Species distribution modelling leads to the discovery of new populations of one of the least known European snakes, Vipera ursinii graeca, in Albania

Vipera ursinii graeca is a restricted-range, endemic snake of the Pindos mountain range in the southwestern Balkans. The subspecies was previously reported from eight localities in Greece and one locality in southern Albania. We used species distribution modelling based on climate data from known localities in Greece to estimate the potential distribution of the subspecies. The model predicted suitable areas for eleven mountains in southern Albania, which we visited in ten field expeditions in four years. Based on 78 live individuals and 33 shed skins, we validated the presence of the snake on eight of the eleven mountains. Six populations (Dhëmbel, Llofiz, Griba, Shendelli, Tomorr and Trebeshinë Mountains) are reported here for the first time. Morphological characters undoubtedly supported that all individuals found at these new localities belong to V. u. graeca . Genetic analysis of mitochondrial DNA sequences also confirmed the identity of the snakes as V. u. graeca and a low number of identified haplotypes suggested low genetic variability among populations despite significant spatial isolation. All localities were subalpine-alpine calcareous meadows above 1600 m. These high montane habitats are separated by deep valleys and are threatened by overgrazing, soil erosion, and a potential increase in the elevation of the tree line due to climate change. Our surveys increased the number of known populations by 60% and the known geographical range of the subspecies by approximately 30%. Our study serves as a baseline for further ecological research and for conservation measures for one of the least known European viperid snakes

Automated deep learning segmentation of high-resolution 7 T postmortem MRI for quantitative analysis of structure-pathology correlations in neurodegenerative diseases

Postmortem MRI allows brain anatomy to be examined at high resolution and to link pathology measures with morphometric measurements. However, automated segmentation methods for brain mapping in postmortem MRI are not well developed, primarily due to limited availability of labeled datasets, and heterogeneity in scanner hardware and acquisition protocols. In this work, we present a high resolution of 135 postmortem human brain tissue specimens imaged at 0.3 mm$^{3}$ isotropic using a T2w sequence on a 7T whole-body MRI scanner. We developed a deep learning pipeline to segment the cortical mantle by benchmarking the performance of nine deep neural architectures, followed by post-hoc topological correction. We then segment four subcortical structures (caudate, putamen, globus pallidus, and thalamus), white matter hyperintensities, and the normal appearing white matter. We show generalizing capabilities across whole brain hemispheres in different specimens, and also on unseen images acquired at 0.28 mm^3 and 0.16 mm^3 isotropic T2*w FLASH sequence at 7T. We then compute localized cortical thickness and volumetric measurements across key regions, and link them with semi-quantitative neuropathological ratings. Our code, Jupyter notebooks, and the containerized executables are publicly available at: https://pulkit-khandelwal.github.io/exvivo-brain-upennComment: Preprint submitted to NeuroImage Project website: https://pulkit-khandelwal.github.io/exvivo-brain-upen

Banking sector development in Central and Eastern Europe

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