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52. Refining the rheological characteristics of high drug loading ointment via SDS and machine learning.

Author

Xilong Qian, Kewei Wang, Yulu Ma, Fang Fang, Xiangsong Meng, Liu Zhou, Yanqiong Pan, Yang Zhang, Yehuang Wang, Xiuxiu Wang, Jing Zhao, Bin Jiang, and Shengjin Liu

Subjects
Medicine, Science
Abstract

This paper presents an optimized preparation process for external ointment using the Definitive Screening Design (DSD) method. The ointment is a Traditional Chinese Medicine (TCM) formula developed by Professor WYH, a renowned TCM practitioner in Jiangsu Province, China, known for its proven clinical efficacy. In this study, a stepwise regression model was employed to analyze the relationship between key process factors (such as mixing speed and time) and rheological parameters. Machine learning techniques, including Monte Carlo simulation, decision tree analysis, and Gaussian process, were used for parameter optimization. Through rigorous experimentation and verification, we have successfully identified the optimal preparation process for WYH ointment. The optimized parameters included drug ratio of 24.5%, mixing time of 8 min, mixing speed of 1175 rpm, petroleum dosage of 79 g, liquid paraffin dosage of 6.7 g. The final ointment formulation was prepared using method B. This research not only contributes to the optimization of the WYH ointment preparation process but also provides valuable insights and practical guidance for designing the preparation processes of other TCM ointments. This advanced DSD method enhances the screening approach for identifying the best preparation process, thereby improving the scientific rigor and quality of TCM ointment preparation processes.

Published
2024
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53. Observation of the Orbital Rashba-Edelstein Magnetoresistance

Author

Ding, Shilei, Liang, Zhongyu, Go, Dongwook, Yun, Chao, Xue, Mingzhu, Liu, Zhou, Becker, Sven, Yang, Wenyun, Du, Honglin, Wang, Changsheng, Yang, Yingchang, Jakob, Gerhard, Kläui, Mathias, Mokrousov, Yuriy, and Yang, Jinbo

Subjects
Condensed Matter - Materials Science
Abstract

We report the observation of magnetoresistance (MR) originating from the orbital angular momentum transport (OAM) in a Permalloy (Py) / oxidized Cu (Cu*) heterostructure: the orbital Rashba-Edelstein magnetoresistance. The angular dependence of the MR depends on the relative angle between the induced OAM and the magnetization in a similar fashion as the spin Hall magnetoresistance (SMR). Despite the absence of elements with large spin-orbit coupling, we find a sizable MR ratio, which is in contrast to the conventional SMR which requires heavy elements. By varying the thickness of the Cu* layer, we confirm that the interface is responsible for the MR, suggesting that the orbital Rashba-Edelstein effect is responsible for the generation of the OAM. Through Py thickness-dependence studies, we find that the effective values for the spin diffusion and spin dephasing lengths of Py are significantly larger than the values measured in Py / Pt bilayers, approximately by the factor of 2 and 4, respectively. This implies that another mechanism beyond the conventional spin-based scenario is responsible for the MR observed in Py / Cu* structures originated in a sizeable transport of OAM. Our findings not only unambiguously demonstrate the current-induced torque without using any heavy element via the OAM channel but also provide an important clue towards the microscopic understanding of the role that OAM transport can play for magnetization dynamics., Comment: 11 pages,3 figures

Published
2021
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54. 顶空-气质联用法鉴别压榨植物油和浸出植物油Identification of pressed vegetable oil and extracted vegetable oil by headspace-gas chromatography-mass spectrometry

Author

杨柳，周侃侃，尚素微，朱杰丽，欧菊芳，王朝仁，王衍彬 YANG Liu, ZHOU Kankan, SHANG Suwei, ZHU Jieli, OU Jufang, WANG Chaoren, WANG Yanbin

Subjects
顶空-气质联用；鉴别；压榨；浸出；植物油, headspace-gas chromatography-mass spectrometry, identification, press, extraction, vegetable oil, Oils, fats, and waxes, TP670-699
Abstract

