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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 217 records · Page 12
All sourcesNASA NTRSDOE OSTI
DOE OSTI2021

Persistence and plasticity in bacterial gene regulation

Organisms orchestrate cellular functions through transcription factor (TF) interactions with their target genes, although these regulatory relationships are largely unknown in most species. Here we report a high-throughput approach for characterizing TF-target gene interactions across species and its application to 354 TFs across 48 bacteria, generating 17,000 genome-wide binding maps. This dataset revealed themes of ancient conservation and rapid evolution of regulatory modules. We observed rewiring, where the TF sensing and regulatory role is maintained while the arrangement and identity of target genes diverges, in some cases encoding entirely new functions. We further integrated phenotypic information to define new functional regulatory modules and pathways. So, we identified 242 new TF DNA binding motifs, including a 70% increase of known Escherichia coli motifs and the first annotation in Pseudomonas simiae, revealing deep conservation in bacterial promoter architecture. Our method provides a versatile tool for functional characterization of genetic pathways in prokaryotes and eukaryotes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Black Swan Event Small-Sample Transfer Learning (BEST-L) and its Case Study on Electrical Power Prediction in COVID-19

The black swan event will usually cause a great impact on the normal operation of society. The scarcity of such events leads to a lack of relevant data and challenges in dealing with related problems. Different situations also make the traditional methods invalid. In this paper, a transfer learning framework and a convolutional neuron network are proposed to deal with the black swan small-sample events (BEST-L). Taking the COVID-19 as a typical black swan event, the BEST-L is utilized to achieve accurate mid-term load forecasting using the relationship between economy and electricity consumption. The experiment results show that the transfer learning model can effectively learn the basic knowledge about the relationship between the adopted input and output data and use a relatively small amount of data during the black swan event to improve the target areas' generalization. The approach and results can provide an effective approach to respond and react to sudden changes quickly and effectively in similar open problems.

black swan event↗
DOE OSTI2021

Developing a Peptide That Inhibits DNA Repair by Blocking the Binding of Artemis and DNA Ligase IV to Enhance Tumor Radiosensitivity

Artemis and DNA Ligase IV are 2 critical elements in the nonhomologous end joining pathway of DNA repair, acting as the nuclease and DNA ligase, respectively. Enhanced cellular radiosensitivity by inhibition of either protein contributes to a promising approach to develop molecular targeted radiosensitizers. The interaction between Artemis and DNA Ligase IV is required for the activation of Artemis as nuclease at 3’overhang DNA; thus, we aim to generate an inhibitory peptide targeting the interaction between Artemis and DNA Ligase IV for novel radiosensitizer development.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2023

Plasmonic hybrid core-shell (HyCoS) AgPt NP template hybridized with GQDs for SERS enhancement of 4-MBA and BT

Surface-enhanced Raman spectroscopy (SERS) is an attractive vibrational spectroscopic technique that can enable a non-destructive and ultra-sensitive detection down to the single-molecule level. Herein, a novel hybrid SERS platform is developed based on hybrid core-shell (HyCoS) AgPt nanoparticles (NPs) and graphene quantum dots (GQDs) for the enhancement of Raman vibration of 4-mercaptobenzoic acid (4-MBA) and benzenethiol (BT). The unique design of HyCoS AgPt NPs induces strong electromagnetic mechanism (EM) enhancement through the amplification of electromagnetic fields by the excitation of high-density surface plasmons and hot spots. Superior localized surface plasmon resonance (LSPR) is generated by the AgPt core-shell and background Ag NP coupling, which is systematically investigated by the optical properties and FDTD simulations. The background Ag NPs can further increase the coverage of metallic NPs, leading to higher-density hot spots and enhanced SERS response. At the same time, GQDs can provide plentiful accessible edges for the charge transfer to the HOMO and LUMO of 4-MBA and BT based on the chemical mechanism (CM) enhancement. The mixing approach of GQDs and target molecules on the HyCoS AgPt NPs can significantly amplify the Raman signals via the strong adsorption of probe molecules by the π – π interaction. The enhancement factors of proposed SERS platform can reach ~107 and ~105 for the 4-MBA and BT respectively.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Conditional guide RNA deactivation by mRNA and small molecule triggers in Saccharomyces cerevisiae

CRISPR interference (CRISPRi) technologies have revolutionized bioengineering by providing precise tools for gene expression modulation, enabling targeted gene perturbation and metabolic pathway optimization. Despite these advances, achieving dynamic control over gene expression by CRISPR-based regulation remains a challenge due to its inherently static nature. Utilizing toehold-mediated strand displacement and ligand-responsive ribozymes (aptazymes), this study introduces switchable guide RNAs (gRNAs) that facilitate tunable gene expression mediated by mRNA or small molecule signals. We demonstrate complete silencing of gRNA via strategically designed 5’ or 3’ extensions that impede the gRNA spacer or the dCas9 handle, with subsequent restoration of function through sequestration or cleavage of the obstructive sequence. The resulting toehold-embedded or aptazyme-embedded gRNAs can be deactivated by specific signals, including two full-length translatable mRNAs and two small molecule triggers, thereby lifting CRISPRi repression on targeted genes. This modular approach allows for gRNA-based biocomputing through multi-layer or multi-input genetic logic gates in Saccharomyces cerevisiae . Offering a versatile strategy for post-CRISPR regulation in response to environmental signals or cellular states, this methodology expands the toolkit in eukaryotic systems for reversible control of gene expression.

