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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

The DESI-Lensing Mock Challenge: large-scale cosmological analysis of 3x2-pt statistics

The current generation of large galaxy surveys will test the cosmological model by combining multiple types of observational probes. Realising the statistical promise of these new datasets requires rigorous attention to all aspects of analysis including cosmological measurements, modelling, covariance and parameter likelihood. In this paper we present the results of an end-to-end simulation study designed to test the analysis pipeline for the combination of the Dark Energy Spectroscopic Instrument (DESI) Year 1 galaxy redshift dataset and separate weak gravitational lensing information from the Kilo-Degree Survey, Dark Energy Survey and Hyper-Suprime-Cam Survey. Our analysis employs the 3x2-pt correlation functions including cosmic shear and galaxy-galaxy lensing, together with the projected correlation function of the spectroscopic DESI lenses. We build realistic simulations of these datasets including galaxy halo occupation distributions, photometric redshift errors, weights, multiplicative shear calibration biases and magnification. We calculate the analytical covariance of these correlation functions including the Gaussian, noise and super-sample contributions, and show that our covariance determination agrees with estimates based on the ensemble of simulations. We use a Bayesian inference platform to demonstrate that we can recover the fiducial cosmological parameters of the simulation within the statistical error margin of the experiment, investigating the sensitivity to scale cuts. This study is the first in a sequence of papers in which we present and validate the large-scale 3x2-pt cosmological analysis of DESI-Y1.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2023

Guided construction of single cell reference for human and mouse lung

Accurate cell type identification is a key and rate-limiting step in single-cell data analysis. Single-cell references with comprehensive cell types, reproducible and functionally validated cell identities, and common nomenclatures are much needed by the research community for automated cell type annotation, data integration, and data sharing. Here, we develop a computational pipeline utilizing the LungMAP CellCards as a dictionary to consolidate single-cell transcriptomic datasets of 104 human lungs and 17 mouse lung samples to construct LungMAP single-cell reference (CellRef) for both normal human and mouse lungs. CellRefs define 48 human and 40 mouse lung cell types catalogued from diverse anatomic locations and developmental time points. We demonstrate the accuracy and stability of LungMAP CellRefs and their utility for automated cell type annotation of both normal and diseased lungs using multiple independent methods and testing data. We develop user-friendly web interfaces for easy access and maximal utilization of the LungMAP CellRefs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Evaluation of co-circulating pathogens and microbiome from COVID-19 infections

Co-infections or secondary infections with SARS-CoV-2 have the potential to affect disease severity and morbidity. Additionally, the potential influence of the nasal microbiome on COVID-19 illness is not well understood. In this study, we analyzed 203 residual samples, originally submitted for SARS-CoV-2 testing, for the presence of viral, bacterial, and fungal pathogens and non-pathogens using a comprehensive microarray technology, the Lawrence Livermore Microbial Detection Array (LLMDA). Eighty-seven percent of the samples were nasopharyngeal samples, and 23% of the samples were oral, nasal and oral pharyngeal swabs. We conducted bioinformatics analyses to examine differences in microbial populations of these samples, as a proxy for the nasal and oral microbiome, from SARS-CoV-2 positive and negative specimens. We found 91% concordance with the LLMDA relative to a diagnostic RT-qPCR assay for detection of SARS-CoV-2. Sixteen percent of all the samples (32/203) revealed the presence of an opportunistic bacterial or frank viral pathogen with the potential to cause co-infections. The two most detected bacteria, Streptococcus pyogenes and Streptococcus pneumoniae , were present in both SARS-CoV-2 positive and negative samples. Human metapneumovirus was the most prevalent viral pathogen in the SARS-CoV-2 negative samples. Sequence analysis of 16S rRNA was also conducted to evaluate bacterial diversity and confirm LLMDA results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Interpreting High-resolution Spectroscopy of Exoplanets using Cross-correlations and Supervised Machine Learning

