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An easy, rapid and inexpensive method of preparing RNA template for a reverse transcription qPCR assay for avocado sunblotch viroid (ASBVd) is described. This method depends on the principle of reversible binding of viroid RNA to filter paper under different concentrations of monovalent cation. Lysis buffers containing either sodium chloride or lithium chloride were compared, and 1.5 M lithium chloride was shown to be optimal for the adsorption of the viroid RNA to the filter paper. The extraction method was validated using field samples and equivalent yields of viroid RNA were obtained using this method and either a commercial RNA extraction kit or a dsRNA chromatography method. The filter paper method of RNA extraction is ideally suited for the large-scale surveillance for ASBVd.
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http://dx.doi.org/10.1016/j.jviromet.2022.114455 | DOI Listing |
Food Res Int
November 2025
German Federal Institute for Risk Assessment (BfR), Department Food Safety, National Reference Laboratory for Animal Protein in Feed, Max-Dohrn-Str. 8-10, 10589 Berlin, Germany. Electronic address:
Processing food and feed sets off a variety of reactions (Maillard, (lipid) oxidation), which may be traced by covalent changes to e.g. proteins.
View Article and Find Full Text PDFFood Res Int
November 2025
Institute of Food Research, National Agriculture and Food Research Organization (NARO), 2-1-12 Kannondai, Tsukuba, Ibaraki 305-8642, Japan.
Accurately assessing saltiness perception in solid foods is essential for the development of low-sodium foods that maintain saltiness despite possessing a lower sodium content. This study aimed to develop a practical system for evaluating human-perceived saltiness during oral processing, particularly when food was initially placed on the tongue. As a basis for system design, sensory evaluations demonstrated that higher local salt concentrations (inhomogeneous distribution) on the tongue significantly enhanced perceived saltiness intensity compared to a homogeneous distribution, despite equal total salt content.
View Article and Find Full Text PDFInt J Biol Macromol
September 2025
Plant Fiber Material Science Research Center, State Key Laboratory of Advanced Papermaking and Paper-based Materials, South China University of Technology, Guangzhou, 510640, China.
The development of cellulose-based electromagnetic shielding materials is critical for the advancement of sustainable, lightweight, and flexible electronic devices. Most high-performance composites rely on nanocellulose, which is expensive and energy-intensive to produce. In this work, we employ chemically modified conventional eucalyptus pulp fibers (non-nano) to fabricate Janus-structured cellulose/MXene composite papers.
View Article and Find Full Text PDFNeural Netw
September 2025
Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ), Shenzhen, China. Electronic address:
Automatic segmentation of retinal vessels from retinography images is crucial for timely clinical diagnosis. However, the high cost and specialized expertise required for annotating medical images often result in limited labeled datasets, which constrains the full potential of deep learning methods. Recent advances in self-supervised pretraining using unlabeled data have shown significant benefits for downstream tasks.
View Article and Find Full Text PDFPLoS One
September 2025
Smart Manufacturing and Artificial Intelligence, Micron Memory Malaysia Sdn. Bhd., Batu Kawan, Penang, Malaysia.
Advances in data collection have resulted in an exponential growth of high-dimensional microarray datasets for binary classification in bioinformatics and medical diagnostics. These datasets generally possess many features but relatively few samples, resulting in challenges associated with the "curse of dimensionality", such as feature redundancy and an elevated risk of overfitting. While traditional feature selection approaches, such as filter-based and wrapper-based approaches, can help to reduce dimensionality, they often struggle to capture feature interactions while adequately preserving model generalization.
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