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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 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Raman Spectral Imaging Based Histopathology of Human Skin Tissue

Confocal Raman microspectral imaging (CRMI) provides a versatile tool to extract biochemical information on histologically defined regions with improved spatial resolution and sensitivity. Here, we investigate spectral features and perform both uni- and multivariate analyses to obtain precise correlation between the biochemical profile and histological architecture of ex vivo human skin tissue. The skin dermis layer is visualized by integrated images of collagen peaks at 855, 940, and 1248 cm{sup −1}, while the stratum corneum layer is described by chemical images of ceramides. M elanin accumulation patterns in epidermis are obtained by integration of 1375 and 1580 cm{sup −1} Raman peaks. In addition, distinct spectral features of the melanin precursor are revealed by certain Raman vibrations, which structurally characterize the molecular composition and organization of hair follicles. These results offer a unique insight into the skin histology, biochemistry, and biophysics, which provide the possibility of interpreting the underlying mechanisms of skin malignant processes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Multivariate Analogs of Classical Univariate Discrete Distributions and Their Properties

Some discrete distributions such as Bernoulli, binomial, geometric, negative binomial, Poisson, Polya–Aeppli, and others play an important role in applied problems of probability theory and mathematical statistics. We propose a variant of a multivariate distribution whose components have a given univariate discrete distribution. In fact we consider some very general variant of the so-called reduction method. We find the explicit form of the mass function and generating function of such distribution and study their properties. We prove that our construction is unique in natural exponential families of distributions. Our results are the generalization and unification of many results of other authors.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2011

Geochemical Fingerprinting of Coltan Ores by Machine Learning on Uneven Datasets

Two modern machine learning techniques, Linear Programming Boosting (LPBoost) and Support Vector Machines (SVMs), are introduced and applied to a geochemical dataset of niobium-tantalum ('coltan') ores from Central Africa to demonstrate how such information may be used to distinguish ore provenance, i.e., place of origin. The compositional data used include uni- and multivariate outliers and elemental distributions are not described by parametric frequency distribution functions. The 'soft margin' techniques of LPBoost and SVMs can be applied to such data. Optimization of their learning parameters results in an average accuracy of up to c. 92%, if spot measurements are assessed to estimate the provenance of ore samples originating from two geographically defined source areas. A parameterized performance measure, together with common methods for its optimization, was evaluated to account for the presence of uneven datasets. Optimization of the classification function threshold improves the performance, as class importance is shifted towards one of those classes. For this dataset, the average performance of the SVMs is significantly better compared to that of LPBoost.

42 ENGINEERING↗
DOE OSTI2012

Implementation of the Iterative Proportion Fitting Algorithm for Geostatistical Facies Modeling

In geostatistics, most stochastic algorithm for simulation of categorical variables such as facies or rock types require a conditional probability distribution. The multivariate probability distribution of all the grouped locations including the unsampled location permits calculation of the conditional probability directly based on its definition. In this article, the iterative proportion fitting (IPF) algorithm is implemented to infer this multivariate probability. Using the IPF algorithm, the multivariate probability is obtained by iterative modification to an initial estimated multivariate probability using lower order bivariate probabilities as constraints. The imposed bivariate marginal probabilities are inferred from profiles along drill holes or wells. In the IPF process, a sparse matrix is used to calculate the marginal probabilities from the multivariate probability, which makes the iterative fitting more tractable and practical. This algorithm can be extended to higher order marginal probability constraints as used in multiple point statistics. The theoretical framework is developed and illustrated with estimation and simulation example.

