The simulation analysis of contact characteristics of biomimetic flexible surfaces

Based on the foot structure of the climbing biology and multivariate coupling bionic technology, the bionic flexible convex surface was designed and a 3D model was created using the digital modeling software. Finite Element Analysis software was used for contacting analysis to the bionic flexible convex foot structure in the state of dry friction and wet adhesion, and then studied frictional contact performance. The results of Finite Element Analysis shows that the contact stress of the convex is much larger than the stress of the area around it in the dry friction state and the deformation is mainly concentrated in the convex’s top. The friction between the hemispherical convex surface and the contact surface is the maximum and the cylindrical convex surface is the minimum. The friction between the bionic flexible convex structure and the solid contact surface in wet adhesion state is larger than dry state.Keywords: Bionic, flexible, contact, finite element, wet adhesio

Significant Impact of Sequence Variations in the Nucleoprotein on CD8 T Cell-Mediated Cross-Protection against Influenza A Virus Infections

Background: Memory CD8 T cells to influenza A viruses are widely detectable in healthy human subjects and broadly cross-reactive for serologically distinct influenza A virus subtypes. However, it is not clear to what extent such pre-existing cellular immunity can provide cross-subtype protection against novel emerging influenza A viruses. Methodology/Principal: Findings We show in the mouse model that naturally occurring sequence variations of the conserved nucleoprotein of the virus significantly impact cross-protection against lethal disease in vivo. When priming and challenge viruses shared identical sequences of the immunodominant, protective NP366/Db epitope, strong cross-subtype protection was observed. However, when they did not share complete sequence identity in this epitope, cross-protection was considerably reduced. Contributions of virus-specific antibodies appeared to be minimal under these circumstances. Detailed analysis revealed that the magnitude of the memory CD8 T cell response triggered by the NP366/Db variants was significantly lower than those triggered by the homologous NP366/Db ligand. It appears that strict specificity of a dominant public TCR to the original NP366/Db ligand may limit the expansion of cross-reactive memory CD8 T cells to the NP366/Db variants. Conclusions/Significance: Pre-existing CD8 T cell immunity may provide substantial cross-protection against heterosubtypic influenza A viruses, provided that the priming and the subsequent challenge viruses share the identical sequences of the immunodominant, protective CTL epitopes

Antioxidative Activity Evaluation of High Purity and Micronized Tartary Buckwheat Flavonoids Prepared by Antisolvent Recrystallization

Tartary buckwheat, a healthy food, is associated with a reduced risk of certain human chronic diseases. However, the bioactive component flavonoids in Tartary buckwheat have poor solubility and low absorption in vivo. To improve these points, 60.00% Tartary buckwheat total flavonoids (TFs) were obtained by ethanol refluxing method, which were purified and micronized by antisolvent recrystallization (ASR) using methanol as a solvent and deionized water as an antisolvent. By using High Performance Liquid Chromatography (HPLC) and electrospray ionized mass spectrometry (ESI-MS), the main flavonoid in pure flavonoids (PF) were rutin (RU), kaempferol-3-O-rutinoside (KA) and quercetin (QU); the content of TF is 99.81% after purification. It is more worthy of our attention that micronized flavonoids contribute more to antioxidant activity because of good solubility. These results provide a theoretical reference for the micronization of other flavonoids

Coal and Gas Outburst Risk Prediction and Management Based on WOA-ELM

A gas outburst risk level prediction method, based on the Whale Optimization Algorithm (WOA) Improved Extreme Learning Machine (ELM), is proposed to predict the coal and gas outburst hazard level more accurately. Based on this method, recommendations are given according to the gas outburst risk level with the help of the Case-Based Reasoning (CBR) method. Firstly, we analyze the accident reports of gas outburst accidents, select the gas outburst risk prediction index, and construct the gas outburst risk prediction index system by combining the gas outburst prevention and control process. The WOA-ELM model was used to predict the gas outburst risk level by selecting data from 150 accident reports from 2008 to 2021. Again, based on the coal and gas outburst risk level, CBR is used to match the cases and give corresponding suggestions for different levels of gas outburst risk conditions to help reduce the gas outburst risk. The results show that the WOA-ELM algorithm has better performance and faster convergence than the ELM algorithm, when compared in terms of accuracy and the error of gas outburst hazard prediction. The use of CBR to manage prediction results can be helpful for decision-makers

Coal and Gas Outburst Risk Prediction and Management Based on WOA-ELM

A gas outburst risk level prediction method, based on the Whale Optimization Algorithm (WOA) Improved Extreme Learning Machine (ELM), is proposed to predict the coal and gas outburst hazard level more accurately. Based on this method, recommendations are given according to the gas outburst risk level with the help of the Case-Based Reasoning (CBR) method. Firstly, we analyze the accident reports of gas outburst accidents, select the gas outburst risk prediction index, and construct the gas outburst risk prediction index system by combining the gas outburst prevention and control process. The WOA-ELM model was used to predict the gas outburst risk level by selecting data from 150 accident reports from 2008 to 2021. Again, based on the coal and gas outburst risk level, CBR is used to match the cases and give corresponding suggestions for different levels of gas outburst risk conditions to help reduce the gas outburst risk. The results show that the WOA-ELM algorithm has better performance and faster convergence than the ELM algorithm, when compared in terms of accuracy and the error of gas outburst hazard prediction. The use of CBR to manage prediction results can be helpful for decision-makers