首页|Jilin Agricultural University Reports Findings in Machine Learning [Enhanced biodegradation of phenol under Cr(VI) stress by microbial collaboration and potential application of machine learning for phenol biodegradation]
Jilin Agricultural University Reports Findings in Machine Learning [Enhanced biodegradation of phenol under Cr(VI) stress by microbial collaboration and potential application of machine learning for phenol biodegradation]
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By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News-New research on Machine Learning is th e subject of a report. According to news reporting from Changchun, People's Repu blic of China, by NewsRx journalists, research stated, "Cr(VI) and phenol common ly coexist in wastewater, posing a great threat to the environment and human hea lth. However, it is still a challenge for microorganisms to degrade phenol under high Cr(VI) stress."Funders for this research include National Natural Science Foundation of China, Education Department of Jilin Province of China. The news correspondents obtained a quote from the research from Jilin Agricultur al University, "In this study, the phenol-degrading strain ZWB3 was co-cultured with the Cr(VI)-reducing strain MZ-1 to enhance phenol biodegradation under Cr(V I) stress. Compared with phenol-degrading strain ZWB3, which has weak tolerance to Cr(VI), and Cr(VI)-reducing strain MZ-1, which has no phenol-degrading abilit y, the co-culture of two strains could significantly increase the degraded rate and capacity of phenol. In addition, the co-cultured strains exhibited phenol de gradation ability over a wide pH range (7-10). The reduced content of intracellu lar proteins and polysaccharides produced by the co-cultured strains contributed to the enhancement of phenol degradation and Cr(VI) tolerance. The determinatio n coefficients , RMSE, and MAPE showed that the BP-ANN model could predict the d egradation of phenol under various conditions, which saved time and economic cos t. The metabolic pathway of microbial degradation of phenol was deduced by metab olic analysis."
ChangchunPeople's Republic of ChinaA siaCyborgsEmerging TechnologiesMachine LearningPhenols
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