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Institution

Southwest University

EducationChongqing, China
About: Southwest University is a education organization based out in Chongqing, China. It is known for research contribution in the topics: Gene & Population. The organization has 29772 authors who have published 27755 publications receiving 409441 citations. The organization is also known as: Southwest University in Chongqing & SWU.
Topics: Gene, Population, Catalysis, Bombyx mori, Adsorption


Papers
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Journal ArticleDOI
Shunbi Xie1, Jiawei Ye1, Yali Yuan1, Yaqin Chai1, Ruo Yuan1 
TL;DR: The synthesized hemin@MOFs represented a new paradigm for multifunctional materials since it combined three different functions including serving as catalysts, redox mediators and loading platforms within a single material.
Abstract: A new type of multifunctional metal–organic framework (MOF) has been synthesized by encapsulating hemin into the nano-sized Fe-MIL-88 MOFs (hemin@MOFs) and first applied in an electrochemical aptasensor to detect thrombin (TB) with the aid of an enzyme for signal amplification. The gold nanoparticle functionalized hemin@MOFs (Au/hemin@MOFs) have not only simultaneously served as redox mediators and solid electrocatalysts, but have also been utilized as an ideal loading platform to immobilize a large number of biomolecules. In this aptasensor, Au/hemin@MOFs conjugated with glucose oxidase (GOD) and thrombin binding aptamer (TBA II) were used as the secondary aptamer bioconjugates (Au/hemin@MOF–TBA II–GOD bioconjugates), and TB was sandwiched between Au/hemin@MOF–TBA II–GOD bioconjugates and the amino-terminated TBA I which was self-assembled on the gold nanoparticle (AuNP) modified electrode. The GOD could oxidize glucose into gluconic acid accompanied by the generation of H2O2. The generated H2O2 on the electrode surface was further electrocatalyzed by hemin@MOFs to amplify the electrochemical signal of hemin contained in hemin@MOFs. Therefore, the synthesized hemin@MOFs represented a new paradigm for multifunctional materials since it combined three different functions including serving as catalysts, redox mediators and loading platforms within a single material. With such an ingenious design, a wide linear range of 0.0001 nM to 30 nM was acquired with a relatively low detection limit of 0.068 pM for TB detection.

155 citations

Journal ArticleDOI
TL;DR: Experimental result demonstrates that the proposed approach outperforms most state-of-the-art methods on seizure prediction, including common spatial pattern (CSP) and convolutional neural network (CNN).
Abstract: Epilepsy seizure prediction paves the way of timely warning for patients to take more active and effective intervention measures. Compared to seizure detection that only identifies the inter-ictal state and the ictal state, far fewer researches have been conducted on seizure prediction because the high similarity makes it challenging to distinguish between the pre-ictal state and the inter-ictal state. In this paper, a novel solution on seizure prediction is proposed using common spatial pattern (CSP) and convolutional neural network (CNN). Firstly, artificial pre-ictal EEG signals based on the original ones are generated by combining the segmented pre-ictal signals to solve the trial imbalance problem between the two states. Secondly, a feature extractor employing wavelet packet decomposition and CSP is designed to extract the distinguishing features in both the time domain and the frequency domain. It can improve overall accuracy while reducing the training time. Finally, a shallow CNN is applied to discriminate between the pre-ictal state and the inter-ictal state. Our proposed solution is evaluated on 23 patients’ data from Boston Children's Hospital-MIT scalp EEG dataset by employing a leave-one-out cross-validation, and it achieves a sensitivity of 92.2% and false prediction rate of 0.12/h. Experimental result demonstrates that the proposed approach outperforms most state-of-the-art methods.

155 citations

Journal ArticleDOI
Zhongwei Wang1, Qing Li1, Zuxin She1, Funan Chen1, Longqin Li1 
TL;DR: In this article, a low-cost and large-scale fabrication method to an environmentally-friendly superhydrophobic coating on magnesium alloy is reported in which rough surface structure could be facilely obtained by electrodeposition of copper.
Abstract: A low-cost and large-scale fabrication method to an environmentally-friendly superhydrophobic coating on magnesium alloy is reported in this paper. The rough surface structure could be facilely obtained by electrodeposition of copper. Then the rough surface could be changed from hydrophilic to superhydrophobic via modification with lauric acid. The as-prepared coatings were characterized using scanning electron microscopy (SEM), Fourier transform infrared (FTIR) spectroscopy, atomic force microscopy (AFM), X-ray diffractometry (XRD), electrochemical corrosion test and electrochemical impedance spectroscopy (EIS). The as-prepared superhydrophobic coatings have ultra-low slide angles (2°) and contact angles of 154°. Our method is of great value for the industrial fabrication of superhydrophobic coatings. It has an excellent anti-corrosion effect and self-cleaning effect.

