A physico-mechanical model of postnatal craniofacial growth in human.

Our fundamental understanding of the physico-mechanical forces that drive the size and shape changes of the cranium during ontogeny are limited. Biomechanical models based on finite element method present a huge opportunity to address this critical gap in our knowledge. Here, we describe a validated computational framework to predict normal craniofacial growth.

Exploring motion using geometric morphometrics in microscopic aquatic invertebrates: 'modes' and movement patterns during feeding in a bdelloid rotifer model species.

Background: Movement is a defining aspect of animals, but it is rarely studied using quantitative methods in microscopic invertebrates. Bdelloid rotifers are a cosmopolitan class of aquatic invertebrates of great scientific interest because of their ability to survive in very harsh environment and also because they represent a rare example of an ancient lineage that only includes asexually reproducing species. In this class, Adineta ricciae has become a model species as it is unusually easy to culture.

Normal human craniofacial growth and development from 0 to 4 years.

Knowledge of human craniofacial growth (increase in size) and development (change in shape) is important in the clinical treatment of a range of conditions that affects it. This study uses an extensive collection of clinical CT scans to investigate craniofacial growth and development over the first 48 months of life, detail how the cranium changes in form (size and shape) in each sex and how these changes are associated with the growth and development of various soft tissues such as the brain, eyes and tongue and the expansion of the nasal cavity. This is achieved through multivariate analyses of cranial form based on 3D landmarks and semi-landmarks and by analyses of linear dimensions, and cranial volumes.

A Comparison of Semilandmarking Approaches in the Analysis of Size and Shape.

Often, few landmarks can be reliably identified in analyses of form variation and covariation. Thus, 'semilandmarking' algorithms have increasingly been applied to surfaces and curves. However, the locations of semilandmarks depend on the investigator's choice of algorithm and their density.