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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 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Tau Positron Emission Tomography for Predicting Dementia in Individuals With Mild Cognitive Impairment

An accurate prognosis is especially pertinent in mild cognitive impairment (MCI), when individuals experience considerable uncertainty about future progression. To evaluate the prognostic value of tau positron emission tomography (PET) to predict clinical progression from MCI to dementia. This was a multicenter cohort study with external validation and a mean (SD) follow-up of 2.0 (1.1) years. Data were collected from centers in South Korea, Sweden, the US, and Switzerland from June 2014 to January 2024. Participant data were retrospectively collected and inclusion criteria were a baseline clinical diagnosis of MCI; longitudinal clinical follow-up; a Mini-Mental State Examination (MMSE) score greater than 22; and available tau PET, amyloid-β (Aβ) PET, and magnetic resonance imaging (MRI) scan less than 1 year from diagnosis. A total of 448 eligible individuals with MCI were included (331 in the discovery cohort and 117 in the validation cohort). None of these participants were excluded over the course of the study. Exposures included Tau PET, Aβ PET, and MRI. Positive results on tau PET (temporal meta–region of interest), Aβ PET (global; expressed in the standardized metric Centiloids), and MRI (Alzheimer disease [AD] signature region) was assessed using quantitative thresholds and visual reads. Clinical progression from MCI to all-cause dementia (regardless of suspected etiology) or to AD dementia (AD as suspected etiology) served as the primary outcomes. The primary analyses were receiver operating characteristics. In the discovery cohort, the mean (SD) age was 70.9 (8.5) years, 191 (58%) were male, the mean (SD) MMSE score was 27.1 (1.9), and 110 individuals with MCI (33%) converted to dementia (71 to AD dementia). Only the model with tau PET predicted all-cause dementia (area under the receiver operating characteristic curve [AUC], 0.75; 95% CI, 0.70-0.80) better than a base model including age, sex, education, and MMSE score (AUC, 0.71; 95% CI, 0.65-0.77; P = .02), while the models assessing the other neuroimaging markers did not improve prediction. In the validation cohort, tau PET replicated in predicting all-cause dementia. Compared to the base model (AUC, 0.75; 95% CI, 0.69-0.82), prediction of AD dementia in the discovery cohort was significantly improved by including tau PET (AUC, 0.84; 95% CI, 0.79-0.89; P < .001), tau PET visual read (AUC, 0.83; 95% CI, 0.78-0.88; P = .001), and Aβ PET Centiloids (AUC, 0.83; 95% CI, 0.78-0.88; P = .03). In the validation cohort, only the tau PET and the tau PET visual reads replicated in predicting AD dementia. In this study, tau-PET showed the best performance as a stand-alone marker to predict progression to dementia among individuals with MCI. This suggests that, for prognostic purposes in MCI, a tau PET scan may be the best currently available neuroimaging marker.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2001

Consequences of contamination of the spacecraft environment: immunologic consequences

Long-term space voyages pose numerous known and unknown health hazards, to the human immune system. Well-studied clinical examples of secondary immunodeficiencies created on Earth, lead one to predict that the conditions of prolonged space flight would weaken the human immune responses that normally hold infection and cancer in check. From evidence gathered from humans flown for prolonged periods in space and from human models of space flight studied on Earth it is reasonable to suspect that space travelers to the planet Mars would experience a weakening of immunity. Subtle defects of immune cell structure and function have been observed in astronauts, such as weakening of specific T-lymphocyte recall of specific antigens. Ground-based models also have demonstrated alterations of immune function, such as the elevation of neuroendocrine immune system messengers, interleukin-6, and soluble tumor necrosis factor-alpha receptor in sleep deprivation. Since severe immune compromise the clinical consequences of reactivation of latent virus infections and the development of cancer, has yet to be seen in space flight or in the Earth models, it is extremely important to begin to quantify early changes in immunity to predict the development of immune system collapse with poor clinical outcomes. This approach is designed to validate a number of surrogate markers that will predict trouble ahead. Inherent in this research is the development of countermeasures to reduce the risks of infection and cancer in the first humans going to Mars.

