Temporal neural networks for downscaling climate variability and extremes.

This paper presents an application of temporal neural networks for downscaling global climate models (GCMs) output. Because of computational constraints, GCMs are usually run at coarse grid resolution (in the order of 100s of kilometres) and as a result they are inherently unable to present local sub-grid scale features and dynamics. Consequently, outputs from these models cannot be used directly in many climate change impact studies.

Artificial neural network modeling of apoptosis in gamma irradiated human lymphocytes.

Purpose: To develop an artificial neural network (ANN) model of apoptotic response in gamma irradiated human lymphocytes. To assess the feasibility of training ANN radiobiological models using data collected with flow cytometry.

Materials And Methods: Irradiated isolated human lymphocytes were labelled with Annexin V-Fluorescein Isothiocyanate (FITC) and 7-Amino-Actinomycin D (7AAD) then analysed using flow cytometry.