Search bioRxivSearch

Biology subjects

Hammerschmidt, K.

Publications and source records attributed to Hammerschmidt, K..

2 recordsLinked to original sources

Meta-population structure and the evolutionary transition to multicellularity

AO_SCPLOWBSTRACTC_SCPLOWThe evolutionary transition to multicellularity has occurred on numerous occasions, but transitions to complex life forms are rare. While the reasons are unclear, relevant factors include the intensity of within-versus between-group selection that are likely to have shaped the course of life cycle evolution. A highly structured environment eliminates the possibility of mixing between evolving lineages, thus ensuring strong competition between groups. Less structure intensifies competition within groups, decreasing opportunity for group-level evolution. Here, using populations of the bacterium Pseudomonas fluorescens, we report the results of experiments that explore the effect of lineage mixing on the evolution of nascent multicellular groups. Groups were propagated under regimes requiring reproduction via a life cycle replete with developmental and dispersal (propagule) phases, but in one treatment lineages never mixed, whereas in a second treatment, cells from different lineages experienced intense competition during the dispersal phase. The latter treatment favoured traits promoting cell growth at the expense of traits underlying group fitness - a finding that is supported by results from a mathematical model. Together our results show that the transition to multicellularity benefits from ecological conditions that maintain discreteness not just of the group (soma) phase, but also of the dispersal (germline) phase.

evolutionary biology

Classification of mouse ultrasonic vocalizations using deep learning

Vocalizations are a widespread means of communication in the animal kingdom. Mice use a large repertoire of ultrasonic vocalizations (USVs) in different social contexts, for instance courtship, territorial dispute, dominance and mother-pup interaction. Previous studies have pointed to differences in the USVs in different context, sexes, strains and individuals, however, in many cases the outcomes of the analyses remained inconclusive.\n\nWe here provide a more general approach to automatically classify USVs using deep neural networks (DNN). We classified the sex of the emitting mouse (C57Bl/6) based on the vocalizations spectrogram, reaching unprecedented performance (~84% correct) in comparison with other techniques (Support Vector Machines: 64%, Ridge regression: 52%). Vocalization characteristics of individual mice only contribute mildly, and sex-only classification reaches ~78%. The performance can only partially be explained by a set of classical shape features, with duration, volume and bandwidth being the most useful predictors. Splitting estimation into two DNNs, from spectrograms to features (57-82%) and features to sex (67%) does not reach the single-step performance.\n\nIn summary, the emitters sex can be successfully predicted from their spectrograms using DNNs, excelling over other classification techniques. In contrast to previous research, this suggests that male and female vocalizations differ in their spectrotemporal structure, recognizable even in single vocalizations.

bioinformatics