Frontiers in Physics最新文献

The effect of temperature on SARS-CoV-2 is frequently debated upon. There is evidence of temperature sensitivity of the viral proteins; however, how heat influences the protein–protein interaction between a SARS-CoV-2 protein and the human angiotensin-converting enzyme 2 (ACE2) receptor remains to be elucidated. Here, we studied the receptor-binding domain of the surface glycoprotein of SARS-CoV-2 wild type and variants of concern bound to the human ACE2 receptor at different temperatures through atomistic simulations. We found that although there were no major conformation changes in the protein complexes at high temperatures, the dynamics of the proteins significantly increased. There was loss of protein–protein contacts and interaction energies. Thus, the protein–protein interaction was found to be rather strong. This study would be useful for viral protein studies and the design of peptide-based vaccines and therapeutics.

The European X-ray Free Electron Laser is a research facility located close to Hamburg, offering X-ray pulses with ultra-high brilliance and femtosecond duration at megahertz repetition rates. The detection systems necessary to unlock the full scientific potential made possible by this machine poses considerable challenges both in terms of data volume and rate, as well as the interpretation of their recorded signal. To provide optimal data quality, expert and detector-specific knowledge not easily accessible to external facility users is essential, and its implementation must cope with the generated volumes. We therefore aim to perform these preparatory processing steps and offer users a dataset suitable for further analysis as the primary data product. This work describes the machinery and workflows providing this data to users in an automatic, configurable and reproducible manner, both online during the experiment, and offline for scientific analysis afterward on the way to publication.

Humans collaborate in different contexts such as in creative or scientific projects, in workplaces and in sports. Depending on the project and external circumstances, a newly formed collaboration may include people that have collaborated before in the past, and people with no collaboration history. Such existing relationships between team members have been reported to influence the performance of teams. However, it is not clear how existing relationships between team members should be quantified, and whether some relationships are more likely to occur in new collaborations than others. Here we introduce a new family of structural patterns, m-patterns, which formalize relationships between collaborators and we study the prevalence of such structures in data and a simple random-hypergraph null model. We analyze the frequency with which different collaboration structures appear in our null model and show how such frequencies depend on size and hyperedge density in the hypergraphs. Comparing the null model to data of human and non-human collaborations, we find that some collaboration structures are vastly under- and overrepresented in empirical datasets. Finally, we find that structures of scientific collaborations on COVID-19 papers in some cases are statistically significantly different from those of non-COVID-19 papers. Examining citation counts for 4 different scientific fields, we also find indications that repeat collaborations are more successful for 2-author scientific publications and less successful for 3-author scientific publications as compared to other collaboration structures.

Emergency disposal is a critical aspect for railway stations to ensure safety. This requires the implementation of emergency plan simulations and cost-effective immersive drills. In the paper, we incorporate a set of disposals for events into emergency processes and model the personnel, supplies, and equipment to create multiple emergency scenarios. Additionally, we introduce a digital twin-based solution for multiple scenarios of emergencies. This solution completely restores the key components of a station in the information space and provides an immersive way for emergency disposals. The four-dimensional model used in the solution simulates and interacts with the station, and it is composed of the details of passenger station physical entity, multi-scenario emergency virtual entity, digital twin connection, and emergency twin service. The digital twin for versatile emergency events such as fire disaster, natural disaster (e.g., flood, earthquake), social security incident, and public health event have been constructed using the model. The solution was tested at Qinghe, a station during Beijing-Zhangjiakou high-speed railway in China. By utilizing the actual operation data of Qinghe, we validated the multi-scenario emergency drills, simulated the corresponding emergency disposal plans, and assessed the proposed solution from three aspects: the fidelity of the real-world simulation, coverage of the multiple emergency scenarios, and the user-friendliness. The evaluations indicate that the proposed solution attains good score and the acceptable level of system usability scale.