Search NASA⌕ Search

SEARCH · Search NASA

Results for “FUNCTIONALS”

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 1,369 records · Page 76
All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

Sphingomonas clade and functional distribution with simulated climate change

Microbes are essential for the functioning of all ecosystems, and as global warming and anthropogenic pollution threaten ecosystems, it is critical to understand how microbes respond to these changes. We investigated the climate response of Sphingomonas, a widespread gram-negative bacterial genus, during an 18-month microbial community reciprocal transplant experiment across a Southern California climate gradient. We hypothesized that after 18 months, the transplanted Sphingomonas clade and functional composition would correspond with site conditions and reflect the Sphingomonas composition of native communities. We extracted Sphingomonas sequences from metagenomic data across the gradient and assessed their clade and functional composition. Representatives of at least 12 major Sphingomonas clades were found at varying relative abundances along the climate gradient, and transplanted Sphingomonas clade composition shifted after 18 months. Site had a significant effect (PERMANOVA; P < 0.001) on the distribution of both Sphingomonas functional (R 2 = 0.465) and clade composition (R 2 = 0.400), suggesting that Sphingomonas composition depends on climate parameters. Additionally, for both Sphingomonas clade and functional composition, ordinations revealed that the transplanted communities shifted closer to the native Sphingomonas composition of the grassland site compared with the site they were transplanted into. Overall, our results indicate that climate and substrate collectively determine Sphingomonas clade and functional composition.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Identification and characterization of proteins of unknown function (PUFs) in Clostridium thermocellum DSM 1313 strains as potential genetic engineering targets

Abstract Background Mass spectrometry-based proteomics can identify and quantify thousands of proteins from individual microbial species, but a significant percentage of these proteins are unannotated and hence classified as proteins of unknown function (PUFs). Due to the difficulty in extracting meaningful metabolic information, PUFs are often overlooked or discarded during data analysis, even though they might be critically important in functional activities, in particular for metabolic engineering research. Results We optimized and employed a pipeline integrating various “guilt-by-association” (GBA) metrics, including differential expression and co-expression analyses of high-throughput mass spectrometry proteome data and phylogenetic coevolution analysis, and sequence homology-based approaches to determine putative functions for PUFs in Clostridium thermocellum . Our various analyses provided putative functional information for over 95% of the PUFs detected by mass spectrometry in a wild-type and/or an engineered strain of C. thermocellum . In particular, we validated a predicted acyltransferase PUF (WP_003519433.1) with functional activity towards 2-phenylethyl alcohol, consistent with our GBA and sequence homology-based predictions. Conclusions This work demonstrates the value of leveraging sequence homology-based annotations with empirical evidence based on the concept of GBA to broadly predict putative functions for PUFs, opening avenues to further interrogation via targeted experiments.

09 BIOMASS FUELS↗
DOE OSTI2025

The impact of kidney function on Alzheimer’s disease blood biomarkers: implications for predicting amyloid-β positivity

