PMCNA_RS00975 activates NF-κB and ERK1/2 through TLR2 and contributes to the virulence of Pasteurella multocida

IntroductionPasteurella multocida is a pathogenic bacterium known to cause hemorrhagic septicemia and pneumonia in poultry. Reports have indicated that certain proteins, either directly involved in or regulating iron metabolism, are important virulence factors of P. multocida. Therefore, understanding virulent factors and analyzing the role of pro-inflammatory cytokines can help us elucidate the underlying pathogenesis.MethodsIn this study, the PMCNA_RS00975 protein, a putative encapsuling protein encoded by a gene from a specific prophage island of the pathogenic strain C48-1 of P. multocida, was investigated. To further explore the impact of the PMCNA_RS00975 protein on pathogenicity, a PMCNA_RS00975 gene mutant of P. multocida strain C48-1 was constructed using positive selection technology. Subcellular localization was performed to determine the location of the PMCNA_RS00975 protein within P. multocida. The recombinant protein PMCNA_RS00975 of P. multocida was soluble expressed, purified, and its role in pro-inflammatory cytokines was investigated.ResultsThe mutant exhibited significantly reduced pathogenicity in the mice model. Furthermore, subcellular localization indicated that the PMCNA_RS00975 protein was located at the outer membrane and expressed during infection of P. multocida. Additionally, our experiments revealed that recombinant PMCNA_RS00975 protein promotes the secretion of the IL-6 pro-inflammatory cytokines triggered by the TLR2 receptor via NF-κB and ERK1/2 signaling pathways in the macrophages.DiscussionThis study identified a novel virulence factor in the C48-1 strain, providing a basis for understanding the pathogenesis and directions for the development of attenuated vaccines against P. multocida

Outsourced Secure Face Recognition Based on CKKS Homomorphic Encryption in Cloud Computing

With the enhancement of the performance of cloud servers, face recognition applications are becoming more and more popular, but it also has some security problems, such as user privacy data leakage. This article proposes a face recognition scheme based on homomorphic encryption in cloud environment. The article first uses the MTCNN algorithm to detect face and correct the data and extracts the face feature vector through the FaceNet algorithm. Then, the article encrypts the facial features with the CKKS homomorphic encryption scheme and builds a database of the encrypted facial feature in the cloud server. The process of face recognition is as follows: calculate the distance between the encrypted feature vectors and the maximum value of the ciphertext result, decrypt it, and compare the threshold to determine whether it is a person. The experimental results show that when the scheme is based on the LFW data set, the threshold is 1.1236, and the recognition accuracy in the ciphertext is 94.8837%, which proves the reliability of the proposed scheme.</jats:p

Secure KNN Classification Scheme Based on Homomorphic Encryption for Cyberspace

With the advent of the intelligent era, more and more artificial intelligence algorithms are widely used and a large number of user data are collected in the cloud server for sharing and analysis, but the security risks of private data breaches are also increasing in the meantime. CKKS homomorphic encryption has become a research focal point in the cryptography field because of its ability of homomorphic encryption for floating-point numbers and comparable computational efficiency. Based on the CKKS homomorphic encryption, this paper implements a secure KNN classification scheme in cloud servers for Cyberspace (CKKSKNNC) and supports batch calculation. This paper uses the CKKS homomorphic encryption scheme to encrypt user data samples and then uses Euclidean distance, Pearson similarity, and cosine similarity to compute the similarity between ciphertext data samples. Finally, the security classification of the samples is realized by voting rules. This paper selects IRIS data set for experimental, which is the classification data set commonly used in machine learning. The experimental results show that the accuracy of the other three similarity algorithms of the IRIS data is around 97% except for the Pearson correlation coefficient, which is almost the same as that in plaintext, which proves the effectiveness of this scheme. Through comparative experiments, the efficiency of this scheme is proved.</jats:p