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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 379 records · Page 21
All sourcesNASA NTRSDOE OSTI
DOE OSTI2015

MicroRNA-196b promotes cell proliferation and suppress cell differentiation in vitro

Highlights: • miRNA-196b increases proliferation and blocks differentiation of progenitor cell. • miRNA-196b inhibits apoptosis and increases viability of cells lines. • Forced expression of miR-196b blocks the differentiation of THP1 induced by PMA. - Abstract: MicroRNA-196b (miR-196b) is frequently amplified and aberrantly overexpressed in acute leukemias. To investigate the role of miR-196b in acute leukemias, it has been observed that forced expression of this miRNA increases proliferation and inhibits apoptosis in human cell lines. More importantly, we show that this miRNA can significantly increase the colony-forming capacity of mouse normal bone marrow progenitor cells alone, as well as partially blocking the cells from differentiation. Taken together, our studies suggest that miRNA-196b may play an essential role in the development of MLL-associated leukemias through inhibiting cell differentiation and apoptosis, while promoting cell proliferation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Inhibition of FGF signaling accelerates neural crest cell differentiation of human pluripotent stem cells

Neural crest (NC) is a transient population, arising during embryonic development and capable of differentiating into various somatic cells. The defects of neural crest development leads to neurocristopathy. Several signaling pathways were revealed their significance in NC cell specification. Fibroblast growth factor (FGF) is recognized as an important signaling during NC development, for instance Xenopus and avian; however, its contributions in human species are remained elusive. Here we used human pluripotent stem cells (hPSCs) to investigate the consequences of FGF inhibition during NC cell differentiation. The specific-FGF receptor inhibitor, SU5402, was used in this investigation. The inhibition of FGF did not found to affect the proliferation or death of hPSC-derived NC cells, but promoted hPSCs to commit NC cell fate. NC-specific genes, including PAX3, SLUG, and TWIST1, were highly upregulated, while hPSC genes, such as OCT4, and E-CAD, rapidly reduced upon FGF signaling blockage. Noteworthy, TFAP-2α, a marker of migratory NC cells, abundantly presented in SU5402-induced cells. This accelerated NC cell differentiation could be due to the activation of Notch signaling upon the blockage of ERK1/2 phosphorylation, since NICD was increased by SU5402. Altogether, this study proposed the contributions of FGF signaling in controlling human NC cell differentiation from hPSCs, the crosstalk between FGF and Notch, and might imply to the influences of FGF signaling in neurocristophatic diseases.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Effect of essential amino acids on enteroids: Methionine deprivation suppresses proliferation and affects differentiation in enteroid stem cells

We investigated the effects of essential amino acids on intestinal stem cell proliferation and differentiation using murine small intestinal organoids (enteroids) from the jejunum. By selectively removing individual essential amino acids from culture medium, we found that 24 h of methionine (Met) deprivation markedly suppressed cell proliferation in enteroids. This effect was rescued when enteroids cultured in Met deprivation media for 12 h were transferred to complete medium, suggesting that Met plays an important role in enteroid cell proliferation. In addition, mRNA levels of the stem cell marker leucine-rich repeat-containing G protein-coupled receptor 5 (Lgr5) decreased in enteroids grown in Met deprivation conditions. Consistent with this observation, Met deprivation also attenuated Lgr5-EGFP fluorescence intensity in enteroids. In contrast, Met deprivation enhanced mRNA levels of the enteroendocrine cell marker chromogranin A (ChgA) and markers of K cells, enterochromaffin cells, goblet cells, and Paneth cells. Immunofluorescence experiments demonstrated that Met deprivation led to an increase in the number of ChgA-positive cells. These results suggest that Met deprivation suppresses stem cell proliferation, thereby promoting differentiation. In conclusion, Met is an important nutrient in the maintenance of intestinal stem cells and Met deprivation potentially affects cell differentiation. - Highlights: • Met influences the proliferation of enteroids. • Met plays a crucial role in the maintenance of stem cells. • Met deprivation potentially promotes differentiation into secretory cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

