Transonic buffet is a flow instability phenomenon that arises from the interaction between the shock wave and the separated boundary layer. This flow phenomenon is considered to be highly detrimental during flight and poses a significant risk to the structural strength and fatigue life of aircraft. Up to now, there has been a lack of an accurate, efficient, and intuitive metric to predict buffet and impose a feasible constraint on aerodynamic design. In this paper, a Physics-Assisted Variational Autoencoder (PAVAE) is proposed to identify dominant features of transonic buffet, which combines unsupervised reduced-order modeling with additional physical information embedded via a buffet classifier. Specifically, four models with various weights adjusting the contribution of the classifier are trained, so as to investigate the impact of buffet information on the latent space. Statistical results reveal that buffet state can be determined exactly with just one latent space when a proper weight of classifier is chosen. The dominant latent space further reveals a strong relevance with the key flow features located in the boundary layers downstream of shock. Based on this identification, the displacement thickness at 80% chordwise location is proposed as a metric for buffet prediction. This metric achieves an accuracy of 98.5% in buffet state classification, which is more reliable than the existing separation metric used in design. The proposed method integrates the benefits of feature extraction, flow reconstruction, and buffet prediction into a unified framework, demonstrating its potential in low-dimensional representations of high-dimensional flow data and interpreting the "black box" neural network.
Introduction The potential contamination of herbal medicinal products poses a significant concern for consumer health. Given the limited availability of genetic information concerning Ajuga species, it becomes imperative to incorporate supplementary molecular markers to enhance and ensure accurate species identification. Methods In this study, the chloroplast (cp) genomes of seven species of the genus Ajuag were sequenced, de novo assembled and characterized. Results exhibiting lengths ranging from 150,342 bp to 150,472 bp, encompassing 86 - 88 protein-coding genes (PCGs), 35 - 37 transfer RNA, and eight ribosomal RNA. The repetitive sequences, codon uses, and cp genomes of seven species were highly conserved, and PCGs were the reliable molecular markers for investigating the phylogenetic relationship within the Ajuga genus. Moreover, four mutation hotspot regions (accD-psaI, atpH-atpI, ndhC-trnV(UAC), and ndhF-rpl23) were identified within cp genomes of Ajuga, which could help distinguish A. bracteosa and its contaminants. Based on cp genomes and PCGs, the phylogenetic tree preliminary confirmed the position of Ajuga within the Lamiaceae family. It strongly supported a sister relationship between Subsect. Genevense and Subsect. Biflorae, suggesting the merger of Subsect. Biflorae and Subsect. Genevenses into one group rather than maintaining separate categorizations. Additionally, molecular clock analysis estimated the divergence time of Ajuga to be around 7.78 million years ago. Discussion The species authentication, phylogeny, and evolution analyses of the Ajuga species may benefit from the above findings.
Current high-throughput protein-protein interaction (PPI) data do not provide information about the condition(s) under which the interactions occur. Thus, the identification of condition-responsive PPI sub-networks is of great importance for investigating how a living cell adapts to changing environments.In this article, we propose a novel edge-based scoring and searching approach to extract a PPI sub-network responsive to conditions related to some investigated gene expression profiles. Using this approach, what we constructed is a sub-network connected by the selected edges (interactions), instead of only a set of vertices (proteins) as in previous works. Furthermore, we suggest a systematic approach to evaluate the biological relevance of the identified responsive sub-network by its ability of capturing condition-relevant functional modules. We apply the proposed method to analyze a human prostate cancer dataset and a yeast cell cycle dataset. The results demonstrate that the edge-based method is able to efficiently capture relevant protein interaction behaviors under the investigated conditions.Supplementary data are available at Bioinformatics online.
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