, Zhenqiang, Heinemann, Klaus, Heinemeyer, Sven, Heng, Yuekun, Herna´ndez-Ruíz, María A., Hong, Jiamin, Hor, Yuenkeung, Hou, George W. S., Hou, Xiantao, Hou, Xiaonan, Hou, Zhilong, Hou, Suen, Hu, Caishi, Hu, Chen, Hu, Dake, Hu, Haiming, Hu, Jiagen, Hu, Jun, Hu, Kun, Hu, Shouyang, Hu, Yongcai, Hu, Yu, Hu, Zhen, Hua, Zhehao, Hua, Jianfei, Huang, Chao-Shang, Huang, Fa Peng, Huang, Guangshun, Huang, Jinshu, Huang, Ke, Huang, Liangsheng, Huang, Shuhui, Huang, Xingtao, Huang, Xu-Guang, Huang, Yanping, Huang, Yonggang, Huang, Yongsheng, Huang, Zimiao, Huanyuan, Chen, Huh, Changgi, Hui, Jiaqi, Huo, Lihua, Hussain, Talab, Hwang, Kyuyeong, Ioannisian, Ara, Iqbal, Munawar, Jackson, Paul, Jafarzade, Shahriyar, Jang, Haeun, Jang, Seoyun, Ji, Daheng, Ji, Qingping, Ji, Quan, Ji, Xiaolu, Jia, Jingguang, Jia, Jinsheng, Jia, Xuewei, Jia, Zihang, Jiang, Cailian, Jiang, Han Ren, Jiang, Houbing, Jiang, Jun, Jiang, Xiaowei, Jiang, Xin, Jiang, Xuhui, Jiang, Yongcheng, Jiang, Zhongjian, Jiang, Cheng, Jiao, Ruiqi, Jin, Dapeng, Jin, Shan, Jin, Song, Jin, Yi, Jis, Junji, Jung, Sunghoon, Kacarevic, Goran, Kajfasz, Eric, Kalinovskaya, Lidia, Kampf, Aleksei, Kang, Wen, Kang, Xian-Wei, Kang, Xiaolin, Karmakar, Biswajit, Ke, Zhiyong, Keloth, Rijeesh, Khan, Alamgir, Khanpour, Hamzeh, Khosonthongkee, Khanchai, KhanchaiKhosonthongkee, Kim, Bobae, Kim, Dongwoon, Kim, Mi Ran, Kim, Minsuk, Kim, Sungwon, Kim, On, Klasen, Michael, Ko, Sanghyun, Koop, Ivan, Kornienko, Vitaliy, Kortman, Bryan, Kozlov, Gennady, Kuang, Shiqing, Kumar, Mukesh, Kuo, Chia Ming, Kwok, Tsz Hong, Lagarde, Franc¸ois Sylvain Ren, Lai, Pei-Zhu, Laktineh, Imad, Lan, Xiaofei, Lan, Zuxiu, Lavezzi, Lia, Lee, Justin, Lee, Junghyun, Lee, Sehwook, Lei, Ge, Lemmon, Roy, Leng, Yongxiang, Leung, Sze Ching, Li, Hai Tao, Li, Bingzhi, Li, Bo, Li, Bo, Li, Changhong, Li, Chao, Li, Cheng, Li, Cheng, Li, Chunhua, Li, Cui, Li, Dazhang, Li, Dikai, Li, Fei, Li, Gang, Li, Gang, Li, Gang, Li, Gaosong, Li, Haibo, Li, Haifeng, Li, Hai-Jun, Li, Haotian, Li, Hengne, Li, Honglei, Li, Huijing, Li, Jialin, Li, Jingyi, Li, Jinmian, Li, Jun, Li, Leyi, Li, Liang, Li, Ling, Li, Mei, Li, Meng, Li, Minxian, Li, Pei-Rong, Li, Qiang, Li, Shaopeng, Li, Shenghe, Li, Shu, Li, Shuo, Li, Teng, Li, Tiange, Li, Tong, Li, Weichang, Li, Weidong, Li, Wenjun, Li, Xiaoling, Li, Xiaomei, Li, Xiaonan, Li, Xiaoping, Li, Xiaoting, Li, Xin, Li, Xinqiang, Li, Xuekang, Li, Yang, Li, Yanwei, Li, Yiming, Li, Ying, Li, Ying-Ying, Li, Yonggang, Li, Yonglin, Li, Yufeng, Li, Yuhui, Li, Zhan, Li, Zhao, Li, Zhiji, Li, Tong, Li, Lingfeng, Li, Fei, Liang, Jing, Liang, Jinhan, Liang, Zhijun, Liao, Guangrui, Liao, Hean, Liao, Jiajun, Liao, Libo, Liao, Longzhou, Liao, Yi, Liao, Yipu, Limphirat, Ayut, AyutLimphirat, Lin, Tao, Lin, Weiping, Lin, Yufu, Lin, Yugen, Liu, Beijiang, Liu, Bo, Liu, Danning, Liu, Dong, Liu, Fu-Hu, Liu, Hongbang, Liu, Huangcheng, Liu, Hui, Liu, Huiling, Liu, Jia, Liu, Jia, Liu, Jiaming, Liu, Jianbei, Liu, Jianyi, Liu, Jingdong, Liu, Jinhua, Liu, Kai, Liu, Kang, Liu, Kun, Liu, Mengyao, Liu, Peng, Liu, Pengcheng, Liu, Qibin, Liu, Shan, Liu, Shidong, Liu, Shuang, Liu, Shubin, Liu, Tao, Liu, Tao, Liu, Tong, Liu, Wei, Liu, Xiang, Liu, Xiao-Hai, Liu, Xiaohui, Liu, Xiaoyu, Liu, Xin, Liu, Xinglin, Liu, Xingquan, Liu, Yang, Liu, Yanlin, Liu, Yao-Bei, Liu, Yi, Liu, Yiming, Liu, Yong, Liu, Yonglu, Liu, Yu, Liu, Yubin, Liu, Yudong, Liu, Yulong, Liu, Zhaofeng, Liu, Zhen, Liu, Zhenchao, Liu, Zhi, Liu, Zhi-Feng, Liu, Zhiqing, Liu, Zhongfu, Liu, Zuowei, Liu, Mia, Liu, Zhen, Liu, Xiaoyang, Lou, Xinchou, Lu, Cai-Dian, Lu, Jun-Xu, Lu, Qiu Zhen, Lu, Shang, Lu, Shang, Lu, Wenxi, Lu, Xiaohan, Lu, Yunpeng, Lu, Zhiyong, Lu, Xianguo, Lu, Wei, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Lukanov, Arslan, Luo, Jinliang, Luo, Tao, Luo, xiaoan, Luo, Xiaofeng, Luo, Xiaolan, Lv, Jindong, Lyu, Feng, Lyu, Xiao-Rui, Lyu, Kun-Feng, Ma, Ande, Ma, Hong-Hao, Ma, Jun-Li, Ma, Kai, Ma, Lishuang, Ma, Na, Ma, Renjie, Ma, Weihu, Ma, Xinpeng, Ma, Yanling, Ma, Yan-Qing, Ma, Yongsheng, Ma, Zhonghui, Ma, Zhongjian, Ma, Yang, Maity, Mousam, Mao, Lining, Mao, Yanmin, Mao, Yaxian, Martens, Aure´lien, Maria, Caccia