Aptazyme↗
DOE OSTI2021

Solving the puzzle of Fe homeostasis by integrating molecular, mathematical, and societal models

To ensure optimal utilization and bioavailability, iron uptake, transport, subcellular localization, and assimilation are tightly regulated in plants. In this work, we examine recent advances in our understanding of cellular responses to Fe deficiency. We then use intracellular mechanisms of Fe homeostasis to discuss how formalizing cell biology knowledge via a mathematical model can advance discovery even when quantitative data is limited. Using simulation-based inference to identify plausible systems mechanisms that conform to known emergent phenotypes can yield novel, testable hypotheses to guide targeted experiments. However, this approach relies on the accurate encoding of domain-expert knowledge in exploratory mathematical models. We argue that this would be facilitated by fostering more “systems thinking” life scientists and that diversifying your research team may be a practical path to achieve that goal.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2020

Scattering of electron from a disk in 2D electron gas: Full cross section, transport cross section, and the interaction correction

It is known that the presence of the Fermi sea modifies the scattering of an electron from a point-like impurity. This is due to the Friedel oscillations of the electron density around the impurity. These oscillations create an additional scattering potential for incident electrons. The closer the energy of the incident electron to the Fermi level, the stronger the additional scattering. Here, we study this effect for the case when the impurity is not point-like but rather a hard disk, with a radius much bigger than the de Broglie wavelength. We start with a careful examination of the full and transport cross sections from an extended target. Both cross sections approach their limiting values upon increasing the wave vector of the incident electron. We establish that the transport cross section saturates much faster than the full cross section. With regard to the interaction correction, we establish that it vanishes for the full cross section, while for the transport cross section, it is enhanced compared to the case of a point-like scatterer.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

High expression of Notch2 drives tongue squamous cell carcinoma carcinogenesis

Tongue squamous cell carcinoma (TSCC) is one of the most common cancers in the oral cavity. Notch signaling is frequently dysregulated in cancer. However, the role of Notch2 in TSCC is not well understood. The aim of this study was to investigate the effect of abnormal expression of Notch2 in TSCC. The expression of Notch2 was tested in 47 pairs of tissues from tongue cancer and normal samples by using immunohistochemical staining. Tongue cancer cells were transfected with siRNA or plasmid. The proliferation of the cells was tested by the CCK8 assay and colony formation assay. Subcutaneous tumor model was established to observe tumor growth. Transwell assay was used to detect the changes of cell migration and invasion ability. A humanized anti-Notch2 antibody was used to TSCC cells. We found that Notch2 was upregulated in tongue carcinoma tissues. Knocking down the expression of Notch2 by siRNA in the TSCC cell lines decreased proliferation ability both in vitro and in vivo. In addition, migration and invasion abilities were inhibited by knockdown of Notch2 in the TSCC cells. However, overexpression of Notch2 increased tongue cancer cell proliferation, invasion and migration. The humanized anti-Notch2 antibody inhibited TSCC cell growth. The results indicated that Notch2 is an oncogene in tongue squamous cell carcinoma and may become the target of a new approach for treating TSCC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2024

Generating Protein Structures for Pathway Discovery Using Deep Learning

Resolving the intricate details of biological phenomena at the molecular level is fundamentally limited by both length- and time scales that can be probed experimentally. Molecular dynamics (MD) simulations at various scales are powerful tools frequently employed to offer valuable biological insights beyond experimental resolution. However, while it is relatively simple to observe long-lived, stable configurations of, for example, proteins, at the required spatial resolution, simulating the more interesting rare transitions between such states often takes orders of magnitude longer than what is feasible even on the largest supercomputers available today. One common aspect of this challenge is pathway discovery, where the start and end states of a scientific phenomenon are known or can be approximated, but the mechanistic details in between are unknown. Here, we propose a representation-learning-based solution that uses interpolation and extrapolation in an abstract representation space to synthesize potential transition states, which are automatically validated using MD simulations. The new simulations of the synthesized transition states are subsequently incorporated into the representation learning, leading to an iterative framework for targeted path sampling. Our approach is demonstrated by recovering the transition of a RAS-RAF protein domain (CRD) from membrane-free to interacting with the membrane using coarse-grain MD simulations.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2023

