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DOE OSTI2019

Strong Stability Preserving Integrating Factor Two-Step Runge-Kutta Methods

In this report, problems with components that feature significantly different time scales, where the stiff time-step restriction comes from a linear component, implicit-explicit (IMEX) methods alleviate this restriction if the concern is linear stability. However, when nonlinear non-inner-product stability properties are of interest, such as in the evolution of hyperbolic partial differential equations with shocks or sharp gradients, linear inner-product stability is no longer sufficient for convergence, and so strong stability preserving (SSP) methods are often needed. Where the SSP property is needed, IMEX SSP Runge-Kutta (SSP-IMEX) methods have very restrictive time-steps. An alternative to SSP-IMEX schemes is to adopt an integrating factor approach to handle the linear component exactly and step the transformed problem forward using some time-evolution method. The strong stability properties of integrating factor Runge-Kutta methods were established in Isherwood et al. (SIAM J Numer Anal 56(6):3276–3307, 2018), where it was shown that it is possible to define explicit integrating factor Runge-Kutta methods that preserve strong stability properties satisfied by each of the two components when coupled with forward Euler time-stepping. It was proved that the solution will be SSP if the transformed problem is stepped forward with an explicit SSP Runge-Kutta method that has non-decreasing abscissas. However, explicit SSP Runge-Kutta methods have an order barrier of p=4, and sometimes higher order is desired. In this work we consider explicit SSP two-step Runge-Kutta integrating factor methods to raise the order. We show that strong stability is ensured if the two-step Runge-Kutta method used to evolve the transformed problem is SSP and has non-decreasing abscissas. We find such methods up to eighth order and present their SSP coefficients. Adding a step allows us to break the fourth order barrier on explicit SSP Runge-Kutta methods; furthermore, our explicit SSP two-step Runge-Kutta methods with non-decreasing abscissas typically have larger SSP coefficients than the corresponding one-step methods. A selection of our methods are tested for convergence and demonstrate the design order. We also show, for selected methods, that the SSP time-step predicted by the theory is a lower bound of the allowable time-step for linear and nonlinear problems that satisfy the total variation diminishing (TVD) condition. We compare some of the non-decreasing abscissa SSP two-step Runge-Kutta methods to previously found methods that do not satisfy this criterion on linear and nonlinear TVD test cases to show that this non-decreasing abscissa condition is indeed necessary in practice as well as theory. We also compare these results to our SSP integrating factor Runge-Kutta methods designed in Isherwood et al. (2018).

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2018

A Correction to the Stress–Strain Curve During Multistage Hot Deformation of 7150 Aluminum Alloy Using Instantaneous Friction Factors

Multistage stress–strain curve correction based on an instantaneous friction factor was studied for axisymmetric uniaxial hot compression of 7150 aluminum alloy. Experimental friction factors were calculated based on continuous isothermal axisymmetric uniaxial compression tests at various deformation parameters. Then, an instantaneous friction factor equation was fitted by mathematic analysis. After verification by comparing single-pass flow stress correction with traditional average friction factor correction, the instantaneous friction factor equation was applied to correct multistage stress–strain curves. The corrected results were reasonable and validated by multistage relative softening calculations. This research provides a broad potential for implementing axisymmetric uniaxial compression in multistage physical simulations and friction optimization in finite element analysis.

36 MATERIALS SCIENCE↗

DOE OSTI2016

Engineering growth factors for regenerative medicine applications.

Growth factors are important morphogenetic proteins that instruct cell behavior and guide tissue repair and renewal. Although their therapeutic potential holds great promise in regenerative medicine applications, translation of growth factors into clinical treatments has been hindered by limitations including poor protein stability, low recombinant expression yield, and suboptimal efficacy. This review highlights current tools, technologies, and approaches to design integrated and effective growth factor-based therapies for regenerative medicine applications. The first section describes rational and combinatorial protein engineering approaches that have been utilized to improve growth factor stability, expression yield, biodistribution, and serum half-life, or alter their cell trafficking behavior or receptor binding affinity. The second section highlights elegant biomaterial-based systems, inspired by the natural extracellular matrix milieu, that have been developed for effective spatial and temporal delivery of growth factors to cell surface receptors. Although appearing distinct, these two approaches are highly complementary and involve principles of molecular design and engineering to be considered in parallel when developing optimal materials for clinical applications.

