Identification of catalytic residues plays a key role in understanding how enzymes work. Although numerous computational methods have been developed to predict catalytic residues and active sites, the prediction accuracy remains relatively low with high false positives. In this work, we developed a novel predictor based on the Random Forest algorithm (RF) aided by the maximum relevance minimum redundancy (mRMR) method and incremental feature selection (IFS). We incorporated features of physicochemical/biochemical properties, sequence conservation, residual disorder, secondary structure and solvent accessibility to predict active sites of enzymes and achieved an overall accuracy of 0.885687 and MCC of 0.689226 on an independent test dataset. Feature analysis showed that every category of the features except disorder contributed to the identification of active sites. It was also shown via the site-specific feature analysis that the features derived from the active site itself contributed most to the active site determination. Our prediction method may become a useful tool for identifying the active sites and the key features identified by the paper may provide valuable insights into the mechanism of catalysis.
:Quasi-cavitation is caused by the vortex formed between the propeller and water surface when the propeller rotates at high speed near the water surface, which leads to a great reduction of thruster's efficiency, thrust loss and noise. As the only power source of most underwater vehicles, quasi-cavitation on the thruster will significantly decrease the stability of motion control. A novel approach for thrust prediction of underwater blade-propeller-type thrusters under quasi-cavitation is proposed, which can realize thrust prediction with high accuracy. The mechanism of quasi-cavitation is introduced and revealed. A Bayesian estimation based thrust model (BETM) is established to perform accurate thrust prediction without quasi-cavitation. On this basis, a quasi-cavitation thrust model based on Gaussian process (QCTM-GP) is proposed, which utilizes BETM and error compensation based on Gaussian process to complete the prediction of quasi-cavitation. The accuracy and validity of the proposed prediction model are verified via experiments. QCTM-GP lays a foundation for the high accurate motion control of underwater vehicles near surface.
Background: The molecular mechanisms regulating the therapeutic effects of plant-based ingredients on the exercise-induced fatigue (EIF) remain unclear. The therapeutic effects of both tea polyphenols (TP) and fruit extracts of Lycium ruthenicum (LR) on mouse model of EIF were investigated.Methods: The variations in the fatigue-related biochemical factors, i.e., lactate dehydrogenase (LDH), superoxide dismutase (SOD), tumor necrosis factor-α (TNF-α), interleukin-1β (IL-1β), interleukin-2 (IL-2), and interleukin-6 (IL-6), in mouse models of EIF treated with TP and LR were determined. The microRNAs involved in the therapeutic effects of TP and LR on the treatment of mice with EIF were identified using the next-generation sequencing technology.Results: Our results revealed that both TP and LR showed evident anti-inflammatory effect and reduced oxidative stress. In comparison with the control groups, the contents of LDH, TNF-α, IL-6, IL-1β, and IL-2 were significantly decreased and the contents of SOD were significantly increased in the experimental groups treated with either TP or LR. A total of 23 microRNAs (21 upregulated and 2 downregulated) identified for the first time by the high-throughput RNA sequencing were involved in the molecular response to EIF in mice treated with TP and LR. The regulatory functions of these microRNAs in the pathogenesis of EIF in mice were further explored based on Gene Ontology (GO) annotation and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses with a total of over 20,000–30,000 target genes annotated and 44 metabolic pathways enriched in the experimental groups based on GO and KEGG databases, respectively.Conclusion: Our study revealed the therapeutic effects of TP and LR and identified the microRNAs involved in the molecular mechanisms regulating the EIF in mice, providing strong experimental evidence to support further agricultural development of LR as well as the investigations and applications of TP and LR in the treatment of EIF in humans, including the professional athletes.
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