7,060 results on '"XIA, Z."'
Search Results
202. Understanding interfaces and mechanical properties of ceramic matrix composites
- Author
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Xia, Z., primary and Li, L., additional
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- 2014
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203. Ferroelectric polarization reversal in multiferroic MnWO4 via a rotating magnetic field up to 52 T
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Wang, J. F., primary, Liu, W. X., additional, He, Z. Z., additional, Liu, C. B., additional, Tokunaga, M., additional, Li, M., additional, Dong, C., additional, Han, X. T., additional, Herlach, F., additional, Lu, C. L., additional, Ouyang, Z. W., additional, Xia, Z. C., additional, Kindo, K., additional, Li, L., additional, and Yang, M., additional
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- 2021
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204. Imaging orbital ferromagnetism in a moiré Chern insulator
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Tschirhart, C. L., primary, Serlin, M., additional, Polshyn, H., additional, Shragai, A., additional, Xia, Z., additional, Zhu, J., additional, Zhang, Y., additional, Watanabe, K., additional, Taniguchi, T., additional, Huber, M. E., additional, and Young, A. F., additional
- Published
- 2021
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205. UPDATED EFFICACY AND SAFETY RESULTS OF ORELABRUTINIB IN THE TREATMENT OF RELAPSED OR REFRACTORY CHRONIC LYMPHOCYTIC LEUKEMIA/SMALL CELL LEUKEMIA
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Xu, W., primary, Song, Y., additional, Wang, T., additional, Yang, S., additional, Liu, L., additional, Hu, Y., additional, Zhang, W., additional, Zhou, J., additional, Gao, S., additional, Ding, K., additional, Zhang, H., additional, Zhu, Z., additional, Wang, S., additional, Xu, B., additional, Hu, J., additional, Liu, T., additional, Ji, C., additional, Xia, Z., additional, Li, Y., additional, Wang, X., additional, Zhao, R., additional, Zhang, B., additional, and Li, J., additional
- Published
- 2021
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206. Discovery of SARS-CoV-2 Papain-like Protease Inhibitors through a Combination of High-Throughput Screening and a FlipGFP-Based Reporter Assay
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Ma, C. Sacco, M.D. Xia, Z. Lambrinidis, G. Townsend, J.A. Hu, Y. Meng, X. Szeto, T. Ba, M. Zhang, X. Gongora, M. Zhang, F. Marty, M.T. Xiang, Y. Kolocouris, A. Chen, Y. Wang, J.
- Abstract
The papain-like protease (PLpro) of SARS-CoV-2 is a validated antiviral drug target. Through a fluorescence resonance energy transfer-based high-throughput screening and subsequent lead optimization, we identified several PLpro inhibitors including Jun9-72-2 and Jun9-75-4 with improved enzymatic inhibition and antiviral activity compared to GRL0617, which was reported as a SARS-CoV PLpro inhibitor. Significantly, we developed a cell-based FlipGFP assay that can be applied to predict the cellular antiviral activity of PLpro inhibitors in the BSL-2 setting. X-ray crystal structure of PLpro in complex with GRL0617 showed that binding of GRL0617 to SARS-CoV-2 induced a conformational change in the BL2 loop to a more closed conformation. Molecular dynamics simulations showed that Jun9-72-2 and Jun9-75-4 engaged in more extensive interactions than GRL0617. Overall, the PLpro inhibitors identified in this study represent promising candidates for further development as SARS-CoV-2 antivirals, and the FlipGFP-PLpro assay is a suitable surrogate for screening PLpro inhibitors in the BSL-2 setting. ©
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- 2021
207. Spatio-temporal pain estimation network with measuring pseudo heart rate gain
- Author
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Huang, D. (Dong), Feng, X. (Xiaoyi), Zhang, H. (Haixi), Yu, Z. (Zitong), Peng, J. (Jinye), Zhao, G. (Guoying), Xia, Z. (Zhaoqiang), Huang, D. (Dong), Feng, X. (Xiaoyi), Zhang, H. (Haixi), Yu, Z. (Zitong), Peng, J. (Jinye), Zhao, G. (Guoying), and Xia, Z. (Zhaoqiang)
- Abstract
Pain is a significant indicator that shows people are suffering from an unwell experience and its automatic estimation has attracted much interest in recent years. Of late, most estimation methods are designed to capture the dynamic pain information from visual signals while a few physiological-signal based methods can provide extra potential cues to analyze the pain more accurately. However, it is still challenging to capture the physiological data from patients as it requires contact devices and patients’ cooperation. In this paper, we propose to leverage the pseudo physiological information by generating new modal data from the original visual videos and jointly estimating the pain by an end-to-end network. To extract the representations from bi-modal data, we design a spatio-temporal pain estimation network, which employs a dual-branch framework for extracting pain-aware visual and pseudo physiological features separately and fuses the features in a probabilistic way. The inherent vital sign, i.e., heart rate gain (HRG), from pseudo physiological information can be utilized as an auxiliary signal and integrated with the visual pain estimation framework. Moreover, specially-designed 3D convolution filters and attention structures are employed to extract spatio-temporal features for both branches. To use the HRG as an auxiliary way for pain estimation, we propose a probabilistic inference model by jointly considering the visual branch and physiological branch, which makes our model estimate the pain comprehensively. Experiments on two publicly-available datasets show the effectiveness of introducing the pseudo modality, and the proposed method can outperform the state-of-the-art methods.
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- 2021
208. Cross-View Matching for Vehicle Localization by Learning Geographically Local Representations
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Xia, Z. (author), Booij, Olaf (author), Manfredi, Marco (author), Kooij, J.F.P. (author), Xia, Z. (author), Booij, Olaf (author), Manfredi, Marco (author), and Kooij, J.F.P. (author)
- Abstract
Cross-view matching aims to learn a shared image representation between ground-level images and satellite or aerial images at the same locations. In robotic vehicles, matching a camera image to a database of geo-referenced aerial imagery can serve as a method for self-localization. However, existing work on cross-view matching only aims at global localization, and overlooks the easily accessible rough location estimates from GNSS or temporal filtering. We argue that the availability of coarse location estimates at test time should already be considered during training. We adopt a simple but effective adaptation to the common triplet loss, resulting in an image representation that is more discriminative within the geographically local neighborhood, without any modifications to a baseline deep neural network. Experiments on the CVACT dataset confirm that the improvements generalize across spatial regions. On a new benchmark constructed from the Oxford RobotCar dataset, we also show generalization across recording days within the same region. Finally, we validate that improvements on these image-retrieval benchmarks also translate to a real-world localization task. Using a particle filter to fuse the cross-view matching scores of a vehicle's camera stream with real GPS measurements, our learned geographically local representation reduces the mean localization error by 17\% compared to the standard global representation learned by the current state-of-the-art., Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public., Intelligent Vehicles
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- 2021
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209. Revisiting pixel-wise supervision for face anti-spoofing
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Yu, Z. (Zitong), Li, X. (Xiaobai), Shi, J. (Jingang), Xia, Z. (Zhaoqiang), Zhao, G. (Guoying), Yu, Z. (Zitong), Li, X. (Xiaobai), Shi, J. (Jingang), Xia, Z. (Zhaoqiang), and Zhao, G. (Guoying)
- Abstract
Face anti-spoofing (FAS) plays a vital role in securing face recognition systems from the presentation attacks (PAs). As more and more realistic PAs with novel types spring up, it is necessary to develop robust algorithms for detecting unknown attacks even in unseen scenarios. However, deep models supervised by traditional binary loss (e.g., ’0’ for bonafide vs. ’1’ for PAs) are weak in describing intrinsic and discriminative spoofing patterns. Recently, pixel-wise supervision has been proposed for the FAS task, intending to provide more fine-grained pixel/patch-level cues. In this paper, we firstly give a comprehensive review and analysis about the existing pixel-wise supervision methods for FAS. Then we propose a novel pyramid supervision, which guides deep models to learn both local details and global semantics from multi-scale spatial context. Extensive experiments are performed on five FAS benchmark datasets to show that, without bells and whistles, the proposed pyramid supervision could not only improve the performance beyond existing pixel-wise supervision frameworks, but also enhance the model’s interpretability (i.e., locating the patch-level positions of PAs more reasonably). Furthermore, elaborate studies are conducted for exploring the efficacy of different architecture configurations with two kinds of pixel-wise supervisions (binary mask and depth map supervisions), which provides inspirable insights for future architecture/supervision design.
