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At least 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Path-BigBird: An AI-Driven Transformer Approach to Classification of Cancer Pathology Reports

PURPOSE Surgical pathology reports are critical for cancer diagnosis and management. To accurately extract information about tumor characteristics from pathology reports in near real time, we explore the impact of using domain-specific transformer models that understand cancer pathology reports. METHODS We built a pathology transformer model, Path-BigBird, by using 2.7 million pathology reports from six SEER cancer registries. We then compare different variations of Path-BigBird with two less computationally intensive methods: Hierarchical Self-Attention Network (HiSAN) classification model and an offthe-shelf clinical transformer model (Clinical BigBird). We use five pathology information extraction tasks for evaluation: site, subsite, laterality, histology, and behavior. Model performance is evaluated by using macro and micro F 1 scores. RESULTS We found that Path-BigBird and Clinical BigBird outperformed the HiSAN in all tasks. Clinical BigBird performed better on the site and laterality tasks. Versions of the Path-BigBird model performed best on the two most difficult tasks: subsite (micro F 1 score of 72.53, macro F 1 score of 35.76) and histology (micro F 1 score of 80.96, macro F 1 score of 37.94). The largest performance gains over the HiSAN model were for histology, for which a Path-BigBird model increased the micro F 1 score by 1.44 points and the macro F 1 score by 3.55 points. Overall, the results suggest that a Path-BigBird model with a vocabulary derived from wellcurated and deidentified data is the best-performing model. CONCLUSION The Path-BigBird pathology transformer model improves automated information extraction from pathology reports. Although Path-BigBird outperforms Clinical BigBird and HiSAN, these less computationally expensive models still have utility when resources are constrained.

60 APPLIED LIFE SCIENCES↗
NASA NTRS2002

Impact of lossy compression on diagnostic accuracy of radiographs for periapical lesions

OBJECTIVES: The purpose of this study was to evaluate the lossy Joint Photographic Experts Group compression for endodontic pretreatment digital radiographs. STUDY DESIGN: Fifty clinical charge-coupled device-based, digital radiographs depicting periapical areas were selected. Each image was compressed at 2, 4, 8, 16, 32, 48, and 64 compression ratios. One root per image was marked for examination. Images were randomized and viewed by four clinical observers under standardized viewing conditions. Each observer read the image set three times, with at least two weeks between each reading. Three pre-selected sites per image (mesial, distal, apical) were scored on a five-scale score confidence scale. A panel of three examiners scored the uncompressed images, with a consensus score for each site. The consensus score was used as the baseline for assessing the impact of lossy compression on the diagnostic values of images. The mean absolute error between consensus and observer scores was computed for each observer, site, and reading session. RESULTS: Balanced one-way analysis of variance for all observers indicated that for compression ratios 48 and 64, there was significant difference between mean absolute error of uncompressed and compressed images (P <.05). After converting the five-scale score to two-level diagnostic values, the diagnostic accuracy was strongly correlated (R (2) = 0.91) with the compression ratio. CONCLUSION: The results of this study suggest that high compression ratios can have a severe impact on the diagnostic quality of the digital radiographs for detection of periapical lesions.

NASA Discipline Space Human Factors↗
NASA NTRSDate not supplied

Artificial Neural Networks to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
NASA NTRSDate not supplied

Machine Learning Models to Predict Cognitive Impairment of Rodents Subjected to Space Radiation

