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1,004 results on '"Zhang, Jingbo"'

107. The design and sensitivity of JUNO’s scintillator radiopurity pre-detector OSIRIS

Author

Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, An, Fengpeng, An, Guangpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Babic, Andrej, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Bergnoli, Antonio, Birkenfeld, Thilo, Blin, Sylvie, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Buscemi, Mario, Busto, Jose, Butorov, Ilya, Cabrera, Anatael, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Chang, Jinfan, Chang, Yun, Chen, Pingping, Chen, Po-An, Chen, Shaomin, Chen, Xurong, Chen, Yi-Wen, Chen, Yixue, Chen, Yu, Chen, Zhang, Cheng, Jie, Cheng, Yaping, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Lorenzo, Selma Conforti Di, Corti, Daniele, Costa, Salvatore, Corso, Flavio Dal, Dalager, Olivia, De La Taille, Christophe, Deng, Jiawei, Deng, Zhi, Deng, Ziyan, Depnering, Wilfried, Diaz, Marco, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dracos, Marcos, Druillole, Frédéric, Du, Shuxian, Dusini, Stefano, Dvorak, Martin, Enqvist, Timo, Enzmann, Heike, Fabbri, Andrea, Fajt, Lukas, Fan, Donghua, Fan, Lei, Fang, Can, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Fekete, Vladko, Feng, Li-Cheng, Feng, Qichun, Ford, Richard, Formozov, Andrey, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Genster, Christoph, Giammarchi, Marco, Giaz, Agnese, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Grewing, Christian, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guardone, Nunzio, Gul, Maria, Guo, Cong, Guo, Jingyuan, Guo, Wanlei, Guo, Xinheng, Guo, Yuhang, Hackspacher, Paul, Hagner, Caren, Han, Ran, Han, Yang, Hassan, Muhammad Sohaib, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hong, Daojin, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Zhuojun, Huang, Chunhao, Huang, Guihong, Huang, Hanxiong, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Ioannisian, Ara, Isocrate, Roberto, Jelmini, Beatrice, Jen, Kuo-Lun, Jeria, Ignacio, Ji, Xiaolu, Ji, Xingzhao, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Di, Jiang, Xiaoshan, Jin, Ruyi, Jing, Xiaoping, Jollet, Cécile, Joutsenvaara, Jari, Jungthawan, Sirichok, Kalousis, Leonidas, Kampmann, Philipp, Kang, Li, Karagounis, Michael, Kazarian, Narine, Khan, Waseem, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kruth, Andre, Kutovskiy, Nikolay, Kuusiniemi, Pasi, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Haitao, Li, Huiling, Li, Jiaqi, Li, Mengzhao, Li, Min, Li, Nan, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Shanfeng, Li, Tao, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Liang, Hao, Liang, Hao, Liang, Jingjing, Liao, Jiajun, Liebau, Daniel, Limphirat, Ayut, Limpijumnong, Sukit, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Lippi, Ivano, Liu, Fang, Liu, Haidong, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shuangyu, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Yan, Liu, Yunzhe, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Shuxiang, Lu, Xiaoxu, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Luo, Fengjiao, Luo, Guang, Luo, Pengwei, Luo, Shu, Luo, Wuming, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Malyshkin, Yury, Mantovani, Fabio, Manzali, Francesco, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Marium, Sadia, Martellini, Cristina, Martin-Chassard, Gisele, Martini, Agnese, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Mezzetto, Mauro, Miller, Jonathan, Miramonti, Lino, Montini, Paolo, Montuschi, Michele, Müller, Axel, Muralidharan, Pavithra, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, NguyenThi, MinhThuan, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Pan, Hsiao-Ru, Paoloni, Alessandro, Parkalian, Nina, Parmeggiano, Sergio, Pei, Yatian, Pelliccia, Nicomede, Peng, Anguo, Peng, Haiping, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Pratumwan, Wathan, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Rajput, Muhammad Usman, Ranucci, Gioacchino, Raper, Neill, Re, Alessandra, Rebber, Henning, Rebii, Abdel, Ren, Bin, Ren, Jie, Ricci, Barbara, Robens, Markus, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Roth, Christian, Ruan, Xiangdong, Ruan, Xichao, Rujirawat, Saroj, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sanfilippo, Simone, Sangka, Anut, Sanguansak, Nuanwan, Sawangwit, Utane, Sawatzki, Julia, Sawy, Fatma, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settanta, Giulio, Settimo, Mariangela, Shao, Zhuang, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Slupecki, Maciej, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Štefánik, Dušan, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Sun, Gongxing, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Suwonjandee, Narumon, Szelezniak, Michal, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Treskov, Konstantin, Triossi, Andrea, Troni, Giancarlo, Trzaska, Wladyslaw, Tuve, Cristina, Ushakov, Nikita, van den Boom, Johannes, van Waasen, Stefan, Vanroyen, Guillaume, Vassilopoulos, Nikolaos, Vedin, Vadim, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Cornelius, Volpe, Cristina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Kunyu, Wang, Lu, Wang, Meifen, Wang, Meng, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuman, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Wang, Zongyi, Waqas, Muhammad, Watcharangkool, Apimook, Wei, Lianghong, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wen, Liangjian, Wiebusch, Christopher, Wong, Steven Chan-Fai, Wonsak, Bjoern, Wu, Diru, Wu, Fangliang, Wu, Qun, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xie, Yuguang, Xie, Zhangquan, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Taylor, Yan, Wenqi, Yan, Xiongbo, Yan, Yupeng, Yang, Anbo, Yang, Changgen, Yang, Huan, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yang, Yifan, Yao, Haifeng, Yasin, Zafar, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yegin, Ugur, Yermia, Frédéric, Yi, Peihuai, Yin, Na, Yin, Xiangwei, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yuan, Ziyi, Yue, Baobiao, Zafar, Noman, Zambanini, Andre, Zavadskyi, Vitalii, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Feiyang, Zhang, Guoqing, Zhang, Haiqiong, Zhang, Honghao, Zhang, Jiawen, Zhang, Jie, Zhang, Jingbo, Zhang, Jinnan, Zhang, Peng, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Tao, Zhang, Xiaomei, Zhang, Xuantong, Zhang, Xueyao, Zhang, Yan, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Fengyi, Zhao, Jie, Zhao, Rong, Zhao, Shujun, Zhao, Tianchi, Zheng, Dongqin, Zheng, Hua, Zheng, Minshan, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jiang, Zhu, Kangpu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, and Zou, Jiaheng

Published
2021
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108. Radioactivity control strategy for the JUNO detector