ERK1/2 signaling mediated naringin-induced osteogenic differentiation of immortalized human periodontal ligament stem cells

Periodontal ligament stem cells (PDLSCs) are promising tools for the investigations of cell differentiation and bone regeneration. However, the limited life span significantly restricts their usefulness. In this study, we established an immortalized PDLSC cell line by the introduction of Bmi1 (PDLSC-Bmi1). Several genes related to cell cycle, cell replication and stemness were found to be changed with the overexpression of Bmi1. Compared with primary PDLSCs, the immortalized cells had a slower aging rate, maintained in a proliferative state without crisis for more than 30 passages, and retained the molecular markers and biological functions of primary ones. Using the PDLSC-Bmi1, we confirmed the promotive effect of naringin on osteogenesis. Naringin promoted the osteogenic differentiation of PDLSC-Bmi1 manifested as the increased activity of alkaline phosphatase (ALP), expression of the runt-related transcription factor 2 (Runx2) and osteocalcin (OCN), and formation of mineralized nodules. In addition, the extracellular regulated protein kinases (ERK) 1/2 was found to be activated by naringin, and the ERK1/2 specific inhibitor significantly inhibited naringin-induced osteogenic differentiation in PDLSC-Bmi1. Our results indicated that the overexpression of Bmi1 extended the life span of PDLSCs without perturbing their biological functions, and that naringin promoted the osteogenesis of PDLSC-Bmi1 at least partially through the ERK1/2 signaling pathway. - Highlights: • The PDLSCs were immortalized by introduction of Bmi1. • The immortalized PDLSCs maintained the characteristics of primary cells. • Naringin promoted the osteogenic differentiation of immortalized PDLSCs. • ERK1/2 signaling mediated naringin-induced the osteogenesis of PDLSCs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

The ferroptosis inducer erastin promotes proliferation and differentiation in human peripheral blood mononuclear cells

Peripheral blood mononuclear cells (PBMCs) contain multipotent progenitor cell populations and possess the potential to differentiate into various types of immune cells under both physiological and pathological conditions. Ferroptosis is a type of oxidative stress-associated cell death that is mainly mediated by lipid peroxidation. However, the function of ferroptosis in cell differentiation remains unknown. Here, we showed that the ferroptosis inducer erastin did not cause cell death in human PBMCs. In contrast, erastin-induced lipid peroxidation promoted human PBMC proliferation and differentiation into B cells and natural killer cells through inhibition of bone morphogenetic protein family expression. These findings uncover a new immune modulation function of erastin in promoting PBMC proliferation and differentiation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2011

Effects of sub-lethal neurite outgrowth inhibitory concentrations of chlorpyrifos oxon on cytoskeletal proteins and acetylcholinesterase in differentiating N2a cells

Previous work in our laboratory has shown that sub-lethal concentrations (1-10 {mu}M) of chlorpyrifos (CPF), diazinon (DZ) and diazinon oxon (DZO) inhibit the outgrowth of axon-like neurites in differentiating mouse N2a neuroblastoma cells concomitant with altered levels and/or phosphorylation state of axonal cytoskeleton and growth-associated proteins. The aim of the present work was to determine whether chlorpyrifos oxon (CPO) was capable of inhibiting N2a cell differentiation in a similar manner. Using experimental conditions similar to our previous work, sub-lethal concentrations (1-10 {mu}M) of CPO were found to inhibit N2a cell differentiation. However, unlike previous studies with DZ and DZO, there was a high level of sustained inhibition of acetylcholinesterase (AChE) in CPO treated cells. Impairment of neurite outgrowth was also associated with reduced levels of growth associated protein-43 and neurofilament heavy chain (NFH), and the distribution of NFH in cells stained by indirect immunofluorescence was disrupted. However, in contrast to previous findings for DZO, the absolute level of phosphorylated NFH was unaffected by CPO exposure. Taken together, the findings suggest that sub-lethal concentrations of CPO inhibit axon outgrowth in differentiating N2a cells and that this effect involves reduced levels of two proteins that play key roles in axon outgrowth and maintenance. Although the inhibition of neurite outgrowth is unlikely to involve AChE inhibition directly, further work will help to determine whether the persistent inhibition of AChE by CPO can account for the different effects induced by CPO and DZO on the levels of total and phosphorylated NFH. -- Highlights: Black-Right-Pointing-Pointer Sub-lethal levels of chlorpyrifos oxon inhibit neurite outgrowth in N2a cells Black-Right-Pointing-Pointer Acetylcholinesterase exhibits sustained inhibition throughout exposure Black-Right-Pointing-Pointer The levels of neurofilament heavy chain and GAP-43 protein are reduced Black-Right-Pointing-Pointer Neurofilament heavy chain forms aggregates in cell bodies Black-Right-Pointing-Pointer Thus at least two axon-associated cytoskeletal proteins are disrupted by this agent.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

