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182 results on '"Shou, L."'

1. Subcritical behavior for quasi-periodic Schr\'odinger cocycles with trigonometric potentials

Author

Marx, C. A., Shou, L. H., and Wellens, J. L.

Subjects
Mathematical Physics
Abstract

We give a criterion implying subcritical behavior for quasi-periodic Schr\"odinger operators where the potential sampling function is given by a trigonometric polynomial. Subcritical behavior, in the sense of Avila's global theory, is known to imply purely absolutely continuous spectrum for all irrational frequencies and all phases., Comment: to appear in the Journal of Spectral Theory

Published
2015

2. The Efficacy and Safety of the Shouzu Ning Decoction Treatment for Multi-Kinase Inhibitors-Associated Severe Hand–Foot Skin Reaction

Author

Shou L, Shao T, Zhao F, Chen S, Chen Q, and Shu Q

Subjects
hand-foot skin reaction, multi-kinase inhibitors, shouzu ning decoction, efficacy, safety, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Liumei Shou,1,2,* Tianyu Shao,2,* Fangmin Zhao,2 Shuyi Chen,1 Qunwei Chen,1 Qijin Shu1 1Department of Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China; 2The First Clinical Medicine College, Zhejiang Chinese Medical University, Hangzhou, People’s Republic of China*These authors contributed equally to this workCorrespondence: Qijin ShuDepartment of Oncology, The First Affiliated Hospital of Zhejiang Chinese Medical University, Hangzhou 310006, People’s Republic of ChinaTel +86-13605706566Fax +86-571-6707-2196Email shuqjhz@163.comBackground: Multi-kinase inhibitors (MKIs) treatment plays an important role in cancer therapy, but still suffers from a high incidence of hand–foot skin reaction (HFSR), leading to MKIs dose modification or termination. Thus, there is a high need for therapeutic strategy for HFSR.Patients and Methods: This prospective analysis included twenty patients, who were continuously administered with MKIs treatment and presented with a grade 3 HFSR during January 2018 to December 2019. All the patients were treated with the Shouzu Ning Decoction (SND) twice a day, in addition to the MKIs treatment. Grading of HFSR was assessed by National Cancer Institute Common Terminology Criteria for Adverse Events version 5.0. Pain intensity was evaluated using the numerical rating scale (NRS). Quality of life was assessed using the Hand–Foot Quality of Life Scale (HF-QoLS).Results: The median time from MKIs initiation to onset of grade 3 HFSR was 26.2 days. Following the SND treatment, seventeen (17/20) patients displayed grade 2 HFSR with a median time of 5.1 days. Among whom, seven (7/17) finally transformed to grade 1 with a median time of 9.9 days. While all of the grade 1 patients (7/7) had local recurrence, and retreatment of the SND was effective. In addition, after the SND treatment, the score of NRS and HF-QoLS decreased to 1.60 ± 1.14 (P < 0.01) and 26.75 ± 11.76 (P < 0.01), respectively.Conclusion: The SND treatment could alleviate symptoms, relieve pain and improve quality of life in HFSR patients. The SND treatment was proved to be an effective and well-tolerated treatment for MKIs-associated grade 3 HFSR patients for the first time. Indeed, further randomized controlled trails with large-scale, multi-center are require to fully determine the clinical application of the SND in MKIs-associated HFSR.Keywords: hand-foot skin reaction, multi-kinase inhibitors, Shouzu Ning Decoction, efficacy, safety

Published
2021

3. The molecular mechanisms underlying BCR/ABL degradation in chronic myeloid leukemia cells promoted by Beclin1-mediated autophagy

Author

Huang X, Li Y, Shou L, Li L, Chen Z, Ye X, and Qian W

Subjects
CML, autophagy, BCR/ABL, Beclin1, leukemia stem cell, Neoplasms. Tumors. Oncology. Including cancer and carcinogens, RC254-282
Abstract

Xianbo Huang,1,* Ying Li,1,* Lihong Shou,2 Li Li,1 Zhenzhen Chen,3 Xiujin Ye,1 Wenbin Qian1,41Department of Hematology, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, People’s Republic of China; 2Department of Hematology, The Central Hospital of Huzhou City, Huzhou 313000, People’s Republic of China; 3Department of Hematology, Hangzhou First People’s Hospital, Hangzhou 310003, People’s Republic of China; 4Malignant Lymphoma Diagnosis and Therapy Center, The First Affiliated Hospital, College of Medicine, Zhejiang University, Hangzhou 310003, People’s Republic of China*These authors contributed equally to this work Background: The development of drug resistance and the persistence of leukemia stem cells are major obstacles for the use of tyrosine kinase inhibitors (TKIs) in the treatment of chronic myeloid leukemia (CML). The induction of autophagic death in tumor cells represents a new route for leukemia treatment. Our previous study showed that infection of CML cells with oncolytic viruses carrying the autophagy gene Beclin1 downregulated BCR/ABL protein expression and significantly increased the killing effect of the oncolytic viruses on CML cells via autophagy activation. However, the specific molecular mechanisms underlying the regulation of BCR/ABL and Beclin1-dependent CML cell killing remain unclear.Methods: A physical interaction between BCR/ABL and Beclin1 was characterized via GST-pulldown, co-IP and dual-luciferase reporter assays. Cell proliferation was examined via CCK-8 and clone formation assays. The expression levels of the related proteins were measured via Western blotting. Autophagosomes were observed under transmission electron microscopy. Lentiviral vectors carrying Atg7/UVRAG shRNA or the Beclin1 gene were used to modulate the expression levels of the indicated genes. Immunofluorescence were performed to examine colocalization of BCR/ABL and p62/SQSTM1. CD34+,CD38−, cells were isolated from bone marrow samples from CML patients via fluorescence-activated cell sorting.Results: In this study, we observed that Beclin1 directly interacts with BCR/ABL. Beclin1 inhibited the activity of the BCR/ABL promoter to downregulate the level of BCR/ABL protein and to promote the downstream colocalization of p62/SQSTM1 and BCR/ABL to autolysosomes for degradation via activation of the autophagy signaling pathway. In CML cell lines, primary cells and CD34+,CD38−, leukemia stem cells, Beclin1 overexpression significantly inhibited cell growth and proliferation and induced autophagy.Conclusion: Taken together, our results suggest that autophagy induction via Beclin1 overexpression might offer new approaches for treating TKI-resistant CML and for promoting the clearance of leukemia stem cells, both of which have important clinical implications.Keywords: CML, autophagy, BCR/ABL, Beclin1, leukemia stem cell

