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A novel composite consisting of fluorine-doped carbon and graphene double-coated LiMn0.6Fe0.4PO4 (LMFP) nanorods was synthesized via a facile low-temperature solvothermal method that employs a hybrid glucose and polyvinylidene fluoride as carbon and fluorine sources. As revealed by physicochemical characterization, F-doped carbon coating and graphene form a 'point-to-surface' conductive network, facilitating rapid electron transport and mitigating electrochemical polarization. Furthermore, the uniform thickness of the F-doped carbon coating alters the growth of nanoparticles and prevents direct contact between the material and the electrolyte, thereby enhancing structural stability. The strongly electronegative F− can inhibit the structural changes in LMFP during charge/discharge, thus reducing the Jahn–Teller effect of Mn3+. The distinctive architecture of the LMFP/C-F/G cathode material exhibits excellent electrochemical properties, exhibiting an initial discharge capacity of 163.1 mAh g−1 at 0.1 C and a constant Coulombic efficiency of 99.7% over 100 cycles. Notably, the LMFP/C-F/G cathode material achieves an impressive energy density of 607.6 Wh kg−1, surpassing that of commercial counterparts. Moreover, it delivers a reversible capacity of 90.3 mAh g−1 at a high current rate of 5 C. The high-capacity capability and energy density of the prepared materials give them great potential for use in next-generation lithium-ion batteries. [ABSTRACT FROM AUTHOR]

The recently reported machine learning- or deep learning-based scoring functions (SFs) have shown exciting performance in predicting protein–ligand binding affinities with fruitful application prospects. However, the differentiation between highly similar ligand conformations, including the native binding pose (the global energy minimum state), remains challenging that could greatly enhance the docking. In this work, we propose a fully differentiable, end-to-end framework for ligand pose optimization based on a hybrid SF called DeepRMSD+Vina combined with a multi-layer perceptron (DeepRMSD) and the traditional AutoDock Vina SF. The DeepRMSD+Vina, which combines (1) the root mean square deviation (RMSD) of the docking pose with respect to the native pose and (2) the AutoDock Vina score, is fully differentiable; thus is capable of optimizing the ligand binding pose to the energy-lowest conformation. Evaluated by the CASF-2016 docking power dataset, the DeepRMSD+Vina reaches a success rate of 94.4%, which outperforms most reported SFs to date. We evaluated the ligand conformation optimization framework in practical molecular docking scenarios (redocking and cross-docking tasks), revealing the high potentialities of this framework in drug design and discovery. Structural analysis shows that this framework has the ability to identify key physical interactions in protein–ligand binding, such as hydrogen-bonding. Our work provides a paradigm for optimizing ligand conformations based on deep learning algorithms. The DeepRMSD+Vina model and the optimization framework are available at GitHub repository https://github.com/zchwang/DeepRMSD-Vina_Optimization. [ABSTRACT FROM AUTHOR]

Predicted interactions are a valuable complement to experimentally reported interactions in molecular mechanism studies, particularly for higher organisms, for which reported experimental interactions represent only a small fraction of their total interactomes. With careful engineering consideration of the lessons from previous efforts, the Predicted Arabidopsis Interactome Resource (PAIR;) presents 149,900 potential molecular interactions, which are expected to cover ~24% of the entire interactome with ~40% precision. This study demonstrates that, although PAIR still has limited coverage, it is rich enough to capture many significant functional linkages within and between higher-order biological systems, such as pathways and biological processes. These inferred interactions can nicely power several network topology-based systems biology analyses, such as gene set linkage analysis, protein function prediction, and identification of regulatory genes demonstrating insignificant expression changes. The drastically expanded molecular network in PAIR has considerably improved the capability of these analyses to integrate existing knowledge and suggest novel insights into the function and coordination of genes and gene networks. [ABSTRACT FROM AUTHOR]

Objective: To analyze the effect of predictive nursing on postoperative rehabilitation index and complications in patients after hip replacement with maintenance hemodialysis., Methods: A total of 81 cases of patients underwent hip replacement and maintenance hemodialysis in our hospital were selected as the research objects and divided into study group (n=41) and control group (n=40) with retrospective analysis method based on different intervention method. Patients in the study group received predictive nursing, while patients in the control group received routine nursing. The hip function and activity, duration of walking with and without crutches, adverse emotions, pain and the incidence of various complications after intervention were compared between the two groups., Results: There was no significant difference in Harris score between the two groups at 7 days after intervention ( P >0.05); the Harris scores of the study group were significantly higher than those of the control group ( P <0.05) at 1, 3 and 6 months after intervention. Before intervention, there was no significant difference in hip activity between the two groups ( P >0.05); 3 months surgery operation, the hip extension, abduction and rotation angle of the study group were significantly higher than those of the control group ( P <0.05); the duration of walking with and without crutches in the study group were significantly shorter than those in the control group ( P <0.05); the scores of adverse emotions, pain and complications in the study group were significantly lower than those in the control group ( P <0.05)., Conclusion: The implementation of predictive nursing to patients underwent hip replacement and maintenance hemodialysis can improve hip activity and joint function after surgery, accelerate postoperative recovery, relieve postoperative pain symptoms, and reduce the incidence of various complications., Competing Interests: None., (AJTR Copyright © 2021.)