155 citations

Journal ArticleDOI
Jin Han1, Yubin Niu1, Shu-Juan Bao1, Ya-Nan Yu1, Shi-Yu Lu1, Maowen Xu1 
TL;DR: Novel nanocubic KTi2(PO4)3 was successfully fabricated via a facile hydrothermal method combined with a subsequent annealing treatment and further evaluated as an electrode material for potassium-ion batteries for the first time.

154 citations

Journal ArticleDOI
Michael R. Kanost1, Estela L. Arrese2, Xiaolong Cao2, Yun-Ru Chen3, Sanjay Chellapilla1, Marian R. Goldsmith4, Ewald Grosse-Wilde5, David G. Heckel5, Nicolae Herndon1, Haobo Jiang2, Alexie Papanicolaou6, Jiaxin Qu7, Jose L. Soulages2, Heiko Vogel5, James R. Walters8, Robert M. Waterhouse9, Seung-Joon Ahn5, Francisca C. Almeida10, Chunju An11, Peshtewani K. Aqrawi7, Anne Bretschneider5, William B. Bryant1, Sascha Bucks5, Hsu Chao7, Germain Chevignon12, Jayne M. Christen1, David F. Clarke13, Neal T. Dittmer1, Laura Ferguson14, Spyridoula Garavelou15, Karl H.J. Gordon13, Ramesh T. Gunaratna2, Yi Han7, Frank Hauser16, Yan He2, Hanna M. Heidel-Fischer5, Ariana Hirsh17, Yingxia Hu2, Hongbo Jiang18, Divya Kalra7, Christian Klinner5, Christopher König5, Christie Kovar7, Ashley R. Kroll19, Suyog S. Kuwar5, Sandy Lee7, Rüdiger Lehman20, Kai Li21, Zhaofei Li22, Hanquan Liang23, Shanna Lovelace24, Zhiqiang Lu22, Jennifer H. Mansfield17, Kyle J. McCulloch25, Tittu Mathew7, Brian R. Morton17, Donna M. Muzny7, David Neunemann5, Fiona Ongeri7, Yannick Pauchet5, Ling Ling Pu7, Ioannis Pyrousis15, Xiang Jun Rao26, Amanda J. Redding27, Charles Roesel28, Alejandro Sánchez-Gracia10, Sarah Schaack19, Aditi Shukla17, Guillaume Tetreau29, Yang Wang2, Guang Hua Xiong30, Walther Traut, Tom Walsh13, Kim C. Worley7, Di Wu1, Wenbi Wu1, Yuan Qing Wu7, Xiufeng Zhang2, Zhen Zou30, Hannah Zucker31, Adriana D. Briscoe25, Thorsten Burmester32, Rollie J. Clem1, René Feyereisen33, Cornelis J. P. Grimmelikhuijzen16, Stavros J. Hamodrakas34, Bill S. Hansson5, Elisabeth Huguet12, Lars S. Jermiin13, Que Lan35, Herman K. Lehman31, Marcé D. Lorenzen36, Hans Merzendorfer37, Ioannis Michalopoulos15, David B. Morton38, Subbaratnam Muthukrishnan1, John G. Oakeshott13, William J. Palmer39, Yoonseong Park1, A. Lorena Passarelli1, Julio Rozas10, Lawrence M. Schwartz40, Wendy A. Smith28, Agnes Ayme Southgate41, Andreas Vilcinskas42, Richard G. Vogt43, Ping Wang29, John H. Werren27, Xiao-Qiang Yu44, Jing-Jiang Zhou45, Susan J. Brown1, Steven E. Scherer7, Stephen Richards7, Gary W. Blissard3 
TL;DR: The sequence and annotation of the M. sexta genome, and a survey of gene expression in various tissues and developmental stages, provide an important new resource from a well-studied model insect species and will facilitate further biochemical and mechanistic experimental studies of many biological systems in insects.

154 citations


Authors

Showing all 29978 results

NameH-indexPapersCitations
Frank B. Hu2501675253464
Hongjie Dai197570182579
Jing Wang1844046202769
Chao Zhang127311984711
Jianjun Liu112104071032
Miao Liu11199359811
Jun Yang107209055257
Eric Westhof9847234825
En-Tang Kang9776338498
Chang Ming Li9789642888
Wei Zhou93164039772
Li Zhang9291835648
Heinz Rennenberg8752726359
Tao Chen8682027714
Xun Wang8460632187
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202395
2022461
20213,538
20203,257
20192,923
20182,479