Weiguang Zhou

Bovine respiratory syncytial virus (BRSV) is a major pathogen of the bovine respiratory disease complex (BRDC), causing significant morbidity and economic losses worldwide. However, its molecular epidemiology in Inner Mongolia, one of China’s largest cattle-producing regions, remains poorly characterized, particularly among calves with clinical respiratory disease. In this study, we developed and performed a preliminary evaluation of a multiplex RT-qPCR assay targeting the F and N genes of BRSV, with ABL1 as an endogenous control, for rapid and reliable detection in symptomatic calves. A total of 909 clinical samples collected in 2023 from calves showing respiratory signs were tested, revealing an overall BRSV detection rate of 21.23% among clinically affected calves with respiratory disease. Lung tissues showed higher detection rates (35.43%) than nasal swabs (17.85%). The virus was more frequently detected in central and western regions, and its occurrence exhibited seasonal peaks in summer and winter. Intensive and large-scale farms tended to have higher infection rates and viral loads than pastoral households. Viral load also varied by sample type and season, being highest in lung tissues and during spring–summer. These findings provide baseline molecular epidemiological data on BRSV among clinically affected calves in Inner Mongolia and, although they are not representative of population-level prevalence, they highlight the burden of BRSV detection within this symptomatic diagnostic cohort and suggest that the developed multiplex RT-qPCR assay may serve as a useful tool for diagnostic screening and targeted monitoring in clinically affected herds or outbreak investigations.

The COVID-19 pandemic poses a serious threat to global public health. Anti-coronavirus peptides (ACVPs) exhibit high targeting, low toxicity, and excellent modifiability, making them promising candidates for antiviral drug discovery. These properties offer advantages over traditional small-molecule drugs. To reduce the time and cost spent on large-scale peptide screening and activity validation, there is an urgent need to construct efficient artificial intelligence models that assist in the identification of ACVPs. However, the limited number of experimentally validated ACVPs severely constrains the generalization ability and prediction accuracy of existing computational models. In this study, a new prediction framework, the Multi-modal Feature Extraction and Ensemble learning framework for Anti-Coronavirus Peptide prediction(MFE-ACVP), is proposed for identifying potential candidate peptides for ACVPs. The method generates high-quality ACVPs by introducing an improved Generative Adversarial Network (GAN) with materialization constraints to alleviate the problem of data insufficiency. Simultaneously, fusing sequence, structural, evolutionary, and topological features, we constructed a 100-dimensional cross-scale feature representation. An ensemble architecture integrating five traditional machine learning models with deep neural networks (DNNs) was designed to enhance predictive performance. Compared with the existing models PreAntiCoV, iACVP, ACVPred, and ENNAVIA-C/D, MFE-ACVP achieved 86.37%, 77.62%, and 65.19% of area under the curve (AUC), accuracy (ACC), and Matthew’s correlation coefficient (MCC), respectively, on an independent validation set, showing superior predictive performance and stability. To facilitate ACVP screening, we developed a publicly accessible web server http://bioprediction.sa1.tunnelfrp.com/.

As the global economy has expanded, animal diseases have become significant obstacles to the sustainable development of animal husbandry. Among these diseases, bovine rotavirus coronavirus (BCV or BCoV) exhibits clinical symptoms such as diarrhea, dehydration, and gastroenteritis. The prevention and control of BCV virus remain the primary concern in managing bovine viral diarrheal diseases. We using crawler technology to search for key words of "bovine coronavirus" and "BCV" in the PubMed database, 1410 articles were obtained, and after screening, 927 related articles were obtained. A knowledge graph construction pipeline method called BcvKG has been proposed to convert unstructured data into structured data. We obtained 1422 entities and 584 pairs of valid entity triplets, and visualized them using the Neo4j graph database. Finally, presented in the form of a knowledge graph. Compared with previous methodological methods, this article has completed the information extraction of literature related to bovine viral diarrhea disease for the first time, aiming to construct a knowledge graph.

Introduction: The escalating occurrence of infectious disease outbreaks in humans and animals necessitates innovative, effective, and integrated research to better comprehend their transmission and dynamics. Viral infection in livestock has led to profound economic losses globally. Pneumonia is the prevalent cause of death in sheep. However, very few studies exist regarding virus-related pathogens in sheep. Metagenomics sequencing technologies in livestock research hold significant potential to elucidate these contingencies and enhance our understanding.Methods: Therefore, this study aims to characterize respiratory viromes in paired nasal swabs from Inner Mongolian feedlot sheep in China using metaviromic sequencing. Through deep sequencing, de novo assembly, and similarity searches using translated protein sequences, several previously uncharacterized and known viruses were identified in this study.Results: Among these discoveries, a novel Bovine Rhinitis B Virus (BRBV) (BRBV-sheep) strain was serendipitously detected in the nasal swabs of domestic sheep (Ovis aries). To facilitate further molecular epidemiological studies, the entire genome of BRBV-sheep was also determined. Owing to the unique sequence characteristics and phylogenetic position of BRBV-sheep, genetically distinct lineages of BRBV in sheep may exist. A TaqMan-based qRT-PCR assay targeting the 3D polymerase gene was developed and used to screen 592 clinical sheep specimens. The results showed that 44.59% of the samples (264/592) were positive. These findings suggest that BRBV sheep are widespread among Inner Mongolian herds.Conclusion: This discovery marks the initial identification of BRBV in sheep within Inner Mongolia, China. These findings contribute to our understanding of the epidemiology and genetic evolution of BRBV. Recognizing the presence of BRBV in sheep informs strategies for disease management and surveillance and the potential development of targeted interventions to control its spread.

Background: Bovine viral diarrhea virus (BVDV) can cause diarrhea, abortion, and immunosuppression in cattle, imposing huge economic losses for the global cattle industry. The pathogenic and immune mechanisms of BVDV remain elusive. The development of a BVDV-gene knowledge base can provide clues to reveal the interaction of BVDV with host cells. However, the traditional method of manually establishing a knowledge base is time-consuming and inefficient. The method of developing a knowledge base based on deep learning has noticeably attracted scholars' attention recently. Objective: The study aimed to explore the substitution of deep learning for manual mining of BVDVrelated genes and to develop a knowledge graph of the relationship between BVDV and related genes. Methods: A deep learning-based biomedical knowledge graph development method was proposed, which used deep learning to mine biomedical knowledge, model BVDV and various gene concepts, and store data in a graphical database. First, the PubMed database was used as the data source and crawler technology to obtain abstract data on the relationship between BVDV and various host genes. Pretrained BioBERT model was used for biomedical named entity recognition to obtain all types of gene entities, and the pre-trained BERT model was utilized for relationship extraction to achieve the relationship between BVDV and various gene entities. Then, it was combined with manual proofreading to obtain structured triple data with high accuracy. Finally, the Neo4j graph database was used to store data and to develop the knowledge graph of the relationship between BVDV and related genes. Results: The results showed the obtainment of 71 gene entity types, including PRL4, MMP-7, TGIF1, etc. 9 relation types of BVDV and gene entities were obtained, including "can downregulate expression of", "can upregulate expression of", "can suppress expression of", etc. The knowledge graph was developed using deep learning to mine biomedical knowledge combined with manual proofreading, which was faster and more efficient than the traditional method of establishing knowledge base manually, and the retrieval of semantic information by storing data in graph database was also more efficient. Conclusion: A BVDV-gene knowledge graph was preliminarily developed, which provided a basis for studying the interaction between BVDV and host cells.