We present a new method for performing atmospheric retrieval on ground-based, high-resolution data of exoplanets. Our method combines cross-correlation functions with a random forest, a supervised machine-learning technique, to overcome challenges associated with high-resolution data. A series of cross-correlation functions are concatenated to give a “CCF-sequence” for each model atmosphere, which reduces the dimensionality by a factor of ∼100. The random forest, trained on our grid of ∼65,000 models, provides a likelihood-free method of retrieval. The precomputed grid spans 31 values of both temperature and metallicity, and incorporates a realistic noise model. We apply our method to HARPS-N observations of the ultra-hot Jupiter KELT-9b and obtain a metallicity consistent with solar (logM = − 0.2 ± 0.2). Our retrieved transit chord temperature (T=6000{sub −200}{sup +0}K) is unreliable as strong ion lines lie outside of the extent of the training set, which we interpret as being indicative of missing physics in our atmospheric model. We compare our method to traditional nested sampling, as well as other machine-learning techniques, such as Bayesian neural networks. We demonstrate that the likelihood-free aspect of the random forest makes it more robust than nested sampling to different error distributions, and that the Bayesian neural network we tested is unable to reproduce complex posteriors. We also address the claim in Cobb et al. 2019 that our random forest retrieval technique can be overconfident but incorrect. We show that this is an artifact of the training set, rather than of the machine-learning method, and that the posteriors agree with those obtained using nested sampling.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

From subsidies to stressors: Positively skewed ecological gradients alter biological responses to nutrients in streams

Abstract Subsidy–stress gradients offer a useful framework for understanding ecological responses to perturbation and may help inform ecological metrics in highly modified systems. Historic, region‐wide shifts from bottomland hardwood forest to row crop agriculture can cause positively skewed impact gradients in alluvial plain ecoregions, resulting in tolerant organisms that typically exhibit a subsidy response (increased abundance in response to environmental stressors) shifting to a stress response (declining abundance at higher concentrations). As a result, observed biological tolerance in modified ecosystems may differ from less modified regions, creating significant challenges for detecting biological responses to restoration efforts. Using the agriculturally dominated Mississippi Alluvial Plain (MAP) ecoregion in Mississippi, USA, as a case study, we tested the hypothesis that macroinvertebrate taxa that typically display a subsidy response to nutrient enrichment in less modified ecoregions (i.e., nutrient‐tolerance) shift to a stress response to increasing nutrients in highly modified watersheds with elevated baseline nutrient conditions (i.e., nutrient intolerance). The abundance and diversity of MAP‐specific intolerant taxa identified with threshold indicator taxa analysis were either unresponsive or exhibited a subsidy response to increasing nutrients in less modified ecoregions in Mississippi with less land alteration and lower nutrient concentrations, but declined at higher concentrations, providing evidence for a stress response to elevated nutrients in the MAP. Additionally, MAP‐specific tolerant and intolerant taxa richness responded to increased nutrients predictably and consistently across space and time within the MAP. However, in MAP streams, elevated specific conductance was predicted to dampen the response of tolerant and intolerant taxa richness to increasing nutrient concentrations, highlighting the importance of considering multistressor interactions when interpreting biological data. Lastly, we demonstrate the efficacy of this approach with sediment bacterial communities characterized with amplicon sequencing, which lack sufficient life history characteristics necessary for the development of multimetric indices. Both macroinvertebrate and bacterial communities responded similarly to increasing nutrient concentrations, suggesting DNA‐based approaches may provide an efficient biological assessment tool for monitoring water quality improvements in highly modified watersheds.

DeVilbiss, Stephen E. [U.S. Geological Survey Lowe↗
DOE OSTI2021

Antimicrobial resistance and genomic characterization of Salmonella Dublin isolates in cattle from the United States

Salmonella enterica subspecies enterica serotype Dublin is a host-adapted serotype in cattle, associated with enteritis and systemic disease. The primary clinical manifestation of Salmonella Dublin infection in cattle, especially calves, is respiratory disease. While rare in humans, it can cause severe illness, including bacteremia, with hospitalization and death. In the United States, S . Dublin has become one of the most multidrug-resistant serotypes. The objective of this study was to characterize S . Dublin isolates from sick cattle by analyzing phenotypic and genotypic antimicrobial resistance (AMR) profiles, the presence of plasmids, and phylogenetic relationships. S . Dublin isolates (n = 140) were selected from submissions to the NVSL for Salmonella serotyping (2014–2017) from 21 states. Isolates were tested for susceptibility against 14 class-representative antimicrobial drugs. Resistance profiles were determined using the ABRicate with Resfinder and NCBI databases, AMRFinder and PointFinder. Plasmids were detected using ABRicate with PlasmidFinder. Phylogeny was determined using vSNP. We found 98% of the isolates were resistant to more than 4 antimicrobials. Only 1 isolate was pan-susceptible and had no predicted AMR genes. All S . Dublin isolates were susceptible to azithromycin and meropenem. They showed 96% resistance to sulfonamides, 97% to tetracyclines, 95% to aminoglycosides and 85% to beta-lactams. The most common AMR genes were: sulf2 and tetA (98.6%), aph(6)-Id (97.9%), aph(3’’)-Ib, (97.1%), floR (94.3%), and blaCMY-2 (85.7%). All quinolone resistant isolates presented mutations in gyr A. Ten plasmid types were identified among all isolates with IncA/C2, IncX1, and IncFII(S) being the most frequent. The S . Dublin isolates show low genomic genetic diversity. This study provided antimicrobial susceptibility and genomic insight into S . Dublin clinical isolates from cattle in the U.S. Further sequence analysis integrating food and human origin S . Dublin isolates may provide valuable insight on increased virulence observed in humans.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2026

Rational Design of Lanmodulin Variants for Size-Based Selectivity of Individual Rare Earth Elements

Rare earth elements (REEs) are essential to modern technologies, yet their high physical and chemical similarity makes separation of individual REEs difficult and environmentally taxing. Metalloproteins offer a promising alternative for selective REE binding, as they tend to have high metal ion affinity and specificity. Lanmodulin (LanM), in particular, has arisen as a potential candidate for REE separation as it exhibits picomolar affinity for elements in the REE family. Prior work has shown that the single point mutation D9N can shift LanM’s preference away from lanthanides toward actinides, motivating efforts to tune selectivity of LanM through targeted mutagenesis. Here, we tested the hypothesis that introducing selective aspartic acid to glutamic acid substitutions in the metal coordinating EF hands of LanM would impose steric constraints that would drive LanM affinity away from larger ions, such as La3+, to smaller ions, such as Y3+. To test this hypothesis, a combination of computational and experimental approaches were employed to evaluate the signal mutations LanM D5E and LanM D3E and the double mutants LanM D1ED5E and LanM D3ED9E. Surprisingly, increasing the number of mutations within the metal center did not enhance affinity for smaller REEs, or decrease affinity for larger ions. Only the single point mutation LanM D5E weakened La3+ binding by one order of magnitude relative to LanM wild type (WT), and pairing it with a second mutation to produce LanM D1ED5E drove La3+ affinity to be stronger than that seen for LanM WT. The D3E mutation alone prevented proper expression and folding, but paring it with D9E to produce LanM D3ED9E rescued expression and yielded La3+ affinities comparable to LanM WT. All variants that expressed (LanM D5E, LanM D1ED5E, LanM D3ED9E) displayed Y3+ affinities comparable to LanM WT. Overall, these results highlight the tunability of LanM’s metal-binding environment but also expose current limitations in predicting structural responses to point mutations within a protein sequence. This work establishes a foundation that can be used for refining computational and experimental strategies to engineer metalloproteins with tailored REE selectivity.

Close, Emily [Pacific Northwest National Laborator↗
DOE OSTI2022

Influence of sequential stimulation practices on geochemical alteration of shale

Water-based hydraulic fracturing fluids (HFFs) can chemically interact with formation shale, resulting in altered porosity and permeability of the host rock. Experimental investigations of spatial and temporal shale-HFF interactions are helpful in interpreting chemical compositions of the injectate, as well as predicting alteration of hydraulic properties in the reservoir due to mineral dissolution and precipitation. Most bench-top experiments designed to study shale-HFF chemical interactions, either using batch reactors or flow-through setups, are carried out assuming that the acid spearhead has already become mixed with neutral HFFs. During operations, however, HFFs are typically injected according to a sequenced pumping schedule, starting with a concentrated acid spearhead, followed by multiple additions of near-neutral pH HFFs containing chemical amendments and proppant. In this study, we use geochemical modeling to consider whether this pre-mixed experimental protocol provides results directly comparable to a sequential discrete fluid-shale interaction protocol. Our results show that for the batch system, the transient evolution in major ion concentrations is faster with the sequential procedure. After 2 h of reaction time, the two protocols converge to the same aqueous concentrations. In a flow-through geometry, the pre-mixed model predicts extensive chemical alteration close to the injection point but negligible alteration downstream. In contrast, the sequential model predicts mineral reactions over hundreds of meters along the flow path. The extent of shale alteration in the sequential model at a given location depends on shale mineralogy and where the acid spearhead resides during the shut-in period. The predictive model developed in this study can help experimentalists to design bench-top tests and operators to better translate the results of laboratory experiments into practical applications.

