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All sourcesNASA NTRSDOE OSTI
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Clinical Decision Support - Overview and Update

We are entering a new era in space exploration to return to the moon and explore Mars. These ambitious goals will require significant changes to in-flight and habitat medical care due to constraints on mass, volume, power, crew time and medical evacuation capabilities. These constraints make it absolutely necessary to develop transformative solutions using new technologies. The Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP) pushes the boundary of space medical systems to advance the care of astronauts on future exploration missions beyond low Earth orbit by identifying and testing next-generation medical care and crew health maintenance technologies. The Clinical Decision Support (CDS) project addresses the gap Medical-701 within the Inflight Medical Conditions risk: Enhance medical capabilities within an exploration medical system. For long-duration, deep space missions, computational and data resources will play an important role in maintaining crew health, wellness and performance where the crew will need to be more self-reliant. The aim of the CDS project is to develop and provide recommended requirements for an in-vehicle CDSS that acts as an assistant for delivering optimal health and performance and medical care during exploration missions. The CDSS is envisioned as a software-based tool that will augment a crewmembers’ knowledge, skills and abilities to assist in decision-making and crew health and performance (CHP) management thus increasing CHP systems capabilities. The human interface will be context aware and lessen the cognitive load to assimilate and use information as well as combine large disparate data sets in such a manner that provides the crew with actionable insight to decisions related to crew medical, health and performance management. Crew autonomy will be provided through a CDS that presents knowledge and data in a context aware manner to augment a crew members’ knowledge, skills and abilities during the process of observation, orientation, decisions and action. The CDS project addresses the need for crew members to operate independently during long duration space exploration missions that require medical Levels of Care (LoC) V, the highest level specified by NASA-STD-3001 and described in more detail by the ExMC interpretation of LoC document (NASA/TM-2017-219290), where significant changes to in-flight and habitat medical care necessitate increasing crew autonomy in decision making and task performance. The CDS project will develop and test a series of iterative and increasingly more complex system prototypes. These annual demonstrations of the data system integration with the crew health and performance domain will inform exploration medical system requirements for an on-board Clinical Decision Support System (CDSS) through a series of use cases that guide CDS prototype functionality. CDS concepts are based on ExMC Concept of Operations documents (presented separately) and will highlight architecture extensibility to other more complex analyses and tests using core crew health and performance integrated data management, processing and visualization capabilities. This approach also establishes how externally developed analyses and approaches could be added to expand a clinical decision support system and thus highlight how a comprehensive system can be commercially and/or globally developed. The CDS project will build upon the concept of an integrated data management approach based on the Medical Data Architecture (MDA) project to more fully address challenges associated with in-flight and habitat medical, health and performance care due to constraints on mass, volume, power, crew time and medical evacuation capabilities required for medical LoC V. These requirements will be derived through systems engineering approaches and software prototype developments over the course of the multi-year CDS project to address crew health and performance decision-making and task performance, often autonomously executed by the crew, in a manner that is consistent with the appropriate medical level of care for the mission. This presentation will provide an overview of the vision for the CDS project and highlight the initial accomplishments in project planning, implementation and requirements identification in fiscal year 2020.

clinical decision support system↗
DOE OSTI2021

Assessing boundary condition and parametric uncertainty in numerical-weather-prediction-modeled, long-term offshore wind speed through machine learning and analog ensemble

