Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “variant”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 685 records · Page 38
All sourcesNASA NTRSDOE OSTI
DOE OSTI2025

Develop High-Throughput Workflows for Whole-Genome Sequencing and Insertion Site Screening

The engineering of microbes for biomanufacturing (e.g. of fuels, chemicals, materials) applications has advanced to a stage where researchers screen genetic libraries with millions of variations each for those with enhanced productivity. This screening, however, can be slow and expensive, as screening individual variants in a high-throughput yet cost-effective manner is challenging. In this project, we aimed to reduce by 3-fold costs associated with the sequencing aspects of the screening process (to determine which genetic variant is responsible for an observed change in productivity), while being able to process over 1,000 samples per batch.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Inhomogeneous Distribution and Coarsening of γ″ Precipitates in a Ni-Based Superalloy and Their Effect on Creep

γ″ was found to be the main precipitate strengthening phase with no detectable g′ in both M725 alloys. After creep, the grip/gage sections of the crept M725-Nb/Ta exhibited preferential coarsening and inhomogeneous distribution of g″ platelets, and sandwich-like structures, comprising of cubic γ′ precipitates with small γ″ discs on each facet. This g″ variant selection behavior taking place in the grip section with no stress applied was different from the typical stress-induced variant selection reported in previous literature.

Hung, Chang-Yu↗
DOE OSTI2026

Quantum-Inspired Bayesian Sampling for Uncertainty Quantification and Machine Learning (Final Technical Report)

With increasing simulation and measurement data, machine learning and artificial intelligence have been widely used in computational decision-making of complex engineering systems. The resulting tools, such as uncertainty quantification solvers, reinforcement learning, and physics-informed machine learning, have achieved great success in critical DOE tasks such as material discovery and design, energy system modeling and control, and numerical weather and climate prediction. A core topic in scientific machine learning and artificial intelligence is Bayesian inference: given an observed data set, people want to estimate the posterior distribution of a (possibly large) number of hidden parameters. Due to the flexibility and weak assumptions, Bayesian sampling has been the mainstream Bayesian inference solvers despite the rapid progress of approximate Bayesian inference. Classical Bayesian sampling methods such as Markov-chain Monte Carlo suffer from a low-acceptance rate due to the random walk nature, therefore state-of-the-art techniques use Hamiltonian Monte Carlo and its variants to efficiently draw posterior samples in a high dimension. The key idea of Hamiltonian Monte Carlo and its variants is to simulate the Hamiltonian dynamics of a classical particle with a fixed mass, and their performance significantly degrades when the posterior distribution is highly spiky or has multiple modes. Leveraging the idea of quantum physics, this project has investigated new theory, algorithms and applications of Bayesian inference (especially Bayesian sampling). The main results include: (1) novel quantum-inspired Bayesian sampling methods that can lead to better accuracy for challenging multi-modal or spiky distributions, (2) more scalable machine learning framework leveraging tensor-compressed Bayesian inference, and (3) Bayesian and sampling approaches for verifying the robustness of continuous and binary neural networks.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Development and Evaluation of Embrittlement Resistant Alloys for Advanced LWR Cladding

