Green fluorescent protein (GFP) and GFP-like fluorescent proteins owe their photophysical properties to an autocatalytically formed intrinsic chromophore. According to quantum mechanical calculations, the excited state of chromophore model systems has significant dihedral freedom, which may lead to fluorescence quenching intersystem crossing. Molecular dynamics simulations with freely rotating chromophoric dihedrals were performed on green, yellow, and blue fluorescent proteins in order to model the dihedral freedom available to the chromophore in the excited state. Most current theories suggest that a restriction in the rotational freedom of the fluorescent protein chromophore will lead to an increase in fluorescence brightness and/or quantum yield. According to our calculations, the dihedral freedom of the systems studied (BFP > A5 > YFP > GFP) increases in the inverse order to the quantum yield. In all simulations, the chromophore undergoes a negatively correlated hula twist (also known as a bottom hula twist mechanism).
Discrimination of time series is an important practical problem with applications in various scientific fields. We propose and study a novel approach to this problem. Our approach is applicable to cases where time series in different categories have a different "shape." Although based on the idea of feature extraction, our method is not distance-based, and as such does not require aligning the time series. Instead, features are measured for each time series, and discrimination is based on these individual measures. An AR process with a time-varying variance is used as an underlying model. Our method then uses shape measures or, better, measures of concentration of the variance function, as a criterion for discrimination. It is this concentration aspect or shape aspect that makes the approach intuitively appealing. We provide some mathematical justification for our proposed methodology, as well as a simulation study and an application to the problem of discriminating earthquakes and explosions.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.