Impaired kidney function has a potential confounding effect on blood biomarker levels, including biomarkers for Alzheimer’s disease (AD). Given the imminent use of certain blood biomarkers in the routine diagnostic work-up of patients with suspected AD, knowledge on the potential impact of comorbidities on the utility of blood biomarkers is important. We aimed to evaluate the association between kidney function, assessed through estimated glomerular filtration rate (eGFR) calculated from plasma creatinine and AD blood biomarkers, as well as their influence over predicting Aβ-positivity. We included 242 participants from the Translational Biomarkers in Aging and Dementia (TRIAD) cohort, comprising cognitively unimpaired individuals (CU; n = 124), mild cognitive impairment (MCI; n = 58), AD dementia (n = 34), and non-AD dementia (n = 26) patients all characterized by [ 18 F] AZD-4694. Plasma samples were analyzed for Aβ42, Aβ40, glial fibrillary acidic protein (GFAP), neurofilament light chain (NfL), tau phosphorylated at threonine 181 (p-tau181), 217 (p-tau217), 231 (p-tau231) and N-terminal containing tau fragments (NTA-tau) using Simoa technology. Kidney function was assessed by eGFR in mL/min/1.73 m 2 , based on plasma creatinine levels, age, and sex. Participants were also stratified according to their eGFR-indexed stages of chronic kidney disease (CKD). We evaluated the association between eGFR and blood biomarker levels with linear models and assessed whether eGFR provided added predictive value to determine Aβ-positivity with logistic regression models. Biomarker concentrations were highest in individuals with CKD stage 3, followed by stages 2 and 1, but differences were only significant for NfL, Aβ42, and Aβ40 (not Aβ42/Aβ40). All investigated biomarkers showed significant associations with eGFR except plasma NTA-tau, with stronger relationships observed for Aβ40 and NfL. However, after adjusting for either age, sex or Aβ-PET SUVr, the association with eGFR was no longer significant for all biomarkers except Aβ40, Aβ42, NfL, and GFAP. When evaluating whether accounting for kidney function could lead to improved prediction of Aβ-positivity, we observed no improvements in model fit (Akaike Information Criterion, AIC) or in discriminative performance (AUC) by adding eGFR to a base model including each plasma biomarker, age, and sex. While covariates like age and sex improved model fit, eGFR contributed minimally, and there were no significant differences in clinical discrimination based on AUC values. We found that kidney function seems to be associated with AD blood biomarker concentrations. However, these associations did not remain significant after adjusting for age and sex, except for Aβ40, Aβ42, NfL, and GFAP. While covariates such as age and sex improved prediction of Aβ-positivity, including eGFR in the models did not lead to improved prediction for any biomarker. Our findings indicate that renal function, within the normal to mild impairment range, does not seem to have a clinically relevant impact when using highly accurate blood biomarkers, such as p-tau217, in a biomarker-supported diagnosis.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

The Cross-sectional and Longitudinal Association Between Thyroid Function and Depression: A Population-Based Study

Abstract Context An association of thyroid function with mood disorders has been widely suggested, but very few studies have examined this association longitudinally. Objective We assessed the cross-sectional and longitudinal association between thyroid function and depression in a population-based cohort. Methods A total of 9471 individuals were included in cross-sectional analyses, of whom 8366 had longitudinal data. At baseline, we assessed thyroid function using serum samples (thyrotropin [TSH], free thyroxine (FT4), and thyroid peroxidase antibodies) and depressive symptoms using the Centre for Epidemiologic Studies Depression (CES-D) scale. Incident depressive events (n = 1366) were continuously followed up with the CES-D and clinical interviews. We analyzed the cross-sectional association of thyroid function and thyroid disease with depressive symptoms using linear and logistic regression, and the longitudinal association with Cox proportional hazard models for depressive events. Results Lower TSH levels and lower and higher FT4 levels were cross-sectionally associated with more depressive symptoms with a B value of −0.07 per 1 unit increase of natural log-transformed TSH (95% CI −0.11; −0.04). Furthermore, hypothyroidism was cross-sectionally associated with less depressive symptoms and hyperthyroidism with more depressive symptoms. Longitudinally, there was a U-shaped association between FT4 and incident depressive events but only in euthyroid participants. Conclusion We show a cross-sectional association between thyroid (dys)function with depressive symptoms, and a U-shaped association between FT4 and incident depressive events in euthyroid individuals. Our findings suggest an association of thyroid function with the risk of developing depression, albeit small. Reverse causation and additional underlying factors may also contribute to the association.

Roa Dueñas, Oscar Hernando (ORCID:0000000207522261↗
DOE OSTI2026

Dataset for manuscript "Rotational Memory Function of SPC/E water"

Memory effect are essential for dynamics of condensed materials and are responsible for non-exponential relaxation of correlation functions of dynamic variables through the memory function entering the memory equation. Memory functions of dipole rotations for polar liquids have never been calculated. We present here calculations of memory functions and single-dipole rotations and of the overall system dipole moment for SPC/E water measured by dielectric spectroscopy. The memory functions for single-particle and collective dynamics turn out to be nearly identical. This result validates theories of dielectric spectroscopy in terms of single-particle time correlation function and the connection between the collective and single-particle relaxation times in terms of the Kirkwood factor. The dataset includes single particle and system dipole moments, including their time-dependence.

