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https://www.selleckchem.com/products/rbn-2397.html Finally, we also describe how such forecasts are used to increase lead time for training mechanistic scenario projections. Our work demonstrates that such a real-time high resolution forecasting pipeline can be developed by integrating multiple methods within a performance-based ensemble to support pandemic response. Aniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Peddireddy, Przemys-law Porebski,, Srinivasan Venkatramanan, Bryan Lewis, Madhav Marathe. 2021. All Models Are Useful Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. In . ACM, New York, NY, USA, 9 pages. https//doi.org/10.1145/nnnnnnn.nnnnnnn. Aniruddha Adiga, Lijing Wang, Benjamin Hurt, Akhil Peddireddy, Przemys-law Porebski,, Srinivasan Venkatramanan, Bryan Lewis, Madhav Marathe. 2021. All Models Are Useful Bayesian Ensembling for Robust High Resolution COVID-19 Forecasting. In Proceedings of ACM Conference (Conference'17) . ACM, New York, NY, USA, 9 pages. https//doi.org/10.1145/nnnnnnn.nnnnnnn. The emergence of more transmissible SARS-CoV-2 variants in the United Kingdom (B.1.1.7), South Africa (B1.351) and Brazil (P.1) requires a vigorous public health response, including real time strain surveillance on a global scale. Although new SARS-CoV-2 variants can be most accurately identified by genomic sequencing, this approach is time consuming and expensive. A simple and more rapid screen for the key SARS-CoV-2 mutations that define variant strains is needed. We developed a simple, rapid and high-throughput reverse-transcriptase PCR (RT-PCR) melting temperature assay that identifies the SARS-CoV-2 N501Y mutation, a key mutation which is present in all three known variant strains of concern. RT-PCR primers and two sloppy molecular beacon (SMB) probes were designed to amplify and detect the SARS-CoV-2 N501Y (A23063T) mutation. One SMB was designed with a probe region that was complementary to the wild type sequence (WT) and a second

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