Published
2019

4. Global multi-stakeholder endorsement of the MAFLD definition

Author

Mendez-Sanchez, N, Bugianesi, E, Gish, R, Lammert, F, Tilg, H, Nguyen, M, Sarin, S, Fabrellas, N, Zelber-Sagi, S, Fan, J, Shiha, G, Targher, G, Zheng, M, Chan, W, Vinker, S, Kawaguchi, T, Castera, L, Yilmaz, Y, Korenjak, M, Spearman, C, Ungan, M, Palmer, M, El-Shabrawi, M, Gruss, H, Dufour, J, Dhawan, A, Wedemeyer, H, George, J, Valenti, L, Fouad, Y, Romero-Gomez, M, Eslam, M, Abate, M, Abbas, B, Abbassy, A, Abd El Ghany, W, Abd Elkhalek, A, Abd ElMajeed, E, Abdalgaber, M, Abdallah, M, Abdallah, N, Abdelaleem, S, Abdelghani, Y, Abdelghany, W, Abdelhalim, S, Abdelhamid, W, Abdelhamid, N, Abdelkader, N, Abdelkreem, E, Abdelmohsen, A, Abdelrahman, A, Abd-elsalam, S, Abdeltawab, D, Abduh, A, Abdulhakam, N, Abdulla, M, Abedpoor, N, Abenavoli, L, Aberg, F, Ablack, O, Abo elftouh, M, Abo-Amer, Y, Aboubkr, A, Aboud, A, Abouelnaga, A, Aboufarrag, G, Aboutaleb, A, Abundis, L, Adali, G, Adames, E, Adams, L, Adda, D, Adel, N, Adel Sayed, M, Afaa, T, Afredj, N, Aghayeva, G, Aghemo, A, Aguilar-Salinas, C, Ahlenstiel, G, Ahmady, W, Ahmed, W, Ahmed, A, Ahmed, S, Ahmed, H, Ahmed, R, Aigner, E, Akarsu, M, Akroush, M, Akyuz, U, Al Mahtab, M, Al Qadiri, T, Al Rawahi, Y, AL rubaee, R, Al Saffar, M, Alam, S, Al-Ani, Z, Albillos, A, Alboraie, M, Al-Busafi, S, Al-Emam, M, Alharthi, J, Ali, K, Ali, B, Ali, M, Ali, R, Alisi, A, AL-Khafaji, A, Alkhatry, M, Aller, R, Almansoury, Y, Al-Naamani, K, Alnakeeb, A, Alonso, A, Alqahtani, S, Alrabadi, L, Alswat, K, Altaher, M, Altamimi, T, Altamirano, J, Alvares-da-Silva, M, Aly, E, Alzahaby, A, Alzamzamy, A, Amano, K, Amer, M, Amin, M, Amin, S, Amir, A, Ampuero, J, Anas, N, Andreone, P, Andriamandimby, S, Anees, M, Angela, P, Antonios, M, Arafat, W, Araya, J, Armendariz-Borunda, J, Armstrong, M, Ashktorab, H, Aspichueta, P, Assal, F, Atef, M, Attia, D, Atwa, H, Awad, R, Awad, M, Awny, S, Awolowo, O, Awuku, Y, Ayada, I, Aye, T, Ayman, S, Ayman, H, Ayoub, H, Azmy, H, Babaran, R, Badreldin, O, Badry, A, Bahcecioglu, I, Bahour, A, Bai, J, Balaban, Y, Balasubramanyam, M, Bamakhrama, K, Banales, J, Bangaru, B, Bao, J, Barahona, J, Barakat, S, Barbalho, S, Barbra, B, Barranco, B, Barrera, F, Baumann, U, Bazeed, S, Bech, E, Benayad, A, Benesic, A, Bernstein, D, Bessone, F, Birney, S, Bisseye, C, Blake, M, Bobat, B, Bonfrate, L, Bordin, D, Bosques-Padilla, F, Boursier, J, Boushab, B, Bowen, D, Bravo, P, Brennan, P, Bright, B, Broekaert, I, Buque, X, Burgos-Santamaria, D, Burman, J, Busetto, L, Byrne, C, Cabral-Prodigalidad, P, Cabrera-Alvarez, G, Cai, W, Cainelli, F, Caliskan, A, Canbay, A, Cano-Contreras, A, Cao, H, Cao, Z, Carrion, A, Carubbi, F, Casanovas, T, Castellanos Fernandez, M, Chai, J, Chan, S, Charatcharoenwitthaya, P, Chavez-Tapia, N, Chayama, K, Chen, J, Chen, L, Chen, Z, Chen, H, Chen, S, Chen, Q, Chen, Y, Chen, G, Chen, E, Chen, F, Chen, P, Cheng, R, Cheng, W, Chieh, J, Chokr, I, Cholongitas, E, Choudhury, A, Chowdhury, A, Chukwudike, E, Ciardullo, S, Clayton, M, Clement, K, Cloa, M, Coccia, C, Collazos, C, Colombo, M, Cosar, A, Cotrim, H, Couillerot, J, Coulibaly, A, Crespo, G, Crespo, J, Cruells, M, Cua, I, Dabbous, H, Dalekos, G, D'Alia, P, Dan, L, Dao, V, Darwish, M, Datz, C, Davalos-Moscol, M, Dawoud, H, de Careaga, B, de Knegt, R, de Ledinghen, V, de Silva, J, Debzi, N, Decraecker, M, Del Pozo, E, Delgado, T, Delgado-Blanco, M, Dembinski, L, Depina, A, Derbala, M, Desalegn, H, Desbois-Mouthon, C, Desoky, M, Dev, A, Di Ciaula, A, Diago, M, Diallo, I, Diaz, L, Dirchwolf, M, Dongiovanni, P, Dorofeyev, A, Dou, X, Douglas, M, Doulberis, M, Dovia, C, Doyle, A, Dragojevic, I, Drenth, J, Duan, X, Dulskas, A, Dumitrascu, D, Duncan, O, Dusabejambo, V, Dwawhi, R, Eiketsu, S, El Amrousy, D, El Deeb, A, El Deriny, G, El Din, H, El Kamshishy, S, El Kassas, M, El Raziky, M, Elagamy, O, Elakel, W, Elalfy, D, Elaraby, H, Elawady, H, Elbadawy, R, Eldash, H, Eldefrawy, M, Elecharri, C, Elfaramawy, A, Elfatih, M, Elfiky, M, Elgamsy, M, Elgendy, M, El-Guindi, M, Elhussieny, N, Eliwa, A, Elkabbany, Z, El-Khayat, H, El-Koofy, N, Elmetwalli, A, Elrabat, A, El-Raey, F, Elrashdy, F, Elsahhar, M, Elsaid, E, Elsayed, S, Elsayed, H, Elsayed, A, El-Serafy, M, Elsharkawy, A, Elsheemy, R, Elshemy, E, Elsherbini, S, Eltoukhy, N, Elwakil, R, Emad, O, Emad, S, Embabi, M, Ergenc, I, Ermolova, T, Esmat, G, Esmat, D, Estupinan, E, Ettair, S, Eugen, T, Ezz-Eldin, M, Falcon, L, Fan, Y, Fandari, S, Farag, M, Farahat, T, Fares, E, Fares, M, Fassio, E, Fathy, H, Fathy, D, Fathy, W, Fayed, S, Feng, D, Feng, G, Fernandez-Bermejo, M, Ferreira, C, Ferrer, J, Forbes, A, Fouad, R, Fouad, H, Frisch, T, Fujii, H, Fukunaga, S, Fukunishi, S, Fulya, H, Furuhashi, M, Gaber, Y, Galang, A, Gallardo, J, Galloso, R, Gamal, M, Gamal, R, Gamal, H, Gan, J, Ganbold, A, Gao, X, Garas, G, Garba, T, Garcia-Cortes, M, Garcia-Monzon, C, Garcia-Samaniego, J, Gastaldelli, A, Gatica, M, Gatley, E, Gegeshidze, T, Geng, B, Ghazinyan, H, Ghoneem, S, Giacomelli, L, Giannelli, G, Giannini, E, Giefer, M, Gines, P, Girala, M, Giraudi, P, Goh, G, Gomaa, A, Gong, B, Gonzales, D, Gonzalez, H, Gonzalez-Huezo, M, Graupera, I, Grgurevic, I, Gronbaek, H, Gu, X, Guan, L, Gueye, I, Guingane, A, Gul, O, Gul, C, Guo, Q, Gupta, P, Gurakar, A, Gutierrez, J, Habib, G, Hafez, A, Hagman, E, Halawa, E, Hamdy, O, Hamed, A, Hamed, D, Hamid, S, Hamoudi, W, Han, Y, Haridy, J, Haridy, H, Harris, D, Hart, M, Hasan, F, Hashim, A, Hassan, I, Hassan, A, Hassan, E, Hassan, M, Hassanin, F, Hassnine, A, Haukeland, J, Hawal, A, He, J, He, Q, He, Y, He, F, Hegazy, M, Hegazy, A, Henegil, O, Hernandez, N, Hernandez-Guerra, M, Higuera-de-la-Tijera, F, Hindy, I, Hirota, K, Ho, L, Hodge, A, Hosny, M, Hou, X, Huang, J, Huang, Y, Huang, Z, Huang, A, Huang, X, Hui-ping, S, Hunyady, B, Hussein, M, Hussein, O, Hussien, S, Ibanez-Samaniego, L, Ibdah, J, Ibrahim, L, Ibrahim, M, Ibrahim, I, Icaza-Chavez, M, Idelbi, S, Idilman, R, Ikeda, M, Indolfi, G, Invernizzi, F, Irshad, I, Isa, H, Iskandar, N, Ismaiel, A, Ismail, M, Ismail, Z, Ismail, F, Iwamoto, H, Jack, K, Jacob, R, Jafarov, F, Jafri, W, Jahshan, H, Jalal, P, Jancoriene, L, Janicko, M, Jayasena, H, Jefferies, M, Jha, V, Ji, F, Ji, Y, Jia, J, Jiang, C, Jiang, N, Jiang, Z, Jin, X, Jin, Y, Jing, X, Jingyu, Q, Jinjolava, M, Jong, F, Jucov, A, Julius, I, Kaddah, M, Kamada, Y, Kamal, A, Kamal, E, Kamel, A, Kao, J, Karin, M, Karlas, T, Kashwaa, M, Katsidzira, L, Kaya, E, Kayasseh, M, Keenan, B, Keklikkiran, C, Keml, W, Khalaf, D, Khalefa, R, Khamis, S, Khater, D, Khattab, H, Khavkin, A, Khlynova, O, Khmis, N, Kobyliak, N, Koffas, A, Koike, K, Kok, K, Koller, T, Komas, N, Korochanskaya, N, Koulla, Y, Koya, S, Kraft, C, Kraja, B, Krawczyk, M, Kuchay, M, Kulkarni, A, Kumar, A, Kumar, M, Lakoh, S, Lam, P, Lan, L, Lange, N, Lankarani, K, Lanthier, N, Lapshyna, K, Lashen, S, Laure, K, Lazebnik, L, Lebrec, D, Lee, S, Lee, W, Lee, Y, Leeming, D, Leite, N, Leon, R, Lesmana, C, Li, J, Li, Q, Li, Y, Li, L, Li, M, Liang, H, Lijuan, T, Lim, S, Lim, L, Lin, S, Lin, H, Lin, R, Lithy, R, Liu, Y, Liu, X, Liu, W, Liu, S, Liu, K, Liu, T, Lonardo, A, Lopez, M, Lopez-Benages, E, Lopez-Jaramillo, P, Lu, H, Lu, L, Lu, Y, Lubel, J, Lui, R, Lupasco, I, Luzina, E, Lv, X, Lynch, K, Ma, H, Machado, M, Maduka, N, Madzharova, K, Magdaong, R, Mahadeva, S, Mahfouz, A, Mahmood, N, Mahmoud, E, Mahrous, M, Maiwall, R, Majeed, A, Majumdar, A, Mak, L, Maklouf, M, Malekzadeh, R, Mandato, C, Mangia, A, Mann, J, Mansour, H, Mansouri, A, Mantovani, A, Mao, J, Maramag, F, Marchesini, G, Marcus, C, Marinho, R, Martinez-Chantar, M, Martins, A, Marwan, R, Mason, K, Masoud, G, Massoud, M, Matamoros, M, Mateos, R, Mawed, A, Mbanya, J, Mbendi, C, Mccolaugh, L, Mcleod, D, Medina, J, Megahed, A, Mehrez, M, Memon, I, Merat, S, Mercado, R, Mesbah, A, Meskini, T, Metwally, M, Metwaly, R, Miao, L, Micah, E, Miele, L, Milivojevic, V, Milovanovic, T, Mina, Y, Mishkovik, M, Mishriki, A, Mitchell, T, Mohamed, A, Mohamed, M, Mohamed, S, Mohammed, S, Mohammed, A, Mohan, V, Mohie, S, Mokhtar, A, Moniem, R, Montilla, M, Morales, J, Morata, M, Moreno-Planas, J, Morise, S, Mosaad, S, Moselhy, M, Mostafa, A, Mostafa, E, Mouane, N, Mousa, N, Moustafa, H, Msherif, A, Muller, K, Munoz, C, Munoz-Urribarri, A, Murillo, O, Mustapha, F, Muzurovic, E, Nabil, Y, Nafady, S, Nagamatsu, A, Nakajima, A, Nakano, D, Nan, Y, Nascimbeni, F, Naseef, M, Nashat, N, Natalia, T, Negro, F, Nersesov, A, Neuman, M, Ng'Wanasayi, M, Ni, Y, Nicoll, A, Niizeki, T, Nikolova, D, Ningning, W, Niriella, M, Nogoibaeva, K, Nordien, R, O Sullivan, C, O'Beirne, J, Obekpa, S, Ocama, P, Ochwoto, M, Ogolodom, M, Ojo, O, Okrostsvaridze, N, Oliveira, C, Omana, R, Omar, O, Omar, H, Omar, M, Omran, S, Omran, R, Osman, M, Owise, N, Owusu-Ansah, T, Padilla- Machaca, P, Palle, S, Pan, Z, Pan, X, Pan, Q, Papaefthymiou, A, Paquissi, F, Par, G, Parkash, A, Payawal, D, Peltekian, K, Peng, X, Peng, L, Peng, Y, Pengoria, R, Perez, M, Perez, J, Perez, N, Persico, M, Pessoa, M, Petta, S, Philip, M, Plaz Torres, M, Polavarapu, N, Poniachik, J, Portincasa, P, Pu, C, Purnak, T, Purwanto, E, Qi, X, Qian, Z, Qiang, Z, Qiao, Z, Qiao, L, Queiroz, A, Rabiee, A, Radwan, M, Rahetilahy, A, Ramadan, Y, Ramadan, D, Ramli, A, Ramm, G, Ran, A, Rankovic, I, Rao, H, Raouf, S, Ray, S, Reau, N, Refaat, A, Reiberger, T, Remes-Troche, J, Reyes, E, Richardson, B, Ridruejo, E, Riestra Jimenez, S, Rizk, I, Roberts, S, Roblero, J, Robles, J, Rockey, D, Rodriguez, M, Rodriguez