74 ATOMIC AND MOLECULAR PHYSICS↗
DOE OSTI2019

Evolution of a novel chimeric maltotriose transporter in Saccharomyces eubayanus from parent proteins unable to perform this function

At the molecular level, the evolution of new traits can be broadly divided between changes in gene expression and changes in protein-coding sequence. For proteins, the evolution of novel functions is generally thought to proceed through sequential point mutations or recombination of whole functional units. In Saccharomyces, the uptake of the sugar maltotriose into the cell is the primary limiting factor in its utilization, but maltotriose transporters are relatively rare, except in brewing strains. No known wild strains of Saccharomyces eubayanus, the cold-tolerant parent of hybrid lager-brewing yeasts (Saccharomyces cerevisiae x S. eubayanus), are able to consume maltotriose, which limits their ability to fully ferment malt extract. In one strain of S. eubayanus, we found a gene closely related to a known maltotriose transporter and were able to confer maltotriose consumption by overexpressing this gene or by passaging the strain on maltose. Even so, most wild strains of S. eubayanus lack native maltotriose transporters. To determine how this rare trait could evolve in naive genetic backgrounds, we performed an adaptive evolution experiment for maltotriose consumption, which yielded a single strain of S. eubayanus able to grow on maltotriose. We mapped the causative locus to a gene encoding a novel chimeric transporter that was formed by an ectopic recombination event between two genes encoding transporters that are unable to import maltotriose. In contrast to classic models of the evolution of novel protein functions, the recombination breakpoints occurred within a single functional domain. Thus, the ability of the new protein to carry maltotriose was likely acquired through epistatic interactions between independently evolved substitutions. By acquiring multiple mutations at once, the transporter rapidly gained a novel function, while bypassing potentially deleterious intermediate steps. This study provides an illuminating example of how recombination between paralogs can establish novel interactions among substitutions to create adaptive functions.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Multi-domain automated patterning of DNA-functionalized hydrogels

DNA-functionalized hydrogels are capable of sensing oligonucleotides, proteins, and small molecules, and specific DNA sequences sensed in the hydrogels’ environment can induce changes in these hydrogels’ shape and fluorescence. Fabricating DNA-functionalized hydrogel architectures with multiple domains could make it possible to sense multiple molecules and undergo more complicated macroscopic changes, such as changing fluorescence or changing the shapes of regions of the hydrogel architecture. However, automatically fabricating multi-domain DNA-functionalized hydrogel architectures, capable of enabling the construction of hydrogel architectures with tens to hundreds of different domains, presents a significant challenge. We describe a platform for fabricating multi-domain DNA-functionalized hydrogels automatically at the micron scale, where reaction and diffusion processes can be coupled to program material behavior. Using this platform, the hydrogels’ material properties, such as shape and fluorescence, can be programmed, and the fabricated hydrogels can sense their environment. DNA-functionalized hydrogel architectures with domain sizes as small as 10 microns and with up to 4 different types of domains can be automatically fabricated using ink volumes as low as 50 μL. We also demonstrate that hydrogels fabricated using this platform exhibit responses similar to those of DNA-functionalized hydrogels fabricated using other methods by demonstrating that DNA sequences can hybridize within them and that they can undergo DNA sequence-induced shape change.

