An immunoassay was previously developed as a technique to improve methods for detection and analysis of fly artifacts found at crime scenes. The dot blot assay utilized a polyclonal antiserum (anti-md3) based on a unique digestive cathepsin D found in cyclorrhaphous Diptera. In this study, artifacts produced by adults of Calliphora vicina, Cynomya cadaverina, Sarcophaga bullata, and Protophormia terraenovae were examined using the immunoassay to determine if insect-derived stains could be distinguished from a range of human body fluid stains. A lift technique was developed which permitted transfer of fly artifacts from test materials to filter paper for dot blot analyses. All species readily deposited artifacts on all test household materials regardless of diet consumed. Despite differences in texture and porosity of the household materials, artifacts of all species transferred to the filter paper. With all fly species, anti-md3 serum bound to artifacts produced after feeding on semen, blood, feces, urine, and saliva. By contrast, anti-md3 serum did not react with any of the human fluids tested, nor with any of the lifts from household materials not exposed to flies. There was no evidence of false positives with any of the fly species tested, regardless of diet consumed. There was also no indication of false negatives with any of the dot blot assays. These observations suggest that immunoassays using anti-md3 serum performed on a simple lift of suspected fly artifacts can be used effectively as a confirmatory assay to distinguish fly regurgitate and fecal stains from human body fluids.
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http://dx.doi.org/10.1007/s00414-019-02159-1 | 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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