Robotics & Machine Learning Daily News2024,Issue(Oct.8) :84-84.

University of Burgos Reports Findings in Serratia marcescens (Synergistic biocha r and Serratia marcescens tackle toxic metal contamination: A multifaceted machi ne learning approach)

Robotics & Machine Learning Daily News2024,Issue(Oct.8) :84-84.

University of Burgos Reports Findings in Serratia marcescens (Synergistic biocha r and Serratia marcescens tackle toxic metal contamination: A multifaceted machi ne learning approach)

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Abstract

New research on Gram-Negative Bacteria-Serratia marcescens is the subject of a report. According to news reporting o riginating in Burgos, Spain, by NewsRx journalists, research stated, "Metal cont amination in soil poses environmental and health risks requiring effective remed iation strategies. This study introduces an innovative approach of synergistical ly employing biochar and bacterial inoculum of Serratia marcescens to address toxic metal ™ contamination." The news reporters obtained a quote from the research from the University of Bur gos, "Physicochemical, enzymatic, and microbial analyses were conducted, employi ng integrated biomarker response (IBR) and machine-learning approaches for toxic ity estimation. The combined application significantly reduced the Cd, Cr, and P b concentrations by 71.6, 31.2, and 57.1%, respectively, while the Cu concentration increased by 85% in the individual Serratia marcescens treatment. Biochar enhanced microbial biomass by 33-44% after 25 days. Noteworthy physicochemical improvements included a 44.7% inc rease in organic content and a decrease in pH and electrical conductivity. The K and Ca concentrations increased by 196.9 and 21.6%, respectively, while the Mg content decreased by 86.4%. Network analysis revealed intricate relationships, displaying direct and indirect negative correlations be tween metals and soil physicochemical parameters. The IBR index values indicated effective mitigation of TM toxicity in Serratia marcescens and biochar with individual and combined treatments. Binary classification demo nstrated high sensitivity (80.1 %) and specificity (80.5% ) in identifying TM-contaminated soil."

Key words

Burgos/Spain/Europe/Cyborgs/Emerging Technologies/Enterobacteriaceae/Gammaproteobacteria/Gram-Negative Bacteria/Gram-Negative Facultatively Anaerobic Rods/Machine Learning/Proteobacteria/Ri sk and Prevention/Serratia/Serratia marcescens

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出版年

2024
Robotics & Machine Learning Daily News

Robotics & Machine Learning Daily News

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