Rubanov, Moshe (ORCID:0000000180507407)↗
DOE OSTI2020

The Effects of Atmospheric Models on the Estimation of Infrasonic Source Functions at the Source Physics Experiment

We invert infrasound signals for an equivalent seismoacoustic source function using different atmospheric models to produce the necessary Green’s functions. The infrasound signals were produced by a series of underground chemical explosions as part of the Source Physics Experiment (SPE). In a previous study, we inverted the infrasound data using so-called predictive atmospheric models, which were based on historic, regional-scaled, publicly available weather observations interpolated onto a 3D grid. For the work presented here, we invert the same infrasound data, but using atmospheric models based on weather data collected in a time window that includes the approximate time of the explosion experiments, which we term postdictive models. We build two versions of the postdictive models for each SPE event: one that is based solely on the regional scaled observations, and one that is based on both regional scaled observations combined with on-site observations obtained by a weather sonde released at the time of the SPE. We then invert the observed data set three times, once for each atmospheric model type. We find that the estimated seismoacoustic source functions are relatively similar in waveform shape regardless of which atmospheric model that we used to construct the Green’s functions. However, we find that the amplitude of the estimated source functions is systematically dependent on the atmospheric model type: using the predictive atmospheric models to invert the data generally yields estimated source functions that are larger in amplitude than those estimated using the postdictive models.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Statistics of Green's functions on a disordered Cayley tree and the validity of forward scattering approximation

The accuracy of the forward scattering approximation for two-point Green's functions of the Anderson localization model on the Cayley tree is studied. A relationship between the moments of the Green's function and the largest eigenvalue of the linearized transfer-matrix equation is proved in the framework of the supersymmetric functional-integral method. The new large-disorder approximation for this eigenvalue is derived and its accuracy is established. Using this approximation the probability distribution of the two-point Green's function is found and compared with that in the forward scattering approximation (FSA). It is shown that FSA overestimates the role of resonances and thus the probability for the Green's function to be significantly larger than its typical value. The error of FSA increases with increasing the distance between points in a two-point Green's function.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2014

Noise Decoherence and Errors from Entanglement-function Theory for Quantum Computing

A significant problem in quantum computing is the development of physical realizations of algorithms that are robust against noise. One way to examine and mitigate noise would be to simulate large sets of qubits coupling to the external environment on classical computers. This is extremely challenging as quantum information processing is in some sense tied to computing resources that scale exponentially with the number of computing elements (qubits). In this LDRD, we set the foundation for a computational framework potentially allowing simulations of 1000s of qubits vs. 10s now possible. Exact wave-function-based methods demand exponentially increasing resources with system size. The method proposed, entanglement-functional theory (EFT), requires vastly fewer resources. The crucial step is to map the information contained in the wave-functions into a simpler object with associated 1.) auxiliary gate operations and 2.) entanglement functionals of this object. This is similar to the Time-dependent Density Functional Theory (TDDFT) approach that has revolutionized chemistry and materials science. Instead of dealing with the exponentially large wave-function, EFT works with a polynomially large set of projections (the density) that are easily manipulated through unitary operations. For a given set of quantum gates, an isomorphism exists that relates the sequence of events to the time-dependent density. A system of entangled qubits can be simulated at drastically reduced cost relative to existing state-of-the-art vector-state simulation codes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2019

Density Functional Theory Applied to Transition Metal Elements and binaries: Development Application and Results of the V-DM/16 Test Set

Density functional theory (DFT) is undergoing a shift from a descriptive to a predictive tool in the field of solid state physics, heralded by a spike in “high-throughput” studies. However, methods to rigorously evaluate the validity and accuracy of these studies is lacking, raising serious questions when simulation and experiment disagree. In response, we have developed the V-DM/16 test set, designed to evaluate the experimental accuracy of DFT’s various implementations for pe riodic transition metal solids. Our test set evaluates 26 transition metal elements and 80 transition metal alloys across three physical observables: lattice constants, elastic coefficients, and formation energy of alloys. Whether or not a functional can accurately evaluate the formation energy offers key insights into whether the relevant physics are being captured in a simulation, an especially impor tant question in transition metals where active d-electrons can thwart the accuracy of an otherwise well-performing functional. Our test set captures a wide variety of cases where the unique physics present in transition metal binaries can undermine the effectiveness of “traditional” functionals. By application of the V/DM-16 test set, we aim to better characterize the performance of existing functionals on transition metals, and to offer a new tool to rigorously evaluate the performance of new functionals in the future.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2019

