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Aims: Phytohormones are a group of signal compounds that regulate plant growth, development, health and plant-soil interactions. We aimed to investigate the distribution and controlling factors of a diverse set of phytohormones found in the plant-soil continuum. Methods: Twenty phytohormones belonging to seven categories were studied in maize roots, rhizosphere and bulk soil with ultra-high-performance liquid chromatography–tandem mass spectrometry. In addition, the microbiomes shaped by gamma-sterilization and reinoculation were analyzed using high throughput sequencing. Results: We found three major distribution patterns of the 17 phytohormones detected along the root-soil continuum. In the first of these, the phytohormones were predominant in roots, followed by rhizosphere and then by bulk soil. In the second pattern, concentrations of the phytohormones were smallest in the rhizosphere. While, in the third pattern, concentrations of the phytohormones were greatest in the rhizosphere. The concentrations of the phytohormones were closely related to those of root phytohormone or roots biomass as well as relative abundances of wide range of microbial taxa. Conclusions: The results indicated that the distribution patterns of six categories of phytohormones in the root-soil continuum are driven by a balance between the production and uptake by the roots and the production, utilization and degradation by the soil microbiome in a phytohormone-specific manner. [ABSTRACT FROM AUTHOR]

Detecting moving objects from the video is an important research topic in computer vision. In this paper, we first review some traditional methods for moving objects detection. Then, some related preliminaries about the subspace learning and the mixed norm are introduced due to their remarkable advantages shown in signal processing. Combing the mixed norm and the total variation, an effective regularized framework is proposed to achieve more pure foreground. Furthermore, by introducing the subspace learning, the model has lower computational complexity. An efficient method based on the alternating direction method of multipliers (ADMM) is then developed to deal with the proposed constrained minimization problem. Extensive experiments on the private and real-world datasets show that the proposed approach outperforms the existing state-of-the-art approaches, particularly for the cases with dynamic background, which indicates the worth to integrate the subspace learning, the ℓ2,1 mixed norm and total variation into a low-rank representation model. [ABSTRACT FROM AUTHOR]