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DOE OSTI2017

Exploratory Modeling and Simulation of the Evolutionary Dynamics of Single-Stranded RNA Virus Populations

Infection, replication and mutation govern the population dynamics of viruses and are the key mechanisms driving their evolution. In particular, RNA viruses (such as the causative agents of Ebola, Dengue, Zika, West Nile, and SARS) have the highest mutation rates which enable them to form highly diverse populations within a single host, evade immune responses and develop resistances to drugs. Understanding the complexity of virus evolution is crucial for developing reliable countermeasures. We present an exploratory simulation model to study the evolution of heterogeneous virus populations in heterogeneous cell environments. This is a unique model that operates at three scales and captures the core mechanisms of the evolutionary process. To the best of our knowledge, this is the first HPC-based simulation of its kind.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2025

Robust Solution Verification Experiments on Nonuniform Meshes

The activities of verification, validation, and uncertainty quantification (VVUQ) provide a comprehensive means to assess the credibility of computational models. Within VVUQ, solution verification assesses numerical errors and evaluates whether the simulation is sufficiently accurate for its intended applications. As computational modeling gains traction in the development of complex, high-consequence systems, the need for robust solution verification intensifies, particularly because experimental data for these systems are often limited. This work examines improvements in the robustness of Richardson extrapolation (RE), a method commonly used in solution verification to study the discretization error of computational models using a power law. Nonuniform mesh refinement is discussed alongside other pollutants that affect the robustness of the power law model. Maximum likelihood estimation (MLE) is proposed as a robust strategy to address the uncertainty generated by nonuniform mesh refinement. An exploratory computational fluid dynamics (CFD) study of a 2D planar Poiseuille flow is conducted to determine if nonuniform mesh noise can be modeled with this MLE approach for more robust RE.

Weinmeister, Justin [ORNL] (ORCID:0000000160090237↗

DOE OSTI2014

Profitability of Cellulosic Biomass Production in the Northern Great Lakes Region

Producing bioenergy feedstocks on non‐crop land can largely avoid the food price feedbacks of energy biomass production on cropland. The U.S. northern tier grassland‐to‐forest ecotone offers large areas of marginal land that is not currently cropped. In this ecological transition zone, the relative profitability of grassy vs. woody sources of energy biomass is little studied. This paper reports an exploratory investment analysis of cellulosic biomass production in the northern Great Lakes region. It compares two short‐rotation tree crops, willow ( Salix sachalinensis F. Schmidt)and hybrid poplar ( Populus nigra L. X P. maximowiczii A. Henry), and switchgrass ( Panicum virgatum L.) (a native prairie grass) to conventional mixed hay. Because biomass markets are not yet well developed, this study calculates threshold prices and yields at which biomass crops become at least as profitable as mixed grass hay. At 2010–2012 prices and available production technologies, none of the cellulosic crops is competitive with the hay baseline system. The breakeven price of energy biomass ranges from $90–100 per oven‐dry Mg −1 for all three energy crops. Breakeven yields are much more variable, due to the high cost of harvesting woody biomass. At 2010–2012 prices, necessary biomass yield increases range from 3.5‐fold for switchgrass and willow to over 25‐fold for poplar. While the ratio of input costs to revenue remains relatively constant between the northern and southern Great Lakes regions, the opportunity cost of active cropland in the southern zone is much higher, implying an economic comparative advantage for marginal land of the northern tier of the Great Lakes region.