Author

Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, An, Fengpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Babic, Andrej, Baldini, Wander, Barresi, Andrea, Basilico, Davide, Baussan, Eric, Bellato, Marco, Bergnoli, Antonio, Birkenfeld, Thilo, Blin, Sylvie, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Buscemi, Mario, Busto, Jose, Butorov, Ilya, Cabrera, Anatael, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Chang, Jinfan, Chang, Yun, Chen, Pingping, Chen, Po-An, Chen, Shaomin, Chen, Xurong, Chen, Yi-Wen, Chen, Yixue, Chen, Yu, Chen, Zhang, Cheng, Jie, Cheng, Yaping, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Conforti Di Lorenzo, Selma, Corti, Daniele, Cremonesi, Oliviero, Dal Corso, Flavio, Dalager, Olivia, De La Taille, Christophe, Deng, Jiawei, Deng, Zhi, Deng, Ziyan, Depnering, Wilfried, Diaz, Marco, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dracos, Marcos, Druillole, Frédéric, Du, Shuxian, Dusini, Stefano, Dvorak, Martin, Enqvist, Timo, Enzmann, Heike, Fabbri, Andrea, Fajt, Lukas, Fan, Donghua, Fan, Lei, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Fekete, Vladko, Feng, Li-Cheng, Feng, Qichun, Ford, Richard, Formozov, Andrey, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Giammarchi, Marco, Giaz, Agnese, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Grewing, Christian, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guardone, Nunzio, Gul, Maria, Guo, Cong, Guo, Jingyuan, Guo, Wanlei, Guo, Xinheng, Guo, Yuhang, Hackspacher, Paul, Hagner, Caren, Han, Ran, Han, Yang, Hassan, Muhammad Sohaib, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Zhuojun, Huang, Chunhao, Huang, Guihong, Huang, Hanxiong, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Ioannisian, Ara, Isocrate, Roberto, Jelmini, Beatrice, Jen, Kuo-Lun, Jeria, Ignacio, Ji, Xiaolu, Ji, Xingzhao, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Di, Jiang, Xiaoshan, Jin, Ruyi, Jing, Xiaoping, Jollet, Cécile, Joutsenvaara, Jari, Jungthawan, Sirichok, Kalousis, Leonidas, Kampmann, Philipp, Kang, Li, Karaparambil, Rebin, Kazarian, Narine, Khan, Waseem, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Kruth, Andre, Kutovskiy, Nikolay, Kuusiniemi, Pasi, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Haitao, Li, Huiling, Li, Jiaqi, Li, Mengzhao, Li, Min, Li, Nan, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Shanfeng, Li, Tao, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Liang, Hao, Liang, Hao, Liao, Jiajun, Liebau, Daniel, Limphirat, Ayut, Limpijumnong, Sukit, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Lippi, Ivano, Liu, Fang, Liu, Haidong, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shuangyu, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Yan, Liu, Yunzhe, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Shuxiang, Lu, Xiaoxu, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Luo, Fengjiao, Luo, Guang, Luo, Pengwei, Luo, Shu, Luo, Wuming, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Malyshkin, Yury, Mantovani, Fabio, Manzali, Francesco, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Marium, Sadia, Martellini, Cristina, Martin-Chassard, Gisele, Martini, Agnese, Mayer, Matthias, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Mezzetto, Mauro, Miller, Jonathan, Miramonti, Lino, Montini, Paolo, Montuschi, Michele, Müller, Axel, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Nguyen Thi, Minh Thuan, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Pan, Hsiao-Ru, Paoloni, Alessandro, Parmeggiano, Sergio, Pei, Yatian, Pelliccia, Nicomede, Peng, Anguo, Peng, Haiping, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Piñeres Rico, Luis Felipe, Popov, Artyom, Poussot, Pascal, Pratumwan, Wathan, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Rajput, Muhammad Usman, Ranucci, Gioacchino, Raper, Neill, Re, Alessandra, Rebber, Henning, Rebii, Abdel, Ren, Bin, Ren, Jie, Ricci, Barbara, Robens, Markus, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Roth, Christian, Ruan, Xiangdong, Ruan, Xichao, Rujirawat, Saroj, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Sanfilippo, Simone, Sangka, Anut, Sanguansak, Nuanwan, Sawangwit, Utane, Sawatzki, Julia, Sawy, Fatma, Schever, Michaela, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settanta, Giulio, Settimo, Mariangela, Shao, Zhuang, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Slupecki, Maciej, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Štefánik, Dušan, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Suwonjandee, Narumon, Szelezniak, Michal, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Treskov, Konstantin, Triossi, Andrea, Troni, Giancarlo, Trzaska, Wladyslaw, Tuve, Cristina, Ushakov, Nikita, van den Boom, Johannes, van Waasen, Stefan, Vanroyen, Guillaume, Vassilopoulos, Nikolaos, Vedin, Vadim, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Moritz Cornelius, Volpe, Cristina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Kunyu, Wang, Lu, Wang, Meifen, Wang, Meng, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuman, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Wang, Zongyi, Waqas, Muhammad, Watcharangkool, Apimook, Wei, Lianghong, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wen, Liangjian, Wiebusch, Christopher, Wong, Steven Chan-Fai, Wonsak, Bjoern, Wu, Diru, Wu, Fangliang, Wu, Qun, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xie, Xiaochuan, Xie, Yuguang, Xie, Zhangquan, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Taylor, Yan, Wenqi, Yan, Xiongbo, Yan, Yupeng, Yang, Anbo, Yang, Changgen, Yang, Chengfeng, Yang, Huan, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yang, Yifan, Yao, Haifeng, Yasin, Zafar, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yegin, Ugur, Yermia, Frédéric, Yi, Peihuai, Yin, Na, Yin, Xiangwei, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yuan, Ziyi, Yue, Baobiao, Zafar, Noman, Zambanini, Andre, Zavadskyi, Vitalii, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Feiyang, Zhang, Guoqing, Zhang, Haiqiong, Zhang, Honghao, Zhang, Jiawen, Zhang, Jie, Zhang, Jin, Zhang, Jingbo, Zhang, Jinnan, Zhang, Peng, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Tao, Zhang, Xiaomei, Zhang, Xuantong, Zhang, Xueyao, Zhang, Yan, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Fengyi, Zhao, Jie, Zhao, Rong, Zhao, Shujun, Zhao, Tianchi, Zheng, Dongqin, Zheng, Hua, Zheng, Minshan, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jiang, Zhu, Kangfu, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, and Zou, Jiaheng

Published
2021
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109. JUNO sensitivity to low energy atmospheric neutrino spectra

