A novel BA complex network model of color space is proposed based on two fundamental rules of BA scale-free network model: growth and preferential attachment. The scale-free characteristic of color space is discovered by analyzing evolving process of template's color distribution. And then the template's BA complex network model can be used to select important color pixels which have much larger effects than other color pixels in matching process. The proposed BA complex network model of color space can be easily integrated into many traditional template matching algorithms, such as SSD based matching and SAD based matching. Experiments show the performance of color template matching results can be improved based on the proposed algorithm. To the best of our knowledge, this is the first study about how to model the color space of images using a proper complex network model and apply the complex network model to template matching.
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http://dx.doi.org/10.1155/2014/918453 | DOI Listing |
Mol Ther
January 2025
Department of Molecular Medicine, University of Southern Denmark; Odense, 5230, Denmark. Electronic address:
Neovascular age-related macular degeneration and diabetic macular edema are leading causes of vision-loss evoked by retinal neovascularization and vascular leakage. The glycoprotein microfibrillar-associated protein 4 (MFAP4) is an integrin αβ ligand present in the extracellular matrix. Single-cell transcriptomics reveal MFAP4 expression in cell-types in close proximity to vascular endothelial cells including choroidal vascular mural cells and retinal astrocytes and Müller cells.
View Article and Find Full Text PDFItal J Pediatr
January 2025
Polistudium SRL, Milan, Italy.
Background: The PalliPed project is a nationwide, observational, cross-sectional study designed with the aim of providing a constantly updated national database for the census and monitoring of specialized pediatric palliative care (PPC) activities in Italy. This paper presents the results of the first monitoring phase of the PalliPed project, which was developed through the PalliPed 2022-2023 study, to update current knowledge on the provision of specialized PPC services in Italy.
Methods: Italian specialized PPC centers/facilities were invited to participate and asked to complete a self-reporting, ad-hoc, online survey regarding their clinical activity in 2022-2023, in the revision of the data initially collected in the first PalliPed study of 2021.
BMC Pulm Med
January 2025
Universal Scientific Education and Research Network (USERN), Tehran, Iran.
Objective: Lung cancer (LC), the primary cause for cancer-related death globally is a diverse illness with various characteristics. Saliva is a readily available biofluid and a rich source of miRNA. It can be collected non-invasively as well as transported and stored easily.
View Article and Find Full Text PDFBMC Bioinformatics
January 2025
School of Computer Science and Technology, University of Science and Technology of China, 443 Huangshan Road, Hefei, 230027, China.
Background: Drug-drug interactions (DDIs) especially antagonistic ones present significant risks to patient safety, underscoring the urgent need for reliable prediction methods. Recently, substructure-based DDI prediction has garnered much attention due to the dominant influence of functional groups and substructures on drug properties. However, existing approaches face challenges regarding the insufficient interpretability of identified substructures and the isolation of chemical substructures.
View Article and Find Full Text PDFNPJ Digit Med
January 2025
Neurofibromatosis Type 1 Center and Laboratory for Neurofibromatosis Type 1 Research, Shanghai Ninth People's Hospital, Shanghai Jiao Tong University School of Medicine, Shanghai, 200011, China.
Deep-learning models have shown promise in differentiating between benign and malignant lesions. Previous studies have primarily focused on specific anatomical regions, overlooking tumors occurring throughout the body with highly heterogeneous whole-body backgrounds. Using neurofibromatosis type 1 (NF1) as an example, this study developed highly accurate MRI-based deep-learning models for the early automated screening of malignant peripheral nerve sheath tumors (MPNSTs) against complex whole-body background.
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