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- 2021
210. Guidelines for the use and interpretation of assays for monitoring autophagy (4th edition)
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Klionsky, D, Abdel-Aziz, A, Abdelfatah, S, Abdellatif, M, Abdoli, A, Abel, S, Abeliovich, H, Abildgaard, M, Abudu, Y, Acevedo-Arozena, A, Adamopoulos, I, Adeli, K, Adolph, T, Adornetto, A, Aflaki, E, Agam, G, Agarwal, A, Aggarwal, B, Agnello, M, Agostinis, P, Agrewala, J, Agrotis, A, Aguilar, P, Ahmad, S, Ahmed, Z, Ahumada-Castro, U, Aits, S, Aizawa, S, Akkoc, Y, Akoumianaki, T, Akpinar, H, Al-Abd, A, Al-Akra, L, Al-Gharaibeh, A, Alaoui-Jamali, M, Alberti, S, Alcocer-Gómez, E, Alessandri, C, Ali, M, Alim Al-Bari, M, Aliwaini, S, Alizadeh, J, Almacellas, E, Almasan, A, Alonso, A, Alonso, G, Altan-Bonnet, N, Altieri, D, Álvarez, É, Alves, S, Alves da Costa, C, Alzaharna, M, Amadio, M, Amantini, C, Amaral, C, Ambrosio, S, Amer, A, Ammanathan, V, An, Z, Andersen, S, Andrabi, S, Andrade-Silva, M, Andres, A, Angelini, S, Ann, D, Anozie, U, Ansari, M, Antas, P, Antebi, A, Antón, Z, Anwar, T, Apetoh, L, Apostolova, N, Araki, T, Araki, Y, Arasaki, K, Araújo, W, Araya, J, Arden, C, Arévalo, M, Arguelles, S, Arias, E, Arikkath, J, Arimoto, H, Ariosa, A, Armstrong-James, D, Arnauné-Pelloquin, L, Aroca, A, Arroyo, D, Arsov, I, Artero, R, Asaro, D, Aschner, M, Ashrafizadeh, M, Ashur-Fabian, O, Atanasov, A, Au, A, Auberger, P, Auner, H, Aurelian, L, Autelli, R, Avagliano, L, Ávalos, Y, Aveic, S, Aveleira, C, Avin-Wittenberg, T, Aydin, Y, Ayton, S, Ayyadevara, S, Azzopardi, M, Baba, M, Backer, J, Backues, S, Bae, D, Bae, O, Bae, S, Baehrecke, E, Baek, A, Baek, S, Bagetta, G, Bagniewska-Zadworna, A, Bai, H, Bai, J, Bai, X, Bai, Y, Bairagi, N, Baksi, S, Balbi, T, Baldari, C, Balduini, W, Ballabio, A, Ballester, M, Balazadeh, S, Balzan, R, Bandopadhyay, R, Banerjee, S, Bánréti, Á, Bao, Y, Baptista, M, Baracca, A, Barbati, C, Bargiela, A, Barilà, D, Barlow, P, Barmada, S, Barreiro, E, Barreto, G, Bartek, J, Bartel, B, Bartolome, A, Barve, G, Basagoudanavar, S, Bassham, D, Bast, R, Basu, A, Batoko, H, Batten, I, Baulieu, E, Baumgarner, B, Bayry, J, Beale, R, Beau, I, Beaumatin, F, Bechara, L, Beck, G, Beers, M, Begun, J, Behrends, C, Behrens, G, Bei, R, Bejarano, E, Bel, S, Behl, C, Belaid, A, Belgareh-Touzé, N, Bellarosa, C, Belleudi, F, Belló Pérez, M, Bello-Morales, R, Beltran, J, Beltran, S, Benbrook, D, Bendorius, M, Benitez, B, Benito-Cuesta, I, Bensalem, J, Berchtold, M, Berezowska, S, Bergamaschi, D, Bergami, M, Bergmann, A, Berliocchi, L, Berlioz-Torrent, C, Bernard, A, Berthoux, L, Besirli, C, Besteiro, S, Betin, V, Beyaert, R, Bezbradica, J, Bhaskar, K, Bhatia-Kissova, I, Bhattacharya, R, Bhattacharya, S, Bhattacharyya, S, Bhuiyan, M, Bhutia, S, Bi, L, Bi, X, Biden, T, Bijian, K, Billes, V, Binart, N, Bincoletto, C, Birgisdottir, A, Bjorkoy, G, Blanco, G, Blas-Garcia, A, Blasiak, J, Blomgran, R, Blomgren, K, Blum, J, Boada-Romero, E, Boban, M, Boesze-Battaglia, K, Boeuf, P, Boland, B, Bomont, P, Bonaldo, P, Bonam, S, Bonfili, L, Bonifacino, J, Boone, B, Bootman, M, Bordi, M, Borner, C, Bornhauser, B, Borthakur, G, Bosch, J, Bose, S, Botana, L, Botas, J, Boulanger, C, Boulton, M, Bourdenx, M, Bourgeois, B, Bourke, N, Bousquet, G, Boya, P, Bozhkov, P, Bozi, L, Bozkurt, T, Brackney, D, Brandts, C, Braun, R, Braus, G, Bravo-Sagua, R, Bravo-San Pedro, J, Brest, P, Bringer, M, Briones-Herrera, A, Broaddus, V, Brodersen, P, Brodsky, J, Brody, S, Bronson, P, Bronstein, J, Brown, C, Brown, R, Brum, P, Brumell, J, Brunetti-Pierri, N, Bruno, D, Bryson-Richardson, R, Bucci, C, Buchrieser, C, Bueno, M, Buitrago-Molina, L, Buraschi, S, Buch, S, Buchan, J, Buckingham, E, Budak, H, Budini, M, Bultynck, G, Burada, F, Burgoyne, J, Burón, M, Bustos, V, Büttner, S, Butturini, E, Byrd, A, Cabas, I, Cabrera-Benitez, S, Cadwell, K, Cai, J, Cai, L, Cai, Q, Cairó, M, Calbet, J, Caldwell, G, Caldwell, K, Call, J, Calvani, R, Calvo, A, Calvo-Rubio Barrera, M, Camara, N, Camonis, J, Camougrand, N, Campanella, M, Campbell, E, Campbell-Valois, F, Campello, S, Campesi, I, Campos, J, Camuzard, O, Cancino, J, Candido de Almeida, D, Canesi, L, Caniggia, I, Canonico, B, Cantí, C, Cao, B, Caraglia, M, Caramés, B, Carchman, E, Cardenal-Muñoz, E, Cardenas, C, Cardenas, L, Cardoso, S, Carew, J, Carle, G, Carleton, G, Carloni, S, Carmona-Gutierrez, D, Carneiro, L, Carnevali, O, Carosi, J, Carra, S, Carrier, A, Carrier, L, Carroll, B, Carter, A, Carvalho, A, Casanova, M, Casas, C, Casas, J, Cassioli, C, Castillo, E, Castillo, K, Castillo-Lluva, S, Castoldi, F, Castori, M, Castro, A, Castro-Caldas, M, Castro-Hernandez, J, Castro-Obregon, S, Catz, S, Cavadas, C, Cavaliere, F, Cavallini, G, Cavinato, M, Cayuela, M, Cebollada Rica, P, Cecarini, V, Cecconi, F, Cechowska-Pasko, M, Cenci, S, Ceperuelo-Mallafré, V, Cerqueira, J, Cerutti, J, Cervia, D, Cetintas, V, Cetrullo, S, Chae, H, Chagin, A, Chai, C, Chakrabarti, G, Chakrabarti, O, Chakraborty, T, Chami, M, Chamilos, G, Chan, D, Chan, E, Chan, H, Chan, M, Chan, Y, Chandra, P, Chang, C, Chang, H, Chang, K, Chao, J, Chapman, T, Charlet-Berguerand, N, Chatterjee, S, Chaube, S, Chaudhary, A, Chauhan, S, Chaum, E, Checler, F, Cheetham, M, Chen, C, Chen, G, Chen, J, Chen, L, Chen, M, Chen, N, Chen, Q, Chen, R, Chen, S, Chen, W, Chen, X, Chen, Y, Chen, Z, Cheng, H, Cheng, J, Cheng, S, Cheng, W, Cheng, X, Cheng, Y, Cheng, Z, Cheong, H, Cheong, J, Chernyak, B, Cherry, S, Cheung, C, Cheung, K, Chevet, E, Chi, R, Chiang, A, Chiaradonna, F, Chiarelli, R, Chiariello, M, Chica, N, Chiocca, S, Chiong, M, Chiou, S, Chiramel, A, Chiurchiù, V, Cho, D, Choe, S, Choi, A, Choi, M, Choudhury, K, Chow, N, Chu, C, Chua, J, Chung, H, Chung, K, Chung, S, Chung, Y, Cianfanelli, V, Ciechomska, I, Cifuentes, M, Cinque, L, Cirak, S, Cirone, M, Clague, M, Clarke, R, Clementi, E, Coccia, E, Codogno, P, Cohen, E, Cohen, M, Colasanti, T, Colasuonno, F, Colbert, R, Colell, A, Čolić, M, Coll, N, Collins, M, Colombo, M, Colón-Ramos, D, Combaret, L, Comincini, S, Cominetti, M, Consiglio, A, Conte, A, Conti, F, Contu, V, Cookson, M, Coombs, K, Coppens, I, Corasaniti, M, Corkery, D, Cordes, N, Cortese, K, Costa, M, Costantino, S, Costelli, P, Coto-Montes, A, Crack, P, Crespo, J, Criollo, A, Crippa, V, Cristofani, R, Csizmadia, T, Cuadrado, A, Cui, B, Cui, J, Cui, Y, Culetto, E, Cumino, A, Cybulsky, A, Czaja, M, Czuczwar, S, D'Adamo, S, D'Amelio, M, D'Arcangelo, D, D'Lugos, A, D'Orazi, G, da Silva, J, Dafsari, H, Dagda, R, Dagdas, Y, Daglia, M, Dai, X, Dai, Y, Dal Col, J, Dalhaimer, P, Dalla Valle, L, Dallenga, T, Dalmasso, G, Damme, M, Dando, I, Dantuma, N, Darling, A, Das, H, Dasarathy, S, Dasari, S, Dash, S, Daumke, O, Dauphinee, A, Davies, J, Dávila, V, Davis, R, Davis, T, Dayalan Naidu, S, De Amicis, F, De Bosscher, K, De Felice, F, De Franceschi, L, De Leonibus, C, de Mattos Barbosa, M, De Meyer, G, De Milito, A, De Nunzio, C, De Palma, C, De Santi, M, De Virgilio, C, De Zio, D, Debnath, J, Debosch, B, Decuypere, J, Deehan, M, Deflorian, G, Degregori, J, Dehay, B, Del Rio, G, Delaney, J, Delbridge, L, Delorme-Axford, E, Delpino, M, Demarchi, F, Dembitz, V, Demers, N, Deng, H, Deng, Z, Dengjel, J, Dent, P, Denton, D, Depamphilis, M, Der, C, Deretic, V, Descoteaux, A, Devis, L, Devkota, S, Devuyst, O, Dewson, G, Dharmasivam, M, Dhiman, R, di Bernardo, D, Di Cristina, M, Di Domenico, F, Di Fazio, P, Di Fonzo, A, Di Guardo, G, Di Guglielmo, G, Di Leo, L, Di Malta, C, Di Nardo, A, Di Rienzo, M, Di Sano, F, Diallinas, G, Diao, J, Diaz-Araya, G, Díaz-Laviada, I, Dickinson, J, Diederich, M, Dieudé, M, Dikic, I, Ding, S, Ding, W, Dini, L, Dinić, J, Dinic, M, Dinkova-Kostova, A, Dionne, M, Distler, J, Diwan, A, Dixon, I, Djavaheri-Mergny, M, Dobrinski, I, Dobrovinskaya, O, Dobrowolski, R, Dobson, R, Đokić, J, Dokmeci Emre, S, Donadelli, M, Dong, B, Dong, X, Dong, Z, Dorn Ii, G, Dotsch, V, Dou, H, Dou, J, Dowaidar, M, Dridi, S, Drucker, L, Du, A, Du, C, Du, G, Du, H, Du, L, du Toit, A, Duan, S, Duan, X, Duarte, S, Dubrovska, A, Dunlop, E, Dupont, N, Durán, R, Dwarakanath, B, Dyshlovoy, S, Ebrahimi-Fakhari, D, Eckhart, L, Edelstein, C, Efferth, T, Eftekharpour, E, Eichinger, L, Eid, N, Eisenberg, T, Eissa, N, Eissa, S, Ejarque, M, El Andaloussi, A, El-Hage, N, El-Naggar, S, Eleuteri, A, El-Shafey, E, Elgendy, M, Eliopoulos, A, Elizalde, M, Elks, P, Elsasser, H, Elsherbiny, E, Emerling, B, Emre, N, Eng, C, Engedal, N, Engelbrecht, A, Engelsen, A, Enserink, J, Escalante, R, Esclatine, A, Escobar-Henriques, M, Eskelinen, E, Espert, L, Eusebio, M, Fabrias, G, Fabrizi, C, Facchiano, A, Facchiano, F, Fadeel, B, Fader, C, Faesen, A, Fairlie, W, Falcó, A, Falkenburger, B, Fan, D, Fan, J, Fan, Y, Fang, E, Fang, Y, Fanto, M, Farfel-Becker, T, Faure, M, Fazeli, G, Fedele, A, Feldman, A, Feng, D, Feng, J, Feng, L, Feng, Y, Feng, W, Fenz Araujo, T, Ferguson, T, Fernández, Á, Fernandez-Checa, J, Fernández-Veledo, S, Fernie, A, Ferrante, A, Ferraresi, A, Ferrari, M, Ferreira, J, Ferro-Novick, S, Figueras, A, Filadi, R, Filigheddu, N, Filippi-Chiela, E, Filomeni, G, Fimia, G, Fineschi, V, Finetti, F, Finkbeiner, S, Fisher, E, Fisher, P, Flamigni, F, Fliesler, S, Flo, T, Florance, I, Florey, O, Florio, T, Fodor, E, Follo, C, Fon, E, Forlino, A, Fornai, F, Fortini, P, Fracassi, A, Fraldi, A, Franco, B, Franco, R, Franconi, F, Frankel, L, Friedman, S, Fröhlich, L, Frühbeck, G, Fuentes, J, Fujiki, Y, Fujita, N, Fujiwara, Y, Fukuda, M, Fulda, S, Furic, L, Furuya, N, Fusco, C, Gack, M, Gaffke, L, Galadari, S, Galasso, A, Galindo, M, Gallolu Kankanamalage, S, Galluzzi, L, Galy, V, Gammoh, N, Gan, B, Ganley, I, Gao, F, Gao, H, Gao, M, Gao, P, Gao, S, Gao, W, Gao, X, Garcera, A, Garcia, M, Garcia, V, García-Del Portillo, F, Garcia-Escudero, V, Garcia-Garcia, A, Garcia-Macia, M, García-Moreno, D, Garcia-Ruiz, C, García-Sanz, P, Garg, A, Gargini, R, Garofalo, T, Garry, R, Gassen, N, Gatica, D, Ge, L, Ge, W, Geiss-Friedlander, R, Gelfi, C, Genschik, P, Gentle, I, Gerbino, V, Gerhardt, C, Germain, K, Germain, M, Gewirtz, D, Ghasemipour Afshar, E, Ghavami, S, Ghigo, A, Ghosh, M, Giamas, G, Giampietri, C, Giatromanolaki, A, Gibson, G, Gibson, S, Ginet, V, Giniger, 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Zhonglin, Xilouri, Maria, Xu, Congfeng, Xu, En, Xu, Haoxing, Xu, Jing, Xu, JinRong, Xu, Liang, Xu, Wen Wen, Xu, Xiulong, Xue, Yu, Yakhine-Diop, Sokhna M S, Yamaguchi, Masamitsu, Yamaguchi, Osamu, Yamamoto, Ai, Yamashina, Shunhei, Yan, Shengmin, Yan, Shian-Jang, Yan, Zhen, Yanagi, Yasuo, Yang, Chuanbin, Yang, Dun-Sheng, Yang, Huan, Yang, Huang-Tian, Yang, Hui, Yang, Jin-Ming, Yang, Jing, Yang, Jingyu, Yang, Ling, Yang, Liu, Yang, Ming, Yang, Pei-Ming, Yang, Qian, Yang, Seungwon, Yang, Shu, Yang, Shun-Fa, Yang, Wannian, Yang, Wei Yuan, Yang, Xiaoyong, Yang, Xuesong, Yang, Yi, Yang, Ying, Yao, Honghong, Yao, Shenggen, Yao, Xiaoqiang, Yao, Yong-Gang, Yao, Yong-Ming, Yasui, Takahiro, Yazdankhah, Meysam, Yen, Paul M, Yi, Cong, Yin, Xiao-Ming, Yin, Yanhai, Yin, Zhangyuan, Yin, Ziyi, Ying, Meidan, Ying, Zheng, Yip, Calvin K, Yiu, Stephanie Pei Tung, Yoo, Young H, Yoshida, Kiyotsugu, Yoshii, Saori R, Yoshimori, Tamotsu, Yousefi, Bahman, Yu, Boxuan, Yu, Haiyang, Yu, Jun, Yu, Li, Yu, Ming-Lung, 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Werner, Brand-Saberi, Beate, Dong, X Charlie, Kenchappa, Chandra Shekar, Li, Zuguo, Lin, Yong, Oshima, Shigeru, Rong, Yueguang, Sluimer, Judith C, Stallings, Christina L, and Tong, Chun-Kit
- Abstract
In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.
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- 2021
211. An analytical model to determine the stress singularity and critical bonding angle for an elastic–viscoelastic bonded joint
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Chowdhuri, M. A. K. and Xia, Z.
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- 2012
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212. Luminescence properties and energy transfer in Ba2Y(BO3)2Cl:Ce3+,Tb3+ phosphors
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Sun, J., Lai, J., Xia, Z., Zhang, X., Liu, H., and Du, H.
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- 2012
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213. Exergy analysis of incremental sheet forming
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Dittrich, M. A., Gutowski, T. G., Cao, J., Roth, J. T., Xia, Z. C., Kiridena, V., Ren, F., and Henning, H.
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- 2012
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214. 1556P Pharmacokinetics, pharmacodynamics and exposure response analyses of osemitamab in patients with locally advanced or metastatic solid tumors
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Shen, L., Yao, J., Chen, D., Yu, M., Lin, X-A., Qi, C., Xia, Z., Zhang, L., Yu, S., and Germa, C.
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- 2023
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215. Microplastics Mitigation in Sewage Sludge through Pyrolysis: The Role of Pyrolysis Temperature
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Ni BJ, Zhu ZR, Li WH, Yan X, Wei W, Xu Q, Xia Z, Dai X, and Sun J
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0502 Environmental Science and Management, 0907 Environmental Engineering, 1002 Environmental Biotechnology - Abstract
© 2020 American Chemical Society. All rights reserved. Sewage sludge is an important source of introducing microplastics into the environment, and thus, effective mitigation of microplastics in the sludge is in urgent need. Herein, the effect of pyrolysis on microplastics reduction in sewage sludge was investigated through a lab-scale study. The micro-Raman analysis showed that the microplastics concentrations in sludge residues decreased significantly from 550.8 to 960.9 particles/g to 1.4-2.3 particles/g with the pyrolysis temperature increasing to 500 °C, and no tiny (10-50 μm) microplastics remained. Polyethylene and polypropylene, the two most abundant microplastics in sewage sludge, were entirely degraded when the pyrolysis temperature reached 450 °C. However, during the pyrolysis process, new plastic polymers could be produced through the reaction between original microplastics with organics in sludge, and heavy metals in sludge can also be combined. Moreover, scanning electron microscopy analysis of spiked microplastics showed that incomplete pyrolysis at low temperatures could result in rough surface morphology of microplastics, making it more readily to adsorb contaminants. Overall, the results of this study provide the first insight into the effectiveness of microplastics control in sewage sludge through pyrolysis, but to avoid potential environmental risks induced by incomplete pyrolysis, a pyrolysis temperature of 450 °C should be reached at least.
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- 2020
216. The Value of Preoperative Urodynamics in Surgery Decision for Pelvic Organ Prolapse Patients with Occult Stress Urinary Incontinence
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Xu H, Zhao Y, Hu Q, and Xia Z
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Pelvic organ ,medicine.medical_specialty ,business.industry ,Surgery decision ,Medicine ,Urinary incontinence ,medicine.symptom ,business ,Value (mathematics) ,Occult ,Surgery - Abstract
This retrospective cohort study aims to explore the clinical value of urodynamics in evaluating lower urinary tract function in pelvic organ prolapse (POP) patients, and to investigate the urodynamic characteristics of POP patients with occult stress urinary incontinence (OSUI) in whom moderate or above postoperative SUI eventually occurred after prolapse surgery. The medical records of 626 advanced POP patients who underwent vaginal pelvic reconstructive surgery were analyzed. The patients with anterior compartment prolapse were more susceptible to urodynamic changes than those with apical or posterior compartment prolapse (95.1% vs. 40.15%). The proportion of patients with urodynamic abnormality was increased with prolapse severity (68.8% vs. 78.6%). After 3 months, the incidence of moderate or severe postoperative urinary incontinence in the OSUI patients was 20%, while it is 2.8% in non-OSUI patients. In subgroup analysis of OSUI patients, the urodynamics of patients with moderate or above postoperative urinary leakage showed significantly lower Valsalva leak point pressure (VLPP), maximum urethral closure pressure (MUCP), maximal bladder volume (MBV) and maximum urine flow rate (MUFR). For POP patients with OSUI, anti-incontinence surgery is only recommended for whom with lower VLPP, MUCP, MBV and MUFR to avoid the risk and costs of secondary surgery or overtreatment.
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- 2020
217. Genomic Insights into the Demographic History of Southern Chinese
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Yin L, Yile Li, Huang X, Ma Z, Xia Z, Guanglin He, Jing Zhao, Jianxin Guo, Lin C, Chuan-Chao Wang, Leyi Wei, Rong Hu, and Bin X
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Genetic diversity ,Geography ,Human migration ,business.industry ,Demographic history ,Genetic structure ,Population genetics ,East Asia ,Cline (biology) ,business ,Archaeology ,Holocene - Abstract
Southern China is the birthplace of rice-cultivating agriculture, different language families, and human migrations that facilitated these cultural diffusions. The fine-scale demographic historyin situ, however, remains unclear. To comprehensively cover the genetic diversity in East and Southeast Asia, we generated genome-wide SNP data from 211 present-day Southern Chinese and co-analyzed them with more than 1,200 ancient and modern genomes. We discover that the previously described ‘Southern East Asian’ or ‘Yangtze River Farmer’ lineage is monophyletic but not homogeneous, comprising four regionally differentiated sub-ancestries. These ancestries are respectively responsible for the transmission of Austronesian, Kra-Dai, Hmong-Mien, and Austroasiatic languages and their original homelands successively distributed from East to West in Southern China. Multiple phylogenetic analyses support that the earliest living branching among East Asian-related populations is First Americans (∼27,700 BP), followed by the pre-LGM differentiation between Northern and Southern East Asians (∼23,400 BP) and the pre-Neolithic split between Coastal and Inland Southern East Asians (∼16,400 BP). In North China, distinct coastal and inland routes of south-to-north gene flow had established by the Holocene, and further migration and admixture formed the genetic profile of Sinitic speakers by ∼4,000 BP. Four subsequent massive migrations finalized the complete genetic structure of present-day Southern Chinese. First, a southward Sinitic migration and the admixture with Kra-Dai speakers formed the ‘Sinitic Cline’. Second, a bi-directional admixture between Hmong-Mien and Kra-Dai speakers gave rise to the ‘Hmong-Mien Cline’ in the interior of South China between ∼2,000 and ∼1,000 BP. Third, a southwestward migration of Kra-Dai speakers in recent ∼2,000 years impacted the genetic profile for the majority of Mainland Southeast Asians. Finally, an admixture between Tibeto-Burman incomers and indigenous Austroasiatic speakers formed the Tibeto-Burman speakers in Southeast Asia by ∼2,000 BP.
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- 2020
218. TaC precipitation behaviors in reduced activation martensitic steels
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Xia, Z. X., Zhang, C., Lan, H., and Yang, Z. G.
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- 2011
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219. Effects of nano-structured particles on microstructure and microhardness of Sn–Ag solder alloy
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Tai, F., Guo, F., Xia, Z. D., Lei, Y. P., and Shi, Y. W.
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- 2010
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220. Progress in the Development of the Wuhan High Magnetic Field Center
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Li, L., Peng, T., Ding, H. F., Han, X. T., Xia, Z. C., Ding, T. H., Wang, J. F., Xie, J. F., Wang, S. L., Huang, Y., Duan, X. Z., Yao, K. L., Herlach, F., Vanacken, J., and Pan, Y.
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- 2010
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221. Unusual Hysteresis Loop of (Tb0.2Pr0.8)(Fe0.4Co0.6)1.9−x C x in Pulsed High Magnetic Field
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Xia, Z. C., Cheng, G., Wu, Y. Y., Li, H. N., Chen, Z. Y., Li, L., and Tang, C. Q.
