Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-Researchers detail new data in Machine Learning. According to news reporting originating from Lake Alfred, Florida, by NewsRx correspondents, research stated, "Lasiodiplodia theobromae is a key post harvest pathogen causing Diplodia stem-end rot (SER) disease in grapefruit. Whil e the disease remains quiescent before harvest, its symptoms become evident duri ng the postharvest period." Financial support for this research came from Florida Citrus Packers through a U SDA Technical Assistance for Specialty Crops grant. Our news editors obtained a quote from the research from the University of Flori da, "This dormant behavior poses a challenge in managing fruits after harvest. T o effectively detect asymptomatic fruit and detect SER disease at an early stage , it's crucial to identify early-stage biomarkers that can serve as disease indi cators. In this study, a machine learning-based metabolomics analysis was utiliz ed to identify characteristic metabolites and to elucidate the underlying biosyn thetic mechanisms. Six machine learning algorithms were used, and Gradient boost ing (GBT) exhibited the highest accuracy identifying early biomarkers such as sh ikimate, succinic acid, quinic acid, coumaric acid, tyrosine, phenylalanine, and tryptophan. Moreover, dynamic time warping (DTW), was used to investigate the t rend of metabolites across timepoints. Our result revealed that the metabolic an alysis enabled differentiating infected from non-infected fruits within 1 day, e ven though symptoms appeared after 7 days of inoculation. Pathway enrichment ana lysis indicated that three pathways (biosynthesis of plant hormones, phenylpropa noid biosynthesis, and glutamate metabolism) were strongly involved in the defen se mechanism. Metabolite mapping analysis showed the behavior of each compound a gainst the pathogen."
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