Hernandez, H, Roman, E, Romeiro, F, Romeo, S, Rosales-Zabal, J, Roshdi, G, Rosso, N, Ruf, A, Ruiz, P, Runes, N, Ruzzenente, A, Ryan, M, Saad, A, Sabbagh, E, Sabbah, M, Saber, S, Sabrey, R, Sabry, R, Saeed, M, Said, D, Said, E, Sakr, M, Salah, Y, Salama, R, Salama, A, Saleh, H, Saleh, A, Salem, A, Salifou, A, Salih, A, Salman, A, Samouda, H, Sanai, F, Sanchez-Avila, J, Sanker, L, Sano, T, Sanz, M, Saparbu, T, Sawhney, R, Sayed, F, Sayed, S, Sayed, A, Sayed, M, Sebastiani, G, Secadas, L, Sediqi, K, Seif, S, Semida, N, Senates, E, Serban, E, Serfaty, L, Seto, W, Sghaier, I, Sha, M, Shabaan, H, Shalaby, L, Shaltout, I, Sharara, A, Sharma, V, Shawa, I, Shawkat, A, Shawky, N, Shehata, O, Sheils, S, Shewaye, A, Shi, G, Shi, J, Shimose, S, Shirono, T, Shou, L, Shrestha, A, Shui, G, Sievert, W, Sigurdardottir, S, Sira, M, Siradj, R, Sison, C, Smyth, L, Soliman, R, Sollano, J, Sombie, R, Sonderup, M, Sood, S, Soriano, G, Stedman, C, Stefanyuk, O, Stimac, D, Strasser, S, Strnad, P, Stuart, K, Su, W, Su, M, Sumida, Y, Sumie, S, Sun, D, Sun, J, Suzuki, H, Svegliati-Baroni, G, Swar, M, Taharboucht, S, Taher, Z, Takamura, S, Tan, L, Tan, S, Tanwandee, T, Tarek, S, Tatiana, G, Tavaglione, F, Tecson, G, Tee, H, Teschke, R, Tharwat, M, Thong, V, Thursz, M, Tine, T, Tiribelli, C, Tolmane, I, Tong, J, Tongo, M, Torkie, M, Torre, A, Torres, E, Trajkovska, M, Treeprasertsuk, S, Tsutsumi, T, Tu, T, Tur, J, Turan, D, Turcan, S, Turkina, S, Tutar, E, Tzeuton, C, Ugiagbe, R, Uygun, A, Vacca, M, Vajro, P, Van der Poorten, D, Van Kleef, L, Vashakidze, E, Velazquez, C, Velazquez, M, Vento, S, Verhoeven, V, Vespasiani-Gentilucci, U, Vethakkan, S, Vilaseca, J, Vitek, L, Volkanovska, A, Wallace, M, Wan, W, Wang, Y, Wang, X, Wang, C, Wang, M, Wangchuk, P, Weltman, M, White, M, Wiegand, J, Wifi, M, Wigg, A, Wilhelmi, M, William, R, Wittenburg, H, Wu, S, Wubeneh, A, Xia, H, Xiao, J, Xiao, X, Xiaofeng, W, Xiong, W, Xu, L, Xu, J, Xu, W, Xu, K, Xu, Y, Xu, S, Xu, M, Xu, A, Xu, C, Yan, H, Yang, J, Yang, R, Yang, Y, Yang, Q, Yang, N, Yao, J, Yara, J, Yaras, S, Yilmaz, N, Younes, R, Younes, H, Young, S, Youssef, F, Yu, Y, Yu, M, Yuan, J, Yue, Z, Yuen, M, Yun, W, Yurukova, N, Zakaria, S, Zaky, S, Zaldastanishvili, M, Zapata, R, Zare, N, Zerem, E, Zeriban, N, Zeshuai, X, Zhang, H, Zhang, X, Zhang, Y, Zhang, W, Zhang, Z, Zhao, J, Zhao, R, Zhao, H, Zheng, C, Zheng, Y, Zheng, R, Zheng, T, Zheng, K, Zhou, X, Zhou, Y, Zhou, H, Zhou, L, Zhu, L, Zhu, Y, Zhu, P, Ziada, E, Ziring, D, Ziyi, L, Zou, S, Zou, Z, Zou, H, Zuart Ruiz, R, Mendez-Sanchez N., Bugianesi E., Gish R. G., Lammert F., Tilg H., Nguyen M. H., Sarin S. K., Fabrellas N., Zelber-Sagi S., Fan J. -G., Shiha G., Targher G., Zheng M. -H., Chan W. -K., Vinker S., Kawaguchi T., Castera L., Yilmaz Y., Korenjak M., Spearman C. W., Ungan M., Palmer M., El-Shabrawi M., Gruss H. -J., Dufour J. -F., Dhawan A., Wedemeyer H., George J., Valenti L., Fouad Y., Romero-Gomez M., Eslam M., Abate M. L., Abbas B., Abbassy A. A., Abd El Ghany W., Abd Elkhalek A., Abd ElMajeed E., Abdalgaber M., AbdAllah M., Abdallah M., Abdallah N., Abdelaleem S., Abdelghani Y., Abdelghany W., Abdelhalim S. M., Abdelhamid W., Abdelhamid N., Abdelkader N. A., Abdelkreem E., Abdelmohsen A. M., Abdelrahman A. A., Abd-elsalam S. M., Abdeltawab D., Abduh A., Abdulhakam N., Abdulla M., Abedpoor N., Abenavoli L., Aberg F., Ablack O., Abo elftouh M., Abo-Amer Y. E. -E., Aboubkr A., Aboud A., Abouelnaga A. M., Aboufarrag G. A., Aboutaleb A., Abundis L., Adali G., Adames E., Adams L., Adda D., Adel N., Adel Sayed M., Afaa T. 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H., Hamid S., Hamoudi W., Han Y., Haridy J., Haridy H., Harris D. C. H. H., Hart M., Hasan F., Hashim A., Hassan I., Hassan A., Hassan E. A., Hassan A. A., Hassan M. S., Hassanin F., Hassnine A., Haukeland J. W., Hawal A. I. M., He J., He Q., He Y., He F. -P., Hegazy M., Hegazy A., Henegil O., Hernandez N., Hernandez-Guerra M., Higuera-de-la-Tijera F., Hindy I., Hirota K., Ho L. C., Hodge A., Hosny M., Hou X., Huang J. -F., Huang Y., Huang Z., Huang A., Huang X. -P., Hui-ping S., Hunyady B., Hussein M. A., Hussein O., Hussien S. M., Ibanez-Samaniego L., Ibdah J., Ibrahim L., Ibrahim M., Ibrahim I., Icaza-Chavez M. E., Idelbi S., Idilman R. I., Ikeda M., Indolfi G., Invernizzi F., Irshad I., Isa H. M. A., Iskandar N. J., Ismaiel A., Ismail M., Ismail Z., Ismail F., Iwamoto H., Jack K., Jacob R., Jafarov F., Jafri W., Jahshan H., Jalal P. K., Jancoriene L., Janicko M., Jayasena H., Jefferies M., Jha V., Ji F., Ji Y., Jia J., Jiang C., Jiang N., Jiang Z. -Z., Jin X., Jin Y., Jing X., Jingyu Q., Jinjolava M., Jong F. H. H., Jucov A., Julius I., Kaddah M., Kamada Y., kamal A., Kamal E. M., Kamel A. S., Kao J. -H., Karin M., Karlas T., Kashwaa M., Katsidzira L., Kaya E., Kayasseh M. A., Keenan B., Keklikkiran C., Keml W., Khalaf D. K., Khalefa R., Khamis S., Khater D., khattab H., Khavkin A., Khlynova O., Khmis N., Kobyliak N., Koffas A., Koike K., Kok K. Y. Y., Koller T., Komas N. P., Korochanskaya N. V., Koulla Y., Koya S., Kraft C., Kraja B., Krawczyk M., Kuchay M. S., Kulkarni A. V., Kumar A., Kumar M., Lakoh S., Lam P., Lan L., Lange N. F., Lankarani K. B., Lanthier N., Lapshyna K., Lashen S. A., Laure K. N. J., Lazebnik L., Lebrec D., Lee S. S., Lee W. S., Lee Y. Y., Leeming D. J., Leite N. C., Leon R., Lesmana C. R. A., Li J., Li Q., Li Y. -Y., Li Y., Li L., Li M., li Y., Liang H., Lijuan T., Lim S. G., Lim L. -L., Lin S., Lin H. -C., Lin R., Lithy R., Liu Y., Liu X., Liu W. -Y., Liu S., Liu K., Liu T., Lonardo A., Lopez M. B., Lopez-Benages E., Lopez-Jaramillo P., Lu H., Lu L. G., Lu Y., Lubel J., Lui R., Lupasco I., Luzina E., Lv X. -H., Lynch K., Ma H. -L., Machado M. V., Maduka N., Madzharova K., Magdaong R., Mahadeva S., Mahfouz A., Mahmood N. R. K. N., Mahmoud E., Mahrous M., Maiwall R., Majeed A., Majumdar A., Mak L., Maklouf M. M., Malekzadeh R., Mandato C., Mangia A., Mann J., Mansour H. H., Mansouri A., Mantovani A., Mao J. Q., Maramag F., Marchesini G., Marcus C., Marinho R. A. R. T., Martinez-Chantar M. L., Martins A. A. S., Marwan R., Mason K. F., Masoud G., Massoud M. N., Matamoros M. A., Mateos R. M., Mawed A., Mbanya J. C., Mbendi C., McColaugh L., McLeod D., Medina J. F. R., Megahed A., Mehrez M., Memon I., Merat S., Mercado R., Mesbah A., Meskini T., Metwally M., Metwaly R., Miao L., Micah E., Miele L., Milivojevic V., Milovanovic T., Mina Y. L., Mishkovik M., Mishriki A., Mitchell T., Mohamed A., Mohamed M., Mohamed S., Mohammed S., Mohammed A., Mohan V., Mohie S., Mokhtar A., Moniem R., Montilla M. S., Morales J. A. O., Morata M. M. S., Moreno-Planas J. M., Morise S., Mosaad S., Moselhy M., Mostafa A. M., Mostafa E., Mouane N., Mousa N., Moustafa H. M., Msherif A., Muller K., Munoz C., Munoz-Urribarri A. B., Murillo O. A., Mustapha F. I., Muzurovic E., Nabil Y., Nafady S., Nagamatsu A., Nakajima A., Nakano D., Nan Y., Nascimbeni F., Naseef M. S., Nashat N., Natalia T., Negro F., Nersesov A. V., Neuman M., Ng'wanasayi M., Ni Y., Nicoll A., Niizeki T., Nikolova D., Ningning W., Niriella M., Nogoibaeva K. A., Nordien R., O Sullivan C., O'Beirne J., Obekpa S., Ocama P., Ochwoto M., Ogolodom M. P., Ojo O., Okrostsvaridze N., Oliveira C. P., Omana R. C., Omar O. M., Omar H., Omar M., Omran S., Omran R., Osman M. M., Owise N., Owusu-Ansah T., Padilla- Machaca P. M., Palle S., Pan Z., Pan X. -Y., Pan Q., Papaefthymiou A., Paquissi F. C., Par G., Parkash A., Payawal D., Peltekian K. M., Peng X., Peng L., Peng Y., Pengoria R., Perez M., Perez J. L., Perez N. M., Persico M., Pessoa M. G., Petta S., Philip M., Plaz Torres M. C., Polavarapu N., Poniachik J., Portincasa P., Pu C., Purnak T., Purwanto E., Qi X., Qian Z., Qiang Z., Qiao Z., Qiao L., Queiroz A., Rabiee A., Radwan M., Rahetilahy A. M., Ramadan Y., Ramadan D., Ramli A. S., Ramm G. A., Ran A., Rankovic I., RAO H., Raouf S., Ray S., Reau N., Refaat A., Reiberger T., Remes-Troche J. M., Reyes E. C., Richardson B., Ridruejo E., Riestra Jimenez S., Rizk I., Roberts S., Roblero J. P., Robles J. A. P., Rockey D., Rodriguez M., Rodriguez Hernandez H., Roman E., Romeiro F. G., Romeo S., Rosales-Zabal J. M., Roshdi G. R., Rosso N., Ruf A., Ruiz P. C., Runes N. R., Ruzzenente A., Ryan M., Saad A., Sabbagh E. B., Sabbah M., Saber S., Sabrey R., Sabry R., Saeed M. A., Said D., Said E. M., Sakr M. A., Salah Y., Salama R. M., Salama A., Saleh H., Saleh A., Salem A., Salem A. T., Salifou A., Salih A. F., Salman A., Samouda H., Sanai F., Sanchez-Avila J. F., Sanker L., Sano T., Sanz M., Saparbu T., Sawhney R., Sayed F., Sayed S. A., Sayed A. O., Sayed M., Sebastiani G., Secadas L., Sediqi K. Q., Seif S., Semida N., Senates E., Serban E. D., Serfaty L., Seto W. -K., Sghaier I., Sha M., Shabaan H. M., Shalaby L., Shaltout I., Sharara A. I., Sharma V., Shawa I. T., Shawkat A., Shawky N., Shehata O., Sheils S., Shewaye A. B., Shi G., Shi J., Shimose S., Shirono T., Shou L., Shrestha A., Shui G., Sievert W., Sigurdardottir S., Sira M. M., Siradj R., Sison C., Smyth L., Soliman R., Sollano J. D., Sombie R., Sonderup M., Sood S., Soriano G., Stedman C. A. M., Stefanyuk O., Stimac D., Strasser S., Strnad P., Stuart K., Su W., Su M., Sumida Y., Sumie S., Sun D. -Q., Sun J., Suzuki H., Svegliati-Baroni G., Swar M. O., TAHARBOUCHT S., Taher Z., Takamura S., Tan L., Tan S. -S., Tanwandee T., Tarek S., Tatiana G., Tavaglione F., Tecson G. Y., Tee H. -P., Teschke R., Tharwat M., Thong V. D., Thursz M., Tine T., Tiribelli C., Tolmane I., Tong J., Tongo M., Torkie M., Torre A., Torres E. A., Trajkovska M., Treeprasertsuk S., Tsutsumi T., Tu T., Tur J. A., Turan D., Turcan S., Turkina S., Tutar E., Tzeuton C., Ugiagbe R., Uygun A., Vacca M., Vajro P., Van der Poorten D., Van Kleef L. A., Vashakidze E., Velazquez C. M., Velazquez M. I., Vento S., Verhoeven V., Vespasiani-Gentilucci U., Vethakkan S. R., Vilaseca J., Vitek L., Volkanovska A., Wallace M., Wan W., Wang Y., Wang X., Wang C., Wang M., Wangchuk P., Weltman M., White M., Wiegand J., Wifi M. -N., Wigg A., Wilhelmi M., William R., Wittenburg H., Wu S., Wubeneh A. M., Xia H., Xiao J., Xiao X., Xiaofeng W., Xiong W., Xu L., Xu J., Xu W., Xu J. -H., Xu K., Xu Y., Xu S. -H., Xu M., Xu A., Xu C., Yan H., Yang J., Yang R. -X., Yang Y., Yang Q., Yang N., Yao J., Yara J., Yaras S., Yilmaz N., Younes R., younes H., Young S., Youssef F., Yu Y., Yu M. -L., Yuan J., Yue Z., Yuen M. -F., Yun W., Yurukova N., Zakaria S., Zaky S., Zaldastanishvili M., Zapata R., Zare N., Zerem E., Zeriban N., Zeshuai X., Zhang H., Zhang X., Zhang Y., Zhang W. -H., Zhang Y. -P., Zhang Z. -Q., Zhao J., Zhao R. -R., Zhao H., Zheng C., Zheng Y., Zheng R., Zheng T. -L., Zheng K., Zhou X. Q., Zhou Y., Zhou Y. -J., Zhou H., Zhou L., Zhu L. D., Zhu Y. F., Zhu Y., Zhu P. -W., Ziada E., Ziring D., Ziyi L., Zou S., Zou Z., Zou H., and Zuart Ruiz R.

