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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

Comprehensive analysis of differentially expressed mRNA and circRNA in Ankylosing spondylitis patients’ platelets

Highlights: • 4996 mRNAs and 2942 circRNAs were identified differentially expressed in platelets of AS patients. • Several platelet-derived immune mediators were upregulated to regulate the interaction between platelets and immune cells. • Two downregulated circRNA were identified, and the corresponding circRNA-miRNA-mRNA regulatory network was constructed. • mRNAs and circRNAs found herein could be used as diagnostic biomarkers or new therapeutic targets. Ankylosing spondylitis (AS) is a chronic inflammatory disease significantly decreasing the quality of life. Platelets play an important and active role in the development of AS. Accumulating evidence demonstrated platelets contain diverse RNA repository inherited from megakaryocytes or microvesicles. Platelet RNAs are dynamically affected by pathological conditions and could be used as diagnostic or prognostic biomarkers. However, the role of the platelet RNAs in AS is elusive. In this study, we compared mRNA and circRNA profiles in platelets between AS patients and healthy controls using RNA sequencing and bioinformatic analysis, and found 4996 mRNAs and 2942 circRNAs were differently expressed. The significantly over-expressed mRNAs in AS patients are involved in platelet activity, gap junction, focal adhesion, rap1 and toll and Imd signaling pathway. The previous identified platelet-derived immune mediators such as P2Y1, P2Y12, PF4, GPIbα, CD40L, ICAM2, CCL5 (RANTES), TGF-β (TGF-β1 and TGF-β2) and PDGF (PDGFB and PDGFA) are also included in these over expressed mRNAs, implying these factors may trigger inflammatory cascades and promote the development of AS. Additionally, we found two down-regulated circRNA (circPTPN22 and circFCHSD2) from the intersection analyses of platelets and spinal ligament tissues of AS patients. The circRNA-miRNA-mRNA regulatory network of these two circRNAs was constructed, and the target mRNAs were enriched in Th17 cell differentiation, inflammatory bowel disease, cell adhesion molecules, cytokine-cytokine receptor interaction, Jak-STAT and Wnt signaling pathway, all these pathways participate in the bone remodeling and pro/anti-inflammatory immune regulation in AS. Then, qRT-PCR was performed to validate the expression of selected key mRNAs and circRNAs and the results demonstrated that the expression levels of P2Y12, GPIbα, circPTPN22 and circFCHSD2 were consistent with the sequencing analysis. In addition, the high expression of five predicted miRNAs interacting with circPTPN22 and circFCHSD2 were also detected in AS by qRT-PCR. Taken together, our study presents a comprehensive overview of mRNAs and circRNAs in platelets in AS patients and offers new insight into the mechanisms of platelet involving in the pathogenesis of AS. The mRNAs and circRNAs identified in this study may serve as candidates for diagnosis and targeted treatment of AS.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Long noncoding RNA AK023096 interacts with hnRNP-K and contributes to the maintenance of self-renewal in bladder cancer stem-like cells

Highlights: • BCSLCs play a critical role for the high risk of recurrence and we identified that lncRNA-AK023096 was required for controls stemness of BCSLCs. • LncRNA-AK023096 recruits hnRNP-K and epigenetically upregulated SOX2 expression by modulating H3K4 trimethylation of SOX2 promoter. • Overexpression of lncRNA-AK023096 expression in the tumor tissue was significantly associated with high risk of recurrence following TUR-Bt. LncRNA contribution to self-renewal of bladder cancer stem-like cells (CSLCs) remains largely unknown. We investigated the expression profile and biological function of lncRNAs in urothelial CSLCs by microarray analysis. Among these, lncRNA-AK023096 was identified as potentially playing a role in maintaining self-renewal of CSLCs. Knockdown of this transcript inhibited spheroid formation and tumor formation. We found that AK023096 mediates recruitment of hnRNP-K to SOX2 promoter and increases H3K4 trimethylation status on SOX2 promoter, leading to a robust change in SOX2 mRNA and protein levels. Moreover, AK023096 expression in primary tumors was found to be a powerful predictor of recurrence following transurethral resection in patients with nonmuscle-invasive bladder cancer, highlighting the critical role of lncRNA in the bladder cancer regulatory network.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

G protein nucleolar 3 promotes Non-Hodgkin lymphoma progression by activating the Wnt/β-catenin signaling pathway

