arXiv subjects
Publications and source records attributed to Stefano Iotti.
The purpose of this paper is to elucidate the theory and mathematical modelling behind the sheaf neural network (SNN) algorithm and then show how SNN can effectively answer to biomedical questions in a concrete case study and outperform the most popular graph neural networks (GNNs) as graph convolutional networks (GCNs), graph attention networks (GAT) and GraphSage.
Magnesium is the most abundant intracellular divalent cation. We present an innovative experimental approach to localizing intracellular magnesium that combines elemental and morphological information from individual cells with high-resolution spatial information. Integration of information from scanning fluorescence X-ray microscopy with information from atomic force microscopy was used to generate a magnesium concentration map and to determine the X-ray linear absorption coefficient map within a whole dehydrated mammary epithelial cell.