Published
2022

5. Social attributes aware data forwarding strategy in intermittently connected wireless networks

Author

UDa-peng W, ONGXiao-long K, HANGHong-pei Z, IUQiao-shou L, IONGYu X, and ANGRu-yan W

Subjects
intermittent connection wireless network, data forwarding, social attributes, node relation, Telecommunication, TK5101-6720
Abstract

The nodes in the intermittent connection wireless network have the aggregation feature,the relation between nodes should be considered by the packet transmitting procedure.A social attributes aware data forwarding strategy in ICWN is proposed.According to the multiple parameters of network status,the social attribute of nodes is evaluated with the distribute manner; moreover,the relation between nodes is determined.Consequently,the packets are forwarded to their destination nodes.Results show that the packet delivery ratio and the delay can be optimized by proposed strategy.

Published
2015
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6. PB2011: CHIDAMIDE COMBINED WITH THALIDOMIDE, CYCLOPHOSPHAMIDE AND DEXAMETHASONE (TC2D) AS AN ORAL QUADRUPLET REGIMEN FOR RELAPSED/REFRACTORY MYELOMA PATIENTS: INITIAL RESULTS OF A PHASE IIA, MULTICENTER TRIAL

Author

Li, Y., primary, He, J., additional, Han, X., additional, Zheng, G., additional, Zhang, E., additional, Chen, Q., additional, Yang, Y., additional, He, D., additional, Zhao, Y., additional, Fu, J., additional, Shou, L., additional, Jiang, W., additional, Qian, S., additional, Hu, H., additional, He, H., additional, and Cai, Z., additional

Published
2022
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8. Bridging the Gap between Language Models and Cross-Lingual Sequence Labeling

Author

Chen, N., Shou, L., Gong, M., Pei, J., and Daxin Jiang

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, Computation and Language (cs.CL)
Abstract

Large-scale cross-lingual pre-trained language models (xPLMs) have shown effectiveness in cross-lingual sequence labeling tasks (xSL), such as cross-lingual machine reading comprehension (xMRC) by transferring knowledge from a high-resource language to low-resource languages. Despite the great success, we draw an empirical observation that there is a training objective gap between pre-training and fine-tuning stages: e.g., mask language modeling objective requires local understanding of the masked token and the span-extraction objective requires global understanding and reasoning of the input passage/paragraph and question, leading to the discrepancy between pre-training and xMRC. In this paper, we first design a pre-training task tailored for xSL named Cross-lingual Language Informative Span Masking (CLISM) to eliminate the objective gap in a self-supervised manner. Second, we present ContrAstive-Consistency Regularization (CACR), which utilizes contrastive learning to encourage the consistency between representations of input parallel sequences via unsupervised cross-lingual instance-wise training signals during pre-training. By these means, our methods not only bridge the gap between pretrain-finetune, but also enhance PLMs to better capture the alignment between different languages. Extensive experiments prove that our method achieves clearly superior results on multiple xSL benchmarks with limited pre-training data. Our methods also surpass the previous state-of-the-art methods by a large margin in few-shot data settings, where only a few hundred training examples are available., Comment: 15 pages

Published
2022
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12. Learning from Multiple Noisy Augmented Data Sets for Better Cross-Lingual Spoken Language Understanding

Author

Guo, Y., Shou, L., Pei, J., Gong, M., Xu, M., Wu, Z., and Daxin Jiang

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Artificial Intelligence (cs.AI), Computer Science - Artificial Intelligence, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, 02 engineering and technology, 010501 environmental sciences, Computation and Language (cs.CL), 01 natural sciences, 0105 earth and related environmental sciences
Abstract

Lack of training data presents a grand challenge to scaling out spoken language understanding (SLU) to low-resource languages. Although various data augmentation approaches have been proposed to synthesize training data in low-resource target languages, the augmented data sets are often noisy, and thus impede the performance of SLU models. In this paper we focus on mitigating noise in augmented data. We develop a denoising training approach. Multiple models are trained with data produced by various augmented methods. Those models provide supervision signals to each other. The experimental results show that our method outperforms the existing state of the art by 3.05 and 4.24 percentage points on two benchmark datasets, respectively. The code will be made open sourced on github., Comment: Long paper at EMNLP 2021

Published
2021
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13. WhiteningBERT: An Easy Unsupervised Sentence Embedding Approach

Author

Huang, J., Tang, D., Zhong, W., Lu, S., Shou, L., Gong, M., Daxin Jiang, and Duan, N.

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL)
Abstract

Producing the embedding of a sentence in an unsupervised way is valuable to natural language matching and retrieval problems in practice. In this work, we conduct a thorough examination of pretrained model based unsupervised sentence embeddings. We study on four pretrained models and conduct massive experiments on seven datasets regarding sentence semantics. We have there main findings. First, averaging all tokens is better than only using [CLS] vector. Second, combining both top andbottom layers is better than only using top layers. Lastly, an easy whitening-based vector normalization strategy with less than 10 lines of code consistently boosts the performance.

Published
2021
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14. Reinforced Multi-Teacher Selection for Knowledge Distillation

Author

Yuan, F., Shou, L., Pei, J., Lin, W., Gong, M., Fu, Y., and Daxin Jiang

Subjects
FOS: Computer and information sciences, Computer Science - Machine Learning, Computer Science - Computation and Language, ComputingMilieux_COMPUTERSANDEDUCATION, General Medicine, Computation and Language (cs.CL), Machine Learning (cs.LG)
Abstract

In natural language processing (NLP) tasks, slow inference speed and huge footprints in GPU usage remain the bottleneck of applying pre-trained deep models in production. As a popular method for model compression, knowledge distillation transfers knowledge from one or multiple large (teacher) models to a small (student) model. When multiple teacher models are available in distillation, the state-of-the-art methods assign a fixed weight to a teacher model in the whole distillation. Furthermore, most of the existing methods allocate an equal weight to every teacher model. In this paper, we observe that, due to the complexity of training examples and the differences in student model capability, learning differentially from teacher models can lead to better performance of student models distilled. We systematically develop a reinforced method to dynamically assign weights to teacher models for different training instances and optimize the performance of student model. Our extensive experimental results on several NLP tasks clearly verify the feasibility and effectiveness of our approach., AAAI 2021

Published
2020

15. Habitat selection in two sympatric Chinese skinks, Eumeces elegans and Sphenomorphus indicus: do thermal preferences matter?

Author

Du, W.G., Shou, L., and Shen, J.Y.

Subjects
Habitat (Ecology) -- Research -- Physiological aspects, Zoology and wildlife conservation, Physiological aspects, Research, Natural history
Abstract

Abstract: We studied the habitat selection and thermal biology of two sympatric Chinese skinks (Eumeces elegans Boulenger, 1887 and Sphenomorphus indicus (Schmidt, 1928)) to test the effect of thermal preference [...]

Published
2006

17. Combined ESWT & RSW Therapy for Achilles Tendinopathy: A Prospective Study.

Author

Saxena, A. and Shou, L.

Subjects
*COMBINATION drug therapy, *COMBINED modality therapy, *COMPARATIVE studies, *LONGITUDINAL method, *ULTRASONIC therapy, *TREATMENT effectiveness, *ACHILLES tendinitis, *DESCRIPTIVE statistics
Abstract

Introduction. Achilles tendinopathy is one of the most common overuse injuries of the foot and ankle in the active population. Many studies have shown radial sound wave therapy (RSW) or extra-corporeal shockwave therapy (ESWT) to be a safe and effective conservative treatment options when used independently. Methods. In this prospective study, we examined the outcomes of treatment on Achilles tendinopathy combining these two modalities. We hypothesize improved results with the combination therapy and compare this with previous studies. The current study observes a cohort of 24 patients, who received the both treatments with mean age of 47.2±12.8 years at the time of study. Each patient received three treatments initially and then subsequent treatments at 6 and/or 12 week follow up. Results. Pre-treatment VAS score was 6.3±1.3 and RM score was 3.5±0.5. Ultimately, these were reduced to 1.2±1.6 (P=0.00001) and 1.6±0.9 (P=0.00001) respectively at 17±4.5 month follow-up. Patients with paratendinosis had better outcomes than insertional Achilles pathology. Conclusions. Our results show a significant improvement in outcome measures in patients treated with ESWT and RSW, as compared to other studies. We conclude that the dual treatment method is a safe and improved method of treatment for Achilles tendinopathy compared to isolated use of ESWT or RSW. [ABSTRACT FROM AUTHOR]

Published
2019
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18. Distinguishing a driver from a response system

Author

Shou L. Bu, Ming C. Ho, and I-Min Jiang

Subjects
Correlation dimension, business.industry, Applied Mathematics, General Physics and Astronomy, Statistical and Nonlinear Physics, Pattern recognition, Control theory, Simple (abstract algebra), Present method, Correlation method, Artificial intelligence, business, Mathematical Physics, Mathematics, Response system
Abstract

This study presents an approach for distinguishing a driver from a response system. The proposed method can be applied to both unidirectional or bidirectional interactions, and to identical or structurally different systems. Compared with most previously proposed schemes, the present method is so “simple” that the driver-response relationships can generally be detected using a direct graphic way. On the other hand, quantitative estimation is also developed using the idea of the correlation dimension.

Published
2008

19. Regulation of DNA methylation and tumor suppression gene expression by miR-29b in leukemia patients and related mechanisms.

Author

CHEN, Y.-L., ZHANG, Z.-X., SHOU, L.-H., and DI, J.-Y.

Abstract

OBJECTIVE: Leukemia is characterized as a kind of malignant clonal disease in hematological stem cells. The study showed an abnormal level of DNA methylation in leukemia cells, which further led to an abnormal expression of hematological genes. This study investigated the role of miR-29b on the modulation of DNA methylation and tumor suppressor gene expression in leukemia patients. PATIENTS AND METHODS: A total of 21 leukemia patients were recruited for the collection of monocytes. Methylation levels of promoter sequence of ESR1 and p15 genes were analyzed by methylation assay kit combined with DHPLC. DNA microarray and qRT-PCR were used to measure microRNA expressional profile, and bioinformatics plus luciferase reporter assay confirmed target gene of miR-29b. After transfection with miR-29b, promoter methylation levels of ESR1 and p15 gene were measured. Protein expressions of DNMT1 DNA (cytosine- 5)-methyltransferase 1 (DNMT1), DNA (cytosine-5)-methyltransferase 3A (DNMT3A) and DNA (cytosine-5)-methyltransferase 3B (DNMT3B) were quantified. RESULTS: The methylation levels of the promoter region of ESR1 and p15 genes in monocytes of leukemia patient were significantly elevated (p < 0.05). DNA microarray and qRT-PCR confirmed the down-regulation of miR-29b (p < 0.05). Luciferase reporter assay revealed DNMT1, DNMT3A and DNMT3B as target genes of miR-29b. MiR-29b transfection inhibited the expressions of DNMT3A and DNMT3B in Kasumi-1 cells (p < 0.05), and promoter methylation levels of estrogen Receptor 1 (ESR1) and p15 gene were decreased (p < 0.05). CONCLUSIONS: In leukemia cells, hyper- methylation existed in the promoter region of tumor suppressor gene. The methylation was enhanced in gene DNMT1, DNMT3A and DNMT3B via the reduction of miR-29b in leukemia tumor cells. [ABSTRACT FROM AUTHOR]

Published
2018

25. Cross-cultural comparison of the oldest old in Shanghai and Boston: their lifestyles and contributions

Author

Wu, B., Silverstein, N., Axelrod, D., Song, P., Zhu, J., and Shou, L.

Subjects
Health, Seniors
Abstract

This study presents results from a 1996 telephone survey of 522 community-dwelling elders age 85+ in Eastern Massachusetts and a 1999 telephone survey that was culturally adjusted from the U.S. instrument and validated through in-person interviews of 533 elders age 80+ in Shanghai, China. The study results show that both groups had made contributions to their family, friends/neighbors, and within organizations. In general, the percentage of the elders making contributions was higher among the U.S. respondents. The study further indicates that the contributions that the elders made were focused on family among the Chinese respondents, but the contributions were more likely to be distributed across family, friends/neighbors, and organizations among the U.S. respondents. In conclusion, the informal network appeared to play a more significant role in the daily lives of the oldest old in Shanghai than for the elders in Boston.

Published
2002

27. REVIEW

Author

Shou, L., primary, Chionh, C. H., additional, Huang, Z., additional, Ruan, Y., additional, and Tan, Kian-Lee, additional

Published
2001
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28. Ink Jet Technology

Author

Shou L Hou and Robert D. Carnahan

Subjects
Engineering, ComputerSystemsOrganization_COMPUTERSYSTEMIMPLEMENTATION, Inkwell, business.industry, Astrophysics::High Energy Astrophysical Phenomena, ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION, Mechanical engineering, ComputerApplications_COMPUTERSINOTHERSYSTEMS, Impulse (physics), Industrial and Manufacturing Engineering, Computer Science::Other, Physics::Fluid Dynamics, Physics::Popular Physics, Physical limitations, Control and Systems Engineering, ComputingMethodologies_DOCUMENTANDTEXTPROCESSING, High Energy Physics::Experiment, Electrical and Electronic Engineering, business, ComputingMethodologies_COMPUTERGRAPHICS
Abstract

Based on the nature of ink droplet generation, ink jets may be classified as continuous droplet ink jets, impulse (or ink-on-demand) jets, and electrostatically generated ink jets. The basic theory and the principles of operation are reviewed. The principal physical limitations of each system are discussed, followed by a comparison of commercial systems and a review of developmental activities related to ink jets.

Published
1977
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30. A human soluble suppressor factor affecting lymphocyte responses in vitro.

Author

Shou, L, Schwartz, S A, Good, R A, Peng, R, and Chen, C L

Abstract

A soluble suppressor factor (SSF) has been demonstrated in the supernatant of normal human peripheral blood lymphocyte cultures that exhibits suppressive activity toward the proliferative response of normal lymphocytes to concanavalin A or alloantigens in mixed lymphocyte culture (MLC) or toward pokeweed mitogen-stimulated immunoglobulin synthesis and secretion in vitro. Suppression of the proliferative response in MLC reached maximal levels when added SSF-containing supernatant approximated 20% by volume of the culture medium. Suppression in the MLC was found to act at the proliferative stage. SSF acts independently of cytotoxicity and is stable at 56 degrees C for 30 min but is inactivated at higher temperatures. Addition of SSF to the MLC as late as day 4 after initiation of the culture results in suppression of transformation. This factor(s) may regulate the magnitude of several immune responses in humans.

Published
1980
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31. Suppressor cell activity after concanavalin A treatment of lymphocytes from normal donors.

