The Evolution of Distributed Systems for Graph Neural Networks and their Origin in Graph Processing and Deep Learning: A Survey

Graph Neural Networks (GNNs) are an emerging research field. This specialized Deep Neural Network (DNN) architecture is capable of processing graph structured data and bridges the gap between graph processing and Deep Learning (DL). As graphs are everywhere, GNNs can be applied to various domains including recommendation systems, computer vision, natural language processing, biology and chemistry. With the rapid growing size of real world graphs, the need for efficient and scalable GNN training solutions has come. Consequently, many works proposing GNN systems have emerged throughout the past few years. However, there is an acute lack of overview, categorization and comparison of such systems. We aim to fill this gap by summarizing and categorizing important methods and techniques for large-scale GNN solutions. In addition, we establish connections between GNN systems, graph processing systems and DL systems.Comment: Accepted at ACM Computing Survey

Microscopy-based high-throughput assays enable multi-parametric analysis to assess adverse effects of nanomaterials in various cell lines

Manufactured nanomaterials (MNMs) selected from a library of over 120 different MNMs with varied compositions, sizes, and surface coatings were tested by four different laboratories for toxicity by high-throughput/-content (HT/C) techniques. The selected particles comprise 14 MNMs composed of CeO2, Ag, TiO2, ZnO and SiO2 with different coatings and surface characteristics at varying concentrations. The MNMs were tested in different mammalian cell lines at concentrations between 0.5 and 250 µg/mL to link physical-chemical properties to multiple adverse effects. The cell lines are derived from relevant organs such as liver, lung, colon and the immune system. Endpoints such as viable cell count, cell membrane permeability, apoptotic cell death, mitochondrial membrane potential, lysosomal acidification and steatosis have been studied. Soluble MNMs, Ag and ZnO, were the most toxic in all cell types. TiO2 and SiO2 MNMs also triggered toxicity in some, but not all, cell types and the cell-type specific effects were influenced by the specific coating. CeO2 MNMs were nearly ineffective in our test systems. Differentiated liver cells appear to be most sensitive to MNMs, in particular to TiO2 MNMs. Whereas most of the investigated MNMs showed no acute toxicity, it became clear that some show adverse effects dependent on the assay and cell line. Hence, it is advised that future nanosafety studies utilise a multi-parametric approach such as HT/C screening to avoid missing signs of toxicity. Furthermore, some of the cell type specific effects should be followed up in more detail and might also provide an incentive to address potential adverse effects in vivo in the relevant organ.JRC.F.3-Chemicals Safety and Alternative Method