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5 papers
Recent·Most cited
F. Rose, M. Michaluk, T. Blindauer, B. M. Ignatowska-Jankowska, L. O'Shaughnessy, G. J. Stephens, T. D. Pereira, M. Y. Uusisaari, K. Bozek
DISK uses deep learning to impute missing skeleton data in animal behavior studies, improving reliability and enabling more accurate motion analysis.
bioRxiv · May 3, 2024Animal Behavior & Cognition
L. Butt, D. Unnersjoe-Jess, M. Hoehne, G. Sergei, A. Witasp, A. Wernerson, J. Patrakka, P. F. Hoyer, H. Blom, B. Schermer, K. Bozek, T. Benzing
This paper develops a deep learning method to segment and quantify podocyte foot process morphology in kidney tissue, confirming morphological changes in disease states.
bioRxiv · Jun 14, 2021 · PreprintPathology
D. M. T. Nguyen, M. L. Iuzzolino, A. Mankel, K. Bozek, G. J. Stephens, O. Peleg
The paper studies how honey bees use directional communication through pheromone flow to aggregate efficiently during swarming.
bioRxiv · May 23, 2020Animal Behavior & Cognition
K. Bozek, L. Hebert, A. S. Mikheyev, G. J. Stephens
The paper develops a method for tracking densely packed honeybees in 2D hives using pixel personality for object recognition, achieving 46% trajectory reconstruction in 5 min recordings.
bioRxiv · Feb 15, 2019 · PreprintAnimal Behavior & Cognition
K. Bozek, J. Rangel, J. Arora, M. Tin, E. Crotteau, G. Loper, J. Fewell, A. Mikheyev
The paper studies parallel genomic evolution in two wild honey bee populations responding to Varroa destructor, finding strong genetic correlations and selection on metabolic and developmental pathways.
bioRxiv · Dec 19, 2018 · PreprintEvolutionary Biology