Revolutionizing medical image analysis, the Segment Anything Model (SAM) stands out among emerging foundation models, yet its potential in accurately segmenting small, irregular ROIs from multi-center, diverse dataset...
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Visual place recognition (VPR) is a highly challenging task that has a wide range of applications, including robot navigation and self-driving vehicles. VPR is a difficult task due to duplicate regions and insufficien...
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Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean a...
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ISBN:
(纸本)9798400712203
Protein-Protein Interaction (PPI) provides important insights into the metabolic mechanisms of different biological processes. Although PPIs in some organisms have been investigated systematically, PPIs in the ocean archaea remain largely unexplored. But such species have special investigation value since their adaptation to extreme living conditions may generate unique PPIs. In this paper, we aim to characterize and predict PPIs in ocean archaea to advance understanding of their metabolic networks. First, we collect all ocean archaea PPIs with high confidence from STRING database and analyze the PPI network features, including centrality and enrichment analysis. The functional enrichment results of the largest connecting subgraph in the PPI network show most PPIs in our constructed dataset is related to the translation and transcription processes. Then, we generate an equal number of negative PPI pairs, whose members have either different subcellular locations or GO terms. We also use the generated dataset to test the performance of three pretraining methods and their ensemble methods in the binary PPI prediction task. Our results suggest the ensemble methods could be applied to further improve models’ performance. Fine-tuned models trained on the ocean archaea dataset are expected to predict the other ocean archaea PPIs that are not included in the STRING database and get more understanding about the ocean archaea PPI universe.
Deep convolutional neural networks have significantly advanced color image denoising. However, existing models often apply grayscale denoising techniques to color images without accounting for inter-channel correlatio...
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ISBN:
(数字)9798350368741
ISBN:
(纸本)9798350368758
Deep convolutional neural networks have significantly advanced color image denoising. However, existing models often apply grayscale denoising techniques to color images without accounting for inter-channel correlations, resulting in color distortion, detail loss, and visual artifacts. Moreover, these models frequently neglect salient features within convolutional maps. To address these issues, we propose a quaternion CNN model that captures channel correlations and extracts salient features, thereby enhancing color image denoising performance. Specifically, we convert color images into quaternion matrices to better capture these correlations and design a quaternion convolutional network to learn relevant features. Furthermore, an aggregated feature block is introduced to enhance the extraction of salient features and further refine the denoising process. Experimental results on multiple datasets demonstrate that the proposed model achieves superior performance compared to recent state-of-the-art methods.
This study investigates the reciprocal relationship between investor sentiment and capital market dynamics, leveraging advanced techniques in data extraction and analysis. By employing web crawling and natural languag...
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The seven-field classification plays a key role in ophthalmology, especially in the diagnosis and treatment of diabetic retinopathy. In this paper, we designed a framework that can classify 7 fields of each eye simult...
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We describe NLSExplorer, an interpretable approach for nuclear localization signal (NLS) prediction. By utilizing the extracted information on nuclear-specific sites from the protein language model to assist in NLS de...
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As society increasingly focuses on health issues, the importance of the role of family doctors becomes more apparent. The question of how to use cutting-edge technology to improve the accessibility and efficiency of m...
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An approach for synthetic aperture radar （SAR） image de-noising based on independent component analysis （ICA） basis images is proposed. Firstly, the basis images and the code matrix of the original image are obtai...
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An approach for synthetic aperture radar （SAR） image de-noising based on independent component analysis （ICA） basis images is proposed. Firstly, the basis images and the code matrix of the original image are obtained using ICA algorithm. Then, pointwise Hoder exponent of each basis is computed as a cost criterion for basis enhancement, and then the enhanced basis images are classified into two sets according to a separation rule which separates the clean basis from the original basis. After these key procedures for speckle reduction, the clean image is finally obtained by reconstruction on the clean basis and original code matrix. The reconstructed image shows better visual perception and image quality compared with those obtained by other traditional techniques.
The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive trea...
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The reasonable measuring of particle weight and effective sampling of particle state are consid- ered as two important aspects to obtain better estimation precision in particle filter. Aiming at the comprehensive treatment of above problems, a novel two-stage prediction and update particle filte- ring algorithm based on particle weight optimization in multi-sensor observation is proposed. Firstly, combined with the construction of muhi-senor observation likelihood function and the weight fusion principle, a new particle weight optimization strategy in multi-sensor observation is presented, and the reliability and stability of particle weight are improved by decreasing weight variance. In addi- tion, according to the prediction and update mechanism of particle filter and unscented Kalman fil- ter, a new realization of particle filter with two-stage prediction and update is given. The filter gain containing the latest observation information is used to directly optimize state estimation in the frame- work, which avoids a large calculation amount and the lack of universality in proposal distribution optimization way. The theoretical analysis and experimental results show the feasibility and efficiency of the proposed algorithm.
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