Institution
Jiangxi University of Science and Technology
Education•Ganzhou, China•
About: Jiangxi University of Science and Technology is a education organization based out in Ganzhou, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 6958 authors who have published 5576 publications receiving 50650 citations.
Topics: Microstructure, Alloy, Adsorption, Catalysis, Leaching (metallurgy)
Papers published on a yearly basis
Papers
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TL;DR: This paper proposes a novel approach, namely CNN-FSRF, for predicting PPIs based on protein sequence by combining deep learning Convolution Neural Network (CNN) with Feature-Selective Rotation Forest (FSRF).
Abstract: Protein is an essential component of the living organism. The prediction of protein-protein interactions (PPIs) has important implications for understanding the behavioral processes of life, preventing diseases, and developing new drugs. Although the development of high-throughput technology makes it possible to identify PPIs in large-scale biological experiments, it restricts the extensive use of experimental methods due to the constraints of time, cost, false positive rate and other conditions. Therefore, there is an urgent need for computational methods as a supplement to experimental methods to predict PPIs rapidly and accurately. In this paper, we propose a novel approach, namely CNN-FSRF, for predicting PPIs based on protein sequence by combining deep learning Convolution Neural Network (CNN) with Feature-Selective Rotation Forest (FSRF). The proposed method firstly converts the protein sequence into the Position-Specific Scoring Matrix (PSSM) containing biological evolution information, then uses CNN to objectively and efficiently extracts the deeply hidden features of the protein, and finally removes the redundant noise information by FSRF and gives the accurate prediction results. When performed on the PPIs datasets Yeast and Helicobacter pylori, CNN-FSRF achieved a prediction accuracy of 97.75% and 88.96%. To further evaluate the prediction performance, we compared CNN-FSRF with SVM and other existing methods. In addition, we also verified the performance of CNN-FSRF on independent datasets. Excellent experimental results indicate that CNN-FSRF can be used as a useful complement to biological experiments to identify protein interactions.
66 citations
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TL;DR: The latest progress of Bi-based electrode materials for alkali metal-ion batteries is summarized, mainly focusing on synthesis strategies, structural features, storage mechanisms, and the corresponding electrochemical performance.
Abstract: Alkali metal (Li, Na, K) ion batteries with high energy density are urgently required for large-scale energy storage applications while the lack of advanced anode materials restricts their development. Recently, Bi-based materials have been recognized as promising electrode candidates for alkali metal-ion batteries due to their high volumetric capacity and suitable operating potential. Herein, the latest progress of Bi-based electrode materials for alkali metal-ion batteries is summarized, mainly focusing on synthesis strategies, structural features, storage mechanisms, and the corresponding electrochemical performance. Particularly, the optimization of electrode-electrolyte interphase is also discussed. In addition, the remaining challenges and further perspectives of Bi-based electrode materials are outlined. This review aims to provide comprehensive knowledge of Bi-based materials and offer a guideline toward more applications in high-performance batteries.
65 citations
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TL;DR: magnetic properties investigations showed that complexes 1 and 2 exhibited weak ferromagnetic coupling between the Ni(ii) and Ln(iii) ions, whereas complex 4 displayed lanthanide single-ion magnetic properties, and can be considered as a molecular magnetic and luminescent material.
Abstract: A new family of 3d–4f heterometallic trinuclear complexes, namely [M2LnL2]·2ClO4·H2O (H3L = tris(((2-hydroxy-3-methoxybenzy1)amino)ethyl)amine, where M = Ni, Ln = Gd (1), Dy (2), and M = Zn, Ln = Gd (3), Dy (4)) were synthesized via the reaction of H3L and Ln(NO3)3·6H2O and Ni(ClO4)2·6H2O or Zn(ClO4)2·6H2O in a 2 : 1 : 2 ratio in the solution. Complexes 1–4 consisted of three metal ions arranged in an isosceles triangle manner. Magnetic properties investigations showed that complexes 1 and 2 exhibited weak ferromagnetic coupling between the Ni(II) and Ln(III) ions, whereas complex 4 displayed lanthanide single-ion magnetic properties. The alternating current (ac) magnetic susceptibilities of 4 revealed that both the in-phase (χ′) and out-of-phase (χ′′) signals are frequency- and temperature-dependent, which are typical features of the field-induced slow relaxation of the magnetization with Ueff = 124.5 K. Complex 4 also exhibited an obvious butterfly-shaped hysteresis loop at 2 K, indicating that it is a single-ion magnet. Moreover, complex 4 showed stronger fluorescent emissions, which were typical narrow emission bands of lanthanide ions. Therefore, complex 4 can be considered as a molecular magnetic and luminescent material. Comparably, complex 2 showed very weak DyIII-based emissions because the paramagnetic NiII ions quench the fluorescence and thereby lower the population of the triplet state.
65 citations
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TL;DR: Ag 2 S/Ag 2 WO 4 composite microrods, with lengths of 0.2-1 µm and diameters of 20-30 nm, were fabricated by a facile sonochemical route as mentioned in this paper.
65 citations
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TL;DR: In this paper, a composite scaffold combining the excellent biomechanical properties of PEEK and the remarkable degradability of PVA is proposed. But it requires polyetheretherketone (PEEK) instead of polyvinyl alcohol (PVA).
Abstract: Blending Polyetheretherketone (PEEK) with Polyvinyl alcohol (PVA) is promising to obtain a composite scaffold combining the excellent biomechanical properties of PEEK and the remarkable degradabili...
65 citations
Authors
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Name | H-index | Papers | Citations |
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Hua Zhang | 163 | 1503 | 116769 |
Wei Li | 158 | 1855 | 124748 |
Mingwei Chen | 108 | 536 | 51351 |
Hongjie Zhang | 92 | 760 | 33301 |
Aibing Yu | 86 | 930 | 34127 |
Shiyong Liu | 79 | 266 | 19061 |
Chun-Hua Yan | 73 | 336 | 19972 |
Xiaobo Ji | 73 | 360 | 17916 |
Yang Hou | 64 | 235 | 14113 |
Hao Su | 57 | 302 | 55902 |
Jian Tian | 56 | 175 | 13090 |
Lei Wang | 54 | 1076 | 15189 |
Jiafu Wan | 54 | 167 | 12244 |
Peng Cheng | 52 | 362 | 9193 |
Heng-Yun Ye | 47 | 204 | 9435 |