| 1. |
- Hedi, Wen, et al.
(författare)
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γ-Cyclodextrin-BSA for nano-encapsulation of hydrophobic substance
- 2021
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Ingår i: Food Bioscience. - : Elsevier. - 2212-4292 .- 2212-4306. ; 41
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Tidskriftsartikel (refereegranskat)abstract
- Self-aggregation and the hemolytic effect limit the application of gamma-cyclodextrin (gamma-CD) in bioactive molecular delivery systems. In this study, gamma-CD was modified by grafting onto bovine serum albumin protein (BSA), with epichlorohydrin (ECH) acting as the cross-linking agent. The effects of BSA concentration, reaction temperature, pH and time on the grafting rate were studied, and the gamma-CD-BSA complex with a grafting rate of 99.5 +/- 0.1% +/- 0.06)% was achieved. The complex was confirmed using H-1 NMR and FT-IR spectra. Compared with gamma-CD, the hemolytic effect and self-aggregation of gamma-CD-BSA were significantly reduced, and the encapsulation efficiency of curcumin was increased by 10.8%. The results of scanning electron microscopy showed that both gamma-CD and gamma-CDBSA nanoparticles were formed and the structure of the gamma-CD-BSA complex was more uniform. The pH stability and salt stability of gamma-CD-BSA were higher than gamma-CD. The release rate of gamma-CD-BSA was 15.2 +/- 0.2% after 2 h at pH 1.2, and 57 +/- 1% after 4 h at pH 7.2. The gamma-CD-BSA nanoparticles could protect curcumin in acidic environments and release it in neutral environments. The results suggested a promising wall material for delivery of hydrophobic substances.
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| 2. |
- Sun, Yuxi, et al.
(författare)
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Cross-Modal Hashing With Feature Semi-Interaction and Semantic Ranking for Remote Sensing Ship Image Retrieval
- 2024
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - : Institute of Electrical and Electronics Engineers (IEEE). - 0196-2892 .- 1558-0644. ; 62
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Tidskriftsartikel (refereegranskat)abstract
- Cross-modal hashing plays a pivotal role in large-scale remote sensing (RS) ship image retrieval. RS ship images often exhibit similar overall appearance with subtle differences. Existing hashing methods typically employ feature non-interaction strategies to generate common hash codes, which may not effectively capture the correlations between cross-modal ship images to reduce intermodality discrepancies. To address this issue, we propose a novel cross-modal hashing approach based on feature semi-interaction and semantic ranking (FSISR) for RS ship image retrieval. Our FSISR approach not only captures intricate correlations between different ship image modalities, but also enables the construction of hash tables for large-scale retrieval. FSISR comprises a feature semi-interaction module and a semantic ranking objective function. The semi-interaction module utilizes clustering centers from one modality to learn the correlations between two modalities and generate robust shared representations. The objective function optimizes these representations in a common Hamming space, consisting of a shared semantic alignment loss and a margin-free ranking loss. The alignment loss employs a shared semantic layer to preserve label-level similarity, while the ranking loss incorporates hard examples to establish a margin-free loss that captures similarity ranking relationships. We evaluate the performance of our method on benchmark datasets and demonstrate its effectiveness for cross-modal RS ship image retrieval. https://github.com/sunyuxi/FSISR.
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| 3. |
- Sun, Yuxi, et al.
(författare)
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Multisource Data Reconstruction-Based Deep Unsupervised Hashing for Unisource Remote Sensing Image Retrieval
- 2022
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Ingår i: IEEE Transactions on Geoscience and Remote Sensing. - : Institute of Electrical and Electronics Engineers (IEEE). - 0196-2892 .- 1558-0644. ; 60, s. 1-16
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Tidskriftsartikel (refereegranskat)abstract
- Unsupervised hashing for remote sensing (RS) image retrieval first extracts image features and then uses these features to construct supervised information (e.g., pseudolabels) to train hashing networks. Existing methods usually regard RS images as natural images to extract unisource features. However, these features only contain partial information about ground objects and cannot produce reliable pseudolabels. In addition, existing methods only generate a pseudo-single-label to annotate each RS image, which cannot accurately represent multiple scenes in an RS image. To address these drawbacks, this article proposes a new Multisource data reconstruction-based deep unsupervised Hashing method, called MrHash, which explores the characteristics of RS images to construct reliable pseudolabels. In particular, we first use geographic coordinates to obtain different satellite images and develop a novel autoencoder network to extract multisource features from these images. Then, pseudo-multilabels are designed to deal with the coexistence of multiple scenes in a single image. These labels are generated by a custom probability function with extracted multisource features. Finally, we propose a novel multisemantic hash loss by using the Kullback-Leibler (KL) divergence to preserve the semantic similarity of these pseudo-multilabels in Hamming space. Our newly developed MrHash only uses multisource images to construct supervised information, and hash code generation still relies on a unisource input image. Experiments on benchmark datasets clearly show the superiority of the proposed method over state-of-the-art baselines. We have added detailed descriptions about our source code. Please check them by accessing https://github.com/sunyuxi/MrHash.
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