58 GEOSCIENCES↗
DOE OSTI2015

High-throughput prediction of Acacia and eucalypt lignin syringyl/guaiacyl content using FT-Raman spectroscopy and partial least squares modeling

High-throughput techniques are necessary to efficiently screen potential lignocellulosic feedstocks for the production of renewable fuels, chemicals, and bio-based materials, thereby reducing experimental time and expense while supplanting tedious, destructive methods. The ratio of lignin syringyl (S) to guaiacyl (G) monomers has been routinely quantified as a way to probe biomass recalcitrance. Mid-infrared and Raman spectroscopy have been demonstrated to produce robust partial least squares models for the prediction of lignin S/G ratios in a diverse group of Acacia and eucalypt trees. The most accurate Raman model has now been used to predict the S/G ratio from 269 unknown Acacia and eucalypt feedstocks. This study demonstrates the application of a partial least squares model composed of Raman spectral data and lignin S/G ratios measured using pyrolysis/molecular beam mass spectrometry (pyMBMS) for the prediction of S/G ratios in an unknown data set. The predicted S/G ratios calculated by the model were averaged according to plant species, and the means were not found to differ from the pyMBMS ratios when evaluating the mean values of each method within the 95 % confidence interval. Pairwise comparisons within each data set were employed to assess statistical differences between each biomass species. While some pairwise appraisals failed to differentiate between species, Acacias , in both data sets, clearly display significant differences in their S/G composition which distinguish them from eucalypts. In conclusion, this research shows the power of using Raman spectroscopy to supplant tedious, destructive methods for the evaluation of the lignin S/G ratio of diverse plant biomass materials.

09 BIOMASS FUELS↗
DOE OSTI2019

Additional value of systolic wall thickening in myocardial stunning evaluated by stress-rest gated perfusion SPECT

Aim and patients: The aim of the present study is to evaluate the additional value of systolic wall thickening to myocardial perfusion in diagnosing myocardial stunning in patients with angiography proven coronary artery disease. We selected 91 ischemic patients (82 males; mean age 59.7 ± 10.3) with CAD documented by angiography. Ischemia was defined as a summed difference score ≥5. All patients underwent a 2-day gated perfusion SPECT protocol. The patients received a dose of 740 MBq of {sup 99m}Tc-tetrofosmin after stress and at rest. Treadmill maximal exercise tests were performed on all patients. Results: The post-stress LVEF was significantly lower than rest LVEF (48.1% ± 10.3% vs 50.3% ± 10.7%; P = .0001). The wall thickening summed difference score was 4.44 ± 4.13 (P = .0001). At a multivariate regression analysis, only WT-SDS as independent variable was significantly correlated with myocardial ischemia (SDS). We also divided patients according to SDS in those with mild (SDS < 8) and severe (SDS ≥ 8) ischemia. WT-SDS, but not ∆LVEF, was significantly different between groups. Conclusions: WT-SDS, more than the depression in the global function (∆LVEF) of the left ventricle, correlates with the degree of ischemia and better identifies, when present, the stunning phenomenon.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

Solving numerically nonlinear systems of balance laws by multivariate sigmoidal functions approximation

A numerical method for solving systems of nonlinear one-dimensional balance laws, based on multivariate sigmoidal functions approximation, is developed. Constructive approximation theorems are first established in both, uniform and L{sup p} norms. A priori as well as a posteriori error estimates are derived for the numerical solutions, when various kinds of sigmoidal functions, such as unit step, logistic and Gompertz functions, are chosen. The residual of the numerical method is also estimated. Numerical examples are given to test the performance of the algorithm, a comparison with the Godunov method is made concerning accuracy and computational cost. Finally, the numerical stability of the method is analyzed.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Fast Fourier Transform for multivariate aggregate claims

The Fast Fourier Transform provides an alternative approximate method to evaluate the distribution of aggregate losses in insurance and finance. The efficiency of this method has already been proved for univariate and bivariate insurance models; therefore, in this paper, we extend it to a multivariate setting by considering its application to a particular model that includes losses of different types and dependency between them. Since the Fourier transform method works with truncated claims distributions, it can generate aliasing errors by wrapping around the probability mass that lies at the truncation point below this point. To eliminate this problem, we also discuss a suitable change of measure called exponential tilting that forces the tail of the distribution to decrease at exponential rate. Other possible errors are also discussed. We also illustrate the method on several numerical examples.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Knowledge-matching based computational framework for genome-scale metabolic model refinement