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- 2010
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222. Use of failure-to-rescue to identify international variation in postoperative care in low-, middle- and high-income countries
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Ahmad T., Bouwman R. A., Grigoras I., Aldecoa C., Hofer C., Hoeft A., Holt P., Buhre W., Pearse R. M., Clavien P. -A., Demartines N., Grocott M., Haddow J., Moreno R., Pritchard N., Rhodes A., Wilson M., Ahmed T., Halliwell R., Shulman M., Myles P., Schmid W., Hiesmayr M., Wouters P., De Hert S., Lobo S., Beattie S., Wijeysundera D., Fang X., Rasmussen L., Futier E., Biais M., Venara A., Slim K., Sander M., Koulenti D., Arvaniti K., Chan M., Kulkarni A., Chandra S., Tantri A., Geddoa E., Abbas M., Della Rocca G., Sivasakthi D., Mansor M., Luna P., Bouwman A., Beavis V., Campbell D., Short T., Osinaike T., Matos R., Kirov M., Protsenko D., Biccard B., Chew M., Hubner M., Ditai J., Szakmany T., Fleisher L., Ferguson M., MacMahon M., Cherian R., Currow H., Kanathiban K., Gillespie D., Pathmanathan E., Phillips K., Reynolds J., Rowley J., Douglas J., Kerridge R., Garg S., Bennett M., Jain M., Alcock D., Terblanche N., Cotter R., Leslie K., Stewart M., Zingerle N., Clyde A., Hambidge O., Rehak A., Cotterell S., Huynh W. B. Q., McCulloch T., Ben-Menachem E., Egan T., Cope J., Fellinger P., Markus P., Haselberger S., Holaubek C., Lichtenegger P., Scherz F., Hoffer F., Cakova V., Eichwalder A., Fischbach N., Klug R., Schneider E., Vesely M., Wickenhauser R., Gernot K., Leitgeb M., Lang F., Toro N., Bauer M., Laengle F., Haberl C., Mayrhofer T., Trybus C., Buerkle C., Forstner K., Germann R., Rinoesl H., Schindler E., Trampitsch E., Bogner G., Dankl D., Duenser M., Fritsch G., Gradwohl I. -M., Hartmann A., Hoelzenbein T., Jaeger T., Landauer F., Lindl G., Lux M., Steindl J., Stundner O., Szabo C., Bidgoli J., Verdoodt H., Forget P., Kahn D., Lois F., Momeni M., Pregardien C., Pospiech A., Steyaert A., Veevaete L., De Kegel D., De Jongh K., Foubert L., Smitz C., Vercauteren M., Poelaert J., Van Mossevelde V., Abeloos J., Bouchez S., Coppens M., De Baerdemaeker L., Deblaere I., De Bruyne A., Fonck K., Heyse B., Jacobs T., Lapage K., Moerman A., Neckebroek M., Parashchanka A., Roels N., Van Den Eynde N., Vandenheuvel M., Van Limmen J., Vanluchene A., Vanpeteghem C., Wyffels P., Huygens C., Vandenbempt P., Van De Velde M., Dylst D., Janssen B., Schreurs E., Aleixo F. B., Candido K., Batista H. D., Guimaraes M., Guizeline J., Hoffmann J., Lobo F. R. M., Nascimento V., Nishiyama K., Pazetto L., Souza D., Rodrigues R. S., Dos Santos A. M. V., Jardim J., Sa Malbouisson L. M., Silva J., Do Nascimento P., Baio T. H., De Castro G. I. P., Oliveira H. R. W., Amendola C. P., Cardoso G., Ortega D., Brotto A. F., De Oliveira M. C., Rea-Neto A., Dias F., Travi M. E., Zerman L., Azambuja P., Knibel M. F., Martins A., Almeida W., Neto C. N., Tardelli M. A., Caser E., MacHado M., Aguzzoli C., Baldisserotto S., Tabajara F. B., Bettega F., Rodrigues L. H. C., De Gasperi J., Faina L., Nolasco M. F., Da Costa Fischer B. F., De Campos Ferreira M. F., Hartmann C., Kliemann M., Ribeiro G. L. H., Fraga J. M., Netto T. M., Pozza L. V., Wendling P. R., Azevedo C., Garcia J., Lopes M., Maia B., Maselli P., Melo R., Mendes W., Neves M., Ney J., Piras C., Applewhaite C., Carr A., Chow L., Duttchen K., Foglia J., Greene M., Hinther A., Houston K., McCormick T. J., Mikhayel J., Montasser S., Ragan A., Suen A., Woolsey A., Yu H. C., Funk D., Stephen D., Regina D., Heather D., Faisal D., Pridham J., Rowe B., Sampson S., Thiessen B., Zbitnew G., Bernard A., George R., Jones P., Moor R., Siddiqui N., Wolfer A., Tran D., Winch D., Dobson G., Montasser O., Hall R., Baghirzada L., Curley G., Dai S. Y., Hare G., Lee E., Shastri U., Tsui A., Yagnik A., Alvares D., Choi S., Dwyer H., Flores K., McCartney C., Somascanthan P., Carroll J., Pazmino-Canizares J., Ami N., Chan V., Perlas A., Argue R., Huang Y., Lavis K., Mayson K., Cao Y., Gao H., Hu T., Lv J., Yang J., Yang Y., Zhong Y., Zhou J., Zou X., He M., Li X., Luo D., Wang H., Yu T., Chen L., Wang L., Cai Y., Cao Z., Li Y., Lian J., Sun H., Wang S., Wang Z., Wang K., Zhu Y., Du X., Fan H., Fu Y., Huang L., Hwan H., Luo H., Qu P. -S., Tao F., Wang G., Zhang Y., Zhang X., Chen C., Wang W., Liu Z., Fan L., Tang J., Chen Y., Han Y., Huang C., Liang G., Shen J., Wang J., Yang Q., Zhen J., Zhou H., Chen J., Chen Z., Meng B., Ye H., Bi Y., Cao J., Guo F., Lin H., Liu Y., Lv M., Shi P., Song X., Sun C., Sun Y., Wang Y., Zhang M., Chen R., Hou J., Leng Y., Meng Q. -T., Qian L., Shen Z. -Y., Xia Z. -Y., Xue R., Zhao B., Zhou X. -J., Chen Q., Guo H., Guo Y., Qi Y., Wei J., Zhang W., Zheng L., Bao Q., Fei Y., Hu N., Hu X., Lei M., Lv X., Miao F., Ouyang L., Shen C., Wang D., Wu C., Xu L., Yuan J., Zhang L., Zhang H., Zhao J., Zhao C., Zhao L., Zheng T., Zhou D., Zhou C., Lu K., Zhao T., He C., Chen H., Chen S., Cheng B., He J., Jin L., Li C., Li H., Pan Y., Shi Y., Wen X. H., Wu S., Xie G., Zhang K., Lu X., Chen F., Liang Q., Lin X., Ling Y., Liu G., Tao J., Yang L., Cheng Z., Dai H., Feng Y., Hou B., Gong H., Hua C., Huang H., Huang J., Jiang Z., Li M., Lin J., Liu M., Liu W., Luo F., Ma L., Min J., Shi X., Song Z., Wan X., Xiong Y., Yang S., Zhang Q., Zhao W., Zhu X., Bai Y., Dai Q., Geng W., Han K., He X., Ji B., Jia D., Jin S., Li Q., Liang D., Luo S., Lwang L., Mo Y., Qi X., Qian M., Qin J., Ren Y., Xie J., Yan Y., Yao Y., Zhuang X., Ai Y., Du F., He L., Li Z., Li L., Meng S., Yuan Y., Zhang E., Zhang J., Zhao S., Ji Z., Pei L., Dong B., Li J., Miao Z., Mu H., Qin C., Su L., Wen Z., Xie K., Yu Y., Yuan F., Xiao W., Zhu Z., Fu K., Hu R., Huang S., Liang Y., Yu S., Guo Z., Jing Y., Tang N., Wu J., Yuan D., Zhang R., Zhao X., Bai H. -P., Liu C. -X., Liu F. -F., Ren W., Wang X. -L., Xu G. -J., Li B., Ou Y., Tang Y., Yao S., Zhang S., Kong C. -C., Liu B., Wang T., Lu B., Xia Y., Cai F., Chen P., Hu S., Xu Q., Hu L., Jing L., Liu Q., Dan Z., Qiu X., Ren Q., Tong Y., Wen Y., Wu Q., Xia J., Xiong X., Xu S., Yang T., Yin N., Zeng Q., Zhang B., Zheng K., Cang J., Fan Y., Fu S., Ge X., Guo B., Huang W., Jiang L., Jiang X., Shan Q., Wang F., Christiansen I. C., Granum S. N., Rasmussen B. S., Daugaard M., Gambhir R., Brandsborg B., Steingrimsdottir G. E., Jensen-Gadegaard P., Kovgaard K. O., Siegel H., Eskildsen K. Z., Gatke M. R., Wibrandt I., Heintzelmann S. B., Lange K. H. W., Lundsgaard R. S., Amstrup-Hansen L., Hovendal C., Larsen M., Lenstrup M., Kobborg T., Larsen J. R., Pedersen A. B., Smith S. H., Oestervig R. M., Afshari A., Andersen C., Ekelund K., Lilja E., Beloeil H., Lasocki S., Ouattara A., Sineus M., Molliex S., Legouge M. L., Wallet F., Tesniere A., Gaudin C., Lehur P., Forsans E., De Rudnicki S., Maudet V. 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M., Beretta L., Bignami E., Bocchino S., Cabrini L., Corti D., Landoni G., Meroni R., Moizo E., Monti G., Pintaudi M., Plumari V. P., Taddeo D., Testa V., Winterton D., Zangrillo A., Cloro R. L. M., Colangelo C., Colangelo A., Rotunno G., Paludi M. A., Maria C. P., Pata A., Parrini V., Gatta A., Nastasi M., Tinti C., Baroselli A., Arrigo M., Benevento A., Bottini C., Cannavo M., Gastaldi C., Marchesi A., Pascazio A., Pata F., Pozzi E., Premoli A., Tessera G., Boschi L., D'Andrea R., Ghignone F., Poggioli G., Sibilio A., Taffurelli M., Ugolini G., Ab Majid M. A., Ab Rahman R., Joseph J., Pathan F., Shah M. H. S., Yap H. L., Cheah S., Chin I. I., Looi J. K., Tan S. C., Visvalingam S., Kwok F. Y., Lee C. K., Tan T. S., Wong S. M., Abdullah N. H., Liew C. F., Luxuman L., Mohd Zin N. H., Norddin M. F., Alias R. L. R., Wong J. Y., Yong J., Mustapha M. T. B., Chan W. K., Dzulkipli N., Kuan P. X., Lee Y. C., Alias A., Guok E. C., Jee C. C., Ramon B. R., Wong C. W., Abd Ghafar F. N. 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P., Cobzaru I., Mardare O., Purcarin B., Tutunaru V., Ionita V., Arustei M., Codita A., Busuioc M., Chilinciuc I., Ciobanu C., Belciu I., Tincu E., Blaj M., Grosu R. -M., Sandu G., Bruma D., Corneci D., Dutu M., Krepil A., Copaciu E., Dumitrascu C. O., Jemna R., Mihaescu F., Petre R., Tudor C., Ursache E., Kulikov A., Lubnin A., Grigoryev E., Pugachev S., Tolmasov A., Hussain A., Ilyina Y., Roshchina A., Iurin A., Chazova E., Dunay A., Karelov A., Khvedelidze I., Voldaeva O., Belskiy V., Dzhamullaev P., Grishkowez E., Kretov V., Levin V., Molkov A., Puzanov S., Samoilenko A., Tchekulaev A., Tulupova V., Utkin I., Allorto N. L., Bishop D. G., Builu P. M., Cairns C., Dasrath A., De Wet J., Den Hoedt M., Grey B., Hayes M. P., Kusel B. S., Shangase N., Wise R., Cacala S., Farina Z., Govindasamy V., Kruse C. -H., Lee C., Marais L., Naidoo T. D., Rajah C., Rodseth R. N., Ryan L., Von Rhaden R., Adam S., Alphonsus C., Ameer Y., Anderson F., Basanth S., Bechan S., Bhula C., Biccard B. M., Biyase T., Buccimazza I., Cardosa J., Daya B., Drummond L., Elabib A., Goad E. H. A., Goga I. E., Goga R., Harrichandparsad R., Hodgson R. E., Jordaan J., Kalafatis N., Kampik C., Landers A. T., Loots E., Madansein R., Madaree A., Madiba T. E., Manzini V. T., Mbuyisa M., Moodley R., Msomi M., Mukama I., Naidoo D., Naidoo R., Naidu T. K., Ntloko S., Padayachee E., Padayachee L., Phaff M., Pillay B., Pillay D., Pillay L., Ramnarain A., Ramphal S. R., Ryan P., Saloojee A., Sebitloane M., Sigcu N., Taylor J. L., Torborg A., Visser L., Anderson P., Conradie A., De Swardt M., De Villiers M., Eikman J., Liebenberg R., Mouton J., Paton A., Van Der Merwe L., Wilscott-Davids