旨在为探明植物油出油方式提供技术支持，建立了一种鉴别压榨植物油和浸出植物油的方法。采用顶空-气质联用法测定植物油的残留溶剂，对顶空进样器平衡温度进行了优化，并探究植物油残留溶剂的特征性成分，通过对压榨植物油和浸出植物油样品中特征性成分的分析确定植物油出油方式的鉴别方法。结果表明：六号溶剂主要成分的出峰面积均随着平衡温度的升高而增加，优化的平衡温度为150 ℃；六号溶剂中5种主要成分在0~1.00 mg/kg范围内线性关系良好，相关系数均可达0.999以上，检出限为0.001 0~0.014 4 mg/kg；六号溶剂中不仅含有烷烃、环烷烃，还含有烯烃、芳香烃（包括多环芳烃）等，成分较为复杂，浸出植物油中溶剂残留物主要为2-甲基戊烷、3-甲基戊烷 、正己烷、甲基环戊烷和环己烷，占比达97.5%，与六号溶剂中这5种物质含量（占比达96.6%）相似，因此确定这5种物质为植物油残留溶剂的特征性成分；实际样品测定表明压榨植物油中没有溶剂残留或残留量较低，浸出植物油中均有溶剂残留且残留量较高；总结出鉴别压榨植物油和浸出植物油的方法为若同时检出4种及以上六号溶剂特征性成分或特征性成分总含量大于或等于0.50 mg/kg的样品可判定为浸出植物油，若同时检出小于或等于3种六号溶剂特征性成分且特征性成分总含量小于 0.50 mg/kg的样品可判定为压榨植物油。综上，采用顶空-气质联用法对植物油中六号溶剂的特征性成分进行分析，以特征性成分有无和含量可以鉴别压榨植物油和浸出植物油。In order to provide technical support for the exploration of vegetable oil production mode, a method for identifying pressed vegetable oil and extracted vegetable oil was established. The residual solvent in vegetable oil was determined by headspace-gas chromatography-mass spectrometry. The equilibrium temperature of headspace injection was optimized, and the characteristic components of residual solvent in vegetable oil was identified. By analyzing the characteristic components in pressed and extracted vegetable oil samples, the extraction method of vegetable oil was identified. The results showed that the peak areas of main components of No. 6 solvent increased with the increase of equilibrium temperature, and the optimal equilibrium temperature was 150 ℃. The linearity of five main components was good in the range of 0-1.00 mg/kg, the correlation coefficient was more than 0.999, and the detection limits was 0.001 0-0.014 4 mg/kg. The components of No.6 solvent were relatively complex, including not only alkane and cycloalkanes, but also olefins, aromatic hydrocarbons (including polycyclic aromatic hydrocarbons). The solvent residues in the extracted vegetable oil were mainly 2-methylpentane, 3-methylpentane, n-hexane, methylcyclopentane and cyclohexane, accounting for 97.5%, which were similar to the contents of these five substances in No. 6 solvent(accouting for 96.6%). Therefore, it was determined that these five substances were characteristic components of residual solvents in vegetable oil. The determination of practical samples showed that there was no or low solvent residue in pressed vegetable oil, while there was solvent residue in the extracted vegetable oil and the residue amount was relative high. The methods for identifying pressed vegetable oil and extracted vegetable oil were summarized as follows: if four or more characteristic components of No. 6 solvent at the same time or the total content of characteristic components greater than or equal to 0.50 mg/kg were detected, the sample could be determined as extracted vegetable oil. If less than or equal to three main components of No. 6 solvent at the same time and the total content of characteristic components less than 0.50 mg/kg were detected, the sample could be judged as pressed vegetable oil. By analyzing characteristic components of No. 6 solvent in vegetable oil using headspace-gas chromatography-mass spectrometry, the pressed vegetable oil and extracted vegetable oil can be identified based on the presence or absence and content of its characteristic components.

Published
2023
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59. Regional Practice Variation and Outcomes in the Standard Versus Accelerated Initiation of Renal Replacement Therapy in Acute Kidney Injury (STARRT-AKI) Trial: A Post Hoc Secondary Analysis

