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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 721 records · Page 40
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Graphene modified titanium alloy promote the adhesion, proliferation and osteogenic differentiation of bone marrow stromal cells

We studied the effects of graphene coating on improving the biological activity of a titanium alloy (Ti{sub 6}Al{sub 4}V) widely used in hip and knee joint replacements. The experiments included immunofluorescence staining for observing cellular adhesion, Cell Counting Kit-8 (CCK-8) for evaluating cellular proliferation and reverse transcription-polymerase chain reaction (RT-PCR) for detecting the differentiation of bone marrow stromal cells on different scaffolds. The results showed that G-Ti{sub 6}Al{sub 4}V exhibited a higher mean integrated optical density (IOD) for vinculin and resulted in a higher cell proliferation rate and higher osteoblast-specific gene transcription levels. In summary, graphene could be used as a new nanocoating material for Ti{sub 6}Al{sub 4}V scaffolds to enhance their surface bioactivity. - Highlights: • Graphene coating could promote the adhesion and proliferation of SD rats' BMSCs. • Graphene coating could accelerate BMSCs specific differentiation into osteogenic lineages. • Graphene coating could enhance the surface bioactivity of Ti-based alloys.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Zika virus infected primary microglia impairs NPCs proliferation and differentiation

Highlights: • ZIKV infects primary microglia and induce inflammatory response. • ZIKV infected microglia decreases NPCs proliferation. • ZIKV infected microglia promote astrogenesis at the expense of neurogenesis. • Cytokines secreted by ZIKV infected microglia decrease neurogenesis. Zika virus (ZIKV) can lead to severe birth defects especially microcephaly in newborns by infecting human neural progenitors and impairing brain development. However, as the resident immune cells in the brain, the role of microglia in the ZIKV pathology is not clearly defined. To understand the interplay between immune response and neural cells, we investigate the interaction between microglia and NPCs during ZIKV infection. Our results demonstrate that primary microglia infected with ZIKV induces an inflammatory response similar to that in human, producing high level of tumor necrosis factor alpha (TNF-α), interleukin 6 (IL-6), interleukin 1β (IL-1β) and inducible nitric oxide synthase (iNOS). Furthermore, conditional medium (CM) of ZIKV infected microglia showed inhibitory effects on cell proliferation and neuronal differentiation of neural precursor cells (NPCs) derived from E14 mice brain. Blocking cytokines in the CM remarkably improved neurogenesis and decreased astrocytic differentiation of NPCs. Together, our results suggest that microglia mediated neuroinflammation plays an important role in neuropathogenesis during ZIKV infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Differential effects of voluntary wheel running and toy rotation on the mRNA expression of neurotrophic factors and FKBP5 in a post-traumatic stress disorder rat model with the shuttle-box task

Highlights: • Running wheel or toy rotation produces almost the same behavioral effects. • The running wheel might alter PTSD-like behaviors via neurotrophic factors. • Toy rotation might alter PTSD-like behaviors via the HPA system. Life-threatening experiences can result in the development of post-traumatic stress disorder. We have developed an animal model for post-traumatic stress disorder (PTSD) using a shuttle box in rats. In this paradigm, the rats were exposed to inescapable foot-shock stress (IS) in a shuttle box, and then an avoidance/escape task was performed in the same box 2 weeks after IS. A previous study using this paradigm revealed that environmental enrichment (EE) ameliorated avoidance/numbing-like behaviors, but not hyperarousal-like behaviors, and EE also elevated hippocampal brain-derived neurotrophic factor (BDNF) expression. However, the differential effects of EE components, i.e., running wheel (RW) or toy rotation, on PTSD-like behaviors has remained unclear. In this experiment, we demonstrated that RW, toy rotation, and EE (containing RW and toy rotation) ameliorated avoidance/numbing-like behaviors, induced learning of avoidance responses, and improved depressive-like behaviors in traumatized rats. The RW increased the hippocampal mRNA expression of neurotrophic factors, especially BDNF and glial-cell derived neurotrophic factor. Toy rotation influenced FK506 binding protein 5 mRNA expression, which is believed to be a regulator of the hypothalamic-pituitary-adrenal (HPA)-axis system, in the hippocampus and amygdala. This is the first report to elucidate the differential mechanistic effects of RW and toy rotation. The former appears to exert its effects via neurotrophic factors, while the latter exerts its effects via the HPA axis. Further studies will lead to a better understanding of the influence of environmental factors on PTSD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Development of xeno-free epithelial differentiation media for adherent, non-expanded adipose stromal vascular cell cultures

