Membranes,mitochondria and amoebae: shape models

Most real-world shapes and images are characterized by high variability- they are not rigid, like crystals, for example—but they are strongly structured. Therefore, a fundamental task in the understanding and analysis of such image ensembles is the construction of models that incorporate both variability and structure in a mathematically precise way. The global shape models introduced in Grenander's general pattern theory are intended to do this. In this paper, we describe the representation of two-dimensional mitochondria and membranes in electron microscope photographs, and three-dimensional amoebae in optical sectioning microscopy. There are three kinds of variability to all of these patterns, which these representations accommodate. The first is the variability in shape and viewing orientation. For this, the typical structure is represented via linear, circular and spherical templates, with the variability accomodated via the application of transformations applied to the templates. The transformations form groups: scale, rotation and translation. They are locally applied throughout the continuum and of high dimension. The second is the textural variability; the inside and outside of these basic shapes are subject to random variation, as well as sensor noise. For this, statistical sensor models and Markov random field texture models are used to connect the constituent structures of the shapes to the measured data. The third variability type is associated with the fact that each scene is made up of a variable number of shapes; this number is not assumed to be known a priori. Each scene has a variable number of parameters encoding the transformations of the templates appropriate for that scene. For this, a single posterior distribution is defined over the countable union of spaces representing models of varying numbers of shapes. Bayesian inference is performed via computation of the conditional expectation of the parametrically defined shapes under the posterior. These conditional mean estimates are generated using jump-diffusion processes. Results for membranes, mitochondria and amoebae are shown.     

Correlates of infant pointing frequency in the first year

This study examines the emergence of concurrent correlates of infant pointing frequency with the aim of contributing to its ontogenetic theories. We measured monthly from 8 to 12 months infants' (N = 56) index-finger pointing frequency along with several candidate correlates: (1) family socioeconomic status (SES), (2) mothers' pointing production, and (3) infants' point following to targets in front of and behind them. Results revealed that (1) infants increased their pointing frequency across age, but high-SES infants had a steeper increase, and a higher pointing frequency than low-SES infants from 10 months onward, (2) maternal pointing frequency was not associated with infant pointing frequency at any age, (3) infants' point following abilities to targets behind their visual fields was positively associated with their pointing frequency at 12 months, after pointing had already emerged around 10 months. Findings suggest that family SES impacts infants' pointing development more generally, not just through maternal pointing. The association between pointing and following points to targets behind, but not in front, suggests that a higher level of referential understanding emerges after, and perhaps through the production of pointing.