INTRODUCTION We use artificial neural networks (ANNs) as an example machine learning (ML) tool to predict the cognitive performance impairment of rats induced by irradiation. The experimental data in the analyses is attentional set-shifting (ATSET) test scores from a rodent model exposed to ≤15 cGy of individual galactic cosmic radiation (GCR) ions: 4He, 28Si, or 56Fe, expected for a Lunar or Mars mission [1]. This work investigates rats at a subject-based level and uses applied dose and performance scores taken before irradiation to predict whether a rat will be impaired when irradiated. The results of this study are significant to crewed space missions as they support the potential of predicting an astronaut’s impairment in a specific task before spaceflight through the implementation of appropriately trained ML tools. METHODS Data used in this work are scores from the ATSET, a multi-stage constrained cognitive flexibility test [2]. Our computational model utilizes the number of attempts to reach the criterion to pass a stage as a behavioral performance measure for rats. We use the post-irradiation scores, generate thresholds from cumulative distribution plots of non-irradiated rats, and calculate the percent of irradiated rats whose scores fall below the threshold to infer how each radiation type/dose affects a population. Rats scoring above the threshold are labeled impaired while the others are non-impaired. We then employ ANNs as a typical ML technique, and use each subject’s individual scores taken before radiation along with the applied dose, to predict their personal susceptibility to cognitive impairment due to space radiation exposure. RESULTS AND CONCLUSION A significant finding is the exhibition of a dose-dependent increasing probability of impairment for 1 to 10 cGy of 28Si or 56Fe in the simple discrimination (SD) stage of the ATSET, and for 1 to 10 cGy of 56Fe in the compound discrimination (CD) stage. On a subject-based level, implementing ML classifiers such as ANNs identifies rats that have a higher tendency for impairment after GCR exposure [1]. The receiver operating characteristic (ROC) and the precision-recall (PR) curves of the ML models show a better prediction of impairment when 56Fe is the ion in question in both SD (Figure 1) and CD stages. They, however, do not depict impairment due to 4He in SD (Figure 1) and 28Si in CD, suggesting no dose-dependent impairment response in these cases. In this work, “good” prediction pertains to “better-than-random-chance”, due to the limited sample size and the high inter- and intra-individual variabilities in response to brain stimulation paradigms, as applicable to both animals and humans. More behavioral tests and biomarkers should be investigated on the same subjects, to be fed to the ML models to capture the agents responsible for performance alterations of some individuals versus others.

machine learning↗
DOE OSTI2022

A Novel DNA Repair Gene Signature for Immune Checkpoint Inhibitor-Based Therapy in Gastric Cancer

Gastric cancer is a heterogeneous group of diseases with only a fraction of patients responding to immunotherapy. The relationships between tumor DNA damage response, patient immune system and immunotherapy have recently attracted attention. Accumulating evidence suggests that DNA repair landscape is a significant factor in driving response to immune checkpoint blockade (ICB) therapy. In this study, to explore new prognostic and predictive biomarkers for gastric cancer patients who are sensitive and responsive to immunotherapies, we developed a novel 15-DNA repair gene signature (DRGS) and its related scoring system and evaluated the efficiency of the DRGS in discriminating different molecular and immune characteristics and therapeutic outcomes of patients with gastric adenocarcinoma, using publicly available datasets. The results demonstrated that DRGS high score patients showed significantly better therapeutic outcomes for ICB compared to DRGS low score patients (p < 0.001). Integrated analysis of multi-omics data demonstrated that the patients with high DRGS score were characteristic of high levels of anti-tumor lymphocyte infiltration, tumor mutation burden (TMB) and PD-L1 expression, and these patients exhibited a longer overall survival, as compared to the low-score patients. Results obtained from HPA and IHC supported significant dysregulation of the genes in DRGS in gastric cancer tissues, and a positive correlation in protein expression between DRGS and PD-L1. Therefore, the DRGS scoring system may have implications in tailoring immunotherapy in gastric cancers. A preprint has previously been published (Yuan et al., 2021).

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Hotspot Coevolution Is a Key Identifier of Near-Native Protein Complexes

Protein–protein interactions play a key role in mediating numerous biological functions, with more than half the proteins in living organisms existing as either homo- or hetero-oligomeric assemblies. Protein subunits that form oligomers minimize the free energy of the complex, but exhaustive computational search-based docking methods have not comprehensively addressed the challenge of distinguishing a natively bound complex from non-native forms. Current protein docking approaches address this problem by sampling multiple binding modes in proteins and scoring each mode, with the lowest-energy (or highest scoring) binding mode being regarded as a near-native complex. However, high-scoring modes often match poorly with the true bound form, suggesting a need for improvement of the scoring function. Here in this study, we propose a scoring function, KFC-E, that accounts for both conservation and coevolution of putative binding hotspot residues at protein–protein interfaces. We tested KFC-E on four benchmark sets of unbound examples and two benchmark sets of bound examples, with the results demonstrating a clear improvement over scores that examine conservation and coevolution across the entire interface.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2022

Are Terrestrial Biosphere Models Fit for Simulating the Global Land Carbon Sink?

