SUMMARYBats are one of the most diverse groups of mammals and have radiated into a wide variety of trophic niches. Accordingly, the cranial structure in bats is unusually variable among mammals and thought to reflect specializations for feeding and echolocation. However, recent analyses of cranial structure, feeding behavior and bite force across a wide range of bats suggest that correlations between morphology and performance and/or ecology are not as clearcut as previously thought. For example, most of the variation in bite force across a wide range of phyllostomid bats was explained by differences in body size rather than specific cranial traits. However, remarkably little is known about the muscular components that are responsible for generating the actual bite forces. We have tested which aspects of the cranial muscular system are good predictors of bite force across a wide range of species using a modeling approach. Model calculations of bite force show good correspondence with in vivo data suggesting that they can be used to estimate performance of the cranial system. Moreover, our data show that bite force is strikingly well explained by differences in temporalis muscle mass, temporalis fiber length and masseter muscle mass. Moreover, our data show that evolutionary changes in bite force capacity in bats are associated with evolutionary changes in relative m. temporalis mass and absolute skull length.

Aims:  To evaluate the electronic nose (EN) as method for the identification of ten clinically important micro‐organisms. Methods and Results:  A commercial EN system with a series of ten metal oxide sensors was used to characterize the headspace of the cultured organisms. The measurement procedure was optimized to obtain reproducible results. Artificial neural networks (ANNs) and a k‐nearest neighbour (k‐NN) algorithm in combination with a feature selection technique were used as pattern recognition tools. Hundred percent correct identification can be achieved by EN technology, provided that sufficient attention is paid to data handling. Conclusions:  Even for a set containing a number of closely related species in addition to four unrelated organisms, an EN is capable of 100% correct identification. Significance and Impact of the Study:  The time between isolation and identification of the sample can be dramatically reduced to 17 h.