Reconstruction of respiratory epithelium is critical for the fabrication of bioengineered airway implants. Epithelial differentiation is typically achieved using bovine pituitary extract (BPE). Due to the xenogenic nature and undefined composition of BPE, an alternative for human clinical applications, devoid of BPE, must be developed. The goal of this study was to develop two different BPE-free media, with and without select pituitary hormone (PH), which could initiate epithelial differentiation for use in human implantation.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Circ-SATB2 upregulates STIM1 expression and regulates vascular smooth muscle cell proliferation and differentiation through miR-939

Highlights: • Circ-SATB2 and STIM1 were up-regulated in proliferative VSMCs, while miR-939 were down-regulated. • Circ-SATB2 inhibits miR-939 transcriptional activity and upregulates its target gene STIM1 expression. • Circ-SATB2 regulates proliferation and differentiation of VSMCs through miR-939/STIM1. • Circ-SATB2 promotes migration of VSMCs through miR-939/STIM1. The prevention and treatment of coronary heart disease (CHD) is a difficult problem to be solved. More and more studies have found that circular RNAs (circRNAs) may play important roles in the development of CHD. Here detection of vascular smooth muscle cells (VSMCs) showed that circ-SATB2 and STIM1 were up-regulated in proliferative VSMCs, while miR-939 were down-regulated. Circ-SATB2 and miR-939 did not affect the expression of each other, but circ-SATB2 could promote while miR-939 inhibited the expression of STIM1 (a target gene of miR-939). Circ-SATB2 overexpression could inhibit the expression of SM22-alpha (SM22α, a marker of contractile VSMCs), while the expression of SM22α was promoted by miR-939. STIM1 could promote cell proliferation and migration, and circ-SATB2 had similar effects, but its linear sequence had no such functions. MiR-939 had the opposite effects, could promote cell apoptosis and inhibit cell proliferation and migration, and siRNAs targeting circ-SATB2 had similar effects. When co-transfected with circ-SATB2 over-expression vector and miR-939 mimics or STIM1 siRNAs, the changes of cell proliferation, apoptosis and migration were not significant. Therefore, circ-SATB2 can regulate VSMC phenotypic differentiation, proliferation, apoptosis and migration by promoting the expression of STIM1. This discovery will provide a theoretical reference for exploring the role of circRNA in VSMCs and the pathogenesis of CHD.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Expression profile analysis of differentially expressed genes in ruptured intracranial aneurysms: In search of biomarkers

Highlights: • Bioinformatics can identify potential biomarkers of ruptured intracranial aneurysm. • BASP1, CD74, CEBPB, ECHDC2, GZMK, HLA-DRB3, KLHL3, SLC2A3 & SOCS3 affect IA rupture. • DEGs are involved in antigen processing and presentation and immune response. • Vascular brain injury affects gene expression in IA tissue. Intracranial aneurysms (IAs) result from the bulging of arterial walls secondary to several factors such as flow, vessel morphology, and genetics. Subarachnoid hemorrhage occurs when such walls rupture, leading to high disability and mortality. Despite numerous investigations pertaining to the relationship between geometric characteristics and IA rupture, only a few have obtained consistent results. This study aimed to further identify the potential genes associated with the pathogenesis of IAs, which may provide novel molecular biomarkers. We downloaded and reanalyzed six datasets, which were divided into four groups. IA walls and blood samples were screened for differentially expressed genes (DEGs); then, functional and pathway enrichment analyses were conducted. In total, 158 common DEGs were identified from Groups 1–3 and 396 genes (187 upregulated and 209 downregulated genes) were differentially expressed in Group 4. The functional analysis revealed that the DEGs were mainly associated with the major histocompatibility complex class II protein complex and antigen processing and presentation. Finally, we identified nine key genes, both in aneurysm tissue samples and blood samples, of which three were mostly associated with the progression and rupture of IAs. Bioinformatics was used to analyze the datasets of the ruptured IAs and identify potential biomarkers, which may provide information for the early detection and treatment of IAs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

The interacting domains in cereblon differentially modulate the immunomodulatory drug-mediated ubiquitination and degradation of its binding partners

