The Old World screwworm fly (OWSF), Chrysomya bezziana (Diptera: Calliphoridae), is an important agent of traumatic myiasis and, as such, a major human and animal health problem. In the implementation of OWSF control operations, it is important to determine the geographical origins of such disease-causing species in order to establish whether they derive from endemic or invading populations. Gross morphological and molecular studies have demonstrated the existence of two distinct lineages of this species, one African and the other Asian. Wing morphometry is known to be of substantial assistance in identifying the geographical origin of individuals because it provides diagnostic markers that complement molecular diagnostics. However, placement of the landmarks used in traditional geometric morphometric analysis can be time-consuming and subject to error caused by operator subjectivity. Here we report results of an image-based approach to geometric morphometric analysis for delivering wing-based identifications. Our results indicate that this approach can produce identifications that are practically indistinguishable from more traditional landmark-based results. In addition, we demonstrate that the direct analysis of digital wing images can be used to discriminate between three Chrysomya species of veterinary and forensic importance and between C. bezziana genders.
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http://dx.doi.org/10.1111/mve.12302 | DOI Listing |
J Craniofac Surg
November 2024
Department of Anatomy, Cukurova University Faculty of Medicine.
The present paper was designed to analyze the dimensions of such important bony structures and surgical landmarks, which are used by many clinicians in many surgical interventions, in dry skull, cadaver, and healthy subjects on computed tomography (CT) images, and to determine whether there is a significant difference between these methods, and to obtain reference values from 3 different methods. Eight cadavers and 16 dry skulls and 100 three-dimensional (3D) CT images were studied. Necessary permissions for the study were obtained from Ethics Comittee.
View Article and Find Full Text PDFAdv Mater
January 2025
Department of Mechanical and Aerospace Engineering, Program of Materials Science and Engineering, University of California San Diego, 9500 Gilman Drive, La Jolla, CA, 92093, USA.
Changes in the density and organization of fibrous biological tissues often accompany the progression of serious diseases ranging from fibrosis to neurodegenerative diseases, heart disease and cancer. However, challenges in cost, complexity, or precision faced by existing imaging methodologies and materials pose barriers to elucidating the role of tissue microstructure in disease. Here, we leverage the intrinsic optical anisotropy of the Morpho butterfly wing and introduce Morpho-Enhanced Polarized Light Microscopy (MorE-PoL), a stain- and contact-free imaging platform that enhances and quantifies the birefringent material properties of fibrous biological tissues.
View Article and Find Full Text PDFBiol Lett
January 2025
Department of Agricultural and Environmental Biology, The University of Tokyo, Tokyo, Japan.
Butterfly wing patterns exhibit notable differences between the dorsal and ventral surfaces, and morphological analyses of them have provided insights into the ecological and behavioural characteristics of wing patterns. Conventional methods for dorsoventral comparisons are constrained by the need for homologous patches or shared features between two surfaces, limiting their applicability across species. We used a convolutional neural network (CNN)-based analysis, which can compare images of the two surfaces without focusing on homologous patches or features, to detect dorsoventral bias in two types of intraspecific variation: sexual dimorphism and mimetic polymorphism.
View Article and Find Full Text PDFRadiology
January 2025
From the School of Biomedical Engineering and Imaging Sciences, King's College London, St. Thomas' Hospital, Westminster Bridge Road, Lambeth Wing, 3rd Fl, London SE1 7EH, UK.
Introduction: A chest X-ray (CXR) is the most common imaging investigation performed worldwide. Advances in machine learning and computer vision technologies have led to the development of several artificial intelligence (AI) tools to detect abnormalities on CXRs, which may expand diagnostic support to a wider field of health professionals. There is a paucity of evidence on the impact of AI algorithms in assisting healthcare professionals (other than radiologists) who regularly review CXR images in their daily practice.
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