Biomaterials↗

DOE OSTI2019

Hybrid super homogenization and discontinuity factor method for continuous finite element diffusion

This paper presents a novel homogenization equivalence technique aiming to simultaneously leveragethe simplicity of the Super Homogenization (SPH) method to reproduce reference reaction rates andthe ability to preserve reference leakage rates at desired surfaces through Discontinuity Factors (DF).The need for this new class of methods arises from the inability of the current state-of-the-art SPH technology to properly reproduce the reactor multiplication factor for problems with significant leakage. This work shows that this defect lies in the use of normalization factors in the SPH algorithm: while they are necessary with purely reflecting problems to ensure uniqueness of the solution, they introduce homogenization inconsistencies if at least one vacuum boundary condition is present. Two solutions to this problem are presented in this work: (i) to simply remove the normalization factor or (ii) to introduce additional degrees of freedom in the form of DFs in such a way that the normalization factors can still constrain the problem. While the former clearly offers unrivaled simplicity and can lead to very satisfactory results for leakage-dominated cores or if spatial restriction of the SPH regions is applied – as demonstrated for the High Temperature Test Reactor, the latter is more robust and does not require to scale the fission terms with the reference eigenvalue.

22 GENERAL STUDIES OF NUCLEAR REACTORS↗

DOE OSTI2017

Regulator of G protein signaling 4 is a novel target of GATA-6 transcription factor

GATA transcription factors regulate an array of genes important in cell proliferation and differentiation. Here we report the identification of regulator of G protein signaling 4 (RGS4) as a novel target for GATA-6 transcription factor. Although three sites (a, b, c) within the proximal region of rabbit RGS4 promoter for GATA transcription factors were predicted by bioinformatics analysis, only GATA-a site (16 bp from the core TATA box) is essential for RGS4 transcriptional regulation. RT-PCR analysis demonstrated that only GATA-6 was highly expressed in rabbit colonic smooth muscle cells but GATA-4/6 were expressed in cardiac myocytes and GATA-1/2/3 expressed in blood cells. Adenovirus-mediated expression of GATA-6 but not GATA-1 significantly increased the constitutive and IL-1β-induced mRNA expression of the endogenous RGS4 in colonic smooth muscle cells. IL-1β stimulation induced GATA-6 nuclear translocation and increased GATA-6 binding to RGS4 promoter. These data suggest that GATA factor could affect G protein signaling through regulating RGS4 expression, and GATA signaling may develop as a future therapeutic target for RGS4-related diseases. - Highlights: • GATA-6 is highly expressed in colonic smooth muscle cells. • RGS4 is a novel target for GATA-6 transcription factor. • GATA-a response element is essential to regulate the core promoter of RGS4. • GATA-6 regulates IL-1β-induced RGS4 upregulation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2017

Th17 cell-mediated immune responses promote mast cell proliferation by triggering stem cell factor in keratinocytes

Although mast cells are traditionally thought to function as effector cells in allergic responses, they have increasingly been recognized as important regulators of various immune responses. Mast cells mature locally; thus, tissue-specific influences are important for promoting mast cell accumulation and survival in the skin and the gastrointestinal tract. In this study, we determined the effects of keratinocytes on mast cell accumulation during Th17-mediated skin inflammation. We observed increases in dermal mast cells in imiquimod-induced psoriatic dermatitis in mice accompanied by the expression of epidermal stem cell factor (SCF), a critical mast cell growth factor. Similar to mouse epidermal keratinocytes, SCF was highly expressed in the human HaCaT keratinocyte cell line following stimulation with IL−17. Further, keratinocytes promoted mast cell proliferation following stimulation with IL−17 in vitro. However, the effects of keratinocytes on mast cells were significantly diminished in the presence of anti−CD117 (stem cell factor receptor) blocking antibodies. Taken together, our results revealed that the Th17-mediated inflammatory environment promotes mast cell accumulation through keratinocyte-derived SCF. - Highlights: • Psoriasis-like skin inflammation increase dermal mast cells. • Keratinocyte produce stem cell factor in psoriasis-like skin inflammation. • Keratinocyte promote mast cell proliferation by stem cell factor dependent manner.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2021

