Assessing the in vitro efficacy of new molecules of fungicides against Bipolaris setariae infecting browntop millet

Ramesh, Gutha Venkata; Palanna, KB; Arunkumar,; Bharath, M.; Kumar, HD Vinay; Nagaraja, TE

doi:10.22271/phyto.2021.v10.i2o.13957

Cited by 2 publications

(1 citation statement)

References 3 publications

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“…If so, are Si regulatory mechanisms related to ROS scavengers, metabolite accumulation, and gene expression regulation? Currently, chemical control is the primary method for managing plant diseases caused by B. setariae (Khanum et al, 2009;Ramesh et al, 2021a). However, the excessive use of chemical pesticides has negative consequences, such as leaving chemical residues in agricultural products that can harm human health and lead to soil degradation and environmental damage (Nicolopoulou-Stamati et al, 2016;Rani et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

Silicon induces ROS scavengers, hormone signalling, antifungal metabolites, and silicon deposition against brown stripe disease in sugarcane

Chen,

Li,

Zeng

et al. 2024

Physiologia Plantarum

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Bipolaris setariae is known to cause brown stripe disease in sugarcane, resulting in significant yield losses. Silicon (Si) has the potential to enhance plant growth and biotic resistance. In this study, the impact of Si on brown stripe disease was investigated across susceptible and resistant sugarcane varieties, utilizing four Si concentrations (0, 15, 30, and 45 g per barrel of Na2SiO3·5H2O). Si significantly reduced the incidence of brown stripe disease (7.41–59.23%) and alleviated damage to sugarcane growth parameters, photosynthetic parameters, and photosynthetic pigments. Submicroscopic observations revealed that Si induced the accumulation of silicified cells in leaves, reduced spore accumulation, decreased stomatal size, and protected organelles from B. setariae damage. In addition, Si increased the activity of antioxidant enzymes (superoxide dismutase, peroxidase, and catalase), reduced reactive oxygen species production (malondialdehyde and hydrogen peroxide) and modulated the expression of genes associated with hormone signalling (PR1, TGA, AOS, AOC, LOX, PYL8, and SnRK2), leading to the accumulation of abscisic acid and jasmonic acid and inhibiting SA synthesis. Si also activated the activity of metabolism‐related enzymes (polyphenol oxidase and phenylalanine ammonia lyase) and the gene expression of PAL‐dependent genes (PAL, C4H, and 4CL), regulating the accumulation of metabolites, such as chlorogenic acid and lignin. The antifungal test showed that chlorogenic acid (15ug μL−1) had a significant inhibitory effect on the growth of B. setariae. This study is the first to demonstrate the inhibitory effect of Si on B. setariae in sugarcane, highlighting Si as a promising and environmentally friendly strategy for managing brown stripe disease.

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Section: Introductionmentioning

confidence: 99%

Silicon induces ROS scavengers, hormone signalling, antifungal metabolites, and silicon deposition against brown stripe disease in sugarcane

Chen,

Li,

Zeng

et al. 2024

Physiologia Plantarum

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Application of Reinforcement Learning Algorithm in Delivery Order System under Supply Chain Environment

Huang

Tan²

2021

Mobile Information Systems

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With the intensification of market competition and the development of market globalization, the efficiency of supply chain management orders has become an important part of enterprise competition resources. The competition among enterprises is fierce. To achieve effective customer response quickly, the time for supply chain order management is minimized, and refine the order processing process. This article introduces the strategy research of supply chain management order based on a reinforcement learning algorithm. This article first combines the reinforcement learning algorithm and deep learning algorithm, using the optimal decision-making ability of reinforcement learning algorithm and deep learning algorithm. The combination of data perception and the optimal ability to analyze examine the data of the order process, order cycle, and order delivery process of the supply chain order management and give the optimal decision. The supply chain order management process conducts questionnaire surveys and seminars to understand the current process of supply chain order management and the problems derived from the analysis of data based on the deep learning algorithm. Finally, through the output of the optimal strategy of the reinforcement learning algorithm, the supply chain order management process was improved, and the satisfaction survey was conducted again. The survey showed that the satisfaction was improved, and the satisfaction reached more than 90%.

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Assessing the in vitro efficacy of new molecules of fungicides against Bipolaris setariae infecting browntop millet

Cited by 2 publications

References 3 publications

Silicon induces ROS scavengers, hormone signalling, antifungal metabolites, and silicon deposition against brown stripe disease in sugarcane

Silicon induces ROS scavengers, hormone signalling, antifungal metabolites, and silicon deposition against brown stripe disease in sugarcane

Application of Reinforcement Learning Algorithm in Delivery Order System under Supply Chain Environment

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