To accurately plan and manage wind power plants, not only does the time-varying wind resource at the site of interest need to be assessed but also the uncertainty connected to this estimate. Numerical weather prediction (NWP) models at the mesoscale represent a valuable way to characterize the wind resource offshore, given the challenges connected with measuring hub-height wind speed. The boundary condition and parametric uncertainty associated with modeled wind speed is often estimated by running a model ensemble. However, creating an NWP ensemble of long-term wind resource data over a large region represents a computational challenge. Here, we propose two approaches to temporally extrapolate wind speed boundary condition and parametric uncertainty using a more convenient setup in which a mesoscale ensemble is run over a short-term period (1 year), and only a single model covers the desired long-term period (20 year). We quantify hub-height wind speed boundary condition and parametric uncertainty from the short-term model ensemble as its normalized across-ensemble standard deviation. Then, we develop and apply a gradient-boosting model and an analog ensemble approach to temporally extrapolate such uncertainty to the full 20-year period, for which only a single model run is available. As a test case, we consider offshore wind resource characterization in the California Outer Continental Shelf. Both of the proposed approaches provide accurate estimates of the long-term wind speed boundary condition and parametric uncertainty across the region (R 2 >0.75), with the gradient-boosting model slightly outperforming the analog ensemble in terms of bias and centered root-mean-square error. At the three offshore wind energy lease areas in the region, we find a long-term median hourly uncertainty between 10 % and 14 % of the mean hub-height wind speed values. Finally, we assess the physical variability in the uncertainty estimates. In general, we find that the wind speed uncertainty increases closer to land. Also, neutral conditions have smaller uncertainty than the stable and unstable cases, and the modeled wind speed in winter has less boundary condition and parametric sensitivity than summer.

17 WIND ENERGY↗
DOE OSTI2024

Resource Assessment for Distributed Wind Energy: An Evaluation of Best-Practice Methods in the Continental US

Current wind resources within the United States (US) indicate a potential to profitably install nearly 1,400 gigawatts of distributed wind (DW) capacity. This amount is equivalent to over half of the United States’ current energy demand from electricity, making it enough to power millions of homes and businesses and replace countless fossil fuel-based generating plants. Despite the potential growth of DW in the US, deployments are presently hindered by a lack of confidence in resource estimation methods. One potential challenge is that smaller-scale turbines, with hub heights of 40 meters or less, are disproportionately impacted by obstacles such as buildings and vegetation. These obstacles may produce complex wake effects, best modeled with high-fidelity complex fluid dynamics (CFD) models that are too computationally expensive to use for routine siting and resource assessment. Thus, installers today make use of heuristics and simple equations to approximate the impact of obstacles while also leveraging long-term resource data from commercial or publicly available atmospheric models. This study evaluates these historical and commonly used methods alongside new lower-order obstacle models produced from CFD simulations and measurement-based bias correction. The preliminary results from this study show the importance of taking care in the choice and application of mesoscale atmospheric models and the significant value of bias correction using measurements from nearby meteorological towers. Detailed obstacle modeling provides only modest additional gains in performance and, in some cases, can add error, especially at sites where turbines have already been located to avoid obvious impact from upwind obstacles. These findings reinforce the importance of collecting in situ measurements and suggest that obstacle models may be better applied in practice to automated or computer-aided siting, rather than in economic wind resource assessments.

17 WIND ENERGY↗
DOE OSTI2020

RNAcentral 2021: secondary structure integration, improved sequence search and new member databases

RNAcentral is a comprehensive database of non-coding RNA (ncRNA) sequences that provides a single access point to 44 RNA resources and >18 million ncRNA sequences from a wide range of organisms and RNA types. RNAcentral now also includes secondary (2D) structure information for >13 million sequences, making RNAcentral the world’s largest RNA 2D structure database. The 2D diagrams are displayed using R2DT, a new 2D structure visualization method that uses consistent, reproducible and recognizable layouts for related RNAs. The sequence similarity search has been updated with a faster interface featuring facets for filtering search results by RNA type, organism, source database or any keyword. This sequence search tool is available as a reusable web component, and has been integrated into several RNAcentral member databases, including Rfam, miRBase and snoDB. To allow for a more fine-grained assignment of RNA types and subtypes, all RNAcentral sequences have been annotated with Sequence Ontology terms. The RNAcentral database continues to grow and provide a central data resource for the RNA community. RNAcentral is freely available at https://rnacentral.org.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

RCSB Protein Data Bank: Celebrating 50 years of the PDB with new tools for understanding and visualizing biological macromolecules in 3D