JAX-CanVeg: A Differentiable Land Surface Model

Land surface models consider the exchange of water, energy, and carbon along the soil-canopy-atmosphere continuum, which is challenging to model due to their complex interdependency and associated challenges in representing and parameterizing them. Differentiable modeling provides a new opportunity to capture these complex interactions by seamlessly hybridizing process-based models with deep neural networks (DNNs), benefiting both worlds, that is, the physical interpretation of process-based models and the learning power of DNNs. Here, we developed a differentiable land model, JAX-CanVeg. The new model builds on the legacy CanVeg by incorporating advanced functionalities through JAX in the graphic processing unit support, automatic differentiation, and integration with DNNs. We demonstrated JAX-CanVeg's hybrid modeling capability by applying the model at four flux tower sites with varying aridity. To this end, we developed a hybrid version of the Ball-Berry equation that emulates the water stress impact on stomatal closure to explore the capability of the hybrid model in (a) improving the simulations of latent heat fluxes (LE) and net ecosystem exchange (NEE), (b) improving the optimization trade-off when learning observations of both LE and NEE, and (c) benefiting a multi-layer canopy model setup. Our results show that the proposed hybrid model improved the simulations of LE and NEE at all sites, with an improved optimization trade-off over the process-based model. Additionally, the multi-layer canopy set benefited hybrid modeling at some sites. Anchored in differentiable modeling, our study provides a new avenue for modeling land-atmosphere interactions by leveraging the benefits of both data-driven learning and process-based modeling.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

State-resolved differential and integral cross sections for the Ne + H{sub 2}{sup +} (v = 0–2, j = 0) → NeH{sup +} + H reaction

State-to-state quantum dynamic calculations for the proton transfer reaction Ne + H{sub 2}{sup +} (v = 0–2, j = 0) are performed on the most accurate LZHH potential energy surface, with the product Jacobi coordinate based time-dependent wave packet method including the Coriolis coupling. The J = 0 reaction probabilities for the title reaction agree well with previous results in a wide range of collision energy of 0.2-1.2 eV. Total integral cross sections are in reasonable agreement with the available experiment data. Vibrational excitation of the reactant is much more efficient in enhancing the reaction cross sections than translational and rotational excitation. Total differential cross sections are found to be forward-backward peaked with strong oscillations, which is the indication of the complex-forming mechanism. As the collision energy increases, state-resolved differential cross section changes from forward-backward symmetric peaked to forward scattering biased. This forward bias can be attributed to the larger J partial waves, which makes the reaction like an abstraction process. Differential cross sections summed over two different sets of J partial waves for the v = 0 reaction at the collision energy of 1.2 eV are plotted to illustrate the importance of large J partial waves in the forward bias of the differential cross sections.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2014

A differential equation for Lerch's transcendent and associated symmetric operators in Hilbert space

