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Biology subjects

Wagner, T.

Publications and source records attributed to Wagner, T..

38 records · Page 3Linked to original sources

Large body size variation is linked to low communication success in tandem running ants

Diversity in animal groups is often assumed to increase group performance. In insect colonies, genetic, behavioral and morphological variation among workers can improve colony functioning and resilience. However, it has been hypothesized that during communication processes, differences between workers, e.g. in body size, could also have negative effects. Tandem running is a common recruitment strategy in ants and allows a leader to guide a nestmate follower to resources. A substantial proportion of tandem runs fail because leader and follower loose contact. Using the ant Temnothorax nylanderi as a model system, we tested the hypothesis that tandem running success is impaired if leader and follower differ in size. Indeed, we found that the success rate of tandem pairs drops considerably as size variation increases: only ~7% of tandem runs were successful when the leader-follower size difference exceeded 10%, whereas 80% of tandem runs were successful when ants differed less than 5% in body length. One possible explanation is that ant size is linked to the preferred walking speed. Ants did not choose partners of similar size, but extranidal workers were larger than intranidal workers, which could reduce recruitment mistakes because it reduced the chance that very large and very small ants perform tandem runs together. Our results suggest that phenotypic differences between interacting workers can have negative effects on the efficiency of communication processes. Whether phenotypic variation has positive or negative effects is likely to depend on the task and the phenotypic trait that shows variation.

animal behavior and cognition↗

SPHIRE-crYOLO: A fast and well-centering automated particle picker for cryo-EM

Selecting particles from digital micrographs is an essential step in single particle electron cryomicroscopy (cryo-EM). Since manual selection is a tedious and time-consuming process, many automatic particle pickers have been developed. However, they have problems especially with non-ideal datasets. Here, we present a novel automated particle picking software called crYOLO, which is based on the deep learning object detection system \"You Only Look Once\" (YOLO). After training the network with 500 - 2,500 particles per dataset, it automatically selects the particles with high accuracy and precision reaching a speed of up to six micrographs per second. Importantly, we show that crYOLO can be trained to select previously unseen datasets paving the way for completely automated \"on-the-fly\" cryo-EM data pre-processing during data acquisition. CrYOLO is available as standalone program under http://sphire.mpg.de/ and will be part of the image processing workflow in SPHIRE.

biophysics↗