Assessing the predictive impact of factor fixing with an adaptive uncertainty-based approach .

Despite widespread use of factor fixing in environmental modeling, its effect on model predictions has received little attention and is instead commonly presumed to be negligible. We propose a proof-of-concept adaptive method for systematically investigating the impact of factor fixing. The method uses Global Sensitivity Analysis methods to identify groups of sensitive parameters, then quantifies which groups can be safely fixed at nominal values without exceeding a maximum acceptable error, demonstrated using the 21-dimensional Sobol’ G-function. Furthermore, three error measures are considered for quantities of interest, namely Relative Mean Absolute Error, Pearson Product-Moment Correlation and Relative Variance. Results demonstrate that factor fixing may cause large errors in the model results unexpectedly, when preliminary analysis suggests otherwise, and that the default value selected affects the number of factors to fix. To improve the applicability and methodological development of factor fixing, a new research agenda encompassing five opportunities is discussed for further attention.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2024

Individual low-carbon behaviors and influencing factors: Insights from a behavior survey study in China

Climate change is a major global concern. Greenhouse gas emissions that cause global climate change are directly or indirectly affected by human activities. Individual low-carbon behaviors are crucial in reducing CO 2 emissions and improving environmental and ecological health. To effectively promote individual low-carbon behavior, this study designed a questionnaire on the factors influencing individual low-carbon intentions and behavior based on theoretical models of environmental behavior. A total of 2430 valid questionnaires were collected in China. This study focuses on analyzing the impact of demographic characteristics, internal and external factors on individual low-carbon behaviors and their interrelationships. The research shows correlations between internal and external factors in determining low-carbon intention or behaviors. Internal factors-related low-carbon behavior is not closely linked with demographic variables, whereas the external factors-related low-carbon behavior vary significantly by age, residence, education, marital status, occupation, and income. The findings have important implications for designing effective policies to promote low-carbon behaviors, such as creating a more favorable external environment and increasing the use of policy tools for reducing CO 2 emission.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

A semiparametric latent factor model for large scale temporal data with heteroscedasticity

Large scale temporal data have flourished in a vast array of applications, and their sophisticated structures, especially the heteroscedasticity among subjects with inter- and intra-temporal dependence, have fueled a great demand for new statistical models. In this paper, with covariate information, we consider a flexible model for large scale temporal data with subject-specific heteroscedasticity. Formally, the model employs latent semiparametric factors to simultaneously account for the subject-specific heteroscedasticity and the contemporaneous and/or serial correlations. The subject-specific heteroscedasticity is modeled as the product of the unobserved factor process and subject’s covariate effect, which is further characterized via additive models. For estimation, we propose a two-step procedure. First, the latent factor process and nonparametric loading are recovered through projection-based methods, and following, we estimate the regression components by approaches motivated from the generalized least squares. By scrupulously examining the non-asymptotic rates for recovering the factor process and its loading, we show the consistency and efficiency of estimated regression coefficients in the absence of prior knowledge of latent factor process and subject’s covariate effect. Here, the statistical guarantees remain valid even for finite time points that makes our method particularly appealing when the subjects significantly outnumber the observation time points. Using comprehensive simulations, we demonstrate the finite sample performance of our method, which corroborates the theoretical findings. Finally, we apply our method to a data set of air quality and energy consumption collected at 129 monitoring sites in the United States in 2015.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2015

