Glycomol: A pervasive tool for structure predication of natural saponin products basing on MS data.

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Development a high-sensitivity sandwich ELISA for determining antigen content of porcine circovirus type 2 vaccines.

Porcine circovirus type 2 (PCV2) is intensely prevalent in global pig farms. The PCV2 vaccine is an important means of preventing and controlling PCV2. The quality control of PCV2 vaccines is predominantly based on detection techniques such as animal testing and neutralizing antibody titration. Measuring the content of effective proteins in vaccines to measure vaccine efficacy is an excellent alternative to traditional methods, which can greatly accelerate the development speed and testing time of vaccines. In this study, we screened a monoclonal antibody (mAb) that can effectively recognize not only the exogenous expression of PCV2 Cap protein but also PCV2 virus. The double antibody sandwich ELISA (DAS-ELISA) was developed using this mAb that specifically recognize PCV2 Cap. The minimum protein content detected by this method is 3.5 ng/mL. This method can be used for the quality control of PCV2 inactivated vaccine and subunit vaccine, and the detection results are consistent with the results of mice animal experiments. This method has the advantages of simple operation, good sensitivity, high specificity and wide application. It can detect the effective antigen Cap protein content of various types of PCV2 vaccines, which not only shorten the vaccine inspection time but also save costs.

Small- and medium-sized rice fields identification in hilly areas using all available sentinel-1/2 images.

BACKGROUND: Mastering the spatial distribution and planting area of paddy can provide a scientific basis for monitoring rice production, and planning grain production layout. Previous remote sensing studies on paddy concentrated in the plain areas with large-sized fields, ignored the fact that rice is also widely planted in vast hilly regions. In addition, the land cover types here are diverse, rice fields are characterized by a scattered and fragmented distribution with small- or medium-sized, which pose difficulties for high-precision rice recognition. METHODS: In the paper, we proposed a solution based on Sentinel-1 SAR, Sentinel-2 MSI, DEM, and rice calendar data to focus on the rice fields identification in hilly areas. This solution mainly included the construction of rice feature dataset at four crucial phenological periods, the generation of rice standard spectral curve, and the proposal of spectral similarity algorithm for rice identification. RESULTS: The solution, integrating topographical and rice phenological characteristics, manifested its effectiveness with overall accuracy exceeding 0.85. Comparing the results with UAV, it presented that rice fields with an area exceeding 400 m2 (equivalent to 4 pixels) exhibited a recognition success rate of over 79%, which reached to 89% for fields exceeding 800 m2. CONCLUSIONS: The study illustrated that the proposed solution, integrating topographical and rice phenological characteristics, has the capability for charting various rice field sizes with fragmented and dispersed distribution. It also revealed that the synergy of Sentinel-1 SAR and Sentinel-2 MSI data significantly enhanced the recognition ability of rice paddy fields ranging from 400 m2 to 2000 m2.