Continuous and Discrete Similarity Coefficient for Identifying Essential Proteins Using Gene Expression Data

Essential proteins play a vital role in biological processes,and the combination of gene expression profiles with Protein-Protein Interaction(PPI)networks can improve the identification of essential proteins.However,gene expression data are prone to significant fluctuations due to noise interference in topological networks.In this work,we discretized gene expression data and used the discrete similarities of the gene expression spectrum to eliminate noise fluctuation.We then proposed the Pearson Jaccard coefficient(PJC)that consisted of continuous and discrete similarities in the gene expression data.Using the graph theory as the basis,we fused the newly proposed similarity coefficient with the existing network topology prediction algorithm at each protein node to recognize essential proteins.This strategy exhibited a high recognition rate and good specificity.We validated the new similarity coefficient PJC on PPI datasets of Krogan,Gavin,and DIP of yeast species and evaluated the results by receiver operating characteristic analysis,jackknife analysis,top analysis,and accuracy analysis.Compared with that of node-based network topology centrality and fusion biological information centrality methods,the new similarity coefficient PJC showed a significantly improved prediction performance for essential proteins in DC,IC,Eigenvector centrality,subgraph centrality,betweenness centrality,closeness centrality,NC,PeC,and WDC.We also compared the PJC coefficient with other methods using the NF-PIN algorithm,which predicts proteins by constructing active PPI networks through dynamic gene expression.The experimental results proved that our newly proposed similarity coefficient PJC has superior advantages in predicting essential proteins.

The red deer antler:Bioinspired design of an Al-Si composite with a fenestrated network-particle structure

Conventional Al-Si materials always have a coarse and discontinuous Si structure,which limits their application as thermal management materials.Fortunately,nature has evolved efficient strategies to form complex microstructures that exhibit excellent wear resistance and thermal properties;one such example is found in the red deer antler.Here,inspired by the antler structure,AleSi composites with a novel fenestrated network-particle structure(F-N)and with a common isolated island structure(I-I)are designed and prepared.Subsequently,the dynamic formation of F-N is tracked and studied using computational fluid dynamics(CFD)simulation.To investigate the reinforcement mechanisms of F-N,the wear resistances and thermal properties of F-N and I-I are compared and analyzed.Simulation and experimental results show that the reconstruction of a semi-continuous structure promotes the formation of a striated structure,whereas flowing Si particles provide some sites for the formation of the fenestrated structure.The dynamic formation of F-N is strongly influenced by the convectionediffusion process and the flow path.Moreover,this biomimetic F-N structure exhibits better wear resistance and thermal properties than I-I,owing to its strong structural support and high expansion resistance.This work is expected to provide new perspectives on the microstructural design of thermal management materials with good wear resistance.