Author

Abusleme, Angel, Adam, Thomas, Ahmad, Shakeel, Ahmed, Rizwan, Aiello, Sebastiano, Akram, Muhammad, An, Fengpeng, An, Guangpeng, An, Qi, Andronico, Giuseppe, Anfimov, Nikolay, Antonelli, Vito, Antoshkina, Tatiana, Asavapibhop, Burin, de André, João Pedro Athayde Marcondes, Auguste, Didier, Babic, Andrej, Baldini, Wander, Barresi, Andrea, Baussan, Eric, Bellato, Marco, Bergnoli, Antonio, Bernieri, Enrico, Birkenfeld, Thilo, Blin, Sylvie, Blum, David, Blyth, Simon, Bolshakova, Anastasia, Bongrand, Mathieu, Bordereau, Clément, Breton, Dominique, Brigatti, Augusto, Brugnera, Riccardo, Bruno, Riccardo, Budano, Antonio, Buscemi, Mario, Busto, Jose, Butorov, Ilya, Cabrera, Anatael, Cai, Hao, Cai, Xiao, Cai, Yanke, Cai, Zhiyan, Cammi, Antonio, Campeny, Agustin, Cao, Chuanya, Cao, Guofu, Cao, Jun, Caruso, Rossella, Cerna, Cédric, Chang, Jinfan, Chang, Yun, Chen, Pingping, Chen, Po-An, Chen, Shaomin, Chen, Xurong, Chen, Yi-Wen, Chen, Yixue, Chen, Yu, Chen, Zhang, Cheng, Jie, Cheng, Yaping, Chetverikov, Alexey, Chiesa, Davide, Chimenti, Pietro, Chukanov, Artem, Claverie, Gérard, Clementi, Catia, Clerbaux, Barbara, Lorenzo, Selma Conforti Di, Corti, Daniele, Costa, Salvatore, Corso, Flavio Dal, Dalager, Olivia, De La Taille, Christophe, Deng, Jiawei, Deng, Zhi, Deng, Ziyan, Depnering, Wilfried, Diaz, Marco, Ding, Xuefeng, Ding, Yayun, Dirgantara, Bayu, Dmitrievsky, Sergey, Dohnal, Tadeas, Dolzhikov, Dmitry, Donchenko, Georgy, Dong, Jianmeng, Doroshkevich, Evgeny, Dracos, Marcos, Druillole, Frédéric, Du, Shuxian, Dusini, Stefano, Dvorak, Martin, Enqvist, Timo, Enzmann, Heike, Fabbri, Andrea, Fajt, Lukas, Fan, Donghua, Fan, Lei, Fang, Can, Fang, Jian, Fang, Wenxing, Fargetta, Marco, Fedoseev, Dmitry, Fekete, Vladko, Feng, Li-Cheng, Feng, Qichun, Ford, Richard, Formozov, Andrey, Fournier, Amélie, Gan, Haonan, Gao, Feng, Garfagnini, Alberto, Genster, Christoph, Giammarchi, Marco, Giaz, Agnese, Giudice, Nunzio, Gonchar, Maxim, Gong, Guanghua, Gong, Hui, Gorchakov, Oleg, Gornushkin, Yuri, Göttel, Alexandre, Grassi, Marco, Grewing, Christian, Gromov, Vasily, Gu, Minghao, Gu, Xiaofei, Gu, Yu, Guan, Mengyun, Guardone, Nunzio, Gul, Maria, Guo, Cong, Guo, Jingyuan, Guo, Wanlei, Guo, Xinheng, Guo, Yuhang, Hackspacher, Paul, Hagner, Caren, Han, Ran, Han, Yang, Hassan, Muhammad Sohaib, He, Miao, He, Wei, Heinz, Tobias, Hellmuth, Patrick, Heng, Yuekun, Herrera, Rafael, Hong, Daojin, Hor, YuenKeung, Hou, Shaojing, Hsiung, Yee, Hu, Bei-Zhen, Hu, Hang, Hu, Jianrun, Hu, Jun, Hu, Shouyang, Hu, Tao, Hu, Zhuojun, Huang, Chunhao, Huang, Guihong, Huang, Hanxiong, Huang, Qinhua, Huang, Wenhao, Huang, Xin, Huang, Xingtao, Huang, Yongbo, Hui, Jiaqi, Huo, Lei, Huo, Wenju, Huss, Cédric, Hussain, Safeer, Insolia, Antonio, Ioannisian, Ara, Isocrate, Roberto, Jelmini, Beatrice, Jen, Kuo-Lun, Jeria, Ignacio, Ji, Xiaolu, Ji, Xingzhao, Jia, Huihui, Jia, Junji, Jian, Siyu, Jiang, Di, Jiang, Xiaoshan, Jin, Ruyi, Jing, Xiaoping, Jollet, Cécile, Jungthawan, Sirichok, Kalousis, Leonidas, Kampmann, Philipp, Kang, Li, Karagounis, Michael, Kazarian, Narine, Khan, Waseem, Khosonthongkee, Khanchai, Korablev, Denis, Kouzakov, Konstantin, Krasnoperov, Alexey, Krumshteyn, Zinovy, Kruth, Andre, Kutovskiy, Nikolay, Kuusiniemi, Pasi, Lachenmaier, Tobias, Landini, Cecilia, Leblanc, Sébastien, Lebrin, Victor, Lefevre, Frederic, Lei, Ruiting, Leitner, Rupert, Leung, Jason, Li, Demin, Li, Fei, Li, Fule, Li, Haitao, Li, Huiling, Li, Jiaqi, Li, Mengzhao, Li, Min, Li, Nan, Li, Nan, Li, Qingjiang, Li, Ruhui, Li, Shanfeng, Li, Tao, Li, Weidong, Li, Weiguo, Li, Xiaomei, Li, Xiaonan, Li, Xinglong, Li, Yi, Li, Yufeng, Li, Zhaohan, Li, Zhibing, Li, Ziyuan, Liang, Hao, Liang, Hao, Liang, Jingjing, Liebau, Daniel, Limphirat, Ayut, Limpijumnong, Sukit, Lin, Guey-Lin, Lin, Shengxin, Lin, Tao, Ling, Jiajie, Lippi, Ivano, Liu, Fang, Liu, Haidong, Liu, Hongbang, Liu, Hongjuan, Liu, Hongtao, Liu, Hui, Liu, Jianglai, Liu, Jinchang, Liu, Min, Liu, Qian, Liu, Qin, Liu, Runxuan, Liu, Shuangyu, Liu, Shubin, Liu, Shulin, Liu, Xiaowei, Liu, Xiwen, Liu, Yan, Liu, Yunzhe, Lokhov, Alexey, Lombardi, Paolo, Lombardo, Claudio, Loo, Kai, Lu, Chuan, Lu, Haoqi, Lu, Jingbin, Lu, Junguang, Lu, Shuxiang, Lu, Xiaoxu, Lubsandorzhiev, Bayarto, Lubsandorzhiev, Sultim, Ludhova, Livia, Luo, Fengjiao, Luo, Guang, Luo, Pengwei, Luo, Shu, Luo, Wuming, Lyashuk, Vladimir, Ma, Bangzheng, Ma, Qiumei, Ma, Si, Ma, Xiaoyan, Ma, Xubo, Maalmi, Jihane, Malyshkin, Yury, Mantovani, Fabio, Manzali, Francesco, Mao, Xin, Mao, Yajun, Mari, Stefano M., Marini, Filippo, Marium, Sadia, Martellini, Cristina, Martin-Chassard, Gisele, Martini, Agnese, Mayilyan, Davit, Mednieks, Ints, Meng, Yue, Meregaglia, Anselmo, Meroni, Emanuela, Meyhöfer, David, Mezzetto, Mauro, Miller, Jonathan, Miramonti, Lino, Monforte, Salvatore, Montini, Paolo, Montuschi, Michele, Müller, Axel, Muralidharan, Pavithra, Nastasi, Massimiliano, Naumov, Dmitry