Anthocyanins are often chosen as signal converters of intelligent labels. However, they are degraded by high-temperature oxidation in the process of intelligent label preparation. The color fading seriously affects the sensitivity of color development. In this study, a green 3D printing intelligent label preparation technique was developed, in which gallic acid (GA) was added to a blueberry anthocyanin (BA) solution to enhance the color of the co-pigment to ensure the color sensitivity. The combined effect of GA-BA reduced the fade rate of the anthocyanins from 35.13 % to 26.44 % at 90 °C. The printing ink has shear-thinning viscosity characteristics and yield stresses in the range of 500-600 MPa for high-quality printing. Structural analysis revealed that GA-BA co-pigmentation enhanced the interaction between ovalbumin and cassava starch. In addition, the method of 3D printing to prepare labels was conducive to solving the problem of waste in traditional labeling process. The results of freshness testing of sea shrimp proved that labels can be applied to fresh boxes to reflect the freshness of food. We provide a method for enhancing the color of 3D-printed smart ink to prepare intelligent labels with reproducible and customizable batch shapes.
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http://dx.doi.org/10.1016/j.ijbiomac.2024.135684 | DOI Listing |
J Chem Theory Comput
January 2025
Guizhou Provincial Engineering Technology Research Center for Chemical Drug R&D, School of Pharmacy, Guizhou Medical University, Guiyang, Guizhou 550025, P. R. China.
Traditional machine learning methods face significant challenges in predicting the properties of highly symmetric molecules. In this study, we developed a machine learning model based on graph neural networks (GNNs) to accurately and swiftly predict the thermodynamic and photochemical properties of fullerenols, such as C(OH) ( = 1 to 30). First, we established a global method for generating fullerenol isomers through isomer fingerprinting, which can generate all possible isomers or produce diverse structural types on demand.
View Article and Find Full Text PDFJ Magn Reson Imaging
January 2025
Department of Radiology, Ålesund Hospital, Møre og Romsdal Hospital Trust, Ålesund, Norway.
Background: Deep learning-based segmentation of brain metastases relies on large amounts of fully annotated data by domain experts. Semi-supervised learning offers potential efficient methods to improve model performance without excessive annotation burden.
Purpose: This work tests the viability of semi-supervision for brain metastases segmentation.
Lab Chip
January 2025
Department of Biomedical Engineering, City University of Hong Kong, 83 Tat Chee Avenue, Kowloon Tong, Hong Kong, China.
Revealing how individual cells alter their secretions over time is crucial for understanding their responses to environmental changes. Key questions include: When do cells modify their functions and states? What transitions occur? Insights into the kinetic secretion trajectories of various cell types are essential for unraveling complex biological systems. This review highlights seven microfluidic technologies for time-resolved single-cell secretion analysis: 1.
View Article and Find Full Text PDFInt J Neural Syst
January 2025
Yangtze Delta Region Institute (Huzhou), University of Electronic Science and Technology of China, Huzhou 313001, P. R. China.
Visual semantic decoding aims to extract perceived semantic information from the visual responses of the human brain and convert it into interpretable semantic labels. Although significant progress has been made in semantic decoding across individual visual cortices, studies on the semantic decoding of the ventral and dorsal cortical visual pathways remain limited. This study proposed a graph neural network (GNN)-based semantic decoding model on a natural scene dataset (NSD) to investigate the decoding differences between the dorsal and ventral pathways in process various parts of speech, including verbs, nouns, and adjectives.
View Article and Find Full Text PDFJ Imaging Inform Med
January 2025
Department of Radiology, UC Davis School of Medicine, University of California, Davis, 4860 Y Street, Suite 3100, Sacramento, CA, 95817-2307, USA.
Purpose: To explore the information in routine digital subtraction angiography (DSA) and evaluate deep learning algorithms for automated identification of anatomic location in DSA sequences.
Methods: DSA of the abdominal aorta, celiac, superior mesenteric, inferior mesenteric, and bilateral external iliac arteries was labeled with the anatomic location from retrospectively collected endovascular procedures performed between 2010 and 2020 at a tertiary care medical center. "Key" images within each sequence demonstrating the parent vessel and the first bifurcation were additionally labeled.
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