An Investigational Platform of the Human Brain for Understanding Complex Neural Function

In this project we focused on developing an experimental model of the human central nervous system that more accurately mirrors the function and physiology of the human brain. This in vitro brain-on-a-chip can provide critical insight into human brain function in response to chemical exposure and disease. To develop a more accurate experimental model, we evaluated and optimized key elements designed to more accurately recapitulate neuron function. We demonstrated that neuronal function was more robust when neurons were cultured with other neuronal cells as well as complex extracellular matrix molecules. We also designed a novel threedimensional multi-electrode array (3DMEA) that provides an unprecedented ability to noninvasively monitor and record neuronal function in 3D in a reproducible manner. We also optimized an experimental model of the blood-brain barrier, providing an important tool for understanding how this interface regulates the exchange of molecules from the blood to the brain. Underpinning all of these experimental efforts were novel data analytics and modeling approaches. The successful completion of this project has positioned LLNL to use our novel tools and expertise to understand human brain function in the context of the Lab’s national security mission.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Approximating and incorporating model uncertainty in an inversion for seismic source functions: Preliminary results

We present preliminary work on propagating model uncertainty into the estimation of the time domain source time functions of the seismic source. Our method is based on an estimated model covariance function, which we estimate from the data. The model covariance function is then used to construct a suite of surrogate Greens functions which we use in a Monte Carlo type inversion scheme. The result is a probability density function of the six independent source time functions, each of which corresponds to an individual component of the seismic moment tensor. We compare the results of our method with those obtained using a computationally expensive finite difference Monte Carlo method and find that our new method produces results that are deficient in low frequencies. The advantage of our new method, which we term the Karhunen-Loeve Monte Carlo (KLMC) method, is that is several orders of magnitude faster than our current method, which uses a finite difference scheme to produce the suite of forward models.

42 ENGINEERING↗
DOE OSTI2022

GaAs Neutron Response Functions and Radiation Damage Metrics

The radiation effects community needs clear, well-documented, neutron energy-dependent responses that can be used in assessing radiation-induced material damage to GaAs semiconductors and for correlating observed radiation-induced changes in the GaAs electronic properties with computed damage metrics. In support of the objective, this document provides: a) a clearly defined set of relevant neutron response functions for use in dosimetry applications; b) clear mathematical expressions for the defined response functions; and c) updated quantitative values for the energy- dependent response functions that reflect the best current nuclear data and modelling. This document recaps the legacy response functions. It then surveys the latest nuclear data and updates the recommended response function to support current GaAs damage studies. A detailed tabulation for six of the energy-dependent response functions is provided in an Appendix.

36 MATERIALS SCIENCE↗
DOE OSTI2014

Collinear Structure Functions of the Nucleon: Status and Future

While our ultimate goal is a complete three-dimensional picture of the nucleon in terms of its fundamental constituents, there are still important lessons to be learned about its “one?dimensional” collinear parton distribution functions (PDFs) like f1(x) and g1(x). There are rigorous proofs for factorization and universality (process independence) which make these PDFs fundamental. They also appear as limits of Generalized Parton Distributions (GPD) and as integrals of transverse momentum-dependent (TMD) parton distribution functions. Experimentally, the unpolarized structure functions F1(x,Q 2 ),F2(x,Q 2 ) have been studied over a huge kinematic range in both variables. Information on the polarized structure functions g1(x,Q 2 ),g2(x,Q 2 ) is somewhat more limited, both in kinematics and in statistical precision. In both cases, much less is known about the neutron than the proton, due to the absence of a free neutron target. Accessing these structure functions at large x (where valence quarks dominate) has been challenging due to the high luminosity and the high resolution required. Finally, much information can be extracted from studying higher twist contributions to these structure functions and the connection between the DIS limit and the region where nucleon resonance excitation dominates. I present an overview of recent experimental results (with special emphasis on the valence region and the transition from quark to hadronic degrees of freedom). I will also give an outlook on the next round of experiments coming online with the energy-upgraded Jefferson Lab electron beam, and future projects like the Electron Ion Collider.