Massimo Luigi, Matsumoto, Shigeki, Mellado, Bruce, Meloni, Davide, Men, Lingling, Meng, Cai, Meng, Lingxin, Mi, Zhenghui, Miao, Yuhui, Migliorati, Mauro, Ming, Lei, Mitsou, Vasiliki A., Monaco, Laura, Moraes, Arthur, Mosala, Karabo, Moursy, Ahmad, Mu, Lichao, Mu, Zhihui, Muchnoi, Nickolai, Muenstermann, Daniel, Muenstermann, Daniel, Munbodh, Pankaj, Murray, William John, Nanni, Jérôme, Nanzanov, Dmitry, Nie, Changshan, Nikitin, Sergei, Ning, Feipeng, Ning, Guozhu, Niu, Jia-Shu, Niu, Juan-Juan, Niu, Yan, Nkadimeng, Edward Khomotso, Ohmi, Kazuhito, Oide, Katsunobu, Okawa, Hideki, Ouchemhou, Mohamed, Ouyang, Qun, Paesani, Daniele, Pagani, Carlo, Paganis, Stathes, Pakuza, Collette, Pan, Jiangyang, Pan, Juntong, Pan, Tong, Pan, Xiang, Panda, Papia, Pandey, Saraswati, Pandurovic, Mila, Paparella, Rocco, Pasechnik, Roman, Passemar, Emilie, Pei, Hua, Peng, Xiaohua, Peng, Xinye, Peng, Yuemei, Ping, Jialun, Ping, Ronggang, Adhya, Souvik Priyam, Qi, Baohua, Qi, Hang, Qi, Huirong, Qi, Ming, Qian, Sen, Qian, Zhuoni, Qiao, Congfeng, Qin, Guangyou, Qin, Jiajia, Qin, Laishun, Qin, Liqing, Qin, Qin, Qin, Xiaoshuai, Qin, Zhonghua, Qu, Guofeng, Racioppi, Antonio, Ramsey-Musolf, Michael, Raza, Shabbar, Rekovic, Vladimir, Ren, Jing, Reuter, Ju¨rgen, Robens, Tania, Rossi, Giancarlo, Ruan, Manqi, Ruan, Manqi, Rumyantsev, Leonid, Ryu, Min Sang, Sadykov, Renat, Sang, Minjing, Sanz-Cillero, Juan Jose´, Saur, Miroslav, Savla, Nishil, Schmidt, Michael A., Sertore, Daniele, Settles, Ron, Sha, Peng, Shao, Ding-Yu, Shao, Ligang, Shao, Hua-Sheng, She, Xin, Shen, Chuang, Shen, Hong-Fei, Shen, Jian-Ming, Shen, Peixun, Shen, Qiuping, Shen, Zhongtao, Sheng, Shuqi, Shi, Haoyu, Shi, Hua, Shi, Qi, Shi, Shusu, Shi, Xiaolei, Shi, Xin, Shi, Yukun, Shi, Zhan, Shipsey, Ian, Shiu, Gary, Shu, Chang, Si, Zong-Guo, Sidorenkov, Andrei, Smiljanić, Ivan, Song, Aodong, Song, Huayang, Song, Jiaojiao, Song, Jinxing, Song, Siyuan, Song, Weimin, Song, Weizheng, Song, Zhi, Sourav, Shashwat, Spruzzola, Paolo, Su, Feng, Su, Shengsen, Su, Wei, Su, Shufang, Sui, Yanfeng, Sui, Zexuan, Sullivan, Michael, Sun, Baiyang, Sun, Guoqiang, Sun, Hao, Sun, Hao-Kai, Sun, Junfeng, Sun, Liang, Sun, Mengcheng, Sun, Pengfei, Sun, Sichun, Sun, Xianjing, Sun, Xiaohu, Sun, Xilei, Sun, Xingyang, Sun, Xin-Yuan, Sun, Yanjun, Sun, Yongzhao, Sun, Yue, Sun, Zheng, Sun, Zheng, Suwonjandee, Narumon, Eldin, Elsayed Tag, Tan, Biao, Tang, Bo, Tang, Chuanxiang, Tang, Gao, Tang, Guangyi, Tang, Jian, Tang, Jingyu, Tang, Liang, Tang, Ying’Ao, Tao, Junquan, Tawfik, Abdel Nasser, Taylor, Geoffrey, Telnov, Valery, Tian, Saike, Torre, Riccardo, Trzaska, Wladyslaw Henryk, Tsybychev, Dmitri, Tu, Yanjun, Tuo, Shengquan, Tytgat, Michael, Islam, Ghalib Ul, Ushakov, Nikita, Valencia, German, Velthuis, Jaap, Vicini, Alessandro, Vickey, Trevor, Vidakovic, Ivana, Videau, Henri, Volkas, Raymond, Voronin, Dmitry, Vukasinovic, Natasa, Wan, Xia, Wan, Xuying, Wang, Xiao, Wang, Anqing, Wang, Bin, Wang, Chengtao, Wang, Chuanye, Wang, Ci, Wang, Dayong, Wang, Dou, Wang, En, Wang, Fei, Wang, Fei, Wang, Guanwen, Wang, Guo-Li, Wang, Haijing, Wang, Haolin, Wang, Jia, Wang, Jian, Wang, Jianchun, Wang, Jianli, Wang, Jiawei, Wang, Jin, Wang, Jin-Wei, Wang, Joseph, Wang, Kechen, Wang, Lechun, Wang, Lei, Wang, Liguo, Wang, Lijiao, Wang, Lu, Wang, Meng, Wang, Na, Wang, Pengcheng, Wang, Qian, Wang, Qun, Wang, Shu Lin, Wang, Shudong, Wang, Taofeng, Wang, Tianhong, Wang, Tianyang, Wang, Tong, Wang, Wei, Wang, Wei, Wang, Xiaolong, Wang, Xiaolong, Wang, Xiaoning, Wang, Xiao-Ping, Wang, Xiongfei, Wang, Xujian, Wang, Yaping, Wang, Yaqian, Wang, Yi, Wang, Yiao, Wang, Yifang, Wang, Yilun, Wang, Yiwei, Wang, You-Kai, Wang, Yuanping, Wang, Yuexin, Wang, Yuhao, Wang, Yu-Ming, Wang, Yuting, Wang, Zhen, Wang, Zhigang, Wang, Weiping, Wang, Zeren