The function Ψ(x,y,s)=e{sup iy}Φ(−e{sup iy},s,x), where Φ(z,s,v) is Lerch's transcendent, satisfies the following two-dimensional formally self-adjoint second-order hyperbolic differential equation, where s=1/2+iλ. The corresponding differential expression determines a densely defined symmetric operator (the minimal operator) on the Hilbert space L{sub 2}(Π), where Π=(0,1)×(0,2π). We obtain a description of the domains of definition of some symmetric extensions of the minimal operator. We show that formal solutions of the eigenvalue problem for these symmetric extensions are represented by functional series whose structure resembles that of the Fourier series of Ψ(x,y,s). We discuss sufficient conditions for these formal solutions to be eigenfunctions of the resulting symmetric differential operators. We also demonstrate a close relationship between the spectral properties of these symmetric differential operators and the distribution of the zeros of some special analytic functions analogous to the Riemann zeta function. Bibliography: 15 titles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Efficient quantum algorithm for dissipative nonlinear differential equations

Significance Nonlinear differential equations appear in many domains and are notoriously difficult to solve. Whereas previous quantum algorithms for general nonlinear differential equations have complexity exponential in the evolution time, we give the first quantum algorithm for dissipative nonlinear differential equations that is efficient provided the dissipation is sufficiently strong relative to nonlinear and forcing terms and the solution does not decay too rapidly. We also establish a lower bound showing that differential equations with sufficiently weak dissipation have worst-case complexity exponential in time, giving an almost tight classification of the quantum complexity of simulating nonlinear dynamics. Furthermore, numerical results for the Burgers equation suggest that our algorithm may potentially address complex nonlinear phenomena even in regimes with weaker dissipation.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗
DOE OSTI2021

Differentiable programming for online training of a neural artificial viscosity function within a staggered grid Lagrangian hydrodynamics scheme

Lagrangian methods to solve the inviscid Euler equations produce numerical oscillations near shock waves. A common approach to reducing these oscillations is to add artificial viscosity (AV) to the discrete equations. The AV term acts as a dissipative mechanism that attenuates oscillations by smearing the shock across a finite number of computational cells. However, AV introduces several control parameters that are not determined by the underlying physical model, and hence, in practice are tuned to the characteristics of a given problem. We seek to improve the standard quadratic-linear AV form by replacing it with a learned neural function that reduces oscillations relative to exact solutions of the Euler equations, resulting in a hybrid numerical-neural hydrodynamic solver. Because AV is an artificial construct that exists solely to improve the numerical properties of a hydrodynamic code, there is no offline ‘viscosity data’ against which a neural network can be trained before inserting into a numerical simulation, thus requiring online training. We achieve this via differentiable programming, i.e. end-to-end backpropagation or adjoint solution through both the neural and differential equation code, using automatic differentiation of the hybrid code in the Julia programming language to calculate the necessary loss function gradients. A novel offline pre-training step accelerates training by initializing the neural network to the default numerical AV scheme, which can be learned rapidly by space-filling sampling over the AV input space. We find that online training over early time steps of simulation is sufficient to learn a neural AV function that reduces numerical oscillations in long-term hydrodynamic shock simulations. These results offer an early proof-of-principle that online differentiable training of hybrid numerical schemes with novel neural network components can improve certain performance aspects existing in purely numerical schemes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Validation and Evaluation Uses of Quasi-Differential High-Energy Scattering Data [Slides]

This presentation covers the validation and evaluation uses of quasi-differential high-energy scattering data. This lecture covers motivation behind this work. The lecture then touches on quasi-differential for evaluation, quasi-differential for covariance, and quasi-differential for validation. The presentation concludes with a summary.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2026