The primary challenge preventing the deployment of reactor designs that leverage wrought FeCrAl as an advanced light-water reactor (LWR) cladding is irradiation hardening and embrittlement. Wrought FeCrAl alloys experience a loss of post-irradiation ductility and fracture toughness under low-temperature neutron irradiation (< 350°C) resulting from the combined effects of dislocation loop formation and the irradiation-enhanced precipitation of Cr-rich alpha-prime precipitates throughout the microstructure. Over the past decade, significant improvements in our understanding regarding the effect of Cr and Al content have been enabled through neutron irradiations, but even optimized wrought alloys such as C26M remain vulnerable to brittle failure during storage, transportation, and handling following irradiation. This report summarizes multiple irradiation campaigns initiated over the past 7 years and provides recent insights into the effect of (1) minor alloying composition, (2) alloy processing strategy, (3) crystal structure, and (4) the use of advanced oxide dispersion strengthened (ODS) alloys. The first irradiation campaign irradiated body-centered cubic FeCrAl alloy C26M with and without elements such as Mo and Y. It also included face-centered cubic alumina-forming austenitic (AFA) alloys that have a steeper neutronic penalty but that have far superior high-temperature strength than wrought FeCrAl. Finally, this campaign compared conventional wrought C26M with the same material produced using modern powder metallurgical hotisostatic pressing (PM-HIP). The second irradiation campaign, initiated in 2018, irradiated wrought and FeCrAl-ODS materials up to 50 dpa to assess their irradiation resistance to high-doses. The results of this work indicate that wrought C26M suffers significant degradation following neutron irradiation at LWR-relevant temperatures regardless of Mo and Y content. The PM-HIP variant appears to be more resilient to irradiation-induced ductility loss in comparison with wrought variants but still loses almost all ductility by 8 dpa. Notably, the AFAs retained at least 5% total elongation after irradiation at the same dose and temperature condition, although additional deterioration may be expected at higher fluence levels. FeCrAl-ODS materials show the greatest promise as a transformative longer-term accident-tolerant fuel (LT-ATF) cladding material. The extruded 106ZY10C alloy retained 10% total elongation after 16 dpa irradiation and retained 8% total elongation after 50 dpa. Additional challenges with FeCrAl-ODS alloys also remain, including optimization of end cap joining methodologies, enhancement of fracture toughness, scaling production to prove economic viability, and optimizing post-pilger heat treatments to maximize ductility and irradiation resistance. This series of irradiations demonstrates the efficacy of the LWR testbed available within the United States for the rapid irradiation and down-selection of LT-ATF candidates. As the country rapidly accelerates its timeline for the deployment of advanced reactor concepts, the effective utilization of the High Flux Isotope Reactor for separate-effects style irradiations should continue to be prioritized to answer the final questions pertaining to LT-ATF candidates necessary for the deployment of advanced boiling water reactors and small modular reactors.

11 NUCLEAR FUEL CYCLE AND FUEL MATERIALS↗
DOE OSTI2026

Software Verification of VARPOW

The VARPOW program is a post-processing utility program for DIF3D, specifically DIF3DVARIANT, and it was developed to provide interface files for the thermal analysis program DASSH. The basic methodology of VARPOW is to take the neutron and gamma flux (moments) calculated by DIF3D (or GAMSOR) and combine them with the heating (coefficient) cross sections to calculate the spatial power distributions using the DIF3DVARIANT spatial basis. VARPOW can use the output from GAMSOR (both steady state neutron and gamma flux calculations) or standard DIF3D/REBUS calculations (neutron flux only). The correct approach for defining the power distribution is to use GAMSOR as its purpose was to properly compute the gamma heating throughout the modeled domain. The power desnities calculated by VARPOW are broken into fuel, cladding and coolant terms for which isotope-wise categorization is needed. VARPOW has built in options the user can select for the isotope categorization or VARPOW can import a file that details the isotope categorization. VARPOW can export the solution in the polynomial basis of DIF3D-VARIANT or the monomial basis of DIF3D-VARIANT. Recently the clad neutron damage calculation capability was introduced into VARPOW, and it now allows a pathway to get distributions of clad DPA. The purpose of this work is to verify the power distribution, fast neutron flux and clad DPA results calculated by VARPOW from both the GAMSOR and DIF3D input options and verify that the input and output options are consistent with the manual. Hand calculation and independent numerical calculation are used for this verification work.