Li, Qingyun↗
DOE OSTI2021

Addressing Load Imbalance in Bioinformatics and Biomedical Applications: Efficient Scheduling across Multiple GPUs

Computational bioinformatics and biomedical applications frequently contain heterogeneously sized units of work or tasks, for instance due to variability in the sizes of biological sequences and molecules. Variable-sized workloads lead to load imbalances in parallel implementations which detract from efficiency and performance. Many modern computing resources now have multiple graphics processing units(GPUs) per computer for acceleration. These multiple GPU resources need to be used efficiently through balancing of workloads across the GPUs. OpenMP is a portable directive-based parallel programming API used ubiquitously in bioscience applications to program CPUs; recently, the use of OpenMP directives for GPU acceleration has become possible. Here, motivated by experiences with imbalanced loads in GPU-accelerated bioinformatics applications, we address the load balancing problem using OpenMP task-to-GPU scheduling combined with OpenMP GPU offloading for multiply heterogeneous workloads – loads with both variable input sizes, and simultaneously, variable convergence rates for algorithms with a stochastic component – scheduled across multiple GPUs. We aim to develop strategies which are both easy to use and have lower overheads, and may be incorporated incrementally in existing programs which already make use of OpenMP for CPU-based threading in order to make use of multi-GPU computers. We test different combinations of input size variability and convergence rate variability, and characterize the effects of these different scenarios on the performance of scheduling strategies across multiple GPUs with OpenMP. We present several dynamic scheduling solutions for different parallel patterns, explore optimizations, and provide publicly available example computational kernels to make these strategies easy to use in programs. This work will enable application developers to efficiently and easily use multiple GPUs for imbalanced workloads found in bioinformatics and biomedical applications.

Thavappiragasam, Mathialakan↗
DOE OSTI2021

The Initial–Final Mass Relation for Hydrogen-deficient White Dwarfs

The initial–final mass relation represents the total mass lost by a star during the entirety of its evolution from the zero age main sequence to the white-dwarf cooling track. The semiempirical initial–final mass relation (IFMR) is largely based on observations of DA white dwarfs, the most common spectral type of white dwarf and the simplest atmosphere to model. We present a first derivation of the semiempirical IFMR for hydrogen-deficient (non-DA) white dwarfs in open star clusters. We identify a possible discrepancy between the DA and non-DA IFMRs, with non-DA white dwarfs ≈0.07 M {sub ⊙} less massive at a given initial mass. Such a discrepancy is unexpected based on theoretical models of non-DA formation and observations of field white dwarf mass distributions. If real, the discrepancy is likely due to enhanced mass loss during the final thermal pulse and renewed post-AGB evolution of the star. However, we are dubious that the mass discrepancy is physical and instead is due to the small sample size, to systematic issues in model atmospheres of non-DAs, and to the uncertain evolutionary history of Procyon B (spectral type DQZ). A significantly larger sample size is needed to test these assertions. In addition, we also present Monte Carlo models of the correlated errors for DA and non-DA white dwarfs in the initial–final mass plane. We find the uncertainties in initial–final mass determinations for individual white dwarfs can be significantly asymmetric, but the recovered functional form of the IFMR is grossly unaffected by the correlated errors.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2025

Leveraging Natural Language Processing and Generative Models in Molecular Chemistry: Property Prediction and Novel Compound Generation

The accurate prediction of molecular properties is important for the rational design and the advancement of green chemistry and sustainable materials research. However, the predictive power of traditional computational chemistry methods is limited due to computational restrictions. Here, in this study, we examine an alternative approach to the accurate prediction of properties of organic compounds: natural language processing (NLP)-based molecular embedding. Using viscosity, partition coefficient (log P), and enthalpy of vaporization as test properties through a survey of comprehensive datasets comprising 5695 data points for viscosity, 25 870 data points for log P, and 2296 data points for enthalpy of vaporization. These are important properties for the design of greener, safer, and sustainable chemical processes. Models were trained using NLP methods such as Mol2vec and fine-tuned ChemBERTa, and results were compared with traditional input featurization techniques such as Morgan fingerprints and quantum chemistry derived sigma profiles and DFT features. Among the various machine learning models, Mol2vec demonstrated superior predictive capabilities, achieving the highest correlation coefficient (R 2 = 0.945) and lowest RMSE (0.106 mPa s) for viscosity, as well as high accuracy for log P and enthalpy of vaporization predictions. These findings establish the Mol2vec featurization technique, graph-convolutional neural networks (GCNN), and fine-tuned ChemBERTa model as powerful tools for predictive modeling of organic compounds properties, offering a significant improvement over previously used featurization techniques and opening up strategies for very-high-throughput computational screening. Finally, we integrated ML models with hybrid language-model-based generative adversarial networks (LM-GAN) to generate novel molecular sequences with desirable properties for different research applications. The ability to computationally design solvents with lower viscosity, lower log P, and lower enthalpy of vaporization offers a data-driven route to accelerating the discovery of sustainable alternatives to traditionally toxic solvents.

