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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 361 records · Page 20
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Structural and Dynamical Design Principles for Iron-Sulfur Clusters in Sequence Defined Polymers

This integrated synthetic and computational effort uses sequence defined polymers such as peptoids, and triazines, capable of mimicking a flexible protein environment to control the positioning of side chains around redox cofactors. This strategy will allow modulation of redox properties through both direct and indirect mechanisms. To this end the aim is defined to test the underlying hypothesis: that principles and critical functionalities governing the binding and tuning of naturally occurring electron carrier co-factors can be identified and used to predictably design and synthesize maquettes based on non-natural polymers resulting in new modular biomimetic scaffolds for Electron Transfer Materials. This will be achieved through design and synthesis of ligands that can stabilize iron-sulfur clusters and tune the redox potential based on sequence.

36 MATERIALS SCIENCE↗
DOE OSTI2023

Neutralization profiles of HIV-1 viruses from the VRC01 Antibody Mediated Prevention (AMP) trials

The VRC01 Antibody Mediated Prevention (AMP) efficacy trials conducted between 2016 and 2020 showed for the first time that passively administered broadly neutralizing antibodies (bnAbs) could prevent HIV-1 acquisition against bnAb-sensitive viruses. HIV-1 viruses isolated from AMP participants who acquired infection during the study in the sub-Saharan African (HVTN 703/HPTN 081) and the Americas/European (HVTN 704/HPTN 085) trials represent a panel of currently circulating strains of HIV-1 and offer a unique opportunity to investigate the sensitivity of the virus to broadly neutralizing antibodies (bnAbs) being considered for clinical development. Pseudoviruses were constructed using envelope sequences from 218 individuals. The majority of viruses identified were clade B and C; with clades A, D, F and G and recombinants AC and BF detected at lower frequencies. We tested eight bnAbs in clinical development (VRC01, VRC07-523LS, 3BNC117, CAP256.25, PGDM1400, PGT121, 10–1074 and 10E8v4) for neutralization against all AMP placebo viruses (n = 76). Compared to older clade C viruses (1998–2010), the HVTN703/HPTN081 clade C viruses showed increased resistance to VRC07-523LS and CAP256.25. At a concentration of 1μg/ml (IC80), predictive modeling identified the triple combination of V3/V2-glycan/CD4bs-targeting bnAbs (10-1074/PGDM1400/VRC07-523LS) as the best against clade C viruses and a combination of MPER/V3/CD4bs-targeting bnAbs (10E8v4/10-1074/VRC07-523LS) as the best against clade B viruses, due to low coverage of V2-glycan directed bnAbs against clade B viruses. Overall, the AMP placebo viruses represent a valuable resource for defining the sensitivity of contemporaneous circulating viral strains to bnAbs and highlight the need to update reference panels regularly. Our data also suggests that combining bnAbs in passive immunization trials would improve coverage of global viruses.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

High-throughput functional trait testing for bacterial pathogens

Functional traits are characteristics that affect the fitness and metabolic function of a microorganism. There is growing interest in using high-throughput methods to characterize bacterial pathogens based on functional virulence traits. Traditional methods that phenotype a single organism for a single virulence trait can be time consuming and labor intensive. Alternatively, machine learning of whole-genome sequences (WGS) has shown some success in predicting virulence. However, relying solely on WGS can miss functional traits, particularly for organisms lacking classical virulence factors. We propose that high-throughput assays for functional virulence trait identification should become a prominent method of characterizing bacterial pathogens on a population scale. This work is critical as we move from compiling lists of bacterial species associated with disease to pathogen-agnostic approaches capable of detecting novel microbes. We discuss six key areas of functional trait testing and how advancing high-throughput methods could provide a greater understanding of pathogens.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Adaptive Pore Opening to Form Tailored Adsorption Sites in a Cooperatively Flexible Framework Enables Record Inverse Propane/Propylene Separation