Zhong, Zhaopeng [Argonne National Laboratory (ANL)↗
DOE OSTI2021

Qubit-efficient entanglement spectroscopy using qubit resets

One strategy to fit larger problems on NISQ devices is to exploit a tradeoff between circuit width and circuit depth. Unfortunately, this tradeoff still limits the size of tractable problems since the increased depth is often not realizable before noise dominates. Here, we develop qubit-efficient quantum algorithms for entanglement spectroscopy which avoid this tradeoff. In particular, we develop algorithms for computing the trace of the n -th power of the density operator of a quantum system, T r ( ρ n ) , (related to the Rényi entropy of order n ) that use fewer qubits than any previous efficient algorithm while achieving similar performance in the presence of noise, thus enabling spectroscopy of larger quantum systems on NISQ devices. Our algorithms, which require a number of qubits independent of n , are variants of previous algorithms with width proportional to n , an asymptotic difference. The crucial ingredient in these new algorithms is the ability to measure and reinitialize subsets of qubits in the course of the computation, allowing us to reuse qubits and increase the circuit depth without suffering the usual noisy consequences. We also introduce the notion of effective circuit depth as a generalization of standard circuit depth suitable for circuits with qubit resets. This tool helps explain the noise-resilience of our qubit-efficient algorithms and should aid in designing future algorithms. We perform numerical simulations to compare our algorithms to the original variants and show they perform similarly when subjected to noise. Additionally, we experimentally implement one of our qubit-efficient algorithms on the Honeywell System Model H0, estimating T r ( ρ n ) for larger n than possible with previous algorithms.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2026

Populus_trichocarpa_Breeding_Population_SNPs

These data are from the manuscript “Application of Genomic Prediction in a Populus trichocarpa Breeding Program”, by Brian J. Stanton, David Macaya-Sanz, Chanaka Roshan Abeyratne, David Kainer, Kathy Haiby, Austin Himes, Carlos Gantz, Gerald A. Tuskan, and Stephen P. DiFazio. The data are based on genome resequencing to approximately 10X depth on two collections of Populus trichocarpa trees from Oregon, Washington, California, and British Columbia. The first collection consists of 293 genets collected by Poplar Innovations LLC for a breeding program. The second collection consists of 961 trees collected for the purpose of genome-wide association studies. These genets were sequenced using short, paired-end Illumina sequence reads (Chhetri et al. 2019). Reads were aligned to the P. trichocarpa ′Stettler-14′ reference (Hofmeister et al. 2020), with minor modifications to correct mis-assemblies (Zhou et al. 2020), and variants were called as per methods described in (Abeyratne et al. 2023). Identified variants were filtered using GATK’s VariantFiltration tool (DePristo et al. 2011), with filter expression flag set to “AF < 0.01 || AF > 0.99 || QD < 10.0 || ExcessHet > 20.0 || FS > 10.0 || MQ < 58.0”. SNPs with severe departures from Hardy−Weinberg expectations (exact-test p< 0.01) were also removed using vcftools --hwe flag (Danecek et al. 2011), resulting in 15,627,211 bi-allelic SNPs. The data included here consist of 141,903 high quality bi-allelic genome-wide SNPs obtained by further filtering the original SNP dataset using vcftools with flags --maf 0.05, --max-maf 0.95, --max-missing 0.95, --min-meanDP 10.75, --max-meanDP 43.00, --thin 2000. Collectively, these filtering parameters removed SNPs with 1) a minor allele frequency ≤ 0.05; 2) proportion of missing data for individual loci exceeding 5%; 3) sequencing depth more than 2X mean-depth or less than 0.5X mean-depth; or 4) a distance of

09 BIOMASS FUELS↗
DOE OSTI2023

Targeted mutagenesis and high-throughput screening of diversified gene and promoter libraries for isolating gain-of-function mutations

Targeted mutagenesis of a promoter or gene is essential for attaining new functions in microbial and protein engineering efforts. In the burgeoning field of synthetic biology, heterologous genes are expressed in new host organisms. Similarly, natural or designed proteins are mutagenized at targeted positions and screened for gain-of-function mutations. Here, we describe methods to attain complete randomization or controlled mutations in promoters or genes. Combinatorial libraries of one hundred thousands to tens of millions of variants can be created using commercially synthesized oligonucleotides, simply by performing two rounds of polymerase chain reactions. With a suitably engineered reporter in a whole cell, these libraries can be screened rapidly by performing fluorescence-activated cell sorting (FACS). Within a few rounds of positive and negative sorting based on the response from the reporter, the library can rapidly converge to a few optimal or extremely rare variants with desired phenotypes. Library construction, transformation and sequence verification takes 6–9 days and requires only basic molecular biology lab experience. Screening the library by FACS takes 3–5 days and requires training for the specific cytometer used. Further steps after sorting, including colony picking, sequencing, verification, and characterization of individual clones may take longer, depending on number of clones and required experiments.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Discovering novel therapeutic V H Hs for emerging viruses: perspectives from VEEV selection strategies

