Molecular dynamics (MD) simulations are a widely used technique in modeling complex nanoscale interactions of atoms and molecules. These simulations can provide detailed insight into how molecules behave under certain environmental conditions. This work explores a machine learning (ML) solution to predicting long-term properties of SARS-CoV-2 spike glycoproteins (S-protein) through the analysis of its nanosecond backbone RMSD (root-mean-square deviation) MD simulation data at varying temperatures. The simulation data were denoised with fast Fourier transforms. The performance of the models was measured by evaluating their mean squared error (MSE) accuracy scores in recurrent forecasts for long-term predictions. The models evaluated include k-nearest neighbors (kNN) regression models, as well as GRU (gated recurrent unit) neural networks and LSTM (long short-term memory) autoencoder models. Results demonstrated that the kNN model achieved the greatest accuracy in forecasts with MSE scores over around 0.01 nm less than those of the GRU model and the LSTM autoencoder. Furthermore, it demonstrated that the kNN model accuracy increases with data size but can still forecast relatively well when trained on small amounts of data, having achieved MSE scores of around 0.02 nm when trained on 10,000 ns of simulation data. This study provides valuable information on the feasibility of accelerating the MD simulation process through training and predicting supervised ML models, which is particularly applicable in time-sensitive studies. Graphic abstract SARS-CoV-2 spike glycoprotein molecular dynamics simulation. Extraction and denoising of backbone RMSD data. Evaluation of k-nearest neighbors regression, GRU neural network, and LSTM autoencoder models in recurrent forecasting for long-term property predictions.

Air quality data prediction in urban area is of great significance to control air pollution and protect the public health. The prediction of the air quality in the monitoring station is well studied in existing researches. However, air-quality-monitor stations are insufficient in most cities and the air quality varies from one place to another dramatically due to complex factors. A novel model is established in this paper to estimate and predict the Air Quality Index (AQI) of the areas without monitoring stations in Nanjing. The proposed model predicts AQI in a non-monitoring area both in temporal dimension and in spatial dimension respectively. The temporal dimension model is presented at first based on the enhanced k-Nearest Neighbor (KNN) algorithm to predict the AQI values among monitoring stations, the acceptability of the results achieves 92% for one-hour prediction. Meanwhile, in order to forecast the evolution of air quality in the spatial dimension, the method is utilized with the help of Back Propagation neural network (BP), which considers geographical distance. Furthermore, to improve the accuracy and adaptability of the spatial model, the similarity of topological structure is introduced. Especially, the temporal-spatial model is built and its adaptability is tested on a specific non-monitoring site, Jiulonghu Campus of Southeast University. The result demonstrates that the acceptability achieves 73.8% on average. The current paper provides strong evidence suggesting that the proposed non-parametric and data-driven approach for air quality forecasting provides promising results.

The spike glycoprotein (S protein) of the SARS-CoV-2 that has be studied extensively in vitro is modeled by all-atom molecular dynamics for its conformational states at six pH values ranging from 2 to 11.5. The MD simulations up to 3.7 T demonstrate interesting discoveries while confirming known facts. (1). At pH2, the protein’s time averaged RMSD is 62.5% higher than that of pH7, as the control group, and the receptor binding domain (RBD) deviates from that of pH7 by 200%. (2). For pH4 through pH10.5, the S protein remains relatively stable evident by the invariance of the side chain H bond counts and RMSD from pH7, suggesting high tolerance of the S protein to a wide range of pH values other than the extreme acidic and basic conditions. (3). For pH2 to pH4, the structure of the S protein alters significantly, suggesting the existence of a critical pH value at which the S protein responds to acid sharply. (4). In the residue-based relative entropy analysis, we identify several RBM and RBD residue clusters with maximum deviations that cause the overall protein structure changes.

In the environment of omni-media era with new media technology as the mainstream information communication channel, the Cross-border marketing method of IP industry and makeup joint name can not only enhance brand visibility and visibility, but also strengthen the value of brand image, which has a positive effect on the development of I P and the brand itself. However, the joint-branded market has hidden dangers and chaos. In the process of rapid development, the compatibility of the Co-branded products; the emergence of frequent Co-branded products makes aesthetic fatigue for consumers; and the quality and price of the Co-branded products are uneven. Not only did not achieve the expected income, but also caused some damage to the image of the joint parties. This paper takes the joint name of M · A · C and King of Glory as an example to analyze and study the problems existing in Cross-border marketing and provide marketing suggestions for the development of IP Cross-border marketing activities.

We present the thermal and conformational states of the spike glycoprotein (S-protein) of SARS-CoV-2 at six temperatures ranging from 3℃ to 95℃ by all-atom molecular dynamics (MD) µs-scale simulations. While corroborating with clinical results of the temperature impact on the COVID-19 infection, we examine the potential phase transitions of the S-protein in the temperature range and our simulation results revealed the following thermal properties of the S-protein: (1) It is structurally stable at 3℃, agreeing with observations that the virus stays active for more than two weeks in the cold supply chain; (2) Its structure varies more significantly for temperature window of 60℃ to 80℃ than in all other windows; (3) The sharpest structural variations occur near 60℃, signaling a plausible critical temperature nearby; (4) The maximum deviation of the receptor-binding domain at 37°C suggests the anecdotal observation that the virus is most infective at 37°C; (5) The in silico data agree with reported experiments of the SARS-CoV-2 survival times from weeks to seconds by our clustering approach analysis.