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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 613 records · Page 34
All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Mortality rates and prognostic factors in patients with malignant salivary tumors

Malignancies of the salivary glands represent a multifarious disease. Evaluating the prognostic factors of these malignancies may help predict patient outcome and aid decision-making in choosing the most suitable therapy. We examined the role of various salivary tumorigenic, clinical and therapeutic features in a cohort of 101 patients diagnosed and treated for primary malignant salivary tumors. These include histo-pathological diagnosis, stage, grade and T, N, M values as well as the existence of perineural invasion and extra-parenchymal spread. We also identified the salivary gland involved, the sub-compartment specific location of the tumor and the therapy administered. All these were related to mortality. Of the 101 patients examined, 79 survived and 22 died due to the disease. Tumor staging, distant metastasis and perineural invasion were highly significant predictors of increased lethality. Histo-pathological grading was also a predictor but to a lesser degree. Neither neck metastasis nor tumor size or type had a significant impact on lethality. Performing neck dissections did not decrease lethality rate. Location of the tumor in the parotid gland and more so in its deep lobe adversely affected lethality; extra-parenchymal spread also had an adverse effect. Our results seem to indicate hematogenous rather than lymphogenous spread of metastasis from malignant salivary tumors. The highest therapeutic priority should be achieving full local control of the disease by safe removal of the primary salivary tumor, accompanied by regional control of perineural invasion and extra-parenchymal spread and appropriate systemic treatment aimed at eradicating distant metastasis.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2019

Adenosine stress myocardial perfusion imaging in octogenarians: Safety, tolerability, and long-term prognostic implications of hemodynamic response and SPECT-related variables

Background: Evaluation of tolerability, safety, and prognostic implications of adenosine stress myocardial perfusion imaging (MPI) in octogenarians. Methods: 370 octogenarians (49% known coronary artery disease) were studied. Hemodynamic response, MPI-related data, and rest-left ventricular ejection fraction (LVEF) based on echocardiography were registered per patient, and prospective follow-up was performed to document all-cause death (ACD), cardiac death (CD), myocardial infarction (MI), and late revascularization. Results: No deaths or MIs were observed during adenosine infusion or the short-term post-infusion period. 86% of patients were able to tolerate a 6-minute infusion. All side effects terminated spontaneously after infusion cessation, except for one case of pulmonary oedema. After 9.3 years, there were 124 ACDs, 62 CDs, 16 MIs, and 35 revascularizations. Differences between survival curves of summed stress score (SSS)-based risk groups were significant for all end points (P < .001). SSS and LVEF were independent predictors of all end points (P ≤ .01) and lung uptake of cardiac end points. ΔHR <10 bpm (OR = 1.78, P = .004) and inability to increase HR by >10 bpm and decrease systolic blood pressure by >10 mmHg (OR = 2, P = .02) during adenosine infusion were independent predictors of ACD and CD, respectively. Hemodynamic response variables, SSS, and lung uptake provided incremental prognostic value over pre-test data for ACD and CD. Conclusions: In octogenarians, adenosine stress MPI is well tolerated and provides effective long-term risk stratification.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

An efficient computational algorithm for pricing European, barrier and American options

In this work, we use the spectral collocation technique for spatial derivatives and predictor–corrector method for time integration to solve the Black–Scholes (B–S) equation. If the spectral collocation method is worked properly, then we get high accuracy in the numerical solutions. Firstly, theory of application of Chebyshev spectral collocation technique and domain decomposition method on the B–S equation is presented. This method gets a stiff system of differential equations. Secondly, by using the predictor–corrector method with variable step size, we obtain the high accuracy approximate solution in the numerical integration of the stiff system of DEs. We present the order of accuracy for the proposed method. The numerical results show the efficiency and validity of the method.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Hypothesis-Agnostic Network-Based Analysis of Real-World Data Suggests Ondansetron is Associated with Lower COVID-19 Any Cause Mortality

Background: The COVID-19 pandemic generated a massive amount of clinical data, which potentially hold yet undiscovered answers related to COVID-19 morbidity, mortality, long-term effects, and therapeutic solutions.Objectives: The objectives of this study were (1) to identify novel predictors of COVID-19 any cause mortality by employing artificial intelligence analytics on real-world data through a hypothesis-agnostic approach and (2) to determine if these effects are maintained after adjusting for potential confounders and to what degree they are moderated by other variables.Methods: A Bayesian statistics-based artificial intelligence data analytics tool (bAIcis®) within the Interrogative Biology® platform was used for Bayesian network learning and hypothesis generation to analyze 16,277 PCR+ patients from a database of 279,281 inpatients and outpatients tested for SARS-CoV-2 infection by antigen, antibody, or PCR methods during the first pandemic year in Central Florida. This approach generated Bayesian networks that enabled unbiased identification of significant predictors of any cause mortality for specific COVID-19 patient populations. These findings were further analyzed by logistic regression, regression by least absolute shrinkage and selection operator, and bootstrapping.Results: We found that in the COVID-19 PCR+ patient cohort, early use of the antiemetic agent ondansetron was associated with decreased any cause mortality 30 days post-PCR+ testing in mechanically ventilated patients.Conclusions: The results demonstrate how a real-world COVID-19-focused data analysis using artificial intelligence can generate unexpected yet valid insights that could possibly support clinical decision making and minimize the future loss of lives and resources.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Relative effects of enthalpy and entropy on the phase stability of equiatomic high-entropy alloys