Introduction: Evolution or emergence of a new viral variant is a significant public health concern. Alphaviruses, such as Venezuelan equine encephalitis virus (VEEV), are mosquito-borne viruses which are becoming more prevalent due to expansion of vector habitats. Despite this, there are currently no antiviral therapies or FDA-approved vaccines available to treat or prevent VEEV infection. The increased prevalence of such viruses provides opportunities for novel variants to evolve. Key therapeutic molecules that could be developed against viral pathogens are recombinant antibodies or antibody fragments, such as the variable heavy domain of heavy chain antibodies (V H Hs). Methods: In vitro selections offer a promising pathway for identification of therapeutic antibodies, here we explored isolation of V H Hs using phage and yeast display methodology with three antigen formats 1) recombinant E2, 2) linear peptides of E2, selected based on molecular dynamics analysis, and 3) UV inactivated virus. Results: Here we report four novel “human” V H Hs which bind to the VEEV E2 protein selected using different strategies that include both computational and biochemical design of suitable antigens and whole virus selections. These V H Hs have distinct complementarity-determining regions (CDRs). Multiple VHHs bind to the VEEV viral particles in ELISAs, and we report the peptide epitope recognized by these V H Hs. Discussion: Though non-neutralizing, these V H Hs bind to and sequester VEEV viral particles preventing infection, demonstrating the potential of these V H Hs to perform viral “sponging” which represents a novel therapeutic approach. The selection strategies we report may have applications to further antibody developments against other viruses.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Exploring the Performance Boundaries of a Small Reconfigurable Multi-Mission UAV through Multidisciplinary Analysis

The performance of a small reconfigurable unmanned aerial vehicle (UAV) is evaluated, combining a multidisciplinary approach in the computational analysis of additive manufactured structures, fluid dynamics, and experiments. Reconfigurable UAVs promise cost savings and efficiency, without sacrificing performance, while demonstrating versatility to fulfill different mission profiles. The use of computational fluid dynamics (CFD) in UAV design produces higher accuracy aerodynamic data, which is particularly important for complex aircraft concepts such as blended wing bodies. To address challenges relating to anisotropic materials, the Tsai–Wu failure criterion is applied to the structural analysis, using CFD solutions as load inputs. Aerodynamic performance results show the low-speed variant attains an endurance of 1 h, 48 min, whereas its high-speed counterpart is 29 min at a 66.7% higher cruise speed. Each variant serves different aspects of small UAS deployment, with low speed envisioned for high-endurance surveying, and high speed for long-range or time-critical missions such as delivery. The experimental and simulation results suggest room for design iteration, in wing area and geometry adjustments. Structural simulations demonstrated the need for airframe improvements to the low-speed configuration. This paper highlights the potential of reconfigurable UAVs to be useful across multiple industries, advocating for further research and design improvements.

