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Luong Thi Tham,Nguyen Thi Hong Tham,Nguyen Tien Dat,Le Van Toan,Pham Thi Hong Trang,Ho Thanh-Tam,Nguyen Ngoc-Loi 한국미생물·생명공학회 2024 Journal of microbiology and biotechnology Vol.34 No.1
The widespread application of triazole fungicides (TFs) in agricultural practices can result in the considerable accumulation of active compound residues in the soil and a subsequent negative impact on the soil microbiota and crop health. In this study, we isolated three TF-degrading bacterial strains from contaminated agricultural soils and identified them as Klebsiella sp., Pseudomonas sp., and Citrobacter sp. based on analysis of morphological characteristics and 16S rRNA gene sequences. The strains used three common TFs, namely hexaconazole, difenoconazole, and propiconazole, as their only sources of carbon and energy for growth in a liquid mineral salt medium, with high concentrations (~ 500 mg/l) of each TF. In addition to the ability to degrade fungicides, the isolates also exhibited plant growth-promoting characteristics, such as nitrogen fixation, indole acetic acid production, phosphate dissolution, and cellulose degradation. The synergistic combination of three bacterial isolates significantly improved plant growth and development with an increased survival rate (57%), and achieved TF degradation ranging from 85.83 to 96.59% at a concentration of approximately 50 mg/kg of each TF within 45 days in the soil-plant system. Based on these findings, the three strains and their microbial consortium show promise for application in biofertilizers, to improve soil health and facilitate optimal plant growth.
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Minh Tu Nguyen,Binh Thang Tran,Thanh Gia Nguyen,Minh Tri Phan,Thi Thu Tham Luong,Dinh Duong Le 한국보건의료인국가시험원 2022 보건의료교육평가 Vol.19 No.-
Purpose The current study aimed to use network analysis to investigate medical and health students’ readiness for online learning during the coronavirus disease 2019 (COVID-19) pandemic at the University of Medicine and Pharmacy, Hue University. Methods A questionnaire survey on the students’ readiness for online learning was performed using a Google Form from May 13 to June 22, 2021. In total, 1,377 completed responses were eligible for analysis out of 1,411 participants. The network structure was estimated for readiness scales with 6 factors: computer skills, internet skills, online communication, motivation, self-control, and self-learning. Data were fitted using a Gaussian graphical model with the extended Bayesian information criterion. Results In 1,377 students, a network structure was identified with 6 nodes and no isolated nodes. The top 3 partial correlations were similar in networks for the overall sample and subgroups of gender and grade levels. The self-control node was the strongest for the connection to others, with the highest nodal strength. The change of nodal strength was greatest in online communication for both gender and grade levels. The correlation stability coefficient for nodal strength was achieved for all networks. Conclusion These findings indicated that self-control was the most important factor in students’ readiness network structures for online learning. Therefore, self-control needs to be encouraged during online learning to improve the effectiveness of achieving online learning outcomes for students.
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