Initial Multi-Target Approach Shows Importance of Improved Caprine Arthritis-Encephalitis Virus Control Program in Russia for Hobbyist Goat Farms
VETERINARY WORLD(2021)
Kazan Fed Univ
Abstract
Background and Aim: Several reports described the detection of specific caprine arthritis-encephalitis virus (CAEV) antibodies in Russian goat populations, which indicates the circulation of CAEV in Russian goat farms. The aim of this study was to use a multi-target approach to testing with both serological tests and an in-house real-time (RT) molecular test to investigate the prevalence of CAEV in goats from three hobbyist farms in the Republic of Tatarstan, Russia. Materials and Methods: We applied a multi-target approach to testing with both enzyme-linked immunosorbent assay (ELISA) and an in-house RT polymerase chain reaction test to investigate the prevalence of CAEV in goats. Animals from the three hobbyist farms were used in this study. The animals from two farms (n=13 for F1 and n=8 for F2) had clinical signs of arthritis and mastitis. In the third farm (n=15 for F3), all goats were home-bred and had no contact with imported animals. Results: CAEV antibodies (ELISA targets TM env and gag genes) were detected in serum samples from two farms (F1 and F2), indicating seroprevalence of 87.50-92.31%. Specific CAEV antibodies were also detected in milk samples. CAEV proviral DNA was detected in 53.85-62.50%. The results from all tests performed in the third farm (F3) were negative, indicating that all tests were 100% specific. Conclusion: The results showed that CAEV is circulating and present in small hobbyist goat farms in Russia. Serological and molecular tests could be important for programs to control and eradicate CAEV in Russia for hobbyist goat farms.
MoreTranslated text
Key words
antibodies,antigens,caprine arthritis-encephalitis virus,goat,proviral DNA
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example

Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined