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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 109 records · Page 6
All sourcesNASA NTRSDOE OSTI
NASA NTRS1976

Variability of wetland reflectance and its effect on automatic catergorization of satellite imagery

The author has identified the following significant results. A technique for training automated analysis of satellite multispectral data based on in situ measurements of target reflectance was tested and applied in delineating cover communities in Delaware's tidal wetlands. Land cover categorization of data from the same overpass in four test wetland areas was carried out using a four category classification system. The tests indicate that training data based on in situ reflectance measurements and atmospheric correction of LANDSAT data can produce comparable accuracy of categorization to that achieved using more conventional relative radiance training. Analysis of the four wetlands cover categories (salt marsh cordgrass, salt hay, water, and unvegetated tidal flat) produced average categorization accuracies of 82.1% by conventional relative radiance training and 81.4% by use of in situ reflectance measurements.

Klemas, V.↗
NASA NTRS1982

Development and testing of LANDSAT-assisted procedures for cost-effective forest management

The capability of LANDSAT data to make certain forest management activities on the Clearwater National Forest in Idaho more efficient and/or more effective was examined. One task was designed to evaluate the utility of single-date categorized LANDSAT data as a source of land cover information for use in assessing elk habitat quality. LANDSAT data was used to categorize conifer forest on the basis of the percentage crown closure. This information was used to evaluate elk habitat quality on the basis of the ratio of cover to forage. A preliminary conclusion is that categorized LANDSAT data can be helpful for assessing current elk habitat quality if the relationships between crown closure and hiding cover can be adequately defined. Another task was designed to evaluate the utility of merged two-date LANDSAT data for updating the existing (1972) Clearwater Forest land cover information. LANDSAT data from 1972 and 1981 were merged, and change images were created. These products indicated where major changes were taking place. These areas could then be examined on aerial photography or in the field to further characterize the nature and magnitude of the change. The 1972 land cover information could be subsequently altered in these changed areas, whereas areas with no change would not have to be re-examined.

Colwell, J. E.↗
NASA NTRS1993

Designers' models of the human-computer interface

Understanding design models of the human-computer interface (HCI) may produce two types of benefits. First, interface development often requires input from two different types of experts: human factors specialists and software developers. Given the differences in their backgrounds and roles, human factors specialists and software developers may have different cognitive models of the HCI. Yet, they have to communicate about the interface as part of the design process. If they have different models, their interactions are likely to involve a certain amount of miscommunication. Second, the design process in general is likely to be guided by designers' cognitive models of the HCI, as well as by their knowledge of the user, tasks, and system. Designers do not start with a blank slate; rather they begin with a general model of the object they are designing. The author's approach to a design model of the HCI was to have three groups make judgments of categorical similarity about the components of an interface: human factors specialists with HCI design experience, software developers with HCI design experience, and a baseline group of computer users with no experience in HCI design. The components of the user interface included both display components such as windows, text, and graphics, and user interaction concepts, such as command language, editing, and help. The judgments of the three groups were analyzed using hierarchical cluster analysis and Pathfinder. These methods indicated, respectively, how the groups categorized the concepts, and network representations of the concepts for each group. The Pathfinder analysis provides greater information about local, pairwise relations among concepts, whereas the cluster analysis shows global, categorical relations to a greater extent.

Gillan, Douglas J.↗
NASA NTRS2002

Observations of Closed Structures at the Magnetopause: A Case for Multiple Reconnections

