Phylogenetic and Molecular Analysis of an H7N7 Avian Influenza Virus Isolated in East Dongting Lake in 2012

Objective In March 2012, an H7N7 subtype avian influenza virus （AIV） named A/wild goose/Dongting/PC0360/2022 （H7N7） （DT/PC0360） was recovered from a wild goose in East Dongting Lake. We performed whole-genome sequencing of the isolate, and analyzed the phylogenetic and molecular characterization. Methods RNA was extracted from environment samples （including fecal samples from wild bird or domestic ducks, and water samples） for detecting the presence of Influenza A Virus targeting Matrix gene, using realtime RT-PCR assay. The positive samples were performed virus isolation with embryonated eggs. The subtype of the isolates were identified by RT-PCR assay with the HI-HI6 and N1-N9 primer set. The whole-genome sequencing of isolates were performed. Phylogenetic and molecular characterizations of the eight genes of the isolates were analyzed. Results Our results suggested that all the eight gene segments of DT/PC0360 belonged to the Eurasian gene pool, and the HA gene were belonged to distinct sublineage with H7N9 AIV which caused outbreaks in China's Mainland in 2013. The hemagglutinin cleavage site of HA of DT/PC0360 showed characterization of low pathogenic avian influenza virus. Conclusion Strengthening the surveillance of AlVs of wild waterfowl and poultry in this region is vita for our knowledge of the ecology and mechanism of transmission to prevent an influenza pandemic.

Automated and refined wetland mapping of Dongting Lake using migrated training samples based on temporally dense Sentinel 1/2 imagery

Wetlands provide vital ecological services for both humans and environment,necessitating continuous,refined and up-to-date mapping of wetlands for conservation and management.in this study,we developed an automated and refined wetland mapping framework integrating training sample migration method,supervised machine learning and knowledge-driven rules using Google Earth Engine(GEE)platform and open-source geospatial tools.We applied the framework to temporally dense Sentinel-1/2 imagery to produce annual refined wetland maps of the Dongting Lake Wetland(DLW)during 2015-2021.First,the continuous change detection(CCD)algorithm was utilized to migrate stable training samples.Then,annual 10 m preliminary land cover maps with 9 classes were produced using random forest algorithm and migrated samples.Ultimately,annual 10 m refined wetland maps were generated based on preliminary land cover maps via knowledge-driven rules from geometric features and available water-related inventories,with Overall Accuracy(OA)ranging from 81.82%(2015)to 93.84%(2020)and Kappa Coefficient(KC)between 0.73(2015)and 0.91(2020),demonstrating satisfactory performance and substantial potential for accurate,timely and type-refined wetland mapping.Our methodological framework allows rapid and accurate monitoring of wetland dynamics and could provide valuable information and methodological support for monitoring,conservation and sustainable development of wetland ecosystem.