Diffusion of a protein in configuration space

Simulations of biomolecular dynamics are commonly interpreted in terms of harmonic or quasi-harmonic models for the dynamics of the system. These models assume that biomolecules exhibit oscillations around a single energy minimum. However, spectroscopic data on myoglobin suggest that proteins sample multiple minima. Transitions between minima reveal a broad distribution of energy barriers. This behavior has been observed in other biomolecular systems. To elucidate the nature of protein dynamics the authors have studied a 1.2ns molecular dynamics trajectory of crambin in aqueous solution. This trajectory samples multiple local energy minima. Transitions between minima involve collective motions of amino acids over long distances. The authors show that nonlinear motions are responsible for most of the atomic fluctuations of the protein. These atomic fluctuations are not well described by large motions of individual atoms or a small group of atoms, but rather by concerted motions of many atoms. These nonlinear motions describe transitions between different basins of attraction. The signature of these motions manifests in local and global structural variables. A method for extracting Molecule Optimal Dynamic Coordinates (MODC) is presented

Bankruptcy prediction in Norway: a comparison study

In this article we develop statistical models for bankruptcy prediction of Norwegian firms in the limited liability sector using annual balance sheet information. We fit generalized linear, generalized linear mixed and Generalized Additive Models (GAM) in a discrete hazard setting. It is demonstrated that careful examination of the functional relationship between the explanatory variables and the probability of bankruptcy enhances the models' forecasting performance. Using information on the industry sector we model the unobserved heterogeneity between different sectors through an industry-specific random factor in the generalized linear mixed model. The models developed are shown to outperform the model with Altman's variables.