C., Barrett W. J., Bester M., De Beer J., Geldenhuys J., Gouws H., Potgieter J. -H., Strydom M., Turton E. W., Chetty R. R., Chirkut S., Cronje L., De Vasconcellos K., Dube N. Z., Gama N. S., Green G. M., Green-Thompson R., Kinoo S. M., Kistnasami P., Maharaj K., Moodley M. S., Mothae S. J., Noorbhai M. A. F., Rughubar V., Reddy J., Singh A., Skinner D. L., Smith M. J., Singh B., Misra R., Naidoo M., Ramdharee P., Selibea Y., Sewpersad S., Sham S., Wessels J. D., Africander C., Bejia T., Blakemore S. P., Botes M., Bunwarie B., Hernandez C. B., Jeeraz M. A., Legutko D. A., Lopez A. G., De Meyer J. N., Muzenda T., Naidoo N., Patel M., Pentela R., Junge M., Mansoor N., Rademan L., Scislowski P., Seedat I., Van Den Berg B., Van Der Merwe D., Van Wyk S., Govender K., Naicker D., Ramjee R., Saley M., Kuhn W. P., Matos-Puig R., Moolla Z., Lisi A., Perez G., Beltran A. V., Lozano A., Navarro C. D., Duca A., Ernesto E. P. M., Ferrando C., Fuentes I., Garcia-Perez M. L., Gracia E., Palomares A. I., Katime A., Minana A., Incertis R. R., Romero E., Garcia C. S. R., Rubio C., Artiles T. S., Soro M., Valls P., Laguarda G. A., Benavent P., Cuenca V. C., Cueva A., Lafuente M., Parra A. M., Rodrigo A. R., Sanchez-Morcillo S., Tormo S., Redondo F. J., De Andres Ibanez J. A., Diago L. G., Cadiz M. J. H., Manuel G. G., Peris R., Saiz C., Vivo J. T., Soto M. T. T., Brunete T., Cancho D., Garcia D. R. D., Zamudio D., Del Valle S. G., Serrano M. L., Alonso E., Anillo V., Maseda E., Salgado P., Suarez L., Suarez-De-La-Rica A., Villagran M. J., Alonso J. I., Cabezuelo E., Garcia-Saiz I., Del Moral O. L., Martin S., Gonzalez A. P., Doncel M. S. T., Vera M. A., Sanchez F. J. A., Castano B., Moreira B. C., Risco S. F., Martin D. P., Martin F. P., Poza P., Ruiz A., Martinez W. F. S., Vicente B. V., Dominguez S. V., Fernandez S., Munoz-Lopez A., Bernat M. J., Mas A., Planas K., Jawad M., Saeed Y., Hedin A., Levander H., Holmstrom S., Lonn D., Zoerner F., Akring I., Widmark C., Zettergren J., Liljequist V. A., Nystrom L., Odeberg-Wernerman S., Oldner A., Fagerlund M. J., Reje P., Lyckner S., Sperber J., Adolfsson A., Klarin B., O K., Barras J. -P., Buhrer T., Despotidis V., Helmy N., Holliger S., Raptis D. A., Schmid R., Meyer A., Jaquet Y., Kessler U., Muradbegovic M., Nahum S. R., Rotunno T., Schiltz B., Voruz F., Worreth M., Christoforidis D., Popeskou S. G., Furrer M., Prevost G. A., Stocker A., Lang K., Breitenstein S., Ganter M. T., Geisen M., Soll C., Korkmaz M., Lubach I., Schmitz M., Zu Schwabedissen M. M., Zingg U., Hillermann T., Wildi S., Pinto B. B., Walder B., Mariotti G., Slankamenac K., Namuyuga M., Kyomugisha E., Kituuka O., Shikanda A. W., Kakembo N., Tom C. O., Antonina W., Bua E., Ssettabi E. M., Epodoi J., Kabagenyi F., Kirya F., Dempsey G., Seasman C., Khan R. B. N., Kurasz C., MacGregor M., Shawki B., Francis D., Hariharan V., Chau S., Ellis K., Butt G., Chicken D. -W., Christmas N., Allen S., Daniel G. D., Dempster A., Kemp J., Matthews L., McGlone P., Tambellini J., Trodd D., Freitas K., Garg A., Gupta J. K., Karpate S., O'Hara C., Troko J., Angus K., Bradley J., Brennan E., Brooks C., Brown J., Brown G., Finch A., Gratrix K., Hesketh S., Hill G., Jeffs C., Morgan M., Pemberton C., Slawson N., Spickett H., Swarbrick G., Van Duyvenvoorde G., Brennan A., Briscoe R., Cooper S., Lawton T., Northey M., Senaratne R., Stanworth H., Burrows L., Cain H., Craven R., Davies K., Jonas A., Pachucki M., Walkden G., Davies H., Gudaca M., Hobrok M., Arawwawala D., Fergey L., Gardiner M., Gunn J., Johnson L., Lofting A., Lyle A., Neela F. M., Smolen S., Topliffe J., Williams S., Bland M., Balaji P., Kaura V., Lanka P., Naylor C., Ahmed A., Myatt J., Shenoy R., Soon W. C., Tan J., Karadia S., Self J., Durant E., Tripathi S., Bullock C., Ghosh A., Hughes T., Zsisku L., Bengeri S., Cowton A., Khalid M. S., Limb J., McAdam C., Porritt M., Rafi M. A., Shekar P., Adams D., Harden C., Hollands H., King A., March L., Minto G., Patrick A., Squire R., Waugh D., Kumara P., Simeson K., Yarwood J., Browning J., Hatton J., Julian H., Mitra A., Newton M., Pernu P. 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A., Barraclough, J., Bentley, C., Bergin, C., Carrera, R., Clarkson, A., Collins, M., Denham, S., Griffiths, E., Ip, P., Jeyanthan, S., Joory, K., Kaur, S., Marriott, P., Mitchell, N., Nagaiah, S., Nilsson, A., Parekh, N., Pope, M., Seager, J., Serag, H., Tameem, A., Thomas, A., Thunder, J., Torrance, A., Vohra, R., Whitehouse, A., Wong, T., Blunt, M., Wong, K., Giles, J., Reed, I., Weller, D., Bell, G., Birch, J., Damant, R., Maiden, J., Mewies, C., Prince, C., Radford, J., Reynolds, T., Balain, B., Banerjee, R., Barnett, A., Burston, B., Edwards, J., Evans, C., Ford, D., Gallacher, P., Hill, S., Jaffray, D., Karlakki, S., Kennedy, J., Kiely, N., Lewthwaite, S., Marquis, C., Ockendon, M., Phillips, S., Pickard, S., Richardson, J., Roach, R., Smith, T., Spencer-Jones, R., Steele, N., Steen, J., Van Liefland, M., White, S., Faulds, M., Harris, M., Nicol, S., Pearson, S. A., Chukkambotla, S., Andrew, A., Attrill, E., Campbell, G., Datson, A., Fouracres, A., Graterol, J., Graves, L., Hong, B., Ishimaru, A., Karthikeyan, A., King, H., Lawson, T., Lee, G., Lyons, S., Hall, A. M., Mathoulin, S., Mcintyre, E., Mclaughlin, D., Mulcahy, K., Paddle, J., Robbins, J., Sung, W., Tayo, A., Trembath, L., Venugopal, S., Wigmore, G., Boereboom, C., Downes, C., Humphries, R., Melbourne, S., Tou, S., Ullah, S., Batchelor, N., Boxall, L., Broomby, R., Deen, T., Hellewell, A., Helliwell, L., Hutchings, M., Hutchins, D., Keenan, S., Mackie, D., Potter, A., Smith, F., Stone, L., Thorpe, K., Wassall, R., Woodgate, A., Baillie, S., Campbell, T., James, S., King, C., De Araujo, D. M., Martin, D., Morkane, C., Neely, J., Rajendram, R., Burton, M., James, K., Keevil, E., Minik, O., Morgan, J., Musgrave, A., Rajanna, H., Roberts, T., Adamson, M., Jumbe, S., Kendall, J., Muthuswamy, M. B., Cruikshanks, A., Wrench, I., Zeidan, L., Ardern, D., Harris, B., Hellstrom, J., Martin, J., Thomas, R., Varsani, N., Brown, C. W., Docherty, P., Gillies, M., Mcgregor, E., Usher, H., Craig, J., Bodger, P., Creary, T., Everingham, K., Fowler, A., Hewson, R., Ijuo, E., Januszewska, M., Jones, T., Kantsedikas, I., Lahiri, S., Mclean, A. L., Niebrzegowska, E., Phull, M., Wickboldt, N., Baldwin, J., Doyle, D., Mcmullan, S., Oladapo, M., Owen, T., Williams, A., Gregory, P., Husain, T., Kirk-Bayley, J., Mathers, E., Montague, L., Harper, M., Jack, J., Ridley, C., Avis, J., Cook, T., Dali-Kemmery, L., Kerslake, I., Lambourne, V., Pearson, A., Boyd, C., Callaghan, M., Lawson, C., Mccrossan, R., Nesbitt, V., O'Connor, L., Sinclair, R., Farid, N., Morgese, C., Bhatia, K., Karmarkar, S., Ahmed, J., Branagan, G., Hutton, M., Swain, A., Brookes, J., Cornell, J., Dolan, R., Hulme, J., Van Vuuren, A. J., Jowitt, T., Kalashetty, G., Lloyd, F., Kiran, Patel, Sherwood, N., Brown, L., Chandler, B., Deighton, K., Emma, T., Haunch, K., Cheeseman, M., Dent, K., Gray, C., Hood, M., Jones, D., Juj, J., Rao, R., Walker, T., Al Anizi, M., Cheah, C., Cheing, Y., Coutinho, F., Gondo, P., Hadebe, B., Hove, M. O., Khader, A., Krishnachetty, B., Rhodes, K., Sokhi, J., Baker, K. -A., Bertram, W., Looseley, A., Mouton, R., Hanna, G., Arnold, G., Arya, S., Balfoussia, D., Baxter, L., Harris, J., Jones, C., Knaggs, A., Markar, S., Perera, A., Scott, A., Shida, A., Sirha, R., Wright, S., Frost, V., Andrews, E., Arrandale, L., Barrett, S., Cifra, E., Cooper, M., Dragnea, D., Elna, C., Maclean, J., Meier, S., Milliken, D., Munns, C., Ratanshi, N., Ramessur, S., Salvana, A., Watson, A., Ali, H., Critchley, R., Endersby, S., Hicks, C., Liddle, A., Pass, M., Ritchie, C., Thomas, C., Too, L., Welsh, S., Gill, T., Johnson, J., Reed, J., Davis, E., Papadopoullos, S., Attwood, C., Biffen, A., Boulton, K., Gray, S., Hay, D., Mills, S., Montgomery, J., Riddell, R., Simpson, J., Bhardwaj, N., Paul, E., Uwubamwen, N., Alexander, M., Arrich, J., Arumugam, S., Blackwood, D., Boggiano, V., Lam, Y., Chatterjee, D., Chhabra, A., Christian, R., Costelloe, H., Matthewman, M. C., Dalton, E., Darko, J., Davari, M., Dave, T., Deacon, M., Deepak, S., Edmond, H., Ellis, J., El-Sayed, A., Eneje, P., English, R., Ewe, R., Foers, W., Franklin, J., Gallego, L., Garrett, E., Goldberg, O., Goss, H., Greaves, R., Harris, R., Hennings, C., Jones, E., Kamali, N., Kokkinos, N., Lewis, C., Lignos, L., Malgapo, E. V., Malik, R., Milne, A., Mulligan, J. -P., Nicklin, P., Palipane, N., Parsons, T., Piper, R., Prakash, R., Ramesh, B., Rasip, S., Reading, J., Rela, M., Reyes, A., Stephens, R., Rooms, M., Shah, K., Simons, H., Solanki, S., Spowart, E., Stevens, A., Waggett, H., Yassaee, A., Kennedy, A., Scott, S., Somanath, S., Berg, A., Hernandez, M., Nanda, R., Tank, G., Wilson, N., Wilson, D., Al-Soudaine, Y., Baldwin, M., Cornish, J., Davies, Z., Davies, L., Edwards, M., Frewer, N., Gallard, S., Glasbey, J., Harries, R., Hopkins, L., Kim, T., Koompirochana, V., Lawson, S., Lewis, M., Makzal, Z., Scourfield, S., Ahmad, Y., Bates, S., Blackwell, C., Bryant, H., Collins, H., Coulter, S., Cruickshank, R., Daniel, S., Daubeny, T., Golder, K., Hawkins, L., Helen, B., Hinxman, H., Levett, D., Salmon, K., Seaward, L., Ben, L., Tyrell, B., Wadams, B., Walsgrove, J., Dickson, J., Constantin, K., Karen, M., O'Brien, P., O'Donohoe, L., Payne, H., Sundayi, S., Walker, E., Brooke, J., Cardy, J., Humphreys, S., Kessack, L., Kubitzek, C., Kumar, S., Cotterill, D., Hodzovic, E., Hosdurga, G., Miles, E., Saunders, G., Campbell, M., Chan, P., Jemmett, K., Raj, A., Naik, A., Oshowo, A., Ramamoorthy, R., Shah, N., Sylvan, A., Blyth, K., Burtenshaw, A., Freeman, D., Johnson, E., Lo, P., Martin, T., Plunkett, E., Wollaston, J., Allison, J., Carroll, C., Craw, N., Craw, S., Pitt-Kerby, T., Rowland-Axe, R., Spurdle, K., Mcdonald, A., Simon, D., Sinha, V., Banner-Goodspeed, V., Boone, M., Campbell, K., Lu, F., Scannell, J., Sobol, J., Balajonda, N., Clemmons, K., Conde, C., Elgasim, M., Funk, B., Hopkins, T., Olaleye, O., Omer, O., Pender, M., Porto, A., Waweru, P., Yeh, E., Bodansky, D., Evans, A., Kleopoulos, S., Maril, R., Mathney, E., Sanchez, A., Tinuoye, E., Bateman, B., Eng, K., Jiang, N., Ladha, K., Needleman, J., Chen, L. -L., Lane, R., Robinowitz, D., Ghushe, N., Irshad, M., O'Connor, J., Patel, S., Takemoto, S., Wallace, A., Mazzeffi, M., Rock, P., Wallace, K., Chua, P., Mattera, M., Sharar, R., Thilen, S., Treggiari, M., Morgan, A., Sofjan, I., Subramaniam, K., Avidan, M., Maybrier, H., Muench, M., and Wildes, T.
