The structural effect of Methyl substitution on the binding of Polypyridyl Ru-dppz Complexes to DNA

ABSTRACT: Polypyridyl ruthenium complexes have been intensively studied and possess photophysical properties which are both interesting and useful. They can act as probes for DNA, with a substantial enhancement in emission when bound, and can induce DNA damage upon photoirradiation and therefore, the synthesis and characterization of DNA binding of new complexes is an area of intense research activity. Whilst knowledge of how the binding of derivatives compares to the parent compound is highly desirable, this information can be difficult to obtain. Here we report the synthesis of three new methylated complexes, [Ru(TAP)2(dppz-10-Me).2Cl, [Ru(TAP)2(dppz-10,12-Me2)].2Cl and [Ru(TAP)2(dppz-11-Me)].2Cl, and examine the consequences for DNA binding through the use of atomic resolution X-ray crystallography. We find that the methyl groups are located in discrete positions with a complete directional preference. This may help to explain the quenching behavior which is found in solution for analogous [Ru(phen)2(dppz)]2+ derivatives.

Reducing noise associated with the Monte Carlo Independent Column Approximation for weather forecasting models

The Monte Carlo Independent Column Approximation (McICA) is a flexible method for representing subgrid-scale cloud inhomogeneity in radiative transfer schemes. It does, however, introduce conditional random errors but these have been shown to have little effect on climate simulations, where spatial and temporal scales of interest are large enough for effects of noise to be averaged out. This article considers the effect of McICA noise on a numerical weather prediction (NWP) model, where the time and spatial scales of interest are much closer to those at which the errors manifest themselves; this, as we show, means that noise is more significant. We suggest methods for efficiently reducing the magnitude of McICA noise and test these methods in a global NWP version of the UK Met Office Unified Model (MetUM). The resultant errors are put into context by comparison with errors due to the widely used assumption of maximum-random-overlap of plane-parallel homogeneous cloud. For a simple implementation of the McICA scheme, forecasts of near-surface temperature are found to be worse than those obtained using the plane-parallel, maximum-random-overlap representation of clouds. However, by applying the methods suggested in this article, we can reduce noise enough to give forecasts of near-surface temperature that are an improvement on the plane-parallel maximum-random-overlap forecasts. We conclude that the McICA scheme can be used to improve the representation of clouds in NWP models, with the provision that the associated noise is sufficiently small.