Kells, Bradley J.↗

DOE OSTI2025

Using feature importance as an exploratory data analysis tool on Earth system models

Abstract. Machine learning (ML) models are commonly used to generate predictions, but these models can also support the discovery of new science. Generating accurate predictions necessitates that a model captures the structure of the underlying data. If the structure is properly extracted, ML could be a useful exploratory and evidential tool. In this paper, we present a case study that demonstrates the use of ML for exploratory data analysis (EDA) in the climate space. We apply the ML explainability method of spatiotemporal zeroed feature importance (stZFI) to understand how climate-variable associations evolve over space and time. Our analyses focus on data from ensembles of Earth system models (ESMs) which provide data on different climate states and conditions. We elect to work with ESM ensembles since they allow us to compare feature importance across alternative scenarios not available with observed data. The ensembles also account for natural variability so that we can distinguish between signal and noise due to natural climate variability when computing feature importance. The use of perturbed initial condition ensembles introduces variability mimicking the natural variability in the atmosphere; thus the signals emerging using feature importance (FI) can be evaluated against the natural variability in the climate system. For our analyses, we consider the 1991 volcanic eruption of Mount Pinatubo, which was a large stratospheric aerosol injection. We explore the climate pathway associated with the eruption from aerosols to radiation to temperature at both the near-surface and stratospheric levels. In addition to applying the method to data generated from two different ESMs, we apply stZFI to reanalysis data to compare the associations identified by stZFI. We show how stZFI tracks the importance of aerosol optical depth over time on forecasting temperatures. This case study illustrates usefulness of an ML tool (stZFI) for EDA on a well-studied climate exemplar.

Ries, Daniel (ORCID:0000000250294647)↗

DOE OSTI2020

Exploratory Efforts to Constrain Geologic Material Properties from Remote Sensing Data: Joint Study (FY2020 Final Report)

Identification and characterization of underground events from surface or remote data requires a thorough understanding of the rock material properties. However, material properties usually come from borehole data, which is expensive and not always available. A potential alternative is to use topographic characteristics to approximate the strength, but this has never been done before quantitatively. Here we present the results from the first steps towards this goal. We have found that there are strong correlations between compressive and tensile strengths and slopes, but these correlations vary depending on data analysis details. Rugosity may be better correlated to strength than slope values. More comprehensive analyses are needed to fully understand the best method of predicting strength from topography for this area. We also found that misalignment of multiple GIS datasets can have a large influence on the ability to make interpretations. Lastly, these results will require further study in a variety of climatic conditions before being applicable to other sites.

58 GEOSCIENCES↗

DOE OSTI2026

From microbial diversity to functional potential using dimensionality reduction

The high dimensionality of microbial diversity data from ‘omics observations can be reduced using Machine Learning, with many recent studies showcasing ML utility for exploratory ecological feature finding and process prediction. Here, we compare the Self Organizing Map (SOM) dimensionality reduction method to the well-documented sample-based Principal Coordinate Analysis (PCoA) and taxa-based Weighted Gene Correlation Network Analysis (WGCNA) using near daily 16S rRNA gene amplicon sequencing data from the 2019 to 2020 MOSAiC International Arctic Drift Expedition. We then map k-means clustering outputs from each method to available metagenomes, extracting functionally distinct seasonal microbial ecotypes in the surface Arctic Ocean. Our results indicate the SOM method better represented expected seasonal transitions and identified a greater number of metabolically distinct functional groups than the more traditional PCoA ordination. Ultimately, we identified four community ecotypes with distinct taxonomic and functional cut-offs driven by seasonality, water mass, and substrate turnover, highlighting the importance of succession in functional diversity for the central Arctic Ocean. These results reinforce ML dimensionality reduction as a meaningful translator in the mining of historical amplicon datasets to address modern mechanistic questions and potentially provide ’omics informed ecotype diversity to leverage in mechanistic biogeochemical models.

