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Hernandez-Gutierrez, E.

Publications and source records attributed to Hernandez-Gutierrez, E..

2 recordsLinked to original sources

End-to-end evaluation of white matter microstructure of the visual pathway in asymmetric glaucoma

Diffusion magnetic resonance imaging is a non-invasive neuroimaging technique that enables in vivo evaluation of white matter microstructure, providing sensitivity to tissue abnormalities caused by disease. Glaucoma, the second leading cause of blindness worldwide, is characterized by progressive loss of retinal ganglion cells and axonal damage in the optic nerve, leading to degeneration along the entire visual pathway. This degeneration includes secondary effects on fiber crossings within the optic chiasm, which are challenging to characterize with conventional diffusion models. In this study, we evaluated 31 patients with asymmetric glaucoma and 31 healthy controls using advanced diffusion magnetic resonance imaging methods, including Diffusion Tensor Imaging, Constrained Spherical Deconvolution, multi-tensor fit via Multi-Resolution Discrete Search method, and Fixel-Based Analysis. We found significant differences of diffusion metrics in white matter tracts of the visual system, including the optic nerve, optic chiasm, optic tracts, and optic radiations. Moreover, diffusion metrics correlated with clinical ophthalmological parameters such as cup-to-disc ratio, visual field mean deviation, and retinal nerve fiber layer thickness. These findings support the use of advanced diffusion magnetic resonance imaging models as sensitive tools for detecting Wallerian degeneration and resolving complex white matter architecture in the human visual pathway, and demonstrate their utility to study other fiber-crossing regions throughout the brain.

neuroscience↗

Combined phylogenetic and geographic data can predict plant-pest interactions with high accuracy

O_LINon-native plant pests can pose major threats to biodiversity, with destructive ecological and economic consequences. The ability to predict future threats would allow limited resources to be concentrated on managing the most serious risks. C_LIO_LIWe build a Bayesian model to predict hosts at risk from Agrilus, a beetle genus of over 3,000 species including one of the worlds worst tree pests, using phylogenetic and geographic relationships between known and potential hosts. C_LIO_LIWe assess risk to Quercus (oak), their most common host, by predicting the probability of over 7,000 possible oak-Agrilus interactions to identify species at risk and inform future prevention efforts. Our model detects known hosts with 83.6% accuracy under Leave-One-Out cross-validation, and successfully classifies novel hosts of Agrilus species in new areas, indicating strong predictive performance on independent or misclassified data. Geographic proximity is a strong predictor of host sharing, with likelihood declining rapidly with distance. In general, hosts cluster phylogenetically, with a tendency for closely related oaks to share the same Agrilus species. C_LIO_LIOur approach uses readily available data and could be implemented to assess Agrilus interactions with other plant genera, and extended to additional host-pest systems to help prioritise counter measures against threats worldwide. C_LI

ecology↗