PsmiR159b-PsMYB65 module functions in the resumption of bud growth after endodormancy by affecting the cell cycle in tree peony.

Bud endodormancy in perennial plants is a sophisticated system that adapts to seasonal climatic changes. Growth-promoting signals such as low temperature and gibberellins (GAs) are crucial for facilitating budbreak following endodormancy release (EDR). However, the regulatory mechanisms underlying GA-mediated budbreak in tree peony (Paeonia suffruticosa) remain unclear. In tree peony, the expression of PsmiR159b among three differentially expressed miR159 members was inhibited with the prolonged chilling, and overexpression of PsMIR159b delayed budbreak, whereas silencing PsmiR159b promoted budbreak after dormancy. PsMYB65, a downstream transcription factor in the GA pathway, was induced by prolonged chilling and exogenous GA3 treatments. PsMYB65 was identified as a target of PsmiR159b, and promoted budbreak in tree peony. RNA-seq of PsMYB65-slienced buds revealed significant enrichment in the GO terms regulation of 'cell cycle' and 'DNA replication' among differentially expressed genes. Yeast one-hybrid and electrophoretic mobility shift assays demonstrated that PsMYB65 directly bound to the promoter of the type-D cyclin gene PsCYCD3;1. Dual-luciferase reporter assay indicated that PsMYB65 positively regulate PsCYCD3;1 expression, suggesting that miR159b-PsMYB65 module contributes to budbreak by influencing the cell cycle. Our findings revealed that the PsmiR159b-PsMYB65 module functioned in budbreak after dormancy by regulating cell proliferation, providing valuable insights into the endodormancy release regulation mechanism.

The relationship between night-time light and socioeconomic factors in China and India.

This paper re-examines the relationships between night-time light (NTL) and gross domestic product (GDP), population, road networks, and carbon emissions in China and India. Two treatments are carried out to those factors and NTL, which include simple summation in each administrative region (total data), and summation normalized by region area (density data). A series of univariate regression and multiple regression experiments are conducted in different countries and at different scales, in order to find the changes in the relationship between NTL and every parameter in different situations. Several statistical metrics, such as R2, Mean Relative Error (MRE), multiple regression weight coefficient, and Pearson's correlation coefficient are given special attention. We found that GDP, as a comprehensive indicator, is more representative of NTL when the administrative region is relatively comprehensive or highly developed. However, when these regions are unbalanced or undeveloped, the representation of GDP becomes weak and other factors can have a more important influence on the multiple regression. Differences in the relationship between NTL and GDP in China and India can also be reflected in some other factors. In many cases, regression after normalization with the administrative area has a higher R2 value than the total regression. But it is highly influenced by a few highly developed regions like Beijing in China or Chandigarh in India. After the scale of the administrative region becomes fragmented, it is necessary to adjust the model to make the regression more meaningful. The relationship between NTL and carbon emissions shows obvious difference between China and India, and among provinces and counties in China, which may be caused by the different electric power generation and transmission in China and India. From these results, we can know how the NTL is reflected by GDP and other factors in different situations, and then we can make some adjustments.