The Global Carbon Project estimates that the terrestrial biosphere has absorbed about one-third of anthropogenic CO 2 emissions during the 1959–2019 period. This sink-estimate is produced by an ensemble of terrestrial biosphere models and is consistent with the land uptake inferred from the residual of emissions and ocean uptake. The purpose of our study is to understand how well terrestrial biosphere models reproduce the processes that drive the terrestrial carbon sink. One challenge is to decide what level of agreement between model output and observation-based reference data is adequate considering that reference data are prone to uncertainties. To define such a level of agreement, we compute benchmark scores that quantify the similarity between independently derived reference data sets using multiple statistical metrics. Models are considered to perform well if their model scores reach benchmark scores. Our results show that reference data can differ considerably, causing benchmark scores to be low. Model scores are often of similar magnitude as benchmark scores, implying that model performance is reasonable given how different reference data are. While model performance is encouraging, ample potential for improvements remains, including a reduction in a positive leaf area index bias, improved representations of processes that govern soil organic carbon in high latitudes, and an assessment of causes that drive the inter-model spread of gross primary productivity in boreal regions and humid tropics. The success of future model development will increasingly depend on our capacity to reduce and account for observational uncertainties.

58 GEOSCIENCES↗
DOE OSTI2023

Hydropower Potential at Non-Powered Dams: A Multi-Criteria Decision Analysis Tool based on Grid, Community, Industry, and Environmental Impacts

Non-powered dams (NPDs) are dams that do not include hydraulic turbine (hydropower) equipment. Currently, there are more than 80,000 such dams in the United States, which provide a variety of non-energy benefits, including flood control, water supply, navigation, and recreation. Approximately 500 of these NPDs are identified as having the potential to add hydropower generation (totaling up to a capacity of more than 8200 MW). A large share of investment costs and environmental impacts of dam construction have already been incurred at these NPDs. Hence, adding power to the existing dam structure is hypothesized to be achieved at a lower cost, with less risk, and a shorter timeframe than the development required for new dam construction. The abundance of NPDs, the associated environmental favorability, and cost advantages, combined with the reliability, predictability, and dispatchability of hydropower, make NPDs a strong candidate in the nation’s renewable energy portfolio. To assess the NPD to hydropower conversion potential, in this study, we developed a GIS-based multi-criterial decision analysis tool, which allows users to rank these NPDs based on the grid, community, industry, and environmental impacts (i.e., GCIE impacts). This web-based interactive tool (developed using open-source Python and JavaScript) lets the user choose from a wide range of features to define each of the GCIE impact scores through a user-friendly graphical user interface. These features are related to dam operation, hydropower generation opportunity, power market economy, social vulnerability and risk, proximity to critical infrastructure and energy generating facilities, environmental concerns (air, water, and critical habitat), and exposure to natural hazards. The overall priority score of NPDs is calculated based on user-defined weights for each of the GCIE impact scores. Besides ranking NPDs, the tool can also be used to estimate the energy-storage feasibility (battery, hydrogen, and pump-storage hydropower) at each of the potential sites.

13 HYDRO ENERGY↗
DOE OSTI2022

Language models for the prediction of SARS-CoV-2 inhibitors

The COVID-19 pandemic highlights the need for computational tools to automate and accelerate drug design for novel protein targets. We leverage deep learning language models to generate and score drug candidates based on predicted protein binding affinity. We pre-trained a deep learning language model (BERT) on ∼9.6 billion molecules and achieved peak performance of 603 petaflops in mixed precision. Our work reduces pre-training time from days to hours, compared to previous efforts with this architecture, while also increasing the dataset size by nearly an order of magnitude. For scoring, we fine-tuned the language model using an assembled set of thousands of protein targets with binding affinity data and searched for inhibitors of specific protein targets, SARS-CoV-2 Mpro and PLpro. We utilized a genetic algorithm approach for finding optimal candidates using the generation and scoring capabilities of the language model. Our generalizable models accelerate the identification of inhibitors for emerging therapeutic targets.

