Background: Because there is an urgent need to develop antibacterial therapies other than antibiotics, research has increasingly focused on the high-temperature-requirement protein A (HtrA) family proteases, which have both serine protease and chaperone activities.Objectives: The research progresses of the role of HtrA family proteases in the pathogenesis of bacterial infections are summarized, and the pros and cons of exploiting HtrA inhibitors in antibacterial drug development are proposed. Sources: A search of PubMed was performed to identify relevant studies. Content: HtrA is essential for bacteria to survive in harsh environments, based on the degradation and refolding of misfolded proteins. Moreover, HtrA family protease can lyse the epithelial cell barrier to promote invasion and can also act as or assist virulence factors to enhance pathogenicity. On the other hand, HtrA secreted by certain bacteria can also affect intra-and interspecies biofilm formation (the mechanism of its promotion or inhibition has not yet been proven). Overall, in view of the role of the HtrA family in promoting bacterial pathogenicity, effective HtrA inhibitors may be an exciting direction for drug development. Therefore, the research progress regarding HtrA inhibitors are summarized and the risks of their application are discussed. Implications: This review will be useful both for investigators involved in the HtrA field as well as those wishing to acquire a basic understanding of the role and potential implementations of HtrA. Ruo-Yi Xue,

With the growth of massive data in the current mobile Internet, network recruitment is gradually growing into a new recruitment channel. How to effectively mine available information in the massive network recruitment data has become the technical bottleneck of current education and social supply and demand development. The renewal of talent demand information is carried out every day, which produces a large amount of text data. How to manage these talents’ demand information reasonably becomes more and more important. Artificial classification is time-consuming and laborious, which is unrealistic naturally. Therefore, using automatic text categorization technology to classify and manage this information becomes particularly important. To break through the bottleneck of this technology, a heuristic KNN text categorization algorithm based on ABC (artificial bee colony) is proposed to adjust the weight of features, and the similarity between test observation and training observation is measured by using the method of fuzzy distance measurement. Firstly, the recruitment information is segmented and feature selection and noise data elimination are carried out by using term frequency-inverse document frequency (TF-IDF) algorithm and AP (affinity propagation) clustering algorithm. Finally, the text information is classified by using KNN algorithm combined with heuristic search and fuzzy distance measurement. The experimental results show that this method effectively solves the problem of poor stability and low classification accuracy of traditional KNN algorithm in text categorization method for talent demand.