In this paper, we showcase a complete, end-To-end, fault tolerant, bandwidth and latency optimized architecture for real time utilization of data from multiple sources that allows the collection, transport, storage, processing, and display of both raw data and analytics. This architecture can be applied for a wide variety of applications ranging from automation/control to monitoring and security. We propose a practical, hierarchical design that allows easy addition and reconfiguration of software and hardware components, while utilizing local processing of data at sensor or field site ('fog computing') level to reduce latency and upstream bandwidth requirements. The system supports multiple fail-safe mechanisms to guarantee the delivery of sensor data. We describe the application of this architecture to cyber-physical security (CPS) by supporting security monitoring of an electric distribution grid, through the collection and analysis of distribution-grid level phasor measurement unit (PMU) data, as well as Supervisory Control And Data Acquisition (SCADA) communication in the control area network.

The Carbon Capture Simulation Initiative (CCSI) project has developed and deployed scientific infrastructure called the CCSI Toolset. The CCSI Toolset provides state-of-the-art computational modeling and simulation tools to accelerate the commercialization of carbon capture technologies from discovery to development, demonstration, and ultimately the widespread deployment to hundreds of power plants. Carbon capture technologies have the potential to dramatically reduce the carbon emissions from power plants. The CCSI Toolset provides end users in industry with a comprehensive, integrated suite of leading-edge, scientifically validated models with simulation, uncertainty quantification, optimization, risk analysis and decision making support. The CCSI Toolset has at its core an integrated framework that enables execution of simulations and workflows including optimization and uncertainty parameter sweeps using a wide variety of computing platforms including desktops, clusters, Clouds, and HPC systems. The integration framework enables the running of a variety of commercial process simulation packages as well as custom simulators. Moreover, the framework enables scientists to run and manage thousands of concurrent simulations to perform optimizations and uncertainty quantification. Components of the CCSI Toolset are connected through the use of a data management system that stores data to a repository and enables the tracking of provenance for each simulation as well as its associated components. The data management system tracks all the configurations, models, simulations, and results created during the design of a carbon capture system and supports the design life-cycle as well as decision making. The primary contribution of this paper is thus the design and implementation of the integration framework within the CCSI Toolset, which provides both data management and simulation support for CCSI. This integration framework has been deployed and is in use by several groups of researchers and commercial entities.

Regulatory elements (enhancers) are major drivers of gene expression in mammals and harbor many genetic variants associated with human diseases. Here, we present an updated VISTA Enhancer Browser (https://enhancer.lbl.gov), a database of transgenic enhancer assays conducted in developing mouse embryos in vivo. Since the original publication in 2007, the database grew nearly 20-fold from 250 to over 4500 experiments and currently harbors over 23 500 images. The updated database provides structured information on experiments conducted at different stages of embryonic development, including enhancer activities of human pathogenic and synthetic variants and sequences derived from a variety of species. In addition to manually curated results of thousands of individual experiments, the new database also features hundreds of manually curated comparisons between alleles. The VISTA Enhancer Browser provides a crucial resource for study of human genetic variation, gene regulation and developmental biology.

The development and scale up of cost effective carbon capture processes is of paramount importance to enable the widespread deployment of these technologies to significantly reduce greenhouse gas emissions. The U.S. Department of Energy initiated the Carbon Capture Simulation Initiative (CCSI) in 2011 with the goal of developing a computational toolset that would enable industry to more effectively identify, design, scale up, operate, and optimize promising concepts (Miller et al., 2014). The CCSI Toolset consists of both multi-scale models as well as new computational tools. This paper focuses specifically on the PSE-related computational tools and models that provide new capabilities for integrating multi-scale models with advanced optimization, uncertainty quantification (UQ), and surrogate modeling techniques.