Simon, Wang, Biao, Wang, Hui, Wang, Lian-Tao, Wang, Zihui, Wang, Zirui, Wang, Jia, Wang, Tong, Wei, Daihui, Wei, Shujun, Wei, Wei, Wei, Xiaomin, Wei, Yuanyuan, Wei, Yingjie, Wen, Liangjian, Wen, Xuejun, Wen, Yufeng, White, Martin, Williams, Peter, Wolffs, Zef, Womersley, William John, Wu, Baona, Wu, Bobing, Wu, Guanjian, Wu, Jinfei, Wu, Lei, Wu, Lina, Wu, Linghui, Wu, Minlin, Wu, Peiwen, Wu, Qi, Wu, Qun, Wu, Tianya, Wu, Xiang, Wu, Xiaohong, Wu, Xing-Gang, Wu, Xuehui, Wu, Yaru, Wu, Yongcheng, Wu, Yuwen, Wu, Zhi, Wu, Xin, Xia, Lei, Xia, Ligang, Xia, Shang, Xiang, Benhou, Xiang, Dao, Xiang, Zhiyu, Xiao, Bo-Wen, Xiao, Chu-Wen, Xiao, Dong, Xiao, Guangyan, Xiao, Han, Xiao, Meng, Xiao, Ouzheng, Xiao, Rui-Qing, Xiao, Xiang, Xiao, Yichen, Xiao, Ying, Xiao, Yu, Xiao, Yunlong, Xiao, Zhenjun, Xiao, Hengyuan, Xie, Nian, Xie, Yuehong, Xin, Tianmu, Xing, Ye, Xing, Zhizhong, Xu, Da, Xu, Fang, Xu, Fanrong, Xu, Haisheng, Xu, Haocheng, Xu, Ji, Xu, Miaofu, Xu, Qingjin, Xu, Qingnian, Xu, Wei, Xu, Wei, Xu, Weixi, Xu, Xinping, Xu, Zhen, Xu, Zijun, Xu, Zehua, Xu, Yaoyuan, Xue, Feifei, Yan, Baojun, Yan, Bin, Yan, Fen, Yan, Fucheng, Yan, Jiaming, Yan, Liang, Yan, Luping, Yan, Qi-Shu, Yan, Wenbiao, Yan, Yupeng, Yan, Luping, Yan, Haoyue, Yang, Dong, Yang, Fengying, Yang, Guicheng, Yang, Haijun, Yang, Jin Min, Yang, Jing, Yang, Lan, Yang, Li, Yang, Li Lin, Yang, Lili, Yang, Litao, Yang, Mei, Yang, Qiaoli, Yang, Tiansen, Yang, Xiaochen, Yang, Yingjun, Yang, Yueling, Yang, Zhengyong, Yang, Zhenwei, Yang, Youhua, Yang, Xiancong, Yao, De-Liang, Yao, Shi, Ye, Lei, Ye, Lingxi, Ye, Mei, Ye, Rui, Ye, Rui, Ye, Yecheng, Yermolchyk, Vitaly, Yi, Kai, Yi, Li, Yi, Yang, Yin, Di, Yin, Peng-Fei, Yin, Shenghua, Yin, Ze, Yin, Zhongbao, Yinhong, Zhang, Yoo, Hwi Dong, You, Zhengyun, Young, Charles, Yu, Boxiang, Yu, Chenghui, Yu, Fusheng, Yu, Jie-Sheng, Yu, Jinqing, Yu, Lingda, Yu, Zhao-Huan, Yu, Felix, Yu, Bingrong, Yuan, Changzheng, Yuan, Li, Yuan, Xing-Bo, Yuan, Youjin, Yue, Junhui, Yue, Qian, Yue, Baobiao, Zaib, Un Nisa, Zanzottera, Riccardo, Zeng, Hao, Zeng, Ming, Zhai, Jian, Zhai, Jiyuan, Zhai, Xin Zhe, Zhan, Xi-Jie, Zhang, Ben-Wei, Zhang, Bolun, Zhang, Di, Zhang, Guangyi, Zhang, Hao, Zhang, Hong-Hao, Zhang, Huaqiao, Zhang, Hui, Zhang, Jialiang, Zhang, Jianyu, Zhang, Jianzhong, Zhang, Jiehao, Zhang, Jielei, Zhang, Jingru, Zhang, Jinxian, Zhang, Junsong, Zhang, Junxing, Zhang, Lei, Zhang, Lei, Zhang, Liang, Zhang, Licheng, Zhang, Liming, Zhang, Linhao, Zhang, Luyan, Zhang, Mengchao, Zhang, Rao, Zhang, Shulei, Zhang, Wan, Zhang, Wenchao, Zhang, Xiangzhen, Zhang, Xiaomei, Zhang, Xiaoming, Zhang, Xiaoxu, Zhang, Xiaoyu, Zhang, Xuantong, Zhang, Xueyao, Zhang, Yang, Zhang, Yang, Zhang, Yanxi, Zhang, Yao, Zhang, Ying, Zhang, Yixiang, Zhang, Yizhou, Zhang, Yongchao, Zhang, Yu, Zhang, Yuan, Zhang, Yujie, Zhang, Yulei, Zhang, Yumei, Zhang, Yunlong, Zhang, Zhandong, Zhang, Zhaoru, Zhang, Zhen-Hua, Zhang, Zhenyu, Zhang, Zhichao, Zhang, Zhi-Qing, Zhang, Zhuo, Zhang, Zhiqing, Zhang, Cong, Zhang, Tianliang, Zhang, Luyan, Zhao, Guang, Zhao, Hongyun, Zhao, Jie, Zhao, Jingxia, Zhao, Jingyi, Zhao, Ling, Zhao, Luyang, Zhao, Mei, Zhao, Minggang, Zhao, Mingrui, Zhao, Qiang, Zhao, Ruiguang, Zhao, Tongxian, Zhao, Yaliang, Zhao, Ying, Zhao, Yue, Zhao, Zhiyu, Zhao, Zhuo, Zhemchugov, Alexey, Zheng, Hongjuan, Zheng, Jinchao, Zheng, Liang, Zheng, Ran, zheng, shanxi, Zheng, Xu-Chang, Zhile, Wang, Zhong, Weicai, Zhong, Yi-Ming, Zhou, Chen, Zhou, Daicui, Zhou, Jianxin, Zhou, Jing, Zhou, Jing, Zhou, Ning, Zhou, Qi-Dong, Zhou, Shiyu, Zhou, Shun, Zhou, Sihong, Zhou, Xiang, Zhou, Xingyu, Zhou, Yang, Zhou, Yong, Zhou, Yu-Feng, Zhou, Zusheng, Zhou, Demin, Zhu, Dechong, Zhu, Hongbo, Zhu, Huaxing, Zhu, Jingya, Zhu, Kai, Zhu, Pengxuan, Zhu, Ruilin, Zhu, Xianglei, Zhu, Yingshun, Zhu, Yongfeng, Zhuang, Xiao, Zhuang, Xuai, Zobov, Mikhail, Zong, Zhanguo, Zou, Cong, and Zou, Hongying