Arctic Ocean↗

DOE OSTI2021

Exploratory Efforts to Constrain Geologic Material Properties from Remote Sensing Data: A Joint Study

42 ENGINEERING↗

DOE OSTI2015

Air temperature changes in Svalbard and the surrounding seas from 1865 to 1920

In this article, air temperature variability in the Svalbard region (74–82°N, 6–30°E) from 1865 to 1920 is presented based on a large amount of early instrumental land and marine data. Measurements were taken during many exploratory and scientific expeditions to the study area. In addition, changes of air temperature from historical times to the present day (1981–2010) were investigated. Present-day air temperatures for land were taken from regular meteorological stations and from campaign measurements, while for marine environment data were taken from three reanalysis products: 20CR, ERA-20C and ERA-Interim. Analysis reveals that Svalbard (land data) in the period 1865–1920 was markedly colder than today (1981–2010, by about 3 °C) in all seasons except summer, when air temperature was similar in both periods. However, the majority of mean monthly air temperatures in historical times still lie within two standard deviations (SDs) from the modern mean. Marine data show good correspondence with land data. The May–September average air temperature for the period 1871–1910 was slightly lower in the seas surrounding Svalbard (by 0.4 °C) than today. About 90% of these mean air temperatures lie within the range of ±3SDs from the ERA-Interim present-day mean. Seasonal patterns of air temperature changes in the Svalbard archipelago between historical and standard normal (1961–1990) periods show generally very good correspondence to similar changes, not only for the Atlantic region, but also for some other Arctic regions (e.g. SE Greenland, Canadian Arctic, Barents and Kara seas), as well as for the entire Arctic. All available reconstructions of annual air temperature using isolated series of early instrumental observations reveal that in historical times, air temperatures were colder than the standard normal (1961–1990) by about 0.5–1.0 °C. Compared with the new normal period (1981–2010), those differences rise to about 1.5–2.5 °C.

Meteorology & Atmospheric Sciences↗

DOE OSTI2024

Feasibility of Hybrid Electro- and Acousto-Dynamical Systems for Quantum Optical Networks

We present results on an exploratory research program that aims to study the feasibility of novel, sustained, dense, and efficient quantum information generation, storage, retrieval, relay and distribution devices and systems towards future advanced quantum networking architectures. The pathfinder program is anchored on original ideas and device conceptual designs that turn the phononic-sourced challenges into an opportunity towards optimized transduction chains over a broad range of length and energy/temperature scales. The proposal involves conceptual design and feasibility towards integration, commissioning and benchmarking of challenging transduction devices in a scaled quantum network setup.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2021

Pilot-Scale Testing of an Integrated Circuit for the Extraction of Rare Earth Minerals and Elements from Coal and Coal Byproducts Using Advanced Separation Technologies