A proposed low-energy alternative to the separation of alkanes from alkenes by energy-intensive cryogenic distillation is separation by porous adsorbents. Unfortunately, most adsorbents preferentially take up the desired, high-value major component alkene, requiring frequent regeneration. Adsorbents with inverse selectivity for the minor component alkane would enable the direct production of purified, reagent-grade alkene, greatly reducing global energy consumption. However, such materials are exceedingly rare, especially for propane/propylene separation. Here, we report that through adaptive and spontaneous pore size and shape adaptation to optimize an ensemble of weak noncovalent interactions, the structurally responsive metal-organic framework CdIF-13 (sod-Cd(benzimidazolate) 2 ) exhibits inverse selectivity for propane over propylene with record-setting separation performance under industrially relevant temperature, pressure, and mixture conditions. Powder synchrotron X-ray diffraction measurements combined with first-principles calculations yield atomic-scale insight and reveal the induced fit mechanism of adsorbate-specific pore adaptation and ensemble interactions between ligands and adsorbates. Dynamic column breakthrough measurements confirm that CdIF-13 displays selectivity under mixed-component conditions of varying ratios, with a record measured selectivity factor of α ≈ 3 at 95:5 propylene:propane at 298 K and 1 bar. When sequenced with a low-cost rigid adsorbent, we demonstrated the direct purification of propylene under ambient conditions. In conclusion, this combined atomic-level structural characterization and performance testing firmly establishes how cooperatively flexible materials can be capable of unprecedented separation factors.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2024

Phasor-Domain Modeling of Inverter Based Resources Considering Unbalanced Grid Faults

A significant increase in inverter-based distributed energy resources (DERs) is expected in distribution systems of the near future. The latest phasor modeling approach of DERs with emerging control technologies can capture relevant dynamics in large-scale three-phase distribution systems with unbalanced construction. However, there is a lack of appropriate phasor models to deal with unbalanced fault scenarios. As such, this paper describes per-phase phasor modeling approach appropriate for unbalanced grid disturbance studies in distribution systems with high penetration of DERs. The overall modeling performance of representative DERs is simulated in phasor domain GridLAB-D environment and validated against controller hardware-in-the loop (CHIL) testing or electromagnetic transient (EMT) based switching model simulation in PSCAD with test cases including different unbalanced grid faults. The high accuracy retained from the simulation verifies the effectiveness of the proposed phasor modeling approach considering interactions between positive and negative sequence signals.

Chen, Zhe↗
DOE OSTI2020

Sensitivity of 44 Ti and 56 Ni Production in Core-collapse Supernova Shock-driven Nucleosynthesis to Nuclear Reaction Rate Variations

Recent observational advances have enabled high resolution mapping of 44 Ti in core-collapse supernova (CCSN) remnants. Comparisons between observations and models provide stringent constraints on the CCSN mechanism. However, past work has identified several uncertain nuclear reaction rates that influence 44 Ti and 56 Ni production in postprocessing model calculations. We evolved one-dimensional models of 15 M ⊙ , 18 M ⊙ , 22 M ⊙ , and 25 M ⊙ stars from zero age main sequence through CCSN using Modules for Experiments in Stellar Astrophysics and investigated the previously identified reaction rate sensitivities of 44 Ti and 56 Ni production. In this work, we tested the robustness of our results by making various assumptions about the CCSN explosion energy and mass cut. We found a number of reactions that have a significant impact on the nucleosynthesis of 44 Ti and 56 Ni, particularly for lower progenitor masses. Notably, the reaction rates ${}^{13}{\rm{N}}{(\alpha ,p)}^{16}{\rm{O}}$, ${}^{17}{\rm{F}}{(\alpha ,p)}^{20}\mathrm{Ne}$, ${}^{52}\mathrm{Fe}{(\alpha ,p)}^{55}\mathrm{Co}$, ${}^{56}\mathrm{Ni}{(\alpha ,p)}^{59}\mathrm{Cu}$, ${}^{57}\mathrm{Ni}{(n,p)}^{57}\mathrm{Co}$, ${}^{56}\mathrm{Co}{(p,n)}^{56}\mathrm{Ni}$, ${}^{39}{\rm{K}}{(p,\gamma )}^{40}\mathrm{Ca}$, ${}^{47}{\rm{V}}{(p,\gamma )}^{48}\mathrm{Cr}$, ${}^{52}\mathrm{Mn}{(p,\gamma )}^{53}\mathrm{Fe}$, ${}^{57}\mathrm{Co}{(p,\gamma )}^{58}\mathrm{Ni}$, and ${}^{39}{\rm{K}}{(p,\alpha )}^{36}\mathrm{Ar}$ are influential for a large number of model conditions. Furthermore, we found the list of influential reactions identified by previous postprocessing studies of CCSN shock-driven nucleosynthesis is likely incomplete, motivating future larger-scale sensitivity studies.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Genetic mapping and prediction of flowering time and plant height in a maize Stiff Stalk MAGIC population