Author

Vaara, Suvi T., Serpa Neto, Ary, Bellomo, Rinaldo, Adhikari, Neill K. J., Dreyfuss, Didier, Gallagher, Martin, Gaudry, Stephane, Hoste, Eric, Joannidis, Michael, Pettilä, Ville, Wang, Amanda Y., Kashani, Kianoush, Wald, Ron, Bagshaw, Sean M., Ostermann, Marlies, Bagshaw, Sean M, Wald, Ron, Adhikari, Neill K.J., Bellomo, Rinaldo, Dreyfuss, Didier, Du, Bin, Gallagher, Martin P., Gaudry, Stéphane, Hoste, Eric A., Lamontagne, François, Joannidis, Michael, Liu, Kathleen D., McAuley, Daniel F., McGuinness, Shay P., Nichol, Alistair D., Ostermann, Marlies, Palevsky, Paul M., Qiu, Haibo, Pettilä, Ville, Schneider, Antoine G., Smith, Orla M., Vaara, Suvi T., Weir, Matthew, Bellomo, Rinaldo, Eastwood, Glenn M., Peck, Leah, Young, Helen, Kruger, Peter, Laurie, Gordon, Saylor, Emma, Meyer, Jason, Venz, Ellen, Wetzig, Krista, French, Craig, McGain, Forbes, Mulder, John, Fennessy, Gerard, Koottayi, Sathyajith, Bates, Samantha, Towns, Miriam, Morgan, Rebecca, Tippett, Anna, Udy, Andrew, Mason, Chris, Licari, Elisa, Gantner, Dashiell, McClure, Jason, Nichol, Alistair, McCracken, Phoebe, Board, Jasmin, Martin, Emma, Vallance, Shirley, Young, Meredith, Vladic, Chelsey, McGloughlin, Steve, Gattas, David, Buhr, Heidi, Coles, Jennifer, Hutch, Debra, Wun, James, Cole, Louise, Whitehead, Christina, Lowrey, Julie, Masters, Kristy, Gresham, Rebecca, Campbell, Victoria, Gutierrez, David, Brailsford, Jane, Forbes, Loretta, Murray, Lauren, Maguire, Teena, NiChonghaile, Martina, Orford, Neil, Bone, Allison, Elderkin, Tania, Salerno, Tania, Chimunda, Tim, Fletcher, Jason, Broadfield, Emma, Porwal, Sanjay, Knott, Cameron, Boschert, Catherine, Smith, Julie, Richardson, Angus, Hill, Dianne, Duke, Graeme, Oziemski, Peter, Cegarra, Santiago, Chan, Peter, Welsh, Deborah, Hunter, Stephanie, Roodenburg, Owen, Dyett, John, Kokotsis, Nicos, Moser, Max, Yang, Yang, Padayachee, Laven, Vetro, Joseph, Gangopadhyay, Himangsu, Kaufman, Melissa, Ghosh, Angaj, Said, Simone, Patel, Alpesh, Bihari, Shailesh, Matheson, Elisha, Jin, Xia, Shrestha, Tapaswi, Schwartz, Kate, Gallagher, Martin P., Cross, Rosalba, Cheung, Winston, Wong, Helen, Kol, Mark, Shah, Asim, Wang, Amanda Y., Endre, Zoltan, Bradford, Celia, Janin, Pierre, Finfer, Simon, Diel, Naomi, Gatward, Jonathan, Hammond, Naomi, Delaney, Anthony, Bass, Frances, Yarad, Elizabeth, Buscher, Hergen, Reynolds, Claire, Baker, Nerilee, Joannidis, Michael, Bellmann, Romuald, Peer, Andreas, Hasslacher, Julia, Koglberger, Paul, Klein, Sebastian, Zotter, Klemens, Brandtner, Anna, Finkenstedt, Armin, Ditlbacher, Adelheid, Hartig, Frank, Fries, Dietmar, Bachler, Mirjam, Schenk, Bettina, Wagner, Martin, Staudinger, Thomas, Tiller, Esther, Schellongowski, Peter, Bojic, Andja, Hoste, Eric A., Bracke, Stephanie, De Crop, Luc, Vermeiren, Daisy, Thome, Fernando, Chiella, Bianca, Fendt, Lucia, Antunes, Veronica, Maisonneuve-Rosemont, Lafrance, Jean-Philippe, Lamontagne, François, D’Aragon, Frédérick, St-Arnaud, Charles, Mayette, Michael, Carbonnaeu, Élaine, Marchand, Joannie, Masse, Marie-Hélène, Ladouceur, Marilène, Turgeon, Alexis F., Lauzier, François, Bellemare, David, Langis Francoeur, Charles, LeBlanc, Guillaume, Guilbault, Gabrielle, Grenier, Stéphanie, Cloutier, Eve, Boivin, Annick, Delisle-Thibault, Charles, Giannakouros, Panagiota, Costerousse, Olivier, Cailhier, Jean-François, Carrier, François-Martin, Ghamraoui, Ali, Lebrasseur, Martine, Benettaib, Fatna, Salamé, Maya, Boumahni, Dounia, Tung Sia, Ying, Naud, Jean-François, Roy, Isabelle, Stelfox, Henry T., Ruddell, Stacey, Manns, Braden J., Duggan, Shelley, Carney, Dominic, Barchard, Jennifer, Whitlock, Richard P., Belley-Cote, Emilie, Savija, Nevena, Sabev, Alexandra, Campbell, Troy, Creary, Thais, Devereaux, Kelson, Brodutch, Shira, Rigatto, Claudio, Paunovic, Bojan, Mooney, Owen, Glybina, Anna, Harasemiw, Oksana, Di