The primary objective of this project was to develop and demonstrate an integrated pilot-scale circuitry for recovering high-value rare earth elements (REEs) from coal and coal byproducts. The target performance was to produce a mixed REE product with content of at least two percent by weight on a dry mass basis in a cost-effective and environmentally benign manner. During the first nine months of the project period (Phase 2 Budget Period 2), pilot plant construction was completed including all field site startup activities such as permitting, engineering design, procurement/bidding, unit fabrication, site construction, equipment installation, module assembly, safety training, and circuit shakedown. During the remaining 21 months of project period (Phase 2 Budget Period 3), detailed field-testing activities were performed including feedstock sample collection and preparation, exploratory testing, circuit modification, detailed parametric study, and performance optimization. A detailed techno-economic analysis was performed based on the pilot plant testing findings which provided various scenarios for REE production. The project successfully accomplished the proposed target performance by producing mixed rare earth oxide (REO) with greater than 90% purity by weight in a continuous pilot scale operation from two distinctly different coarse refuse materials (i.e., West Kentucky No. 13 and Fire Clay coal seams), and at least three secondary sources (i.e., heap leach process and naturally formed acid mine drainage system). Project partners included the University of Kentucky, Virginia Tech, West Virginia University, Alliance Coal, Blackhawk Mining, Mineral Refining Company, and Mineral Separation Technologies. The pilot scale test facility was constructed at a former mining complex owned by Alliance Natural Resource Partners (Alliance Coal). The site was rehabilitated to accommodate the equipment installation, construction and fabrication, electrical power requirement, water line management and containment. The process units constructed and installed included X-ray sorting unit, crushing and grinding unit, physical separation unit, acid leaching unit, solvent extraction unit, and wastewater management unit. A rare earth mineral concentration unit was constructed as a standalone unit for flexible operation. A detailed environmental assessment and control plan was carried out to identify and quantify any potential impacts of the pilot-scale processing circuitry on the human and eco-system health and well-being. Corresponding mitigation strategies and control measures were provided. A conceptual flowsheet was developed to effectively remove thorium and uranium from high purity rare earth oxide mix or any potential radionuclide enriched stream. The two distinct feedstock materials were secured from the Blackhawk Mining Complex in eastern Kentucky where the Fire Clay (Hazard No. 4) seam is processed. The West Kentucky No. 13 (Baker) coarse refuse material was collected from an active process stream at an Alliance coal preparation plant located in western Kentucky. Characterization analysis indicated that both of feed materials generated from the two sources contained >300 ppm of TREEs on a dry whole mass basis which met the requirements for a qualified feed stock. The two feedstocks were further upgraded using a dual x-ray sorter to prepare the feed material for hydrometallurgical circuit. Thermal treatment on feed material prior to leaching was found to: 1) improve the leaching recovery of REEs, 2) increase the leaching kinetics, and 3) allow the leaching reaction to occur at lower acidity. Roasting at 600°C was selected as the pre-treatment condition for both West Kentucky No. 13 and Fire Clay coarse refuse material. Over 40% of leaching recovery was achieved by roasting West Kentucky No. 13 material having a top particle size of 3 mm in the pilot scale operation using 1.2M sulfuric acid leaching at 75OC. Initial pilot scale testing involved continuous operation of the pilot plant for 94 hours. The leaching unit was operated at solid-to-liquid ratio of 1 to 10 (w/v) using 0.5M sulfuric acid solution at a temperature of 75°C. The continuous solvent extraction circuit utilized rougher and cleaner units with DEHPA and TBP as the extractants. An innovative stripping circuit was developed to accumulate the REE concentration in the stripping solution to a level above 600 ppm. A bleed stream from the recycled strip solution was treated using oxalic acid precipitation which produced a high grade rare earth oxalate. The oxalate product was roasted to remove the oxalate which produced a rare earth oxide product having a purity greater 90%. Due to high concentrations of contaminant ions in the pregnant leach solution (PLS), a modified flowsheet was developed that involved pre-concentration of the REEs using multiple stages of precipitation and redissolution. The advantage of this process was improved removal of contamination before the downstream purification process and a significant cost reduction relative to the circuit that utilized the solvent extraction process. The modified circuitry included processes involving leaching, multistage precipitation, redissolution, and oxalate precipitation followed by roasting of the oxalate product. The circuit produced a mixed REO that was 92.96% pure from the initial test. A detailed parametric test plan was carried out which involved varying key parameters including solids feed rate, acid flowrate, acid concentration, multistage precipitation pH, redissolution pH, oxalate precipitation dosage and pH. The response variables included REE recovery, contaminant recovery, REO product grade and overall chemical consumption. Test results indicated that the acid-to-solid ratio is the key parameter to leaching efficiency as performance deteriorated with an increase in solids concentration. The optimal pH determined for REE precipitation and redissolution was 6.5 and 2.5, respectively. Additional tests were conducted to further improve the flowsheet. Recirculating a portion of the PLS to the feed of the leach tanks improved the leaching performance by lowering the pH of the leaching system and reducing the contamination recovery by shortening the residence time. Moreover, the removal of Al prior to REE precipitation significantly reduced the oxalic acid consumption in the oxalate precipitation circuit. The modified circuit produced over 90% grade REO by weight from both West Kentucky No. 13 and Fire Clay coarse refuse material in pilot scale continuous test programs. A case analysis model was developed to project the REE and major contaminants concentration in each PLS stream based on the leaching condition, pH cut point, and oxalic acid dosage. A correlation was established using empirical and semi-empirical models. Using the models, chemical consumption required for each stage was predicted based on the projected performance of the hydrometallurgy circuit. After identifying the optimum conditions, validation tests were carried out for the treatment of both West Kentucky No. 13 and Fire Clay coarse refuse materials in the pilot plant. The actual circuit performance and chemical consumptions were very close to the model predictions. Other than the two coarse refuse sources, several secondary feedstocks were also tested in the pilot plant facility. A “heap leach” system was constructed using the coarse refuse material generated from cleaning the West Kentucky No. 13 seam coal. Using the two stage SX rougher and cleaner circuit, a concentrate with a grade >90% REO was produced while recovering >97% of the REEs from the heap leach PLS. Naturally generated acid mine drainage (AMD) from West Kentucky No.13 mine was processed using the multistage precipitation circuit in the pilot plant in a test conducted for a period of 32 hours. The final grade of the mix RE oxide produced from the AMD was 90.84% with an overall circuit recovery of 64%. The primary source of REE was the selective precipitation steps involving iron and aluminum rejection. The hydrophobic-hydrophilic separation (HHS) process was proven to effectively recover coal from fine waste materials. For REM recovery, the HHS process was able to produce concentrates at grades of approximately 1.8% REE on an ash basis; however, recovery values were typically low, <10%, under the optimal conditions determined in the laboratory-scale testing. Staged testing of the pilot-scale HHS process for coal recovery and semi-continuous laboratory testing for REM testing showed that a total concentration ratio of more than 15x was observed for the REM recovery process. A circuit simulation package was developed for REE extraction and purification using a spreadsheet-based platform (Microsoft Excel). The REESim circuit simulation package is configured to track the mass and volume flows of components passing through a series of unit operations specified and configured by the user. The mass rates can then be utilized by the user to determine important performance indicators such as product mass yields, concentrate purity levels, element-by-element recoveries, and so forth. The techno-economic analysis showed that the roasting and leaching operations were the most expensive capital items, each contributing approximately 30% to the total capital cost. One notable contributor to the high production costs was the low REE recovery observed in the pilot scale trials. The product basket price was shown to have a strong influence on the economic viability of the scenarios, with the scandium price being the most significant influencer. Operating cost was shown to be extremely sensitive to REE recovery, REE feed grade, and leaching acid consumption. An analysis of ten different scenarios for a 500 t/h commercial operation revealed that three were economically favorable, producing internal rates of return varying from 27.7% to 33.1% and payback periods of 4 to 5 years. The project successfully developed and demonstrated a process to recover REEs from coal and coal byproducts in a pilot-plant operation which consistently produced over 90% grade REO mix from varies types of feedstocks. Commercialization analysis showed that the technology readiness level successfully achieved TRL 6 at the end of the project and demonstrated the need and the potential for scaling the process to further advance the technologies toward the goal of providing a domestic supply of REEs at a commercial scale.

