ABSTRACT
The severe acute respiratory syndrome coronavirus-2 (SARSCoV-2) replication transcription complex (RTC) is a multi-domain protein responsible for replicating and transcribing the viral mRNA inside a human cell. Attacking RTC function with pharmaceutical compounds is a pathway to treating COVID-19. Conventional tools, e.g., cryo-electron microscopy and all-atom molecular dynamics (AAMD), do not provide sufficiently high resolution or timescale to capture important dynamics of this molecular machine. Consequently, we develop an innovative workflow that bridges the gap between these resolutions, using mesoscale fluctuating finite element analysis (FFEA) continuum simulations and a hierarchy of AI-methods that continually learn and infer features for maintaining consistency between AAMD and FFEA simulations. We leverage a multi-site distributed workflow manager to orchestrate AI, FFEA, and AAMD jobs, providing optimal resource utilization across HPC centers. Our study provides unprecedented access to study the SARS-CoV-2 RTC machinery, while providing general capability for AI-enabled multi-resolution simulations at scale. We acknowledge funding from NIH P41-GM10460, DOE CSGF (DE-FG02-97ER25308), Exascale Computing Project (17-SC-20-SC) and National Virtual Biotechnology Laboratory.Copyright © 2023 The American Society for Biochemistry and Molecular Biology, Inc.
ABSTRACT
The main objective of this project is to ensure the safety precautions in public transportation. This project monitors and stores the database of number of allowed users with their respective temperature. The person's identity is monitored with the help of radio frequency identification (RFID) tags. The sensors and RFID reader are interfaced with the Arduino. If the temperature is increased then the threshold level alert message is passed to the person personal mobile through global system of mobile communication and the bus door does not get opened. Then if any fire, breakdown and accident exist it sends intimation to the nearby hospitals and fire service via the control room. The current location of the bus is known by using global positioning system. The entire module information controlled by IoT and the data are stored in cloud server. This system also can be implemented in theaters\shopping malls to monitor the number of allowed persons with their respective temperature as per government instructions.