Fine-Tuning of Pt Dispersion on Al 2 O 3 and Understanding the Nature of Active Pt Sites for Efficient CO and NH 3 Oxidation Reactions

Fine-tuning the dispersion of active metal species on widely used supports is a research hotspot in the catalysis community, which is vital for achieving a balance between the atomic utilization efficiency and the intrinsic activity of active sites. In this work, using bayerite Al(OH) 3 as support directly or after precalcination at 200 or 550 °C, Pt/Al 2 O 3 catalysts with distinct Pt dispersions from single atoms to clusters (ca. 2 nm) were prepared and evaluated for CO and NH 3 removal. Richer surface hydroxyl groups on AlO x (OH) y support were proved to better facilitate the dispersion of Pt. However, Pt/Al 2 O 3 with relatively lower Pt dispersion could exhibit better activity in CO/NH 3 oxidation reactions. Further reaction mechanism study revealed that the Pt sites on Pt/Al 2 O 3 with lower Pt dispersion could be activated to Pt 0 species much easier under the CO oxidation condition, on which a higher CO adsorption capacity and more efficient O 2 activation were achieved simultaneously. Compared to Pt single atoms, PtO x clusters could also better activate NH 3 into –NH 2 and –HNO species. The higher CO adsorption capacity and the more efficient NH 3 /O 2 activation ability on Pt/Al 2 O 3 with relatively lower Pt dispersion well explained its higher CO/NH 3 oxidation activity. This study emphasizes the importance of avoiding a singular pursuit of single-atom catalyst synthesis and instead focusing on achieving the most effective Pt species on Al 2 O 3 support for targeted reactions. Finally, this approach avoids unnecessary limitations and enables a more practical and efficient strategy for Pt catalyst fabrication in emission control applications.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Large CO 2 removal potential of woody debris preservation in managed forests

Limiting climate warming to 1.5 °C requires reductions in greenhouse gas emissions and CO 2 removal. While various CO 2 removal strategies have been explored to achieve global net-zero greenhouse gas emissions and account for legacy emissions, additional exploration is warranted to examine more durable, scalable and sustainable approaches to achieve climate targets. Here, in this study, we show that preserving woody debris in managed forests can remove gigatonnes of CO 2 from the atmosphere sustainably based on a carbon cycle analysis using three Earth system models. Woody debris is produced from logging, sawmill wastes and abandoned woody products, and can be preserved in deep soil to lengthen its residence time (a measure of durability) by thousands of years. Preserving annual woody debris production in managed forests has the capacity to remove 769–937 GtCO 2 from the atmosphere cumulatively (10.1–12.4 GtCO 2 yr −1 on average) from 2025 to 2100, if its residence time is lengthened for 100–2,000 years and after 5% CO 2 removal is discounted to account for CO 2 emission due to machine operation for wood debris preservation. This translates to a reduction in global temperatures of 0.35–0.42 °C. Given the large potential, relatively low cost and long durability, future efforts should be focused on establishing large-scale demonstration projects for this technology in a variety of contexts, with rigorous monitoring of CO 2 removal, its co-benefits and side-effects.

Luo, Yiqi [Cornell Univ., Ithaca, NY (United State↗
DOE OSTI2021

Limitations of Hartree–Fock with quantum resources

The Hartree–Fock problem provides the conceptual and mathematical underpinning of a large portion of quantum chemistry. As efforts in quantum technology aim to enhance computational chemistry algorithms, the Hartree–Fock method, central to many other numerical approaches, is a natural target for quantum enhanced algorithms. While quantum computers and quantum simulation offer many prospects for the future of modern chemistry, the non-deterministic polynomial-complete Hartree–Fock problem is not a likely candidate. Here, we highlight this fact from a number of perspectives including computational complexity, practical examples, and the full characterization of energy landscapes for simple systems.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

De novo design of D-peptide ligands: Application to influenza virus hemagglutinin

D-peptides hold great promise as therapeutics by alleviating the challenges of metabolic stability and immunogenicity in L-peptides. However, current D-peptide discovery methods are severely limited by specific size, structure, and the chemical synthesizability of their protein targets. Here, we describe a computational method for de novo design of D-peptides that bind to an epitope of interest on the target protein using Rosetta’s hotspot-centric approach. The approach comprises identifying hotspot sidechains in a functional protein–protein interaction and grafting these side chains onto much smaller structured peptide scaffolds of opposite chirality. The approach enables more facile design of D-peptides and its applicability is demonstrated by design of D-peptidic binders of influenza A virus hemagglutinin, resulting in identification of multiple D-peptide lead series. The X-ray structure of one of the leads at 2.38 Å resolution verifies the validity of the approach. This method should be generally applicable to targets with detailed structural information, independent of molecular size, and accelerate development of stable, peptide-based therapeutics.