Abstract The Stiff Stalk heterotic pool is a foundation of US maize seed parent germplasm and has been heavily utilized by both public and private maize breeders since its inception in the 1930s. Flowering time and plant height are critical characteristics for both inbred parents and their test crossed hybrid progeny. To study these traits, a 6-parent multiparent advanced generation intercross population was developed including maize inbred lines B73, B84, PHB47 (B37 type), LH145 (B14 type), PHJ40 (novel early Stiff Stalk), and NKH8431 (B73/B14 type). A set of 779 doubled haploid lines were evaluated for flowering time and plant height in 2 field replicates in 2016 and 2017, and a subset of 689 and 561 doubled haploid lines were crossed to 2 testers, respectively, and evaluated as hybrids in 2 locations in 2018 and 2019 using an incomplete block design. Markers were derived from a practical haplotype graph built from the founder whole genome assemblies and genotype-by-sequencing and exome capture-based sequencing of the population. Genetic mapping utilizing an update to R/qtl2 revealed differing profiles of significant loci for both traits between 635 of the DH lines and 2 sets of 570 and 471 derived hybrids. Genomic prediction was used to test the feasibility of predicting hybrid phenotypes based on the per se data. Predictive abilities were highest on direct models trained using the data they would predict (0.55–0.63), and indirect models trained using per se data to predict hybrid traits had slightly lower predictive abilities (0.49–0.55). Overall, this finding is consistent with the overlapping and nonoverlapping significant quantitative trait loci found within the per se and hybrid populations and suggests that selections for phenology traits can be made effectively on doubled haploid lines before hybrid data is available.

Genetics & Heredity↗
DOE OSTI2023

Membrane‐acting biomimetic peptoids against visceral leishmaniasis

Visceral leishmaniasis (VL) is among the most neglected tropical diseases in the world. Drug cell permeability is essential for killing the intracellular residing parasites responsible for VL, making cell‐permeating peptides a logical choice to address VL. Unfortunately, the limited biological stability of peptides restricts their usage. Sequence‐specific oligo‐ N ‐substituted glycines (‘peptoids’) are a class of peptide mimics that offers an excellent alternative to peptides in terms of ease of synthesis and good biostability. We tested peptoids against the parasite Leishmania donovani in both forms, that is, intracellular amastigotes and promastigotes. N ‐alkyl hydrophobic chain addition (lipidation) and bromination of oligopeptoids yielded compounds with good antileishmanial activity against both forms, showing the promise of these antiparasitic peptoids as potential drug candidates to treat VL.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

microTrait: A Toolset for a Trait-Based Representation of Microbial Genomes

Remote sensing approaches have revolutionized the study of macroorganisms, allowing theories of population and community ecology to be tested across increasingly larger scales without much compromise in resolution of biological complexity. In microbial ecology, our remote window into the ecology of microorganisms is through the lens of genome sequencing. For microbial organisms, recent evidence from genomes recovered from metagenomic samples corroborate a highly complex view of their metabolic diversity and other associated traits which map into high physiological complexity. Regardless, during the first decades of this omics era, microbial ecological research has primarily focused on taxa and functional genes as ecological units, favoring breadth of coverage over resolution of biological complexity manifested as physiological diversity. Recently, the rate at which provisional draft genomes are generated has increased substantially, giving new insights into ecological processes and interactions. From a genotype perspective, the wide availability of genome-centric data requires new data synthesis approaches that place organismal genomes center stage in the study of environmental roles and functional performance. Extraction of ecologically relevant traits from microbial genomes will be essential to the future of microbial ecological research. Here, we present microTrait , a computational pipeline that infers and distills ecologically relevant traits from microbial genome sequences. microTrait maps a genome sequence into a trait space, including discrete and continuous traits, as well as simple and composite. Traits are inferred from genes and pathways representing energetic, resource acquisition, and stress tolerance mechanisms, while genome-wide signatures are used to infer composite, or life history, traits of microorganisms. This approach is extensible to any microbial habitat, although we provide initial examples of this approach with reference to soil microbiomes.