Factors circulating in the blood of type 2 diabetes mellitus patients affect osteoblast maturation – Description of a novel in vitro model

Type 2 diabetes mellitus (T2DM) is one of the most frequent metabolic disorders in industrialized countries. Among other complications, T2DM patients have an increased fracture risk and delayed fracture healing. We have demonstrated that supraphysiological glucose and insulin levels inhibit primary human osteoblasts' maturation. We aimed at developing a more physiologically relevant in vitro model to analyze T2DM-mediated osteoblast changes. Therefore, SCP-1-immortalized pre-osteoblasts were differentiated with T2DM or control (non-obese and obese) sera. Between both control groups, no significant changes were observed. Proliferation was significantly increased (1.69-fold), while AP activity and matrix mineralization was significantly reduced in the T2DM group. Expression levels of osteogenic marker genes and transcription factors were altered, e.g. down-regulation of RUNX2 and SP-7 or up-regulation of STAT1, in the T2DM group. Active TGF-β levels were significantly increased (1.46-fold) in T2DM patients' sera. SCP-1 cells treated with these sera showed significantly increased TGF-β signaling (2.47-fold). Signaling inhibition effectively restored osteoblast maturation in the T2DM group. Summarizing our data, SCP-1 cells differentiated in the presence of T2DM patients' serum exhibit reduced osteoblast function. Thus, this model has a high physiological impact, as it can identify circulating factors in T2DM patients' blood that may affect bone function, e.g. TGF-β. - Highlights: • We present here a physiologically relevant in vitro model for diabetic osteopathy. • Blood of T2DM patients contains factors that affect osteoblasts' function. • The model developed here can be used to identify these factors, e.g. TGF-β. • Blocking TGF-β signaling partly rescues the osteoblasts' function in the T2DM group. • The model is useful to demonstrate the role of single factors in diabetic osteopathy.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2016

Test-Based Calibration of Safety Factors for Capacity Models

In this paper, a simple procedure to calibrate the safety factor of capacity models is presented. The calibration can be carried out based on any available database of experimental tests, even of limited size. The procedure aims to assess the model capability of predicting the test results and to calibrate the safety factor so that the capacity equation meets the target reliability level required by the code or sought by the calibrator. After predicting each test of the database with the capacity equation under consideration, the test-prediction pairs are checked for the property of linearity, and the relative error for the properties of homoscedasticity and normality. Once these properties are fulfilled—which may require a nonlinear transformation of the test values and/or the predictions—the closed-form equation proposed in this paper is employed to compute a target design value. The model safety factor is finally obtained by comparing such target design value with the design value obtained from the code. The paper also proposes two approximate analytical equations to compute the tolerance factor, used to attain any given fractile, as a function of the (even small) number of tests, with any assigned confidence level. A fundamental outcome of the procedure is that it yields an objective indicator of the model accuracy, measured by the standard deviation of its error, which may be regarded as a parameter useful for selecting the most reliable model among different competing ones. In the long run, the application of the proposed procedure will allow achieving a uniform reliability level throughout all capacity models used in codes and guidelines. A further advantage is that the partial safety factors so derived can be straightforwardly updated when more experiments become available. Finally, as an example, the proposed procedure is herein applied to the ACI 318 shear design capacity equation for concrete members unreinforced in shear.

42 ENGINEERING↗

DOE OSTI2017

Effective g factor of low-density two-dimensional holes in a Ge quantum well

Here we report the measurements of the effective g factor of low-density two-dimensional holes in a Ge quantum well. Using the temperature dependence of the Shubnikov-de Haas oscillations, we extract the effective g factor in a magnetic field perpendicular to the sample surface. Very large values of the effective g factor, ranging from ~13 to ~28, are observed in the density range of 1.4×10 10 cm -2 – 1.4×10 11 cm -2 . When the magnetic field is oriented parallel to the sample surface, the effective g factor is obtained from a protrusion in the magneto-resistance data that signify full spin polarization. In the latter orientation, a small effective g factor, ~1.3-1.4, is measured in the density range of 1.5×10 10 cm -2 –2×10 10 cm -2 . Finally, this very strong anisotropy is consistent with theoretical predictions and previous measurements in other 2D hole systems, such as InGaAs and GaSb.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗

DOE OSTI2016

Model-independent determination of the astrophysical S factor in laser-induced fusion plasmas

In this paper, we present a new and general method for measuring the astrophysical S factor of nuclear reactions in laser-induced plasmas and we apply it to 2 H(d,n) 3 He. The experiment was performed with the Texas Petawatt Laser, which delivered 150–270 fs pulses of energy ranging from 90 to 180 J to D 2 or CD 4 molecular clusters (where D denotes 2 H). After removing the background noise, we used the measured time-of-flight data of energetic deuterium ions to obtain their energy distribution. We derive the S factor using the measured energy distribution of the ions, the measured volume of the fusion plasma, and the measured fusion yields. This method is model independent in the sense that no assumption on the state of the system is required, but it requires an accurate measurement of the ion energy distribution, especially at high energies, and of the relevant fusion yields. In the 2 H(d,n) 3 He and 3 He(d,p) 4 He cases discussed here, it is very important to apply the background subtraction for the energetic ions and to measure the fusion yields with high precision. While the available data on both ion distribution and fusion yields allow us to determine with good precision the S factor in the d+d case (lower Gamow energies), for the d+ 3 He case the data are not precise enough to obtain the S factor using this method. Our results agree with other experiments within the experimental error, even though smaller values of the S factor were obtained. This might be due to the plasma environment differing from the beam target conditions in a conventional accelerator experiment.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2016

Flavor dependence of the pion and kaon form factors and parton distribution functions

The separate quark flavor contributions to the pion and kaon valence quark distribution functions are studied, along with the corresponding electromagnetic form factors in the space-like region. The calculations are made using the solution of the Bethe-Salpeter equation for the model of Nambu and Jona-Lasinio with proper-time regularization. Both the pion and kaon form factors and the valence quark distribution functions reproduce many features of the available empirical data. The larger mass of the strange quark naturally explains the empirical fact that the ratio u(K) + (x)/u(pi) + (x) drops below unity at large x, with a value of approximately M$2\atop{u}$/Ms$2\atop{s}$ as x → 1. With regard to the elastic form factors we report a large flavor dependence, with the u-quark contribution to the kaon form factor being an order of magnitude smaller than that of the s-quark at large Q 2 , which may be a sensitive measure of confinement effects in QCD. Surprisingly though, the total K + and π + form factors differ by only 10%. Lastly, in general we find that flavor breaking effects are typically around 20%.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2024

Pion gravitational form factors in the QCD instanton vacuum. I

The pion form factors of the QCD energy-momentum tensor (EMT) are studied in the instanton liquid model (ILM) of the QCD vacuum. In this approach, the breaking of conformal symmetry is encoded in the form of stronger-than-Poisson fluctuations in the number of instantons. For the trace of the EMT, it is shown that the gluonic trace anomaly term contributes half the pion mass, with the other half coming from the quark-mass-dependent σ term. The Q 2 dependence of the form factors is governed by glueball and scalar meson exchanges. For the EMT, the spin-0 (trace) and spin-2 (traceless rank-2 tensor) form factors are computed at next-to-leading order in the instanton density using effective quark operators. Relations between the gluon and quark contributions to the EMT form factors are derived. The form factors are also expressed in terms of the pion light-front wave functions in the ILM. The results at the low resolution scale of the inverse instanton size are evolved to higher scales using the renormalization group equation. The ILM results compare well with those of recent lattice QCD calculations. Published by the American Physical Society 2024