Non-NASA Center↗
DOE OSTI2020

Deep convolutional neural networks for multi-scale time-series classification and application to tokamak disruption prediction using raw, high temporal resolution diagnostic data

In this paper we discuss recent advances in deep convolutional neural networks (CNN) for sequence learning, which allow identifying long-range, multi-scale phenomena in long sequences, such as those found in fusion plasmas. We point out several benefits of these deep CNN architectures, such as not requiring experts such as physicists to hand-craft input data features, the ability to capture longer range dependencies compared to the more common sequence neural networks (recurrent neural networks like long short-term memory (LSTM) networks), and the comparative computational efficiency. We apply this neural network architecture to the popular problem of disruption prediction in fusion energy tokamaks, utilizing raw data from a single diagnostic, the Electron Cyclotron Emission imaging (ECEi) diagnostic from the DIII-D tokamak. Initial results trained on a large ECEi dataset show promise, achieving an F 1 -score of ~91% on individual time-slices using only the ECEi data. This indicates the ECEi diagnostic by itself can be sensitive to a number of pre-disruption markers useful for predicting disruptions on timescales not only for mitigation but also avoidance. Future opportunities for utilizing these deep CNN architectures with fusion data are outlined, including impact of recent upgrades to the ECEi diagnostic.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2021

Deep graph representations embed network information for robust disease marker identification

We report that the accurate disease diagnosis and prognosis based on omics data rely on the effective identification of robust prognostic and diagnostic markers that reflect the states of the biological processes underlying the disease pathogenesis and progression. In this article, we present GCNCC, a Graph Convolutional Network-based approach for Clustering and Classification, that can identify highly effective and robust network-based disease markers. Based on a geometric deep learning framework, GCNCC learns deep network representations by integrating gene expression data with protein interaction data to identify highly reproducible markers with consistently accurate prediction performance across independent datasets possibly from different platforms. GCNCC identifies these markers by clustering the nodes in the protein interaction network based on latent similarity measures learned by the deep architecture of a graph convolutional network, followed by a supervised feature selection procedure that extracts clusters that are highly predictive of the disease state. By benchmarking GCNCC based on independent datasets from different diseases (psychiatric disorder and cancer) and different platforms (microarray and RNA-seq), we show that GCNCC outperforms other state-of-the-art methods in terms of accuracy and reproducibility.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Statistically-driven Experimental Design to Improve Reference-free Quantification of Small Molecules by Liquid Chromatography-Mass Spectrometry

Non-targeted analysis of small molecules and metabolites in unknown, complex samples using liquid chromatography-tandem mass spectrometry remains challenging. One of the main bottlenecks is the extensive unannotated regions of metabolomics mass spectrometry data, resulting in knowledge gaps. Small molecule annotation in mass spectrometry data has conventionally relied on reference standards and libraries for compound identification and confirmation, which can constrain compound identification to those molecules already known, thus limiting the ability to discover new knowledge and new markers. Retention time prediction can facilitate and expedite unknown compound identification in non-targeted analysis of complex metabolomics samples. Additionally, accurate retention time predictions can also inform sample mixture design for LC-MS/MS analyses. However, current machine learning-based methods for retention time prediction are typically developed for specific chromatographic platforms and are not generalizable across scales. And while technologies and methods to improve reference-free metabolite identification for more comprehensive annotation of unknowns has received much attention, development of the same for quantitation without reference standards has been much more limited, despite its importance in toxicological, environmental, food safety, forensics, and clinical applications. We believe that a reference-free quantitation strategy that exploits mass spectrometry data already collected for reference-free identification can provide much more insight on unknowns, and move the metabolomics field for more complete unknowns characterization. As such, we pursue two efforts to improve upon current state-of-the-art methods in non-targeted analysis: (1) machine learning-based retention time prediction and (2) statistical design of experiments framework for reference-free quantitation. In this work, we develop and demonstrate (1) a generalizable retention time prediction capability across chromatographic conditions and scales, and (2) a statistical design-based framework for response factor contribution elucidation and reference-free quantitation. Evaluation of our retention time prediction model, PrediToR, showed approximately 24% improvement over current models, and we observed approximately 10X improvement in concentration estimation accuracy from our statistical design-based response factor model over a primarily ionization efficiency-based model. We expect that future efforts to improve upon these new capabilities will further advance non-targeted analysis of small molecules towards truly reference-free metabolomics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1981

Effect of field of view on performing a low altitude maneuvering task

Visual systems with a single window display are often utilized in ground based simulators used to study helicopter flying qualities during visual low altitude maneuvering tasks. The effects of this limited field of view (FOV) on pilot assessments of flying qualities are uncertain. A study was conducted using a variable stability UH-1H helicopter to compare restricted and unrestricted FOV for a range of flying qualities. With the restricted FOV, the pilots reported reduced ground track precision owing to loss of visual contact with the course markers. However, the predictable ground track of the repeated S-turn task with no obstacles made it easy for them to anticipate their maneuvers, and resulted in only a slight degradation of pilot ratings. This degradation was not sensitive to large changes in helicopter flying qualities.