Blanchard, Andrew E.↗
DOE OSTI2023

Final Technical Report

Statement of the problem or situation that is being addressed in your application. The DOE and its national laboratories developed the Home Energy Score™ (HES) to encourage homeowners to improve their energy performance, lower costs and to share energy information through the MLS listing, appraisal, and financing channels. While the HES is an instrumental tool, it is currently underutilized and consists of technical, structural and sector barriers which need to be addressed in order to scale and many energy efficiency contractors are understandably overwhelmed by the added time and effort and lack of incentive to sell and deliver deep retrofit projects while simultaneously meeting the DOE HES program requirements; consequently, contractors may decide to forgo participation. Home Energy Rating System (HERS) Raters have the opportunity to play the critical Assessor role in producing a Home Energy Score (HES); this role has immense potential but currently is unfulfilled. Lastly, while utilities are interested in their customer base achieving greater energy efficiency, especially to help offset growing residential loads in states like California that are accelerating electrification, utilities do not have access to the market actors who are on the front line of influence to homeowners or review and approve their permits: HERS Raters, assessors, contractors and building departments. General statement of how this problem is being addressed: ConSol will integrate the Home Energy Score™ (HES) to its State of California, approved home energy rating services (HERS) platform (CHEERS) to develop a single tool for contractors nationwide to assess, record and install recommended cost, energy, and emissions saving measures to the 140 million single-family homes throughout the U.S. and 14 million homes in California (CHEERS+HES). The CHEERS high fidelity energy code permitting data will be integrated with HES for simple, accurate, easy-to-use home energy estimation and analysis and will directly gain access to the retrofit and renovations markets with the same upgraded platform. This innovative project will assist the utilities in supporting existing homes in their jurisdictions with HES and develop measures to improve energy efficiency and reduce emissions. How is this problem being addressed? What is the overall project approach? In effort to expand the Home Energy Score™ (HES) by increasing the use of aggregable home energy asset data, ConSol proposes to integrate the DOE HES via Application Programming Interface (API) to its State of California approved home energy rating services platform (CHEERS). Once the CHEERS platform and HES are integrated (CHEERS+HES), this enhanced platform will be instantly available and actively deployed via Phase 1 pilot to HERS Raters, assessors and contractors in California to market-test the solution, understand the rate of adoption and identify opportunities for improvement prior to scaling nationally. The CHEERS high fidelity energy code permitting data will be integrated with HES for simple, accurate, easy-to-use home energy estimation and analysis and will directly gain access to the retrofit and renovations markets with the same upgraded platform. This innovative project will assist the building industry and homeowners with an easy-to-use assessment if energy and carbon impacts of existing homes, and assist the utilities in supporting existing homes in their jurisdictions with HES to improve energy efficiency and reduce emissions. What is to be done in Phase I? During Phase I of this proposed project, ConSol will (1) design software architecture that links CHEERS to the Home Energy ScoreTM via API, (2) solicit partnership from one or more California utilities for a regional pilot, (3) test the new software with its HERS Raters and contractor network in the partnership utility jurisdiction, (4) launch a pilot version of the newly developed software with HERS Raters and contractors in the utility territory, and (5) explore California’s GoGreen energy efficiency homeowner lending program in parallel with the pilot. Commercial Applications and Other Benefits. Summarize the future applications or public benefits if the project is carried over into Phase II or Phase III and beyond. The CHEERS+HES commercialized product will be ready for national market scale following a successful Phase 1 performance. The CHEERS+HES adoption is estimated to reach a 5% adoption growth rate versus the 110,000 baseline, starting in Year 1 after Phase I completion, and continuing each year. As a direct benefit to the DOE, CHEERS will set a goal of 100,000 Home Energy Score assessments for existing home alterations within the first 10 years following Phase 1 performance. The technical benefits of this proposed project include the harmonized, automated, and seamless integration of the DOE HES into the widely used and market leading California energy registry, CHEERS. The social benefits include the aggregate energy, cost and GHG savings by allowing the broader public streamlined access to the CHEERS+HES measurement and the energy efficiency recommended measures that may result. Key Words: Home Energy ScoreTM (HES); Application Programming Interface (API); Home Energy Rating Services (HERS); HERS Raters; contractors; assessors; existing homes, energy asset data; cost, energy, and emissions saving measures; energy code (Title 24) compliance; document repository; utilities; pilot; newly developed software; energy efficiency; homeowner. Summary for Members of Congress: The DOE Home Energy Score™ (HES) is a tool to encourage homeowners to improve their energy performance, lower costs and share energy information but is underutilized and consists of barriers which need to be addressed in order to scale. In effort to expand the HES, CHEERS, Inc. will integrate the HES to its State of California, approved home energy rating services (HERS) platform (CHEERS) to develop a single tool for contractors nationwide to assess, record and install recommended cost, energy, and emissions saving measures to the 140 million single-family homes throughout the U.S. and 14 million homes in California.