Author

Shou, L, Schwartz, S A, and Good, R A

Abstract

Pretreatment of normal human peripheral blood lymphocytes with the plant lectin, concanavalin A (Con A), results in inhibition of blast transformation and [3H]thymidine incorporation by untreated allogeneic lymphocytes from healthy volunteers donors in one-way mixed leukocyte culture. Similarly, responses to mitogens, certain microbial antigens, and allogeneic lymphocytes are inhibited by Con A-treated allogeneic cells. Con A pretreated autologous lymphocytes can also be induced to manifest suppressor activities. This antimitotic effect occurs without evidence of cytotoxicity and is active on de novo lymphocyte responses and does not require prior sensitization of the cells being tested. Suppression of the lymphocyte response to pokeweed mitogen, a potent B-cell stimulator, by Con A-pretreated suppressor cells was not as consistent as was inhibition of response to other mitogens, including phytohemagglutinin and Con A. Furthermore, suppression of lymphocyte transformation to the microbial antigens, tuberculin purified protein derivative, and Canadida albicans extracts could be similarly induced by Con A pretreatment of either allogeneic or autologous cells. Induction of autologous suppressor activity in lymphocytes from healthy donors is compatible with a model that includes a role for suppressor cells in the modulation of the normal immune response.

Published
1976
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32. Suppression of immunoglobulin synthesis and secretion by peripheral blood lymphocytes from normal donors.

Author

Schwartz, S A, Shou, L, Good, R A, and Choi, Y S

Abstract

Concanavalin A (Con A)-treated peripheral blood lymphocytes from healthy volunteer donors have been shown to suppress proliferative responses associated with thymus-derived lymphocytes (T-cells). The present investigations demonstrate that peripheral blood lymphocytes incubated with Con A for 48 hr can abrogate pokeweed mitogen-stimulated differentiation of bone-marrow-derived (B) lymphocytes to immunoglobulin-synthesizing and -secreting plasma cells. This effect was manifested when washed Con A-treated peripheral blood lymphocytes were added to pokeweed mitogen-stimulated cocultures containing fresh autologous or allogeneic mononuclear cells, and it did not appear to involve cytotoxicity. Parallel control cultures consisting of mononuclear leukocytes incubated for 48 hr in the absence of Con A also had immunoglobulin suppressor activity in mixing experiments. This effect, however, was most pronounced when preincubated cells were added to fresh autologous pokeweed mitogen-stimulated peripheral blood lymphocytes. Cell mixtures containing peripheral blood lymphocytes demonstrated a spectrum of immunoregulatory effects ranging from suppression to enhancement of pokeweed mitogen-stimulated immunoglobulin synthesis and secretion. Several functional subclasses of suppressor cells that reflect varying levels of specific activity have thus been demonstrated in human beings. Moreover, a degree of genetic identity appears to be required for the expression of "weak" immunoregulatory influences.

Published
1977
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33. XGLUE: A new benchmark dataset for cross-lingual pre-training, understanding and generation

Author

Liang, Y., Duan, N., Gong, Y., Wu, N., Guo, F., Qi, W., Gong, M., Shou, L., Daxin Jiang, Cao, G., Fan, X., Zhang, R., Agrawal, R., Cui, E., Wei, S., Bharti, T., Qiao, Y., Chen, J. -H, Wu, W., Liu, S., Yang, F., Campos, D., Majumder, R., and Zhou, M.

Subjects
FOS: Computer and information sciences, Computer Science - Computation and Language, Computation and Language (cs.CL)
Abstract

In this paper, we introduce XGLUE, a new benchmark dataset that can be used to train large-scale cross-lingual pre-trained models using multilingual and bilingual corpora and evaluate their performance across a diverse set of cross-lingual tasks. Comparing to GLUE(Wang et al., 2019), which is labeled in English for natural language understanding tasks only, XGLUE has two main advantages: (1) it provides 11 diversified tasks that cover both natural language understanding and generation scenarios; (2) for each task, it provides labeled data in multiple languages. We extend a recent cross-lingual pre-trained model Unicoder(Huang et al., 2019) to cover both understanding and generation tasks, which is evaluated on XGLUE as a strong baseline. We also evaluate the base versions (12-layer) of Multilingual BERT, XLM and XLM-R for comparison.

34. Unsupervised Context Aware Sentence Representation Pretraining for Multi-lingual Dense Retrieval

Author

Wu, N., Liang, Y., Ren, H., Shou, L., Duan, N., Gong, M., and Daxin Jiang

Subjects
FOS: Computer and information sciences, InformationSystems_INFORMATIONSTORAGEANDRETRIEVAL, Information Retrieval (cs.IR), Computer Science - Information Retrieval
Abstract

Recent research demonstrates the effectiveness of using pretrained language models (PLM) to improve dense retrieval and multilingual dense retrieval. In this work, we present a simple but effective monolingual pretraining task called contrastive context prediction (CCP) to learn sentence representation by modeling sentence level contextual relation. By pushing the embedding of sentences in a local context closer and pushing random negative samples away, different languages could form isomorphic structure, then sentence pairs in two different languages will be automatically aligned. Our experiments show that model collapse and information leakage are very easy to happen during contrastive training of language model, but language-specific memory bank and asymmetric batch normalization operation play an essential role in preventing collapsing and information leakage, respectively. Besides, a post-processing for sentence embedding is also very effective to achieve better retrieval performance. On the multilingual sentence retrieval task Tatoeba, our model achieves new SOTA results among methods without using bilingual data. Our model also shows larger gain on Tatoeba when transferring between non-English pairs. On two multi-lingual query-passage retrieval tasks, XOR Retrieve and Mr.TYDI, our model even achieves two SOTA results in both zero-shot and supervised setting among all pretraining models using bilingual data.

38. REVIEW.

Author

Shou, L., Chionh, C. H., Huang, Z., Ruan, Y., and Tan, Kian-Lee

Published
2001
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39. Global multi-stakeholder endorsement of the MAFLD definition