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗

DOE OSTI2017

Axial-vector form factors of the nucleon from lattice QCD

In this paper, we present results for the form factors of the isovector axial vector current in the nucleon state using large scale simulations of lattice QCD. The calculations were done using eight ensembles of gauge configurations generated by the MILC collaboration using the HISQ action with 2 + 1 + 1 dynamical flavors. These ensembles span three lattice spacings a ≈ 0.06 , 0.09, and 0.12 fm and light-quark masses corresponding to the pion masses M π ≈ 135, 225, and 310 MeV. High-statistics estimates allow us to quantify systematic uncertainties in the extraction of G A (Q 2 ) and the induced pseudoscalar form factor G P (Q 2 ) . We perform a simultaneous extrapolation in the lattice spacing, lattice volume and light-quark masses of the axial charge radius r A data to obtain physical estimates. Using the dipole ansatz to fit the Q 2 behavior we obtain r A | dipole = 0.49(3) fm , which corresponds to M A = 1.39(9) GeV , and is consistent with M A = 1.35(17) GeV obtained by the miniBooNE collaboration. The estimate obtained using the z -expansion is r A | z - expansion = 0.46(6) fm, and the combined result is r A | combined = 0.48(4) fm. Analysis of the induced pseudoscalar form factor G P (Q 2 ) yields low estimates for g* P and g π NN compared to their phenomenological values. To understand these, we analyze the partially conserved axial current (PCAC) relation by also calculating the pseudoscalar form factor. Lastly, we find that these low values are due to large deviations in the PCAC relation between the three form factors, and in the pion-pole dominance hypothesis.

72 PHYSICS OF ELEMENTARY PARTICLES AND FIELDS↗

DOE OSTI2026

Nucleon axial form factor from elementary target data

Precise neutrino-nucleon amplitudes are essential ingredients for predicting neutrino event rates in current and upcoming long-baseline neutrino oscillation experiments. A common neutrino interaction with a low reaction threshold and with most of the energy carried by two final state particles is quasielastic scattering, for which the nucleon axial form factor, 𝐹 𝐴⁡ (𝑄 2 ), is a dominant source of uncertainty. Improvements to the nucleon axial form factor rely on neutrino scattering data with elementary targets to reduce or eliminate the need for nuclear modeling systematics. This work examines constraints on the nucleon axial form factor that can be achieved from datasets of neutrino scattering on deuterium targets, Lattice QCD predictions, and from the recent hydrogen target data from the MINERvA Collaboration. Significant tension is found between hydrogen and deuterium target data, suggesting that extractions from deuterium underestimate both the central value and uncertainty of the form factor. Parametrizations for and uncertainties of the nucleon axial form factor using the 𝑧 expansion are provided.

FOS: Physical sciences↗

DOE OSTI2021

DeepGRN: prediction of transcription factor binding site across cell-types using attention-based deep neural networks

Abstract Background Due to the complexity of the biological systems, the prediction of the potential DNA binding sites for transcription factors remains a difficult problem in computational biology. Genomic DNA sequences and experimental results from parallel sequencing provide available information about the affinity and accessibility of genome and are commonly used features in binding sites prediction. The attention mechanism in deep learning has shown its capability to learn long-range dependencies from sequential data, such as sentences and voices. Until now, no study has applied this approach in binding site inference from massively parallel sequencing data. The successful applications of attention mechanism in similar input contexts motivate us to build and test new methods that can accurately determine the binding sites of transcription factors. Results In this study, we propose a novel tool (named DeepGRN) for transcription factors binding site prediction based on the combination of two components: single attention module and pairwise attention module. The performance of our methods is evaluated on the ENCODE-DREAM in vivo Transcription Factor Binding Site Prediction Challenge datasets. The results show that DeepGRN achieves higher unified scores in 6 of 13 targets than any of the top four methods in the DREAM challenge. We also demonstrate that the attention weights learned by the model are correlated with potential informative inputs, such as DNase-Seq coverage and motifs, which provide possible explanations for the predictive improvements in DeepGRN. Conclusions DeepGRN can automatically and effectively predict transcription factor binding sites from DNA sequences and DNase-Seq coverage. Furthermore, the visualization techniques we developed for the attention modules help to interpret how critical patterns from different types of input features are recognized by our model.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