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Bodensteiner, B.

Publications and source records attributed to Bodensteiner, B..

2 recordsLinked to original sources

Universal phylogenetic inertia in body temperature evolution across endothermic and ectothermic tetrapods.

Species must adapt to persist in a changing world. As global temperatures rise, how species adapt and respond to thermal shifts is crucial for anticipating global patterns of biodiversity change. Land vertebrates can be divided into two major thermoregulatory strategies, endothermy and ectothermy. One might hypothesize that, given their reputation as being "cold blooded," ectotherms are thermal generalists, capable of operating across a greater range of body temperatures than endotherms and exhibit greater plasticity and evolvability in body temperature. However, a wide variety of traits and ecologies could modulate responses of thermal physiology to environmental change. Here, we employ macroevolutionary models to estimate the rate of adaptation of thermal physiology across squamates, mammals, and birds in the context of their ecology, physiology, and changing climatic conditions and whether there are fundamental differences in how the three clades respond to their environments. We find stronger relationships between squamates body temperature and their environment than in birds and mammals, significant effects of diel activity (nocturnal and diurnal) on body temperature evolution in all clades, and no effect of aquatic/terrestrial habits and rumination on the evolution of body temperature in mammals. Most surprisingly, our findings suggest shared limits on the evolution of thermal physiology across ectothermic and endothermic groups that argue for universal constraints on the rate of evolution in thermal physiology while explaining disparate patterns of body temperature and niche evolution across groups.

evolutionary biology↗

CelFDrive: Artificial Intelligence assisted microscopy for automated detection of rare events

11.1 SummaryCelFDrive automates high-resolution 3D imaging cells of interest across a variety of fluorescence microscopes, integrating deep learning cell classification from auxiliary low resolution widefield images. CelFDrive enables efficient detection of rare events in large cell populations, such as the onset of cell division, and subsequent rapid switching to 3D imaging modes, increasing the speed for finding cells of interest by an order of magnitude. 1.2 Availability and ImplementationCelFDrive is available freely for academic purposes at the CelFDrive GitHub repository. and can be installed on Windows, macOS or Linux-based machines with relevant conda environments [1]. To interact with microscopy hardware requires additional software; we use SlideBook software from Intelligent Imaging Innovations (3i), but CelFDrive can be deployed with any microscope control software that can interact with a Python environment. Graphical Processing Units (GPUs) are recommended to increase the speed of application but are not required. On 3i systems the software can be deployed with a range of microscopes including their Lattice LightSheet microscope (LLSM) and spinning disk confocal (SDC). 1.3 Contacts.brooks.2@warwick.ac.uk

bioengineering↗