We further analyze a case of Interball LLBL crossing on the dusk flank of geomagnetosphere under southward magnetosheath magnetic field, previously categorized as an interval of highly structured LLBL. These conditions of highly structured LLBL include reconnection signatures. Observed ion velocity distributions with LLBL are quite variable. D-shaped distributions that are associated with the open reconnected flux tube are observed at the boundaries of LLBL transients and sometimes within the LLBL transients. In most cases the ion velocity distributions consist of two magnetosheath-type components with different velocities parallel to the magnetic field, or of three components one of which has nearly zero Vpar. The shapes of ion velocity distributions and their evolution with decreasing number density in LLBL indicate that most of LLBL is located on closed magnetic field lines. These observations strongly favor multiple reconnections between magnetosheath and magnetosphereric flux tubes, creating long spiral flux tube islands at the magnetopause. We report evidence for the simultaneous occurrence of magnetic reconnection at multiple points across the magnetopause, as has been proposed and found to occur in magnetopause simulations. The evidence is in the form of highly structured distributions of ions in velocity parallel to the local magnetic field direction, within the magnetopause and low latitude boundary layer region, from the Interball-Tall spacecraft. We interpret these distributions as a natural consequence of the formation of spiral magnetic flux tubes consisting of a mixture of alternating segments originating from the magnetosheath or interplanetary plasma and from the low latitude boundary layer or magnetospheric plasma. We further analyze a case of Interball LLBL crossing on the dusk flank of geomagnetosphere under southward magnetosheath magnetic field, previously categorized as an interval of highly structured LLBL. These conditions of highly structured LLBL include reconnection signatures. Observed ion velocity distributions with LLBL are quite variable. D-shaped distributions that are associated with the open reconnected flux tube are observed at the boundaries of LLBL transients and sometimes within the LLBL transients. In most cases the ion velocity distributions consist of two magnetosheath-type components with different velocities parallel to the magnetic field, or of three components one of which has nearly zero Vpar. The shapes of ion velocity distributions and their evolution with decreasing number density in LLBL indicate that most of LLBL is located on closed magnetic field lines. These observations strongly favor multiple reconnection between magnetosheath and magnetospheric flux tubes, creating long spiral flux tube islands at the magnetopause. We report evidence for the simultaneous occurrence of magnetic reconnection at multiple points across the magnetopause, as has been proposed and found to occur in magnetopause simulations. The evidence is in the form of highly structured distributions of ions in velocity parallel to the local magnetic field direction, within the magnetopause and low latitude boundary layer region, from the Interball-Tail spacecraft. We interpret these distributions as a natural consequence of the formation of spiral. We further analyze a case of Interball LLBL crossing on the dusk flank of geomagnetosphere under southward magnetosheath magnetic field, previously categorized as an interval of highly structured LLBL. These conditions of highly structured LLBL include reconnection signatures. Observed ion velocity distributions with LLBL are quite variable. D-shaped distributions that are associated with the open reconnected flux tube are observed at the boundaries of LLBL transients and sometimes within the LLBL transients. In most cases the ion velocity distributions consist of two magnetosheath-type components with different velocities parallel to the magnetic field, or of three components one of which has nearly zero Vpar.

Vaisberg, O. L.↗
NASA NTRS2002

Determination of Storm Flashing/Non-Flashing Condition From Convective and Environmental Observations

A simple and fundamental problem in cloud electrification is whether or not a cloud can be determined to be producing lightning or not producing lightning, based solely on knowledge of its microphysical (and perhaps environmental) state. A merged database of TRMM radar, microwave and lightning observations and NCEP reanalysis environmental parameters is used to answer this question, for the tropics. The formal skill of traditional, univariate rule-based approaches (e.g., 35 dBZ occurrence at 6 km altitude) is quantified (via the probability of detection (POD), false alarm rate (FAR) and critical skill index (CSI)). Under indiscriminate application to the tropics, peak rule-based CSI for categorization of flashing storms is approximately 50%, with peak POD approximately 67% and minimum FAR approximately 33%, with peak CSI found for radar reflectivity-based parameters at 7-7.5 km altitude (near -15C). Separation of land and ocean domains yields approximately 5-10% gains in CSI over land. Conventional multivariate categorization techniques (discriminant analysis) are then applied, and less conventional (neural network) categorization techniques are also discussed.

Boccippio, Dennis J.↗
NASA NTRSDate not supplied

An Approach to Shape Parameterization Using Laboratory Hypervelocity Impact Experiments