Published
2024
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10. Sensitivity estimation for beam position monitors using a boundary element method

Author

LI Yong, HE Jun, SUI Yanfeng, YUAN Renxian, DU Yaoyao, ZHANG Wan, WANG Xujian, YIN Di, YU Lingda, LIU Zhi, WEI Shujun, YUE Junhui, and CAO Jianshe

Subjects
boundary element method, beam position monitor, position sensitivity coefficient, high energy photon source, Nuclear engineering. Atomic power, TK9001-9401
Abstract

BackgroundTo measure the beam positions of High Energy Photon Source (HEPS), different types of beam position monitors (BPMs) have been developed. The position sensitivity coefficient is an important parameter of BPMs by which the position of the beam can be calculated.PurposeThis study aims to establish a method for calculating the position sensitivity coefficient of BPMs.MethodsThe position sensitivity coefficients of various types of BPMs, such as round, elliptical, and octagonal pipes, were determined by using the boundary element method (BEM). The azimuth button angles in the elliptical BPM of the HEPS booster and the button distances in an octagonal BPM on the Beijing Electron Positron Collider II (BEPCII) storage ring were derivated by the application of BEM. Furthermore, the position sensitivity mappings of the BPMs was calculated.ResultsThe difference in sensitivity results of the round BPM calculated by the BEM and the analytical value is approximately 1%. The error between the calculated and experimental measurement results of the position sensitivity coefficients of the elliptical and octagonal sections is approximately 2%.ConclusionsThe BEM is a reliable method for calculating the position sensitivity coefficient of BPMs, which can be used in BPM design.

Published
2023
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15. Detection Method of Digital Beam Position Monitor Based on Histogram

Author

TANG Xuhui;GAO Guodong;WEI Shujun;CAO Jianshe;DU Yaoyao;LIU Zhi;YE Qiang;MA Huizhou;HE Jun;JI Daheng; YANG Jing;LI Yukun;SUI Yanfeng;YUE Junhui

Subjects
digital beam position monitor processer, analog to digital converter, histogram, clock jitter, signal-to-noise ratio, Nuclear engineering. Atomic power, TK9001-9401, Nuclear and particle physics. Atomic energy. Radioactivity, QC770-798
Abstract

The digital beam position monitor processer (DBPM) is one of the important beam diagnosis equipments in particle accelerator and it will be massproduced during the construction of Upgrade of Beijing Electron Positron Collider (BEPCⅡ) and high energy photon source (HEPS), which brings great challenges to the testing work. The analog to digital converter (ADC) module is an essential component of DBPM and its conversion accuracy determines the resolution of the beam position measurement. Therefore, in order to ensure that these two projects meet the design targets, it is necessary to test the ADC performance. Its main observation object includes the phaselocked loop, sampling clock jitter, signaltonoise ratio (SNR), etc. To meet the batch test requirements of the selfdeveloped DBPM ADC, a method of testing the sampling performance based on the histogram was proposed. The histogram’s test principle is that the shape of the histogram can detect whether the sampling clock is phaselocked to the input signal, the boundary value of the histogram reflects the amplitude of the input signal to judge if the ADC is saturated, and the average and standard deviation of the data peak on the histogram can be used to calculate the clock jitter that conforms to the normal distribution. In this paper, the mathematical relation between the statistics of each data peak, clock jitter, and SNR was simulated by MATLAB in the situation of phaselocking. The separation algorithm was designed to obtain the data corresponding to each peak of the histogram and compute the clock jitter and SNR. An integrated test platform was built and experiments were carried out to verify the testing effect of the histogram method. The experimental results show that when the amount of sampling data is appropriate, the histogram method can complete the test quickly and accurately, and the measurement result is close to that of the highperformance phase noise analyzer. The clock jitter measurement error is less than 200 fs, the SNR measurement error is less than 2 dB, and the entire test duration is less than 1 min, which fulfills large volume testing demands. Compared with the manual instrument measure method, the histogram approach is simpler and lower cost to test with the same order of magnitude accuracy and has been implemented by programming to reduce labor intensity. The test program has been implemented in the automatic test platform and used to batch detection of DBPM products successfully.

Published
2022

17. Design and implementation of network topology for HEPS fast orbit feedback system

Author

GAO Guodong, LIU Peng, LONG Fengli, JI Daheng, YE Qiang, DU Yaoyao, LIU Zhi, SUI Yanfeng, YUE Junhui, TANG Xuhui, WEI Shujun, and CAO Jianshe

Subjects
heps, fast orbit feedback, fpga, high speed data transmission, digital signal processing, Nuclear engineering. Atomic power, TK9001-9401
Abstract

BackgroundHEPS (High Energy Photon Source) needs to control the beam orbit change within 10% of the cluster size within a certain frequency. In order to meet the beam orbit stability requirements in HEPS, it is necessary to establish a fast orbit feedback (FOFB) system.PurposeThis study aims to design and implement an effective feedback bandwidth of FOFB system that is greater than 500 Hz, and the delay of the whole system is less than 160 µs.MethodsBased on this requirement, a two-layer communication of loop centralized computing system network topology was designed and implemented for FOFB system of HEPS. And on this basis, the FPGA (field-programmable gate array) firmware algorithm of the signaling pathway of FOFB system was realized, including beam position acquisition, loop data transmission, FOFB algorithm, power control interface and the testing logic.ResultsThe measurement and analysis results show that each module in the data transmission link of the FOFB system can be used normally, and the total delay time of the system is about 140.46 µs, which has reached the intended design target.ConclusionsThe FOFB design of this study lays a foundation for the future construction, optimization and debugging of FOFB system on HEPS storage ring with good flexibility and scalability, providing a feasible solution for the future establishment of fast orbit feedback system in other storage rings.