Application Programming Interface (API)↗
DOE OSTI2025

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory Protected Areas Database of the United States (PAD-US) areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
DOE OSTI2026

IM3 Projected US Data Center Locations

IM3 Projected US Data Center Locations This dataset contains model projections of new data center facilities in the contiguous United States (CONUS) through 2035 using the CERF – Data Centers model. Data center locations are modeled across four data center electricity demand growth scenarios (low, moderate, high, higher) and five market gravity scenarios (0%, 25%, 50%, 75%, 100%). Projected locations are intended to be regional representations of feasible siting locations in the future to assess potential grid and water stress impacts. The data center load growth scenarios correspond with the rates outlined in EPRI (2024) and include 3.71%, 5%, 10%, and 15% annual growth of electricity demand for data centers from 2023 values in 37 states across the CONUS. Market gravity scenarios correspond to the relative importance of proximity to data center markets or high population areas compared to locational cost in the siting algorithm. 0% market gravity means that siting decisions were entirely determined by the locational cost in each feasible location. 100% market gravity means that only market proximity was considered when siting. Other scenarios have weight placed on both components where total weight always equals 100%. Locational cost is dependent on facility cooling type and corresponding electricity cost, taxes, and other factors. Facility cooling type is spatially determined where high water stress and/or areas with high summer wet bulb temperatures are assumed to operate with mechanical cooling for a higher fraction of the year rather than evaporative cooling. Feasible data center siting areas are based on geospatial suitability raster data developed with open-source information. The following areas are excluded from siting: Areas within 300 m of a federal airport runway or within an airport area boundary Waterbodies Areas with slope >16% Areas susceptible to sinkholes High coastal or inland flood risk areas Local, state, and federal parks, leisure areas, and cemeteries Areas >2 km away from electric substations Areas >5 km away from a municipal water supplier service area Areas >2 km away from high-speed fiber provider service territory USGS Protected Areas Database of the United States (PAD-US) GAP status 1, 2, or 3 areas US National Parks Wetlands USFWS critical habitats BIA land areas Railroads, major roadways, and minor roadways Military areas and training grounds NLCD developed lands Areas >0.8 km (0.5 miles) from NLCD developed lands Because we use open-source information, proprietary information that can influence siting decisions such as individual tax agreements with cities, detailed fiber line connectivity, electric grid power capacity agreements, and others, are not currently accounted for in the modeling process. Using specific building locations and footprints in the dataset for local planning purposes is not advised. Technical Information Geospatial data is provided in geojson format using the Albers Equal Area Conic (ESRI:102003) coordinate reference system. The datasets contain the following parameters: id - unique identification number within given scenario file growth_scenario – data center demand growth scenario market_gravity_weight – market gravity weight scenario (%) region – name of region (i.e., US State) total_cost_million_usd – locational siting cost ($million) campus_size_square_ft – total land acquired for data center facility (square ft) data_center_it_power_mw – IT power of data center facility (MW) mechanical_cooling_frac – fraction of year when data center uses mechanical cooling system water_cooling_frac– fraction of year when data center uses evaporative cooling system cooling_energy_demand_mwh – total annual facility energy demand for cooling (MWh) cooling_water_demand_mgy – total annual facility water demand for cooling (MG) cooling_water_consumption_mgy – total annual facility water consumed (MG) normalized_locational_cost – normalized total locational cost score for location normalized_gravity_score – normalized market gravity score for location weighted_siting_score – total weighted siting score of locational cost and gravity score geometry – polygon geometry of facility Acknowledgment IM3 is a multi-institutional effort led by Pacific Northwest National Laboratory and supported by the U.S. Department of Energy's Office of Science as part of research in MultiSector Dynamics, Earth and Environmental Systems Modeling Program. License This data is made available under a CCBY4.0 License Disclaimer This material was prepared as an account of work sponsored by an agency of the United States Government. Neither the United States Government nor the United States Department of Energy, nor the Contractor, nor any or their employees, nor any jurisdiction or organization that has cooperated in the development of these materials, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness or any information, apparatus, product, software, or process disclosed, or represents that its use would not infringe privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States Government or any agency thereof, or Battelle Memorial Institute. The views and opinions of authors expressed herein do not necessarily state or reflect those of the United States Government or any agency thereof. PACIFIC NORTHWEST NATIONAL LABORATORYoperated byBATTELLEfor theUNITED STATES DEPARTMENT OF ENERGYunder Contract DE-AC05-76RL01830