01 COAL, LIGNITE, AND PEAT↗

DOE OSTI2022

Reduced Order Models Generation for HTGRs Pebble Shuffling Procedure Optimization Studies

This report provides an initial study for producing reduced-order models (ROMs) of pebble-bed high temperature gas reactor (HTGR) models for the purposes of design optimization. As an initial study, this work is meant to be exploratory---identifying useful workflows and methods for ROM generation---and not meant to be a catch-all analysis of HTGR ROM generation and usage for optimization. This report summarizes three tasks performed in Fiscal Year 2022: 1) the creation of HTGR model, 2) the sensitivity analysis of model design parameters, and 3) an introduction to ROM generation techniques. The representative HTGR model created in this work is a multiphysics equilibrium-core using the BlueCRAB (comprehensive reactor analysis bundle) reactor analysis application, coupling four physical phenomena: neutronics, streamline depletion, porous flow thermal hydraulics, and pebble heat conduction. Part of the model creation was identifying some design parameters and quantities of interest that are relevant in an optimization analysis and adjustable in the model. The sensitivity analysis utilized a polynomial chaos expansion methodology to compute global sensitivity metrics. This analysis showed that thermal hydraulics parameters and quantities of interest had a relatively small impact on simulation results. Finally, the ROM generation work involved exploring three different ROM methodologies: polynomial regression, a Gaussian process, and artificial neural networks. Using a cross-validation technique to characterize ROM performance, the Gaussian process and single-layer artificial neural networks showed the most promising results. Overall, this study was insightful and the lessons learned will be invaluable for the eventual development of an HTGR design optimization workflow.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2017