Published
2023
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22. Optimizing Acute Coronary Syndrome Patient Treatment: Leveraging Gated Transformer Models for Precise Risk Prediction and Management.

Author

Mei, Yingxue, Jin, Zicai, Ma, Weiguo, Ma, Yingjun, Deng, Ning, Fan, Zhiyuan, and Wei, Shujun

Subjects
TRANSFORMER models, ACUTE coronary syndrome, MACHINE learning, MAJOR adverse cardiovascular events, DEEP learning, MEDICAL personnel, SUMATRIPTAN
Abstract

Background: Acute coronary syndrome (ACS) is a severe cardiovascular disease with globally rising incidence and mortality rates. Traditional risk assessment tools are widely used but are limited due to the complexity of the data. Methods: This study introduces a gated Transformer model utilizing machine learning to analyze electronic health records (EHRs) for an enhanced prediction of major adverse cardiovascular events (MACEs) in ACS patients. The model's efficacy was evaluated using metrics such as area under the curve (AUC), precision–recall (PR), and F1-scores. Additionally, a patient management platform was developed to facilitate personalized treatment strategies. Results: Incorporating a gating mechanism substantially improved the Transformer model's performance, especially in identifying true-positive cases. The TabTransformer+Gate model demonstrated an AUC of 0.836, a 14% increase in average precision (AP), and a 6.2% enhancement in accuracy, significantly outperforming other deep learning approaches. The patient management platform enabled healthcare professionals to effectively assess patient risks and tailor treatments, improving patient outcomes and quality of life. Conclusion: The integration of a gating mechanism within the Transformer model markedly increases the accuracy of MACE risk predictions in ACS patients, optimizes personalized treatment, and presents a novel approach for advancing clinical practice and research. [ABSTRACT FROM AUTHOR]

Published
2024
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23. Design and verification of data acquisition clock circuit based on dual-loop phase-locked loop

Author

LIU Zhi, GAO Guodong, YUE Junhui, CAO Jianshe, DU Yaoyao, MA Huizhou, HE Jun, YE Qiang, TANG Xuhui, LI Yukun, YANG Jing, and WEI Shujun

Subjects
data acquisition, dual-loop pll, jitter cleaner, snr of adc, Nuclear engineering. Atomic power, TK9001-9401
Abstract

Background Digital measurement system based on ADCs (analog-to-digital converter) has higher requirement on the signal to noise ratio (SNR) of sampled data. Among all the factors, the jitter of sampling clock has the most prominent effect on SNR. Purpose This study aims to design a clock circuit based on dual-loop phase-locked loop to reduce the jitter of digital measurement system input clock. Methods First of all, the influence of clock jitter on digital measurement system was analyzed. Then, the LMK04610 chip with dual loop PLL architecture of Texas Instruments was employed to design and implement a dual-loop phase-locked loop jitter cleaner circuit. The cores of this design were power supply design and the loop filter design. At last, the performance of the circuit was tested by using Rodschwarz phase noise analyzer. Results After testing, the dual-loop phase-locked loop jitter cleaner circuit can reduce the jitter of the 62.475 MHz source clock from more than 7 ps to less than 2 ps with output frequency of 499.8 MHz. The SNR of the sampled data is close to the theoretical value. Conclusions Dual-loop phase-locked loop jitter cleaner circuit has a good result and can provide reference for designers of digital measurement system.

Published
2022
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24. Development of an automatic calibration system for beam position monitor

Author

TANG Xuhui, HE Jun, YUE Junhui, WEI Shujun, DU Yaoyao, MA Huizhou, LIU Zhi, YANG Jing, GAO Guodong, CAO Jianshe, WANG Anxin, SUI Yanfeng, and YE Qiang

Subjects
beam position monitor, automatic calibration system, epics, data analysis, Nuclear engineering. Atomic power, TK9001-9401
Abstract

BackgroundBeam position monitor (BPM) is an important beam measurement component of accelerators, and it generally needs to be calibrated before online operating. High energy photon source (HEPS) and upgrade of Beijing electron positron collider (BEPCII) will need a large amount of BPM, which brings challenges to the calibration work.PurposeThis study aims to design and implement an automatic BPM calibration system for raising calibration efficiency and improving the accuracy of BPM measurement after calibration.MethodsFirst of all, an analog beam generating device, precision motion and support device, signal processing electronics and industrial computer were employed to compose the hardware of the calibration system. Then, the control software was programmed by Python to realize automation of the calibration, and the binary polynomial regression algorithm was used to determine the probe calibration coefficient. Finally, the appropriate calibration parameters were optimized by the combination experiments with sufficient raw data, and the influence of calibration range, number of calibration points and fitting order on calibration accuracy were analyzed.ResultsThe calibration system can automatically complete the BPM probe calibration. The BPM accuracy can reach 40 μm over the entire calibration range, and 7 μm in the local linear region.ConclusionsThe calibration system of this study meets application requirements and achieves the expected function. The measurement accuracy of the calibrated BPM is related to factors such as the order of the polynomial, the calibration range and the selection of calibration points.

