Leadership and Stewardship of the Laboratory (Objective 4.1) Notable Outcome - Phase II Alternative Analysis and PNNL Site Plan Recommendation

Pacific Northwest National Laboratory (PNNL) and the Pacific Northwest Site Office (PNSO) have recently completed an effort to identify the current state of the campus and gaps that exist with regards to space needs, facilities and infrastructure. This effort has been used to establish a campus strategy to ensure PNNL is ready to further the United States (U.S.) Department of Energy (DOE) mission. Ten-year business projections and the impacts on space needs were assessed and incorporated into the long-term facility plans. In identifying/quantifying the space needs for PNNL, the following categories were addressed: Multi-purpose Programmatic (wet chemistry and imaging laboratory space), Strategic (Systems Engineering and Computation Analytics, and Collaboration space), Remediation (space to offset the loss of the Research Technology Laboratory [RTL] Complex due to decontamination and demolition), and Optimization (the exit of older and less cost-effective facilities). The findings of the space assessment indicate a need for wet chemistry space, imaging space, and strategic space needs associated with systems engineering and collaboration space. Based on the analysis, a 10-year campus strategy evolved that balanced four strategic objectives, as directed by the DOE Office of Science (DOE-SC): • Mission Alignment - maintain customer satisfaction • Reasonable & Achievable - do what makes sense from a practical and cost perspective • Campus Continuity - increase the federal control of assets and follow the Campus Master Plan • Guiding Principles - modern, collaborative, flexible, and sustainable. This strategy considered the following possible approaches to meet the identified space needs: • Institutional General Plant Project (IGPP) funded projects • Third party leased facilities • Science Laboratory Infrastructure (SLI) line item funded projects. Pairing the four strategic objectives with additional key metrics as criteria for selection, an initial recommendation was made to DOE-SC to use all three funding mechanisms to deliver the mission need. DOE-SC provided feedback that third party facilities are not to be pursued at this time. The decision was made by DOE that an IGPP-funded program would be the base plan, while retaining the possibility of a 2019 SLI-funded project. The SLI project will be designed to deliver significant impact on science and technology (S&T) and support the development of a modern, synergistic core campus where a collaborative and innovative environment is fostered. The specific scientific impact will be further defined in the 2015 and 2016 Annual Laboratory Plans. Additionally, opportunities will be explored to construct annexes on current federal facilities, including the Environmental Molecular Sciences Laboratory (EMSL), if proven synergistic and cost effective. The final result of this effort is an actionable, flexible plan with scope, schedule, and cost targets for individual acquisition projects. Implemented as planned, the result will increase federal ownership by approximately 15 percent, reduce the operating cost by approximately 7 percent, and reduce the geographic facility footprint by approximately 66,000 gross square feet (GSF). Reduction of surplus space will be addressed while maintaining customer satisfaction, lowering operating costs, reducing the campus footprint, and increasing the federal control of assets. This strategy is documented in PNNL’s 2014 Laboratory Plan

GANDALF - Graphical Astrophysics code for N-body Dynamics And Lagrangian Fluids

GANDALF is a new hydrodynamics and N-body dynamics code designed for investigating planet formation, star formation and star cluster problems. GANDALF is written in C++, parallelised with both OpenMP and MPI and contains a python library for analysis and visualisation. The code has been written with a fully object-oriented approach to easily allow user-defined implementations of physics modules or other algorithms. The code currently contains implementations of Smoothed Particle Hydrodynamics, Meshless Finite-Volume and collisional N-body schemes, but can easily be adapted to include additional particle schemes. We present in this paper the details of its implementation, results from the test suite, serial and parallel performance results and discuss the planned future development. The code is freely available as an open source project on the code-hosting website github at https://github.com/gandalfcode/gandalf and is available under the GPLv2 license.This research was supported by the DFG cluster of excellence "Origin and Structure of the Universe", DFG Projects 841797-4, 841798-2 (DAH, GPR), the DISCSIM project, grant agreement 341137 funded by the European Research Council under ERC-2013-ADG (GPR, RAB). Some development of the code and simulations have been carried out on the computing facilities of the Computational centre for Particle and Astrophysics (C2PAP) and on the DiRAC Data Analytic system at the University of Cambridge, operated by the University of Cambridge High Performance Computing Service on behalf of the STFC DiRAC HPC Facility (www.dirac.ac.uk); the equipment was funded by BIS National E-infrastructure capital grant (ST/K001590/1), STFC capital grants ST/H008861/1 and ST/H00887X/1, and STFC DiRAC Operations grant ST/K00333X/1

FUS and TARDBP but Not SOD1 Interact in Genetic Models of Amyotrophic Lateral Sclerosis

Mutations in the SOD1 and TARDBP genes have been commonly identified in Amyotrophic Lateral Sclerosis (ALS). Recently, mutations in the Fused in sarcoma gene (FUS) were identified in familial (FALS) ALS cases and sporadic (SALS) patients. Similarly to TDP-43 (coded by TARDBP gene), FUS is an RNA binding protein. Using the zebrafish (Danio rerio), we examined the consequences of expressing human wild-type (WT) FUS and three ALS–related mutations, as well as their interactions with TARDBP and SOD1. Knockdown of zebrafish Fus yielded a motor phenotype that could be rescued upon co-expression of wild-type human FUS. In contrast, the two most frequent ALS–related FUS mutations, R521H and R521C, unlike S57Δ, failed to rescue the knockdown phenotype, indicating loss of function. The R521H mutation caused a toxic gain of function when expressed alone, similar to the phenotype observed upon knockdown of zebrafish Fus. This phenotype was not aggravated by co-expression of both mutant human TARDBP (G348C) and FUS (R521H) or by knockdown of both zebrafish Tardbp and Fus, consistent with a common pathogenic mechanism. We also observed that WT FUS rescued the Tardbp knockdown phenotype, but not vice versa, suggesting that TARDBP acts upstream of FUS in this pathway. In addition we observed that WT SOD1 failed to rescue the phenotype observed upon overexpression of mutant TARDBP or FUS or upon knockdown of Tardbp or Fus; similarly, WT TARDBP or FUS also failed to rescue the phenotype induced by mutant SOD1 (G93A). Finally, overexpression of mutant SOD1 exacerbated the motor phenotype caused by overexpression of mutant FUS. Together our results indicate that TARDBP and FUS act in a pathogenic pathway that is independent of SOD1

Genome evolution predicts genetic interactions in protein complexes and reveals cancer drug targets

Contains fulltext : 118479.pdf (publisher's version ) (Open Access)Genetic interactions reveal insights into cellular function and can be used to identify drug targets. Here we construct a new model to predict negative genetic interactions in protein complexes by exploiting the evolutionary history of genes in parallel converging pathways in metabolism. We evaluate our model with protein complexes of Saccharomyces cerevisiae and show that the predicted protein pairs more frequently have a negative genetic interaction than random proteins from the same complex. Furthermore, we apply our model to human protein complexes to predict novel cancer drug targets, and identify 20 candidate targets with empirical support and 10 novel targets amenable to further experimental validation. Our study illustrates that negative genetic interactions can be predicted by systematically exploring genome evolution, and that this is useful to identify novel anti-cancer drug targets