Insect stains produced by necrophagous flies are indistinguishable morphologically from human bloodstains. At present, no diagnostic tests exist to overcome this deficiency. As the first step toward developing a chemical test to recognize fly artifacts, polyclonal antisera were generated in rats against three distinct antigenic sequences of fly cathepsin D-like proteinase, an enzyme that is structurally distinct in cyclorrhaphous Diptera from other animals. The resulting rat antisera bound to artifacts produced by Protophormia terraenovae and synthetic peptides used to generate the polyclonal antisera, but not with any type of mammalian blood tested in immunoassays. Among the three antisera, anti-md3 serum displayed the highest reactivity for fly stains, demonstrated cross-reactivity for all synthetic peptides representing antigenic sequences of the mature fly enzyme, and bound artifacts originating from the fly digestive tract. Further work is needed to determine whether the antisera are suitable for non-laboratory conditions.
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http://dx.doi.org/10.1111/1556-4029.13756 | DOI Listing |
Sci Adv
September 2024
School of Chemistry, University of Southampton, University Road, Southampton SO17 1BJ, UK.
The current standard method for amino acid signal identification in protein NMR spectra is sequential assignment using triple-resonance experiments. Good software and elaborate heuristics exist, but the process remains laboriously manual. Machine learning does help, but its training databases need millions of samples that cover all relevant physics and every kind of instrumental artifact.
View Article and Find Full Text PDFG3 (Bethesda)
November 2024
School of BioSciences, The University of Melbourne, Parkville Campus, Melbourne, Victoria 3010, Australia.
Med Image Anal
October 2024
Quantitative Healthcare Analysis (QurAI) Group, Informatics Institute, University of Amsterdam, Amsterdam, Noord-Holland, Netherlands; Department of Biomedical Engineering and Physics, Amsterdam University Medical Center, Amsterdam, Noord-Holland, Netherlands.
Deep learning classification models for medical image analysis often perform well on data from scanners that were used to acquire the training data. However, when these models are applied to data from different vendors, their performance tends to drop substantially. Artifacts that only occur within scans from specific scanners are major causes of this poor generalizability.
View Article and Find Full Text PDFBiomed Opt Express
June 2024
Department of Biomedical Engineering, Washington University in St. Louis, St. Louis, MO 63130, USA.
Optical coherence microscopy (OCM) imaging of the (fruit fly) heart tube has enabled the non-invasive characterization of fly heart physiology . OCM generates large volumes of data, making it necessary to automate image analysis. Deep-learning-based neural network models have been developed to improve the efficiency of fly heart image segmentation.
View Article and Find Full Text PDFRecent advancements in ptychography have demonstrated the potential of coded ptychography (CP) for high-resolution optical imaging in a lensless configuration. However, CP suffers imaging throughput limitations due to scanning inefficiencies. To address this, we propose what we believe is a novel 'fly-scan' scanning strategy utilizing two eccentric rotating mass (ERM) vibration motors for high-throughput coded ptychographic microscopy.
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