Genome-scale metabolic models (GEMs) are mathematically structured knowledge base reconstructed from annotated genome of different organisms. With the advancement of next-generation sequencing technology, many organisms have had their genomes sequenced. However, obtaining a high-quality GEM is highly time-consuming, even with the introduction of several genome-scale reconstruction tools that offer automated draft network generation and gap filling. It has been recognized that the iterative process of manual curation and refinement is the limiting step of GEM development, and how to expedite the GEM refinement is still an open question. As cellular metabolism is a complex system with very high degree of freedom and redundancy, the principles and techniques developed in process systems engineering can be adapted to expedite GEM refinement. In this paper we present a knowledge-matching based computation framework for GEM refinement, and demonstrate the effectiveness of the proposed solution using the refinement of a GEM for Clostridium tyrobutyricum.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Thermodynamics of an austenitic stainless steel (AISI-348) under in situ TEM heavy ion irradiation

The stability of the face-centred cubic austenite (γ-Fe) phase in a commercial stainless steel (AISI348) was investigated through in situ transmission electron microscopy (TEM) with heavy ion irradiation at 1073 K up to a fluence of 1.3×10 17 ions·cm -2 (corresponding to a dose of 46 dpa). The γ-Fe phase was observed to decompose at a fluence of around 7.8×10 15 ions·cm -2 (3 dpa) when a new phase nucleated and grew upon increasing irradiation dose. Scanning transmission electron microscopy (STEM) with energy dispersive X-ray (EDX) spectroscopy and multivariate statistical analysis (MVSA) were used to characterise the irradiated specimens. The combination of such experimental techniques with calculated equilibrium phase diagrams using the CALPHAD method led to the conclusion that the new phase formed upon irradiation is the body-centred cubic Cr-rich α 0 phase. At the nanoscale, precipitation of M 23 C 6 (τ-carbide) was also observed. The results indicate that ion irradiation can assist the austenitic stainless steel to reach a non-equilibrium state similar to a calculated equilibrium state observed at lower temperatures in which, under conventional conditions, is suppressed due to kinetic restrictions.

36 MATERIALS SCIENCE↗
DOE OSTI2018

Disordered crystals from first principles I: Quantifying the configuration space

This work represents the first chapter of a project on the foundations of first-principle calculations of the electron transport in crystals at finite temperatures. We are interested in the range of temperatures, where most electronic components operate, that is, room temperature and above. The aim is a predictive first-principle formalism that combines ab-initio molecular dynamics and a finite-temperature Kubo-formula for homogeneous thermodynamic phases. The input for this formula is the ergodic dynamical system (Ω,G,dP) defining the thermodynamic crystalline phase, where Ω is the configuration space for the atomic degrees of freedom, G is the space group acting on Ω and dP is the ergodic Gibbs measure relative to the G-action. The present work develops an algorithmic method for quantifying (Ω,G,dP) from first principles. Using the silicon crystal as a working example, we find the Gibbs measure to be extremely well characterized by a multivariate normal distribution, which can be quantified using a small number of parameters. The latter are computed at various temperatures and communicated in the form of a table. Using this table, one can generate large and accurate thermally-disordered atomic configurations to serve, for example, as input for subsequent simulations of the electronic degrees of freedom.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2025

Local practically safe extremum seeking with assignable rate of attractivity to the safe set

We present Assignably Safe Extremum Seeking (ASfES), an algorithm designed to minimize a measured, static objective function while maintaining a measured, static metric of safety (a control barrier function or CBF) to be positive in a practical sense. We ensure that for trajectories with safe initial conditions, the violation of safety can be made arbitrarily small through appropriately chosen design constants. We also guarantee an assignable “attractivity” rate: from unsafe initial conditions, the trajectories approach the safe set, in the sense of the measured CBF, at a rate no slower than a user-assigned rate. Similarly, from safe initial conditions, the trajectories approach the unsafe set, in the sense of the CBF, no faster than the assigned attractivity rate. The feature of assignable attractivity is not present in the semiglobal version of safe extremum seeking, where the semiglobality of convergence is achieved by slowing the adaptation. We also demonstrate local convergence of the parameter to a neighborhood of the minimum of a quadratic objective function constrained to the safe set with a linear CBF. The ASfES algorithm and analysis are multivariable, but we also extend the algorithm to a Newton-Based ASfES scheme which we show is only useful in the scalar case. The proven properties of the designs are illustrated through simulation examples.