Carico, Dean↗
DOE OSTI2023

A novel transfer learning framework for sorghum biomass prediction using UAV-based remote sensing data and genetic markers

Yield for biofuel crops is measured in terms of biomass, so measurements throughout the growing season are crucial in breeding programs, yet traditionally time- and labor-consuming since they involve destructive sampling. Modern remote sensing platforms, such as unmanned aerial vehicles (UAVs), can carry multiple sensors and collect numerous phenotypic traits with efficient, non-invasive field surveys. However, modeling the complex relationships between the observed phenotypic traits and biomass remains a challenging task, as the ground reference data are very limited for each genotype in the breeding experiment. In this study, a Long Short-Term Memory (LSTM) based Recurrent Neural Network (RNN) model is proposed for sorghum biomass prediction. The architecture is designed to exploit the time series remote sensing and weather data, as well as static genotypic information. As a large number of features have been derived from the remote sensing data, feature importance analysis is conducted to identify and remove redundant features. A strategy to extract representative information from high-dimensional genetic markers is proposed. To enhance generalization and minimize the need for ground reference data, transfer learning strategies are proposed for selecting the most informative training samples from the target domain. Consequently, a pre-trained model can be refined with limited training samples. Field experiments were conducted over a sorghum breeding trial planted in multiple years with more than 600 testcross hybrids. The results show that the proposed LSTM-based RNN model can achieve high accuracies for single year prediction. Further, with the proposed transfer learning strategies, a pre-trained model can be refined with limited training samples from the target domain and predict biomass with an accuracy comparable to that from a trained-from-scratch model for both multiple experiments within a given year and across multiple years.

36 MATERIALS SCIENCE↗
NASA NTRS1977

Development and application of a three dimensional numerical model for predicting pollutant and sediment transport using an Eulerian-Lagrangian marker particle technique

A computer coded Lagrangian marker particle in Eulerian finite difference cell solution to the three dimensional incompressible mass transport equation, Water Advective Particle in Cell Technique, WAPIC, was developed, verified against analytic solutions, and subsequently applied in the prediction of long term transport of a suspended sediment cloud resulting from an instantaneous dredge spoil release. Numerical results from WAPIC were verified against analytic solutions to the three dimensional incompressible mass transport equation for turbulent diffusion and advection of Gaussian dye releases in unbounded uniform and uniformly sheared uni-directional flow, and for steady-uniform plug channel flow. WAPIC was utilized to simulate an analytic solution for non-equilibrium sediment dropout from an initially vertically uniform particle distribution in one dimensional turbulent channel flow.

Pavish, D. L.↗
DOE OSTI2025

Prediction of cccDNA dynamics in hepatitis B patients by a combination of serum surrogate markers

Quantification of intrahepatic covalently closed circular DNA (cccDNA) is a key for evaluating an elimination of hepatitis B virus (HBV) in infected patients. However, quantifying cccDNA requires invasive methods such as a liver biopsy, which makes it impractical to access the dynamics of cccDNA in patients. Although HBV RNA and HBV core-related antigens (HBcrAg) have been proposed as surrogate markers for evaluating cccDNA activity, they do not necessarily estimate the amount of cccDNA. Here, we employed a recently developed multiscale mathematical model describing intra- and intercellular viral propagation and applied it in HBV-infected patients under treatment. We developed a model that can predict intracellular HBV dynamics by use of extracellular viral markers, including HBsAg, HBV DNA, and HBcrAg in peripheral blood. Importantly, the model prediction of the amount of cccDNA in patients over time was confirmed to be well correlated with the data for quantified cccDNA by paired liver biopsy. Thus, our method combining classic and emerging surrogate markers enables us to predict the decay dynamics of cccDNA in patients undergoing treatment.