Mongird, Kendall (ORCID:0000000328077088)↗
NASA NTRS1994

Stochastic decision analysis

Small space flight project design at NASA Langley Research Center goes through a multi-phase process from preliminary analysis to flight operations. The process insures that each system achieves its technical objectives with demonstrated quality and within planned budgets and schedules. A key technical component of early phases is decision analysis, which is a structure procedure for determining the best of a number of feasible concepts based upon project objectives. Feasible system concepts are generated by the designers and analyzed for schedule, cost, risk, and technical measures. Each performance measure value is normalized between the best and worst values and a weighted average score of all measures is calculated for each concept. The concept(s) with the highest scores are retained, while others are eliminated from further analysis. This project automated and enhanced the decision analysis process. Automation of the decision analysis process was done by creating a user-friendly, menu-driven, spreadsheet macro based decision analysis software program. The program contains data entry dialog boxes, automated data and output report generation, and automated output chart generation. The enhancements to the decision analysis process permit stochastic data entry and analysis. Rather than enter single measure values, the designers enter the range and most likely value for each measure and concept. The data can be entered at the system or subsystem level. System level data can be calculated as either sum, maximum, or product functions of the subsystem data. For each concept, the probability distributions are approximated for each measure and the total score for each concept as either constant, triangular, normal, or log-normal distributions. Based on these distributions, formulas are derived for the probability that the concept meets any given constraint, the probability that the concept meets all constraints, and the probability that the concept is within a given amount of the best score. Formulas are also derived for the probability that one concept's total score is within a given amount of a second concept's total score. These probabilistic calculations provide more realistic data entry and output information for designers, enabling designers to better determine which concepts to eliminate and which concepts to retain at the decision points of each design phase.

Lacksonen, Thomas A.↗
NASA NTRS1997

Individual Differences in Response to Sleep Deprivation: Assessment of Fatigue Following Sleep Loss

Previous work has indicated that a small but significant number of participants in sleep deprivation studies or in simulated shift work experiments manifests an exaggerated performance decrement when they reach a critical point in the experiment, usually near the trough of the circadian cycle or the middle of the night. Those who show this exaggerated response do not appear to differ from other non-nal volunteers in any substantial way according to usual screening criteria or baseline values. The present study aims to examine factors that may provide the basis for this extreme response. We propose that a preexisting sleep deficit-as manifested by low values on the Multiple Sleep Latency Test (MSLT)-may account for extreme responders. Roth and colleagues (1993) have shown that among normal volunteers screened for a variety of studies, approximately 20 to 25 percent show low (< 6 minutes) MSLT scores on a consistent basis, whereas a like proportion shows consistently high MSLT scores (> 13 minutes). Additionally, studies by this group have indicated that subjects with low MSLT scores may suffer from chronic insufficient sleep (Roth et al., 1993), as further substantiated by the finding that they have consistently higher nocturnal sleep efficiency and that their MSLT scores rise to normal values when sleep is extended (Roehrs et al., 1996). We hypothesize that the short MSLT subjects have a significant long-term sleep deficit that leads to a marked intolerance for sleep deprivation or shift work. We further suggest that this sleep debt may signify an increased sleep need in these individuals that is not met either due to personal preference or to societal pressures (or both). If this speculation is accurate, then we predict that the tolerance for sleep deprivation in such individuals can be increased by "pretreatment" with sleep extension. Thus, the present study is designed to test the following two hypotheses: subjects with nominal sleep patterns who have low MSLT scores (e.g., Sleepy subjects) will show an exaggerated response (performance decrement) to sleep loss compared to subjects who have high MSLT scores (Alert subjects) on a nominal sleep schedule. when permitted to extend sleep-thus discharging their sleep debt-the Sleepy subjects will show a sleep-loss response resembling that of the Alert subjects.