Published
2022
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28. The genomes of two parasitic wasps that parasitize the diamondback moth

Author

Shi, Min, Wang, Zhizhi, Ye, Xiqian, Xie, Hongqing, Li, Fei, Hu, Xiaoxiao, Wang, Zehua, Yin, Chuanlin, Zhou, Yuenan, Gu, Qijuan, Zou, Jiani, Zhan, Leqing, Yao, Yuan, Yang, Jian, Wei, Shujun, Hu, Rongmin, Guo, Dianhao, Zhu, Jiangyan, Wang, Yanping, Huang, Jianhua, Pennacchio, Francesco, Strand, Michael R., and Chen, Xuexin

Published
2019
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34. Local climate adaptation and gene flow in the native range of two co‐occurring fruit moths with contrasting invasiveness

Author

Chen Jincui, Ary A. Hoffmann, Li-Jun Cao, Jia-Ying Zhu, Wei Shujun, and Bing-Yan Li

Subjects
Gene Flow, Geography, biology, Range (biology), Ecology, Moths, biology.organism_classification, Grapholita molesta, Invasive species, Gene flow, Population genomics, Fruit, Genetics, Animals, Adaptation, Ecosystem, Ecology, Evolution, Behavior and Systematics, Global biodiversity, Local adaptation
Abstract

Invasive species pose increasing threats to global biodiversity and ecosystems. While previous studies have characterized successful invaders based on ecological traits, characteristics related to evolutionary processes have rarely been investigated. Here we compared gene flow and local adaptation using demographic analyses and outlier tests in two co-occurring moth pests across their common native range of China, one of which (the peach fruit moth, Carposina sasakii) has maintained its native distribution, while the other (the oriental fruit moth, Grapholita molesta) has expanded its range globally during the past century. We found that both species showed a pattern of genetic differentiation and an evolutionary history consistent with a common southwestern origin and northward expansion in their native range. However, for the noninvasive species, genetic differentiation was closely aligned with the environment, and there was a relatively low level of gene flow, whereas in the invasive species, genetic differentiation was associated with geography. Genome scans indicated stronger patterns of climate-associated loci in the noninvasive species. While strong local adaptation and reduced gene flow across its native range may have decreased the invasiveness of C. sasakii, this requires further validation with additional comparisons of invasive and noninvasive species across their native range.

Published
2021
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37. Panaxatriol saponin ameliorates myocardial infarction-induced cardiac fibrosis by targeting Keap1/Nrf2 to regulate oxidative stress and inhibit cardiac-fibroblast activation and proliferation

Author

Yao, Huan, primary, He, Qingman, additional, Huang, Cong, additional, Wei, Shujun, additional, Gong, Yuanyuan, additional, Li, Xueping, additional, Liu, Weiwei, additional, Xu, Zhiyi, additional, Wu, Huihui, additional, Zheng, Chuan, additional, and Gao, Yongxiang, additional

Published
2022
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40. Low levels of genetic differentiation with isolation by geography and environment in populations of Drosophila melanogaster from across China

Author

Yan-Hao Lin, Li-Jun Cao, Wei Shujun, Ary A. Hoffmann, Lei Yue, Gong Yajun, and Chen Jincui

Subjects
0106 biological sciences, 0301 basic medicine, China, Range (biology), Demographic history, Population, Biology, DNA, Mitochondrial, 010603 evolutionary biology, 01 natural sciences, Article, 03 medical and health sciences, Geographical distance, Genetic variation, Genetics, Animals, education, Genetics (clinical), education.field_of_study, Geography, Genetic Variation, Drosophila melanogaster, Genetics, Population, 030104 developmental biology, Haplotypes, Genetic distance, Evolutionary biology, Genetic structure, Microsatellite
Abstract

The fruit fly, Drosophila melanogaster, is a model species in evolutionary studies. However, population processes of this species in East Asia are poorly studied. Here we examined the population genetic structure of D. melanogaster across China. There were 14 mitochondrial haplotypes with 10 unique ones out of 23 known from around the globe. Pairwise F(ST) values estimated from 15 novel microsatellites ranged from 0 to 0.11, with geographically isolated populations showing the highest level of genetic uniqueness. STRUCTURE analysis identified high levels of admixture at both the individual and population levels. Mantel tests indicated a strong association between genetic distance and geographical distance as well as environmental distance. Full redundancy analysis (RDA) showed that independent effects of environmental conditions and geography accounted for 62.10% and 31.58% of the total explained genetic variance, respectively. When geographic variables were constrained in a partial RDA analysis, the environmental variables bio2 (mean diurnal air temperature range), bio13 (precipitation of the wettest month), and bio15 (precipitation seasonality) were correlated with genetic distance. Our study suggests that demographic history, geographical isolation, and environmental factors have together shaped the population genetic structure of D. melanogaster after its introduction into China.

Published
2021
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41. Association Between Susceptibility of Thrips palmi to Spinetoram and Frequency of G275E Mutation Provides Basis for Molecular Quantification of Field-Evolved Resistance

Author

Li-Jun Cao, Wei Shujun, Meng-Qing Tang, Hu Li, Yong-Fu Gao, Chen Jincui, Ary A. Hoffmann, Gong Yajun, Pan Shi, and Shao-Kun Guo

Subjects
0106 biological sciences, 0301 basic medicine, Genetics, China, Ecology, biology, Thrips, Resistance (ecology), Thysanoptera, General Medicine, Thripidae, Molecular diagnostics, biology.organism_classification, 01 natural sciences, 010602 entomology, 03 medical and health sciences, 030104 developmental biology, Insect Science, Mutation, Mutation (genetic algorithm), Animals, Macrolides, Thrips palmi, Mutation frequency, Allele frequency
Abstract

Putative mechanisms underlying spinosyn resistance have been identified in controlled studies on many species; however, mechanisms underlying field-evolved resistance and the development of a molecular diagnostic method for monitoring field resistance have lagged behind. Here, we examined levels of resistance of melon thrips, Thrips palmi Karny (Thysanoptera:Thripidae), to spinetoram as well as target site mutations in field populations across China to identify potential mechanisms and useful molecular markers for diagnostic and quantifying purposes. In resistant populations, we identified the G275E mutation, which has previously been linked to spinosyns resistance, and F314V mutation, both located in the α6 subunit of the nicotinic acetylcholine receptor. There was a strong correlation between levels of spinetoram resistance and allele frequency of G275E mutation in field-collected populations (r2 = 0.84) and those reared under laboratory conditions for two to five generations (r2 = 0.91). LC50 ranged from 0.12 to 0.66 mg/liter in populations without G275E mutation, whereas it ranged from 33.12 to 39.91 mg/liter in most populations with a G275E mutation frequency more than 90%. Our results indicate that the field-evolved resistance of T. palmi to spinetoram in China is mainly conferred by the G275E mutation. The frequency of the G275E mutation provides a useful diagnostic for quantifying resistance levels in field populations of T. palmi.