NASA’s Orbital Debris Program Office relies on laboratory-based impact tests to supplement the measurement data of on-orbit events that defines the orbital debris environment. These experiments provide information that is essential to interpreting the radar and optical measurements of orbital fragmentation events into useful metrics, such as characteristic size of the debris, and to providing a better understanding of the distributions of fragment populations in terms of their masses, material constituents, fragment densities, cross-sectional areas, area-to-mass ratios, shapes, etc. The Satellite Orbital Debris Characterization Impact Test (SOCIT) was a notable laboratory impact experiment conducted in 1992 using a surplus U.S. Navy Transit navigation satellite of the 1960s. The data from this ground-based experiment were combined with on-orbit measurements to develop the NASA Standard Satellite Breakup Model (SSBM). To account for advancements in satellite design and construction since, a new impact test series – DebriSat – was conducted in 2014. This test utilized a high-fidelity mock-up spacecraft that better represents the materials and construction techniques used to design and manufacture modern spacecraft. Together, these tests offer valuable data to model an orbital debris environment composed of legacy and modern spacecraft. This paper presents an overview of the two laboratory impact tests, comparing their fragment parameter distributions with each other and with relevant distributions from the NASA SSBM. The categorization and descriptions of fragment shapes are of significant interest for future work, yet there are marked differences in the definitions of shape categories between each dataset. The categorizations of constituent materials, and the measurement techniques employed to populate these two datasets, are also different. New rubrics simplify and equate the categorizations between datasets to aid comparative analyses and to facilitate the potential use of both datasets in tandem with future environmental debris models. A preferred approach to classifying shape across disparate datasets uses the characteristic-length dimensions, and a simplified shape classification based on physical, solid-body dimensions, to mathematically construct an encapsulating right-circular cylinder that represents the fragment. The ratio of cylinder length-to-diameter (L:D) then provides a single continuum value for shape that is strongly correlated with its designated shape and size. This metric can then be used to further assess the distribution of shape with populations of other fragment characteristics within these datasets. The shape parameterization using the L:D ratios of right-circular cylinders is discussed.

John H. Seago↗
NASA NTRSDate not supplied

An Approach to Shape Parameterization Using Laboratory Hypervelocity Impact Experiments

NASA’s Orbital Debris Program Office relies on laboratory-based impact tests to supplement the measurement data of on-orbit events that defines the orbital debris environment. These experiments provide information that is essential to interpreting the radar and optical measurements of orbital fragmentation events into useful metrics, such as characteristic size of the debris, and to providing a better understanding of the distributions of fragment populations in terms of their masses, material constituents, fragment densities, cross-sectional areas, area-to-mass ratios, shapes, etc. The Satellite Orbital Debris Characterization Impact Test (SOCIT) was a notable laboratory impact experiment conducted in 1992 using a surplus U.S. Navy Transit navigation satellite of the 1960s. The data from this ground-based experiment were combined with on-orbit measurements to develop the NASA Standard Satellite Breakup Model (SSBM). To account for advancements in satellite design and construction since, a new impact test series – DebriSat – was conducted in 2014. This test utilized a high-fidelity mock-up spacecraft that better represents the materials and construction techniques used to design and manufacture modern spacecraft. Together, these tests offer valuable data to model an orbital debris environment composed of legacy and modern spacecraft. This paper presents an overview of the two laboratory impact tests, comparing their fragment parameter distributions with each other and with relevant distributions from the NASA SSBM. The categorization and descriptions of fragment shapes are of significant interest for future work, yet there are marked differences in the definitions of shape categories between each dataset. The categorizations of constituent materials, and the measurement techniques employed to populate these two datasets, are also different. New rubrics simplify and equate the categorizations between datasets to aid comparative analyses and to facilitate the potential use of both datasets in tandem with future environmental debris models. A preferred approach to classifying shape across disparate datasets uses the characteristic-length dimensions, and a simplified shape classification based on physical, solid-body dimensions, to mathematically construct an encapsulating right-circular cylinder that represents the fragment. The ratio of cylinder length-to-diameter (L:D) then provides a single continuum value for shape that is strongly correlated with its designated shape and size. This metric can then be used to further assess the distribution of shape with populations of other fragment characteristics within these datasets. The shape parameterization using the L:D ratios of right-circular cylinders is discussed.

John H Seago↗
DOE OSTI2024

Hybrid Parameter Search and Dynamic Model Selection for Mixed-Variable Bayesian Optimization

Herein this article presents a new type of hybrid model for Bayesian optimization (BO) adept at managing mixed variables, encompassing both quantitative (continuous and integer) and qualitative (categorical) types. Our proposed new hybrid models (named hybridM) merge the Monte Carlo Tree Search structure (MCTS) for categorical variables with Gaussian Processes (GP) for continuous ones. hybridM leverages the upper confidence bound tree search (UCTS) for MCTS strategy, showcasing the tree architecture’s integration into Bayesian optimization. Our innovations, including dynamic online kernel selection in the surrogate modeling phase and a unique UCTS search strategy, position our hybrid models as an advancement in mixed-variable surrogate models. Numerical experiments underscore the superiority of hybrid models, highlighting their potential in Bayesian optimization.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2020

Long-Range Ordered Amorphous Atomic Chains as Building Blocks of a Superconducting Quasi-One-Dimensional Crystal