Highlights: • c-Jun, CLC-1, IKZF1, and MEIS2 interact with CRBN at different domains. • IMiDs differentially regulates the level of CRBN-interacting proteins. • IMiDs modulates the ubiquitination of CRBN-interacting proteins in three manners. • Interacting domains affect the IMiD-mediated regulation on CRBN-binding partners. Cereblon (CRBN), a substrate receptor of the cullin-4 RING E3 ligase (CRL4), has been utilized for the targeted protein degradation via small molecular weight CRBN modulators. However, it is unclear whether and how proteins that interact with CRBN at different domains are affected by these modulators. Here, we use CRBN and its four binding partners, c-Jun, chloride channel protein CLC-1, transcription factor IKZF1, and MEIS2, as model proteins to investigate the effect of immunomodulatory drugs (IMiDs) including thalidomide, lenalidomide, and pomalidomide, on their stability, ubiquitination, and interaction with CRBN. Together with previous discoveries, domain mapping experiment shows that these four proteins interact with CRBN at three distinct regions. Immunoblotting analyses reveal that the protein level of CRBN-binding partners could be enhanced, attenuated, or not affected by IMiDs. Interaction analyses and ubiquitination assay demonstrate that IMiDs modulate the interaction between CRBN and its binding partners in three distinct ways and thus differentially regulate their ubiquitination. This work suggests that the binding domain in CRBN is a critical factor which influences the regulation of IMiDs on the ubiquitination and stability of these CRBN-interacting partners.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Differentiable lagrangian shock hydrodynamics with application to stable shock acceleration of density interfaces

We develop a gradient based optimization approach for the equations of compressible, Lagrangian hydrodynamics and demonstrate how it can be employed to automatically uncover strategies to control hydrodynamic instabilities arising from shock acceleration of density interfaces. Strategies for controlling the Richtmyer-Meshkov instability (RMI) are of great benefit for inertial confinement fusion (ICF) where shock interactions with many small imperfections in the density interface lead to instabilities which rapidly grow over time. These instabilities lead to mixing which, in the case of laser driven ICF, quenches the runaway fusion process ruining the potential for positive energy return. Here, we demonstrate that control of these instabilities can be achieved by optimization of initial conditions with ( > 100) parameters. Optimizing over a large parameter space like this is not possible with gradient-free optimization strategies. This requires computation of the gradient of the outputs of a numerical solution to the equations of Lagrangian hydrodynamics with respect to the inputs. We show that the efficient computation of these gradients is made possible via a judicious application of (i) adjoint methods, the exact formal representation of sensitivities involving partial differential equations, and (ii) automatic differentiation (AD), the algorithmic calculation of derivatives of functions. Careful regularization of multiple operators including artificial viscosity and timestep control is required. We perform design optimization of > 100 parameter energy field driving the Richtmyer Meshkov instability showing significant suppression while simultaneously enhancing the acceleration of the interface relative to a nominal baseline case.

Hydrophysics↗
DOE OSTI2023

Cecropin P1 antimicrobial peptide modulates differential expression of immune relevant genes in rainbow trout ( Oncorhynchus mykiss ) gill cell line, RTgill-W1

Accumulated evidence indicates that antimicrobial peptides modulate immune activities in fish. In this study, we profiled the differential expression patterns of representative immune relevant genes in an epithelial-like cell line of rainbow trout gill, RTgill-W1, in response to exposure of cecropin P1 antimicrobial peptide. RTgill-W1 cells were treated with synthetic cecropin P1 over time (0, 2, 4 and 24 h) with or without the present of lipopolysaccharide (LPS) or polyinosinic polycytidylic acid (PolyI:C). The relative abundances of each mRNA were measured by real-time quantitative PCR. The dose-response study revealed significant perturbations of mRNA levels of genes related to pro-inflammation, acute phase, surface proteins and transcription factors at 30 μM of cecropin P1. In addition, cecropin P1 altered the differential expression patterns that were induced by LPS or PolyI:C, at different time points in RTgill-W1. Overall, our results indicate that cecropin P1 exhibits pro-inflammation activity, modulate cell-cell interaction and cytokine signal transduction in rainbow trout gill cell, and may suggest a potential application of this peptide as an immune adjuvant for disease control in aquaculture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2018

Fault Detection for Dynamical Systems using Differential Geometric and Concurrent Learning Approach

This paper presents a fault identification and estimation approach that brings together the differential geometric concept of observability codistribution with data driven concurrent learning. In order to identify faults in presence of unknown disturbances, we utilize the differential geometric approach to design a coordinate transformation, to find a subspace in which the effect of disturbances and system faults can be segregated. We then use concurrent learning to estimate magnitude of the constant fault. We illustrate the approach to fault isolation for a linear helicopter dynamics and a spherical pendulum dynamics. We use Lyapunov stability analysis to show that the fault estimate by concurrent learning converges to the actual fault value, and then illustrate the design of a recovery controller.

