The tail plays a major role in the differing manoeuvrability of two sibling species of mouse-eared bats (Myotis myotis and Myotis blythii)

Two sympatrically occurring bat species, the greater mouse-eared bat (Myotis myotis (Borkhausen, 1797)) and the lesser mouse-eared bat (Myotis blythii (Tomes, 1857)) (Chiroptera, Vespertillionidae), share numerous similarities in morphology, roosting behaviour, and echolocation and are often difficult to distinguish. However, despite these similarities, their foraging behaviour is noticeably different. Our aim was to examine the extent to which these different foraging strategies reflect morphological adaptation. We assessed whether the morphology of the wing, body, and tail differed between M. myotis and M. blythii. In addition, in a laboratory experiment involving an obstacle course, we compared differences in manoeuvrability by relating them to our morphological measurements. The two species differed in their overall size, wing-tip shape, and tail-to-body length ratio. The generally smaller sized M. blythii performed better in the obstacle course and was therefore considered to be more manoeuvrable. Although differences in wing-tip shape were observed, we found the most important characteristic affecting manoeuvrability in both species to be the tail-to-body length ratio. Additionally, when we compared two bats with injured wing membranes with unharmed bats of the same species, we found no difference in manoeuvrability, even when the wing shape was asymmetric. We therefore postulate that morphometric differences between the two species in their overall size and, more importantly, in their tail-to-body length ratio are the main physical characteristics providing proof of adaptation to different foraging and feeding strategies.

Mandibula wing osteotomy for correction of the mandibular plane: A case report

We report a new technique for vertical enlargement of the inferior border of the mandible.

Statistical shape modeling of pathological scoliotic vertebrae: A comparative analysis

Statistical shape models (SSMs) have been used widely as a basis for segmenting and interpreting complex anatomical structures. The robustness of these models are sensitive to the registration procedures, i.e., establishment of a dense correspondence across a training data set. In this work, two SSMs based on the same training data set of scoliotic vertebrae, and registration procedures were compared. The first model was constructed based on the original binary masks without applying any image pre- and post-processing, and the second was obtained by means of a feature preserving smoothing method applied to the original training data set, followed by a standard rasterization algorithm. The accuracies of the correspondences were assessed quantitatively by means of the maximum of the mean minimum distance (MMMD) and Hausdorf distance (H(D)). Anatomical validity of the models were quantified by means of three different criteria, i.e., compactness, specificity, and model generalization ability. The objective of this study was to compare quasi-identical models based on standard metrics. Preliminary results suggest that the MMMD distance and eigenvalues are not sensitive metrics for evaluating the performance and robustness of SSMs.

Validation of statistical shape model based reconstruction of the proximal femur - A morphology study

Seventeen bones (sixteen cadaveric bones and one plastic bone) were used to validate a method for reconstructing a surface model of the proximal femur from 2D X-ray radiographs and a statistical shape model that was constructed from thirty training surface models. Unlike previously introduced validation studies, where surface-based distance errors were used to evaluate the reconstruction accuracy, here we propose to use errors measured based on clinically relevant morphometric parameters. For this purpose, a program was developed to robustly extract those morphometric parameters from the thirty training surface models (training population), from the seventeen surface models reconstructed from X-ray radiographs, and from the seventeen ground truth surface models obtained either by a CT-scan reconstruction method or by a laser-scan reconstruction method. A statistical analysis was then performed to classify the seventeen test bones into two categories: normal cases and outliers. This classification step depends on the measured parameters of the particular test bone. In case all parameters of a test bone were covered by the training population's parameter ranges, this bone is classified as normal bone, otherwise as outlier bone. Our experimental results showed that statistically there was no significant difference between the morphometric parameters extracted from the reconstructed surface models of the normal cases and those extracted from the reconstructed surface models of the outliers. Therefore, our statistical shape model based reconstruction technique can be used to reconstruct not only the surface model of a normal bone but also that of an outlier bone.