42 ENGINEERING↗
DOE OSTI2015

Elevated expression of Thoc1 is associated with aggressive phenotype and poor prognosis in colorectal cancer

The THO complex 1 (Thoc1) is a nuclear matrix protein playing vital roles in transcription elongation and mRNA export. Recently, aberrant expression of Thoc1 has been reported in an increasing array of tumor types. However, the clinical significance of Thoc1 expression in colorectal cancer (CRC) is still unknown. The present study aimed to characterize the expression of Thoc1 in human CRC and evaluate its clinical significance. Quantitative real-time polymerase chain reaction (qRT-PCR) and Western blotting analyses showed that the mRNA and protein expression of Thoc1 in CRC specimens was significantly higher than that in adjacent normal colon mucosae. Immunohistochemistry (IHC) was conducted to characterize the expression pattern of Thoc1 in 185 archived paraffin-embedded CRC specimens. Statistical analyses revealed that high levels of Thoc1 expression were associated with the clinical stages and tumor differentiation. CRC patients with high levels of Thoc1 expression had poorer overall-survival and disease-free survival, whereas those with lower levels of Thoc1 expression survived longer. Furthermore, multivariate Cox regression analyses demonstrated that Thoc1 expression remained an independent prognostic factor for increased disease recurrence and decreased survival. Our results suggest for the first time that Thoc1 is involved in the development and progression of CRC, and elevated expression of Thoc1 is associated with aggressive phenotype and poor prognosis in CRC. These findings may prove to be clinically useful for developing a new therapeutic target of CRC treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

miR-625 suppresses cell proliferation and migration by targeting HMGA1 in breast cancer

Dysregulation of microRNA contributes to the high incidence and mortality of breast cancer. Here, we show that miR-625 was frequently down-regulated in breast cancer. Decrease of miR-625 was closely associated with estrogen receptor (P = 0.004), human epidermal growth factor receptor 2 (P = 0.003) and clinical stage (P = 0.001). Kaplan–Meier and multivariate analyses indicated miR-625 as an independent factor for unfavorable prognosis (hazard ratio = 2.654, 95% confident interval: 1.300–5.382, P = 0.007). Re-expression of miR-625 impeded, whereas knockdown of miR-625 enhanced cell viabilities and migration abilities in breast cancer cells. HMGA1 was confirmed as a direct target of miR-625. The expressions of HMGA1 mRNA and protein were induced by miR-625 mimics, but reduced by miR-625 inhibitor. Re-introduction of HMGA1 in cells expressing miR-625 distinctly abrogated miR-625-mediated inhibition of cell growth. Taken together, our data demonstrate that miR-625 suppresses cell proliferation and migration by targeting HMGA1 and suggest miR-625 as a promising prognostic biomarker and a potential therapeutic target for breast cancer. - Highlights: • miR-625 expression was significantly decreased in breast cancer. • Decrease of miR-625 was associated with poor clinical outcomes and unfavorable overall survival. • miR-625 overexpression inhibits cell proliferation and migration in vitro. • miR-625 directly targets and suppresses the expression of HMGA1.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Tenascin-C is a potential cancer-associated fibroblasts marker and predicts poor prognosis in prostate cancer

