Tracking Membrane Protein Association in Model Membranes

Membrane proteins are essential in the exchange processes of cells. In spite of great breakthrough in soluble proteins studies, membrane proteins structures, functions and interactions are still a challenge because of the difficulties related to their hydrophobic properties. Most of the experiments are performed with detergent-solubilized membrane proteins. However widely used micellar systems are far from the biological two-dimensions membrane. The development of new biomimetic membrane systems is fundamental to tackle this issue

A flexible approach for modelling a proportion response variable: Loss given default

Loss given default (LGD) is a proportion of a credit exposure that is lost if the obligor defaults on a loan. Response variable LGD contains values between 0 and 1 including both 0 and 1, where 0 means that the balance is fully recovered while 1 means total loss of exposure at default. This article addresses two alternative semi parametric approaches for modelling loss given default, which is measured on the interval [0,1]. The class of models are very flexible and can accommodate skewness and bimodal characteristics of LGD data. The dependence of the predictors of each of the parameters (of the proposed model distribution for LGD) on explanatory variables can be additive P-splines, regression trees or neural network models. The proposed models are applied to a loss given default data set and compared with current popular models