The grain weight of wheat is strongly influenced by filling. Polyamines (PA) are involved in regulating plant growth. However, the effects of PA on wheat grain filling and its mechanism of action are unclear. The objective of the present study was to investigate the relationship between PAs and hormones in the regulation of wheat grain filling. Three PAs, spermidine (Spd), spermine (Spm), and putrescine (Put), were exogenously applied, and the grain filling characteristics and changes in endogenous PA and hormones, i.e., indole-3-acetic acid (IAA), zeatin (Z) + zeatin riboside (ZR), abscisic acid (ABA), ethylene (ETH) and gibberellin 1+4 (GAs), were quantified during wheat grain filling. Exogenous applications of Spd and Spm significantly increased the grain filling rate and weight, but exogenous Put had no significant effects on these measures. Exogenous Spd and Spm significantly increased the endogenous Spd, Spm, Z+ZR, ABA, and IAA contents and significantly decreased ETH evolution in grains. The endogenous Spd, Spm and Z+ZR contents were positively and significantly correlated with the grain filling rate and weight of wheat, and the endogenous ETH evolution was negatively and significantly correlated with the wheat grain filling rate and weight. Based upon these results, we concluded that PAs were involved in the balance of hormones that regulated the grain filling of wheat.
Rapid urbanization has accelerated land use and land cover changes, and generated the urban heat island effect (UHI). Previous studies have reported positive effects of neighborhood landscapes on mitigating urban surface temperatures. However, the influence of neighborhood landscape spatial patterns on enhancing cooling effects has not yet been fully investigated. The main objective of this study was to assess the relationships between neighborhood landscape spatial patterns and land surface temperatures (LST) by using multi-regression models considering spatial autocorrelation issues. To measure the influence of neighborhood landscape spatial patterns on LST, this study analyzed neighborhood environments of 15,862 single-family houses in Austin, Texas, USA. Using aerial photos, geographic information systems (GIS), and remote sensing, FRAGSTATS was employed to calculate values of several landscape indices used to measure neighborhood landscape spatial patterns. After controlling for the spatial autocorrelation effect, results showed that larger and better-connected landscape spatial patterns were positively correlated with lower LST values in neighborhoods, while more fragmented and isolated neighborhood landscape patterns were negatively related to the reduction of LST.
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