Karaoz, Ulas↗
DOE OSTI2023

Biotic countermeasures that rescue Nannochloropsis gaditana from a Bacillus safensis infection

The natural assemblage of a symbiotic bacterial microbiome (bacteriome) with microalgae in marine ecosystems is now being investigated as a means to increase algal productivity for industry. When algae are grown in open pond settings, biological contamination causes an estimated 30% loss of the algal crop. Therefore, new crop protection strategies that do not disrupt the native algal bacteriome are needed to produce reliable, high-yield algal biomass. Bacteriophages offer an unexplored solution to treat bacterial pathogenicity in algal cultures because they can eliminate a single species without affecting the bacteriome. To address this, we identified a highly virulent pathogen of the microalga Nannochloropsis gaditana, the bacterium Bacillus safensis, and demonstrated rescue of the microalgae from the pathogen using phage. 16S rRNA amplicon sequencing showed that phage treatment did not alter the composition of the bacteriome. It is widely suspected that the algal bacteriome could play a protective role against bacterial pathogens. To test this, we compared the susceptibility of a bacteriome-attenuated N. gaditana culture challenged with B. safensis to a N. gaditana culture carrying a growth-promoting bacteriome. We showed that the loss of the bacteriome increased the susceptibility of N. gaditana to the pathogen. Transplanting the microalgal bacteriome to the bacteriome-attenuated culture reconstituted the protective effect of the bacteriome. Finally, the success of phage treatment was dependent on the presence of beneficial bacteriome. This study introduces two synergistic countermeasures against bacterial pathogenicity in algal cultures and a tractable model for studying interactions between microalgae, phages, pathogens, and the algae microbiome.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2020

Ion Binding Properties of a Naturally Occurring Metalloantibody

LT1009 is a humanized version of murine LT1002 IgG1 that employs two bridging Ca2+ ions to bind its antigen, the biologically active lipid sphingosine-1-phosphate (S1P). We crystallized and determined the X-ray crystal structure of the LT1009 Fab fragment in 10 mM CaCl2 and found that it binds two Ca2+ in a manner similar to its antigen-bound state. Flame atomic absorption spectroscopy (FAAS) confirmed that murine LT1002 also binds Ca2+ in solution and inductively-coupled plasma-mass spectrometry (ICP-MS) revealed that, although Ca2+ is preferred, LT1002 can bind Mg2+ and, to much lesser extent, Ba2+. Isothermal titration calorimetry (ITC) indicated that LT1002 binds two Ca2+ ions endothermically with a measured dissociation constant (KD) of 171 μM. Protein and genome sequence analyses suggested that LT1002 is representative of a small class of confirmed and potential metalloantibodies and that Ca2+ binding is likely encoded for in germline variable chain genes. To test this hypothesis, we engineered, expressed, and purified a Fab fragment consisting of naïve murine germline-encoded light and heavy chain genes from which LT1002 is derived and observed that it binds Ca2+ in solution. We propose that LT1002 is representative of a class of naturally occurring metalloantibodies that are evolutionarily conserved across diverse mammalian genomes.

Farokhi, Elinaz↗
DOE OSTI2020

A Hybrid AI/ML and Computational Mechanics Based Approach for Time-Series State and Fatigue Life Estimation of Nuclear Reactor Components