Published
2021
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42. Chromosome‐level genome assembly of the aphid parasitoid Aphidius gifuensis using Oxford Nanopore sequencing and Hi‐C technology

Author

Wei Shujun, Hailin Yang, Hu Li, Yanbi Yu, Li Tian, Bing-Yan Li, Fan Song, Zhenyong Du, Wanzhi Cai, Huang Zhihua, and Limeng Zhang

Subjects
0106 biological sciences, 0301 basic medicine, Technology, Aphidius gifuensis, Genome, Insect, Sequence assembly, chromosome‐level genome, 010603 evolutionary biology, 01 natural sciences, Genome, Parasitoid wasp, Chromosome conformation capture, 03 medical and health sciences, Genetics, Animals, Resource Article, nanopore, Permanent Genetic Resources, Gene, Ecology, Evolution, Behavior and Systematics, Phylogeny, Contig, biology, Hi‐C, fungi, RESOURCE ARTICLES, Molecular Sequence Annotation, biology.organism_classification, Chromosomes, Insect, Nanopore Sequencing, 030104 developmental biology, Aphids, Nanopore sequencing, parasitoid wasp, Biotechnology, Reference genome
Abstract

Aphidius gifuensis is a parasitoid wasp that has been commercially bred and released in large scale as a biocontrol agent for the management of aphid pests. As a highly efficient endoparasitoid, it is also an important model for exploring mechanisms of parasitism. Currently, artificially bred populations of this wasp are facing rapid decline with undetermined cause, and mechanisms underlying its parasitoid strategy remain poorly understood. Exploring the mechanism behind its population decline and the host–parasitoid relationship is impeded partly due to the lack of a comprehensive genome data for this species. In this study, we constructed a high‐quality reference genome of A. gifuensis using Oxford Nanopore sequencing and Hi‐C (proximity ligation chromatin conformation capture) technology. The final genomic assembly was 156.9 Mb, with a contig N50 length of 3.93 Mb, the longest contig length of 10.4 Mb and 28.89% repetitive sequences. 99.8% of genome sequences were anchored onto six linkage groups. A total of 11,535 genes were predicted, of which 90.53% were functionally annotated. Benchmarking Universal Single‐Copy Orthologs (BUSCO) analysis showed the completeness of assembled genome is 98.3%. We found significantly expanded gene families involved in metabolic processes, transmembrane transport, cell signal communication and oxidoreductase activity, in particular ATP‐binding cassette (ABC) transporter, Cytochrome P450 and venom proteins. The olfactory receptors (ORs) showed significant contraction, which may be associated with the decrease in host recognition. Our study provides a solid foundation for future studies on the molecular mechanisms of population decline as well as host–parasitoid relationship for parasitoid wasps.

Published
2021

46. Effects of chlorantraniliprole and chromafenozide on mortality and feeding cessation of the fall webworm, Hyphantria cunea (Lepidoptera: Arctiidae)

Author

Chen Jincui, Li-Jun Cao, Pan Shi, Wei Shujun, Shao-Kun Guo, Gong Yajun, Ming-Liang Li, and Ary A. Hoffmann

Subjects
0106 biological sciences, 0301 basic medicine, Larva, Diamondback moth, biology, Pesticide, biology.organism_classification, 01 natural sciences, Toxicology, Lepidoptera genitalia, 010602 entomology, 03 medical and health sciences, 030104 developmental biology, Insect Science, Toxicity, Hyphantria, Instar, PEST analysis
Abstract

The fall webworm (FWW) Hyphantria cunea, native to North America, is a globally invasive pest of a wide range of forest and fruit trees. Spraying of pesticides is the primary method for the control of FWW. In this study, toxicity and feeding cessation of two potential pesticides against the FWW, chlorantraniliprole, and chromafenozide, were evaluated. Both pesticides were slow to affect FWW. For chlorantraniliprole, the highest mortality of third instar larvae occurred at 72 hours with an LC50 of 10.34 mg/L, while for chromafenozide, the highest mortality occurred at 72 hours with an LC50 value 74.0950 mg/L. Low concentrations of both pesticides led to larvae ceasing to feed after six hours (chlorantraniliprole) and 24 hours (chromafenozide). Both pesticides had persistent effects; thirty days after being applied at concentrations of 16, 26.67, and 35.56 mg/L to leaves, 93.33% of newly contacted larvae died after seven days. Our study showed that chlorantraniliprole and chromafenozide could be alternatively used against FWW and form a component of integrated control programs. The results provide information to guide the usage of chlorantraniliprole and chromafenozide in FWW control.

Published
2020
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47. Chromosome‐level genome of the peach fruit moth Carposina sasakii (Lepidoptera: Carposinidae) provides a resource for evolutionary studies on moths

Author

Li-Jun Cao, Shao-Kun Guo, Wei Shujun, Lei Yue, Gong Yajun, Chen Jincui, Ary A. Hoffmann, and Wei Song

Subjects
0106 biological sciences, 0301 basic medicine, Genome, Insect, Sequence assembly, Context (language use), Moths, Biology, 010603 evolutionary biology, 01 natural sciences, Genome, Lepidoptera genitalia, 03 medical and health sciences, Genetics, Animals, Gene family, Carposinidae, Gene, Ecology, Evolution, Behavior and Systematics, Prunus persica, Contig, food and beverages, Chromosome, biology.organism_classification, Chromosomes, Insect, 030104 developmental biology, Fruit, Biotechnology
Abstract

The peach fruit moth (PFM), Carposina sasakii Matsumura, is a major phytophagous orchard pest widely distributed across Northeast Asia. Here, we report the chromosome-level genome for the PFM, representing the first genome for the family Carposinidae, from the lepidopteran superfamily Copromorphoidea. The genome was assembled into 404.83 Mb sequences using PacBio long-read and Illumina short-read sequences, including 275 contigs, with a contig N50 length of 2.62 Mb. All contigs were assembled into 31 linkage groups assisted by the Hi-C technique, including 30 autosomes and a Z chromosome. BUSCO analysis showed that 98.3% of genes were complete and 0.4% of genes were fragmented, while 1.3% of genes were missing in the assembled genome. In total, 21,697 protein-coding genes were predicted, of which 84.80% were functionally annotated. Because of the importance of diapause triggered by photoperiod in PFM, five circadian genes in the PFM as well as in the other related species were annotated, and potential genes related to diapause and photoperiodic reaction were also identified from transcriptome sequencing. In addition, manual annotation of detoxification gene families was undertaken and showed a higher number of glutathione S-transferase (GST) gene in PFM than in most other lepidopterans, in contrast to a lower number of uridine diphosphate (UDP)-glycosyltransferase (UGT) gene, carboxyl/cholinesterases (CCE) gene and cytochrome P450 monooxygenase (P450) gene, suggesting different detoxication pathways in this moth. The high-quality genome provides a resource for comparative evolutionary studies of this moth and its relatives within the context of radiations across Lepidoptera.