Differential Privacy in Grid Kitchen: Implementation & Software Documentation

Sharing of power grid feeder models faces significant challenges due to the potential risk of exposing sensitive operational information. Traditional anonymization techniques have shown notable limitations in other sensitive domains, as evidenced by documented re-identification attacks that combine supposedly anonymized datasets with auxiliary information, raising concerns that similar vulnerabilities could affect power grid data. Consequently, there is a pressing need for a more rigorous privacy protection strategy that not only delivers formal mathematical guarantees but also preserves the analytical value of the shared models. To address this challenge, we have enhanced the Grid Kitchen framework by implementing differential privacy mechanisms within the distribution model dehydration pipeline. This implementation carefully calibrates and applies noise to sensitive attributes in feeder models according to configurable privacy levels—low, moderate, and high—each offering different balances between data utility and privacy protection. Our approach uses established noise functions (Gaussian for continuous data and Discrete Laplace for integer values) with parameters carefully calibrated so that the impact of individual data points is effectively masked in the final output. The integration leverages our Noise Catalog, which we developed to categorize feeder model properties by component type, data type, and sensitivity. This catalog guides the application of appropriate noise functions and privacy parameters ($\varepsilon$ and $\delta$) to each attribute, ensuring consistent privacy protection across the model while maintaining its structural integrity and analytical usefulness. This implementation also includes evaluation tools that allow model owners to assess the impact of privacy-preserving transformations before sharing data with external parties. This report provides documentation for the differential privacy capabilities added to the Grid Kitchen project. It includes a primer on differential privacy concepts and their importance in modern data sharing, details the architecture of our implementation, explains the privacy modes and parameter configurations, and offers practical guidance on using the code for applying differential privacy to grid feeder models. Through examples and code snippets, we demonstrate the effective application of these privacy-enhancing technologies, enabling utility operators and researchers to confidently share grid data while protecting sensitive information.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Differential Probability Functions for Investigating Long-term Changes in Local and Regional Air Pollution Sources

Conditional probability functions are commonly used for source identification purposes in air pollution studies. CBPF (conditional bivariate probability function) categorizes the probability of high concentrations being observed at a location by wind direction/speed and investigate the directionality of local sources. PSCF (potential source contribution function), a trajectory-ensemble method, identifies the source regions most likely to be associated with high measured concentrations. However, these techniques do not allow the direct identification of areas where changes in emissions have occurred. This study presents an extension of conditional probability methods in which the differences between conditional probability values for temporally different sets of data can be used to explore changes in emissions from source locations. The differential CBPF and differential PSCF were tested using a long-term series of air quality data (12 years; 2005/2016) collected in Rochester, NY. The probability functions were computed for each of 4 periods that represent known changes in emissions. Correlation analyses were also performed on the results to find pollutants undergoing similar changes in local and regional sources. The differential probability functions permitted the identification of major changes in local and regional emission location. In Rochester, changes in local air pollution were related to the shutdown of a large coal power plant (SO 2 ) and to the abatement measures applied to road and off-road traffic (primary pollutants). The concurrent effects of these changes in local emissions were also linked to reduced concentrations of nucleation mode particles. Changes in regional source areas were related to the decreases in secondary inorganic aerosol and organic carbon. As a result, the differential probabilities for sulfate, nitrate, and organic aerosol were consistent with differences in the available National Emission Inventory annual emission values. Changes in the source areas of black carbon and PM2.5 mass concentrations were highly correlated.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2022

Reverse-mode differentiation in arbitrary tensor network format: with application to supervised learning.

This paper describes an efficient reverse-mode differentiation algorithm for contraction operations of tensor networks that may have arbitrary and unconventional network topologies. The approach leverages the tensor contraction tree of Evenbly and Pfeifer (2014), which provides an instruction set for the contraction sequence of a network. We show that this tree can be efficiently leveraged for differentiation of a full tensor network contraction using a recursive scheme that exploits (1) the bilinear property of contraction and (2) the property that trees have single path from root to leaves. While differentiation of tensor-tensor contraction is already possible in most automatic differentiation packages, we show that exploiting these two additional properties in the specific context of contraction sequences can improve efficiency. Following a description of the algorithm and computational complexity analysis, we investigate its utility for gradient-based supervised learning for low-rank function recovery and for fitting real-world unstructured datasets. We demonstrate improved performance over alternating least-squares optimization approaches and the capability to handle heterogeneous and arbitrary tensor network formats. When compared to alternating minimization algorithms, we find that the gradient-based approach requires a smaller oversampling ratio (number of samples compared to number model parameters) for recovery. This increased efficiency extends to fitting unstructured data of varying dimensionality and when employing a variety of tensor network formats. Here, we show improved learning using the hierarchical Tucker method over the tensor-train in high-dimensional settings on a number of benchmark problems.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Osteogenic differentiation of human adipose derived stem cells on chemically crosslinked carbon nanomaterial coatings