G protein nucleolar 3 (GNL3), which acts as an oncoprotein in various carcinomas, is associated with tumor progression; however, little is known regarding GNL3 function in non-Hodgkin lymphoma (NHL). In this study, we first used in silico analysis to determine associations between GNL3 and diffuse large B-cell lymphoma (DLBCL). We then examined the effect of GNL3 on NHL progression, including cell proliferation, apoptosis, and cell cycle progression, and determined its underlying molecular mechanism using in vitro lymphoma cell lines and in vivo mouse xenograft models. We found that GNL3 mRNA levels were markedly higher in DLBCL tissues than in normal tissues, with these higher levels associated with poor prognosis. Additionally, GNL3 overexpression promoted NHL cell proliferation and cell cycle progression and reduced apoptosis in vitro, and enhanced tumorigenesis in an in vivo xenograft model. Moreover, we found that GNL3 upregulated the levels of Wnt/β-catenin signaling pathway-related factors and downstream target genes, whereas the opposite result was observed in GNL3-silenced cells. Furthermore, a rescue experiment using a Wnt/β-catenin inhibitor (XAV939) confirmed that GNL3 promotes NHL progression by activating the Wnt/β-catenin signaling pathway. These findings demonstrated that GNL3 functions as an oncogenic driver in NHL via the Wnt/β-catenin pathway.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Mast1 mediates radiation-induced gastric injury via the P38 MAPK pathway

Radiation-induced gastric injury is a serious adverse effect and reduces the efficacy of radiotherapy treatment. However, the mechanisms underlying radiation-induced stomach injury remain unclear. Here, mouse stomach and gastric epithelial cells were irradiated with different doses of X-ray radiation. The results showed that radiation induced gastric injury in vivo and in vitro. Differentially expressed functional mRNAs in irradiation-induced gastric tissues were screened from the Gene Expression Omnibus (GEO) database. We found that the expression of microtubule-associated serine/threonine kinase 1 (Mast1) was downregulated in mouse gastric tissues and gastric epithelial cells after irradiation. Furthermore, functional assays showed that knockdown of Mast1 inhibited growth and promoted apoptosis in gastric epithelial cells, while overexpression of Mast1 protected gastric epithelial cells from radiation damage. Mechanistically, Mast1 negatively regulated radiation-induced injury in gastric epithelial cells by inhibiting the activation of P38. The apoptosis caused by knockdown of Mast1 in gastric epithelial cells could be partially reversed by the P38 inhibitor SB203580. Moreover, data from several gastric cancer cell lines and online databases revealed that Mast1 was not involved in the development of gastric cancer. Collectively, our findings demonstrated that Mast1 is essential for radiation-induced gastric injury, providing a promising prognostic and therapeutic target.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Targeting ROR1 in combination with osimertinib in EGFR mutant lung cancer cells

Highlights: • The role of ROR1 was investigated in EGFR mutant lung cancer cells in vitro. • Cell growth and colony formation were suppressed and apoptosis was increased in cell lines treated with siROR1. • A combination of osimertinib with siROR1 was effective for EGFR mutant cell lines. Lung cancer that exhibits epidermal growth factor receptor (EGFR) gene mutation is sensitive to EGFR-tyrosine kinase inhibitors (TKIs), such as osimertinib. Receptor tyrosine kinase-like orphan receptor 1 (ROR1) may be involved in overcoming EGFR-TKI resistance. Growth inhibition, colony formation, apoptosis, and mRNA/protein levels in four osimertinib-sensitive and resistant cell lines transfected with small interfering RNA (siRNA) targeting ROR1 (siROR1) were evaluated. Cell growth and colony formation were suppressed and apoptosis was increased in all cell lines treated with siROR1. Although EGFR, AKT, and ERK phosphorylation were not suppressed in all cell lines, TGF-β2, AXL, CDH2, PARP1, PEG10, and TYMS mRNA expression levels were reduced. The combination of osimertinib with siROR1 was effective for the four cell lines, particularly in the two osimertinib-sensitive lines. In conclusion, targeting ROR1 in combination with osimertinib in EGFR mutant lung cancer may be a novel therapeutic option.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Learning Physically Interpretable Atmospheric Models From Data With WSINDy

The multiscale and turbulent nature of Earth's atmosphere has historically rendered accurate weather modeling a hard problem. Recently, there has been an explosion of interest surrounding data-driven approaches to weather modeling, which in many cases show improved forecasting accuracy and computational efficiency when compared to traditional methods. However, many of the current data-driven approaches employ highly parameterized neural networks, often resulting in uninterpretable models and limited gains in scientific understanding. In this work, we address the interpretability problem by explicitly discovering partial differential equations governing atmospheric phenomena, identifying symbolic mathematical models with direct physical interpretations. The purpose of this paper is to demonstrate that, in particular, the weak-form sparse identification of nonlinear dynamics (WSINDy) algorithm can learn effective atmospheric models from both simulated and assimilated data. Our approach adapts the standard WSINDy algorithm to work with high-dimensional fluid data of arbitrary spatial dimension.