Environmental fatigue modeling is a complex problem due to multiple failure modes and their intermixing. The failure modes are function of various underlying causes in addition to the corrosive effect of reactor coolant environment. Some of the major causes are time-dependence of material associated with cyclic loading, load sequence effect associated with random/variable amplitude loading, effect of strain amplitude and rates, effect of varying temperature (along both temporal and spatial directions) and the effect of mean strain and stress. The nonlinear intermixing of failure modes associated with above mentioned causing parameters makes the environmental fatigue modeling is a challenging task. Because of this challenge, fatigue is traditionally being modeled based on experimental data. However, test based empirical approach often requires hundreds of fatigue tests to model the above-mentioned intermixing failure causes even for a single material system. The problem is further exaggerated for reactor component made from multi-material systems such as made from both carbon and stainless-steel base metals and their similar and dissimilar metal welds. With the difficulty of conducting hundreds of fatigue tests to capture the above-mentioned intermixing failure causes, fatigue modeling approaches often depends on empirical models based on limited available test data such as available through ASME code and NUREG 6909. However, these limited test-data-based models may not be enough to accurately predict the life of reactor components. Accurate prediction of life of reactor component would become a necessity, particularly when the license of the reactors to be extended for long-term-operation (LTO) that is for well beyond its original design life of 40 years. The requirement of extending the license of reactor under LTO requires hundreds of fatigue tests to be conducted to understand the mechanism associated with the above-mentioned interdependent failure causes. However, conducting large number of fatigue tests is not a feasibility due to the cost involved. To address this issues Argonne National Laboratory (ANL) with the sponsorship of DOE Light Water Reactor Sustainability (LWRS) program trying to develop a hybrid predictive modeling approach. This is based on limited experiment-data, Artificial-intelligence (AI) – Machine-Learning (ML) - Deep-Learning (DL) based techniques and Multiphysics-computational-mechanics based modeling tools. The hybrid approach not-only can improve the accuracy of the existing stress analysis and fatigue modeling approach but also can reduce the over-dependency on test-based approach. Towards this goal following are some of the major contributions based on ANL’s FY-20 environmental fatigue modeling activities: 1) A cyclic plasticity material model database for 82/182 dissimilar metal weld, which can be readily shared with US nuclear industry and regulatory agency on request. 2) A well validated analytical modeling methodology to perform cycle-by-cycle stress prediction under both constant amplitude fatigue loading and variable amplitude fatigue loading (with load-sequence effect). 3) An AI/ML/DL based methodology to predict unmeasurable cyclic strain based on other available sensor signals. This type of approach can be used for estimating strain in real reactor components from other sensor readings. 4) An AI/ML based approach to improve the US capability on environmental fatigue testing. This is by improving ANL’s existing environmental fatigue testing capacity to conduct ASME required strain-controlled tests (by controlling strain amplitudes and its rate), while not measuring the strain (due to the difficulty of placing an extensometer in a narrow autoclave in a PWR-water-test system). 5) A simulation and experiment based probabilistic modeling methodology for time-series fatigue state and life estimation of reactor metal such as dissimilar metal weld.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗
DOE OSTI2023

MArVD2: a machine learning enhanced tool to discriminate between archaeal and bacterial viruses in viral datasets

Abstract Our knowledge of viral sequence space has exploded with advancing sequencing technologies and large-scale sampling and analytical efforts. Though archaea are important and abundant prokaryotes in many systems, our knowledge of archaeal viruses outside of extreme environments is limited. This largely stems from the lack of a robust, high-throughput, and systematic way to distinguish between bacterial and archaeal viruses in datasets of curated viruses. Here we upgrade our prior text-based tool (MArVD) via training and testing a random forest machine learning algorithm against a newly curated dataset of archaeal viruses. After optimization, MArVD2 presented a significant improvement over its predecessor in terms of scalability, usability, and flexibility, and will allow user-defined custom training datasets as archaeal virus discovery progresses. Benchmarking showed that a model trained with viral sequences from the hypersaline, marine, and hot spring environments correctly classified 85% of the archaeal viruses with a false detection rate below 2% using a random forest prediction threshold of 80% in a separate benchmarking dataset from the same habitats.

