Theoretical Study on Raman Characteristic Peaks of Coronavirus Spike Protein Based on Deep Learning

COVID-19, which has lasted for a year, has caused great damage to the global economy. In order to control COVID-19 effectively, rapid detection of COVID-19 (SARS-CoV-2) is an urgent problem. Spike protein is the detection point of Raman spectroscopy to detect SARS-CoV-2. The construction of spike protein Raman characteristic peaks plays an important role in the rapid detection of SARS-CoV-2 using Raman technology. In this paper, we used Deep Neural Networks to construct the amide I and III characteristic peak model of spike proteins based on simplified exciton model, and combined with the experimental structures of seven coronaviruses (HCoV-229E, HCoV-HKUl, HCoV-NL63, HCoV-OC43, MERS-CoV, SARS-CoV, SARS-CoV-2) spike proteins, analyzed the differences of amide I and III characteristic peaks of seven coronaviruses. The results showed that seven coronaviruses could be divided into four groups according to the amide I and III characteristic peaks of spike proteins: SARS-CoV-2, SARS-CoV, MERS-CoV form a group;HCoV-HKUl, HCoV-NL63 form a group;HCoV-229E and HCoV-OC43 form a group independently. The frequency of amide I and III in the same group is relatively close,and it is difficult to distinguish spike proteins by the frequency of amide I and III ;the characteristic peaks of amide I and III in different groups are quite different, and spike proteins can be distinguished by Raman spectroscopy. The results provide a theoretical basis for the development of Raman spectroscopy for rapid detection of SARS-CoV-2. © 2022 Science Press. All rights reserved.

Opinion Mining about Online Education Basing on GDELT and Twitter Data

This public health incident transformed teaching activities from offline to online. The media content and comments on social media provide a dataset for digging the public opinion on online learning. This study uses the GDELT and TWITTER platforms' data, searching "COVID" and "online education" as keywords;the relevant information is collected and analyzed in python. The results of this public opinion mining will play an essential role in discovering the problem of online teaching in this pandemic.