Nella, Michelle, Harmon, John, Mehta, Navdeep, Lakatos, Louis, Haslam, Nicole, Lellouche, Francois, Simon, Mathieu, Tung, Ying, Lizotte, Patricia, Bourchard, Pierre-Alexandre, Rochwerg, Bram, Karachi, Tim, Millen, Tina, Muscedere, John, Maslove, David, Gordon Boyd, J., Sibley, Stephanie, Drover, John, Hunt, Miranda, Georgescu, Ilinca, Wax, Randy, Lenga, Ilan, Sridhar, Kavita, Steele, Andrew, Fusco, Kelly, Ghate, Taneera, Tolibas, Michael, Robinson, Holly, Weir, Matthew A., Taneja, Ravi, Ball, Ian M., Garg, Amit, Campbell, Eileen, Ovsenek, Athena, Bagshaw, Sean M., van Diepen, Sean, Baig, Nadia, Magder, Sheldon, Yao, Han, Alam, Ahsan, Campisi, Josie, MacIntyre, Erika, Rokosh, Ella, Scherr, Kimberly, Lapinsky, Stephen, Mehta, Sangeeta, Shah, Sumesh, Niven, Daniel J., Stelfox, Henry T., Ruddell, Stacey, Russell, Michael, Jim, Kym, Brown, Gillian, Oxtoby, Kerry, Hall, Adam, Benoit, Luc, Sokolowski, Colleen, Prasad, Bhanu, Rao, Jag, Giebel, Shelley, Kutsogiannis, Demetrios J., Thompson, Patricia, Thompson, Tayne, Cirone, Robert, Kavikondala, Kanthi, Soth, Mark, Clarke, France, Takaoka, Alyson, Wald, Ron, Mazer, David, Burns, Karen, Friedrich, Jan, Klein, David, Sandhu, Gyan, Santos, Marlene, Khalid, Imrana, Hodder, Jennifer, Dodek, Peter, Ayas, Najib, Alcuaz, Victoria, Suen, Gabriel, Rewa, Oleksa, Singh, Gurmeet, Norris, Sean, Gibson, Neil, Arias, Castro, Shami, Aysha, Pelletier, Celine, Adhikari, Neill K.J., Zahirieh, Alireza, Amaral, Andre, Marinoff, Nicole, Kaur, Navjot, Perez, Adic, Wang, Jane, Haljan, Gregory, Condin, Christopher, McIntyre, Lauralyn, Gomes, Brigette, Porteous, Rebecca, Watpool, Irene, Hiremath, Swapnil, Clark, Edward, Herridge, Margaret S., Backhouse, Felicity, Elizabeth Wilcox, M., Walczak, Karolina, Ki, Vincent, Sharman, Asheer, Romano, Martin, Bagshaw, Sean M., Noel Gibney, R.T., Romanovsky, Adam S., Rewa, Oleksa, McCoshen, Lorena, Baig, Nadia, Wood, Gordon, Ovakim, Daniel, Auld, Fiona, Carney, Gayle, Duan, Meili, Ji, Xiaojun, Guo, Dongchen, Qi, Zhili, Lin, Jin, Zhang, Meng, Dong, Lei, Liu, Jingfeng, Liu, Pei, Zhi, Deyuan, Bai, Guoqiang, Qiu, Yu, Yang, Ziqi, Bai, Jing, Liu, Zhuang, Zhuang, Haizhou, Wang, Haiman, Li, Jian, Zhao, Mengya, Zhou, Xiao, Shi, Xianqing, Ye, Baning, Liu, Manli, Wu, Jing, Fu, Yongjian, Long, Dali, Pan, Yu, Wang, Jinlong, Mei, Huaxian, Zhang, Songsong, Wen, Mingxiang, Yang, Enyu, Mu, Sijie, Li, Jianquan, Hu, Tingting, Qin, Bingyu, Li, Min, Wang, Cunzhen, Dong, Xin, Wang, Kaiwu, Wang, Haibo, Yang, Jianxu, Du, Bin, Wang, Chuanyao, Wang, Dongxin, Li, Nan, Yu, Zhui, Xu, Song, Yao, Lan, Hou, Guo, Liu, Zhou, Lu, Liping, Lian, Yingtao, Wang, Chunting, Zhang, Jichen, Ding, Ruiqi, Qi, Guoqing, Wang, Qizhi, Wang, Peng, Meng, Zhaoli, Chen, Man, Hu, Xiaobo, He, Xiandi, Zhao, Shibing, Hang, Lele, Li, Rui, Qin, Suhui, Lu, Kun, Dun, Shijuan, Liu, Cheng, Zhou, Qi, Chen, Zhenzhen, Mei, Jing, Zhang, Minwei, Xu, Hao, Lin, Jincan, Shi, Qindong, Fu, Lijuan, Zeng, Qinjing, Ma, Hongye, Yan, Jinqi, Gao, Lan, Liu, Hongjuan, Zhang, Lei, Li, Hao, He, Xiaona, Fan, Jingqun, Guo, Litao, Liu, Yu, Wang, Xue, Sun, Jingjing, Liu, Zhongmin, Yang, Juan, Ding, Lili, Sheng, Lulu, Liu, Xingang, Yan, Jie, Wang, Quihui, Wang, Yifeng, Zhao, Dan, Zhao, Shuangping, Hu, Chenghuan, Li, Jing, Deng, Fuxing, Qiu, Haibo, Yang, Yi, Mo, Min, Pan, Chun, Wu, Changde, Huang, Yingzi, Huang, Lili, Liu, Airan, Pettilä, Ville, Vaara, Suvi T., Korhonen, Anna-Maija, Törnblom, Sanna, Sutinen, Sari, Pettilä, Leena, Heinonen, Jonna, Lappi, Eliria, Suhonen, Taria, Karlsson, Sari, Hoppu, Sanna, Jalkanen, Ville, Kuitunen, Anne, Levoranta, Markus, Långsjö, Jaakko, Ristimäki, Sanna, Malila, Kaisa, Wootten, Anna, Varila, Simo, Järvisalo, Mikko J, Inkinen, Outi, Kentala, Satu, Leivo, Keijo, Haltia, Paivi, Dreyfuss, Didier, Ricard, Jean-Damien, Messika, Jonathan, Tiagarajah, Abirami, Emery, Malo, Dechanet, Aline, Gernez, Coralie, Roux, Damien, Martin-Lefevre, Laurent, Fiancette, Maud, Vinatier, Isabelle, Claude