V., Naumova, Elena, Navas-Nicolas, Diana, Nemchenok, Igor, Thi, Minh Thuan Nguyen, Ning, Feipeng, Ning, Zhe, Nunokawa, Hiroshi, Oberauer, Lothar, Ochoa-Ricoux, Juan Pedro, Olshevskiy, Alexander, Orestano, Domizia, Ortica, Fausto, Othegraven, Rainer, Pan, Hsiao-Ru, Paoloni, Alessandro, Parkalian, Nina, Parmeggiano, Sergio, Pei, Yatian, Pelliccia, Nicomede, Peng, Anguo, Peng, Haiping, Perrot, Frédéric, Petitjean, Pierre-Alexandre, Petrucci, Fabrizio, Pilarczyk, Oliver, Rico, Luis Felipe Piñeres, Popov, Artyom, Poussot, Pascal, Pratumwan, Wathan, Previtali, Ezio, Qi, Fazhi, Qi, Ming, Qian, Sen, Qian, Xiaohui, Qian, Zhen, Qiao, Hao, Qin, Zhonghua, Qiu, Shoukang, Rajput, Muhammad Usman, Ranucci, Gioacchino, Raper, Neill, Re, Alessandra, Rebber, Henning, Rebii, Abdel, Ren, Bin, Ren, Jie, Rezinko, Taras, Ricci, Barbara, Robens, Markus, Roche, Mathieu, Rodphai, Narongkiat, Romani, Aldo, Roskovec, Bedřich, Roth, Christian, Ruan, Xiangdong, Ruan, Xichao, Rujirawat, Saroj, Rybnikov, Arseniy, Sadovsky, Andrey, Saggese, Paolo, Salamanna, Giuseppe, Sanfilippo, Simone, Sangka, Anut, Sanguansak, Nuanwan, Sawangwit, Utane, Sawatzki, Julia, Sawy, Fatma, Schever, Michaela, Schuler, Jacky, Schwab, Cédric, Schweizer, Konstantin, Selyunin, Alexandr, Serafini, Andrea, Settanta, Giulio, Settimo, Mariangela, Shao, Zhuang, Sharov, Vladislav, Shaydurova, Arina, Shi, Jingyan, Shi, Yanan, Shutov, Vitaly, Sidorenkov, Andrey, Šimkovic, Fedor, Sirignano, Chiara, Siripak, Jaruchit, Sisti, Monica, Slupecki, Maciej, Smirnov, Mikhail, Smirnov, Oleg, Sogo-Bezerra, Thiago, Sokolov, Sergey, Songwadhana, Julanan, Soonthornthum, Boonrucksar, Sotnikov, Albert, Šrámek, Ondřej, Sreethawong, Warintorn, Stahl, Achim, Stanco, Luca, Stankevich, Konstantin, Štefánik, Dušan, Steiger, Hans, Steinmann, Jochen, Sterr, Tobias, Stock, Matthias Raphael, Strati, Virginia, Studenikin, Alexander, Sun, Gongxing, Sun, Shifeng, Sun, Xilei, Sun, Yongjie, Sun, Yongzhao, Suwonjandee, Narumon, Szelezniak, Michal, Tang, Jian, Tang, Qiang, Tang, Quan, Tang, Xiao, Tietzsch, Alexander, Tkachev, Igor, Tmej, Tomas, Treskov, Konstantin, Triossi, Andrea, Troni, Giancarlo, Trzaska, Wladyslaw, Tuve, Cristina, Ushakov, Nikita, Boom, Johannes van den, Waasen, Stefan van, Vanroyen, Guillaume, Vassilopoulos, Nikolaos, Vedin, Vadim, Verde, Giuseppe, Vialkov, Maxim, Viaud, Benoit, Vollbrecht, Moritz Cornelius, Volpe, Cristina, Vorobel, Vit, Voronin, Dmitriy, Votano, Lucia, Walker, Pablo, Wang, Caishen, Wang, Chung-Hsiang, Wang, En, Wang, Guoli, Wang, Jian, Wang, Jun, Wang, Kunyu, Wang, Lu, Wang, Meifen, Wang, Meng, Wang, Meng, Wang, Ruiguang, Wang, Siguang, Wang, Wei, Wang, Wei, Wang, Wenshuai, Wang, Xi, Wang, Xiangyue, Wang, Yangfu, Wang, Yaoguang, Wang, Yi, Wang, Yi, Wang, Yifang, Wang, Yuanqing, Wang, Yuman, Wang, Zhe, Wang, Zheng, Wang, Zhimin, Wang, Zongyi, Waqas, Muhammad, Watcharangkool, Apimook, Wei, Lianghong, Wei, Wei, Wei, Wenlu, Wei, Yadong, Wen, Liangjian, Wiebusch, Christopher, Wong, Steven Chan-Fai, Wonsak, Bjoern, Wu, Diru, Wu, Fangliang, Wu, Qun, Wu, Wenjie, Wu, Zhi, Wurm, Michael, Wurtz, Jacques, Wysotzki, Christian, Xi, Yufei, Xia, Dongmei, Xie, Yuguang, Xie, Zhangquan, Xing, Zhizhong, Xu, Benda, Xu, Cheng, Xu, Donglian, Xu, Fanrong, Xu, Hangkun, Xu, Jilei, Xu, Jing, Xu, Meihang, Xu, Yin, Xu, Yu, Yan, Baojun, Yan, Taylor, Yan, Wenqi, Yan, Xiongbo, Yan, Yupeng, Yang, Anbo, Yang, Changgen, Yang, Huan, Yang, Jie, Yang, Lei, Yang, Xiaoyu, Yang, Yifan, Yang, Yifan, Yao, Haifeng, Yasin, Zafar, Ye, Jiaxuan, Ye, Mei, Ye, Ziping, Yegin, Ugur, Yermia, Frédéric, Yi, Peihuai, Yin, Na, Yin, Xiangwei, You, Zhengyun, Yu, Boxiang, Yu, Chiye, Yu, Chunxu, Yu, Hongzhao, Yu, Miao, Yu, Xianghui, Yu, Zeyuan, Yu, Zezhong, Yuan, Chengzhuo, Yuan, Ying, Yuan, Zhenxiong, Yuan, Ziyi, Yue, Baobiao, Zafar, Noman, Zambanini, Andre, Zavadskyi, Vitalii, Zeng, Shan, Zeng, Tingxuan, Zeng, Yuda, Zhan, Liang, Zhang, Aiqiang, Zhang, Feiyang, Zhang, Guoqing, Zhang, Haiqiong, Zhang, Honghao, Zhang, Jiawen, Zhang, Jie, Zhang, Jingbo, Zhang, Jinnan, Zhang, Peng, Zhang, Qingmin, Zhang, Shiqi, Zhang, Shu, Zhang, Tao, Zhang, Xiaomei, Zhang, Xuantong, Zhang, Xueyao, Zhang, Yan, Zhang, Yinhong, Zhang, Yiyu, Zhang, Yongpeng, Zhang, Yuanyuan, Zhang, Yumei, Zhang, Zhenyu, Zhang, Zhijian, Zhao, Fengyi, Zhao, Jie, Zhao, Rong, Zhao, Shujun, Zhao, Tianchi, Zheng, Dongqin, Zheng, Hua, Zheng, Minshan, Zheng, Yangheng, Zhong, Weirong, Zhou, Jing, Zhou, Li, Zhou, Nan, Zhou, Shun, Zhou, Tong, Zhou, Xiang, Zhu, Jiang, Zhu, Kejun, Zhu, Zhihang, Zhuang, Bo, Zhuang, Honglin, Zong, Liang, and Zou, Jiaheng

