The use of nanoparticles in medicine is ever increasing, and it is important to understand their targeted and non-targeted effects. We have previously shown that nanoparticles can cause DNA damage to cells cultured below a cellular barrier without crossing this barrier. Here, we show that this indirect DNA damage depends on the thickness of the cellular barrier, and it is mediated by signalling through gap junction proteins following the generation of mitochondrial free radicals. Indirect damage was seen across both trophoblast and corneal barriers. Signalling, including cytokine release, occurred only across bilayer and multilayer barriers, but not across monolayer barriers. Indirect toxicity was also observed in mice and using ex vivo explants of the human placenta. If the importance of barrier thickness in signalling is a general feature for all types of barriers, our results may offer a principle with which to limit the adverse effects of nanoparticle exposure and offer new therapeutic approaches.

Economic optimization of photovoltaic water pumping systems for irrigation.Energy Conversion and Management, http://dx.doi.org/10.1016/j.enconman. 2015.01.066 Access to the published version may require subscription. N.B. When citing this work, cite the original published paper.

Abstract. Mesoscale numerical weather prediction (NWP) models are gaining more attention in providing highresolution rainfall forecasts at the catchment scale for realtime flood forecasting. The model accuracy is however negatively affected by the "spin-up" effect and errors in the initial and lateral boundary conditions. Synoptic studies in the meteorological area have shown that the assimilation of operational observations, especially the weather radar data, can improve the reliability of the rainfall forecasts from the NWP models. This study aims at investigating the potential of radar data assimilation in improving the NWP rainfall forecasts that have direct benefits for hydrological applications. The Weather Research and Forecasting (WRF) model is adopted to generate 10 km rainfall forecasts for a 24 h storm event in the Brue catchment (135.2 km 2 ) located in southwest England. Radar reflectivity from the lowest scan elevation of a C-band weather radar is assimilated by using the three-dimensional variational (3D-Var) dataassimilation technique. Considering the unsatisfactory quality of radar data compared to the rain gauge observations, the radar data are assimilated in both the original form and an improved form based on a real-time correction ratio developed according to the rain gauge observations. Traditional meteorological observations including the surface and upper-air measurements of pressure, temperature, humidity and wind speed are also assimilated as a bench mark to better evaluate and test the potential of radar data assimilation. Four modes of data assimilation are thus carried out on different types/combinations of observations: (1) traditional meteorological data; (2) radar reflectivity; (3) corrected radar reflectivity; (4) a combination of the original reflectivity and meteorological data; and (5) a combination of the corrected reflectivity and meteorological data. The WRF rainfall forecasts before and after different modes of data assimilation are evaluated by examining the rainfall temporal variations and total amounts which have direct impacts on rainfall-runoff transformation in hydrological applications. It is found that by solely assimilating radar data, the improvement of rainfall forecasts are not as obvious as assimilating meteorological data; whereas the positive effect of radar data can be seen when combined with the traditional meteorological data, which leads to the best rainfall forecasts among the five modes. To further improve the effect of radar data assimilation, limitations of the radar correction ratio developed in this study are discussed and suggestions are made on more efficient utilisation of radar data in NWP data assimilation.