Author

Nahum Méndez-Sánchez, Elisabetta Bugianesi, Robert G Gish, Frank Lammert, Herbert Tilg, Mindie H Nguyen, Shiv K Sarin, Núria Fabrellas, Shira Zelber-Sagi, Jian-Gao Fan, Gamal Shiha, Giovanni Targher, Ming-Hua Zheng, Wah-Kheong Chan, Shlomo Vinker, Takumi Kawaguchi, Laurent Castera, Yusuf Yilmaz, Marko Korenjak, C Wendy Spearman, Mehmet Ungan, Melissa Palmer, Mortada El-Shabrawi, Hans-Juergen Gruss, Jean-François Dufour, Anil Dhawan, Heiner Wedemeyer, Jacob George, Luca Valenti, Yasser Fouad, Manuel Romero‐Gomez, Mohammed Eslam, Maria Lorena Abate, Bahaa Abbas, Ahmed Amr Abbassy, Waleed Abd El Ghany, Amira Abd Elkhalek, Emad Abd ElMajeed, Mohammad Abdalgaber, Mohamed AbdAllah, Marwa Abdallah, Nourhan Abdallah, Shereen Abdelaleem, Yasser Abdelghani, Wael Abdelghany, Safaa Mohamed Abdelhalim, Wafaa Abdelhamid, Nehal Abdelhamid, Nadia A. Abdelkader, Elsayed Abdelkreem, Aly Mohamed Abdelmohsen, Awny Ali Abdelrahman, Sherief M Abd-elsalam, Doaa Abdeltawab, Abdulbaset Abduh, Nada Abdulhakam, Maheeba Abdulla, Navid Abedpoor, Ludovico Abenavoli, Fredrik Åberg, Omala Ablack, Mostafa Abo elftouh, Yousry Esam-Eldin Abo-Amer, Ashraf Aboubkr, Alaa Aboud, Amr M. Abouelnaga, Galal A. Aboufarrag, Ashraf Aboutaleb, Leticia Abundis, Gupse Adalı, Enrique Adames, Leon Adams, Danjuma Adda, Noor Adel, Nada Adel, Muhammad Adel Sayed, Taiba Jibril Afaa, Nawal Afredj, Gulnara Aghayeva, Alessio Aghemo, Carlos A. Aguilar-Salinas, Golo Ahlenstiel, Walid Ahmady, Wafaa Ahmed, Amira Ahmed, Samah Nasser Ahmed, Heba Mostafa Ahmed, Rasha Ahmed, Elmar Aigner, Mesut Akarsu, Maisam Akroush, Umit Akyuz, Mamun Al Mahtab, Tahani Al Qadiri, Yusriya Al Rawahi, Razzaq AL rubaee, Muna Al Saffar, Shahinul Alam, Zaid Al-Ani, Agustín Albillos, Mohamed Alboraie, Said Al-Busafi, Mohamed Al-Emam, Jawaher Alharthi, Kareem Ali, Basma Abdelmoez Ali, Mohammad Ali, Raja Affendi Raja Ali, Anna Alisi, Ali Raad AL-Khafaji, Maryam Alkhatry, Rocio Aller, Yahya Almansoury, Khalid Al-Naamani, Alaa Alnakeeb, Anna Alonso, Saleh A. Alqahtani, Leina Alrabadi, Khalid Alswat, Mahir Altaher, Turki Altamimi, Jose Altamirano, Mario R. Alvares-da-Silva, Elsragy Adel M. Aly, Amgad Alzahaby, Ahmed Alzamzamy, Keisuke Amano, Maysa A. Amer, Mona A. Amin, Sayed A. Amin, Ashraf A. Amir, Javier Ampuero, Noha Anas, Pietro Andreone, Soa Fy Andriamandimby, Mahmoud Anees, Peltec Angela, Manal Antonios, Wael Arafat, Jose Moreno Araya, Juan Armendariz-Borunda, Matthew J. Armstrong, Hassan Ashktorab, Patricia Aspichueta, Fathia Assal, Mira Atef, Dina Attia, Hoda Atwa, Reham Awad, Mohyeldeen Abd Elaziz Awad, Sally Awny, Obafemi Awolowo, Yaw Asante Awuku, Ibrahim Ayada, Than Than Aye, Sherif Ayman, Hedy Ayman, Hesham Ayoub, Hosny M. Azmy, Romiro P. Babaran, Omneya Badreldin, Ahmed Badry, İbrahim Halil Bahçecioğlu, Amira Bahour, Jiajia Bai, Yasemin Balaban, Muthuswamy Balasubramanyam, Khaled Bamakhrama, Jesus M Banales, Babu Bangaru, Jianfeng Bao, Jorge Suazo Barahona, Salma Barakat, Sandra Maria Barbalho, Bikwa Barbra, Beatriz Barranco, Francisco Barrera, Ulrich Baumann, Shamardan Bazeed, Eva Bech, Aourarh Benayad, Andreas Benesic, David Bernstein, Fernando Bessone, Susie Birney, Cyrille Bisseye, Martin Blake, Bilal Bobat, Leonilde Bonfrate, Dmitry S Bordin, Francisco Bosques-Padilla, Jerome Boursier, Boushab Mohamed Boushab, David Bowen, Patricia Medina Bravo, Paul N Brennan, Bisi Bright, Ilse Broekaert, Xabier Buque, Diego Burgos-Santamaría, Julio Burman, Luca Busetto, Chris D. Byrne, Patricia Anne I. Cabral-Prodigalidad, Guillermo Cabrera-Alvarez, Wei Cai, Francesca Cainelli, Ali Riza Caliskan, Ali Canbay, Ana Cano-Contreras, Hai-Xia Cao, Zhujun Cao, Andres Carrion, Francesca Carubbi, Teresa Casanovas, Marlen Ivón Castellanos Fernández, Jin Chai, Siew Pheng Chan, Phunchai Charatcharoenwitthaya, Norberto Chavez-Tapia, Kazuaki Chayama, Jinjun Chen, Lin Chen, Zhong-Wei Chen, Huiting Chen, Sui-Dan Chen, Qiang Chen, Yaxi Chen, Gang Chen, En-Quang Chen, Fei Chen, Pei-Jer Chen, Robert Cheng, Wendy Cheng, Jack Tan Wei Chieh, Imad Chokr, Evangelos Cholongitas, Ashok Choudhury, Abhijit Chowdhury, Evaristus Sunday Chukwudike, Stefano Ciardullo, Michelle Clayton, Karine Clement, Marie Michelle Cloa, Cecilia Coccia, Cristina Collazos, Massimo Colombo, Arif Mansur Cosar, Helma Pinchemel Cotrim, Joris Couillerot, Alioune Coulibaly, Gonzalo Crespo, Javier Crespo, Maria Cruells, Ian Homer Y. Cua, Hesham K. 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Dwawhi, Sho Eiketsu, Doaa El Amrousy, Ahmed El Deeb, Ghada El Deriny, Hesham Salah El Din, Salwa El Kamshishy, Mohamed El Kassas, Maissa El Raziky, Osama A Elagamy, Wafaa Elakel, Dina Elalfy, Hanaa Elaraby, Heba ElAwady, Reda Elbadawy, Hanaa Hassan Eldash, Manal S. Eldefrawy, Carol Lezama Elecharri, Amel Elfaramawy, Mohammed Elfatih, Mahmoud Elfiky, Mohamed Elgamsy, Mohamed Elgendy, Mohamed A. El-Guindi, Nagi Elhussieny, Ahmed Maher Eliwa, Zeineb Elkabbany, Hesham El-Khayat, Nehal M. El-Koofy, Alaa Elmetwalli, Amr Elrabat, Fathiya El-Raey, Fatma Elrashdy, Medhat Elsahhar, Esraa M. Elsaid, Shimaa Elsayed, Hany Elsayed, Aly Elsayed, Amr M. Elsayed, Hamdy Elsayed, Magdy El-Serafy, Ahmed M. Elsharkawy, Reem Yehia Elsheemy, Eman Elsayed Elshemy, Sara Elsherbini, Naglaa Eltoukhy, Reda Elwakil, Ola Emad, Shaimaa Emad, Mohamed Embabi, Ilkay Ergenç, Tatiana Ermolova, Gamal Esmat, Doaa M. 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Hawal, Jinfan He, Qiong He, Yong He, Fang-Ping He, Mona Hegazy, Adham Hegazy, Osama Henegil, Nelia Hernández, Manuel Hernández-Guerra, Fatima Higuera-de-la-Tijera, Ibrahim Hindy, Keisuke Hirota, Lee Chi Ho, Alexander Hodge, Mohamed Hosny, Xin Hou, Jiao-Feng Huang, Yan Huang, Zhifeng Huang, Yuan Huang, Ang Huang, Xiao-Ping Huang, Sheng Hui-ping, Bela Hunyady, Mennatallah A. Hussein, Osama Hussein, Shahinaz Mahmoud Hussien, Luis Ibáñez-Samaniego, Jamal Ibdah, Luqman Ibrahim, Miada Ibrahim, Ibrahim Ibrahim, Maria E. 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Khalaf, Rofida Khalefa, Sherin Khamis, Doaa Khater, Hamed khattab, Anatoly Khavkin, Olga Khlynova, Nabil Khmis, Nazarii Kobyliak, Apostolos Koffas, Kazuhiko Koike, Kenneth Y.Y. Kok, Tomas Koller, Narcisse Patrice Komas, Nataliya V. Korochanskaya, Yannoula Koulla, Shunji Koya, Colleen Kraft, Bledar Kraja, Marcin Krawczyk, Mohammad Shafi Kuchay, Anand V Kulkarni, Ashish Kumar, Manoj Kumar, Sulaiman Lakoh, Philip Lam, Ling Lan, Naomi F. Lange, Kamran Bagheri Lankarani, Nicolas Lanthier, Kateryna Lapshyna, Sameh A. Lashen, Konang Nguieguia Justine Laure, Leonid Lazebnik, Didier Lebrec, Samuel S. 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D, Bravo, P, Brennan, P, Bright, B, Broekaert, I, Buque, X, Burgos-Santamaria, D, Burman, J, Busetto, L, Byrne, C, Cabral-Prodigalidad, P, Cabrera-Alvarez, G, Cai, W, Cainelli, F, Caliskan, A, Canbay, A, Cano-Contreras, A, Cao, H, Cao, Z, Carrion, A, Carubbi, F, Casanovas, T, Castellanos Fernandez, M, Chai, J, Chan, S, Charatcharoenwitthaya, P, Chavez-Tapia, N, Chayama, K, Chen, J, Chen, L, Chen, Z, Chen, H, Chen, S, Chen, Q, Chen, Y, Chen, G, Chen, E, Chen, F, Chen, P, Cheng, R, Cheng, W, Chieh, J, Chokr, I, Cholongitas, E, Choudhury, A, Chowdhury, A, Chukwudike, E, Ciardullo, S, Clayton, M, Clement, K, Cloa, M, Coccia, C, Collazos, C, Colombo, M, Cosar, A, Cotrim, H, Couillerot, J, Coulibaly, A, Crespo, G, Crespo, J, Cruells, M, Cua, I, Dabbous, H, Dalekos, G, D'Alia, P, Dan, L, Dao, V, Darwish, M, Datz, C, Davalos-Moscol, M, Dawoud, H, de Careaga, B, de Knegt, R, de Ledinghen, V, de Silva, J, Debzi, N, Decraecker, M, Del Pozo, E, Delgado, T, Delgado-Blanco, M, Dembinski, L, Depina, A, Derbala, M, Desalegn, H, Desbois-Mouthon, C, Desoky, M, Dev, A, Di Ciaula, A, Diago, M, Diallo, I, Diaz, L, Dirchwolf, M, Dongiovanni, P, Dorofeyev, A, Dou, X, Douglas, M, Doulberis, M, Dovia, C, Doyle, A, Dragojevic, I, Drenth, J, Duan, X, Dulskas, A, Dumitrascu, D, Duncan, O, Dusabejambo, V, Dwawhi, R, Eiketsu, S, El Amrousy, D, El Deeb, A, El Deriny, G, El Din, H, El Kamshishy, S, El Kassas, M, El Raziky, M, Elagamy, O, Elakel, W, Elalfy, D, Elaraby, H, Elawady, H, Elbadawy, R, Eldash, H, Eldefrawy, M, Elecharri, C, Elfaramawy, A, Elfatih, M, Elfiky, M, Elgamsy, M, Elgendy, M, El-Guindi, M, Elhussieny, N, Eliwa, A, Elkabbany, Z, El-Khayat, H, El-Koofy, N, Elmetwalli, A, Elrabat, A, El-Raey, F, Elrashdy, F, Elsahhar, M, Elsaid, E, Elsayed, S, Elsayed, H, Elsayed, A, El-Serafy, M, Elsharkawy, A, Elsheemy, R, Elshemy, E, Elsherbini, S, Eltoukhy, N, Elwakil, R, Emad, O, Emad, S, Embabi, M, Ergenc, I, Ermolova, T, Esmat, G, Esmat, D, Estupinan, E, Ettair, S, Eugen, T, Ezz-Eldin, M, Falcon, L, Fan, Y, Fandari, S, Farag, M, Farahat, T, Fares, E, Fares, M, Fassio, E, Fathy, H, Fathy, D, Fathy, W, Fayed, S, Feng, D, Feng, G, Fernandez-Bermejo, M, Ferreira, C, Ferrer, J, Forbes, A, Fouad, R, Fouad, H, Frisch, T, Fujii, H, Fukunaga, S, Fukunishi, S, Fulya, H, Furuhashi, M, Gaber, Y, Galang, A, Gallardo, J, Galloso, R, Gamal, M, Gamal, R, Gamal, H, Gan, J, Ganbold, A, Gao, X, Garas, G, Garba, T, Garcia-Cortes, M, Garcia-Monzon, C, Garcia-Samaniego, J, Gastaldelli, A, Gatica, M, Gatley, E, Gegeshidze, T, Geng, B, Ghazinyan, H, Ghoneem, S, Giacomelli, L, Giannelli, G, Giannini, E, Giefer, M, Gines, P, Girala, M, Giraudi, P, Goh, G, Gomaa, A, Gong, B, Gonzales, D, Gonzalez, H, Gonzalez-Huezo, M, Graupera, I, Grgurevic, I, Gronbaek, H, Gu, X, Guan, L, Gueye, I, Guingane, A, Gul, O, Gul, C, Guo, Q, Gupta, P, Gurakar, A, Gutierrez, J, Habib, G, Hafez, A, Hagman, E, Halawa, E, Hamdy, O, Hamed, A, Hamed, D, Hamid, S, Hamoudi, W, Han, Y, Haridy, J, Haridy, H, Harris, D, Hart, M, Hasan, F, Hashim, A, Hassan, I, Hassan, A, Hassan, E, Hassan, M, Hassanin, F, Hassnine, A, Haukeland, J, Hawal, A, He, J, He, Q, He, Y, He, F, Hegazy, M, Hegazy, A, Henegil, O, Hernandez, N, Hernandez-Guerra, M, Higuera-de-la-Tijera, F, Hindy, I, Hirota, K, Ho, L, Hodge, A, Hosny, M, Hou, X, Huang, J, Huang, Y, Huang, Z, Huang, A, Huang, X, Hui-ping, S, Hunyady, B, Hussein, M, Hussein, O, Hussien, S, Ibanez-Samaniego, L, Ibdah, J, Ibrahim, L, Ibrahim, M, Ibrahim, I, Icaza-Chavez, M, Idelbi, S, Idilman, R, Ikeda, M, Indolfi, G, Invernizzi, F, Irshad, I, Isa, H, Iskandar, N, Ismaiel, A, Ismail, M, Ismail, Z, Ismail, F, Iwamoto, H, Jack, K, Jacob, R, Jafarov, F, Jafri, W, Jahshan, H, Jalal, P, Jancoriene, L, Janicko, M, Jayasena, H, Jefferies, M, Jha, V, Ji, F, Ji, Y, Jia, J, Jiang, C, Jiang, N, Jiang, Z, Jin, X, Jin, Y, Jing, X, Jingyu, Q, Jinjolava, M, Jong, F, Jucov, A, Julius, I, Kaddah, M, Kamada, Y, Kamal, A, Kamal, E, Kamel, A, Kao, J, Karin, M, Karlas, T, Kashwaa, M, Katsidzira, L, Kaya, E, Kayasseh, M, Keenan, B, Keklikkiran, C, Keml, W, Khalaf, D, Khalefa, R, Khamis, S, Khater, D, Khattab, H, Khavkin, A, Khlynova, O, Khmis, N, Kobyliak, N, Koffas, A, Koike, K, Kok, K, Koller, T, Komas, N, Korochanskaya, N, Koulla, Y, Koya, S, Kraft, C, Kraja, B, Krawczyk, M, Kuchay, M, Kulkarni, A, Kumar, A, Kumar, M, Lakoh, S, Lam, P, Lan, L, Lange, N, Lankarani, K, Lanthier, N, Lapshyna, K, Lashen, S, Laure, K, Lazebnik, L, Lebrec, D, Lee, S, Lee, W, Lee, Y, Leeming, D, Leite, N, Leon, R, Lesmana, C, Li, J, Li, Q, Li, Y, Li, L, Li, M, Liang, H, Lijuan, T, Lim, S, Lim, L, Lin, S, Lin, H, Lin, R, Lithy, R, Liu, Y, Liu, X, Liu, W, Liu, S, Liu, K, Liu, T, Lonardo, A, Lopez, M, Lopez-Benages, E, Lopez-Jaramillo, P, Lu, H, Lu, L, Lu, Y, Lubel, J, Lui, R, Lupasco, I, Luzina, E, Lv, X, Lynch, K, Ma, H, Machado, M, Maduka, N, Madzharova, K, Magdaong, R, Mahadeva, S, Mahfouz, A, Mahmood, N, Mahmoud, E, Mahrous, M, Maiwall, R, Majeed, A, Majumdar, A, Mak, L, Maklouf, M, Malekzadeh, R, Mandato, C, Mangia, A, Mann, J, Mansour, H, Mansouri, A, Mantovani, A, Mao, J, Maramag, F, Marchesini, G, Marcus, C, Marinho, R, Martinez-Chantar, M, Martins, A, Marwan, R, Mason, K, Masoud, G, Massoud, M, Matamoros, M, Mateos, R, Mawed, A, Mbanya, J, Mbendi, C, Mccolaugh, L, Mcleod, D, Medina, J, Megahed, A, Mehrez, M, Memon, I, Merat, S, Mercado, R, Mesbah, A, Meskini, T, Metwally, M, Metwaly, R, Miao, L, Micah, E, Miele, L, Milivojevic, V, Milovanovic, T, Mina, Y, Mishkovik, M, Mishriki, A, Mitchell, T, Mohamed, A, Mohamed, M, Mohamed, S, Mohammed, S, Mohammed, A, Mohan, V, Mohie, S, Mokhtar, A, Moniem, R, Montilla, M, Morales, J, Morata, M, Moreno-Planas, J, Morise, S, Mosaad, S, Moselhy, M, Mostafa, A, Mostafa, E, Mouane, N, Mousa, N, Moustafa, H, Msherif, A, Muller, K, Munoz, C, Munoz-Urribarri, A, Murillo, O, Mustapha, F, Muzurovic, E, Nabil, Y, Nafady, S, Nagamatsu, A, Nakajima, A, Nakano, D, Nan, Y, Nascimbeni, F, Naseef, M, Nashat, N, Natalia, T, Negro, F, Nersesov, A, Neuman, M, Ng'Wanasayi, M, Ni, Y, Nicoll, A, Niizeki, T, Nikolova, D, Ningning, W, Niriella, M, Nogoibaeva, K, Nordien, R, O Sullivan, C, O'Beirne, J, Obekpa, S, Ocama, P, Ochwoto, M, Ogolodom, M, Ojo, O, Okrostsvaridze, N, Oliveira, C, Omana, R, Omar, O, Omar, H, Omar, M, Omran, S, Omran, R, Osman, M, Owise, N, Owusu-Ansah, T, Padilla- Machaca, P, Palle, S, Pan, Z, Pan, X, Pan, Q, Papaefthymiou, A, Paquissi, F, Par, G, Parkash, A, Payawal, D, Peltekian, K, Peng, X, Peng, L, Peng, Y, Pengoria, R, Perez, M, Perez, J, Perez, N, Persico, M, Pessoa, M, Petta, S, Philip, M, Plaz Torres, M, Polavarapu, N, Poniachik, J, Portincasa, P, Pu, C, Purnak, T, Purwanto, E, Qi, X, Qian, Z, Qiang, Z, Qiao, Z, Qiao, L, Queiroz, A, Rabiee, A, Radwan, M, Rahetilahy, A, Ramadan, Y, Ramadan, D, Ramli, A, Ramm, G, Ran, A, Rankovic, I, Rao, H, Raouf, S, Ray, S, Reau, N, Refaat, A, Reiberger, T, Remes-Troche, J, Reyes, E, Richardson, B, Ridruejo, E, Riestra Jimenez, S, Rizk, I, Roberts, S, Roblero, J, Robles, J, Rockey, D, Rodriguez, M, Rodriguez Hernandez, H, Roman, E, Romeiro, F, Romeo, S, Rosales-Zabal, J, Roshdi, G, Rosso, N, Ruf, A, Ruiz, P, Runes, N, Ruzzenente, A, Ryan, M, Saad, A, Sabbagh, E, Sabbah, M, Saber, S, Sabrey, R, Sabry, R, Saeed, M, Said, D, Said, E, Sakr, M, Salah, Y, Salama, R, Salama, A, Saleh, H, Saleh, A, Salem, A, Salifou, A, Salih, A, Salman, A, Samouda, H, Sanai, F, Sanchez-Avila, J, Sanker, L, Sano, T, Sanz, M, Saparbu, T, Sawhney, R, Sayed, F, Sayed, S, Sayed, A, Sayed, M, Sebastiani, G, Secadas, L, Sediqi, K, Seif, S, Semida, N, Senates, E, Serban, E, Serfaty, L, Seto, W, Sghaier, I, Sha, M, Shabaan, H, Shalaby, L, Shaltout, I, Sharara, A, Sharma, V, Shawa, I, Shawkat, A, Shawky, N, Shehata, O, Sheils, S, Shewaye, A, Shi, G, Shi, J, Shimose, S, Shirono, T, Shou, L, Shrestha, A, Shui, G, Sievert, W, Sigurdardottir, S, Sira, M, Siradj, R, Sison, C, Smyth, L, Soliman, R, Sollano, J, Sombie, R, Sonderup, M, Sood, S, Soriano, G, Stedman, C, Stefanyuk, O, Stimac, D, Strasser, S, Strnad, P, Stuart, K, Su, W, Su, M, Sumida, Y, Sumie, S, Sun, D, Sun, J, Suzuki, H, Svegliati-Baroni, G, Swar, M, Taharboucht, S, Taher, Z, Takamura, S, Tan, L, Tan, S, Tanwandee, T, Tarek, S, Tatiana, G, Tavaglione, F, Tecson, G, Tee, H, Teschke, R, Tharwat, M, Thong, V, Thursz, M, Tine, T, Tiribelli, C, Tolmane, I, Tong, J, Tongo, M, Torkie, M, Torre, A, Torres, E, Trajkovska, M, Treeprasertsuk, S, Tsutsumi, T, Tu, T, Tur, J, Turan, D, Turcan, S, Turkina, S, Tutar, E, Tzeuton, C, Ugiagbe, R, Uygun, A, Vacca, M, Vajro, P, Van der Poorten, D, Van Kleef, L, Vashakidze, E, Velazquez, C, Velazquez, M, Vento, S, Verhoeven, V, Vespasiani-Gentilucci, U, Vethakkan, S, Vilaseca, J, Vitek, L, Volkanovska, A, Wallace, M, Wan, W, Wang, Y, Wang, X, Wang, C, Wang, M, Wangchuk, P, Weltman, M, White, M, Wiegand, J, Wifi, M, Wigg, A, Wilhelmi, M, William, R, Wittenburg, H, Wu, S, Wubeneh, A, Xia, H, Xiao, J, Xiao, X, Xiaofeng, W, Xiong, W, Xu, L, Xu, J, Xu, W, Xu, K, Xu, Y, Xu, S, Xu, M, Xu, A, Xu, C, Yan, H, Yang, J, Yang, R, Yang, Y, Yang, Q, Yang, N, Yao, J, Yara, J, Yaras, S, Yilmaz, N, Younes, R, Younes, H, Young, S, Youssef, F, Yu, Y, Yu, M, Yuan, J, Yue, Z, Yuen, M, Yun, W, Yurukova, N, Zakaria, S, Zaky, S, Zaldastanishvili, M, Zapata, R, Zare, N, Zerem, E, Zeriban, N, Zeshuai, X, Zhang, H, Zhang, X, Zhang, Y, Zhang, W, Zhang, Z, Zhao, J, Zhao, R, Zhao, H, Zheng, C, Zheng, Y, Zheng, R, Zheng, T, Zheng, K, Zhou, X, Zhou, Y, Zhou, H, Zhou, L, Zhu, L, Zhu, Y, Zhu, P, Ziada, E, Ziring, D, Ziyi, L, Zou, S, Zou, Z, Zou, H, Zuart Ruiz, R, and Global Multi-Stakeholder Consensus on the Redefinition of Fatty Liver Disease