60 APPLIED LIFE SCIENCES↗
NASA NTRS1999

Predicting cancer rates in astronauts from animal carcinogenesis studies and cellular markers

The radiation space environment includes particles such as protons and multiple species of heavy ions, with much of the exposure to these radiations occurring at extremely low average dose-rates. Limitations in databases needed to predict cancer hazards in human beings from such radiations are significant and currently do not provide confidence that such predictions are acceptably precise or accurate. In this article, we outline the need for animal carcinogenesis data based on a more sophisticated understanding of the dose-response relationship for induction of cancer and correlative cellular endpoints by representative space radiations. We stress the need for a model that can interrelate human and animal carcinogenesis data with cellular mechanisms. Using a broad model for dose-response patterns which we term the "subalpha-alpha-omega (SAO) model", we explore examples in the literature for radiation-induced cancer and for radiation-induced cellular events to illustrate the need for data that define the dose-response patterns more precisely over specific dose ranges, with special attention to low dose, low dose-rate exposure. We present data for multiple endpoints in cells, which vary in their radiosensitivity, that also support the proposed model. We have measured induction of complex chromosome aberrations in multiple cell types by two space radiations, Fe-ions and protons, and compared these to photons delivered at high dose-rate or low dose-rate. Our data demonstrate that at least three factors modulate the relative efficacy of Fe-ions compared to photons: (i) intrinsic radiosensitivity of irradiated cells; (ii) dose-rate; and (iii) another unspecified effect perhaps related to reparability of DNA lesions. These factors can produce respectively up to at least 7-, 6- and 3-fold variability. These data demonstrate the need to understand better the role of intrinsic radiosensitivity and dose-rate effects in mammalian cell response to ionizing radiation. Such understanding is critical in extrapolating databases between cellular response, animal carcinogenesis and human carcinogenesis, and we suggest that the SAO model is a useful tool for such extrapolation.

Non-NASA Center↗
NASA NTRS2006

Markers of bone resorption and calcium metabolism are related to dietary intake patterns in male and female bed rest subjects

Dietary potassium and protein intakes predict net endogenous acid production in humans. Intracellular buffers, including exchangeable bone mineral, play a crucial role in balancing chronic acid-base perturbations in the body; subsequently, chronic acid loads can potentially contribute to bone loss. Bone is lost during space flight, and a dietary countermeasure would be desirable for many reasons. We studied the ability of diet protein and potassium to predict levels of bone resorption markers in males and females. Identical twin pairs (8 M, 7 F) were assigned to 2 groups: bed rest (sedentary, SED) or bed rest with supine treadmill exercise in a lower body negative pressure chamber (EX). Diet was controlled for 3 d before and 30 d of bed rest (BR). Urinary Ca, N-telopeptide (NTX), and pyridinium crosslinks (PYD) were measured before and on days 5, 12, 19, and 26 of BR. Data were analyzed by Pearson correlation (P<0.05). The ratio of dietary animal protein/potassium intake was not correlated with NTX before BR for males or females, but they were positively correlated in both groups of males during bed rest. Dietary animal protein/potassium and urine Ca were correlated before and during bed rest for the males, and only during bed rest for the females. Conversely, the ratio of dietary vegetable protein/potassium intake was negatively correlated with urinary calcium during bed rest for the females, but there was no relationship between vegetable protein/potassium intake and bone markers for the males. These data suggest that the ratio of animal protein/potassium intake may affect bone, particularly in bed rest subjects. These data show that the type of protein and gender may be additional factors that modulate the effect of diet on bone metabolism during bed rest. Altering this ratio may help prevent bone loss on Earth and during space flight.

Smith, Scott M.↗
NASA NTRS1997

Aircraft Vortex Spacing System (AVOSS) Concept and Development

The AVOSS goal is to: (1) Support TAP goal of improving instrument operations capacity 12-15% while maintaining safety; (2) Provide dynamical aircraft wake vortex spacing criteria to ATC systems at capacity limited facilities with required lead time and stability for use in establishing aircraft arrival scheduling; and (3) System development and concept demonstration. The AVOSS system concept is to separate aircraft from encounters with wake vortices of an operationally unacceptable strength. In doing so, define protected corridor from outer marker to runway and predict time for vortex to clear ("Transport Time"), define operationally unacceptable wake strength and predict time to decay ("Decay Time"), combine and provide to ATC automation ("Residence Time"), and monitor safety and provide predictor feedback with wake vortex detection subsystem.