Published
2021
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120. Innovative Neuroimaging Biomarker Distinction of Major Depressive Disorder and Bipolar Disorder through Structural Connectome Analysis and Machine Learning Models.

Author

Huang, Yang, Zhang, Jingbo, He, Kewei, Mo, Xue, Yu, Renqiang, Min, Jing, Zhu, Tong, Ma, Yunfeng, He, Xiangqian, Lv, Fajin, Lei, Du, and Liu, Mengqi

Subjects
*MACHINE learning, *MENTAL depression, *BIPOLAR disorder, *DEFAULT mode network, *BIOMARKERS
Abstract

Major depressive disorder (MDD) and bipolar disorder (BD) share clinical features, which complicates their differentiation in clinical settings. This study proposes an innovative approach that integrates structural connectome analysis with machine learning models to discern individuals with MDD from individuals with BD. High-resolution MRI images were obtained from individuals diagnosed with MDD or BD and from HCs. Structural connectomes were constructed to represent the complex interplay of brain regions using advanced graph theory techniques. Machine learning models were employed to discern unique connectivity patterns associated with MDD and BD. At the global level, both BD and MDD patients exhibited increased small-worldness compared to the HC group. At the nodal level, patients with BD and MDD showed common differences in nodal parameters primarily in the right amygdala and the right parahippocampal gyrus when compared with HCs. Distinctive differences were found mainly in prefrontal regions for BD, whereas MDD was characterized by abnormalities in the left thalamus and default mode network. Additionally, the BD group demonstrated altered nodal parameters predominantly in the fronto-limbic network when compared with the MDD group. Moreover, the application of machine learning models utilizing structural brain parameters demonstrated an impressive 90.3% accuracy in distinguishing individuals with BD from individuals with MDD. These findings demonstrate that combined structural connectome and machine learning enhance diagnostic accuracy and may contribute valuable insights to the understanding of the distinctive neurobiological signatures of these psychiatric disorders. [ABSTRACT FROM AUTHOR]

Published
2024
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121. Sinomenine Attenuates Angiotensin II-Induced Autophagy via Inhibition of P47-Phox Translocation to the Membrane and Influences Reactive Oxygen Species Generation in Podocytes.

Author

Wang, Weili, Cai, Juan, Tang, Sha, Zhang, Ying, Gao, Xuejing, Xie, Lijiao, Mou, Zhirong, Wu, Yuzhang, Wang, Li, and Zhang, Jingbo

Subjects
ANGIOTENSIN II, REACTIVE oxygen species, AUTOPHAGY, ANGIOTENSINS, TRANSMISSION electron microscopy, OXYGEN consumption
Abstract

Background/Aims: Sinomenine, a pure alkaloid extracted from the Chinese medicinal plant Sinomenium acutum, and sinomenine hydrochloride (SN) has been successfully used for the therapy of rheumatoid arthritis (RA) and kidney diseases. Autophagy is a cytoprotective mechanism used by podocytes and other cells to alleviate the effects of oxidative stress, and angiotensin II (Ang II) significantly promotes podocyte autophagy. However, excessive autophagy may lead to cell death and podocyte depletion. The present study evaluated the effect of SN in podocytes induced by Ang II. Methods: Podocytes were pretreated with graded concentrations (10-8 M ∼ 10-4 M) of SN and then stimulated with Ang II. The LC3B protein and the p47-phox membrane fraction were measured by Western blot. Autolysosomes were assessed by transmission electron microscopy. FACS was used to quantify the ROS produced by podocytes. The translocation of p47-phox to the membrane was investigated by immunofluorescence. Results: The 10-8 M ∼ 10-4 M of SN alone did not effect ROS generation or podocyte autophagy. The 10-8 M and 10-6 M SN attenuated Ang II-induced autophagy in podocytes. Furthermore, SN decreased the level of ROS generation in Ang II-induced podocytes via inhibition of NOX subunit p47-phox translocation to the membrane. Conclusion: The appropriate concentration of SN attenuated Ang II-induced podocyte autophagy through ROS generation, at least in part, by regulating NOX subunit p47-phox translocation to the membrane. [ABSTRACT FROM AUTHOR]

Published
2024
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122. Preparation of Low-Defect Aluminum-Doped Zinc Oxide Nanostructure-Based Compact Layer by Vacuum Ultraviolet Irradiation for Quantum Dot-Sensitized Solar Cells.