ChemBERTa↗
DOE OSTI2020

Evaluation of Changes to the MTR Fuel Dissolution Flowsheet to Increase the Number of L-Bundles Charged to a Dissolver

Material Test Reactor (MTR) fuels are dissolved in the H-Canyon facility using a Hg-catalyzed, HNO₃ dissolution flowsheet. The flowsheet is based on experimental work in which laboratory-scale experiments were used to provide bounding values for both offgas and H₂ generation rates. The generation of H₂ gas, primarily from the dissolution of Al, is controlled by limiting the surface area exposed to the solution. The number of L-Bundles charged and their submergence level defines the exposed surface area which is directly proportional to the H₂ generation rate. For the MTR fuel dissolution flowsheet, the maximum number of L-Bundles which can be charged to a dissolver was specified as a function of the Al concentration in the solution and the submergence level of the bundle. The MTR fuels are currently dissolved in the 6.1D dissolver. To reach the desired terminal Al concentration, a total of 12 L-Bundles of fuel are dissolved per batch. Depending on the submergence level being used in the dissolver, the 12 bundles are dissolved in three charges of four bundles per charge or in a sequence of three, four, and five bundles. However, if six L-Bundles could be dissolved in the initial charge, a dissolver batch could be completed in two dissolutions instead of three. To assess the feasibility of dissolving six bundles in the first charge, the Savannah River National Laboratory evaluated changes to the MTR flowsheet which increase the number of bundles which can be charged to a dissolver. Since the development of the existing MTR fuel dissolution flowsheet, improved offgas measurement methods have been developed to more accurately calculate the offgas composition and generation rate during laboratory-scale dissolutions. In the first part of this study, improvements in the calibration model for the Raman spectrometer (used for offgas characterization) and a more effective data smoothing routine were applied to existing data. The uncertainty in the offgas composition data used to define the MTR fuel dissolution flowsheet were subsequently re-evaluated which resulted in an approximate 50% reduction in the uncertainty of the H₂ gas concentration measurements. The reduced uncertainty was used in new calculations to define the maximum number of L-Bundles which can be charged to either the 6.1D or 6.4D dissolver as a function of the Al concentration. With the reduced uncertainty in the H₂ concentration measurements, a flowsheet was defined which supported initially charging 6 bundles to a dissolver. This flowsheet is based on the use of 0.002 M Hg, an L-Bundle immersion depth of 54 in., an air sparge/purge flowrate of 40 scfm, an iodine reactor operating temperature of 200 °C, and not exceeding 60% of the H₂ LFL in the dissolver offgas. In the second phase of this study, calculations were performed to assess the impact of changing various operating parameters to increase the number of L-Bundles charged to an H-Canyon dissolver. Applying the improved offgas measurement techniques including the reduced uncertainty in the H₂ concentration measurements to the existing data, the number of bundles of MTR fuel which can be charged to either the 6.1D or 6.4D dissolver were calculated based on varying the bundle immersion depth (44-60 in.), air sparge/purge flowrate (40-60 scfm), iodine reactor operating temperature (150-200 °C), and the percentage of the H₂ LFL which must not be exceeded (60-76%). Qualitatively, the percentage of the LFL had the largest influence on the maximum number of bundles which can be charged to a dissolver followed by the air sparge/purge flowrate, L-Bundle immersion depth, and the iodine reactor operating temperature. During the evaluation of each variable, the other variables were held at the baseline flowsheet conditions.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2021

Spectroscopy of the T = 2 mirror nuclei 48 Fe/ 48 Ti using mirrored knockout reactions