42 ENGINEERING↗
DOE OSTI2025

You Only Look Once v5 and Multi-Template Matching for Small-Crack Defect Detection on Metal Surfaces

This paper compares the performance of Deep Learning (DL) and multi-template matching (MTM) models for detecting small defects. DL models extract distinguishing features of objects but require a large dataset of images. In contrast, alternative computer vision techniques like MTM need a relatively small dataset. The lack of large datasets for small metal-surface defects has inhibited the adoption of automation in small-defect detection in remanufacturing settings. This motivated this preliminary study to compare template-based approaches, like MTM, with feature-based approaches, such as DL models, for small-defect detection on an initial laboratory and remanufacturing industry dataset. This study used You Only Look Once v5 (YOLOv5) as the DL model and compared its performance against the MTM model for small-crack detection. The findings of our preliminary investigation are as follows: (i) YOLOv5 demonstrated higher performance than MTM in detecting small cracks; (ii) an extra-large variant of YOLOv5 outperformed a small-size variant; (iii) the size and object variety of the data are crucial in achieving robust pre-trained weights for use in transfer learning; and (iv) enhanced image resolution contributes to precise object detection.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Acclimation and Compensating Metabolite Responses to UV-B Radiation in Natural and Transgenic Populus spp. Defective in Lignin Biosynthesis

Plants have evolved to protect leaf mesophyll tissue from damage caused by UV-B radiation by producing an array of UV-absorbing secondary metabolites. Flavonoids (phenolic glycosides) and sinapate esters (hydroxycinnamates) have been implicated as UV-B protective compounds because of the accumulation in the leaf epidermis and the strong absorption in the wavelengths corresponding to UV. Environmental adaptations by plants also generate a suite of responses for protection against damage caused by UV-B radiation, with plants from high elevations or low latitudes generally displaying greater adaptation or tolerance to UV-B radiation. In an effort to explore the relationships between plant lignin levels and composition, the origin of growth elevation, and the hierarchical synthesis of UV-screening compounds, a collection of natural variants as well as transgenic Populus spp. were examined for sensitivity or acclimation to UV-B radiation under greenhouse and laboratory conditions. Noninvasive, ecophysiological measurements using epidermal transmittance and chlorophyll fluorescence as well as metabolite measurements using UPLC-MS generally revealed that the synthesis of anthocyanins, flavonoids, and lignin precursors are increased in Populus upon moderate to high UV-B treatment. However, poplar plants with genetic modifications that affect lignin biosynthesis, or natural variants with altered lignin levels and compositions, displayed complex changes in phenylpropanoid metabolites. A balance between elevated metabolic precursors to protective phenylpropanoids and increased biosynthesis of these anthocyanins, flavonoids, and lignin is proposed to play a role in the acclimation of Populus to UV-B radiation and may provide a useful tool in engineering plants as improved bioenergy feedstocks.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

The Local Topological Free Energy of the SARS-CoV-2 Spike Protein

The novel coronavirus SARS-CoV-2 infects human cells using a mechanism that involves binding and structural rearrangement of its Spike protein. Understanding protein rearrangement and identifying specific amino acids where mutations affect protein rearrangement has attracted much attention for drug development. In this manuscript, we use a mathematical method to characterize the local topology/geometry of the SARS-CoV-2 Spike protein backbone. Our results show that local conformational changes in the FP, HR1, and CH domains are associated with global conformational changes in the RBD domain. The SARS-CoV-2 variants analyzed in this manuscript (alpha, beta, gamma, delta Mink, G614, N501) show differences in the local conformations of the FP, HR1, and CH domains as well. Finally, most mutations of concern are either in or in the vicinity of high local topological free energy conformations, suggesting that high local topological free energy conformations could be targets for mutations with significant impact of protein function. Namely, the residues 484, 570, 614, 796, and 969, which are present in variants of concern and are targeted as important in protein function, are predicted as such from our model.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