Author

Dong, Zhuo, Men, Jiao, Wang, Jiaduo, Zhai, Zeyu, Xie, Xiaoying, and Zhang, Jingbo

Abstract

Al-doped ZnO (AZO) nanostructure-based compact thin films were successfully prepared via the irradiation of the precursor films containing zinc nitrate and aluminum nitrate under vacuum ultraviolet (VUV) radiation at 172 nm. The VUV irradiation can generate hydroxyl free radicals to make up for the oxygen defects on the as-prepared AZO thin films. Meanwhile, the photoactivation effect triggered by VUV irradiation can desorb the amorphous oxygen species adsorbed on the surface, thus significantly reducing the number of surface defects. By changing the ratio of aluminum nitrate to zinc nitrate, the doped amount of Al can be rationally adjusted for the promotion of electron mobility and conductivity. When the Al loading is 3%, the resultant AZO thin film has the highest electron mobility and conductivity. When used as a base compact layer in CdS/PbS quantum dot-sensitized solar cells (QDSSCs), the nanostructure-based AZO thin film could suppress the recombination of the photogenerated electrons and holes between the TiO2 mesoporous layer and FTO conductive glass. As a result, the assembled QDSSC based on the AZO thin film achieved a photoelectric conversion efficiency of 4.97%, higher than that based on the traditional AZO compact layer prepared by sintering at 450 °C (3.70%). Therefore, VUV irradiation is a promising strategy for the preparation of nanostructure-based compact thin films doped by metal oxides under low temperatures. [ABSTRACT FROM AUTHOR]

Published
2023
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133. Machine Learning Model of ResNet50-Ensemble Voting for Malignant–Benign Small Pulmonary Nodule Classification on Computed Tomography Images.

Author

Li, Weiming, Yu, Siqi, Yang, Runhuang, Tian, Yixing, Zhu, Tianyu, Liu, Haotian, Jiao, Danyang, Zhang, Feng, Liu, Xiangtong, Tao, Lixin, Gao, Yan, Li, Qiang, Zhang, Jingbo, and Guo, Xiuhua

Subjects
SUPPORT vector machines, SOLITARY pulmonary nodule, MACHINE learning, ACQUISITION of data, RANDOM forest algorithms, CANCER patients, COMPARATIVE studies, T-test (Statistics), QUESTIONNAIRES, MEDICAL records, DESCRIPTIVE statistics, CHI-squared test, RESEARCH funding, COMPUTED tomography, DATA analysis software, ALGORITHMS
Abstract

Simple Summary: Machine learning methods have shown promise in accurately identifying small lung nodules. However, further exploration is needed to fully harness the potential of machine learning in distinguishing between benign and malignant nodules. This study aimed to develop and evaluate a ResNet50-Ensemble Voting model for detecting the nature (benign or malignant) of small pulmonary nodules (less than 20 mm) based on CT images. This study involved 834 CT imaging data from 396 patients with small pulmonary nodules. CT image features were extracted using ResNet50 and VGG16 algorithms, and classification was performed using XGBoost, SVM, and Ensemble Voting techniques, incorporating ten different combinations of machine learning classifiers. Among the models tested, the ResNet50-Ensemble Voting algorithm demonstrated the highest performance in the test set, achieving an accuracy of 0.943 (0.938, 0.948), with sensitivity and specificity values of 0.964 and 0.911, respectively. The implementation of machine learning models, particularly the ResNet50-Ensemble Voting approach, showed excellent performance in accurately identifying benign and malignant small pulmonary nodules (less than 20 mm) from diverse sources. These models have the potential to assist doctors in accurately diagnosing the nature of early-stage lung nodules in clinical practice. Background: The early detection of benign and malignant lung tumors enabled patients to diagnose lesions and implement appropriate health measures earlier, dramatically improving lung cancer patients' quality of living. Machine learning methods performed admirably when recognizing small benign and malignant lung nodules. However, exploration and investigation are required to fully leverage the potential of machine learning in distinguishing between benign and malignant small lung nodules. Objective: The aim of this study was to develop and evaluate the ResNet50-Ensemble Voting model for detecting the benign and malignant nature of small pulmonary nodules (<20 mm) based on CT images. Methods: In this study, 834 CT imaging data from 396 patients with small pulmonary nodules were gathered and randomly assigned to the training and validation sets in an 8:2 ratio. ResNet50 and VGG16 algorithms were utilized to extract CT image features, followed by XGBoost, SVM, and Ensemble Voting techniques for classification, for a total of ten different classes of machine learning combinatorial classifiers. Indicators such as accuracy, sensitivity, and specificity were used to assess the models. The collected features are also shown to investigate the contrasts between them. Results: The algorithm we presented, ResNet50-Ensemble Voting, performed best in the test set, with an accuracy of 0.943 (0.938, 0.948) and sensitivity and specificity of 0.964 and 0.911, respectively. VGG16-Ensemble Voting had an accuracy of 0.887 (0.880, 0.894), with a sensitivity and specificity of 0.952 and 0.784, respectively. Conclusion: Machine learning models that were implemented and integrated ResNet50-Ensemble Voting performed exceptionally well in identifying benign and malignant small pulmonary nodules (<20 mm) from various sites, which might help doctors in accurately diagnosing the nature of early-stage lung nodules in clinical practice. [ABSTRACT FROM AUTHOR]

Published
2023
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134. Room-temperature processed TiO2 to construct composite electron transport layers for efficient planar perovskite solar cells.

Author

Wang, Jiaduo, Dong, Zhuo, Wang, Jiajun, Zhang, Junwei, Zhai, Zeyu, Qiu, Fazheng, Wu, Jinpeng, Lin, Yuan, and Zhang, Jingbo

Abstract

The power conversion efficiency (PCE) of perovskite solar cells (PSCs) is seriously affected by the conductivity and energy alignment of the electron transport layer (ETL). However, poor film quality leads to low conductivity and interface energy level mismatch, which limits the performance of PSCs. Herein, a simple room-temperature (<35 °C) method is used to produce high-quality amorphous TiO2 thin films by vacuum ultraviolet (VUV) light (172 nm), to form composite ETLs with SnO2 for PSCs. The introduction of TiO2 produced perovskite films with larger grains and low trap density. The experimental results and density functional theory (DFT) analysis show that the composite ETL structure improves the electron mobility and conductivity, and the interaction with SnO2 accelerates the electron extraction due to favorable energy level alignment with the perovskite layer. Eventually, the composite ETL-based PSCs obtain a champion PCE of 24.59% (certified efficiency of 24.24%) and retain 92.4% of their initial PCE after 500 h of continuous light exposure. [ABSTRACT FROM AUTHOR]

Published
2023
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135. The Functions of an NAC Transcription Factor, GhNAC2-A06, in Cotton Response to Drought Stress.

