Abstract

Abstract
Adverse Drug Events (ADEs) are a major health problem, and developing accurate prediction methods may have a significant impact in public health. Ideally, we would like to have predictive methods, that could pinpoint possible ADRs during the drug development process. Unfortunately, most relevant information on possible ADRs is only available after the drug is commercially available. As a first step, we propose using prior information on existing interactions through recommendation systems algorithms. We have evaluated our proposal using data from the ADReCS database with promising results.

Abstract
Human pluripotent stem cell-based in vitro models that reflect human physiology have the potential to reduce the number of drug failures in clinical trials and offer a cost-effective approach for assessing chemical safety. Here, human embryonic stem (ES) cell-derived neural progenitor cells, endothelial cells, mesenchymal stem cells, and microglia/macrophage precursors were combined on chemically defined polyethylene glycol hydrogels and cultured in serum-free medium to model cellular interactions within the developing brain. The precursors self-assembled into 3D neural constructs with diverse neuronal and glial populations, interconnected vascular networks, and ramified microglia. Replicate constructs were reproducible by RNA sequencing (RNA-Seq) and expressed neurogenesis, vasculature development, and microglia genes. Linear support vector machines were used to construct a predictive model from RNA-Seq data for 240 neural constructs treated with 34 toxic and 26 nontoxic chemicals. The predictive model was evaluated using two standard hold-out testing methods: a nearly unbiased leave-one-out cross-validation for the 60 training compounds and an unbiased blinded trial using a single hold-out set of 10 additional chemicals. The linear support vector produced an estimate for future data of 0.91 in the cross-validation experiment and correctly classified 9 of 10 chemicals in the blinded trial.

Abstract

Abstract

Abstract
Computer networking is a central topic in computer science courses curricula offered by higher education institutions. Network virtualization and simulation tools, like GNS3, allows students and practitioners to test real world networking configuration scenarios and to configure complex network scenarios by configuring virtualized equipments, such as routers and switches, through each one's virtual console. The configuration of advanced network topics in GNS3 requires that students have to apply basic and very repetitive IP configuration tasks in all network equipments. As the network topology grows, so does the amount of network equipments to be configured, which may lead to logical configuration errors. In this paper we propose an extension for GNS3 network virtualizer, to automatically generate a valid configuration of all the network equipments in a GNS3 scenario. Our implementation is able to automatically produce an initial IP and routing configuration of all the Cisco virtual equipments by using the GNS3 specification files. We tested this extension against a set of networked scenarios which proved the robustness, readiness and speedup of the overall configuration tasks. In a learning environment, this feature may save time for all networking practitioners, both beginners or advanced, who aim to configure and test network topologies, since it automatically produces a valid and operational configuration for all the equipments designed in a GNS3 environment.