Crystalline and amorphous structures are two of the most common solid-state phases. Crystals having orientational and periodic translation symmetries are usually both short-range and long-range ordered, while amorphous materials have no long-range order. Short-range ordered but long-range disordered materials are generally categorized into amorphous phases. In contrast to the extensively studied crystalline and amorphous phases, the combination of short-range disordered and long-range ordered structures at the atomic level is extremely rare and so far has only been reported for solvated fullerenes under compression. Here, a report on the creation and investigation of a superconducting quasi-1D material with long-range ordered amorphous building blocks is presented. Using a diamond anvil cell, monocrystalline (TaSe4)(2)I is compressed and a system is created where the TaSe(4)atomic chains are in amorphous state without breaking the orientational and periodic translation symmetries of the chain lattice. Strikingly, along with the amorphization of the atomic chains, the insulating (TaSe4)(2)I becomes a superconductor. The data provide critical insight into a new phase of solid-state materials. The findings demonstrate a first ever case where superconductivity is hosted by a lattice with periodic but amorphous constituent atomic chains.

combination of crystalline and amorphous structure↗
DOE OSTI2024

Modern chemical graph theory

Abstract Graph theory has a long history in chemistry. Yet as the breadth and variety of chemical data is rapidly changing, so too do graph encoding methods and analyses that yield qualitative and quantitative insights. Using illustrative cases within a basic mathematical framework, we showcase modern chemical graph theory's utility in Chemists' analysis and model development toolkit. The encoding of both experimental and simulation data is discussed at various levels of granularity of information. This is followed by a discussion of the two major classes of graph theoretical analyses: identifying connectivity patterns and partitioning methods. Measures, metrics, descriptors, and topological indices are then introduced with an emphasis upon enhancing interpretability and incorporation into physical models. Challenging data cases are described that include strategies for studying time dependence. Throughout, we incorporate recent advancements in computer science and applied mathematics that are propelling chemical graph theory into new domains of chemical study. This article is categorized under: Molecular and Statistical Mechanics > Molecular Dynamics and Monte‐Carlo Methods Structure and Mechanism > Computational Materials Science Structure and Mechanism > Molecular Structures

Leite, Leonardo S. G.↗
DOE OSTI2020

Coordinative Entities: Forms of Organizing in Data Intensive Science

Scientific collaboration is a long-standing subject of CSCW scholarship that typically focuses on the development and use of computing systems to facilitate research. The research presented in this article investigates the sociality of science by identifying and describing particular, common forms of organizing that researchers in four different scientific realms employ to conduct work in both local contexts and as part of distributed, global projects. This paper introduces five prototypical forms of organizing we categorize as coordinative entities: the Principal Group, Intermittent Exchange, Sustained Aggregation, Federation, and Facility Organization. Coordinative entities as a categorization help specify, articulate, compare, and trace overlapping and evolving arrangements scientists use to facilitate data intensive research. We use this typology to unpack complexities of data intensive scientific collaboration in four cases, showing how scientists invoke different coordinative entities across three types of research activities: data collection, processing, and analysis. Finally, our contribution scrutinizes the sociality of scientific work to illustrate how these actors engage in relational work within and among diverse, dispersed forms of organizing across project, funding, and disciplinary boundaries.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2021

Classification of orthostatic intolerance through data analytics

Imbalance in the autonomic nervous system can lead to orthostatic intolerance manifested by dizziness, lightheadedness, and a sudden loss of consciousness (syncope); these are common conditions, but they are challenging to diagnose correctly. Uncertainties about the triggering mechanisms and the underlying pathophysiology have led to variations in their classification. This study uses machine learning to categorize patients with orthostatic intolerance. Here we use random forest classification trees to identify a small number of markers in blood pressure, and heart rate time-series data measured during head-up tilt to (a) distinguish patients with a single pathology and (b) examine data from patients with a mixed pathophysiology. Next, we use Kmeans to cluster the markers representing the time-series data. We apply the proposed method analyzing clinical data from 186 subjects identified as control or suffering from one of four conditions: postural orthostatic tachycardia (POTS), cardioinhibition, vasodepression, and mixed cardioinhibition and vasodepression. Classification results confirm the use of supervised machine learning. We were able to categorize more than 95% of patients with a single condition and were able to subgroup all patients with mixed cardioinhibitory and vasodepressor syncope. Clustering results confirm the disease groups and identify two distinct subgroups within the control and mixed groups. The proposed study demonstrates how to use machine learning to discover structure in blood pressure and heart rate time-series data. The methodology is used in classification of patients with orthostatic intolerance. Diagnosing orthostatic intolerance is challenging, and full characterization of the pathophysiological mechanisms remains a topic of ongoing research. This study provides a step toward leveraging machine learning to assist clinicians and researchers in addressing these challenges.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Street context of various demographic groups in their daily mobility