Author

Saimi, Gulisitan, Wang, Ziyu, Liusui, Yunhao, Guo, Yanjun, Huang, Gengqing, Zhao, Huixin, and Zhang, Jingbo

Subjects
DROUGHTS, CHLOROPHYLL in water, TRANSCRIPTION factors, GENE silencing, SUPEROXIDE dismutase, CROP growth
Abstract

Drought stress imposes severe constraints on crop growth and yield. The NAC transcription factors (TF) play a pivotal role in regulating plant stress responses. However, the biological functions and regulatory mechanisms of many cotton NACs have not been explored. In this study, we report the cloning and characterization of GhNAC2-A06, a gene encoding a typical cotton NAC TF. The expression of GhNAC2-A06 was induced by PEG treatment, drought stress, and ABA treatment. Furthermore, we investigated its function using the virus-induced gene silencing (VIGS) method. GhNAC2-A06 silenced plants exhibited a poorer growth status under drought stress conditions compared to the controls. The GhNAC2-A06 silenced cotton plants had a lower leaf relative water and chlorophyll content and a higher MDA content compared to the controls under the drought treatment. The levels of superoxide dismutase (SOD), peroxidase (POD), and catalase (CAT) enzyme activity in the GhNAC2-A06 silenced plants were found to be lower compared to the controls when exposed to drought stress. Additionally, the downregulation of the drought stress-related genes, GhSAP12-D07, GhNCED1-A01, GhLEA14-A11, GhZAT10-D02, GhPROT2-A05, GhABF3-A03, GhABF2-D05, GhSAP3-D07, and GhCPK1-D04, was observed in the GhNAC2-A06 silenced cotton. Together, our research reveals that GhNAC2-A06 plays a role in the reaction of cotton to drought stress by affecting the expression of genes related to drought stress. The data obtained from this study lay the theoretical foundation for further in-depth research on the biological function and regulatory mechanisms of GhNAC2-A06. [ABSTRACT FROM AUTHOR]

Published
2023
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136. A novel drug-drug interactions prediction method based on a graph attention network.

Author

Tan, Xian, Fan, Shijie, Duan, Kaiwen, Xu, Mengyue, Zhang, Jingbo, Sun, Pingping, and Ma, Zhiqiang

Subjects
DRUG interactions, PREDICTION models, DRUG development, POLYPHARMACY, ARTIFICIAL intelligence
Abstract

s t r i n g U t i l s. c o n v e r t A b s t r a c t M a t h H t m l (formulaUtilTools.convertMathHtml( s t r i n g U t i l s. c o n v e r t M m l (!article.abstractinfoEn))) With the increasing need for public health and drug development, combination therapy has become widely used in clinical settings. However, the risk of unanticipated adverse effects and unknown toxicity caused by drug-drug interactions (DDIs) is a serious public health issue for polypharmacy safety. Traditional experimental methods for detecting DDIs are expensive and time-consuming. Therefore, many computational methods have been developed in recent years to predict DDIs with the growing availability of data and advancements in artificial intelligence. In silico methods have proven to be effective in predicting DDIs, but detecting potential interactions, especially for newly discovered drugs without an existing DDI network, remains a challenge. In this study, we propose a predicting method of DDIs named HAG-DDI based on graph attention networks. We consider the differences in mechanisms between DDIs and add learning of semantic-level attention, which can focus on advanced representations of DDIs. By treating interactions as nodes and the presence of the same drug as edges, and constructing small subnetworks during training, we effectively mitigate potential bias issues arising from limited data availability. Our experimental results show that our method achieves an F1-score of 0.952, proving that our model is a viable alternative for DDIs prediction. The codes are available at: https://github.com/xtnenu/DDIFramework. [ABSTRACT FROM AUTHOR]

Published
2023
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137. Leaf–Root–Soil Stoichiometric Characteristics in Different Shrub Ages of Ammopiptanthus mongolicus.

Author

Dong, Xue, Xu, Dehao, Wang, Danyang, Han, Chunxia, Huang, Yaru, and Zhang, Jingbo

Subjects
SHRUBS, PHOSPHATE fertilizers, MIDDLE age, NITROGEN fixation, BIOINDICATORS, PLANT nutrients, HOMEOSTASIS
Abstract

The ecological indicators for the growth and restoration of A. mongolicus populations are important for grasping the regulatory mechanisms of the biogeochemistry cycle, and for providing basic data for the prediction and evaluation of the evolution characteristics of natural A. mongolicus populations. We conducted studies on the eco-stoichiometric characteristics of natural A. mongolicus in different shrub ages, in order to understand the nutrient limitations for the growth and development of A. mongolicus and the synergy between the soil, leaves and roots, and to explore the C, N and P stoichiometric characteristics on A. mongolicus. The results showed the following: (1) The response of C, N and P stoichiometric characteristics in the leaves, roots and soil to changes in shrub age was not completely consistent. The leaf C content was young shrub> mature shrub> middle age shrub. The C content in the root system and C and N content in the soil showed an upward trend with increasing shrub age. The N and P contents of the root system and the P content of the soil showed a downward trend with increasing shrub age. The stoichiometric ratios C:N, C:P and N:P in the leaves, roots and soil showed an upward trend, and the N:P ratios in the leaves and roots were similar. (2) Among the stoichiometric characteristics of the leaves, C, N and P, leaves P and C:P are the most sensitive to shrub age changes, and have ecological implications for the growth and population dynamics of A. mongolicus. The average N:P ratios of young A. mongolicus leaves in young, middle-aged and mature shrubs were 15.32, 18.23 and 21.76, respectively. It can be seen that with an increase in shrub age, the growth of A. mongolicus gradually shifted from being jointly restricted by N and P to being more restricted by P. (3) The N content and the C∶N and N∶P ratios of A. mongolicus are classified as "strictly homoeostasis ", which shows strong plant homoeostasis for environmental adaptability. The N supplemented by symbiotic nitrogen fixation makes A. mongolicus have strong N internal homoeostasis. Therefore, in a desert grassland with low N content, the growth process of A. mongolicus may be easily restricted by P due to the additional N absorbed by it. (4) The C, N and P contents of the leaves, roots and soils of the three shrubs were shown as leaf > root > soil, and the difference was significant (p < 0.05). The correlation analysis showed that the C, N and P contents of the soil, roots and leaves and their stoichiometric ratio characteristics of the three shrubs showed a certain correlation. Among them, the P content of the soil was significantly related to the N:P ratio of the leaves and roots. Therefore, P is likely to become a limiting factor in the plant growth and repair process of the plant ecosystem in the A. mongolicus population. In summary, during the growth of A. mongolicus, special attention should be paid to the balance of nutrients. In order to improve its productivity, it is recommended to reasonably apply P fertilizers in the process of tending management to enhance the soil nutrient status and improve plant nutrient utilization efficiency and homoeostasis. [ABSTRACT FROM AUTHOR]

Published
2023
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138. Applications and Research Advances in the Delivery of CRISPR/Cas9 Systems for the Treatment of Inherited Diseases.