Integrated Nondestructive Assay Systems to Estimate Plutonium in Spent Fuel Assemblies

An integrated nondestructive assay (NDA) system combining active (neutron generator) and passive neutron detection and passive gamma (PG) detection is being analyzed in order to estimate the amount of plutonium, verify initial enrichment, burnup, and cooling time, and detect partial defects in a spent fuel assembly (SFA). Active signals are measured using the differential die-away (DDA), delayed neutron (DN), and delayed gamma (DG) techniques. Passive signals are measured using total neutron (TN) counts and both gross and spectral resolved gamma counts. To quantify how a system of several NDA techniques is expected to perform, all of the relevant NDA techniques listed above were simulated as a function of various reactor conditions such as initial enrichment, burnup, cooling time, assembly shuffling pattern, reactor operating conditions (including temperature, pressure, and the presence of burnable poisons) by simulating the NDA response for five sets of light water reactor assemblies. In this study, we compare the performance of several exploratory model-fitting options (including neural networks, adaptive regression with splines, iterative bias reduction smoothing, projection pursuit regression, and regression with quadratic terms and interaction terms) to relate data simulated with measurement and model error effects from various subsets of the NDA techniques to the total Pu mass. Isotope masses for SFAs and expected detector responses (DRs) for several NDA techniques are simulated using MCNP, and the DRs become inputs to the fitting process. Such responses include eight signals from DDA, one from DN, one from TN, and up to seven from PG; the DG signal will be examined separately. Results are summarized using the root-mean-squared estimation error for plutonium mass in held-out subsets of the data for a range of model and measurement error variances. Different simulation assumptions lead to different spent fuel libraries relating DRs to Pu mass. Some results for training with one library and testing with another library are also given.

98 NUCLEAR DISARMAMENT, SAFEGUARDS, AND PHYSICAL P↗

DOE OSTI2013

Occurrence and Stabilization of Colloidal and Nanoparticulate Mercury Sulfides (Final Technical Report)

