40 research outputs found
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 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