58 GEOSCIENCES↗
DOE OSTI2024

Coarse-graining Hamiltonian systems using WSINDy

Abstract Weak form equation learning and surrogate modeling has proven to be computationally efficient and robust to measurement noise in a wide range of applications including ODE, PDE, and SDE discovery, as well as in coarse-graining applications, such as homogenization and mean-field descriptions of interacting particle systems. In this work we extend this coarse-graining capability to the setting of Hamiltonian dynamics which possess approximate symmetries associated with timescale separation. A smooth $$\varepsilon$$ ε -dependent Hamiltonian vector field $$X_\varepsilon$$ X ε possesses an approximate symmetry if the limiting vector field $$X_0=\lim _{\varepsilon \rightarrow 0}X_\varepsilon$$ X 0 = lim ε → 0 X ε possesses an exact symmetry. Such approximate symmetries often lead to the existence of a Hamiltonian system of reduced dimension that may be used to efficiently capture the dynamics of the symmetry-invariant dependent variables. Deriving such reduced systems, or approximating them numerically, is an ongoing challenge. We demonstrate that WSINDy can successfully identify this reduced Hamiltonian system in the presence of large perturbations imparted in the $$\varepsilon >0$$ ε > 0 regime, while remaining robust to extrinsic noise. This is significant in part due to the nontrivial means by which such systems are derived analytically. WSINDy naturally preserves the Hamiltonian structure by restricting to a trial basis of Hamiltonian vector fields. The methodology is computationally efficient, often requiring only a single trajectory to learn the global reduced Hamiltonian, and avoiding forward solves in the learning process. In this way, we argue that weak-form equation learning is particularly well-suited for Hamiltonian coarse-graining. Using nearly-periodic Hamiltonian systems as a prototypical class of systems with approximate symmetries, we show that WSINDy robustly identifies the correct leading-order system, with dimension reduced by at least two, upon observation of the relevant degrees of freedom. While our main contribution is computational, we also provide a contribution to the literature on averaging theory by proving that first-order averaging at the level of vector fields preserves Hamiltonian structure in nearly-periodic Hamiltonian systems. This provides theoretical justification for our approach as WSINDy’s computations occur at the level of Hamiltonian vector fields. We illustrate the efficacy of our proposed method using physically relevant examples, including coupled oscillator dynamics, the Hénon–Heiles system for stellar motion within a galaxy, and the dynamics of charged particles.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

Evolution of a σ–(c-di-GMP)–anti-σ switch

Filamentous actinobacteria of the genus Streptomyces have a complex lifecycle involving the differentiation of reproductive aerial hyphae into spores. We recently showed c-di-GMP controls this transition by arming a unique anti-σ, RsiG, to bind the sporulation-specific σ, WhiG. The Streptomyces venezuelae RsiG–(c-di-GMP) 2 –WhiG structure revealed that a monomeric RsiG binds c-di-GMP via two E(X) 3 S(X) 2 R(X) 3 Q(X) 3 D repeat motifs, one on each helix of an antiparallel coiled-coil. Here we show that RsiG homologs are found scattered throughout the Actinobacteria. Strikingly, RsiGs from unicellular bacteria descending from the most basal branch of the Actinobacteria are small proteins containing only one c-di-GMP binding motif, yet still bind their WhiG partners. Our structure of a Rubrobacter radiotolerans (RsiG) 2 –(c-di-GMP) 2 –WhiG complex revealed that these single-motif RsiGs are able to form an antiparallel coiled-coil through homodimerization, thereby allowing them to bind c-di-GMP similar to the monomeric twin-motif RsiGs. Further data show that in the unicellular actinobacterium R. radiotolerans , the (RsiG) 2 –(c-di-GMP) 2 –WhiG regulatory switch controls type IV pilus expression. Phylogenetic analysis indicates the single-motif RsiGs likely represent the ancestral state and an internal gene-duplication event gave rise to the twin-motif RsiGs inherited elsewhere in the Actinobacteria. Thus, these studies show how the anti-σ RsiG has evolved through an intragenic duplication event from a small protein carrying a single c-di-GMP binding motif, which functions as a homodimer, to a larger protein carrying two c-di-GMP binding motifs, which functions as a monomer. Consistent with this, our structures reveal potential selective advantages of the monomeric twin-motif anti-σ factors.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Asymptotic consistency of the WSINDy algorithm in the limit of continuum data