We report the Research Collaboratory for Structural Bioinformatics Protein Data Bank (RCSB PDB), funded by the US National Science Foundation, National Institutes of Health, and Department of Energy, has served structural biologists and Protein Data Bank (PDB) data consumers worldwide since 1999. RCSB PDB, a founding member of the Worldwide Protein Data Bank (wwPDB) partnership, is the US data center for the global PDB archive housing biomolecular structure data. RCSB PDB is also responsible for the security of PDB data, as the wwPDB-designated Archive Keeper. Annually, RCSB PDB serves tens of thousands of three-dimensional (3D) macromolecular structure data depositors (using macromolecular crystallography, nuclear magnetic resonance spectroscopy, electron microscopy, and micro-electron diffraction) from all inhabited continents. RCSB PDB makes PDB data available from its research-focused RCSB.org web portal at no charge and without usage restrictions to millions of PDB data consumers working in every nation and territory worldwide. In addition, RCSB PDB operates an outreach and education PDB101.RCSB.org web portal that was used by more than 800,000 educators, students, and members of the public during calendar year 2020. This invited Tools Issue contribution describes (i) how the archive is growing and evolving as new experimental methods generate ever larger and more complex biomolecular structures; (ii) the importance of data standards and data remediation in effective management of the archive and facile integration with more than 50 external data resources; and (iii) new tools and features for 3D structure analysis and visualization made available during the past year via the RCSB.org web portal.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2021

Exploration Medical Capability Clinical Decision Support System Concept of Operations

The Clinical Decision Support (CDS) project supports the Exploration Medical Capability (ExMC) Element of the Human Research Program (HRP). Specifically, the CDS project addresses the ExMC gap, Medical-701: Enhance medical capabilities within an exploration medical system. For long-duration, deep space missions, computational and data resources will play an important role in maintaining crew health, wellness and performance where the crew will need to be more self-reliant as we enter a new era in space exploration to return to the moon and explore Mars. These ambitious goals will require significant change in in-flight medical care due to constraints on mass, volume, power, crew time, skills reduction over time and medical evacuation capabilities. These constraints make it absolutely necessary to develop transformative solutions using new technologies. Unlike the current paradigm for crew health in low-Earth orbit missions that rely on constant communication with Mission Control, the deep space missions will experience communication delays and possibly, no communications for finite periods of time. Hence, crew health management will benefit from analytics’ capabilities to augment decision support. A comprehensive, multi-functional on-board clinical decision support system (CDSS) will help crews assess and diagnose conditions, decide appropriate responses, and guide the provision of tailored and evidence-based treatments, while reflecting contextual factors and constraints. The context may include present and historical data, viable diagnostic equipment, available supplies and medications, and vehicle and environmental health. Communication time with ground-based personnel is delayed or non-existent during significant portions of the mission so the crew will need to autonomously respond to health, performance and medical situations, particularly those that are unplanned. The CDSS must also provide additional capabilities as complex as training for an emergency situation while augmenting non-expert practitioner skillsets if the Crew Medical Officer (CMO) is incapacitated, and as routine as facilitating delayed communication with flight surgeons on the ground. The CDSS must connect complex issues involving health, wellness, task performance and environmental domains. Furthermore, CDSS functionality will focus on semi-autonomous and autonomous decision-making by the crew that is necessary to address challenges in executing a self-contained medical system that enables health care without assistance from ground clinical experts. The document, ExMC CDSS Architecture Recommendation, (HRP- 48032) establishes a description of the envisioned CDSS architecture. The analytics, descriptive or advanced, contained in a CDSS will interface with the integrated crew health and performance architecture that provides the appropriate data sets. The aim of the CDS project is to develop requirements for a CDSS through a series of test-bed prototype developments and demonstrations.