Lacherade, Jean, Colin, Gwenhaël, Lebert, Christine, Azais, Marie-Ange, Yehia, Aihem, Pouplet, Caroline, Henry- Lagarrigue, Matthieu, Seguin, Amélie, Crosby, Laura, Maizel, Julien, Titeca-Beauport, Dimitri, Combes, Alain, Nieszkowska, Ania, Masi, Paul, Demoule, Alexandre, Mayaux, Julien, Dres, Martin, Morawiec, Elise, Decalvele, Maxens, Demiri, Suela, Faure, Morgane, Marios, Clémence, Mallet, Maxime, Amélie Ordon, Marie, Morizot, Laura, Cantien, Marie, Pousset, François, Gaudry, Stéphane, Poirson, Florent, Cohen, Yves, Argaud, Laurent, Cour, Martin, Bitker, Laurent, Simon, Marie, Hernu, Romain, Baudry, Thomas, De La Salle, Sylvie, Robine, Adrien, Sedillot, Nicholas, Tchenio, Xavier, Bouisse, Camille, Roux, Sylvie, Barbar, Davide, Trusson, Rémi, Tamion, Fabienne, Grangé, Steven, Carpentier, Dorothée, Chevrel, Guillaume, Ensenyat-Martin, Luis, Marque, Sophie, Quenot, Jean-Pierre, Andreu, Pascal, Dargent, Auguste, Large, Audrey, Chudeau, Nicolas, Landais, Mickael, Derrien, Benoit, Christophe Callahan, Jean, Guitton, Christophe, Le Moal, Charlène, Robert, Alain, Asehnoune, Karim, Cinotti, Raphaël, Grillot, Nicolas, Demeure, Dominique, Vinsonneau, Christophe, Rahmani, Imen, Marzouk, Mehdi, Dekeyser, Thibault, Sejourne, Caroline, Verlay, Mélanie, Thevenin, Fabienne, Delecolle, Lucie, Didier Thevenin, Lens, Souweine, Bertrand, Coupez, Elisabeth, Adda, Mireille, Eraldi, Jean-Pierre, Marchalot, Antoine, De Prost, Nicolas, Mekontso Dessap, Armand, Razazi, Keyvan, Meziani, Ferhat, Boisrame-Helms, Julie, Clere-Jehl, Raphael, Delabranche, Xavier, Kummerlen, Christine, Merdji, Hamid, Monnier, Alexandra, Rabouel, Yannick, Rahmani, Hassene, Allam, Hayat, Chenaf, Samir, Franja, Vincenta, Pons, Bertrand, Carles, Michel, Martino, Frédéric, Richard, Régine, Zuber, Benjamin, Lacave, Guillaume, Lakhal, Karim, Rozec, Bertrand, Dang Van, Hoa, Boulet, Éric, Dubos, René, Fadel, Fouad, Cleophax, Cedric, Dufour, Nicolas, Grant, Caroline, Thuong, Marie, Reignier, Jean, Canet, Emmanuel, Nicolet, Laurent, Boulain, Thierry, Nay, Mai-Anh, Benzekri, Dalila, Barbier, François, Bretagnol, Anne, Kamel, Toufik, Mathonnet, Armelle, Muller, Grégoire, Skarzynski, Marie, Rossi, Julie, Pradet, Amandine, Dos Santos, Sandra, Guery, Aurore, Muller, Lucie, Felix, Luis, Bohé, Julien, Thiéry, Guillaume, Aissaoui, Nadia, Vimpere, Damien, Commeureuc, Morgane, Diehl, Jean-Luc, Guerot, Emmanuel, Liangos, Orfeas, Wittig, Monika, Zarbock, Alexander, Küllmar, Mira, van Waegeningh, Thomas, Rosenow, Nadine, Nichol, Alistair D., Brickell, Kathy, Doran, Peter, Murray, Patrick T., Landoni, Giovanni, Lembo, Rosalba, Zangrillo, Alberto, Monti, Giacomo, Tozzi, Margherita, Marzaroli, Matteo, Lombardi, Gaetano, Paternoster, Gianluca, Vitiello, Michelangelo, McGuinness, Shay, Parke, Rachael, Butler, Magdalena, Gilder, Eileen, Cowdrey, Keri-Anne, Wallace, Samantha, Hallion, Jane, Woolett, Melissa, Neal, Philippa, Duffy, Karina, Long, Stephanie, McArthur, Colin, Simmonds, Catherine, Chen, Yan, McConnochie, Rachael, Newby, Lynette, Knight, David, Henderson, Seton, Mehrtens, Jan, Morgan, Stacey, Morris, Anna, Vander Hayden, Kymbalee, Burke, Tara, Bailey, Matthew, Freebairn, Ross, Chadwick, Lesley, Park, Penelope, Rolls, Christine, Thomas, Liz, Buehner, Ulrike, Williams, Erin, Albrett, Jonathan, Kirkham, Simon, Jackson, Carolyn, Browne, Troy, Goodson, Jennifer, Jackson, David, Houghton, James, Callender, Owen, Higson, Vicki, Keet, Owen, Dominy, Clive, Young, Paul, Hunt, Anna, Judd, Harriet, Lawrence, Cassie, Olatunji, Shaanti, Robertson, Yvonne, Latimer-Bell, Charlotte, Hendry, Deborah, Mckay-Vucago, Agnes, Beehre, Nina, Lesona, Eden, Navarra, Leanlove, Robinson, Chelsea, Jang, Ryan, Junge, Andrea, Lambert, Bridget, Schneider, Antoine