In this work we study the asymptotic consistency of the weak-form sparse identification of nonlinear dynamics algorithm (WSINDy) in the identification of differential equations from noisy samples of solutions. We prove that the WSINDy estimator is unconditionally asymptotically consistent for a wide class of models that includes the Navier–Stokes, Kuramoto–Sivashinsky and Sine–Gordon equations. We thus provide a mathematically rigorous explanation for the observed robustness to noise of weak-form equation learning. Conversely, we also show that, in general, the WSINDy estimator is only conditionally asymptotically consistent, yielding discovery of spurious terms with probability one if the noise level exceeds a critical threshold σ c . We provide explicit bounds on σ c in the case of Gaussian white noise and we explicitly characterize the spurious terms that arise in the case of trigonometric and/or polynomial libraries. Furthermore, we show that, if the data is suitably denoised (a simple moving average filter is sufficient), then asymptotic consistency is recovered for models with locally-Lipschitz, polynomial-growth nonlinearities. Our results reveal important aspects of weak-form equation learning, which may be used to improve future algorithms. We demonstrate our findings numerically using the Lorenz system, the cubic oscillator, a viscous Burgers-growth model and a Kuramoto–Sivashinsky-type high-order PDE.

asymptotic consistency↗
DOE OSTI2024

Weak-form inference for hybrid dynamical systems in ecology

Species subject to predation and environmental threats commonly exhibit variable periods of population boom and bust over long timescales. Understanding and predicting such behaviour, especially given the inherent heterogeneity and stochasticity of exogenous driving factors over short timescales, is an ongoing challenge. A modelling paradigm gaining popularity in the ecological sciences for such multi-scale effects is to couple short-term continuous dynamics to long-term discrete updates. We develop a data-driven method utilizing weak-form equation learning to extract such hybrid governing equations for population dynamics and to estimate the requisite parameters using sparse intermittent measurements of the discrete and continuous variables. The method produces a set of short-term continuous dynamical system equations parametrized by long-term variables, and long-term discrete equations parametrized by short-term variables, allowing direct assessment of interdependencies between the two timescales. We demonstrate the utility of the method on a variety of ecological scenarios and provide extensive tests using models previously derived for epizootics experienced by the North American spongy moth ( Lymantria dispar dispar ).

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2023

Bayesian refinement of covariant energy density functionals

The last five years have seen remarkable progress in our quest to determine the equation of state of neutron rich matter. Here, recent advances across the theoretical, experimental, and observational landscape have been incorporated in a Bayesian framework to refine existing covariant energy density functionals previously calibrated by the properties of finite nuclei. In particular, constraints on the maximum neutron star mass from pulsar timing, on stellar radii from the NICER mission, on tidal deformabilities from the LIGO-Virgo collaboration, and on the dynamics of pure neutron matter as predicted from chiral effective field theories have resulted in significant refinements to the models, particularly to those predicting a stiff symmetry energy. Still, even after these improvements, we find it challenging to reproduce simultaneously the neutron skin thickness of both 208 Pb and 48 Ca recently reported by the PREX/CREX collaboration.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗
DOE OSTI2025

Nonperturbative Guiding Center Model for Magnetized Plasmas

Perturbative guiding center theory adequately describes the slow drift motion of charged particles in the strongly magnetized regime characteristic of thermal particle populations in various magnetic fusion devices. However, it breaks down for particles with large-enough energy. Here, we report on a data-driven method for learning a nonperturbative guiding center model from full-orbit particle simulation data. We show the data-driven model significantly outperforms traditional asymptotic theory in magnetization regimes appropriate for fusion-born α particles in stellarators, thus opening the door to nonperturbative guiding center calculations.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗
DOE OSTI2023

Mechanical and Thermal Characterization of Phase-Change Material and High-Density Polyethylene Functional Composites for Thermal Energy Storage

Abstract Phase-change materials (PCMs) can be used to develop thermal energy storage systems as they absorb large amount of latent heat nearly at a constant temperature when changing phase from a solid to a liquid. To prevent leakage when in a liquid state, PCM is shape stabilized in a polymer matrix of high-density polyethylene (HDPE). The present research explores the injection-molded mechanical and thermal properties of different PCM/HDPE composite ratios. The tensile strength and modulus of elasticity at room temperature and with the PCM fully melted within the composite are measured. Additionally, the hardness, latent heat of fusion, phase-change temperature, and thermal conductivity are investigated. An analysis of microstructures of the composite is used to support the findings. The PCM within the PCM/HDPE composite gives it the benefit of thermal storage but causes a decrease in mechanical properties.