AEPF: Attention-Enabled Point Fusion for 3D Object Detection

Current state-of-the-art (SOTA) LiDAR-only detectors perform well for 3D object detection tasks, but point cloud data are typically sparse and lacks semantic information. Detailed semantic information obtained from camera images can be added with existing LiDAR-based detectors to create a robust 3D detection pipeline. With two different data types, a major challenge in developing multi-modal sensor fusion networks is to achieve effective data fusion while managing computational resources. With separate 2D and 3D feature extraction backbones, feature fusion can become more challenging as these modes generate different gradients, leading to gradient conflicts and suboptimal convergence during network optimization. To this end, we propose a 3D object detection method, Attention-Enabled Point Fusion (AEPF). AEPF uses images and voxelized point cloud data as inputs and estimates the 3D bounding boxes of object locations as outputs. An attention mechanism is introduced to an existing feature fusion strategy to improve 3D detection accuracy and two variants are proposed. These two variants, AEPF-Small and AEPF-Large, address different needs. AEPF-Small, with a lightweight attention module and fewer parameters, offers fast inference. AEPF-Large, with a more complex attention module and increased parameters, provides higher accuracy than baseline models. Experimental results on the KITTI validation set show that AEPF-Small maintains SOTA 3D detection accuracy while inferencing at higher speeds. AEPF-Large achieves mean average precision scores of 91.13, 79.06, and 76.15 for the car class’s easy, medium, and hard targets, respectively, in the KITTI validation set. Results from ablation experiments are also presented to support the choice of model architecture.

Chemistry↗
DOE OSTI2023

Neutrino Flavor Model Building and the Origins of Flavor and CP Violation

The neutrino sector offers one of the most sensitive probes of new physics beyond the Standard Model of Particle Physics (SM). The mechanism of neutrino mass generation is still unknown. The observed suppression of neutrino masses hints at a large scale, conceivably of the order of the scale of a rand unified theory (GUT), which is a unique feature of neutrinos that is not shared by the charged fermions. The origin of neutrino masses and mixing is part of the outstanding puzzle of fermion masses and mixings, which is not explained ab initio in the SM. Flavor model building for both quark and lepton sectors is important in order to gain a better understanding of the origin of the structure of mass hierarchy and flavor mixing, which constitute the dominant fraction of the SM parameters. Recent activities in neutrino flavor model building based on non-Abelian discrete flavor symmetries and modular flavor symmetries have been shown to be a promising direction to explore. The emerging models provide a framework that has a significantly reduced number of undetermined parameters in the flavor sector. In addition, such a framework affords a novel origin of CP violation from group theory due to the intimate connection between physical CP transformation and group theoretical properties of non-Abelian discrete groups. Model building based on non-Abelian discrete flavor symmetries and their modular variants enables the particle physics community to interpret the current and anticipated upcoming data from neutrino experiments. Non-Abelian discrete flavor symmetries and their modular variants can result from compactification of a higher-dimensional theory. Pursuit of flavor model building based on such frameworks thus also provides the connection to possible UV completions: in particular, to string theory. We emphasize the importance of constructing models in which the uncertainties of theoretical predictions are smaller than, or at most compatible with, the error bars of measurements in neutrino experiments. While there exist proof-of-principle versions of bottom-up models in which the theoretical uncertainties are under control, it is remarkable that the key ingredients of such constructions were discovered first in top-down model building. We outline how a successful unification of bottom-up and top-down ideas and techniques may guide us towards a new era of precision flavor model building in which future experimental results can give us crucial insights into the UV completion of the SM.

Almumin, Yahya (ORCID:0000000196396772)↗
DOE OSTI2022

Low Pathogenicity H7N3 Avian Influenza Viruses Have Higher Within-Host Genetic Diversity Than a Closely Related High Pathogenicity H7N3 Virus in Infected Turkeys and Chickens

Within-host viral diversity offers a view into the early stages of viral evolution occurring after a virus infects a host. In recent years, advances in deep sequencing have allowed for routine identification of low-frequency variants, which are important sources of viral genetic diversity and can potentially emerge as a major virus population under certain conditions. We examined within-host viral diversity in turkeys and chickens experimentally infected with closely related H7N3 avian influenza viruses (AIVs), specifically one high pathogenicity AIV (HPAIV) and two low pathogenicity AIV (LPAIVs) with different neuraminidase protein stalk lengths. Consistent with the high mutation rates of AIVs, an abundance of intra-host single nucleotide variants (iSNVs) at low frequencies of 2–10% was observed in all samples collected. Furthermore, a small number of common iSNVs were observed between turkeys and chickens, and between directly inoculated and contact-exposed birds. Notably, the LPAIVs have significantly higher iSNV diversities and frequencies of nonsynonymous changes than the HPAIV in both turkeys and chickens. These findings highlight the dynamics of AIV populations within hosts and the potential impact of genetic changes, including mutations in the hemagglutinin gene that confers the high pathogenicity pathotype, on AIV virus populations and evolution.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