Abstract Carbon nanomaterial coatings have been widely investigated for many biomedical applications including bone tissue engineering. Current methods to fabricate carbon nanomaterial coatings are limited by specific substrate requirements and the lack of strong bonds between the nanomaterials. Furthermore, few studies compare the effect of carbon nanoparticle architecture on stem cell differentiation and mineralization for osteogenic differentiation. Herein, we report a study comparing chemically crosslinked carbon nanotubes (of various diameters), graphene nanoplatelets, and graphene nanoribbons coatings for adipose derived stem cell differentiation toward an osteogenic lineage. We observed greatest autodeposition of calcium on graphene nanoribbon surfaces, while multiwalled carbon nanotubes of high diameter had the greatest influence on stem cell fate (by alkaline phosphatase activity, calcium deposition, and osteocalcin measurements). Studies indicate the cause for multiwalled carbon nanotube related stem cell differentiation, may be related to early timepoint toxicity as indicated by lactose dehydrogenase release. These results indicate suggestions for orthopedic tissue engineering applications for carbon nanomaterial coatings. © 2018 Wiley Periodicals, Inc. J Biomed Mater Res Part A: 106A: 1189–1199, 2018.

Patel, Sunny C.↗
DOE OSTI2017

Near Activation and Differential Activation in Enzymatic Reactions

A framework that introduces the concept of near-activation complexation and differential free energy of activation is presented to extend the capabilities of the classical transition-state theory in enzymatic reactions. In our approach, reaching a near-equilibrium energy level is assumed to be necessary for complexation near the activation point, whereas an additional differential energy level is required for the near-equilibrium complex to activate and release reaction products. Integration of these energy levels within the transition-state theory explains the thermodynamic nature of the Michaelis–Menten (affinity) constant and its relationship with the rate constant under the quasi-steady-state assumption. The concepts of near-activation complexation and differential free energy of activation were tested on 57 independent experiments of NH$+\atop{4}$ and NO$-\atop{3}$ uptake by various microalgae and bacteria at temperatures ranging between 1 and 45°C. Results showed that near-activation complexation was always favored, whereas the differential energy of activation led to an apparent energy barrier consistent with earlier observations. Temperature affected all energy levels within this framework but did not alter substantially their thermodynamic features. Finally, the approach (1) mutually links the thermodynamics and kinetics of Michaelis–Menten and rate constants with a mathematical expression; (2) describes the likelihood of formation of sub-, super-, and activated complexes; and (3) shows direction and thermodynamic likelihood of each reaction branch within the transition state.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2026

Combination and interpretation of differential Higgs boson production cross sections in proton-proton collisions at $ \sqrt{s}=13 $ TeV

Precision measurements of Higgs boson differential production cross sections are a key tool to probe the properties of the Higgs boson and test the standard model. New physics can affect both Higgs boson production and decay, leading to deviations from the distributions that are expected in the standard model. In this paper, combined measurements of differential spectra in a fiducial region matching the experimental selections are performed, based on analyses of four Higgs boson decay channels (γγ, ZZ$^{(*)}$, WW$^{(*)}$, and ττ) using proton-proton collision data recorded with the CMS detector at $ \sqrt{s}=13 $ TeV, corresponding to an integrated luminosity of 138 fb$^{−1}$. The differential measurements are extrapolated to the full phase space and combined to provide the differential spectra. A measurement of the total Higgs boson production cross section is also performed using the γγ and ZZ decay channels, with a result of $ {53.4}_{-2.9}^{+2.9}{\left(\textrm{stat}\right)}_{-1.8}^{+1.9}\left(\textrm{syst}\right) $ pb, consistent with the standard model prediction of 55.6 ± 2.5 pb. The fiducial measurements are used to compute limits on Higgs boson couplings using the κ-framework and the SM effective field theory.[graphic not available: see fulltext]

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