Background: African Swine Fever (ASF) is a highly contagious and lethal viral disease of swine, the presence of which in groups of pigs leads to enormous economic losses in the farming industry. However, vaccines and drugs to treat ASF have yet to be developed. To control the spread of the African Swine Fever Virus (ASFV), a diagnostic method that can be applied rapidly and can detect the disease during the early stages of infection is urgently needed.
Methods: In this study, we demonstrate a rapid and easy-to-use ASFV detection method that combines loop-mediated isothermal amplification (LAMP) and image processing with the hue-saturation-value (HSV) color model. This method was validated through use of synthetic ASFV DNA.
Results: The method shows high sensitivity, as it detects as few as 10 copies per reaction within 20 min. The speed and sensitivity of this newly developed assay are superior to those reported in previous studies. In addition, through HSV color space transformation, the colorimetric result of this LAMP assay can be used for a semi-quantitative analysis for ASFV (ranging from 10 to 10 copies per reaction) and improve the discern to low concentration samples from a negative control.
Conclusion: These results show that the combination of ASFV-LAMP assay and HSV color space transformation may accelerate the screening process of pigs for ASFV infection. Overall, this study provides a rapid, sensitive, early-stage, on-site diagnosis of ASFV infection and has potential to be applied to other infectious diseases.
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http://dx.doi.org/10.1016/j.jmii.2020.08.003 | DOI Listing |
RSC Adv
January 2025
Department of Chemical and Materials Engineering, University of Alberta Edmonton AB T6G 1H9 Canada
Non-destructive color sensors are widely applied for rapid analysis of various biological and healthcare point-of-care applications. However, existing red, green, blue (RGB)-based color sensor systems, relying on the conversion to human-perceptible color spaces like hue, saturation, lightness (HSL), hue, saturation, value (HSV), as well as cyan, magenta, yellow, key (CMYK) and the CIE L*a*b* (CIELAB) exhibit limitations compared to spectroscopic methods. The integration of machine learning (ML) techniques presents an opportunity to enhance data analysis and interpretation, enabling insights discovery, prediction, process automation, and decision-making.
View Article and Find Full Text PDFJ Periodontol
January 2025
Discipline of Periodontics, School of Dentistry, University of São Paulo, São Paulo, Brazil.
Background: Gingivitis, a widely prevalent oral health condition, affects up to 80% of the population. Traditional assessment methods for gingivitis rely heavily on subjective clinical evaluation. This study seeks to explore the efficacy of interpreting the color metrics from intraoral scans to objectively differentiate between healthy and inflamed gingiva.
View Article and Find Full Text PDFJ Colloid Interface Sci
January 2025
State Key Laboratory of Fine Chemicals, Dalian University of Technology, 2# Linggong Road, Dalian 116024, China. Electronic address:
The utilization of structural colors in 3D printing was anticipated due to their eco-friendliness and sustainability. However, the manufacturing of homogeneous structural colors with intricate 3D architectures remains a great challenge, particularly in hydrogels. Herein, we added 0.
View Article and Find Full Text PDFIEEE Trans Neural Netw Learn Syst
November 2024
The selection and utilization of different color spaces significantly impact the recognition performance of deep learning models in downstream tasks. Existing studies typically leverage image information from various color spaces through model integration or channel concatenation. However, these methods result in excessive model size and suboptimal utilization of image information.
View Article and Find Full Text PDFSci Rep
November 2024
College of Material Science and Art Design, Inner Mongolia Agricultural University, Hohhot, 010018, China.
Aiming at the veneer defect image acquisition process is prone to the problems of blurred edges, inconspicuous contrast and distortion, which cannot show the defects clearly.To improve image analyzability and clarity, a veneer defect image enhancement method based on AMEF-AGC is proposed herein. First, a veneer defect image is subjected to Gamma correction to obtain multiple underexposed image sequences for which Gaussian and Laplacian pyramids are constructed to determine the weights of the multiple exposure sequence group images.
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