National Prevalence of Caprine Prion Protein Genetic Variability at Codons 146, 211, and 222 in Goat Herds in the United States

Scrapie is a neurodegenerative disease that impacts sheep and goats, characterized by gradual and progressive changes in neurological function. Recent research shows that the scrapie incubation period is significantly influenced by specific variations in amino acids within the prion protein gene (PRNP). The objective of this study was to estimate the national prevalence of caprine PRNP genetic variability at codons 146, 211, and 222 in goat populations across the United States. A total of 3052 blood, ear tissue, and brain tissue samples were collected from goats from 50 states. The participating states were categorized into four Veterinary Service (VS) district regions. The samples underwent DNA extraction, and the PRNP variants corresponding to codons 146, 211, and 222 were amplified and sequenced. The analysis of PRNP variants, when compared to the PRNP reference sequence, revealed seven alleles in twelve genotypes. The homozygous 146NN, 211RR, and 222QQ alleles, which have been linked to an increased risk of scrapie, were found to be the most prevalent among all the goats. The heterozygous 222QK, 211RQ, 146SD, 146ND, and 146NS alleles and the homozygous 222KK, 146SS, and 146DD alleles, known to be associated with reduced scrapie susceptibility and a prolonged incubation period after experimental challenge, were found in 1.098% (222QK), 2.33% (211RQ), 0.58% (146SD), 3.13% (146ND), 20.68% (146NS), 0.005% (222KK), 3.31% (146SS), and 0.67% (146DD) of goats, respectively. The 222QK allele was found most frequently in goats tested from the east (VS District 1, 1.59%) and southwest (VS District 4, 1.08%) regions, whereas the 211RQ allele was found most often in goats tested from the Midwest (VS District 2, 8.03%) and east (VS District 1, 6.53%) regions. The 146NS allele was found most frequently in goats tested from the northwest (VS District 3, 29.02%) and southwest (VS District 4, 20.69%) regions. Our results showed that the prevalence of less susceptible genotypes at PRNP codon 146 may be sufficient to use genetic susceptibility testing in some herds. This may reduce the number of goats removed as part of a herd clean-up plan and may promote the selective breeding goats for less susceptible alleles in high-risk herds at the national level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Propagating Uncertainties in the SALT3 Model-training Process to Cosmological Constraints

Type Ia supernovae (SNe Ia) are standardizable candles that must be modeled empirically to yield cosmological constraints. To understand the robustness of this modeling to variations in the model-training procedure, we build an end-to-end pipeline to test the recently developed SALT3 model. We explore the consequences of removing pre-2000s low-z or poorly calibrated U-band data, adjusting the amount and fidelity of SN Ia spectra, and using a model-independent framework to simulate the training data. We find that the SALT3 model surfaces are improved by having additional spectra and U-band data, and can be shifted by ~5% if host-galaxy contamination is not sufficiently removed from SN spectra. We find that resulting measurements of w are consistent to within 2.5% for all of the training variants explored in this work, with the largest shifts coming from variants that add color-dependent calibration offsets or host-galaxy contamination to the training spectra and those that remove pre-2000s low-z data. These results demonstrate that the SALT3 model-training procedure is largely robust to reasonable variations in the training data, but that additional attention must be paid to the treatment of spectroscopic data in the training process. We also find that the training procedure is sensitive to the color distributions of the input data—the resulting w measurement can be biased by ~2% if the color distribution is not sufficiently wide. Future low-z data, particularly u-band observations and high signal-to-noise ratio SN Ia spectra, will help to significantly improve SN Ia modeling in the coming years.

79 ASTRONOMY AND ASTROPHYSICS↗