Mercury contaminates the soils at several DOE sites, including facilities at Oak Ridge, Hanford, and the Savannah River site. This contamination poses a hazard to surrounding ecosystems because the metal continuously leaches to local streams and rivers and is converted to methylmercury, a neurotoxic and highly bioaccumulative form of mercury. Anaerobic microorganisms such as sulfate-reducing and iron-reducing bacteria are primarily responsible for methylmercury production in the environment. The remediation of mercury-contaminated ecosystems is difficult because we lack a poor understanding of the factors that control mercury mobilization and methylation in sediments. The objective of this project was to investigate the forms of mercury that are bioavailable to methylating bacteria. This project specifically focused on mercury-sulfide-organic matter geochemistry and the persistence of nanoparticulate HgS that are intermediates of metal sulfide precipitation reactions occurring in the presence of natural organic matter (NOM). This work involved two specific aims: 1) Investigate environmental processes that facilitate the occurrence and stabilization of nanoparticulate HgS and other metal sulfides; and (2) Investigate the occurrence of nanoparticulate HgS in sediments of the East Fork Poplar Creek, located in near a DOE facility in Oak Ridge, TN. For specific aim #1, laboratory experiments were performed to simulate metal sulfide precipitation processes in environmental systems. A key experimental variable was the type and concentration of NOM that was used in the experiments simulating metal sulfide precipitation. The characteristics of the metal sulfide nanoparticles were analyzed using a suite of analytical techniques that included X-ray based methods at DOE synchrotron facilities (National Synchrotron Light Source and Advanced Photon Source). Field sampling in Oak Ridge was performed in collaboration with colleague at the Oak Ridge National Laboratory. The experimental results demonstrated that a combination of photon scattering and spectroscopic tools can be used to characterize nanoscale structures that form during heterogeneous metal-sulfide-NOM precipitation. ZnS precipitation experiments with a low molecular weight thiol indicated the importance of specific metal-ligand binding for altering the kinetics of ZnS nanoparticle aggregation and monomer growth. However, NOM consists of macromolecular structures that may interact with nano-HgS in non-specific ways (e.g. electrosteric surface interactions. The methylation potential of nano-HgS in sediments may depend on size and crystallinity of nanoparticles and interactions with NOM. For specific aim #2, sediment core samples were collected in three separate locations along the East Fork Poplar Creek in Oak Ridge, TN in June 2011. Analysis of the sediment samples indicated that more than 90% of the Hg in the porewater is associated with colloidal material. These results highlight the importance of small particles for dominating Hg speciation in sediment porewater. Overall, this project highlights the importance of nanoscale phases for mercury speciation. Nanoparticulate HgS will be of particular importance in the anaerobic habitats of methylating microorganisms. In previous models for Hg bioavailability, nanoparticles of Hg are presumed to be unavailable for methylating bacteria. However, recent work shows that nanoparticles contribute to bioavailable Hg that greatly exceeds the contribution from larger HgS microparticles (Zhang et al., 2012). The work performed for this exploratory project lays the groundwork for further study on nanoscale mercury sulfide geochemistry and contributes to an improved understanding of Hg bioavailability and methylation potential.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2013

Approach to Cerebrospinal Fluid (CSF) Biomarker Discovery and Evaluation in HIV Infection

Central nervous system (CNS) infection is a nearly universal facet of systemic HIV infection that varies in character and neurological consequences. While clinical staging and neuropsychological test performance have been helpful in evaluating patients, cerebrospinal fluid (CSF) biomarkers present a valuable and objective approach to more accurate diagnosis, assessment of treatment effects and understanding of evolving pathobiology. We review some lessons from our recent experience with CSF biomarker studies. We have used two approaches to biomarker analysis: targeted, hypothesis-driven and non-targeted exploratory discovery methods. We illustrate the first with data from a cross-sectional study of defined subject groups across the spectrum of systemic and CNS disease progression and the second with a longitudinal study of the CSF proteome in subjects initiating antiretroviral treatment. Both approaches can be useful and, indeed, complementary. The first is helpful in assessing known or hypothesized biomarkers while the second can identify novel biomarkers and point to broad interactions in pathogenesis. Common to both is the need for well-defined samples and subjects that span a spectrum of biological activity and biomarker concentrations. Previouslydefined guide biomarkers of CNS infection, inflammation and neural injury are useful in categorizing samples for analysis and providing critical biological context for biomarker discovery studies. CSF biomarkers represent an underutilized but valuable approach to understanding the interactions of HIV and the CNS and to more objective diagnosis and assessment of disease activity. Both hypothesis-based and discovery methods can be useful in advancing the definition and use of these biomarkers.