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operative ,Male ,Pediatrics ,GLOBAL HEALTH ,Cohort Studies ,0302 clinical medicine ,Postoperative Complications ,030202 anesthesiology ,surgical procedures, operative, mortality ,Health care ,statistics and numerical data ,030212 general & internal medicine ,Hospital Mortality ,Prospective Studies ,Prospective cohort study ,Aged, 80 and over ,HIGH-RISK SURGERY ,COMPLICATIONS ,OUTCOMES ,Incidence (epidemiology) ,Middle Aged ,PATIENT MORTALITY ,Female ,SURGICAL MORTALITY ,Cohort study ,Adult ,medicine.medical_specialty ,methods ,03 medical and health sciences ,Anesthesiology ,medicine ,QUALITY ,Humans ,Elective surgery ,postoperative care, statistics and numerical data ,Aged ,Quality of Health Care ,Postoperative Care ,business.industry ,Postoperative complication ,mortality ,CANCER-SURGERY ,surgical procedures ,Anesthesiology and Pain Medicine ,Logistic Models ,VOLUME ,postoperative care, methods ,Human medicine ,Complication ,business - Abstract
Background.The incidence and impact of postoperative complications are poorly described. Failure-to-rescue, the rate of death following complications, is an important quality measure for perioperative care but has not been investigated across multiple health care systems.Methods.We analysed data collected during the International Surgical Outcomes Study, an international 7-day cohort study of adults undergoing elective inpatient surgery. Hospitals were ranked by quintiles according to surgical procedural volume (Q1 lowest to Q5 highest). For each quintile we assessed in-hospital complications rates, mortality, and failure-to-rescue. We repeated this analysis ranking hospitals by risk-adjusted complication rates (Q1 lowest to Q5 highest).Results.A total of 44 814 patients from 474 hospitals in 27 low-, middle-, and high-income countries were available for analysis. Of these, 7508 (17%) developed one or more postoperative complication, with 207 deaths in hospital (0.5%), giving an overall failure-to-rescue rate of 2.8%. When hospitals were ranked in quintiles by procedural volume, we identified a three-fold variation in mortality (Q1: 0.6% vs Q5: 0.2%) and a two-fold variation in failure-to-rescue (Q1: 3.6% vs Q5: 1.7%). Ranking hospitals in quintiles by risk-adjusted complication rate further confirmed the presence of important variations in failure-to-rescue, indicating differences between hospitals in the risk of death among patients after they develop complications.Conclusions.Comparison of failure-to-rescue rates across health care systems suggests the presence of preventable postoperative deaths. Using such metrics, developing nations could benefit from a data-driven approach to quality improvement, which has proved effective in high-income countries.
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- 2017
223. The technology of fabrication and application of Bi-2223 HTS wires at InnoST
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Wang, S.-L., Xia, Z.-Z., Chen, S.-S., Ding, S.-Y., Zhang, J.-S., and Sun, J.
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Bismuth -- Magnetic properties ,Bismuth -- Electric properties ,Bismuth -- Thermal properties ,High temperature superconductors -- Usage ,Scanning microscopy -- Usage ,Superconducting magnets -- Design and construction ,Business ,Electronics ,Electronics and electrical industries - Published
- 2010
224. Polytetrafluoroethylene piezoelectrets prepared by sintering process
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Zhang, X., Wang, X., Cao, G., Pan, D., and Xia, Z.
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- 2009
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225. Laparoscopic intersphincteric resection for low rectal cancer: comparison of stapled and manual coloanal anastomosis
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Cong, J. C., Chen, C. S., Ma, M. X., Xia, Z. X., Liu, D. S., and Zhang, F. Y.
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- 2014
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226. Luminescence properties of a new green emitting Eu2+-doped barium chlorosilicate phosphor
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Du, H., Sun, J., Xia, Z., and Sun, J.
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- 2009
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227. Effects of the probiotic Arthrobacter sp. CW9 on the survival and immune status of white shrimp (Penaeus vannamei)
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Xia, Z., Zhu, M., and Zhang, Y.
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- 2014
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228. An engineering approach to improve the stamping robustness of high strength steels
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Wang, Wu-Rong, Hou, Bo, Lin, Zhong-Qin, and Xia, Z. Cedric
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Steel -- Production processes ,Steel -- Mechanical properties ,Monte Carlo method -- Research ,Metal stamping -- Technology application ,Technology application ,Engineering and manufacturing industries ,Science and technology - Abstract
High strength steels (HSSs) are one of the light-weight sheet metals well suited for reducing vehicle weight due to their higher strength-to-weight ratio. However, HSS tend to have bigger variations in their mechanical properties due to more complex rolling techniques involved in the steel-making process. Such uncertainties, when combined with variations in the process parameters such as friction and blank holder force, pose a significant challenge in maintaining the robustness of HSS sheet metal stamping. The paper presents a systematic and robust approach, combining the power of the finite element method and stochastic statistics to decrease the sensitivity of HSS stamping in the presence of abovementioned uncertainties. First, the statistical distribution of sheet metal properties of selected HSS is characterized from a material sampling database. Then a separate interval adaptive response surface methodology (RSM) is applied in modeling sheet metal stamping. The new method significantly improves the model accuracy when compared with the conventional RSM within a single interval. Finally, the Monte Carlo method is employed to simulate the stochastic response of material/process variations to stamping quality and to provide optimal process parameter designs to reduce the sensitivity of these effects. The experiment with the obtained optimal process design demonstrates the improvements of stamping robustness using small-batch experiments. Keywords: robustness, stamping, high strength steels [DOI: 10.1115/1.4000333]
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- 2009
229. Measurement of n - and l -resolved State-selective Charge Exchange in Ar 8+ Collision with He.
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Xia, Z. H., Ren, B., Zhang, R. T., Wei, L., Han, J., Meng, T., Wang, J., Ma, P., Zhang, Y., Tu, B., Xiao, J., Yao, K., Zou, Y., Zhu, X. L., Guo, D. L., Ma, X., and Wei, B.
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CHARGE exchange , *ION exchange (Chemistry) , *SOFT X rays , *COLLISION broadening , *NEUTRON capture , *ANGULAR momentum (Mechanics) , *ATOMIC physics - Abstract
The state-resolved capture cross sections for principal n and orbital angular momentum l play an important role in modeling soft X-ray emissions induced by charge exchange for many astrophysical environments. However, the empirical and semiclassical theories used to produce these data of n - and l -resolved state-selective capture have not been well tested. Using the cold target recoil ion momentum spectroscopy apparatus at Fudan University, we perform a series of measurements of Ar8+ ion charge exchange with He in the collision energy range from 1.4 to 20 keV uâ'1. We find that electrons are mainly captured in the n = 4 state of Ar7+ ions. This agrees with the prediction of the scaling law for n capture. Moreover, the relative cross sections are reported for 4 s-, 4 p-, 4 d-, and 4 f -resolved state-selective capture. The often used analytical l distributions in the astrophysical literature are evaluated by comparing to the measurements. [ABSTRACT FROM AUTHOR]
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- 2022
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230. Origin of Magnetic Dielectric Effect in Geometry Frustrated CuFe1−xMnxO2 Single Crystal.
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Xiao, G. L., Xia, Z. C., Wu, Y. Y., and Shi, L. R.