Author

Lu, Xinyue, Zhang, Miaomiao, Li, Ge, Zhang, Shixin, Zhang, Jingbo, Fu, Xiaoge, and Sun, Fengying

Subjects
GENETIC disorders, THERAPEUTICS, CRISPRS, DUCHENNE muscular dystrophy, GENE therapy, BLOOD coagulation factor VIII, NUCLEIC acids
Abstract

The rapid advancements in gene therapy have opened up new possibilities for treating genetic disorders, including Duchenne muscular dystrophy, thalassemia, cystic fibrosis, hemophilia, and familial hypercholesterolemia. The utilization of the clustered, regularly interspaced short palindromic repeats (CRISPR)-CRISPR-associated protein (Cas) system has revolutionized the field of gene therapy by enabling precise targeting of genes. In recent years, CRISPR/Cas9 has demonstrated remarkable efficacy in treating cancer and genetic diseases. However, the susceptibility of nucleic acid drugs to degradation by nucleic acid endonucleases necessitates the development of functional vectors capable of protecting the nucleic acids from enzymatic degradation while ensuring safety and effectiveness. This review explores the biomedical potential of non-viral vector-based CRISPR/Cas9 systems for treating genetic diseases. Furthermore, it provides a comprehensive overview of recent advances in viral and non-viral vector-based gene therapy for genetic disorders, including preclinical and clinical study insights. Additionally, the review analyzes the current limitations of these delivery systems and proposes avenues for developing novel nano-delivery platforms. [ABSTRACT FROM AUTHOR]

Published
2023
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144. Study of Constitutive Models of Reconstituted Clay with High Initial Water Content.

Author

Liu, Jie, Hu, Shijun, Liu, Gang, Zhang, Jingbo, and Lu, Zheng

Abstract

The compression and shear behavior of reconstituted clay are closely related to the initial water content of the reconstituted soil. It is difficult to obtain the compression and shear test data of clay with a high initial water content. This study aims to propose a model to predict the compression deformation and strength characteristics of reconstituted clay prepared with any initial water content using less data. Based on the concept of the disturbed state, this paper establishes a mathematical model that can simulate the compression and triaxial shear characteristics of reconstituted clay with different initial water contents. This model uses three compression curves of reconstituted clay with different initial water contents to calibrate the model parameters, and can predict the compression deformation characteristics of reconstituted clay prepared with any initial water content. The model can simulate the consolidated undrained shear behavior of clay reconstituted with different initial water contents. Comparing the measured data shows that the model is in good agreement with the measured compression curve and the triaxial stress–strain curve. The error of the predicted pore ratio and test pore ratio before yield is within 5%, and the error of the predicted pore ratio and test pore ratio after yield is within 10%. The stress–strain relationship of clay hardening with different water contents can be captured. The model can provide a preliminary prediction for the mechanical properties of clay with a high initial water content. [ABSTRACT FROM AUTHOR]

Published
2023
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145. Integrated mRNA and microRNA profiling in lung tissue and blood from human silicosis.

Author

Zhang, Jingbo, Hu, Weijiang, Liu, Kai, Liu, Jie, Zheng, Yuxin, Sun, Xin, Mei, Liangying, Qian, Zushu, Sun, Qiangguo, Liu, Qiang, Wu, Zhijun, Zhang, Hengdong, Li, Yanping, Sun, Daoyuan, and Ye, Meng

Abstract

Background: The overwhelming majority of subjects in the current silicosis mRNA and microRNA (miRNA) expression profile are of human blood, lung cells or a rat model, which puts limits on the understanding of silicosis pathogenesis and therapy. To address the limitations, our investigation was focused on differentially expressed mRNA and miRNA profiles in lung tissue from silicosis patients to explore potential biomarker for early detection of silicosis. Methods: A transcriptome study was conducted based on lung tissue from 15 silicosis patients and eight normal people, and blood samples from 404 silicosis patients and 177 normal people. Three early stage silicosis, five advanced silicosis and four normal lung tissues were randomly selected for microarray processing and analyze. The differentially expressed mRNAs were further used to conduct Gene Ontology and pathway analyses. Series test of cluster was performed to explore possible changes in differentially expressed mRNA and miRNA expression patterns during the process of silicosis. The blood samples and remaining lung tissues were used in a quantitative real‐time PCR (RT‐qPCR) (RT‐qPCR). Results: In total, 1417 and 241 differentially expressed mRNAs and miRNAs were identified between lung tissue from silicosis patients and normal people (p < 0.05). However, there was no significant difference in most mRNA or miRNA expression between early stage and advanced stage silicosis lung tissues. RT‐qPCR validation results in lung tissues showed expression of four mRNAs (HIF1A, SOCS3, GNAI3 and PTEN) and seven miRNAs was significantly down‐regulated compared to those of control group. Nevertheless, PTEN and GNAI3 expression was significantly up‐regulated (p < 0.001) in blood samples. The bisulfite sequencing PCR demonstrated that PTEN had significantly decreased the methylation rate in blood samples of silicosis patients. Conclusions: PTEN might be a potential biomarker for silicosis as a result of low methylation in the blood. [ABSTRACT FROM AUTHOR]

Published
2023
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148. A Label-Free and Antibody-Free Molecularly Imprinted Polymer-Based Impedimetric Sensor for NSCLC-Cells-Derived Exosomes Detection.

Author

Zhang, Jingbo, Chen, Quancheng, Gao, Xuemin, Lin, Qian, Suo, Ziqin, Wu, Di, Wu, Xijie, and Chen, Qing

Subjects
EXOSOMES, CARBON electrodes, NON-small-cell lung carcinoma, DETECTORS
Abstract

In this study, a label-free and antibody-free impedimetric biosensor based on molecularly imprinting technology for exosomes derived from non-small-cell lung cancer (NSCLC) cells was established. Involved preparation parameters were systematically investigated. In this design, with template exosomes anchored on a glassy carbon electrode (GCE) by decorated cholesterol molecules, the subsequent electro-polymerization of APBA and elution procedure afforded a selective adsorption membrane for template A549 exosomes. The adsorption of exosomes caused a rise in the impedance of the sensor, so the concentration of template exosomes can be quantified by monitoring the impedance of GCEs. Each procedure in the establishment of the sensor was monitored with a corresponding method. Methodological verification showed great sensitivity and selectivity of this method with an LOD = 2.03 × 103 and an LOQ = 4.10 × 104 particles/mL. By introducing normal cells and other cancer cells derived exosomes as interference, high selectivity was proved. Accuracy and precision were measured, with an obtained average recovery ratio of 100.76% and a resulting RSD of 1.86%. Additionally, the sensors' performance was retained at 4 °C for a week or after undergoing elution and re-adsorption cycles seven times. In summary, the sensor is competitive for clinical translational application and improving the prognosis and survival for NSCLC patients. [ABSTRACT FROM AUTHOR]

Published
2023
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