Virulence factors (VFs) are critical determinants of bacterial pathogenicity, but current homology-based identification methods often miss novel or divergent VFs, and many machine learning approaches neglect functional classification. Here, we present VirulentHunter, a novel deep learning framework that enable simultaneous VF identification and classification directly from protein sequences by leveraging the crucial step of fine-tuning pretrained protein language model. We curate a comprehensive VF database by integrating diverse public resources and expanding VF category annotations. Our benchmarking results demonstrate that VirulentHunter outperforms existing methods, particularly in identifying VFs lacking detectable homologs. Additionally, strain-level analysis using VirulentHunter highlights distinct pathogenicity profiles between Mycobacterium tuberculosis and Mycobacterium avium, revealing enrichment in VFs related to adherence, effector delivery systems, and immune modulation in M. tuberculosis, compared to biofilm formation and motility in M. avium. Furthermore, metagenomic profiling of gut microbiota from inflammatory bowel disease patient reveals a depletion of VFs associated with immune homeostasis. These results underscore the versatility of VirulentHunter as a powerful tool for VF analysis across diverse applications. To facilitate broader accessibility, we provide a freely accessible web service for VF prediction (http://www.unimd.org/VirulentHunter), accommodating protein sequences, genomes, and metagenomic data.

Proof of Work(PoW) as a critical consensus algorithm plays an essential role in Cryptocurrency fields. In PoW, computation power is the most vital resources. The security of a PoW system heavily relies on the computation power all honest users controlled. PoW systems distribute some rewards among participants according to their contributions as an incentive. Users who focus on earning such rewards are known as miners. Miners can join pools to share and reduce the variance of their rewards. In this work, we will compare different reward distribution mechanisms in a long-term condition. The result shows that all existed mechanisms cannot motivate miners continuously mining in the pool. For solving this flaw, a novel mechanism is proposed to improve the utility and motivation of miners.

With the growth of cloud computing technology, more and more Cloud Service Providers (CSPs) begin to provide cloud computing service to users and ask for users’ permission of using their data to improve the quality of service (QoS). Since these data are stored in the form of plain text, they bring about users’ worry for the risk of privacy leakage. However, the existing watermark embedding and encryption technology is not suitable for protecting the Right to Be Forgotten. Hence, we propose a new Cloud-User protocol as a solution for plain text outsourcing problem. We only allow users and CSPs to embed the ciphertext watermark, which is generated and embedded by Trusted Third Party (TTP), into the ciphertext data for transferring. Then, the receiver decrypts it and obtains the watermarked data in plain text. In the arbitration stage, feature extraction and the identity of user will be used to identify the data. The fixed Hamming distance code can help raise the system’s capability for watermarks as much as possible. Extracted watermark can locate the unauthorized distributor and protect the right of honest CSP. The results of experiments demonstrate the security and validity of our protocol.