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This study evaluated the potential for antibody‐dependent enhancement (ADE) in serum samples from patients exposed to Middle East respiratory syndrome coronavirus (MERS‐CoV). Furthermore, we evaluated the effect of severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2) vaccination on ADE in individuals with a MERS infection history. We performed ADE assay in sera from MERS recovered and SARS‐CoV‐2‐vaccinated individuals using BHK cells expressing FcgRIIa, SARS‐CoV‐2, and MERS‐CoV pseudoviruses (PVs). Further, we analyzed the association of ADE to serum IgG levels and neutralization. Out of 16 MERS patients, nine demonstrated ADE against SARS‐CoV‐2 PV, however, none of the samples demonstrated ADE against MERS‐CoV PV. Furthermore, out of the seven patients exposed to SARS‐CoV‐2 vaccination after MERS‐CoV infection, only one patient (acutely infected with MERS‐CoV) showed ADE for SARS‐CoV‐2 PV. Further analysis indicated that IgG1, IgG2, and IgG3 against SARS‐CoV‐2 S1 and RBD subunits, IgG1 and IgG2 against the MERS‐CoV S1 subunit, and serum neutralizing activity were low in ADE‐positive samples. In summary, samples from MERS‐CoV‐infected patients exhibited ADE against SARS‐CoV‐2 and was significantly associated with low levels of neutralizing antibodies. Subsequent exposure to SARS‐CoV‐2 vaccination resulted in diminished ADE activity while the PV neutralization assay demonstrated a broadly reactive antibody response in some patient samples. [ABSTRACT FROM AUTHOR]

Environment, price, regulation, and other factors influence the price of agricultural products, which is a social signal of product supply and demand. The price of many agricultural products fluctuates greatly due to the asymmetry between production and marketing details. Horticultural goods are particularly price sensitive because they cannot be stored for long periods of time. It is very important and helpful to forecast the price of horticultural products which is crucial in designing a cropping plan. The proposed method guides the farmers in agricultural product production and harvesting plans. Farmers can benefit from long-term forecasting since it helps them plan their planting and harvesting schedules. Customers can also profit from daily average price estimates for the short term. This paper study the time series models such as ARIMA, SARIMA, and neural network models such as BPN, LSTM and are used for wheat cost prediction in India. A large scale available data set is collected and tested. The results shows that since ARIMA and SARIMA models are well suited for smallscale, continuous, and periodic data, the BPN and LSTM provide more accurate and faster results for predicting well weekly and monthly trends of price fluctuation. [ABSTRACT FROM AUTHOR]