Kuhn, Sebastian E.↗
DOE OSTI2022

Open library of g-functions for 34,321 configurations

Thermal response functions, known as g-functions, are commonly used in ground heat exchanger design tools and whole building energy simulation programs to simulate the ground heat exchanger performance. Calculation of g-functions can be quite computationally time-consuming, particularly as the number of boreholes gets large. However, once the g-function is computed, the actual simulation time can be quite short, particularly if a hybrid time-step (Cullin and Spitler 2011) approach is used. Because of this, pre-computed g-function libraries are commonly used in design tools and building simulation tools. This paper describes development of a new, publicly available library containing g-functions for 34,321 borehole field configurations; for each configuration, gfunctions are provided for 5 depths to allow interpolation between different borehole-to-height ratios. The available configurations include configurations in standard shapes: lines, rectangles, open rectangles, L-shapes, and U-shapes. It also includes new configurations: C-shapes, lopsided-U-shapes, and zoned rectangles, which are rectangular configurations with different interior and perimeter spacing of the boreholes.

Spitler, Jeffery↗
DOE OSTI2024

Influence functions for the Delaware Oil and Gas Basin Tower Network

This data consists of influence functions created for the Delaware tower network for the purpose of calculating methane enhancements in the region. Two different footprint models were used. The Lagrangian Particle Dispersion model (LPDM, Uliasz 1993) was run from March 2020-May 2023. The model uses in-house WRF simulations for the meteorological transport as described in Barkley et al., (2023). Nine hundred (900) particles were released during each hourly period and are traced back 72 hours backwards in time. A static binning height of 110 m AGL was used to calculate surface influence. After May 2023, the LPDM simulations were phased out and replaced with influence functions created using HYSPLIT under the STILT configuration (Lin et al., 2003). The STILT influence functions use HRRR for the meteorological transport (Dowell et al., 2022). Nine hundred (900) particles were released during each hourly period and are traced back 72 hours backwards in time. A binning height of half the boundary layer height was used to calculate surface influence. Footprints are currently available from Sept 2020-December 2024 and will be updated periodically as more are generated in time. No STILT influence functions were created for tower site Maljimar, which was decommissioned in March 2022. Lat/Lon grid information for the LPDM influence functions is contained in lpdm-lat-lon-grid.nc. STILT grid information is contained within the influence functions themselves. Tower coordinates and heights can be found in tower-info.csv

Barkley, Z. R.↗
DOE OSTI2025

Developing a media formulation to sustain ex vivo chloroplast function

Chloroplasts are critical organelles in plants and algae responsible for accumulating biomass through photosynthetic carbon fixation and cellular maintenance through metabolism in the cell. Chloroplasts are increasingly appreciated for their role in biomanufacturing, as they can produce many useful molecules, and a deeper understanding of chloroplast regulation and function would provide more insight for the biotechnological applications of these organelles. However, traditional genetic approaches to manipulate chloroplasts are slow, and generation of transgenic organisms to study their function can take weeks to months, significantly delaying the pace of research. To develop chloroplasts themselves as a quicker and more defined platform, we isolated chloroplasts from the green algae, Chlamydomonas reinhardtii, and examined their photosynthetic function after extraction. Combined with a metabolic modeling approach using flux-balance analysis, we identified key metabolic reactions essential to chloroplast function and leveraged this information into reagents that can be used in a “chloroplast media” capable of maintaining chloroplast photosynthetic function over time ex vivo compared to buffer alone. We envision this could serve as a model platform to enable more rapid design-build-test-learn cycles to study and improve chloroplast function in combination with genetic modifications and potentially as a starting point for the bottom-up design of a synthetic organelle-containing cell.

Chlamydomonas reinhardtii↗