Carskadon, Mary A.↗
NASA NTRS1997

Individual Differences in Response to Sleep Deprivation: Assessment of Fatigue Following Sleep Loss

Previous work has indicated that a small but significant number of participants in sleep deprivation studies or in simulated shift work experiments manifests an exaggerated performance decrement when they reach a critical point in the experiment, usually near the trough of the circadian cycle or the middle of the night. Those who show this exaggerated response do not appear to differ from other non-nal volunteers in any substantial way according to usual screening criteria or baseline values. The present study aims to examine factors that may provide the basis for this extreme response. We propose that a preexisting sleep deficit-as manifested by low values on the Multiple Sleep Latency Test (MSLT)-may account for extreme responders. It has been shown that among normal volunteers screened for a variety of studies, approximately 20 to 25 percent show low (< 6 minutes) MSLT scores on a consistent basis, whereas a like proportion shows consistently high MSLT scores (> 13 minutes). Additionally, studies by this group have indicated that subjects with low MSLT scores may suffer from chronic insufficient sleep, as further substantiated by the finding that they have consistently higher nocturnal sleep efficiency and that their MSLT scores rise to normal values when sleep is extended. We hypothesize that the short MSLT subjects have a significant long-term sleep deficit that leads to a marked intolerance for sleep deprivation or shift work. We further suggest that this sleep debt may signify an increased sleep need in these individuals that is not met either due to personal preference or to societal pressures (or both). If this speculation is accurate, then we predict that the tolerance for sleep deprivation in such individuals can be increased by "pretreatment" with sleep extension. Thus, the present study is designed to test the following two hypotheses: (1) subjects with nominal sleep patterns who have low MSLT scores (e.g., Sleepy subjects) will show an exaggerated response (performance decrement) to sleep loss compared to subjects who have high MSLT scores (Alert subjects) on a nominal sleep schedule; (2) when permitted to extend sleep--thus discharging their sleep debt-the Sleepy subjects will show a sleep-loss response resembling that of the Alert subjects.

Carskadon, Mary A.↗
NASA NTRS1997

Individual Differences in Response to Sleep Deprivation: Assessment of Fatigue Following Sleep Loss

Previous work has indicated that a small but significant number of participants in sleep deprivation studies or in simulated shift work experiments manifests an exaggerated performance decrement when they reach a critical point in the experiment, usually near the trough of the circadian cycle or the middle of the night. Those who show this exaggerated response do not appear to differ from other normal volunteers in any substantial way according to usual screening criteria or baseline values. The present study aims to examine factors that may provide the basis for this extreme response. We propose that a preexisting sleep deficit-as manifested by low values on the Multiple Sleep Latency Test (MSLT)-may account for extreme responders. Roth and colleagues (1993) have shown that among normal volunteers screened for a variety of studies, approximately 20 to 25 percent show low (< or = 6 minutes) MSLT scores on a consistent basis, whereas a like proportion shows consistently high MSLT scores (> or = 13 minutes). Additionally, studies by this group have indicated that subjects with low MSLT scores may suffer from chronic insufficient sleep (Roth et al., 1993), as further substantiated by the finding that they have consistently higher nocturnal sleep efficiency and that their MSLT scores rise to normal values when sleep is extended (Roehrs et al., 1996). We hypothesize that the short MSLT subjects have a significant long-term sleep deficit that leads to a marked intolerance for sleep deprivation or shift work. We further suggest that this sleep debt may signify an increased sleep need in these individuals that is not met either due to personal preference or to societal pressures (or both). If this speculation is accurate, then we predict that the tolerance for sleep deprivation in such individuals can be increased by "pretreatment" with sleep extension. Thus, the present study is designed to test the following two hypotheses: subjects with nominal sleep patterns who have low MSLT scores (e.g., Sleepy subjects) will show an exaggerated response (performance decrement) to sleep loss compared to subjects who have high MSLT scores (Alert subjects) on a nominal sleep schedule. When permitted to extend sleep-thus discharging their sleep debt-the Sleepy subjects will show a sleep-loss response resembling that of the Alert subjects.

