| 1. |
- Beal, Jacob, et al.
(författare)
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Robust estimation of bacterial cell count from optical density
- 2020
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Ingår i: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Tidskriftsartikel (refereegranskat)abstract
- Optical density (OD) is widely used to estimate the density of cells in liquid culture, but cannot be compared between instruments without a standardized calibration protocol and is challenging to relate to actual cell count. We address this with an interlaboratory study comparing three simple, low-cost, and highly accessible OD calibration protocols across 244 laboratories, applied to eight strains of constitutive GFP-expressing E. coli. Based on our results, we recommend calibrating OD to estimated cell count using serial dilution of silica microspheres, which produces highly precise calibration (95.5% of residuals <1.2-fold), is easily assessed for quality control, also assesses instrument effective linear range, and can be combined with fluorescence calibration to obtain units of Molecules of Equivalent Fluorescein (MEFL) per cell, allowing direct comparison and data fusion with flow cytometry measurements: in our study, fluorescence per cell measurements showed only a 1.07-fold mean difference between plate reader and flow cytometry data.
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| 2. |
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| 3. |
- Liao, Wenlong, et al.
(författare)
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Fault diagnosis of power transformers using graph convolutional network
- 2021
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Ingår i: CSEE Journal of Power and Energy Systems. - : Power System Technology Press. - 2096-0042. ; 7:2, s. 241-249
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Tidskriftsartikel (refereegranskat)abstract
- Existing methods for transformer fault diagnosis either train a classifier to fit the relationship between dissolved gas and fault type or find some similar cases with unknown samples by calculating the similarity metrics. Their accuracy is limited, since they are hard to learn from other algorithms to improve their own performance. To improve the accuracy of transformer fault diagnosis, a novel method for transformer fault diagnosis based on graph convolutional network (GCN) is proposed. The proposed method has the advantages of two kinds of existing methods. Specifically, the adjacency matrix of GCN is utilized to fully represent the similarity metrics between unknown samples and labeled samples. Furthermore, the graph convolutional layers with strong feature extraction ability are used as a classifier to find the complex nonlinear relationship between dissolved gas and fault type. The back propagation algorithm is used to complete the training process of GCN. The simulation results show that the performance of GCN is better than that of the existing methods such as convolutional neural network, multi-layer perceptron, support vector machine, extreme gradient boosting tree, k-nearest neighbors and Siamese network in different input features and data volumes, which can effectively meet the needs of diagnostic accuracy.
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| 4. |
- Xiang, Yusen, et al.
(författare)
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Ginkgolic acids inhibit SARS-CoV-2 and its variants by blocking the spike protein/ACE2 interplay
- 2023
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Ingår i: International Journal of Biological Macromolecules. - : Elsevier. - 0141-8130 .- 1879-0003. ; 226, s. 780-792
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Tidskriftsartikel (refereegranskat)abstract
- Targeting the interaction between the spike protein receptor binding domain (S-RBD) of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and angiotensin-converting enzyme 2 (ACE2) is a potential therapeutic strategy for treating coronavirus disease 2019 (COVID-19). However, we still lack small-molecule drug candidates for this target due to the missing knowledge in the hot spots for the protein-protein interaction. Here, we used NanoBiT technology to identify three Ginkgolic acids from an in-house traditional Chinese medicine (TCM) library, and they interfere with the S-RBD/ACE2 interplay. Our pseudovirus assay showed that one of the compounds, Ginkgolic acid C17:1 (GA171), significantly inhibits the entry of original SARS-CoV-2 and its variants into the ACE2-overexpressed HEK293T cells. We investigated and proposed the binding sites of GA171 on S-RBD by combining molecular docking and molecular dynamics simulations. Site-directed mutagenesis and surface plasmon resonance revealed that GA171 specifically binds to the pocket near R403 and Y505, critical residues of S-RBD for S-RBD interacting with ACE2. Thus, we provide structural insights into developing new small-molecule inhibitors and vaccines against the proposed S-RBD binding site.
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| 5. |
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- 2019
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Tidskriftsartikel (refereegranskat)
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