Tenascin-C (TNC), as a member of the extracellular matrix (ECM), plays an important role in cancer cell proliferation and migration and tumor invasion in various types of cancer. Here, we attempted to investigate the role of TNC as a prognostic factor in prostate cancer. We studied TNC expression via immunohistochemistry in 145 prostate cancer tissue samples. The clinicopathological relevance of TNC expression was examined, as well as other cancer-associated fibroblasts (CAFs)-related factors. Our results showed that the high levels of TNC expression in prostate cancer stroma was significantly associated with lymph node metastasis (P = 0.024) and clinical stage (P = 0.032). Furthermore, TNC was positively correlated with increased micro-vessel density (MVD) (P = 0.017) and tumor associated macrophage (TAM) population (P = 0.025). In both univariate and multivariate Cox regression analyses, TNC (P < 0.001) was an independent poor prognostic factor for overall survival in prostate cancer patients. Moreover, over-expression of TNC (P < 0.001), SMA (P = 0.042) and vimentin (P = 0.010) were significantly correlated with the lower overall survival. In addition, TNC expression in prostate cancer stroma was significantly associated with FSP1 (P = 0.011), SMA (P = 0.021), and vimentin (P = 0.002). In conclusion, our study revealed that high level of TNC as a potential biomarker of CAFs was significantly correlated with the poor prognosis for prostate cancer patients. - Highlights: • The cancer stroma carries a significant prognostic information. • TNC and CAFs are important in promoting tumor growth and metastasis. • CAFs could affect the invasive potential of cancer cells by producing TNC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Predicting concrete compressive strength using hybrid ensembling of surrogate machine learning models

This study aims to implement a hybrid ensemble surrogate machine learning technique in predicting the compressive strength (CS) of concrete, an important parameter used for durability design and service life prediction of concrete structures in civil engineering projects. For this purpose, an experimental database consisting of 1030 records has been compiled from the machine learning repository of the University of California, Irvine. The database was used to train and validate four conventional machine learning (CML) models, namely Artificial Neural Network (ANN), Linear and Non-Linear Multivariate Adaptive Regression Splines (MARS-L and MARS-C), Gaussian Process Regression (GPR), and Minimax Probability Machine Regression (MPMR). Subsequently, the predicted outputs of CML models were combined and trained using ANN to construct the Hybrid Ensemble Model (HENSM). It is observed that the proposed HENSM produces higher predictive accuracy compared to the CML models used in the present study. The predictive performance of all models for CS prediction was compared using the testing dataset and it is found that the HENSM model attained the highest predictive accuracy in both phases. Based on the experimental results, the newly constructed HENSM model is very potential to be a new alternative in handling the overfitting issues of CML models and hence, can be used to predict the concrete CS, including the design of less polluting and more sustainable concrete constructions.

36 MATERIALS SCIENCE↗
DOE OSTI2015

Multivariate pluvial flood damage models

Depth–damage-functions, relating the monetary flood damage to the depth of the inundation, are commonly used in the case of fluvial floods (floods caused by a river overflowing). We construct four multivariate damage models for pluvial floods (caused by extreme rainfall) by differentiating on the one hand between ground floor floods and basement floods and on the other hand between damage to residential buildings and damage to housing contents. We do not only take into account the effect of flood-depth on damage, but also incorporate the effects of non-hazard indicators (building characteristics, behavioural indicators and socio-economic variables). By using a Tobit-estimation technique on identified victims of pluvial floods in Flanders (Belgium), we take into account the effect of cases of reported zero damage. Our results show that the flood depth is an important predictor of damage, but with a diverging impact between ground floor floods and basement floods. Also non-hazard indicators are important. For example being aware of the risk just before the water enters the building reduces content damage considerably, underlining the importance of warning systems and policy in this case of pluvial floods. - Highlights: • Prediction of damage of pluvial floods using also non-hazard information • We include ‘no damage cases’ using a Tobit model. • The damage of flood depth is stronger for ground floor than for basement floods. • Non-hazard indicators are especially important for content damage. • Potential gain of policies that increase awareness of flood risks.

54 ENVIRONMENTAL SCIENCES↗