07/2019 - Content-aware image restoration for electron microscopy
Multiple approaches to use deep neural networks for image restoration have recently been proposed. Training such networks requires well registered pairs of high and low-quality images. While this is easily achievable for many imaging modalities, e.g., fluorescence light microscopy, for others it is not. Here we summarize on a number of recent developments in the […]
07/2019 - Resolving genetic heterogeneity in cancer
To a large extent, cancer conforms to evolutionary rules defined by the rates at which clones mutate, adapt and grow. Next-generation sequencing has provided a snapshot of the genetic landscape of most cancer types, and cancer genomics approaches are driving new insights into cancer evolutionary patterns in time and space. In contrast to species evolution, […]
06/2019 - Better to stay apart: asset commonality, bipartite network centrality, and investment strategies
By exploiting a bipartite network representation of the relationships between mutual funds and portfolio holdings, we propose an indicator that we derive from the analysis of the network, labelled the Average Commonality Coefficient (ACC), which measures how frequently the assets in the fund portfolio are present in the portfolios of the other funds of the […]
06/2019 - Yeast membraneless compartments revealed by correlative light microscopy and electron tomography
Yeast essential enzymes are able to assemble and form membrane-less compartments in the cytoplasm during stress conditions (Narayanaswamy et al., 2009). These microcompartments form rapidly under ATP-depletion upon cellular regulation of pH and molecular crowding (Munder et al., 2016). So far, the behavior of most of these enzymes has been characterized by live imaging using fluorescence […]