HRP↗
DOE OSTI2025

Accelerating discoveries at DIII-D with the Integrated Research Infrastructure

DIII-D research is being accelerated by leveraging high performance computing (HPC) and data resources available through the National Energy Research Scientific Computing Center (NERSC) Superfacility initiative. As part of this initiative, a high-resolution, fully automated, whole discharge kinetic equilibrium reconstruction workflow was developed that runs at the NERSC for most DIII-D shots in under 20 min. This has eliminated a long-standing research barrier and opened the door to more sophisticated analyses, including plasma transport and stability. These capabilities would benefit from being automated and executed within the larger Department of Energy Advanced Scientific Computing Research program’s Integrated Research Infrastructure (IRI) framework. The goal of IRI is to empower researchers to meld DOE’s world-class research tools, infrastructure, and user facilities seamlessly and securely in novel ways to radically accelerate discovery and innovation. For transport, we are looking at producing flux matched profiles and also using particle tracing to predict fast ion heat deposition from neutral beam injection before a shot takes place. Our starting point for evaluating plasma stability focuses on the pedestal limits that must be navigated to achieve better confinement. This information is meant to help operators run more effective experiments, so it needs to be available rapidly inside the DIII-D control room. So far this has been achieved by ensuring the data is available with existing tools, but as more novel results are produced new visualization tools must be developed. In addition, all of the high-quality data we have generated has been collected into databases that can unlock even deeper insights. This has already been leveraged for model and code validation studies as well as for developing AI/ML surrogates. The workflows developed for this project are intended to serve as prototypes that can be replicated on other experiments and can be run to provide timely and essential information for ITER, as well as next stage fusion power plants.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2025

Visualizing and analyzing 3D biomolecular structures using Mol* at RCSB.org: Influenza A H5N1 virus proteome case study

The easiest and often most useful way to work with experimentally determined or computationally predicted structures of biomolecules is by viewing their three-dimensional (3D) shapes using a molecular visualization tool. Mol* was collaboratively developed by RCSB Protein Data Bank (RCSB PDB, RCSB.org) and Protein Data Bank in Europe (PDBe, PDBe.org) as an open-source, web-based, 3D visualization software suite for examination and analyses of biostructures. It is capable of displaying atomic coordinates and related experimental data of biomolecular structures together with a variety of annotations, facilitating basic and applied research, training, education, and information dissemination. Across RCSB.org, the RCSB PDB research-focused web portal, Mol* has been implemented to support single-mouse-click atomic-level visualization of biomolecules (e.g., proteins, nucleic acids, carbohydrates) with bound cofactors, small-molecule ligands, ions, water molecules, or other macromolecules. RCSB.org Mol* can seamlessly display 3D structures from various sources, allowing structure interrogation, superimposition, and comparison. Using influenza A H5N1 virus as a topical case study of an important pathogen, we exemplify how Mol* has been embedded within various RCSB.org tools—allowing users to view polymer sequence and structure-based annotations integrated from trusted bioinformatics data resources, assess patterns and trends in groups of structures, and view structures of any size and compositional complexity. In addition to being linked to every experimentally determined biostructure and Computed Structure Model made available at RCSB.org, Standalone Mol* is freely available for visualizing any atomic-level or multi-scale biostructure at rcsb.org/3d-view.

3D biostructure↗
DOE OSTI2016

Chapter 1: Direct Normal Radiation

This chapter addresses the quantitative and qualitative aspects of the solar resource, the direct solar radiation. It discusses the total or integrated broadband direct beam extraterrestrial radiation (ETR). This total integrated irradiance is comprised of photons of electromagnetic radiation. The chapter also discusses the impact of the atmosphere and its effect upon the direct normal irradiance (DNI) beam radiation. The gases and particulates present in the atmosphere traversed by the direct beam reflect, absorb, and scatter differing spectral regions and proportions of the direct beam, and act as a variable filter. Knowledge of the available broadband DNI beam radiation resource data is essential in designing a concentrating photovoltaic (CPV) system. Spectral variations in the DNI beam radiation affect the performance of a CPV system depending on the solar cell technology used. The chapter describes propagation and scattering processes of circumsolar radiation (CSR), which includes the Mie scattering from large particles.

14 SOLAR ENERGY↗
DOE OSTI2021

Quantifying sensitivity in numerical weather prediction-modeled offshore wind speeds through an ensemble modeling approach