Carskadon, Mary A.↗
NASA NTRS1999

Sequence information signal processor

An electronic circuit is used to compare two sequences, such as genetic sequences, to determine which alignment of the sequences produces the greatest similarity. The circuit includes a linear array of series-connected processors, each of which stores a single element from one of the sequences and compares that element with each successive element in the other sequence. For each comparison, the processor generates a scoring parameter that indicates which segment ending at those two elements produces the greatest degree of similarity between the sequences. The processor uses the scoring parameter to generate a similar scoring parameter for a comparison between the stored element and the next successive element from the other sequence. The processor also delivers the scoring parameter to the next processor in the array for use in generating a similar scoring parameter for another pair of elements. The electronic circuit determines which processor and alignment of the sequences produce the scoring parameter with the highest value.

Peterson, John C.↗
NASA NTRSDate not supplied

Behavioral consequences of low dose radiation and sex differences in MCAT mouse model

Our study used 1-year old C57BL/6NJ male and female mice (astronaut-relevant age) that underwent exposure to 0.5 gray of gamma radiation and were euthanized 12 weeks after. In this study, we used an MCAT mouse model for mitochondrial ROS quenching, which overexpress human catalase. MCAT mice were shown to live longer and age better. Hence, in this study we determined whether quenching ROS in the mitochondria will mitigate the adverse effects of ionizing radiation exposure on spaceflight-relevant tissues. As part of the analysis, we have completed 5 different behavioral tests which focus on memory, physical stance, stress, anxiety, and other mission relevant behaviors. In the Neuro-score battery, performed after both 1and 8 weeks post IR we saw that all female groups had significantly higher scores compared to males. When comparing the baseline vs 8 weeks of radiation, we saw that all the male groups (including the sham) had lower neuro-score, pointing out to aging effect in addition to IR. In the female groups only the female IR group had lower neuro-score and the MCAT group was protected from this effect. In the Nestlet building test we saw similarly that only females were affected by radiation, having lower scores and this effect was mitigated in the MCAT animals as well. In the Catwalk test we saw that females were faster, had higher swing speed and stride length in all four paws. Males had higher stand, step cycle and max contact area. Aging is associated with slowing of gait speed, swing speed and shortening of stride length which we see in males, this is consistent with physical appearance where males look markedly older. In the Light-Dark Box test we saw that females were more frequently present in the light side and altered zones more frequently, pointing out to a more exploratory and less anxious pattern of behavior. Similarly, to what was detected in the Nest building and Neuro-score test, in the Barnes maze test, during the acquisition phase (learning) we saw that IR affected more the females who did not do better in the maze after 4 days. On the other hand, during the probe phase of the test (spatial memory) the females visited the target hole and the box quadrant more often, but also had more errors vs males, which points out to possible serial escape vs spatial escape strategy. Overall, we see that older females look physically better are faster and perform better almost in all behavioral tests compared to their male counterparts. On the other hand, they are more sensitive to low dose radiation in many cases, in some cases this effect was mitigated in the MCAT model pointing out to the importance of ROS in these stressors. In the near future we will focus on corelating these behavioral tests with molecular findings such as for example brain IHC, plasma and hippocampal cytokines in order to find specific biomarkers for behavioral deficits.

radiation↗