| 1. |
- Beal, Jacob, et al.
(author)
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Robust estimation of bacterial cell count from optical density
- 2020
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In: Communications Biology. - : Springer Science and Business Media LLC. - 2399-3642. ; 3:1
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Journal article (peer-reviewed)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. |
- Wang, Shudong, et al.
(author)
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MSHGANMDA : Meta-Subgraphs Heterogeneous Graph Attention Network for miRNA-Disease Association Prediction
- 2023
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In: IEEE journal of biomedical and health informatics. - : Institute of Electrical and Electronics Engineers (IEEE). - 2168-2194 .- 2168-2208. ; 27:10, s. 4639-4648
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Journal article (peer-reviewed)abstract
- MicroRNAs (miRNAs) influence several biological processes involved in human disease. Biological experiments for verifying the association between miRNA and disease are always costly in terms of both money and time. Although numerous biological experiments have identified multi-types of associations between miRNAs and diseases, existing computational methods are unable to sufficiently mine the knowledge in these associations to predict unknown associations. In this study, we innovatively propose a heterogeneous graph attention network model based on meta-subgraphs (MSHGATMDA) to predict the potential miRNA-disease associations. Firstly, we define five types of meta-subgraph from the known miRNA-disease associations. Then, we use meta-subgraph attention and meta-subgraph semantic attention to extract features of miRNA-disease pairs within and between these five meta-subgraphs, respectively. Finally, we apply a fully-connected layer (FCL) to predict the scores of unknown miRNA-disease associations and cross-entropy loss to train our model end-to-end. To evaluate the effectiveness of MSHGATMDA, we apply five-fold cross-validation to calculate the mean values of evaluation metrics Accuracy, Precision, Recall, and F1-score as 0.8595, 0.8601, 0.8596, and 0.8595, respectively. Experiments show that our model, which primarily utilizes multi-types of miRNAdisease association data, gets the greatest ROC-AUC value of 0.934 when compared to other state-of-the-art approaches. Furthermore, through case studies, we further confirm the effectiveness of MSHGATMDA in predicting unknown diseases.
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| 3. |
- Lawniczak, Mara K. N., et al.
(author)
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Standards recommendations for the Earth BioGenome Project
- 2022
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In: Proceedings of the National Academy of Sciences of the United States of America. - : Proceedings of the National Academy of Sciences (PNAS). - 0027-8424 .- 1091-6490. ; 119:4
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Journal article (peer-reviewed)abstract
- A global international initiative, such as the Earth BioGenome Project (EBP), requires both agreement and coordination on standards to ensure that the collective effort generates rapid progress toward its goals. To this end, the EBP initiated five technical standards committees comprising volunteer members from the global genomics scientific community: Sample Collection and Processing, Sequencing and Assembly, Annotation, Analysis, and IT and Informatics. The current versions of the resulting standards documents are available on the EBP website, with the recognition that opportunities, technologies, and challenges may improve or change in the future, requiring flexibility for the EBP to meet its goals. Here, we describe some highlights from the proposed standards, and areas where additional challenges will need to be met.
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| 4. |
- Qiao, Sibo, et al.
(author)
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HCMMNet : Hierarchical Conv-MLP-Mixed Network for Medical Image Segmentation in Metaverse for Consumer Health
- 2024
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In: IEEE transactions on consumer electronics. - : Institute of Electrical and Electronics Engineers (IEEE). - 0098-3063 .- 1558-4127. ; 70:1, s. 2078-2089
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Journal article (peer-reviewed)abstract
- In the burgeoning metaverse for consumer health (MCH), medical image segmentation methods with high accuracy and generalization capability are essential to drive personalized healthcare solutions and enhance the patient experience. To address the inherent challenges of capturing complex structures and features in medical image segmentation, we propose a convolutional neural network (CNN) and multi-layer-perceptron (MLP) mixed module named HCMM, which hierarchically incorporates local priors of CNN into fully-connected (FC) layers, ingeniously capturing specific details and a broader range of contextual information of the focused object from diverse perspectives. Then, we propose an MLP-based information fusion module (MIF) designed to dynamically merge feature maps of varying levels from different pathways, enhancing feature expression and discriminative power. Based on the above-proposed modules, we design a novel segmentation model, HCMMNet, which can adeptly capture feature maps from input medical images at different scales and perspectives. Through comparative experiments, we demonstrate the outstanding performance of the HCMMNet for medical image segmentation on three publicly available datasets and one self-organized dataset. Notably, our HCMMNet showcases remarkable efficacy while maintaining an extraordinarily lightweight profile, weighing in at a mere 3M, rendering it ideal for MCH application.
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| 5. |
- Zhao, Yundong, et al.
(author)
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Irradiation engineered lattice distortion in Ti-Ni shape memory alloy achieving enhanced elastocaloric effect
- 2022
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In: Journal of Alloys and Compounds. - : Elsevier BV. - 0925-8388 .- 1873-4669. ; 906
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Journal article (peer-reviewed)abstract
- Large lattice distortion is the design idea to obtain a high elastocaloric effect of shape memory alloys. In the present work, a new method of He ion irradiation is used to obtain a large lattice distortion, resulting in a 6.5 K of adiabatic temperature in Ti-Ni microwires, compared to the 2.9 K of the unirradiated sample. In addition, the isothermal entropy change is 68 J/kg·K at 293 K, which is twice as high as that of unirradiated alternatives. The microwires have a larger unit cell lattice distortion after irradiation across the stress-induced martensite transformation, therefore, the irradiated sample has a larger lattice entropy change, resulting in the improvement of the phase transformation entropy change. This is the main reason that the elastocaloric effect of Ti-Ni shape memory microwires is significantly improved after He ion irradiation. This method will provide a new idea for the design of colossal elastocaloric effect materials and provide the possibility for the application of solid-state refrigeration.
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