A decade of research has shown that numerical weather prediction (NWP)-modeled wind speeds can be highly sensitive to the inputs and setups within the NWP model. For wind resource characterization applications, this sensitivity is often addressed by constructing a range of setups and selecting the one that best validates against observations. However, this approach is not possible in areas that lack high-quality hub height observations, especially offshore wind areas. In such cases, techniques to quantify and disseminate confidence in NWP-modeled wind speeds in the absence of observations are needed. We address this need in the present study and propose best practices for quantifying the spread in NWP-modeled wind speeds. We implement an ensemble approach in which we consider 24 different setups to the Weather Research and Forecasting (WRF) model. We construct the ensemble by considering variations in WRF version, WRF namelist, atmospheric forcing, and sea surface temperature (SST) forcing. Our analysis finds that the standard deviation produces more consistent estimates compared to the interquartile range and tends to be the more conservative estimator for ensemble variability. We further find that model spread increases closer to the surface and on shorter time scales. In conclusion, we explore methods to attribute total ensemble variability to the different ensemble components (e.g., atmospheric forcing and SST product) and find that contributions by components also vary depending on time scale. We anticipate that the methods and results presented in this paper will provide a reasonable foundation for further research into ensemble-based wind resource data sets.

17 WIND ENERGY↗
DOE OSTI2020

D3R grand challenge 4: blind prediction of protein–ligand poses, affinity rankings, and relative binding free energies

The Drug Design Data Resource (D3R) aims to identify best practice methods for computer aided drug design through blinded ligand pose prediction and affinity challenges. Herein, we report on the results of Grand Challenge 4 (GC4). GC4 focused on proteins beta secretase 1 and Cathepsin S, and was run in an analogous manner to prior challenges. In Stage 1, participant ability to predict the pose and affinity of BACE1 ligands were assessed. Following the completion of Stage 1, all BACE1 co-crystal structures were released, and Stage 2 tested affinity rankings with co-crystal structures. Finally, we provide an analysis of the results and discuss insights into determined best practice methods.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Temporal complementarity of marine renewables with wind and solar generation: Implications for GB system benefits

Wave and tidal energy have the potential to provide benefits to power systems with high proportions of stochastic renewable generation. This is particularly applicable with wind and solar photovoltaics, as the offsetting of these renewable resources results in more reliable renewable generation. This study utilises ten metrics to quantify the temporal complementarity and supply-demand balancing requirements of the energy mix in Great Britain, to investigate the potential magnitude of these system benefits. Wave and tidal generation profiles are created using historical resource data and hydrodynamic models. The results show that the inclusion of wave and tidal generation creates a renewable energy mix which is more available under multiple conditions: throughout a year of operation; at times of peak demand; for multiple consecutive hourly time periods; and at times when wind and solar generation are not available. Three regional case studies also show that the inclusion of marine energy allows for improved regional supply-demand matching, reducing instances of energy shortage and excess and potentially relieving transmission congestion at particularly constrained locations within GB. Finally, the implications of these findings are discussed in terms of GB wholesale market operation, system balancing and system security.

16 TIDAL AND WAVE POWER↗
DOE OSTI2015

Identification and proteomic analysis of osteoblast-derived exosomes

Exosomes are nanometer-sized vesicles with the function of intercellular communication, and they are released by various cell types. To reveal the knowledge about the exosomes from osteoblast, and explore the potential functions of osteogenesis, we isolated microvesicles from supernatants of mouse Mc3t3 by ultracentrifugation, characterized exosomes by electron microscopy and immunoblotting and presented the protein profile by proteomic analysis. The result demonstrated that microvesicles were between 30 and 100 nm in diameter, round shape with cup-like concavity and expressed exosomal marker tumor susceptibility gene (TSG) 101 and flotillin (Flot) 1. We identified a total number of 1069 proteins among which 786 proteins overlap with ExoCarta database. Gene Oncology analysis indicated that exosomes mostly derived from plasma membrane and mainly involved in protein localization and intracellular signaling. The Ingenuity Pathway Analysis showed pathways are mostly involved in exosome biogenesis, formation, uptake and osteogenesis. Among the pathways, eukaryotic initiation factor 2 pathways played an important role in osteogenesis. Our study identified osteoblast-derived exosomes, unveiled the content of them, presented potential osteogenesis-related proteins and pathways and provided a rich proteomics data resource that will be valuable for further studies of the functions of individual proteins in bone diseases. - Highlights: • We for the first time identified exosomes from mouse osteoblast. • Osteoblasts-derived exosomes contain osteoblast peculiar proteins. • Proteins from osteoblasts-derived exosomes are intently involved in EIF2 pathway. • EIF2α from the EIF2 pathway plays an important role in osteogenesis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

