Population of nonnative states of lysozyme variants drives amyloid fibril formation.

The propensity of protein molecules to self-assemble into highly ordered, fibrillar aggregates lies at the heart of understanding many disorders ranging from Alzheimer's disease to systemic lysozyme amyloidosis. In this paper we use highly accurate kinetic measurements of amyloid fibril growth in combination with spectroscopic tools to quantify the effect of modifications in solution conditions and in the amino acid sequence of human lysozyme on its propensity to form amyloid fibrils under acidic conditions. We elucidate and quantify the correlation between the rate of amyloid growth and the population of nonnative states, and we show that changes in amyloidogenicity are almost entirely due to alterations in the stability of the native state, while other regions of the global free-energy surface remain largely unmodified. These results provide insight into the complex dynamics of a macromolecule on a multidimensional energy landscape and point the way for a better understanding of amyloid diseases.

A high-resolution atlas and statistical model of the human heart from multislice CT

Atlases and statistical models play important roles in the personalization and simulation of cardiac physiology. For the study of the heart, however, the construction of comprehensive atlases and spatio-temporal models is faced with a number of challenges, in particular the need to handle large and highly variable image datasets, the multi-region nature of the heart, and the presence of complex as well as small cardiovascular structures. In this paper, we present a detailed atlas and spatio-temporal statistical model of the human heart based on a large population of 3D+time multi-slice computed tomography sequences, and the framework for its construction. It uses spatial normalization based on nonrigid image registration to synthesize a population mean image and establish the spatial relationships between the mean and the subjects in the population. Temporal image registration is then applied to resolve each subject-specific cardiac motion and the resulting transformations are used to warp a surface mesh representation of the atlas to fit the images of the remaining cardiac phases in each subject. Subsequently, we demonstrate the construction of a spatio-temporal statistical model of shape such that the inter-subject and dynamic sources of variation are suitably separated. The framework is applied to a 3D+time data set of 138 subjects. The data is drawn from a variety of pathologies, which benefits its generalization to new subjects and physiological studies. The obtained level of detail and the extendability of the atlas present an advantage over most cardiac models published previously. © 1982-2012 IEEE.

A neurophysiologically plausible population code model for feature integration explains visual crowding.

An object in the peripheral visual field is more difficult to recognize when surrounded by other objects. This phenomenon is called "crowding". Crowding places a fundamental constraint on human vision that limits performance on numerous tasks. It has been suggested that crowding results from spatial feature integration necessary for object recognition. However, in the absence of convincing models, this theory has remained controversial. Here, we present a quantitative and physiologically plausible model for spatial integration of orientation signals, based on the principles of population coding. Using simulations, we demonstrate that this model coherently accounts for fundamental properties of crowding, including critical spacing, "compulsory averaging", and a foveal-peripheral anisotropy. Moreover, we show that the model predicts increased responses to correlated visual stimuli. Altogether, these results suggest that crowding has little immediate bearing on object recognition but is a by-product of a general, elementary integration mechanism in early vision aimed at improving signal quality.