Science & Technology - Other Topics↗
DOE OSTI2025

Optimal parameterizations for observational constraints on thawing dark energy

Time-varying dark energy is often modeled in observational analyses through generic parameterizations of its equation of state w(z), which typically use two free parameters {w 0 , w a } to span a broad range of behaviors as a function of redshift. However, this broad range of behaviors can only approximately capture the dynamics of any given microphysical theory of dark energy. A complementary approach is to use targeted parameterizations designed to model specific classes of dynamical dark energy with greater precision. Focusing on the class of thawing dark energy, we quantify and compare the precision with which nineteen generic and targeted parameterizations can capture the dynamics of physically motivated thawing quintessence theories. We find that a targeted parameterization derived from a Padé expansion of w is the most reliable of these, producing accurate reconstructions of w(z), the expansion history H(z), and cosmological parameters such as H 0 and Ω m for a broad range of microphysical theories.

dark energy experiments↗
DOE OSTI2025

Downscaled CMIP5 projections of physical fire risk understate historical trends

Reliable projections of wildfire risk are important for multi-sector impacts analysis. Statistically downscaled and bias-corrected Earth system model ensemble products are routinely used to analyze regional physical wildfire risk, but evaluations of historical observed trends and variability are lacking. Here, we evaluate physical fire risk over the western United States using the Canadian Forest Fire Weather Index (FWI) by comparing model outputs from the Coupled Model Intercomparison Project Phase 5 (CMIP5), statistically downscaled via the Multivariate Adaptive Constructed Analogs (MACA) approach, against the observational target dataset gridMET, a gridded high-resolution surface meteorological product. We analyze multidecadal trends and interannual variability in seasonal average FWI for the historical period and future projections under two emissions scenarios, and we compare MACA-CMIP5 ensemble results with a simple time series model that generates historical and future projections of seasonal FWI based on bootstrapping observed historical trends and variability. Our findings indicate that MACA-CMIP5 accurately captures the magnitude and spatial patterns of seasonally averaged FWI but tends to underestimate historical decadal trends. We show that future increases in fire risk may be underestimated relative to the simple time series model that projects historical variability into the future. We also highlight that model biases in relative humidity contribute significantly to model-data differences. Our results underscore the importance of historical hindcasting exercises for informing broader multi-sector applications.

FWI↗
DOE OSTI2021

Global Microscopic Description of Nucleon-Nucleus Scattering with Quantified Uncertainties

We develop for the first time a microscopic global nucleon-nucleus optical potential with quantified uncertainties suitable for analyzing nuclear reaction experiments at next-generation rare-isotope beam facilities. Within the improved local density approximation and without any adjustable parameters, we begin by computing proton-nucleus and neutron-nucleus optical potentials from a set of five nuclear forces from chiral effective field theory for 1800 target nuclei in the mass range 12 ≤ A ≤ 242 for energies between 0 MeV < E ≲ 150 MeV. We then parameterize a global optical potential for each chiral force that depends smoothly on the projectile energy as well as the target nucleus mass number and isospin asymmetry. Uncertainty bands for elastic scattering observables are generated from a full covariance analysis of the parameters entering in the description of our global optical potential and benchmarked against existing experimental data for stable target nuclei. Since our approach is purely microscopic, we anticipate a similar quality of the model for nucleon scattering on unstable isotopes.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2022

Semantic Property Graph for Scalable Knowledge Graph Analytics

Graphs are a natural and fundamental representation to describe entities, relationships, activities, and evolution of complex systems. Many domains such as communication, citation, procurement, biology, social media, and transportation can be modeled as a set of entities and their relationships. Resource Description Framework (RDF) and Labeled Property Graph (LPG) are two of the most used data models to encode information in a graph. Both models are similar in terms of using basic graph elements such as nodes and edges but differ in terms of the modeling approach, expressibility, serialization, and target applications. RDF is a flexible data exchange model for expressing information about entities but it tends to a have high memory footprint and inefficient storage, which does not make it a natural choice to perform scalable graph analytics. In contrast, LPG has gained traction as a reliable model to perform scalable graph analytic tasks such as sub-graph matching, network alignment, and real-time knowledge graph query. It provides efficient storage, fast traversal, and flexibility to model various real-world domains. At the same time, the LPG lacks the support of a formal knowledge representation such as an ontology to provide automated knowledge inference. We propose Semantic Property Graph (SPG) as a logical projection of reified RDF into the LPG model. SPG continues to use RDF ontology to define the type hierarchy of the projected graph and validate it against a given ontology. We present a framework to convert reified RDF graphs into SPG using two different computing environments. We also present cloud-based graph migration capabilities using Amazon Web Services.

Purohit, Sumit↗