Vik, Dean (ORCID:000000027546899X)↗
DOE OSTI2020

Efficient DNA sequence compression with neural networks

Abstract Background The increasing production of genomic data has led to an intensified need for models that can cope efficiently with the lossless compression of DNA sequences. Important applications include long-term storage and compression-based data analysis. In the literature, only a few recent articles propose the use of neural networks for DNA sequence compression. However, they fall short when compared with specific DNA compression tools, such as GeCo2. This limitation is due to the absence of models specifically designed for DNA sequences. In this work, we combine the power of neural networks with specific DNA models. For this purpose, we created GeCo3, a new genomic sequence compressor that uses neural networks for mixing multiple context and substitution-tolerant context models. Findings We benchmark GeCo3 as a reference-free DNA compressor in 5 datasets, including a balanced and comprehensive dataset of DNA sequences, the Y-chromosome and human mitogenome, 2 compilations of archaeal and virus genomes, 4 whole genomes, and 2 collections of FASTQ data of a human virome and ancient DNA. GeCo3 achieves a solid improvement in compression over the previous version (GeCo2) of $2.4\%$, $7.1\%$, $6.1\%$, $5.8\%$, and $6.0\%$, respectively. To test its performance as a reference-based DNA compressor, we benchmark GeCo3 in 4 datasets constituted by the pairwise compression of the chromosomes of the genomes of several primates. GeCo3 improves the compression in $12.4\%$, $11.7\%$, $10.8\%$, and $10.1\%$ over the state of the art. The cost of this compression improvement is some additional computational time (1.7–3 times slower than GeCo2). The RAM use is constant, and the tool scales efficiently, independently of the sequence size. Overall, these values outperform the state of the art. Conclusions GeCo3 is a genomic sequence compressor with a neural network mixing approach that provides additional gains over top specific genomic compressors. The proposed mixing method is portable, requiring only the probabilities of the models as inputs, providing easy adaptation to other data compressors or compression-based data analysis tools. GeCo3 is released under GPLv3 and is available for free download at https://github.com/cobilab/geco3.

Silva, Milton↗
DOE OSTI2021

A generic method for interfacing IEDs using low voltage interfaces to real-time simulators with hardware in the loop

This study presents an interface method (IM) to interconnect real-time simulators (RTSs) without amplifiers for different intelligent electronic devices (IEDs), such as protective relays, smart meters, and other devices. It is a significant topic for the power systems protection community because there are many protection devices with different requirements. These protection devices have pinouts that measure bus voltages/currents and breaker pole states and generate trip/close pulse signals. Most of the emulation test beds with RTSs and HIL need amplifiers to connect IEDs from different product manufacturers because the low-voltage interface levels are not available in some instruction manuals. Additionally, commercial amplifiers have cutoff frequency limitations and have an excessive cost when several IEDs are wired onto test beds. Then, the validation of protection and control systems with RTSs and IEDs in the loop from different vendors without amplifiers results in an unfeasible alternative. In this paper, the IM was based on using an interface box, power source, and RTS to identify IED inputs/outputs. Here, the sequence to recognize IED pinouts and calculate the current/voltage scaling factors is described. The IM was validated satisfactorily with RTS and a protection device (with unknown pinouts) in the loop.

42 ENGINEERING↗
DOE OSTI2021

Novel compound heterozygous CPLANE1 variants identified in a Chinese family with Joubert syndrome

Abstract Joubert syndrome (JS) and JS‐related disorders (JSRD) are a group of neurodevelopmental diseases that share the “molar tooth sign” on axial brain magnetic resonance imaging (MRI), accompanied by cerebellar vermis hypoplasia, ataxia, hypotonia, and developmental delay. To identify variants responsible for the clinical symptoms of a Chinese family with JS and to explore the genotype–phenotype associations, we conducted a series of clinical examinations, including blood tests, brain MRI scans, ultrasound imaging, and ophthalmologic examination. Genomic DNA was extracted from the peripheral blood of the six‐person family, and the pathogenic variants were detected by whole‐exome sequencing (WES) and verified by Sanger sequencing. WES revealed two novel compound heterozygous variants in CPLANE1 : c.1270C>T (p.Arg424*) in exon 10 and c.8901C>A (p.Tyr2967*) in exon 48 of one child, inherited from each parent. Both variants were absent in ethnically matched Chinese control individuals and were either absent or present at very low frequencies in public databases, suggesting that these variants could be the pathogenic triggers of the JS phenotype. Notably, these CPLANE1 sequence variants were related to the pathogenesis of autosomal recessive JS in this study. The newly discovered variants expand the mutation spectrum of CPLANE1 , which assists in understanding the molecular mechanism underlying JS and improving the recognition of genetic counseling, particularly for families with a history of autosomal recessive JS.

