Materials are not only the foundation of the national economy, but also the carrier of high-tech. Machine learning combined with computer science, database theory, statistics, computational mathematics and engineering cannot only show faster calculation speed and reliable predictive ability, significantly improve the efficiency of material calculations, and it can also effectively deal with some systems and problems that are difficult to use traditional simulation computing methods. This article will briefly outline the basic principles of machine learning, introduce several typical algorithms in machine learning models and how machine learning is the application progress in the research of new materials, and the prospects for the future development of machine learning in the field of materials science.
Anaplasma phagocytophilum, the aetiologic agent of human granulocytic anaplasmosis (HGA), is an obligate intracellular Gram-negative bacterium. During infection, A. phagocytophilum enhances the adhesion of neutrophils to the infected endothelial cells. However, the bacterial factors contributing to this phenomenon remain unknown. In this study, we characterized a type IV secretion system substrate of A. phagocytophilum, AFAP (an actin filament-associated Anaplasma phagocytophilum protein) and found that it dynamically changed its pattern and subcellular location in cells and enhanced cell adhesion. Tandem affinity purification combined with mass spectrometry identified host nucleolin as an AFAP-interacting protein. Further study showed the disruption of nucleolin by RNA interference, and the treatment of a nucleolin-binding DNA aptamer AS1411 attenuated AFAP-mediated cell adhesion, indicating that AFAP enhanced cell adhesion in a nucleolin-dependent manner. The characterization of cell adhesion-enhancing AFAP and the identification of host nucleolin as its interaction partner may help understand the mechanism underlying A. phagocytophilum-promoting cell adhesion, facilitating the elucidation of HGA pathogenesis.
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