G., Thibault, Michel, Eckert, Philippe, Kissling, Sébastien, Polychronopoulos, Erietta, Poli, Elettra, Altarelli, Marco, Schnorf, Madeleine, Abed Mallaird, Samia, Heidegger, Claudia, Perret, Aurelie, Montillier, Philippe, Sangla, Frederic, Neils, Seigenthaller, De Watteville, Aude, Phull, Mandeep-Kaur, George, Aparna, Hussain, Nauman, Pogreban, Tatiana, Lobaz, Steve, Daniels, Alison, Cunningham, Mishell, Kerr, Deborah, Nicholson, Alice, Shanmugasundaram, Pradeep, Abrams, Judith, Manso, Katarina, Hambrook, Geraldine, McKerrow, Elizabeth, Salva, Juvy, Foulkes, Stephen, Wise, Matthew, Morgan, Matt, Brooks, Jenny, Cole, Jade, Michelle Davies, Tracy, Hill, Helen, Thomas, Emma, Vizcaychipi, Marcela, Baharlo, Behrad, Carungcong, Jaime, Costa, Patricia, Martins, Laura, Kapoor, Ritoo, Hazelton, Tracy, Moon, Angela, Musselwhite, Janine, Shelley, Ben, McCall, Philip, Ostermann, Marlies, Arbane, Gill, Bociek, Aneta, Marotti, Martina, Lim, Rosario, Campos, Sara, Grau Novellas, Neus, Cennamo, Armando, Slack, Andrew, Wyncoll, Duncan, Camporota, Luigi, Sparkes, Simon, Tilley, Rosalinde, Rattray, Austin, Moreland, Gayle, Duffy, Jane, McGonigal, Elizabeth, Hopkins, Philip, Finney, Clare, Smith, John, Noble, Harriet, Watson, Hayley, Harris, Claire-Louise, Clarey, Emma, Corcoran, Eleanor, Beck, James, Howcroft, Clare, Youngs, Nora, Wilby, Elizabeth, Ogg, Bethan, Wolverson, Adam, Lee, Sandra, Butler, Susie, Okubanjo, Maryanne, Hindle, Julia, Welters, Ingeborg, Williams, Karen, Johnson, Emily, Patrick-Heselton, Julie, Shaw, David, Waugh, Victoria, Stewart, Richard, Mwaura, Esther, Wren, Lynn, Mew, Louise, Sutherland, Sara-Beth, Adderley, Jane, Ruddy, Jim, Harkins, Margaret, Kaye, Callum, Scott, Teresa, Mitchell, Wendy, Anderson, Felicity, Willox, Fiona, Jagannathan, Vijay, Clark, Michele, Purv, Sarah, Sharman, Andrew, Meredith, Megan, Ryan, Lucy, Conner, Louise, Peters, Cecilia, Harvey, Dan, Roshdy, Ashraf, Collins, Amy, Sim, Malcolm, Henderson, Steven, Chee, Nigel, Pitts, Sally, Bowman, Katie, Dilawershah, Maria, Vamplew, Luke, Howe, Elizabeth, Rogers, Paula, Hernandez, Clara, Prendergast, Clara, Benton, Jane, Rosenberg, Alex, Forni, Lui G., Grant, Alice, Carvelli, Paula, Raithatha, Ajay, Bird, Sarah, Richardson, Max, Needham, Matthew, Hirst, Claire, Ball, Jonathan, Leaver, Susannah, Howlett, Luisa, Castro Delgado, Carlos, Farnell-Ward, Sarah, Farrah, Helen, Gray, Geraldine, Joseph, Gipsy, Robinson, Francesca, Tridente, Ascanio, Harrop, Clare, Shuker, Karen, McLaughlan, Derek, Ramsey, Judith, Meehan, Sharon, Oliver Rose, Bernd, Reece-Anthony, Rosie, Gurung, Babita, Whitehouse, Tony, Snelson, Catherine, Veenith, Tonny, Johnston, Andy, Cooper, Lauren, Carrera, Ron, Ellis, Karen, Fellows, Emma, Harkett, Samanth, Bergin, Colin, Spruce, Elaine, Despy, Liesl, Goundry, Stephanie, Dooley, Natalie, Mason, Tracy, Clark, Amy, Dignam, Gemma, Ward, Geraldine, Attwood, Ben, Parsons, Penny, Mason, Sophie, Margarson, Michael, Lord, Jenny, McGlone, Philip, Hodgson, Luke E., Chadbourn, Indra, Gomez, Raquel, Margalef, Jordi, Pretorius, Rinus, Hamshere, Alexandra, Carter, Joseph, Cahill, Hazel, Grainger, Lia, Howard, Kate, Forshaw, Greg, Guy, Zoe, Kashani, Kianoush B., Albright, Robert C., Amsbaugh, Amy, Stoltenberg, Anita, Niven, Alexander S., Lynch, Matthew, O’Mara, AnnMarie, Naeem, Syed, Sharif, Sairah, McKenney Goulart, Joyce, Lynch, Matthew, O’Mara, AnnMarie, Naeem, Syed, Sharif, Sairah, McKenney Goulart, Joyce, Tolwani, Ashita, Lyas, Claretha, Latta, Laura, Bihorac, Azra, Hashemighouchani, Haleh, Efron, Philip, Ruppert, Matthew, Cupka, Julie, Kiley, Sean, Carson, Joshua, White, Peggy, Omalay, George, Brown, Sherry, Velez, Laura, Marceron, Alina, Neyra, Javier A., Carlos Aycinena, Juan, Elias, Madona, Ortiz-Soriano, Victor M., Hauschild, Caroline, and Dorfman, Robert