A proteogenomic portrait of lung squamous cell carcinoma

Lung squamous cell carcinoma (LSCC) remains a leading cause of cancer death with few therapeutic options. We characterized the proteogenomic landscape of LSCC, providing a deeper exposition of LSCC biology with potential therapeutic implications. We identify NSD3 as an alternative driver in FGFR1-amplified tumors and low-p63 tumors overexpressing the therapeutic target survivin. SOX2 is considered undruggable, but our analyses provide rationale for exploring chromatin modifiers such as LSD1 and EZH2 to target SOX2-overexpressing tumors. Our data support complex regulation of metabolic pathways by crosstalk between post-translational modifications including ubiquitylation. Numerous immune-related proteogenomic observations suggest directions for further investigation. Proteogenomic dissection of CDKN2A mutations argue for more nuanced assessment of RB1 protein expression and phosphorylation before declaring CDK4/6 inhibition unsuccessful. Finally, triangulation between LSCC, LUAD, and HNSCC identified both unique and common therapeutic vulnerabilities. These observations and proteogenomics data resources may guide research into the biology and treatment of LSCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Genome-wide fetalization of enhancer architecture in heart disease

Heart disease is associated with re-expression of key transcription factors normally active only during prenatal development of the heart. However, the impact of this reactivation on the regulatory landscape in heart disease is unclear. Here, we use RNA-seq and ChIP-seq targeting a histone modification associated with active transcriptional enhancers to generate genome-wide enhancer maps from left ventricle tissue from up to 26 healthy controls, 18 individuals with idiopathic dilated cardiomyopathy (DCM), and five fetal hearts. Healthy individuals have a highly reproducible epigenomic landscape, consisting of more than 33,000 predicted heart enhancers. In contrast, we observe reproducible disease-associated changes in activity at 6,850 predicted heart enhancers. Combined analysis of adult and fetal samples reveals that the heart disease epigenome and transcriptome both acquire fetal-like characteristics, with 3,400 individual enhancers sharing fetal regulatory properties. We also provide a comprehensive data resource (http://heart.lbl.gov) for the mechanistic exploration of DCM etiology.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

BrainXcan identifies brain features associated with behavioral and psychiatric traits using large-scale genetic and imaging data

Advances in brain MRI have enabled many discoveries in neuroscience. Case-control comparisons of brain MRI features have highlighted potential causes of psychiatric and behavioral disorders. However, due to the cost and difficulty of collecting MRI data, most studies have small sample sizes, limiting their reliability. Furthermore, reverse causality complicates interpretation because many observed brain differences are the result rather than the cause of the disease. Here we propose a method (BrainXcan) that leverages the power of large-scale genomewide association studies (GWAS) and reference brain MRI data to discover new mechanisms of disease etiology and validate existing ones. BrainXcan tests the association with genetic predictors of brain MRI-derived features and complex traits to pinpoint relevant brain-wide and region-specific features. Requiring only genetic data, BrainXcan allows us to test a host of hypotheses on mental illness, across many MRI modalities, using public data resources. For example, our method shows that reduced axonal density across the brain is associated with schizophrenia risk, consistent with the disconnectivity hypothesis. We also find that the hippocampus volume is associated with schizophrenia risk, highlighting the potential of our approach. Taken together, our results show the promise of BrainXcan to provide insights into the biology of GWAS traits.

Association study↗
DOE OSTI2011

Experts' perspective on the performance of Chinese technical guidelines for Plan Environmental Impact Assessment

This research was to evaluate the Technical Guideline (HJ/T130-2003) effectiveness based on data resource from a questionnaire survey and interviews on EIA experts. The result demonstrated that the guideline served mainly as a quality control measure for PEIA products, and only a small proportion of experts considered it effective due to its inconsistency with the newly released Chinese PEIA Regulation (2009) and poor practicability. Current status of SEA implementation in China was also studied in terms of application fields, effectiveness evaluation, and major issues to affect SEA, to conclude that SEA implementation in China is premature, and with limited effectiveness. The major problem was the lack of effective methodologies and technical tools. Recommendations for revising the PEIA Guideline and advices to improving SEA implementation in China were accordingly proposed.

54 ENVIRONMENTAL SCIENCES↗