A sequence of excited states has been established for the first time in the proton-rich nucleus 48 Fe (Z=26, N=22). The technique of mirrored (i.e. analogue) one-nucleon knockout reactions was applied, in which the T z = ±2 mirror pair, 48 Fe/ 48 Ti were populated via one-neutron/one-proton knockout from the secondary beams 49 Fe/ 49 V, respectively. The analogue properties of the reactions were used to help establish the new level scheme of 48 Fe. The inclusive and exclusive cross sections were determined for the populated states. Large differences between the cross sections for the two mirrored reactions were observed and have been interpreted in terms of different degrees of binding of the mirror nuclei and in the context of the recent observations of suppression of spectroscopic strength as a function of nuclear binding, for knockout reactions on light solid targets. Mirror energy differences (MED) have been determined between the analogue T = 2 states and compared with the shell model predictions. MED for this mirror pair, due to their location in the shell, are especially sensitive to excitations out of the f 7/2 shell, and present a stringent test of the shell-model prescription.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Cross-platform analysis of public responses to the 2019 Ridgecrest earthquake sequence on Twitter and Reddit

Online social networks (OSNs) have become a powerful tool to study collective human responses to extreme events such as earthquakes. Most previous research concentrated on a single platform and utilized users’ behaviors on a single platform to study people’s general responses. In this study, we explore the characteristics of people’s behaviors on different OSNs and conduct a cross-platform analysis of public responses to earthquakes. Our findings support the Uses and Gratification theory that users on Reddit and Twitter are engaging with platforms that they may feel best reflect their sense of self. Using the 2019 Ridgecrest earthquakes as our study cases, we collected 510,579 tweets and 45,770 Reddit posts (including 1437 submissions and 44,333 comments) to answer the following research questions: (1) What were the similarities and differences between public responses on Twitter and Reddit? (2) Considering the different mechanisms of Twitter and Reddit, what unique information of public responses can we learn from Reddit as compared with Twitter? By answering these research questions, we aim to bridge the gap of cross-platform public responses research towards natural hazards. Our study evinces that the users on the two different platforms have both different topics of interest and different sentiments towards the same earthquake, which indicates the necessity of investigating cross-platform OSNs to reveal a more comprehensive picture of people’s general public responses towards certain disasters. Our analysis also finds that r/conspiracy subreddit is one of the major venues where people discuss the 2019 Ridgecrest earthquakes on Reddit and different misinformation/conspiracies spread on Twitter and Reddit platforms (e.g., “Big one is coming” on Twitter and “Nuclear test” on Reddit).

58 GEOSCIENCES↗
DOE OSTI2024

Diverse PFAS produce unique transcriptomic changes linked to developmental toxicity in zebrafish

Per- and polyfluoroalkyl substances (PFAS) are a widespread and persistent class of contaminants posing significant environmental and human health concerns. Comprehensive understanding of the modes of action underlying toxicity among structurally diverse PFAS is mostly lacking. To address this need, we recently reported on our application of developing zebrafish to evaluate a large library of PFAS for developmental toxicity. In the present study, we prioritized 15 bioactive PFAS that induced significant morphological effects and performed RNA-sequencing to characterize early transcriptional responses at a single timepoint (48 h post fertilization) after early developmental exposures (8 h post fertilization). Internal concentrations of 5 of the 15 PFAS were measured from pooled whole fish samples across multiple timepoints between 24–120 h post fertilization, and additional temporal transcriptomics at several timepoints (48–96 h post fertilization) were conducted for Nafion byproduct 2. A broad range of differentially expressed gene counts were identified across the PFAS exposures. Most PFAS that elicited robust transcriptomic changes affected biological processes of the brain and nervous system development. While PFAS disrupted unique processes, we also found that similarities in some functional head groups of PFAS were associated with the disruption in expression of similar gene sets. Body burdens after early developmental exposures to select sulfonic acid PFAS, including Nafion byproduct 2, increased from the 24–96 h post fertilization sampling timepoints and were greater than those of sulfonamide PFAS of similar chain lengths. In parallel, the Nafion byproduct 2-induced transcriptional responses increased between 48 and 96 h post fertilization. PFAS characteristics based on toxicity, transcriptomic effects, and modes of action will contribute to further prioritization of PFAS structures for testing and informed hazard assessment.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Formal verification and validation of run-to-completion style state charts using Event-B