Published
2024
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62. Covariant propagator and chiral power counting

Author

Liu, Zhou, Wen, Li-Hong, and Yang, J. -F.

Subjects
High Energy Physics - Theory, High Energy Physics - Phenomenology, Nuclear Theory
Abstract

Some one-loop diagrams with one and two external baryons/nucleons are revisited using covariant baryon propagators in chiral effective theory. We showed that it is enough to separate and subtract all the local terms that violate chiral power counting to recover chiral power counting, no need to introduce extra operations. The structures of leading chiral corrections and IR enhancement or threshold effects are 'stable' or persist as long as covariant propagators are employed for all particles., Comment: 9 pages, 11 figures, final version, accepted for publication in Nuclear Physics B

Published
2020
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63. Mixed Noise Removal with Pareto Prior

Author

Liu, Zhou, Yu, Lei, Xia, Gui-Song, and Sun, Hong

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Denoising images contaminated by the mixture of additive white Gaussian noise (AWGN) and impulse noise (IN) is an essential but challenging problem. The presence of impulsive disturbances inevitably affects the distribution of noises and thus largely degrades the performance of traditional AWGN denoisers. Existing methods target to compensate the effects of IN by introducing a weighting matrix, which, however, is lack of proper priori and thus hard to be accurately estimated. To address this problem, we exploit the Pareto distribution as the priori of the weighting matrix, based on which an accurate and robust weight estimator is proposed for mixed noise removal. Particularly, a relatively small portion of pixels are assumed to be contaminated with IN, which should have weights with small values and then be penalized out. This phenomenon can be properly described by the Pareto distribution of type 1. Therefore, armed with the Pareto distribution, we formulate the problem of mixed noise removal in the Bayesian framework, where nonlocal self-similarity priori is further exploited by adopting nonlocal low rank approximation. Compared to existing methods, the proposed method can estimate the weighting matrix adaptively, accurately, and robust for different level of noises, thus can boost the denoising performance. Experimental results on widely used image datasets demonstrate the superiority of our proposed method to the state-of-the-arts.

Published
2020

64. Learning Cluster Structured Sparsity by Reweighting

Author

Jiang, Yulun, Yu, Lei, Zhang, Haijian, and Liu, Zhou

Subjects
Computer Science - Machine Learning, Electrical Engineering and Systems Science - Signal Processing, Statistics - Machine Learning
Abstract

Recently, the paradigm of unfolding iterative algorithms into finite-length feed-forward neural networks has achieved a great success in the area of sparse recovery. Benefit from available training data, the learned networks have achieved state-of-the-art performance in respect of both speed and accuracy. However, the structure behind sparsity, imposing constraint on the support of sparse signals, is often an essential prior knowledge but seldom considered in the existing networks. In this paper, we aim at bridging this gap. Specifically, exploiting the iterative reweighted $\ell_1$ minimization (IRL1) algorithm, we propose to learn the cluster structured sparsity (CSS) by rewegihting adaptively. In particular, we first unfold the Reweighted Iterative Shrinkage Algorithm (RwISTA) into an end-to-end trainable deep architecture termed as RW-LISTA. Then instead of the element-wise reweighting, the global and local reweighting manner are proposed for the cluster structured sparse learning. Numerical experiments further show the superiority of our algorithm against both classical algorithms and learning-based networks on different tasks.

Published
2019

65. Prevalence and risk factors for colonisation and infection with carbapenem-resistant Enterobacterales in intensive care units: A prospective multicentre study

Author

Wu, Yi-Le, Hu, Xiao-Qian, Wu, De-Quan, Li, Ruo-Jie, Wang, Xue-Ping, Zhang, Jin, Liu, Zhou, Chu, Wen-Wen, Zhu, Xi, Zhang, Wen-Hui, Zhao, Xue, Guan, Zi-Shu, Jiang, Yun-Lan, Wu, Jin-Feng, Cui, Zhuo, Zhang, Ju, Li, Jia, Wang, Ru-Mei, Shen, Shi-Hua, Cai, Chao-Yang, Zhu, Hai-Bin, Jiang, Quan, Zhang, Jing, Niu, Jia-Lan, Xiong, Xian-Peng, Tian, Zhen, Zhang, Jian-She, Zhang, Jun-Lin, Tang, Li-Ling, Liu, An-Yun, Wang, Cheng-Xiang, Ni, Ming-Zhu, Jiang, Jing-Jing, Yang, Xi-Yao, Yang, Min, and Zhou, Qiang

Published
2023
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72. Glycyrrhizic acid glycosides reduces extensive tripterygium glycosides-induced lipid deposition in hepatocytes

Author

Yifei Yang, Xiaotong Fu, Bing Xia, Liu Zhou, Haijing Zhang, Chun Li, Xiao Ye, and Ting Liu

Subjects
Tripterygium glycosides, Glycyrrhizic acid glycosides, Liver, Hepatotoxicity, Lipid metabolism, Science (General), Q1-390, Social sciences (General), H1-99
Abstract