Hinton, David A.↗
NASA NTRS2017

Characterizing the Effects of Chronic 2G Centrifugation on the Rat Skeletal System

During weightlessness, the skeletal system of astronauts is negatively affected by decreased calcium absorption and bone mass loss. Therefore, it is necessary to counteract these changes for long-term skeletal health during space flights. Our long-term plan is to assess artificial gravity (AG) as a possible solution to mitigate these changes. In this study, we aim to determine the skeletal acclimation to chronic centrifugation. We hypothesize that a 2G hypergravity environment causes an anabolic response in growing male rats. Specifically, we predict chronic 2G to increase tissue mineral density, bone volume fraction of the cancellous tissue and to increase overall bone strength. Systemically, we predict that bone formation markers (i.e., osteocalcin) are elevated and resorption markers (i.e., tartrate resistant acid phosphatase) are decreased or unchanged from controls. The experiment has three groups, each with an n8: chronic 2g, cage control (housed on the centrifuge, but not spun), and a vivarium control (normal rat caging). Pre-pubescent, male Long-Evans rats were used to assess our hypothesis. This group was subject to 90 days of 2G via centrifugation performed at the Chronic Acceleration Research Unit (CARU) at University of California Davis. After 90 days, animals were euthanized and tissues collected. Blood was drawn via cardiac puncture and the right leg collected for structural (via microcomputed tomography) and strength quantification. Understanding how counteract these skeletal changes will have major impacts for both the space-faring astronauts and the people living on Earth.

rat↗
DOE OSTI2020

National Cancer Institute Think-Tank Meeting Report on Proteomic Cartography and Biomarkers at the Single-Cell Level: Interrogation of Premalignant Lesions

A Think-Tank Meeting was convened by the National Cancer Institute (NCI) to solicit experts’ opinion on the development and application of multi-omic single-cell analyses, and especially single-cell proteomics, to improve the development of a new generation of biomarkers for cancer risk, early detection, diagnosis, prognosis as well as to discuss the discovery of new targets for prevention and therapy. It is anticipated that such markers and targets will be based on cellular, subcellular, molecular and functional aberrations within the lesion and within individual cells. Additionally, single-cell proteomic data will be essential for the establishment of new tools that with searchable and scalable features that include spatial and temporal cartographies of premalignant and malignant lesions. Challenges and potential solutions that were discussed included: • The best way/s to analyze single-cells from fresh and preserved tissue • Detection and analysis of secreted molecules and from single cells, especially from a tissue slice • Detection of new, previously undocumented cell type/s in the premalignant and early stage cancer tissue microenvironment • Multi-omic integration of data to support and inform proteomic measurements • Subcellular organelles – identifying abnormal structure, function, distribution and location within individual premalignant and malignant cells • How to improve the dynamic range of single-cell proteomic measurements for discovery of differentially expressed proteins and their post translational modifications (PTM) • The depth of coverage measured concurrently using single-cell techniques • Quantitation - absolute or semiquantitative? • Single methodology or multiplexed combinations? • Application of analytical methods for identification of biologically significant subsets • Data visualization of N-dimensional datasets • How to construct intercellular signaling networks in individual cells within premalignant tumor microenvironments (TME) • Associations between intrinsic cellular processes and extrinsic stimuli • How to predict cellular responses to stress inducing stimuli • Identification of new markers for prediction of progression from precursor, benign and localized lesions to invasive cancer, based on spatial and temporal changes within individual cells • Identification of new targets for immunoprevention or immunotherapy – identification of neoantigens and surfactome of individual cells within a lesion

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Prediction of the development of islet autoantibodies through integration of environmental, genetic, and metabolic markers

The Environmental Determinants of the Diabetes in the Young (TEDDY) study has prospectively followed, from birth, children at increased genetic risk of type 1 diabetes. We evaluated the potential of machine learning to identify new biomarkers that predict imminent (within 6 months) development of persistent islet autoantibodies to insulin, GAD or IA-2 in TEDDY participants through integration of time-invariant risk factors with time-varying metabolomics. The predictive modeling was initiated with over 220 potential biomarkers; through ensemble-based feature evaluation, the optimal model included 42 biomarkers, returning a cross-validated receiver operating characteristic area under the curve of 0.74. The model identified a principal set of 20 time-invariant markers, including 16 single nucleotide polymorphisms and two HLA-DR genotypes, gestational age, and exposure to a prebiotic formula. Integration of the metabolome identified 22 high-priority metabolites and lipids, including adipic acid and ceramide d42:0, that predicted development of islet autoantibodies, dependent upon the time horizon. The majority (86%) of metabolites that predicted development of islet autoantibodies belonged to 3 pathways: lipid oxidation, phospholipase A2 signaling, and pentose phosphate pathway. TEDDY data suggest that these metabolic processes may play a role in triggering islet autoimmunity.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Investigating genomic prediction strategies for grain carotenoid traits in a tropical/subtropical maize panel