Published
2020
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48. Geographic patterns of Lucanus (Coleoptera: Lucanidae) species diversity and environmental determinants in China

Author

Li-Jun Cao, Xia Wan, Dan Chen, Wei Shujun, and Jin-Ling Zhao

Subjects
0106 biological sciences, 0303 health sciences, Ecology, biology, Biogeography, Primary production, Species diversity, Lucanus maculifemoratus, Subspecies, biology.organism_classification, 010603 evolutionary biology, 01 natural sciences, 03 medical and health sciences, Geography, Habitat, Species richness, China, Ecology, Evolution, Behavior and Systematics, Original Research, 030304 developmental biology, Nature and Landscape Conservation
Abstract

Clarifying the geographic patterns of species diversity and the determinant factors can provide essential information for species conservation and management. Stag beetles (Coleoptera: Lucanidae) of Lucanus are important saproxylic insects and can be used for biomonitoring forests. Most of Lucanus species are facing conservation concerns due to their limited distribution and fragmented habitats, particularly in China, which has the richest species diversity of this genus. The distribution patterns of species diversity of Lucanus at large spatial scales remain portly understood. We studied the distribution patterns of Lucanus and its environmental and geographic determinants in China. Distribution data for 72 species and subspecies were examined. All these species are distributed in southern China except for Lucanus maculifemoratus dybowskyi, which is mainly distributed in north China. The hotspot for Lucanus in China is southeastern Tibet. Our study indicated that the species richness of Lucanus in China was shaped by the precipitation of the wettest and driest month, net primary productivity, digital elevation model, and latitude at a large scale. These variables collectively explained 56.2% of the variation in species richness; precipitation contributed the most (44.1%). Our results provide valuable insights to improve the conservation of Lucanus and can contribute to furthering our understanding of the biogeography of stag beetles in China., We describe the diversity of the stag beetle species and further explore its formation. The result indicated that the variables of geography vegetation, temperature, and precipitation collectively shaped the distributed pattern, and geographical variables are most important.

Published
2020
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49. Increased density of endosymbiotic Buchnera related to pesticide resistance in yellow morph of melon aphid

Author

Li-Jun Cao, Ary A. Hoffmann, Qiong Yang, Chen Jincui, Wei Shujun, Gong Yajun, Pan Shi, and Shao-Kun Guo

Subjects
0106 biological sciences, Aphid, animal structures, Pesticide resistance, genetic structures, Homoptera, fungi, Zoology, Biology, Pesticide, biology.organism_classification, 010603 evolutionary biology, 01 natural sciences, 010602 entomology, chemistry.chemical_compound, chemistry, Imidacloprid, Aphis gossypii, Buchnera, Agronomy and Crop Science, psychological phenomena and processes, reproductive and urinary physiology, Symbiotic bacteria
Abstract

Many invertebrates develop into different color morphs in response to changes in environmental conditions. Color morphs can differ in their biology and stress tolerance including pesticide resistance, but underlying mechanisms are unclear. In this study, we found that yellow morphs (predominant in hot summer conditions) of an agricultural pest, the melon aphid Aphis gossypii, have higher levels of resistance to the commonly used pesticides imidacloprid and sulfoxaflor compared to the green morphs (predominant in cooler spring and autumn conditions), while no difference was found in morph resistance to the antibiotic/pesticide avermectin. Transcriptome analysis and biochemical assays of enzyme activities revealed no differences in metabolic processes between the two color morphs except for differentially expressed genes related to wing development. Microbiome analysis revealed that the endosymbiont Buchnera aphidicola is the dominant bacterium in both morphs, representing 86.76–99.88% of the microbiome. The yellow morph had a higher density of Buchnera compared to the green morph. When yellow morphs were treated with avermectin and antibiotics, the density of Buchnera was reduced to levels similar to the green morph, and their susceptibility to imidacloprid was simultaneously increased. These results indicate an association between resistance in the yellow morph of A. gossypii and symbiotic bacteria, providing novel insights into pesticide resistance mechanisms and the plasticity of stress adaptation.

Published
2020
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50. Variable resistance to spinetoram in populations of Thrips palmi across a small area unconnected to genetic similarity

Author

Li-Jun Cao, Wei Shujun, Ary A. Hoffmann, Lei Yue, Gong Yajun, Yong-Fu Gao, Pan Shi, Hu Li, Shao-Kun Guo, and Chen Jincui

Subjects
0106 biological sciences, 0301 basic medicine, Thrips palmi, Pesticide resistance, Population, lcsh:Evolution, Zoology, Biology, 010603 evolutionary biology, 01 natural sciences, Gene flow, resistance, 03 medical and health sciences, Genetics, lcsh:QH359-425, population genetic structure, education, dispersal, Ecology, Evolution, Behavior and Systematics, education.field_of_study, Resistance (ecology), Thrips, Original Articles, spinetoram, biology.organism_classification, 030104 developmental biology, Biological dispersal, Original Article, PEST analysis, General Agricultural and Biological Sciences
Abstract

The melon thrips, Thrips palmi, is an increasingly important pest of vegetables in northern China. Some populations have developed resistance in the field to the insecticide spinetoram. Understanding the origin and dispersal of insecticide‐resistant populations can shed light on resistance management strategies. In this study, we tested susceptibility of seven greenhouse populations of T. palmi to spinetoram collected from a small area of about 300 km2 in Shandong Province and examined population genetic structure across the area based on a segment of mitochondrial cox1 gene and 22 microsatellite loci to infer the possible origin and dispersal of insecticide resistance. Levels of resistance to spinetoram differed among seven populations, which included one population with high resistance (LC50 = 759.34 mg/L), three populations with medium resistance (LC50 ranged from 28.69 to 34.79 mg/L), and three populations with low resistance (LC50 ranged from 7.61 to 8.97 mg/L). The populations were genetically differentiated into two groups unrelated to both levels of resistance and geographic distance. The molecular data indicated high levels of gene flow between populations with different levels of resistance to spinetoram and low gene flow among populations with the same level of resistance, pointing to a likely separate history of resistance evolution. Resistance levels of two tested populations to spinetoram decreased 23 and 4.6 times after five generations without any exposure to the pesticide. We therefore suspect that resistance of T. palmi most likely evolved in response to local applications of the insecticide. Our study suggests that the development of resistance could be avoided or resistance even reversed by reducing usage of spinetoram.

Published
2020

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