Aim: Tripterygium glycosides (TG) extracted from the plant Tripterygium wilfordii Hook F has been used to treat chronic kidney diseases for many years. However, hepatotoxicity limits its clinical application. Glycyrrhizic acid glycosides (GA) can reduce TG hepatotoxicity, however, further investigation into the underlying molecular mechanisms by which GA attenuates TG-induced hepatotoxicity is required. Methods: Sprague‒Dawley rats were randomly divided into the control group, the TG groups (TG189 mg/kg group, TG472.5 mg/kg group), and the TG + GA groups (TG189 mg/kg + GA20.25 mg/kg group, TG472.5 mg/kg + GA20.25 mg/kg group). After 21 consecutive days of intragastric administration, structural and molecular changes in hepatocytes were detected. Results: After 21 days of TG treatment, the serum level of the total bilirubin, triglyceride, total cholesterol, and low-density lipoprotein cholesterol increased in the TG189 mg/kg and TG472.5 mg/kg groups when compared to the control group. High-density lipoprotein cholesterol levels were reduced in both TG groups. The ultrastructure of hepatocytes and the structural integrity of the liver were compromised. In addition, the relevant molecular level of the peroxisome proliferators-activated receptor α (PPARα) and acyl-CoA synthetase long-chain family members (ACSLs) pathway was modulated. With the addition of 20.25 mg/kg GA, the serum biochemical indexes and liver tissue structure ultrastructure of hepatocytes were improved, and the PPARα-ACSLs pathway was corrected. Conclusion: The combined application of GA and TG improved abnormal lipid metabolism, repaired liver structure, reduced lipid deposition in hepatocytes, and reduced TG-induced hepatotoxicity.

Published
2023
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73. CRNet: Image Super-Resolution Using A Convolutional Sparse Coding Inspired Network

Author

Zhang, Menglei, Liu, Zhou, and Yu, Lei

Subjects
Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition
Abstract

Convolutional Sparse Coding (CSC) has been attracting more and more attention in recent years, for making full use of image global correlation to improve performance on various computer vision applications. However, very few studies focus on solving CSC based image Super-Resolution (SR) problem. As a consequence, there is no significant progress in this area over a period of time. In this paper, we exploit the natural connection between CSC and Convolutional Neural Networks (CNN) to address CSC based image SR. Specifically, Convolutional Iterative Soft Thresholding Algorithm (CISTA) is introduced to solve CSC problem and it can be implemented using CNN architectures. Then we develop a novel CSC based SR framework analogy to the traditional SC based SR methods. Two models inspired by this framework are proposed for pre-/post-upsampling SR, respectively. Compared with recent state-of-the-art SR methods, both of our proposed models show superior performance in terms of both quantitative and qualitative measurements., Comment: 10 pages, 12 figures, 3 tables

Published
2019

77. Image Super-Resolution via RL-CSC: When Residual Learning Meets Convolutional Sparse Coding

Author

Zhang, Menglei, Liu, Zhou, and Yu, Lei

Subjects
Computer Science - Computer Vision and Pattern Recognition, I.4.4
Abstract

We propose a simple yet effective model for Single Image Super-Resolution (SISR), by combining the merits of Residual Learning and Convolutional Sparse Coding (RL-CSC). Our model is inspired by the Learned Iterative Shrinkage-Threshold Algorithm (LISTA). We extend LISTA to its convolutional version and build the main part of our model by strictly following the convolutional form, which improves the network's interpretability. Specifically, the convolutional sparse codings of input feature maps are learned in a recursive manner, and high-frequency information can be recovered from these CSCs. More importantly, residual learning is applied to alleviate the training difficulty when the network goes deeper. Extensive experiments on benchmark datasets demonstrate the effectiveness of our method. RL-CSC (30 layers) outperforms several recent state-of-the-arts, e.g., DRRN (52 layers) and MemNet (80 layers) in both accuracy and visual qualities. Codes and more results are available at https://github.com/axzml/RL-CSC., Comment: 10 pages, 8 figures, 4 tables

Published
2018

79. Research on a Rolling Missile Analysis Method Based on the Angle of Attack

Author

Dou, Guohui, Yang, Yunjun, Shi, Lei, Liu, Zhou, Zhou, Weijiang, Angrisani, Leopoldo, Series Editor, Arteaga, Marco, Series Editor, Panigrahi, Bijaya Ketan, Series Editor, Chakraborty, Samarjit, Series Editor, Chen, Jiming, Series Editor, Chen, Shanben, Series Editor, Chen, Tan Kay, Series Editor, Dillmann, Rüdiger, Series Editor, Duan, Haibin, Series Editor, Ferrari, Gianluigi, Series Editor, Ferre, Manuel, Series Editor, Hirche, Sandra, Series Editor, Jabbari, Faryar, Series Editor, Jia, Limin, Series Editor, Kacprzyk, Janusz, Series Editor, Khamis, Alaa, Series Editor, Kroeger, Torsten, Series Editor, Li, Yong, Series Editor, Liang, Qilian, Series Editor, Martín, Ferran, Series Editor, Ming, Tan Cher, Series Editor, Minker, Wolfgang, Series Editor, Misra, Pradeep, Series Editor, Möller, Sebastian, Series Editor, Mukhopadhyay, Subhas, Series Editor, Ning, Cun-Zheng, Series Editor, Nishida, Toyoaki, Series Editor, Pascucci, Federica, Series Editor, Qin, Yong, Series Editor, Seng, Gan Woon, Series Editor, Speidel, Joachim, Series Editor, Veiga, Germano, Series Editor, Wu, Haitao, Series Editor, Zamboni, Walter, Series Editor, Zhang, Junjie James, Series Editor, Wu, Meiping, editor, Niu, Yifeng, editor, Gu, Mancang, editor, and Cheng, Jin, editor

Published
2022
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