Abstract We present an urban science framework to characterize phone users’ exposure to different street context types based on network science, geographical information systems (GIS), daily individual trajectories, and street imagery. We consider street context as the inferred usage of the street, based on its buildings and construction, categorized in nine possible labels. The labels define whether the street is residential, commercial or downtown, throughway or not, and other special categories. We apply the analysis to the City of Boston, considering daily trajectories synthetically generated with a model based on call detail records (CDR) and images from Google Street View. Images are categorized both manually and using artificial intelligence (AI). We focus on the city’s four main racial/ethnic demographic groups (White, Black, Hispanic and Asian), aiming to characterize the differences in what these groups of people see during their daily activities. Based on daily trajectories, we reconstruct most common paths over the street network. We use street demand (number of times a street is included in a trajectory) to detect each group’s most relevant streets and regions. Based on their street demand, we measure the street context distribution for each group. The inclusion of images allows us to quantitatively measure the prevalence of each context and points to qualitative differences on where that context takes place. Other AI methodologies can further exploit these differences. This approach presents the building blocks to further studies that relate mobile devices’ dynamic records with the differences in urban exposure by demographic groups. The addition of AI-based image analysis to street demand can power up the capabilities of urban planning methodologies, compare multiple cities under a unified framework, and reduce the crudeness of GIS-only mobility analysis. Shortening the gap between big data-driven analysis and traditional human classification analysis can help build smarter and more equal cities while reducing the efforts necessary to study a city’s characteristics.

Salgado, Ariel (ORCID:0000000177015372)↗
DOE OSTI2025

Soft costs and EVSE – Knowledge gaps as a barrier to successful projects

There has been a recent push to increase access to electric vehicle (EV) charging infrastructure. The National Electric Vehicle Infrastructure (NEVI) program, part of the Bipartisan Infrastructure Law (BIL) has made significant funding available for major charging infrastructure projects along state thruways, and many state and local incentives exist for EV owners to install chargers in their homes. However, deployment of these chargers has not kept up with demand, primarily due to issues in project planning, permitting processes, and unforeseen delays. This paper serves as a review of the current understanding of these and other non-hardware costs in EV charging infrastructure projects (collectively known as “soft costs”). We found that soft costs in EV charging infrastructure projects are not well understood. Specifically, there is little agreement on how soft costs should be categorized and tracked, and less agreement still on best practices for controlling these costs and lowering barriers to infrastructure deployment. A broader review of EV charging infrastructure cost analyses shows that these costs can have significant impacts on project outcomes. EV charging infrastructure projects may be able to examine the success of the solar industry in lowering soft costs, and a similar effort may lower project costs significantly. Further work on standardizing and collecting data on EV charging infrastructure costs is required to begin addressing and controlling these costs.

32 - ENERGY CONSERVATION, CONSUMPTION, AND UTILIZA↗
DOE OSTI2021

Evaluation of nature and intensity of fire concrete spalling by frequency analysis of sound records

Highlights: • Original sound-based NDT method to assess the spalling form and intensity within the time of the test is proposed. • Frequency analysis of recorded sound provides a quantitative and qualitative assessment of concrete propensity to spalling. • Nature of fire concrete spalling can be categorized by a well-defined frequency value. • Waveform and spectrogram analysis delivers information on the exact number of spalling events within the performed test. The paper presents a new method for identifying the form and intensity of spalling using an analysis of the acoustic signal emitted by concrete. To assess the intensity and monitor the course of concrete spalling during fire exposure, the analysis of the recorded sound from the furnace chamber is used. The spalling intensity was determined based on signal amplitude analysis. Investigation of sound records showed that the character of spalling can be categorized by a well-defined frequency value. Based on the analysis of the Fast Fourier Transform (FFT) spectrum, the authors have distinguished the following phenomena that are the source of the signal by assigning the corresponding frequency of the sound wave vibration: explosive spalling, aggregate spalling and destructive spalling.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The effect of lighting environment on task performance in buildings – A review