Zhang, Cheng↗
DOE OSTI2023

Novel machine-learning method for spin classification of neutron resonances

The performance of nuclear reactors and other nuclear systems depends on a precise understanding of the neutron interaction cross sections for materials used in these systems. These cross sections exhibit resonant structure whose shape is determined in part by the angular-momentum quantum numbers of the resonances. The correct assignment of the quantum numbers of neutron resonances is, therefore, paramount. In this project, we apply machine learning to automate the quantum number assignments using only the resonances' energies and widths and not relying on detailed transmission or capture measurements. The classifier used for quantum number assignment is trained using stochastically generated resonance sequences whose distributions mimic those of real data. Here we explore the use of several physics-motivated features for training our classifier. These features amount to out-of-distribution tests of a given resonance's widths and resonance-pair spacings. We pay special attention to situations where either capture widths cannot be trusted for classification purposes or where there is insufficient information to classify resonances by the total spin J. We demonstrate the efficacy of our classification approach using simulated and actual 52 Cr resonance data.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2026

Field and Model Data Associated with the Manuscript “Drivers of Streamflow Intermittency in Humid Regions: 2. Evaluating Controls on Flow Persistence in an Urbanized Catchment”

This package contains field data, modeling files, and scripts supporting the investigation of the drivers of streamflow intermittency in an urbanized catchment. It includes the field data collected from electrical resistivity tomography (ERT) surveys, distributed temperature sensing (DTS), continuous self-potential (SP) monitoring, groundwater and stilling well. In addition, it contains the data and results of the coupled water- and electrical-flow model developed using the COMSOL Multiphysics and Advanced Terrestrial Simulator (ATS), as well as software files and Jupyter notebooks used to process the data and generate figures in the manuscript submitted for peer review. The data archive is organized in the following directories: 1) Climate Includes hourly precipitation and daily evapotranspiration time series (2024 – 2025) provided as CSV files, alongside a text file detailing dataset units. 2) Coupled_model Field_Application subfolder contains the ATS XML input scripts, data files, output data for the SP site. It also contains the Jupyter notebook (Plot_final_calib.ipynb) to visualize the results of the modeled SP, stream-groundwater exchange and moisture content. The flow model simulation is executed using the ATS XML scripts and the included Python script (generate_data_set.py) to convert ATS output to COMSOL-ready input. COMSOL Multiphysics template (.m can only be used with COMSOL with MATLAB) is executed using the ATS output data to simulate the potential field. 3) Discharge Includes the electrical conductivity (EC) time series (provided as CSV files) from salt slug injections. It also includes the Jupyter notebook (Discharge_process.ipynyb) used to estimate discharge. All discharge measurements collated into rating_curve_processed.csv 4) DTS Contains collated DTS data including raw Stokes and anti-Stokes measurement (provided as .h5 file). It also includes DTS processing.ipynb, a Jupyter notebook for calibrating the DTS data using dts_calibration Python package. cooler_calibration.csv is the DTS calibration CSV used in the calibration sequence. 5) ERT Contains raw resistivity data (provided as CSV files), spatial location of each of the electrodes (provided as CSV files), and files used for the resistivity inversion. 6) Slug_test Includes the slug test data at all the groundwater wells provided as CSV files, as well as the Jupyter notebook (Slug_test.ipynb) for calculating hydraulic conductivity. 7) SP Contains the SP data collected in field at the SP sites (provided as CSV files). 8) Well_data Contains two subfolders: 1) Raw, which provides unprocessed pressure, electrical conductivity and temperature timeseries downloaded from the loggers in all the groundwater and stilling wells, and 2) Processed, which contains sorted, QA/QC timeseries data for each well. The data archive also contains data_process.ipynb, a Jupyter notebook used for field data analysis and generating figures (plotting well, SP, climate, and discharge data, as well as calculating head gradient at sites with nested groundwater wells). Note: Code files (.ipynb, .py, .xml) can be opened in any standard code editor, .exo file can be viewed using Paraview, .h5 files can be opened using HDFView software and h5py Python package, and .resipy file can be opened with the open-source ResIPy software.

ATS↗