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  • MDPI AG  (3)
  • Zhang, Ying  (3)
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  • MDPI AG  (3)
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  • 1
    Online Resource
    Online Resource
    Fluorescent Nanoparticle-RNAi-Mediated Silencing of Sterol Carrier Protein-2 Gene Expression Suppresses the Growth, Development, and Reproduction of Helicoverpa armigera
    MDPI AG ; 2023
    In:  Nanomaterials Vol. 13, No. 2 ( 2023-01-06), p. 245-
    Details
    In: Nanomaterials, MDPI AG, Vol. 13, No. 2 ( 2023-01-06), p. 245-
    Abstract: Helicoverpa armigera is a polyphagous destructive lepidopteran pest with strong Bacillus thuringiensis (Bt) resistance. Cholesterol, a vital component for insect growth, can only be obtained from food, and its transfer and metabolism are regulated by sterol carrier protein-2 (SCP-2). This study examined whether H. armigera SCP-2 (HaSCP-2) gene expression, involved in cholesterol absorption, can be silenced by nanocarrier fluorescent nanoparticle-RNA interference (FNP-RNAi) by larval feeding and whether the silencing affected H. armigera development. Fluorescence microscopy showed that nanoparticle-siRNA was distributed in Ha cells and the larval midgut. FNP-HaSCP-2 siRNA suppressed HaSCP-2 expression by 52.5% in H.armigera Ha cells. FNP can effectively help deliver siRNA into cells, protect siRNA, and is not affected by serum. FNP-siRNA in vivo biological assays showed that HaSCP-2 transcript levels were inhibited by 70.19%, 68.16%, and 67.66% in 3rd, 4th, and 5th instar larvae, leading to a decrease in the cholesterol level in the larval and prepupal fatbodies. The pupation rate and adult emergence were reduced to 26.0% and 56.52%, respectively. This study demonstrated that FNP could deliver siRNA to cells and improve siRNA knockdown efficiency. HaSCP-2 knockdown by FNP-siRNA in vivo hindered H. armigera growth and development. FNP could enhance RNAi efficiency to achieve pest control by SCP-2-targeted FNP-RNAi.
    Type of Medium: Online Resource
    ISSN: 2079-4991
    URL: Article
    DOI: 10.3390/nano13020245
    Language: English
    Publisher: MDPI AG
    Publication Date: 2023
    detail.hit.zdb_id: 2662255-5
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    Online Resource
  • 2
    Online Resource
    Online Resource
    Multi-Modal Graph Interaction for Multi-Graph Convolution Network in Urban Spatiotemporal Forecasting
    MDPI AG ; 2022
    In:  Sustainability Vol. 14, No. 19 ( 2022-09-29), p. 12397-
    Details
    In: Sustainability, MDPI AG, Vol. 14, No. 19 ( 2022-09-29), p. 12397-
    Abstract: Graph convolution network-based approaches have been recently used to model region-wise relationships in region-level prediction problems in urban computing. Each relationship represents a kind of spatial dependency, such as region-wise distance or functional similarity. To incorporate multiple relationships into a spatial feature extraction, we define the problem as a multi-modal machine learning problem on multi-graph convolution networks. Leveraging the advantage of multi-modal machine learning, we propose to develop modality interaction mechanisms for this problem in order to reduce the generalization error by reinforcing the learning of multi-modal coordinated representations. In this work, we propose two interaction techniques for handling features in lower layers and higher layers, respectively. In lower layers, we propose grouped GCN to combine the graph connectivity from different modalities for a more complete spatial feature extraction. In higher layers, we adapt multi-linear relationship networks to GCN by exploring the dimension transformation and freezing part of the covariance structure. The adapted approach, called multi-linear relationship GCN, learns more generalized features to overcome the train–test divergence induced by time shifting. We evaluated our model on a ride-hailing demand forecasting problem using two real-world datasets. The proposed technique outperforms state-of-the art baselines in terms of prediction accuracy, training efficiency, interpretability and model robustness.
    Type of Medium: Online Resource
    ISSN: 2071-1050
    URL: Article
    DOI: 10.3390/su141912397
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2518383-7
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  • 3
    Online Resource
    Online Resource
    Prediction of the Short-Term Therapeutic Effect of Anti-VEGF Therapy for Diabetic Macular Edema Using a Generative Adversarial Network with OCT Images
    MDPI AG ; 2022
    In:  Journal of Clinical Medicine Vol. 11, No. 10 ( 2022-05-19), p. 2878-
    Details
    In: Journal of Clinical Medicine, MDPI AG, Vol. 11, No. 10 ( 2022-05-19), p. 2878-
    Abstract: Purpose: To generate and evaluate individualized post-therapeutic optical coherence tomography (OCT) images that could predict the short-term response of anti-vascular endothelial growth factor (VEGF) therapy for diabetic macular edema (DME) based on pre-therapeutic images using generative adversarial network (GAN). Methods: Real-world imaging data were collected at the Department of Ophthalmology, Qilu Hospital. A total of 561 pairs of pre-therapeutic and post-therapeutic OCT images of patients with DME were retrospectively included in the training set, 71 pre-therapeutic OCT images were included in the validation set, and their corresponding post-therapeutic OCT images were used to evaluate the synthetic images. A pix2pixHD method was adopted to predict post-therapeutic OCT images in DME patients that received anti-VEGF therapy. The quality and similarity of synthetic OCT images were evaluated independently by a screening experiment and an evaluation experiment. Results: The post-therapeutic OCT images generated by the GAN model based on big data were comparable to the actual images, and the response of edema resorption was also close to the ground truth. Most synthetic images (65/71) were difficult to differentiate from the actual OCT images by retinal specialists. The mean absolute error (MAE) of the central macular thickness (CMT) between the synthetic OCT images and the actual images was 24.51 ± 18.56 μm. Conclusions: The application of GAN can objectively demonstrate the individual short-term response of anti-VEGF therapy one month in advance based on OCT images with high accuracy, which could potentially help to improve treatment compliance of DME patients, identify patients who are not responding well to treatment and optimize the treatment program.
    Type of Medium: Online Resource
    ISSN: 2077-0383
    URL: Article
    DOI: 10.3390/jcm11102878
    Language: English
    Publisher: MDPI AG
    Publication Date: 2022
    detail.hit.zdb_id: 2662592-1
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