The effects of indoor environmental conditions on human health, satisfaction, and performance have been the focal point of research for decades. This paper reviews and summarizes the impact of lighting environment on task performance, specifically for the built environment audience. Existing studies included a variety of performance tests on cognitive performance and perception, visual acuity and reaction, memory, reasoning, and labor productivity. Illuminance, luminance ratio and correlated color temperature were found to affect performance in different ways, reflecting the impact of experimental techniques, conditions, performance evaluation methods used and data analysis methods. These were reviewed and categorized, with discussion on limitations related to sample size, modeling approach, carryover effects and other factors affecting individual differences in performance, with recommendations for future improvement. Although no universal conclusions can be made, in general, task performance seems to improve with higher illuminances, contrast ratios in the range of 7–11:1 (while always making sure that glare will not occur in the space) and higher correlated color temperature, while spectral tuning in the red or blue wavelengths has also shown positive effects. To obtain more generic evidence, future studies should be more consistent in terms of experimental procedures and overall light conditions, and also consider the effects of vertical illuminance, daylight provision/control, and outside views on task performance. Finally, studying performance with multi-factorial designs in a human-centered optimized manner (such as deploying variable lighting scenarios optimized for various tasks) can lead to deeper understanding of lighting effects on task performance, and ultimately to improved lighting design and operation in buildings overall.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2023

Allometric-kinetic model predictions of concentration ratios for anthropogenic radionuclides across animal classes and food selection

Protection of the environment from radiation fundamentally relies on dose assessments for non-human biota. These dose assessments use measured or predicted concentrations of radionuclides in soil or water combined with Concentration Ratios (CRs) to estimate tissue concentrations in animals and plants, yet there is a paucity of CR data relative to the vast number of potential taxa and radioactive contaminants in the environment and their taxon-specific ecosystems. Because there are many taxa each having very different behaviors and biology and many possible bioavailable radionuclides, CRs have the potential to vary by orders-of-magnitude, as often seen in published data. Given the diversity of taxa, the International Commission on Radiological Protection (ICRP) has categorized non-human biota into 12 representative organisms (ROs), while the U.S. Department of Energy (DOE) uses the non-taxon specific categories of terrestrial, riparian, and aquatic animals. The question we examine here is: do these categorizations adequately “represent” the broad diversity of animals? To explore this question, here we analyze an Allometric-Kinetic (A-K) model to calculate radionuclide-specific CRs for common animal classes which are then further subcategorized into herbivores, omnivores, carnivores, and invertebrate detritivores. Comparisons in CRs among animal classes exhibited only small differences, but there was order of magnitude differences between herbivores, carnivores, and especially detritivores, for many radionuclides of interest. These findings suggest that the ICRP ROs and the DOE categories are reasonable, but their accuracy could be improved by including sub-categories related to animal dietary ecology. Finally, comparisons of A-K model predicted CR values to published CRs show order-of-magnitude variations, providing justification for additional studies of assimilation across radionuclides and animal classes.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗
DOE OSTI2022

An expert-driven literature review of “negative” chemicals for developmental neurotoxicity (DNT) in vitro assay evaluation

To date, approximately 200 chemicals have been tested in US Environmental Protection Agency (EPA) or Organization for Economic Co-operation and Development (OECD) developmental neurotoxicity (DNT) guideline studies, leaving thousands of chemicals without traditional animal information on DNT hazard potential. To address this data gap, a battery of in vitro DNT new approach methodologies (NAMs) has been proposed. Evaluation of the performance of this battery will increase the confidence in its use to determine DNT chemical hazards. One approach to evaluate DNT NAM performance is to use a set of chemicals to evaluate sensitivity and specificity. Since a list of chemicals with potential evidence of in vivo DNT has been established, this study aims to develop a curated list of “negative” chemicals for inclusion in a “DNT NAM evaluation set”. A workflow, including a literature search followed by an expert-driven literature review, was used to systematically screen 39 chemicals for lack of DNT effect. Expert panel members evaluated the scientific robustness of relevant studies to inform chemical categorizations. Following review, the panel discussed each chemical and made categorical determinations of “Favorable”, “Not Favorable”, or “Indeterminate” reflecting acceptance, lack of suitability, or uncertainty given specific limitations and considerations, respectively. Further, the panel determined that 10, 22, and 7 chemicals met the criteria for “Favorable”, “Not Favorable”, and “Indeterminate”, for use as negatives in a DNT NAM evaluation set. Ultimately, this approach not only supports DNT NAM performance evaluation but also highlights challenges in identifying large numbers of negative DNT chemicals.

59 BASIC BIOLOGICAL SCIENCES↗