Fault detection and diagnosis↗
DOE OSTI2021

Deep Learning Explicit Differentiable Predictive Control Laws for Buildings

We present a differentiable predictive control (DPC) methodology for learning constrained control laws for unknown nonlinear systems. DPC poses an approximate solution to multiparametric programming problems emerging from explicit nonlinear model predictive control (MPC). Contrary to approximate MPC, DPC does not require supervision by an expert controller. Instead, a system dynamics model is learned from a small dataset of recorded observations of the perturbed system's dynamics and the control law is optimized offline by interaction with the learned system model. The DPC method is based on two sequential steps, i) system identification using a constrained neural state-space model, and ii) optimization of an explicit control law parametrized by another neural network in closed-loop simulation with the identified neural state-space model. The combination of a differentiable closed-loop system and penalty methods for constraint handling of system outputs and inputs allows us to optimize the control law's parameters directly by backpropagating economic MPC loss through the learned system model. By incorporating domain knowledge and leveraging established techniques from optimal control, our method leverages deep neural networks as nonlinear function approximators for system identification and control while avoiding concomitant costs of intractably large datasets, and computationally expensive over-parametrized models. The scalability, data efficiency, and constrained optimal control capability of the proposed DPC method are demonstrated in simulation using a multi-zone building emulator.

Drgona, Jan↗
DOE OSTI2018

Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations

Hejre, we introduce physics-informed neural networks – neural networks that are trained to solve supervised learning tasks while respecting any given laws of physics described by general nonlinear partial differential equations. In this work, we present our developments in the context of solving two main classes of problems: data-driven solution and data-driven discovery of partial differential equations. Depending on the nature and arrangement of the available data, we devise two distinct types of algorithms, namely continuous time and discrete time models. The first type of models forms a new family of data-efficient spatio-temporal function approximators, while the latter type allows the use of arbitrarily accurate implicit Runge–Kutta time stepping schemes with unlimited number of stages. Moreover, the effectiveness of the proposed framework is demonstrated through a collection of classical problems in fluids, quantum mechanics, reaction–diffusion systems, and the propagation of nonlinear shallow-water waves.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Group Projected subspace pursuit for IDENTification of variable coefficient differential equations (GP-IDENT)

We propose an effective and robust algorithm for identifying partial differential equations (PDEs) with space-time varying coefficients from the noisy observation of a single solution trajectory. Identifying unknown differential equations from noisy data is a difficult task, and it is even more challenging with space and time varying coefficients in the PDE. The proposed algorithm, GP-IDENT, has three ingredients: (i) we use B-spline bases to express the unknown space and time varying coefficients, (ii) we propose Group Projected Subspace Pursuit (GPSP) to find a sequence of candidate PDEs with various levels of complexity, and (iii) we propose a new criterion for model selection using the Reduction in Residual (RR) to choose an optimal one among a pool of candidates. The new GPSP considers group projected subspaces which is more robust than existing methods in distinguishing correlated group features. We test GP-IDENT on a variety of PDEs and PDE systems, and compare it with the state-of-the-art parametric PDE identification algorithms under different settings to illustrate its outstanding performance. Furthermore, our experiments show that GP-IDENT is effective in identifying the correct terms from a large dictionary, and our model selection scheme is robust to noise.

Data-driven method↗
DOE OSTI2023

Feature-adjacent multi-fidelity physics-informed machine learning for partial differential equations

Physics-informed neural networks have emerged as an alternative method for solving partial differential equations. However, for complex problems, the training of such networks can still require high-fidelity data which can be expensive to generate. To reduce or even eliminate the dependency on high-fidelity data, we propose a novel multi-fidelity architecture which is based on a feature space shared by the low- and high-fidelity solutions. In the feature space, the projections of the low-fidelity and high-fidelity solutions are adjacent by constraining their relative distance. The feature space is represented with an encoder and its mapping to the original solution space is effected through a decoder. In conclusion, the proposed multi-fidelity approach is validated on forward and inverse problems for steady and unsteady problems described by partial differential equations.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2025

Differential Ligation Alters Electronic State and Coupling Signals of Iron-Sulfur Clusters in Flavin-Based Electron Bifurcation

