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Yuejun Fu,Xing Li,Shuhua Zheng,Jun Du,Aihua Liang 한국생물공학회 2015 Biotechnology and Bioprocess Engineering Vol.20 No.5
Autographa californica multiple nucleopolyhedrovirus (AcMNPV) have been applied in the pesticide prevention as new bioinsecticides. Many studies have been carried out to assess the effects of insecticide on microorganism communities in different environments. However, little is known about whether the pesticides affect the microbial community in soil. Therefore, we performed high-throughput sequencing of V3–V4 hypervariable regions of the 16S rRNA genes from the bacteria in soil treated with AcMNPV and compared the difference of microbiota in these soil samples. In the study, a total of 80,301 validated reads were obtained, and the bacteria found belonged to 31 phyla and 748 genera. Statistical analysis showed that AcMNPV contributed the growth of Fusobacteria, Tenericutes, Cyanobacteria/Chloroplast, Actinobacteria, Gemmatimonadetes. AcMNPV inhibited the growth of Fibrobacteres, Crenarchaeota, Firmicutes, DeinococcusThermus, TM7, Chlorobi, Synergistetes, BRC1, Chlamydiae, Euryarchaeota, Planctomycetes, Acidobacteria, Verrucomicrobia, Bacteroidetes, Elusimicrobia, Nitrospira, Armatimonadetes, Proteobacteria, WS3, OD1, Chloroflexi, Spirochaetes. AcMNPV had no effect on SR1, OP11, Thermodesulfobacteria, Aquificae. Alpha Diversity analysis showed that the diversity of bacterial community for the soil treated with AcMNPV was lower than that of the soil before treatment or the control group. Meanwhile, the similarity of soil samples from AcMNPV treated group compared with samples from either untreated or prior treatment group was low as shown by Beta Diversity analysis. These findings provide previously unknown information about the impact of AcMNPV on the soil bacterial community structure and also lay a foundation for further investigations of AcMNPV how influences the development and progression of bioinsecticides.
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Li Ming,An Yuejun,Hou Qiang,An Hui 대한전기학회 2024 Journal of Electrical Engineering & Technology Vol.19 No.1
When calculating the can loss, the fnite element method considering time harmonics needs the working current waveform, and the analytical method considering the harmonic adopts superposition principle. Based on the power conservation law, this paper proposes a method to obtain the can loss that can take into account the time harmonics from inverter and the harmonic loss. The method can obtain the calculation accuracy similar to that of the fnite element method without relying the current waveform of the motor, and has the advantages of simple calculation and saving time. In addition, the method proposed in this paper is used to analyze the can loss under diferent loads, and the infuence law of the can parameters on the can loss is explored. It provides a reference for the determination and optimization of the can loss of the canned permanent magnet synchronous motor under non-sinusoidal excitation.
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Ming YongFei,Li Yin,Chu JianZhi,Zhou XiaoShuang,Huang YuXuan,Yang ShuDe,Mu YueJun,Wang Lin,Zhang Rui,Cheng XianHao 한국응용생명화학회 2024 Applied Biological Chemistry (Appl Biol Chem) Vol.67 No.-
Taiwanofungus camphoratus has attracted much attention because it can abundantly produce various active substances that exhibit blood-sugar lowering, immunity improving, and antioxidant properties. Currently, T. camphoratus is cultured using four main methods: cutting wood culture, solid-state fermentation, submerged fermentation, and dish culture. T. camphoratus produces different metabolites under different culture methods. In this study, nontargeted metabolomics was used to compare the metabolites of T. camphoratus produced under these four culture methods. Principal component analysis and supervised partial least squares-discriminant analysis were used to analyze the differences in the metabolites. Moreover, in vitro hypoglycemic activity of T. camphoratus extracts produced under four culture methods was compared by assessing their ability to inhibit the activity of α-glucosidase, α-amylase, and sucrase. A total of 186 metabolites were identified. In total, 127 metabolites were common under the four culture methods. Under solid-state fermentation, submerged fermentation, and cutting wood culture, 12, 1, and 4 metabolites were unique, respectively. The differential metabolites produced by T. camphoratus under four culture methods were mainly triterpenoids, phenolic compounds, and fatty acid compounds. α-glucosidase, α-amylase, and sucrase activity inhibition was the best using T. camphoratus extract obtained under cutting wood culture; the inhibition rates were 55.97%, 51.96%, and 78.02%, respectively, which were comparable to those exhibited by 0.001, 3, and 12 mg/mL acarbose (positive control). The metabolites produced by T. camphoratus and α-glucosidase, α-amylase, and sucrase inhibitory activities were different under the four culture methods. Cutting wood culture exhibited the best enzyme inhibitory activity. This study provided a theoretical basis for further use and development of various culture methods for the rational production of active metabolites of T. camphoratus.
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Adaptive Attention Annotation Model: Optimizing the Prediction Path through Dependency Fusion
( Fangxin Wang ),( Jie Liu ),( Shuwu Zhang ),( Guixuan Zhang ),( Yang Zheng ),( Xiaoqian Li ),( Wei Liang ),( Yuejun Li ) 한국인터넷정보학회 2019 KSII Transactions on Internet and Information Syst Vol.13 No.9
Previous methods build image annotation model by leveraging three basic dependencies: relations between image and label (image/label), between images (image/image) and between labels (label/label). Even though plenty of researches show that multiple dependencies can work jointly to improve annotation performance, different dependencies actually do not "work jointly" in their diagram, whose performance is largely depending on the result predicted by image/label section. To address this problem, we propose the adaptive attention annotation model (AAAM) to associate these dependencies with the prediction path, which is composed of a series of labels (tags) in the order they are detected. In particular, we optimize the prediction path by detecting the relevant labels from the easy-to-detect to the hard-to-detect, which are found using Binary Cross-Entropy (BCE) and Triplet Margin (TM) losses, respectively. Besides, in order to capture the inforamtion of each label, instead of explicitly extracting regional featutres, we propose the self-attention machanism to implicitly enhance the relevant region and restrain those irrelevant. To validate the effective of the model, we conduct experiments on three well-known public datasets, COCO 2014, IAPR TC-12 and NUSWIDE, and achieve better performance than the state-of-the-art methods.
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