1,110 research outputs found

    Numerical Investigation of Hypersonic Unsteady Flow Around a Spiked Blunt-body

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    AbstractThe obvious unsteady flow characteristics of the flow field around the spiked blunt-body have negative effect on the drag reduction of vehicle and the thermo-protection of the head. The hypersonic self-sustained oscillatory flow was numerical simulated, and the flow structure and mechanism of the three process of one cycle: collapse, inflation and withhold, were discussed in detail. The correspondence between the drag coefficient curve and the different flow structure was obtained, which will provide basis to the drag reduction and thermo-protection of hypersonic blunt-body vehicle through flow control in the future

    A phage-displayed peptide recognizing porcine aminopeptidase N is a potent small molecule inhibitor of PEDV entry

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    Three phage-displayed peptides designated H, S and F that recognize porcine aminopeptidase N (pAPN), the cellular receptor of porcine transmissible gastroenteritis virus (TGEV) were able to inhibit cell infection by TGEV. These same peptides had no inhibitory effects on infection of Vero cells by porcine epidemic diarrhea virus (PEDV). However, when PEDV, TGEV and porcine pseudorabies virus were incubated with peptide H (HVTTTFAPPPPR), only infection of Vero cells by PEDV was inhibited. Immunofluorescence assays indicated that inhibition of PEDV infection by peptide H was independent of pAPN. Western blots demonstrated that peptide H interacted with PEDV spike protein and that pre-treatment of PEDV with peptide H led to a higher inhibition than synchronous incubation with cells. These results indicate direct interaction with the virus is necessary to inhibit infectivity. Temperature shift assays demonstrated that peptide H inhibited pre-attachment of the virus to the cells

    Effect of the Sodium Silicate Modulus and Slag Content on Fresh and Hardened Properties of Alkali-Activated Fly Ash/Slag

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    This paper presents the results of an experimental study performed to investigate the effect of activator modulus (SiO2/Na2O) and slag addition on the fresh and hardened properties of alkali-activated fly ash/slag (AAFS) pastes. Four activator moduli (SiO2/Na2O), i.e., 0.0, 1.0, 1.5, and 2.0, and five slag-to-binder ratios, i.e., 0, 0.3, 0.5, 0.7, 1.0, were used to prepare AAFS mixtures. The setting time, flowability, heat evolution, compressive strength, microstructure, and reaction products of AAFS pastes were studied. The results showed that the activator modulus and slag content had a combined effect on the setting behavior and workability of AAFS mixtures. Both the activator modulus and slag content affected the types of reaction products formed in AAFS. The coexistence of N-A-S-H gel and C-A-S-H gel was identified in AAFS activated with high pH but low SiO2 content (low modulus). C-A-S-H gel had a higher space-filling ability than N-A-S-H gel. Thus, AAFS with higher slag content had a finer pore structure and higher heat release (degree of reaction), corresponding to a higher compressive strength. The dissolution of slag was more pronounced when NaOH (modulus of 0.0) was applied as the activator. The use of Na2SiO3 as activator significantly refined the pores in AAFS by incorporating soluble Si in the activator, while further increasing the modulus from 1.5 to 2.0 prohibited the reaction process of AAFS, resulting in a lower heat release, coarser pore structure, and reduced compressive strength. Therefore, in view of the strength and microstructure, the optimum modulus is 1.5

    Identification of Protein Pupylation Sites Using Bi-Profile Bayes Feature Extraction and Ensemble Learning

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    Pupylation, one of the most important posttranslational modifications of proteins, typically takes place when prokaryotic ubiquitin-like protein (Pup) is attached to specific lysine residues on a target protein. Identification of pupylation substrates and their corresponding sites will facilitate the understanding of the molecular mechanism of pupylation. Comparing with the labor-intensive and time-consuming experiment approaches, computational prediction of pupylation sites is much desirable for their convenience and fast speed. In this study, a new bioinformatics tool named EnsemblePup was developed that used an ensemble of support vector machine classifiers to predict pupylation sites. The highlight of EnsemblePup was to utilize the Bi-profile Bayes feature extraction as the encoding scheme. The performance of EnsemblePup was measured with a sensitivity of 79.49%, a specificity of 82.35%, an accuracy of 85.43%, and a Matthews correlation coefficient of 0.617 using the 5-fold cross validation on the training dataset. When compared with other existing methods on a benchmark dataset, the EnsemblePup provided better predictive performance, with a sensitivity of 80.00%, a specificity of 83.33%, an accuracy of 82.00%, and a Matthews correlation coefficient of 0.629. The experimental results suggested that EnsemblePup presented here might be useful to identify and annotate potential pupylation sites in proteins of interest. A web server for predicting pupylation sites was developed
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