Flavin-based electron bifurcation (FBEB) is employed by microorganisms for controlling pools of redox equivalents by reversibly splitting electron pairs into high- and low-energy levels from an initial midpoint potential. Our ability to harness this phenomenon is crucial for biocatalytic design which is limited by our understanding of energy coupling in the bifurcation system. In Pyrococcus furiosus, FBEB is carried out by the NADH-dependent ferredoxin:NADP+-oxidoreductase (NfnSL), coupling the uphill reduction of ferredoxin in NfnL to the downhill reduction of NAD+ in NfnS from oxidation of NADPH. Flanking the bifurcating flavin are two site-differentiated iron-sulfur clusters; the nearest is a glutamate-ligated [4Fe-4S] cluster in NfnL. Recent biochemical experiments substituting the native glutamate with cysteine led to loss of coupling between the uphill and downhill pathways, in contrast to the tight thermodynamic coupling in the native system. To understand how this decoupling is biochemically manifested by the cysteine-substituted [4Fe-4S] in NfnL, we employed electron paramagnetic resonance (EPR) spectroscopy to identify changes in electronic architecture and square wave voltammetry (SWV) to probe thermodynamic shifts produced by the substitution. We observed notable g-value shifts in the EPR for the cysteine-substituted iron-sulfur cluster in addition to significant downward shifts in the redox potential, as well as the disappearance of several low-field signals observed in the native NfnSL complex. These results suggest the site-differentiated glutamate residue facilitates higher spin states in the [4Fesingle bond4S] cluster to bridge energetic gaps in electron transfer to the bifurcating flavin in the native complex, preventing unwanted short-circuiting seen in the cysteine-substituted complex.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

An isotopic perspective on growth and differentiation of Proterozoic orogenic crust: From subduction magmatism to cratonization

The in situ chemical differentiation of continental crust ultimately leads to the long-term stability of the continents. This process, more commonly known as ‘cratonization’, is driven by deep crustal melting with the transfer of those melts to shallower regions resulting in a strongly chemically stratified crust, with a refractory, dehydrated lower portion overlain by a complementary enriched upper portion. Since the lower to mid portions of continental crust are rarely exposed, investigation of the cratonization process must be through indirect methods. In this study we use in situ Hf and O isotope compositions of both magmatic and inherited zircons from several felsic magmatic suites in the Capricorn Orogen of Western Australia to highlight the differentiation history (i.e. cratonization) of this portion of late Archean to Proterozoic orogenic crust. The Capricorn Orogen shows a distinct tectonomagmatic history that evolves from an active continental margin through to intracratonic reworking, ultimately leading to thermally stable crust that responds similarly to the bounding Archean Pilbara and Yilgarn Cratons.

Johnson, Simon P.↗
DOE OSTI2025

Understanding latent timescales in neural ordinary differential equation models of advection-dominated dynamical systems

The neural ordinary differential equation (ODE) framework has shown considerable promise in recent years in developing highly accelerated surrogate models for complex physical systems characterized by partial differential equations (PDEs). For PDE-based systems, state-of-the-art neural ODE strategies leverage a two-step procedure to achieve this acceleration: a nonlinear dimensionality reduction step provided by an autoencoder, and a time integration step provided by a neural-network based model for the resultant latent space dynamics (the neural ODE). This work explores the applicability of such autoencoder-based neural ODE strategies for PDEs in which advection terms play a critical role. More specifically, alongside predictive demonstrations, physical insight into the sources of model acceleration (i.e., how the neural ODE achieves its acceleration) is the scope of the current study. Such investigations are performed by quantifying the effects of both autoencoder and neural ODE components on latent system time-scales using eigenvalue analysis of dynamical system Jacobians. To this end, the sensitivity of various critical training parameters – de-coupled versus end-to-end training, latent space dimensionality, and the role of training trajectory length, for example – to both model accuracy and the discovered latent system timescales is quantified. Furthermore, this work specifically uncovers the key role played by the training trajectory length (the number of rollout steps in the loss function during training) on the latent system timescales: larger trajectory lengths correlate with an increase in limiting neural ODE time-scales, and optimal neural ODEs are found to recover the largest time-scales of the full-order (ground-truth) system. Demonstrations are performed across fundamentally different unsteady fluid dynamics configurations influenced by advection: (1) the Kuramoto–Sivashinsky equations (2) Hydrogen-Air channel detonations (the compressible reacting Navier–Stokes equations with detailed chemistry), and (3) 2D Atmospheric flow.

Advection-dominated dynamical systems↗