Biomarkers↗

DOE OSTI2017

Hidden Chloroplast Functions Revealed Through Deep Genomic Analysis

Photosynthesis is a highly complex and orchestrated process that is tuned to plant development and the environment. This project has focused on the gene regulatory mechanisms underlying the elaboration of the photosynthetic apparatus. Particular emphasis has been placed on the regulation of genes encoded in the chloroplast compartment, where photosynthesis takes place. These genes specify both major core proteins of the photosynthetic complexes, as well as proteins that have structural or regulatory properties. The project initially identified structures and sequences in the mRNAs encoding these proteins, that allowed them to be properly regulated during plant development, as well as regulatory proteins that interact with these sequences and structures. It was shown that much of the regulation of chloroplast genes was exerted at the RNA level, rather than at the level of gene transcription. Then, insights were obtained into the enzyme functions that are required to express these RNAs correctly. A series of ribonucleases was studied, with each one having particular specificities and roles in RNA maturation and decay. In the latter part of the project, new genomics tools were employed to study gene regulation in a data-intensive and exploratory manner. Through this work, a new class of “non-coding” and mainly antisense RNAs was identified in chloroplasts, and some of these appear to in fact encode novel proteins that may have unstudied roles in photosynthesis. Overall, the project has taken a trajectory from gene-by-gene analysis to a holistic study of chloroplast coding capacity, including opening the door to identifying new chloroplast-encoded photosynthetic functions.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2020

Advancing Diagnostic Model Evaluation to Better Understand Water Shortage Mechanisms in Institutionally Complex River Basins

Abstract Water resources systems models enable valuable inferences on consequential system stressors by representing both the geophysical processes determining the movement of water and the human elements distributing it to its various competing uses. This study contributes a diagnostic evaluation framework that pairs exploratory modeling with global sensitivity analysis to enhance our ability to make inferences on water scarcity vulnerabilities in institutionally complex river basins. Diagnostic evaluation of models representing institutionally complex river basins with many stakeholders poses significant challenges. First, it needs to exploit a large and diverse suite of simulations to capture important human‐natural system interactions as well as institutionally aware behavioral mechanisms. Second, it needs to have performance metrics that are consequential and draw on decision‐relevant model outputs that adequately capture the multisector concerns that emerge from diverse basin stakeholders. We demonstrate the proposed model diagnostic framework by evaluating how potential interactions between changing hydrologic conditions and human demands influence the frequencies and durations of water shortages of varying magnitudes experienced by hundreds of users in a subbasin of the Colorado River. We show that the dominant factors shaping these effects vary both across users and, for an individual user, across percentiles of shortage magnitude. These differences hold even for users sharing diversion locations, demand levels or water right seniority. Our findings underline the importance of detailed institutional representation for such basins, as institutions strongly shape how dominant factors of stakeholder vulnerabilities propagate through the complex network of users.

Hadjimichael, Antonia↗

DOE OSTI2020

Trait Vs. Skill: Individual Differences -- Survey Questions

This document is UUR survey questions for use in an exploratory express LDRD experiment. The purpose of the study is to understand if people overestimate their performance only in some situations or some people are more prone to it do to an underlying trait. To investigate our aims, we must use 3 experimental tasks: two domain general (an English grammar task a logic task) and a domain specific task (a science & technology questionnaire). The reason we are using these tasks is to see if people overestimate their abilities on tasks they are more familiar with (grammar and logic) but not on domains in which they are more specialized (science and technology). To understand the traits and characteristics of our participants, we are using 7 well-validated assessments from the field of psychology. All questionnaires are available for research and teaching purposes. Citations for all materials have been included.

99 GENERAL AND MISCELLANEOUS↗

DOE OSTI2021

A Publicly Available, Annotated Dataset for Naturalistic Driving Study and Computer Vision Algorithm Development

Oak Ridge National Laboratory developed and implemented a data collection effort to create a dataset for use in evaluating and testing algorithms for analyzing driver behavior under controlled settings for support of the Federal Highway Administration’s Exploratory Advanced Research Program. This collection is called the ORNL Naturalistic Driving Study Sample (ONDSS). The dataset is designed to emulate aspects of the Second Strategic Highway Research Project (SHRP2), which contained a massive naturalistic driving study (NDS) with over 3000 drivers between 2010 and 2013 using their personal vehicles, with over 4300 person-years of data collected [HANKEY].

42 ENGINEERING↗