Fluorescence recovery after photobleaching in material and life sciences: putting theory into practice.

Fluorescence recovery after photobleaching (FRAP) is a versatile tool for determining diffusion and interaction/binding properties in biological and material sciences. An understanding of the mechanisms controlling the diffusion requires a deep understanding of structure-interaction-diffusion relationships. In cell biology, for instance, this applies to the movement of proteins and lipids in the plasma membrane, cytoplasm and nucleus. In industrial applications related to pharmaceutics, foods, textiles, hygiene products and cosmetics, the diffusion of solutes and solvent molecules contributes strongly to the properties and functionality of the final product. All these systems are heterogeneous, and accurate quantification of the mass transport processes at the local level is therefore essential to the understanding of the properties of soft (bio)materials. FRAP is a commonly used fluorescence microscopy-based technique to determine local molecular transport at the micrometer scale. A brief high-intensity laser pulse is locally applied to the sample, causing substantial photobleaching of the fluorescent molecules within the illuminated area. This causes a local concentration gradient of fluorescent molecules, leading to diffusional influx of intact fluorophores from the local surroundings into the bleached area. Quantitative information on the molecular transport can be extracted from the time evolution of the fluorescence recovery in the bleached area using a suitable model. A multitude of FRAP models has been developed over the years, each based on specific assumptions. This makes it challenging for the non-specialist to decide which model is best suited for a particular application. Furthermore, there are many subtleties in performing accurate FRAP experiments. For these reasons, this review aims to provide an extensive tutorial covering the essential theoretical and practical aspects so as to enable accurate quantitative FRAP experiments for molecular transport measurements in soft (bio)materials.

Internal validation of near-crashes in naturalistic driving studies: a continuous and multivariate approach.

Large naturalistic driving studies give extremely detailed insight into how traffic accidents happen and what causes them. However, even in very large studies there are only relatively few crashes. Hence one additionally selects and studies crash surrogates, so called "near-crashes", i.e. situations when a crash almost happened. The selection procedures invariably entail severe risks of causing bias. In this paper we use extreme value statistics to develop two methods to study the extent and form of this bias. The methods are applied to a large naturalistic driving study, the 100-car study. Both methods identified a severe discrepancy between the rear-striking near-crashes and the rear-striking crashes. Perhaps surprisingly, one conclusion is that, for rear-striking and in this study, the crashes have little relevance for increasing traffic safety. We believe substantial efforts should be made to develop statistical methods for using near-crashes and crashes in future large naturalistic driving studies (such as the SHRP2 study).