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Phylogenetic Status of an Undiscovered Zygomycete Species, Syncephalastrum monosporum, in Korea
( Tham Thi Duong ),( Thi Thuong Thuong Nguyen ),( Sun Jeong Jeon ),( Hyang Burm Lee ) 한국균학회 2016 韓國菌學會誌 Vol.44 No.4
During a survey of undiscovered taxa in Korea, two zygomycete fungal isolates, EML-BT5-1 and EML-BT5-2, were isolated from the seed of a pumpkin (Cucurbita pepo) fruit in Korea. Based on their morphological characteristics and a sequence analysis of four genes, ITS1-5.8S-ITS2, 18S, 28S rDNA, and EF-1α, the isolates were confirmed to be Syncephalastrum monosporum in the family Syncephalastraceae. To our knowledge, the zygomycete fungal species S. monosporum has not been previously described in Korea.
( Thi Thuong Thuong Nguyen ),( Tham Thi Duong ),( Hyang Burm Lee ) 한국균학회 2016 Mycobiology Vol.44 No.4
While surveying the diversity of fungi of the order Mucorales, two isolates, EML-PUKI12-1 and EML-PUKI06-1, were obtained from the gut of soldier fly larvae inhabiting the bulrush at a pond located in the Chonnam National University Arboretum, Gwangju, Korea. The isolates were confirmed as Mucor irregularis and Mucor fragilis species, respectively, based on the morphological characteristics and phylogenetic analysis of rDNA internal transcribed spacer region. Such mucoralean species belonging to undiscovered taxa has not previously been described in Korea.
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.