Abstract Vitamin A deficiency remains prevalent on a global scale, including in regions where maize constitutes a high percentage of human diets. One solution for alleviating this deficiency has been to increase grain concentrations of provitamin A carotenoids in maize (Zea mays ssp. mays L.)—an example of biofortification. The International Maize and Wheat Improvement Center (CIMMYT) developed a Carotenoid Association Mapping panel of 380 inbred lines adapted to tropical and subtropical environments that have varying grain concentrations of provitamin A and other health-beneficial carotenoids. Several major genes have been identified for these traits, 2 of which have particularly been leveraged in marker-assisted selection. This project assesses the predictive ability of several genomic prediction strategies for maize grain carotenoid traits within and between 4 environments in Mexico. Ridge Regression-Best Linear Unbiased Prediction, Elastic Net, and Reproducing Kernel Hilbert Spaces had high predictive abilities for all tested traits (β-carotene, β-cryptoxanthin, provitamin A, lutein, and zeaxanthin) and outperformed Least Absolute Shrinkage and Selection Operator. Furthermore, predictive abilities were higher when using genome-wide markers rather than only the markers proximal to 2 or 13 genes. These findings suggest that genomic prediction models using genome-wide markers (and assuming equal variance of marker effects) are worthwhile for these traits even though key genes have already been identified, especially if breeding for additional grain carotenoid traits alongside β-carotene. Predictive ability was maintained for all traits except lutein in between-environment prediction. The TASSEL (Trait Analysis by aSSociation, Evolution, and Linkage) Genomic Selection plugin performed as well as other more computationally intensive methods for within-environment prediction. The findings observed herein indicate the utility of genomic prediction methods for these traits and could inform their resource-efficient implementation in biofortification breeding programs.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Imitating the “breeder's eye”: Predicting grain yield from measurements of non‐yield traits

Abstract Plant breeding relies on information gathered from field trials to select promising new crop varieties for release to farmers and to develop genomic prediction models that can enhance the efficiency of genetic improvement in future breeding cycles. However, generating the genetic marker data required to apply genomic prediction at the early stages of a breeding program remains costly for many public‐sector breeding programs as well as for many plant breeders operating in developing countries. As the pace of climate change intensifies, the time lag of developing and deploying new crop varieties requires plant breeders to make selection decisions without knowing the future environments those crop varieties will encounter in farmers’ fields. Therefore, both lower cost and higher accuracy methods for prediction of crop performance are essential for creating and maintaining resilient agricultural systems in the latter half of the 21 st century. To address this challenge, we conducted linked yield trials of 752 public maize ( Zea mays ) genotypes in two distinct environments. We developed and trained a phenotypic prediction model to predict yield from manually scored plant traits. The phenotypic prediction approach we employed outperformed genomic prediction in predicting yields in a second environment, with 8.7%–63% higher R 2 and 4%–13% less root mean square error than the genomic prediction. The phenotypic prediction has the potential to be applied to a wider range of breeding programs, including those that lack the resources to genotype large populations, such as programs in the developing world, breeding programs for specialty crops, and public sector programs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Topological Phenomena in Artificial Quantum Materials Revealed by Local Chern Markers

A striking example of frustration in physics is Hofstadter’s butterfly, a fractal structure that emerges from the competition between a crystal’s lattice periodicity and the magnetic length of an applied field. Current methods for predicting the topological invariants associated with Hofstadter’s butterfly are challenging or impossible to apply to a range of materials, including those that are disordered or lack a bulk spectral gap. Here, in this work, we demonstrate a framework for predicting a material’s local Chern markers using its position-space description and validate it against experimental observations of quantum transport in artificial graphene in a semiconductor heterostructure, inherently accounting for fabrication disorder strong enough to close the bulk spectral gap. By resolving local changes in the system’s topology, we reveal the topological origins of antidot-localized states that appear in artificial graphene in the presence of a magnetic field. Moreover, we show the breadth of this framework by simulating how Hofstadter’s butterfly emerges from an initially unpatterned 2D electron gas as the system’s potential strength is increased and predict that artificial graphene becomes a topological insulator at the critical magnetic field. Overall, we anticipate that a position-space approach to determine a material’s Chern invariant without requiring prior knowledge of its occupied states or bulk spectral gaps will enable a broad array of fundamental inquiries and provide a novel route to material discovery, especially in metallic, aperiodic, and disordered systems.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