Severe fever with thrombocytopenia syndrome (SFTS) is an emerging infectious disease caused by the SFTS virus (SFTSV). Hubei Province is a major epidemic area for SFTS in China. In this study, quantitative reverse-transcription polymerase chain reaction (qRT-PCR) and serological testing (IgM) were used simultaneously for laboratory detection of SFTS; however, testing results showed poor consistency between these two methods. Further analysis revealed that time post-onset was the main factor leading to inconsistent results. Thus, qRT-PCR is unable to detect all SFTS cases, and serological testing is essential. Here, 15 strains of SFTSV were successfully isolated from serum samples of acute SFTSV infection and their complete genomes were sequenced and submitted to GenBank. Phylogenetic analysis showed that the 15 SFTS virus strains clustered into four independent genotypes (A, B, D, and E), demonstrating that at least four genotypes of SFTSV have been co-circulating in Hubei Province. Furthermore, four strains of our isolates (HB2014-31, HB2014-35, HB2014-36, and HB2014-37) clustered in genotype E, which was the predominant genotype in Japan and South Korea. In this study, we identified multiple co-prevalent genotypes and confirmed the existence of genotype E viruses circulating in the Dabie Mountains of Hubei, central China. We concluded that SFTSV strains from Hubei exhibit most of the genetic diversity found in China.
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http://dx.doi.org/10.1007/s00705-018-3993-5 | DOI Listing |
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Laboratory of Agricultural Animal Genetics, Breeding and Reproduction of Ministry of Education, Huazhong Agricultural University, Wuhan, China.
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State Key Laboratory of Agricultural Microbiology and College of Life Science and Technology, Hubei Hongshan Laboratory, Huazhong Agricultural University, Shizishan Road No.1, Hongshan District, 430070 Wuhan, China.
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January 2025
School of Computer Science, Hubei University of Technology, Wuhan 430068, China.
Large visual language models like Contrastive Language-Image Pre-training (CLIP), despite their excellent performance, are highly vulnerable to the influence of adversarial examples. This work investigates the accuracy and robustness of visual language models (VLMs) from a novel multi-modal perspective. We propose a multi-modal fine-tuning method called Multi-modal Depth Adversarial Prompt Tuning (MDAPT), which guides the generation of visual prompts through text prompts to improve the accuracy and performance of visual language models.
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College of Electronics and Information Engineering, South-Central Minzu University, Wuhan 430074, China.
Drones are extensively utilized in both military and social development processes. Eliminating the reliance of drone positioning systems on GNSS and enhancing the accuracy of the positioning systems is of significant research value. This paper presents a novel approach that employs a real-scene 3D model and image point cloud reconstruction technology for the autonomous positioning of drones and attains high positioning accuracy.
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January 2025
Key Laboratory of Automotive Power Train and Electronics, Hubei University of Automotive Technology, Shiyan 442002, China.
Autonomous driving has demonstrated impressive driving capabilities, with behavior decision-making playing a crucial role as a bridge between perception and control. Imitation Learning (IL) and Reinforcement Learning (RL) have introduced innovative approaches to behavior decision-making in autonomous driving, but challenges remain. On one hand, RL's policy networks often lack sufficient reasoning ability to make optimal decisions in highly complex and stochastic environments.
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