Subjects
Renal disorders Radboud Institute for Molecular Life Sciences [Radboudumc 11], Hepatology, Non-alcoholic Fatty Liver Disease, NAFLD, consensu, Gastroenterology, MAFLD, definition, Humans, MAFLD, NAFLD, Human medicine
Abstract

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Published
2022
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40. Exploration of genetic characterization in hyperprogressive disease after immunotherapy retreatment in a patient with LCNEC: A case report.

Author

Zhang Y, Yang J, Shao T, Chen J, Shu Q, and Shou L

Subjects
Humans, Immunotherapy adverse effects, Antibodies, Monoclonal, Retreatment, Disease Progression, DNA Helicases, Nuclear Proteins, Transcription Factors, Immune Checkpoint Inhibitors, Carcinoma, Neuroendocrine
Abstract

Immune checkpoint inhibitors (ICIs) have emerged as a promising therapeutic option for large cell neuroendocrine carcinoma (LCNEC). However, various studies have suggested a potential risk of hyperprogressive disease (HPD) in patients receiving ICI, which might be associated with gene alterations. Here, this is the first report on an unknown primary LCNEC patient who had achieved a long-term response from ICI treatment (atezolizumab), but developed HPD after tumor progression due to receiving another ICI agent (serplulimab). The mutation region of FAT4, SMARCA4, CYLD, CTNNB1, and KIT was altered prior to serplulimab treatment compared to before atezolizumab treatment. This case suggested a potential association between these mutated genes and HPD. Patients with the aforementioned genes should caution when selecting ICI treatment. These findings required further confirmation in a larger study cohort.

Published
2024
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41. Bioinformatics-based modeling of lung squamous cell carcinoma prognosis and prediction of immunotherapy response.

Author

Zhang Q, He H, Wei Y, Li G, and Shou L

Abstract

Lung squamous cell carcinoma (LUSC) is a subtype of non-small cell lung cancer. It has a grim prognosis for patients, primarily because the disease often remains asymptomatic in its early stages. As a result, it is frequently diagnosed at an advanced stage, limiting treatment options. This underscores the importance of studying potential biomarkers and developing personalized treatment strategies. In this study, we used an advanced bioinformatics approach, integrating two authoritative databases, NCBI's GEO and TCGA, to perform a large-scale cross-platform gene expression analysis. To deeply mine the gene expression data of a large number of lung squamous carcinoma samples, we used a screening strategy based on median absolute deviation to select genes that differed significantly in multiple datasets. The expression variations of these genes between normal and cancerous tissues provided us with valuable clues revealing key molecules that may be involved in the disease process. Through rigorous statistical tests, we identified 36 genes that were significantly associated with patient survival, and further constructed a model using Cox proportional risk model containing 11 key genes (MRPL40, GABPB1AS1, PTPN3, SNCA, PYGB, RAP1, VDR, PHPT1, KIAA0100, TBC1D30, CYP7B1) in a risk prediction model. The prediction model not only reflects the strong correlation between gene expression and LUSC prognosis, but also provides clinicians with an effective tool to predict patients' survival prospects. In the future, this model is expected to guide the development of individualized treatment plans, thereby improving the quality of life and overall prognosis of patients., Competing Interests: Declarations. Competing interests: The authors declare no competing interests., (© 2024. The Author(s).)

Published
2024
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42. Assessment of macrobenthos in evaluating the restoration effects of artificial mangrove planting on tidal flats in Zhejiang, China.

Author

Shao O, Li Y, Gu W, Zhang R, Tang Y, Xu H, Shou L, Zeng J, and Liao Y

Abstract

Artificially planted mangroves are widely used for tidal flat restoration, but their effectiveness in restoring these ecosystems remains unclear. Macrobenthos, as key indicators of tidal flat ecosystem health, can reflect changes in environmental conditions and quality resulting from the introduction of artificial mangroves. This study aimed to investigate the ecological restoration effects of artificially planted mangroves in northern China by surveying macrobenthic communities across four regions in June 2023. We compared the macrobenthic community structures in mangrove zones with those in Spartina alterniflora or unvegetated mudflat control zones by estimating habitat quality indices and community structure analyses. Additionally, we explored the relationship between community structures and environmental factors to identify key drivers of community structure changes. Significant differences in species abundance and biomass were observed across the four regions, with mudflat zones exhibiting the highest values, followed by mangrove and Spartina alterniflora zones. Beta diversity was higher in mangrove zones compared to control zones, driven primarily by turnover components. The AMBI index indicated excellent ecological conditions in 18-year-old mangrove zone on Maoyan Island, while poorer conditions were observed in the mangrove zone of Zhoushan City. Environmental factors, such as sand content, organic carbon content, and elevation, significantly influenced macrobenthic community distribution. Overall, our findings suggest that the 18-year-old mangrove zone on Maoyan Island is ecologically stable and demonstrates significant restoration effects. Using macrobenthos to assess the restoration efficacy of mangroves proves to be both effective and feasible. Furthermore, while the mangroves have successfully established and grown, their long-term survival depends on the active involvement of local stakeholders, highlighting the importance of long-term monitoring and adaptive management strategies., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Published
2024
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43. Identification of TXN and F5 as novel diagnostic gene biomarkers of the severe asthma based on bioinformatics and machine learning analysis.

Author

Shou L, He H, Wei Y, Xu X, Wang W, and Zheng J

Subjects
Humans, Gene Expression Profiling, MicroRNAs genetics, Severity of Illness Index, Transcriptome, Gene Expression Regulation, Asthma genetics, Asthma diagnosis, Asthma immunology, Machine Learning, Computational Biology methods, Biomarkers, Thioredoxins genetics, Thioredoxins metabolism
Abstract

Asthma poses a major threat to human health. The aim of this study was to identify genetic markers of severe asthma and analyze the relationship between key genes and immune infiltration. Differentially expressed genes (DEGs) were first screened by downloading the training set GSE69683 and validation set GSE137268 from the GEO dataset. SVM-RFE analysis and the LASSO regression model were used to screen key genes, and CIBERSORT was used to assess immune infiltration in the samples. A total of 20 DEGs were identified in this study, mainly enriched for lymph node-like receptors, b-cell receptors, and neutrophil extracellular trap pathway. Comparative validation set GSE137268 identified thioredoxin (TXN) and coagulation factor V (F5) were identified as diagnostic markers of severe asthma. CIBERSORT analysis revealed that TXN and F5 are associated with multiple immune cell infiltrates. In addition, we identified miRNA and TF at the transcriptional level that may regulate F5 and TXN, and found that several commonly used drugs may exert therapeutic effects by targeting F5 and TXN. Taken together, TXN and F5 may be key genes in the development of severe asthma and are associated with immune infiltration. Our study can help to better understand the pathogenesis of asthma and provide new ideas for clinical treatment.

Published
2024
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44. Photovoltaic Power Station Impacts on the Benthic Ecosystem and Sediment Carbon Storage in Tidal Flats in China.

Author

Jin L, Yu P, Liu C, Liu Q, Liu Q, Zhang R, Tang Y, Shou L, Zeng J, Chen Q, and Liao Y

Subjects
China, Biodiversity, Environmental Monitoring, Geologic Sediments chemistry, Ecosystem, Carbon
Abstract

Photovoltaic power is a rapidly growing component of the renewable energy sector. Photovoltaic power stations (PVPSs) on coastal tidal flats offer benefits, but the lack of information on the effects of PVPSs on benthic ecosystems and sediment carbon storage can hamper the development of eco-friendly renewable energy. We sampled the macrobenthos and sediment cores at a PVPS on a coastal tidal flat in eastern China. The biodiversity indicators and benthic ecological quality based on macrobenthos were mostly higher under the photovoltaic panels than elsewhere. These variations were primarily driven by pH, sediment grain size, and chlorophyll- a content. However, the PVPS had exerted a considerable influence on the macrobenthic community structure. Furthermore, the carbon stocks in the sediment cores from under the photovoltaic panels were similar to those in the reference sites. These results suggest that this PVPS has not had discernible short-term adverse effects on the benthic ecosystems or sediment carbon storage of the tidal flat. Nevertheless, the potentially long-term and cascading risks throughout the ecosystem warrant caution. Therefore, we recommend that policymakers adopt a cautious development strategy and implement long-term, high-frequency monitoring to ensure the sustainability of renewable energy production on coastal tidal flats.

Published
2024
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45. Analysis of the association between non-alcoholic fatty liver disease and mortality in United States adults.

Author

Rong F, Mai Y, Shou L, Zhu X, Li M, Zhu L, Sun X, and Zhao X

Abstract

Background: Non-alcoholic Fatty Liver Disease (NAFLD) is a prevalent condition characterized by the accumulation of fat in the liver, often linked with increased risk for multi-systemic diseases. This study aims to investigate the relationship between NAFLD and mortality, particularly all-cause and cardiovascular mortality, among United States adults., Methods: Data from the National Health and Nutrition Examination Survey (NHANES) were utilized, encompassing 80,312 participants from 2003-2004 to 2017-2018. After exclusions for incomplete data, potential other liver diseases, and significant alcohol consumption, the final analytical cohort included 32,698 participants. The Fatty Liver Index (FLI), a non-invasive diagnostic tool, was used to identify NAFLD. Covariates included demographic characteristics, lifestyle factors, and biochemical parameters. Survival analysis was conducted using a weighted Cox proportional hazards regression model to quantify the impact of NAFLD on mortality., Results: The study revealed that NAFLD was significantly associated with increased risks of all-cause and cardiovascular disease (CVD) mortality. The hazard ratios (HRs) from the survival analysis consistently indicated a higher risk among participants with NAFLD compared to those without. Subgroup analyses further confirmed the association, with notable exceptions in certain subgroups such as those with high school education and diabetes. Additionally, a nonlinear relationship between serum uric acid (SUA) levels and mortality risk was identified among NAFLD participants., Conclusion: Non-alcoholic Fatty Liver Disease is a significant risk factor for all-cause and CVD mortality in US adults. The findings underscore the importance of early detection and intervention for NAFLD to mitigate its impact on public health. Further research is needed to explore the complex interactions between NAFLD, SUA levels, and mortality, particularly in high-risk subgroups., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Rong, Mai, Shou, Zhu, Li, Zhu, Sun and Zhao.)

Published
2024
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46. Caffeine intake is nonlinearly associated with muscle mass in young and middle-aged US adults.

Author

Zhou L, Qu H, Wang J, Shou L, Zhang Q, and Zhang C

Subjects
Humans, Male, Female, Adult, Cross-Sectional Studies, Middle Aged, Young Adult, United States epidemiology, Coffee adverse effects, Absorptiometry, Photon, Caffeine administration & dosage, Caffeine adverse effects, Sarcopenia epidemiology, Muscle, Skeletal drug effects, Nutrition Surveys
Abstract

Background: Recent studies have indicated that coffee consumption is inversely correlated with sarcopenia in the elderly population. Data regarding the association between caffeine intake and muscle mass in young adults are scarce., Objective: We aimed to investigate how dietary caffeine correlates with muscle mass and sarcopenia in the young and middle-aged people., Methods: We performed a cross-sectional study utilizing data from NHANES. Muscle mass was evaluated using DXA and caffeine intake was derived from 24-h dietary recalls. Multivariable regression analysis was adopted to explore association between caffeine and sarcopenia. Restricted cubic spline analysis was conducted to investigate dose-response effect of dietary caffeine on muscle mass. Mediation effect of high-sensitivity C reactive protein was examined by mediation analysis., Results: A total of 9116 adults aged from 20 to 59 years old were included. Higher ingestion of caffeine was not associated with sarcopenia. Association between dietary caffeine and muscle mass was found to be W-shaped in males and U-shaped in young females, wherein mediation effect of hs-CRP was not discovered., Conclusions: Caffeine consumption is associated with muscle mass in a nonlinear pattern. ASMI peaks at a daily caffeine intake of 1.23 mg/kg in young adults, while 0.64-1.49 mg/kg is recommended for middle-aged men., Competing Interests: Declarations Ethics approval and consent to participate The NHANES study protocols had already been approved by the National Center for Health Statistics (NCHS) Research Ethics Review Board, and all NHANES participants included in current study had signed a written informed consent form in person. Consent for publication Not applicable. Competing interests The authors declare no competing interests., (© 2024. The Author(s).)

Published
2024
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47. Diversity patterns and ecological assembly mechanisms of bacterial communities in the northeastern Indian Ocean epipelagic waters during the northeast monsoon.

Author

Guo R, Ma X, Zhu C, Liu C, Shou L, Zhang J, Li H, Li Z, Dai X, Priyadarshani WNC, Jayathilake RMRM, Lwin SM, Thu CA, Li G, Wang P, and Zhou F

Subjects
Indian Ocean, Salinity, Environmental Monitoring, Water Microbiology, Ecosystem, Seawater microbiology, Bacteria classification, Microbiota, Biodiversity
Abstract

Disentangling microbial community diversity patterns and assembly mechanisms is critical for understanding ecological processes and evaluating biogeochemical cycling in ecosystems. However, the diversity patterns and assembly mechanism of the microbial communities in the epipelagic waters in the northeastern Indian Ocean (NEIO) on the spatial scale are still unclear. In this study, we investigated the spatial dynamics, geographic distribution pattern, and assembly process of the bacterial community using 532 samples collected from the epipelagic waters in the NEIO during the northeast monsoon. The results indicate that the bacterial richness and Bray-Curtis dissimilarity exhibited the strongest correlations with depth compared to the latitudinal and longitudinal scales. The dissolved oxygen was identified as the most important environmental factor affecting the bacterial richness and Bray-Curtis dissimilarity compared to temperature and salinity. The distance-decay relationship (DDR) of the bacterial community strengthened with increasing water depth. Turnover was the predominant β-diversity component influencing the spatial changes in the whole bacterial community. The dispersal limitation of the stochastic process and homogeneous selection of the deterministic process governed the bacterial ecological assembly process of the whole bacterial community. Abundant and rare subcommunities differed in terms of the niche breath, composition changes. The abundant subcommunities exhibited a much wider niche breath than the rare subcommunities. Regarding the abundant subcommunity species changes, the contributions of the turnover and nestedness varied with the water depth and oceanic region. In contrast, turnover was the major β-diversity component regarding the changes in the rare species. These data improve our understanding of the ecological processes of bacterial community assemblages in the NEIO., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2024 Elsevier B.V. All rights reserved.)

Published
2024
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48. Advancements in Bacteriophages for the Fire Blight Pathogen Erwinia amylovora .

Author

Ke D, Luo J, Liu P, Shou L, Ijaz M, Ahmed T, Shahid MS, An Q, Mustać I, Ondrasek G, Wang Y, Li B, and Lou B

Subjects
Genome, Viral, Rosaceae microbiology, Rosaceae virology, Phage Therapy methods, Erwinia amylovora virology, Bacteriophages physiology, Bacteriophages genetics, Plant Diseases microbiology, Plant Diseases prevention & control, Host Specificity
Abstract

Erwinia amylovora , the causative agent of fire blight, causes significant economic losses for farmers worldwide by inflicting severe damage to the production and quality of plants in the Rosaceae family. Historically, fire blight control has primarily relied on the application of copper compounds and antibiotics, such as streptomycin. However, the emergence of antibiotic-resistant strains and growing environmental concerns have highlighted the need for alternative control methods. Recently, there has been a growing interest in adopting bacteriophages (phages) as a biological control strategy. Phages have demonstrated efficacy against the bacterial plant pathogen E. amylovora , including strains that have developed antibiotic resistance. The advantages of phage therapy includes its minimal impact on microbial community equilibrium, the lack of a detrimental impact on plants and beneficial microorganisms, and its capacity to eradicate drug-resistant bacteria. This review addresses recent advances in the isolation and characterization of E. amylovora phages, including their morphology, host range, lysis exertion, genomic characterization, and lysis mechanisms. Furthermore, this review evaluates the environmental tolerance of E. amylovora phages. Despite their potential, E. amylovora phages face certain challenges in practical applications, including stability issues and the risk of lysogenic conversion. This comprehensive review examines the latest developments in the application of phages for controlling fire blight and highlights the potential of E. amylovora phages in plant protection strategies.

Published
2024
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49. Genomic Characterization of Phage ZP3 and Its Endolysin LysZP with Antimicrobial Potential against Xanthomonas oryzae pv. oryzae .

Author

Zhang M, Xu X, Lv L, Luo J, Ahmed T, Alsakkaf WAA, Ali HM, Bi J, Yan C, Gu C, Shou L, and Li B

Subjects
Endopeptidases pharmacology, Endopeptidases genetics, Endopeptidases chemistry, Endopeptidases metabolism, Phylogeny, Plant Leaves virology, Plant Leaves microbiology, China, Genomics methods, Xanthomonas virology, Xanthomonas drug effects, Bacteriophages genetics, Bacteriophages physiology, Bacteriophages isolation & purification, Oryza microbiology, Oryza virology, Plant Diseases microbiology, Plant Diseases virology, Genome, Viral
Abstract

Xanthomonas oryzae pv. oryzae (Xoo) is a significant bacterial pathogen responsible for outbreaks of bacterial leaf blight in rice, posing a major threat to rice cultivation worldwide. Effective management of this pathogen is crucial for ensuring rice yield and food security. In this study, we identified and characterized a novel Xoo phage, ZP3, isolated from diseased rice leaves in Zhejiang, China, which may offer new insights into biocontrol strategies against Xoo and contribute to the development of innovative approaches to combat bacterial leaf blight. Transmission electron microscopy indicated that ZP3 had a short, non-contractile tail. Genome sequencing and bioinformatic analysis showed that ZP3 had a double-stranded DNA genome with a length of 44,713 bp, a G + C content of 52.2%, and 59 predicted genes, which was similar to other OP1-type Xoo phages belonging to the genus Xipdecavirus . ZP3's endolysin LysZP was further studied for its bacteriolytic action, and the N -terminal transmembrane domain of LysZP is suggested to be a signal-arrest-release sequence that mediates the translocation of LysZP to the periplasm. Our study contributes to the understanding of phage-Xoo interactions and suggests that phage ZP3 and its endolysin LysZP could be developed into biocontrol agents against this phytopathogen.

Published
2024
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50. [Long-term hypomethylating agents in patients with myelodysplastic syndromes: a multi-center retrospective study].

Author

Liu XZ, Zhou SJ, Huang J, Zhao CF, Jiang LX, Zhang YD, Mei C, Ma LY, Zhou XP, Shao YP, Wu GQ, Xiao XB, Yao RX, Du XH, Hu TL, Qian SX, Li Y, Yan XF, Huang L, Wang ML, Fu JP, Shou LH, Jiang WH, Jin WM, Li LJ, Le J, Luo WJ, Zhang Y, Zhou XJ, Zhang H, Lang XH, Zhou M, Jin J, Jiang HF, Zhang J, Ouyang GF, and Tong HY

Subjects
Humans, Male, Middle Aged, Female, Retrospective Studies, Aged, Adult, Aged, 80 and over, Young Adult, Adolescent, Treatment Outcome, Azacitidine therapeutic use, Myelodysplastic Syndromes drug therapy
Abstract

Objective: To evaluate the efficacy and safety of hypomethylating agents (HMA) in patients with myelodysplastic syndromes (MDS) . Methods: A total of 409 MDS patients from 45 hospitals in Zhejiang province who received at least four consecutive cycles of HMA monotherapy as initial therapy were enrolled to evaluate the efficacy and safety of HMA. Mann-Whitney U or Chi-square tests were used to compare the differences in the clinical data. Logistic regression and Cox regression were used to analyze the factors affecting efficacy and survival. Kaplan-Meier was used for survival analysis. Results: Patients received HMA treatment for a median of 6 cycles (range, 4-25 cycles) . The complete remission (CR) rate was 33.98% and the overall response rate (ORR) was 77.02%. Multivariate analysis revealed that complex karyotype ( P =0.02, OR =0.39, 95% CI 0.18-0.84) was an independent favorable factor for CR rate. TP53 mutation ( P =0.02, OR =0.22, 95% CI 0.06-0.77) was a predictive factor for a higher ORR. The median OS for the HMA-treated patients was 25.67 (95% CI 21.14-30.19) months. HMA response ( P =0.036, HR =0.47, 95% CI 0.23-0.95) was an independent favorable prognostic factor, whereas complex karyotype ( P =0.024, HR =2.14, 95% CI 1.10-4.15) , leukemia transformation ( P <0.001, HR =2.839, 95% CI 1.64-4.92) , and TP53 mutation ( P =0.012, HR =2.19, 95% CI 1.19-4.07) were independent adverse prognostic factors. There was no significant difference in efficacy and survival between the reduced and standard doses of HMA. The CR rate and ORR of MDS patients treated with decitabine and azacitidine were not significantly different. The median OS of patients treated with decitabine was longer compared with that of patients treated with azacitidine (29.53 months vs 20.17 months, P =0.007) . The incidence of bone marrow suppression and pneumonia in the decitabine group was higher compared with that in the azacitidine group. Conclusion: Continuous and regular use of appropriate doses of hypomethylating agents may benefit MDS patients to the greatest extent if it is tolerated.

Published
2024
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