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SINGLE crystals , *POLARIZATION (Electricity) , *MAGNETIC structure , *DIELECTRICS , *MAGNETIC fields - Abstract
This work aimed to investigate low temperature magnetic dielectric behavior of CuFe1−xMnxO2 (x = 0, 0.01, 0.03, and 0.05). We also discussed the influence of different Mn3+-doped concentrations and analyzed the origin of magnetic dielectric coupling effect in CuFeO2. The obvious variation of dielectric constant with different magnetic fields and temperatures was observed. The critical magnetic fields and critical temperatures, where dielectric constant changed obviously, were closely related to ion-doped concentration. In the ferroelectric incommensurate (FEIC) phase, the dopant enhanced electric polarization by regulating magnetic order. Electric polarization was modified by external magnetic fields, which shows strong magnetoelectric coupling. Based on the special spin-frustrated configuration of the "Fe3+(Mn3+) scalene triangular" in CuFe1−xMnxO2 and the magnetostriction effect, a possible mechanism for the observed dielectric behavior was assumed. In the mechanism, the amplitudes, directions of oxygen ion shift, were mediated by both the applied magnetic field and Mn3+-doping level. Furthermore, the experimental results show that dielectric behavior of CuFe1−xMnxO2 has a close relationship with magnetic structure and geometry frustrated, which may open up a new vista for studies of magnetoelectric materials. Based on the experimental results, the effects of Mn3+ doped on magnetic dielectric and possible mechanism diagrams were assumed. [ABSTRACT FROM AUTHOR]
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- 2022
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231. Preliminary Design of the Fusion Power Shutdown System Unit for ITER
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Li, B., primary, Xia, Z. W., additional, Pan, Y. D., additional, Fang, T. Z., additional, Zhang, B., additional, Liu, S., additional, Li, W., additional, Yang, Y., additional, Kiss, G., additional, Maruyama, S., additional, Kruezi, U., additional, Liu, X. G., additional, Villers, F., additional, Huang, X. M., additional, Conroy, M., additional, and He, F., additional
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- 2021
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232. A nationwide population‐based survey on the prevalence and risk factors of symptomatic pelvic organ prolapse in adult women in China – a pelvic organ prolapse quantification system‐based study
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Pang, H, primary, Zhang, L, additional, Han, S, additional, Li, Z, additional, Gong, J, additional, Liu, Q, additional, Liu, X, additional, Wang, J, additional, Xia, Z, additional, Lang, J, additional, Xu, T, additional, and Zhu, L, additional
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- 2021
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233. Nanocellulose Extracted from Waste Polyester/Cotton Fabric by Chemical-Mechanical Separation Technology
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Sun, Y Y, primary, Xia, Z P, additional, Yang, A P, additional, Li, J X, additional, Wang, L, additional, Chen, H, additional, Zheng, X, additional, and Liu, Y, additional
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- 2021
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234. Rituximab in combination with CHOP chemotherapy for the treatment of diffuse large B cell lymphoma in Chinese patients
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Li, J. M., Wang, L., Shen, Y., Xia, Z. G., Chen, Y., Chen, Q. S., Chen, Y., Zeng, X. Y., You, J. H., Qian, Y., and Shen, Z. X.
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- 2007
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235. A Modified Mroz Model for Springback Prediction
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Zeng, Danielle and Xia, Z. Cedric
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- 2007
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236. Shell Buckling of Imperfect Multiwalled Carbon Nanotubes—Experiments and Analysis
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Guduru, P. R. and Xia, Z.
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- 2007
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237. Exploring anti-nonalcoholic fatty liver disease mechanism of Gardeniae Fructus by combining an animal model with network pharmacology and molecular docking
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Li L, Tang Z, Lu L, and Xia Z
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Animal model ,Chemistry ,Mechanism (biology) ,Network pharmacology ,Nonalcoholic fatty liver disease ,medicine ,Computational biology ,medicine.disease - Abstract
Background Gardeniae Fructus (GF), a traditional Chinese medicine in clinic for the treatment of nonalcoholic fatty liver disease (NAFLD). However, the mechanisms of action of GF was still margin. To explore the efficacy and mechanism of action of GF for the treatment of NAFLD, we proposed a strategy combined in vivo efficacy verification, network pharmacology analysis, molecular docking, and validity assay of target protein. Methods Firstly, an animal model induced by the high fat diet feed was established, then orally administrated with GF, the mRNA expression levels of lipogenesis was performed by RT-PCR, the liver tissue specimens were stained by hematoxylin and eosin (H&E), then observed by light microscopy. Secondly, network pharmacology studies clarified the relationship among the active constituents, target protein, and pathways, and then explored by the molecular docking. Finally, validity assay of target protein was performed in surface plasmon resonance (SPR) test. Results GF protected against NAFLD in rats. Network pharmacology showed that quercetin, oleanolic acid, and geniposide, targeted on PPARα, PPARγ, and CA2 genes, through regulating PPAR, AMPK, and cGMP-PKG signal pathways, to protect against NAFLD. And the Conclusion GF could alleviate NAFLD through the molecular mechanisms explored by network pharmacology, molecular docking, and surface plasmon resonance, those method can be effective tools to clarify the mechanisms of actions of traditional Chinese medicine from a holistic perspective.
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- 2020
238. Risk assessment of progression to severe conditions for patients with COVID-19 pneumonia: a single-center retrospective study
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Xia Z, Zongru Li, Zeng L, Lei Liu, Jie Li, Jing Yuan, Mingfeng Liao, Shi Q, Huang P, Hua R, Kun Qiao, Ning X, Yuhui Liao, Qing He, Mo J, Liu D, Mingxia Zhang, Liang Wang, Zhou Z, Yan Zhang, and Fu Y
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medicine.medical_specialty ,business.industry ,Retrospective cohort study ,macromolecular substances ,Single Center ,medicine.disease ,Pneumonia ,Internal medicine ,Cohort ,Medicine ,Stage (cooking) ,Risk assessment ,business ,Body mass index ,Survival analysis - Abstract
BackgroundManagement of high mortality risk due to significant progression requires prior assessment of time-to-progression. However, few related methods are available for COVID-19 pneumonia.MethodsWe retrospectively enrolled 338 adult patients admitted to one hospital between Jan 11, 2020 to Feb 29, 2020. The final follow-up date was March 8, 2020. We compared characteristics between patients with severe and non-severe outcome, and used multivariate survival analyses to assess the risk of progression to severe conditions.ResultsA total of 76 (31.9%) patients progressed to severe conditions and 3 (0.9%) died. The mean time from hospital admission to severity onset is 3.7 days. Age, body mass index (BMI), fever symptom on admission, co-existing hypertension or diabetes are associated with severe progression. Compared to non-severe group, the severe group already demonstrated, at an early stage, abnormalities in biomarkers indicating organ function, inflammatory responses, blood oxygen and coagulation function. The cohort is characterized with increasing cumulative incidences of severe progression up to 10 days after admission. Competing risks survival model incorporating CT imaging and baseline information showed an improved performance for predicting severity onset (mean time-dependent AUC = 0.880).ConclusionsMultiple predisposition factors can be utilized to assess the risk of progression to severe conditions at an early stage. Multivariate survival models can reasonably analyze the progression risk based on early-stage CT images that would otherwise be misjudged by artificial analysis.
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- 2020
239. Combination of isoflurane and propofol, a means for general anesthesia in the orthopedic surgery of perioperative cerebral hypoperfusion rats to avoid cognitive impairment Anesthesia of perioperative cerebral hypoperfusion
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Yang Z, Yang C, Xia Z, Liu G, Wang H, Wang G, Sun Y, Bu X, Li T, Guo D, Wang J, and Ma J
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medicine.medical_specialty ,Cerebral hypoperfusion ,Isoflurane ,business.industry ,Anesthesia ,Orthopedic surgery ,Medicine ,Perioperative ,business ,Propofol ,Cognitive impairment ,medicine.drug - Abstract
Background: Perioperative cerebral hypoperfusion often occurs. However, the underlying mechanism of cognitive impairment resulting when perioperative cerebral hypoperfusion occurs remain mostly to be determined. Anesthetic isoflurane induces neuronal injury via endoplasmic reticulum (ER) stress, whereas sub-anesthetic dose of propofol improves postoperative cognitive function. However, the effects of the combination of isoflurane plus propofol, which is a common combination of anesthesia for patient, on ER stress and the associated cognitive function remain unknown. Methods: We therefore set out to determine the effects of isoflurane plus propofol on the ER stress and cognitive function in the rats insulted by cerebral hypoperfusion. A ligation of bilateral common carotid arteries (CCA) surgery was adopted to prepare rats as cerebral hypoperfusion (CH) animal model. A second surgery, open reduction and internal fixation (ORIF), requiring general anesthesia, was operated 30 days later so that the effects of anesthetics on cognitive function of these CH rats could be assessed. The rats received isoflurane alone (1.9%), propofol alone (40 mg·kg -1 ·h -1 ) or a combination of isoflurane and propofol (1% and 20 mg·kg -1 ·h -1 or 1.4% and 10 mg·kg -1 ·h -1 ). Behavior studies (Fear Conditioning test), histological analyses (Nissl staining) and biochemical analyses (western blotting for the harvested rat brain tissues) were employed in the studies. Results: We found that the combination of 1% isoflurane plus 20 mg·kg -1 ·h -1 propol did not aggravate the cognitive impairment and the ER stress in aging rats with cerebral hypoperusion and being subjected to an ORIF surgery. Conclusions: These data suggest that ER stress contributes to the underlying mechanism of cognitive impairment and the combination of isoflurane and propofol did not aggravate the cognitive impairment and the ER stress in aging rats with cerebral hypoperfusion and being subjected to an ORIF surgery.
