Previous studies have reported reduction in cell division in seedlings of plants harbouring mutations in the alpha subunit of GTP-binding protein (gpa1). Notably, gpa1 mutants display reduced numbers of hypocotyl cells. Here we show that relative transcript level of the Arabidopsis minichromosome maintenance gene 2 (AtMCM2) is lower in gpa1 mutants than in wild-type Arabidopsis. Furthermore, expression of the AtMCM2 gene under the AtRb promoter restored hypocotyl cell number in gpa1 mutants. These results indicate that reduction in AtMCM2 gene is responsible for the reduction in hypocotyl cell division induced by loss of function of the alpha subunit of GTP-binding protein.
With the development of the economy and industrial construction, air quality deteriorates dramatically in China and seriously threatens people’s health. To investigate which factors most affect air quality and provide a useful tool to assist the prediction and early warning of air pollution in urban areas, we applied a sensor that observed air quality big data, information theory-based predictor significance identification, and PEK-based machine learning to air quality index (AQI) analysis and prediction in this paper. We found that the stability of air quality has a high relationship with absolute air quality, and that improvement of air quality can also improve stability. Air quality in southern and western cities is better than that of northern and eastern cities. AQI time series of cities with closer geophysical locations have a closer relationship with others. PM2.5, PM10, and SO2 are the most important impact factors. The machine learning-based prediction is useful for AQI prediction and early warning. This tool could be applied to other city’s air quality monitoring and early warning to further verify its effectiveness and robustness. Finally, we suggested the use of a training data sample with better quality and representatives to further improve AQI prediction model performance in future research.