Zinc supplementation and light intensity affect 2-acetyl-1-pyrroline (2AP) formation in fragrant rice.

BACKGROUND: Improving the yield and aroma content of fragrant rice is the focus of fragrant rice research. Light and Zinc (Zn) management generally cause regulations in the 2-acetyl-1-pyrroline (2AP) accumulation in fragrant rice. In addition, Zn promotes rice growth and improves rice yield, which has the potential to compensate for the negative impact of low light on fragrant rice yield. However, the potential of Zn to improve fragrant rice yield and 2AP content under shading conditions has not been verified. METHODS: Field experiments were conducted in the rice season (May-September) in 2019 to 2021. Two light i.e., normal light (NL) and low light (LL) and four Zn levels i.e., 0 kg Zn ha- 1 (N0), 1 kg Zn ha- 1 (Zn1), 2 kg Zn ha- 1(Zn2), and 3 kg Zn ha- 1 (Zn3), which applied at booting stage was set up. The grain yield, 2AP contents, Zn content in polished rice, photosynthesis related indicators, MDA content, antioxidant enzyme activity and the biochemical parameters related to 2AP formation were investigated. RESULTS: Shading reduced yield by 8.74% and increased 2AP content by 24.37%. In addition, shading reduced net photosynthetic rate (Pn), superoxide dismutase (SOD), peroxidase (POD) and catalase (CAT), and increased proline, γ-aminobutyric acid (GABA), and pyrroline-5-carboxylic acid (P5C), proline dehydrogenase (PDH), △1-pyrroline-5-carboxylic acid synthetase (P5CS), malondialdehyde (MDA). With increasing Zn application levels, yield, 2AP, Zn content in polished rice, Pn, proline, P5C, GABA, PDH, P5CS, SOD, CAT and POD increased, and MDA decreased. Significant Light and Zn interaction effect on 2AP content was detected, and both shading and increasing Zn application increased the 2AP content. CONCLUSION: Shading can increase the 2AP content but reduce the yield of fragrant rice. Increasing Zn application under shading conditions can further promote the biosynthesis of 2AP, but the effect of improving yield is limited.

Saliency Detection via Absorbing Markov Chain With Learnt Transition Probability.

In this paper, we propose a bottom-up saliency model based on absorbing Markov chain (AMC). First, a sparsely connected graph is constructed to capture the local context information of each node. All image boundary nodes and other nodes are, respectively, treated as the absorbing nodes and transient nodes in the absorbing Markov chain. Then, the expected number of times from each transient node to all other transient nodes can be used to represent the saliency value of this node. The absorbed time depends on the weights on the path and their spatial coordinates, which are completely encoded in the transition probability matrix. Considering the importance of this matrix, we adopt different hierarchies of deep features extracted from fully convolutional networks and learn a transition probability matrix, which is called learnt transition probability matrix. Although the performance is significantly promoted, salient objects are not uniformly highlighted very well. To solve this problem, an angular embedding technique is investigated to refine the saliency results. Based on pairwise local orderings, which are produced by the saliency maps of AMC and boundary maps, we rearrange the global orderings (saliency value) of all nodes. Extensive experiments demonstrate that the proposed algorithm outperforms the state-of-the-art methods on six publicly available benchmark data sets.