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  • Hindawi Limited  (2)
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  • Hindawi Limited  (2)
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  • 1
    In: BioMed Research International, Hindawi Limited, Vol. 2018 ( 2018-06-26), p. 1-14
    Abstract: Magnetic resonance imaging (MRI) based on the ferritin heavy chain 1 (FTH1) reporter gene has been used to trace stem cells. However, whether FTH1 expression is affected by stem cell differentiation or whether cell differentiation is affected by reporter gene expression remains unclear. Here, we explore the relationship between FTH1 expression and neural differentiation in the differentiation of mesenchymal stem cells (MSCs) carrying FTH1 into neuron-like cells and investigate the feasibility of using FTH1 as an MRI reporter gene to detect neurally differentiated cells. By inducing cell differentiation with all-trans retinoic acid and a modified neuronal medium, MSCs and MSCs-FTH1 were successfully differentiated into neuron-like cells (Neurons and Neurons-FTH1), and the neural differentiation rates were (91.56±7.89)% and (92.23±7.64)%, respectively. Neuron-specific markers, including nestin, neuron-specific enolase, and microtubule-associated protein-2, were significantly expressed in Neurons-FTH1 and Neurons without noticeable differences. On the other hand, FTH1 was significantly expressed in MSCs-FTH1 and Neurons-FTH1 cells, and the expression levels were not significantly different. The R2 value was significantly increased in MSCs-FTH1 and Neurons-FTH1 cells, which was consistent with the findings of Prussian blue staining, transmission electron microscopy, and intracellular iron measurements. These results suggest that FTH1 gene expression did not affect MSC differentiation into neurons and was not affected by neural differentiation. Thus, MRI reporter gene imaging based on FTH1 can be used for the detection of neurally differentiated cells from MSCs.
    Type of Medium: Online Resource
    ISSN: 2314-6133 , 2314-6141
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2018
    detail.hit.zdb_id: 2698540-8
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  • 2
    In: Computational Intelligence and Neuroscience, Hindawi Limited, Vol. 2022 ( 2022-4-21), p. 1-10
    Abstract: In recent years, analysis and optimization algorithm based on image data is a research hotspot. Aircraft detection based on aerial images can provide data support for accurately attacking military targets. Although many efforts have been devoted, it is still challenging due to the poor environment, the vastness of the sky background, and so on. This paper proposes an aircraft detection method named TransEffiDet in aerial images based on the EfficientDet method and Transformer module. We improved the EfficientDet algorithm by combining it with the Transformer which models the long-range dependency for the feature maps. Specifically, we first employ EfficientDet as the backbone network, which can efficiently fuse the different scale feature maps. Then, deformable Transformer is used to analyze the long-range correlation for global feature extraction. Furthermore, we designed a fusion module to fuse the long-range and short-range features extracted by EfficientDet and deformable Transformer, respectively. Finally, object class is produced by feeding the feature map to the class prediction net and the bounding box predictions are generated by feeding these fused features to the box prediction net. The mean Average Precision (mAP) is 86.6%, which outperforms the EfficientDet by 5.8%. The experiment shows that TransEffiDet is more robust than other methods. Additionally, we have established a public aerial dataset for aircraft detection, which will be released along with this paper.
    Type of Medium: Online Resource
    ISSN: 1687-5273 , 1687-5265
    Language: English
    Publisher: Hindawi Limited
    Publication Date: 2022
    detail.hit.zdb_id: 2388208-6
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