- Published
- 2020
240. Experimental and numerical investigations of a split-ring test for springback
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Xia, Z. Cedric, Miller, Craig E., and Ren, Feng
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Springs (Mechanism) -- Research ,Springs (Mechanism) -- Structure ,Strains and stresses -- Research ,Stress relaxation (Materials) -- Research ,Stress relieving (Materials) -- Research ,Engineering and manufacturing industries ,Science and technology - Abstract
This paper presents an in-depth experimental and numerical investigation of a split-ring test, which provides a simple yet effective benchmark for correlating forming and springback predictive capabilities with experimental measurements. The experimental procedure consists of deep drawing a circular 6111-T4 aluminum alloy into a cylindrical cup of 55 mm depth, crosscutting nine rings each of 5 mm wide from the cup, splitting the rings, and measuring their opening displacement, i.e., the springback amount. Experimental data obtained included punch force trajectories, drawn cup profile, thickness distribution after forming, and the ring openings after splitting. A numerical model is built to analyze the process, and both transversely isotropic and fully orthotropic yield criteria are investigated. Simulation results are validated against experimental data. A detailed numerical analysis is also conducted for stress distributions in each ring after each step and their relationship to the total springback amount. Stress and strain signatures suggested that the test is well suited for validating material models, such as anisotropic yield surface models and hardening models. [DOI: 10.1115/1.2673341]
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- 2007
241. A Systematic Review and Meta-Analysis of Short-Term Ambient Ozone Exposure and COPD Hospitalizations
- Author
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Gao, H. (Hui), Wang, K. (Kan), W Au, W. (William), Zhao, W. (Wensui), Xia, Z.-L. (Zhao-Lin), Gao, H. (Hui), Wang, K. (Kan), W Au, W. (William), Zhao, W. (Wensui), and Xia, Z.-L. (Zhao-Lin)
- Abstract
Chronic obstructive pulmonary disease (COPD) is the third leading cause of death globally and ozone exposure is a
- Published
- 2020
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242. Functional informed genome-wide interaction analysis of body mass index, diabetes and colorectal cancer risk
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Xia, Z, Su, Y-R, Petersen, P, Qi, L, Kim, AE, Figueiredo, JC, Lin, Y, Nan, H, Sakoda, LC, Albanes, D, Berndt, SI, Bezieau, S, Bien, S, Buchanan, DD, Casey, G, Chan, AT, Conti, DV, Drew, DA, Gallinger, SJ, Gauderman, WJ, Giles, GG, Gruber, SB, Gunter, MJ, Hoffmeister, M, Jenkins, MA, Joshi, AD, Le Marchand, L, Lewinger, JP, Li, L, Lindor, NM, Moreno, V, Murphy, N, Nassir, R, Newcomb, PA, Ogino, S, Rennert, G, Song, M, Wang, X, Wolk, A, Woods, MO, Brenner, H, White, E, Slattery, ML, Giovannucci, EL, Chang-Claude, J, Pharoah, PDP, Hsu, L, Campbell, PT, Peters, U, Xia, Z, Su, Y-R, Petersen, P, Qi, L, Kim, AE, Figueiredo, JC, Lin, Y, Nan, H, Sakoda, LC, Albanes, D, Berndt, SI, Bezieau, S, Bien, S, Buchanan, DD, Casey, G, Chan, AT, Conti, DV, Drew, DA, Gallinger, SJ, Gauderman, WJ, Giles, GG, Gruber, SB, Gunter, MJ, Hoffmeister, M, Jenkins, MA, Joshi, AD, Le Marchand, L, Lewinger, JP, Li, L, Lindor, NM, Moreno, V, Murphy, N, Nassir, R, Newcomb, PA, Ogino, S, Rennert, G, Song, M, Wang, X, Wolk, A, Woods, MO, Brenner, H, White, E, Slattery, ML, Giovannucci, EL, Chang-Claude, J, Pharoah, PDP, Hsu, L, Campbell, PT, and Peters, U
- Abstract
BACKGROUND: Body mass index (BMI) and diabetes are established risk factors for colorectal cancer (CRC), likely through perturbations in metabolic traits (e.g. insulin resistance and glucose homeostasis). Identification of interactions between variation in genes and these metabolic risk factors may identify novel biologic insights into CRC etiology. METHODS: To improve statistical power and interpretation for gene-environment interaction (G × E) testing, we tested genetic variants that regulate expression of a gene together for interaction with BMI (kg/m2 ) and diabetes on CRC risk among 26 017 cases and 20 692 controls. Each variant was weighted based on PrediXcan analysis of gene expression data from colon tissue generated in the Genotype-Tissue Expression Project for all genes with heritability ≥1%. We used a mixed-effects model to jointly measure the G × E interaction in a gene by partitioning the interactions into the predicted gene expression levels (fixed effects), and residual G × E effects (random effects). G × BMI analyses were stratified by sex as BMI-CRC associations differ by sex. We used false discovery rates to account for multiple comparisons and reported all results with FDR <0.2. RESULTS: Among 4839 genes tested, genetically predicted expressions of FOXA1 (P = 3.15 × 10-5 ), PSMC5 (P = 4.51 × 10-4 ) and CD33 (P = 2.71 × 10-4 ) modified the association of BMI on CRC risk for men; KIAA0753 (P = 2.29 × 10-5 ) and SCN1B (P = 2.76 × 10-4 ) modified the association of BMI on CRC risk for women; and PTPN2 modified the association between diabetes and CRC risk in both sexes (P = 2.31 × 10-5 ). CONCLUSIONS: Aggregating G × E interactions and incorporating functional information, we discovered novel genes that may interact with BMI and diabetes on CRC risk.
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- 2020
243. Structured Decomposition for Reversible Boolean Functions
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Jiang, J, Jiang, J, Sun, X, Sun, Y, Wu, K, Xia, Z, Jiang, J, Jiang, J, Sun, X, Sun, Y, Wu, K, and Xia, Z
- Abstract
Reversible Boolean function (RBF) is a one-to-one function which maps n-bit input to n-bit output. Reversible logic synthesis has been widely studied due to its connection with low-energy computation as well as quantum computation. In this paper, we give a structured decomposition for even RBFs. Specifically, for n≥q 6, any even n-bit RBF can be decomposed to 7 blocks of (n-1)-bit RBF, where 7 is a constant independent of n and the positions of these blocks have a large degree of freedom. Moreover, if the (n-1)-bit RBFs are required to be even as well, we show for n≥q 10, even n-bit RBF can be decomposed to 10 even (n-1)-bit RBFs. In short, our decomposition has block depth 7 and even block depth 10. Our result improves Selinger's work in block depth model, by reducing the constant from 9 to 7 and from 13 to 10, when the blocks are limited to be even. We emphasize that our setting is a bit different from Selinger's work. In Selinger's constructive proof, each block is placed in one of two specific positions and thus the decomposition has an alternating structure. We relax this restriction and allow each block to act on arbitrary (n-1) bits. This relaxation keeps the block structure and provides more candidates when choosing the positions of blocks.
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- 2020
244. Mix dimension in Poincaré geometry for 3D skeleton-based action recognition
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Peng, W. (Wei), Shi, J. (Jingang), Xia, Z. (Zhaoqiang), Zhao, G. (Guoying), Peng, W. (Wei), Shi, J. (Jingang), Xia, Z. (Zhaoqiang), and Zhao, G. (Guoying)
- Abstract
Graph Convolutional Networks (GCNs) have already demonstrated their powerful ability to model the irregular data, e.g., skeletal data in human action recognition, providing an exciting new way to fuse rich structural information for nodes residing in different parts of a graph. In human action recognition, current works introduce a dynamic graph generation mechanism to better capture the underlying semantic skeleton connections and thus improves the performance. In this paper, we provide an orthogonal way to explore the underlying connections. Instead of introducing an expensive dynamic graph generation paradigm, we build a more efficient GCN on a Riemann manifold, which we think is a more suitable space to model the graph data, to make the extracted representations fit the embedding matrix. Specifically, we present a novel spatial-temporal GCN (ST-GCN) architecture which is defined via the Poincaré geometry such that it is able to better model the latent anatomy of the structure data. To further explore the optimal projection dimension in the Riemann space, we mix different dimensions on the manifold and provide an efficient way to explore the dimension for each ST-GCN layer. With the final resulted architecture, we evaluate our method on two current largest scale 3D datasets, i.e., NTU RGB+D and NTU RGB+D 120. The comparison results show that the model could achieve a superior performance under any given evaluation metrics with only 40% model size when compared with the previous best GCN method, which proves the effectiveness of our model.
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- 2020
245. Revealing the invisible with model and data shrinking for composite-database micro-expression recognition
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Xia, Z. (Zhaoqiang), Peng, W. (Wei), Khor, H.-Q. (Huai-Qian), Feng, X. (Xiaoyi), Zhao, G. (Guoying), Xia, Z. (Zhaoqiang), Peng, W. (Wei), Khor, H.-Q. (Huai-Qian), Feng, X. (Xiaoyi), and Zhao, G. (Guoying)
- Abstract
Composite-database micro-expression recognition is attracting increasing attention as it is more practical for real-world applications. Though the composite database provides more sample diversity for learning good representation models, the important subtle dynamics are prone to disappearing in the domain shift such that the models greatly degrade their performance, especially for deep models. In this article, we analyze the influence of learning complexity, including input complexity and model complexity, and discover that the lower-resolution input data and shallower-architecture model are helpful to ease the degradation of deep models in composite-database task. Based on this, we propose a recurrent convolutional network (RCN) to explore the shallower-architecture and lower-resolution input data, shrinking model and input complexities simultaneously. Furthermore, we develop three parameter-free modules (i.e., wide expansion, shortcut connection and attention unit) to integrate with RCN without increasing any learnable parameters. These three modules can enhance the representation ability in various perspectives while preserving not-very-deep architecture for lower-resolution data. Besides, three modules can further be combined by an automatic strategy (a neural architecture search strategy) and the searched architecture becomes more robust. Extensive experiments on the MEGC2019 dataset (composited of existing SMIC, CASME II and SAMM datasets) have verified the influence of learning complexity and shown that RCNs with three modules and the searched combination outperform the state-of-the-art approaches.
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- 2020
246. Spatiotemporal recurrent convolutional networks for recognizing spontaneous micro-expressions
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Xia, Z. (Zhaoqiang), Hong, X. (Xiaopeng), Gao, X. (Xingyu), Feng, X. (Xiaoyi), Zhao, G. (Guoying), Xia, Z. (Zhaoqiang), Hong, X. (Xiaopeng), Gao, X. (Xingyu), Feng, X. (Xiaoyi), and Zhao, G. (Guoying)
- Abstract
Recently, the recognition task of spontaneous facial micro-expressions has attracted much attention with its various real-world applications. Plenty of handcrafted or learned features have been employed for a variety of classifiers and achieved promising performances for recognizing micro-expressions. However, the micro-expression recognition is still challenging due to the subtle spatiotemporal changes of micro-expressions. To exploit the merits of deep learning, we propose a novel deep recurrent convolutional networks based micro-expression recognition approach, capturing the spatiotemporal deformations of micro-expression sequence. Specifically, the proposed deep model is constituted of several recurrent convolutional layers for extracting visual features and a classificatory layer for recognition. It is optimized by an end-to-end manner and obviates manual feature design. To handle sequential data, we exploit two ways to extend the connectivity of convolutional networks across temporal domain, in which the spatiotemporal deformations are modeled in views of facial appearance and geometry separately. Besides, to overcome the shortcomings of limited and imbalanced training samples, two temporal data augmentation strategies as well as a balanced loss are jointly used for our deep network. By performing the experiments on three spontaneous micro-expression datasets, we verify the effectiveness of our proposed micro-expression recognition approach compared to the state-of-the-art methods.
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- 2020
247. Transforming active sites in nickel–nitrogen–carbon catalysts for efficient electrochemical CO2 reduction to CO
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Daiyan R, Zhu X, Tong Z, Gong L, Razmjou A, Liu RS, Xia Z, Lu X, Dai L, Amal R, Daiyan R, Zhu X, Tong Z, Gong L, Razmjou A, Liu RS, Xia Z, Lu X, Dai L, and Amal R
- Abstract
© 2020 Nitrogen-coordinated single-atom catalysts (SACs) catalyzed electrochemical reduction of CO2 (CO2RR) to CO has emerged as a promising strategy in the management of the global carbon cycle. Herein, we carried out density functional theory (DFT) calculations to investigate the role of possible Ni-Nx and Ni–C4 coordinations in CO2RR catalysis. We discover that the free energy change for CO2RR is lowered with a decrease in Ni-Nx coordination number, with Ni–C4 displaying the lowest overpotential for CO2RR. Using these findings, we develop an effective strategy to transform Ni–N4 to Ni–C4 active sites by removing N moieties within Ni embedded in a hollow nitrogen-doped carbon shell (Ni@NCH). We demonstrate an improvement in CO selectivity with this transformation of active sites and the optimized Ni@NCH-1000 catalyst is capable of converting CO2 to CO with high Faradaic efficiency for CO (FECO) of 96% and a current density (j) of −35 mA cm−2 at an applied potential of −1 V vs Reversible Hydrogen Electrode (RHE). When adopted in a high-throughput gas diffusion electrolyzer, the newly-developed superhydrophobic catalyst is capable of maintaining CO selectivity >95% over a wide range of applied cell voltages from 2.4 V to 3 V with high current densities (~100 mA cm−2 at 3 V). Our insights and findings with active site transformation in Ni–N–C SACs can serve as guidelines for designing highly active SACs for large-scale CO2RR systems.
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- 2020
248. Design and Development of Three-Dimensional Scaffolds for Tissue Engineering
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Liu, C., Xia, Z., and Czernuszka, J.T.
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- 2007
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249. Growth process of atomically flat anodic films on titanium under potentiostatical electrochemical treatment in H 2SO 4 solution
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Xia, Z., Nanjo, H., Aizawa, T., Kanakubo, M., Fujimura, M., and Onagawa, J.
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- 2007
- Full Text
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250. The surface nanostructure of pure iron after combined electrochemical passivation and thermal annealing treatments
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Nanjo, H., Fujimura, M., Laycock, N.J., Xia, Z., Ishikawa, I., and Onagawa, J.
- Published
- 2007
- Full Text
- View/download PDF
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