Energy & Fuels↗
DOE OSTI2022

In search of autophagy biomarkers in breast cancer: Receptor status and drug agnostic transcriptional changes during autophagy flux in cell lines

Autophagy drives drug resistance and drug-induced cancer cell cytotoxicity. Targeting the autophagy process could greatly improve chemotherapy outcomes. The discovery of specific inhibitors or activators has been hindered by challenges with reliably measuring autophagy levels in a clinical setting. We investigated drug-induced autophagy in breast cancer cell lines with differing ER/PR/Her2 receptor status by exposing them to known but divergent autophagy inducers each with a unique molecular target, tamoxifen, trastuzumab, bortezomib or rapamycin. Differential gene expression analysis from total RNA extracted during the earliest sign of autophagy flux showed both cell- and drug-specific changes. We analyzed the list of differentially expressed genes to find a common, cell- and drug-agnostic autophagy signature. Twelve mRNAs were significantly modulated by all the drugs and 11 were orthogonally verified with Q-RT-PCR (Klhl24, Hbp1, Crebrf, Ypel2, Fbxo32, Gdf15, Cdc25a, Ddit4, Psat1, Cd22, Ypel3). The drug agnostic mRNA signature was similarly induced by a mitochondrially targeted agent, MitoQ. In-silico analysis on the KM-plotter cancer database showed that the levels of these mRNAs are detectable in human samples and associated with breast cancer prognosis outcomes of Relapse-Free Survival in all patients (RSF), Overall Survival in all patients (OS), and Relapse-Free Survival in ER + Patients (RSF ER + ). High levels of Klhl24, Hbp1, Crebrf, Ypel2, CD22 and Ypel3 were correlated with better outcomes, whereas lower levels of Gdf15, Cdc25a, Ddit4 and Psat1 were associated with better prognosis in breast cancer patients. This gene signature uncovers candidate autophagy biomarkers that could be tested during preclinical and clinical studies to monitor the autophagy process.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Evaluation of co-circulating pathogens and microbiome from COVID-19 infections

Co-infections or secondary infections with SARS-CoV-2 have the potential to affect disease severity and morbidity. Additionally, the potential influence of the nasal microbiome on COVID-19 illness is not well understood. In this study, we analyzed 203 residual samples, originally submitted for SARS-CoV-2 testing, for the presence of viral, bacterial, and fungal pathogens and non-pathogens using a comprehensive microarray technology, the Lawrence Livermore Microbial Detection Array (LLMDA). Eighty-seven percent of the samples were nasopharyngeal samples, and 23% of the samples were oral, nasal and oral pharyngeal swabs. We conducted bioinformatics analyses to examine differences in microbial populations of these samples, as a proxy for the nasal and oral microbiome, from SARS-CoV-2 positive and negative specimens. We found 91% concordance with the LLMDA relative to a diagnostic RT-qPCR assay for detection of SARS-CoV-2. Sixteen percent of all the samples (32/203) revealed the presence of an opportunistic bacterial or frank viral pathogen with the potential to cause co-infections. The two most detected bacteria, Streptococcus pyogenes and Streptococcus pneumoniae , were present in both SARS-CoV-2 positive and negative samples. Human metapneumovirus was the most prevalent viral pathogen in the SARS-CoV-2 negative samples. Sequence analysis of 16S rRNA was also conducted to evaluate bacterial diversity and confirm LLMDA results.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

The structural basis of mRNA recognition and binding by yeast pseudouridine synthase PUS1

The chemical modification of RNA bases represents a ubiquitous activity that spans all domains of life. Pseudouridylation is the most common RNA modification and is observed within tRNA, rRNA, ncRNA and mRNAs. Pseudouridine synthase or ‘PUS’ enzymes include those that rely on guide RNA molecules and others that function as ‘stand-alone’ enzymes. Among the latter, several have been shown to modify mRNA transcripts. Although recent studies have defined the structural requirements for RNA to act as a PUS target, the mechanisms by which PUS1 recognizes these target sequences in mRNA are not well understood. Here we describe the crystal structure of yeast PUS1 bound to an RNA target that we identified as being a hot spot for PUS1-interaction within a model mRNA at 2.4 Å resolution. The enzyme recognizes and binds both strands in a helical RNA duplex, and thus guides the RNA containing the target uridine to the active site for subsequent modification of the transcript. The study also allows us to show the divergence of related PUS1 enzymes and their corresponding RNA target specificities, and to speculate on the basis by which PUS1 binds and modifies mRNA or tRNA substrates.

59 BASIC BIOLOGICAL SCIENCES↗