State chart notations with ‘run to completion’ semantics are popular with engineers for designing controllers that react to environment events with a sequence of state transitions but lack formal refinement and rigorous verification methods. State chart models are typically used to design complex control systems that respond to environmental triggers with a sequential process. The model is usually constructed at a concrete level and verified and validated using animation techniques relying on human judgement. Event-B, on the other hand, is based on refinement from an initial abstraction and is designed to make formal verification by automatic theorem provers feasible. Abstraction and formal verification provide greater assurance that critical (e.g. safety or security) properties are not violated by the control system. In this paper, we introduce a notion of refinement into a ‘run to completion’ state chart modelling notation and leverage Event-B’s tool support for theorem proving. We describe the difficulties in translating ‘run to completion’ semantics into Event-B refinements and suggest a solution. We illustrate our approach and show how models can be validated at different refinement levels using our scenario checker animation tools. We show how critical invariant properties can be verified by proof despite the reactive nature of the system and how behavioural aspects of the system can be verified by testing the expected reactions using a temporal logic, model checking approach. To verify liveness, we outline a proof that the run to completion is deadlock-free and converges to complete the run.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Observational constraints of an anisotropic boost due to the projection effects using redMaPPer clusters

Optical clusters identified from red-sequence galaxies suffer from projection effects, where interloper galaxies along the line of sight to a cluster are mistaken as genuine members of the cluster. In the previous study, we found that the projection effects cause the boost on the amplitudes of clustering and lensing on large scale compared to the expected amplitudes in the absence of any projection effects. These boosts are caused by preferential selections of filamentary structure aligned to the line of sight due to distance uncertainties in photometric surveys. We model the projection effects with two simple assumptions and develop a novel method to quantify the size of the boost using cluster-galaxy cross-correlation functions. We validate our method using mock cluster catalogues built from cosmological N-body simulations and find that we can obtain unbiased constraints on the boost parameter with our model. We then apply our analysis on the SDSS redMaPPer clusters and find that the size of the boost is roughly 20 per cent for all the richness bins except the cluster sample with the richness bin λ ∈ [30, 40]. This is the first study to constrain the boost parameter independent from cluster cosmology studies and provides a self-consistency test for the projection effects.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Assessment of human nuclear and mitochondrial DNA qPCR assays for quantification accuracy utilizing NIST SRM 2372a

In forensic DNA casework, a highly accurate real-time quantitative polymerase chain reaction (qPCR) assay is recommended per the Scientific Working Group on DNA Analysis Methods (SWGDAM) (SWGDAM Validation Guidelines for DNA Analysis Methods [1]) to determine whether a DNA sample is of sufficient quantity and robust quality to move forward with downstream short tandem repeats (STR) or sequencing analyses. Most of these assays rely on a standard curve, referred to herein and traditionally as absolute qPCR, in which an unknown is compared, relative to that curve. However, one fundamental issue with absolute qPCR is the quantifiable concentration of commercial assay standards can vary depending on (1) origin, i.e., whether from a cell line or a human subject, (2) supplier, (3) lot number, (4) shipping method, etc. In 2018, the National Institute for Standards and Technology (NIST) released a human DNA standard reference material for evaluating qPCR quantification standards, Standard Reference Material (SRM) 2372a, Romsos et al. (2018) [2] which contains three well-characterized human genomic DNA samples: Component A) a single male1 donor, Component B) a single female 1 donor, and Component C) a 1:3 male 2 :female 2 donor, each with certification data for nDNA and informational mitochondrial DNA(mtDNA)/nuclear DNA (nDNA) ratio data. The SRM 2372a was used to assess four qPCR assays: (1) Quantifiler Trio (Thermo Fisher Scientific, Waltham, MA) for nDNA quantification, (2) NovaQUANT (EMD Millipore Corporation, San Diego, CA) for nDNA and mtDNA quantification, (3) a custom duplex mtDNA assay, and (4) a custom triplex mtDNA assay. Additionally, extracts from eighteen (18) skeletal remains were tested with the latter three assays for concordance of DNA concentration and with assays (2) and (3), for the degradation state. Our assessment revealed that an accurate, efficient, and reproducible qPCR assay is dependent on (1) the quality and reliability of the DNA standard, (2) the qPCR chemistry, and (3) the specific primers, and probes (if applicable), used in an assay. Finally, our findings indicate qPCR assays may not always quantify as expected and that performance of each lot should be verified using a well-characterized DNA standard such as the NIST SRM 2372a and adjusted if warranted.

59 BASIC BIOLOGICAL SCIENCES↗