High configurational entropies have been hypothesized to stabilize solid solutions in equiatomic, multi-element alloys which have attracted much attention recently as high-entropy alloys with potentially interesting properties. To evaluate the usefulness of configurational entropy as a predictor of single-phase (solid solution) stability, we prepared five new equiatomic, quinary alloys by replacing individual elements one at a time in a CoCrFeMnNi alloy that was previously shown to be single-phase [1]. An implicit assumption here is that, if any one element is replaced by another, while keeping the total number of elements constant, the configurational entropy of the alloy is unchanged; therefore, the new alloys should also be single-phase. Additionally, the substitute elements that we chose, Ti for Co, Mo or V for Cr, V for Fe, and Cu for Ni, had the same room-temperature crystal structure and comparable size/electronegativity as the elements being replaced to maximize solid solubility consistent with the Hume-Rothery rules. For comparison, the base CoCrFeMnNi alloy was also prepared. After three-day anneals at elevated temperatures, multiple phases were observed in all but the base CoCrFeMnNi alloy suggesting that, by itself, configurational entropy is generally not able to override competing driving forces that also govern phase stability. Thermodynamic analyses were carried out for each of the constituent binaries in the investigated alloys (Co-Cr, Fe-Ni, Mo-Mn, etc,). Experimental results combined with the thermodynamic analyses suggest that, in general, enthalpy and non-configurational entropy have bigger influences on phase stability in equiatomic, multi-component alloys. Only when the alloy microstructure is a single-phase, approximately ideal solid solution does the contribution of configurational entropy to the total Gibbs free energy become dominant. Thus, high configurational entropy provides a way to rationalize, after the fact, why a solid solution forms (if it forms) but it is not a useful a priori predictor of which of the so-called high-entropy alloys will form single-phase solid solutions.

High-entropy alloys↗
DOE OSTI2019

Impact of ethanol blending into gasoline on aromatic compound evaporation and particle emissions from a gasoline direct injection engine

This study examined the interactions of ethanol with aromatic compounds on aromatic species evolution during gasoline evaporation and the consequent impacts on particle emissions from a single-cylinder, gasoline direct injection engine. From a chemical kinetic standpoint, the dilution of the aromatic species in gasoline by ethanol blending should reduce particle emissions because aromatics are the primary species that form particles during gasoline combustion. However, increased evaporative cooling from the ethanol and ethanol's effect on when aromatics in the fuel droplet (or surface film or pool) evaporate can drive an increase in particle emissions. The objective of this study was to quantify these competing effects. The results show that a combination of ethanol's increased evaporative cooling and impact on aromatic compound vapor-liquid equilibrium can extend droplet lifetime and cause aromatics to evaporate later in the droplet evaporation process than would be the case without ethanol. When fuels were burned in a direct-injection single-cylinder engine at high speed (2500 rpm) and relatively high load, ethanol blending caused an increase in particle emissions for fuels containing a low vapor pressure aromatic. At a lower speed (1500 rpm), no statistically significant increase in particle emissions was observed, likely because more time was available for evaporation and mixing to occur. In contrast, particle emissions from a fuel blended with a high vapor pressure aromatic were either insensitive to or reduced by ethanol blending, likewise to engine operating conditions. Current models for predicting particle emissions from gasoline engines, such as particulate matter index (PMI), generally lack variables for non-linear interactions between ethanol and gasoline hydrocarbons. To identify nonlinear interactions between predictor variables that might better explain the observed particle emissions, the least absolute shrinkage and selection operator (LASSO) regularized regression approach was employed. Among the predictor variables evaluated was the Yield Sooting Index (YSI) which is as an improved measure to quantify the aromatic's chemical tendency to generate particle emissions. Following this analysis, two variables were selected based on their frequent appearance. The first was ethanol mole percent multiplied by aromatic mole percent divided by aromatic vapor pressure [(EtOH * Aro%)/Aro VP@443 K]. The second was aromatic mole percent multiplied by aromatic YSI divided by aromatic molecular weight [(Aro% * Aro YSI)/Aro MW]. The results of linear regression including only these two combined explanatory variables achieved an r 2 = 0.959, substantially improved over the PMI model (r 2 = 0.688) and provides insight into how the PMI formalism might be modified to account for non-linear oxygenate-hydrocarbon coupling.

09 BIOMASS FUELS↗
DOE OSTI2013

In silico analysis of stomach lineage specific gene set expression pattern in gastric cancer

Highlights: •Identified stomach lineage specific gene set (SLSGS) was found to be under expressed in gastric tumors. •Elevated expression of SLSGS in gastric tumor is a molecular predictor of metabolic type gastric cancer. •In silico pathway scanning identified estrogen-α signaling is a putative regulator of SLSGS in gastric cancer. •Elevated expression of SLSGS in GC is associated with an overall increase in the survival of GC patients. -- Abstract: Stomach lineage specific gene products act as a protective barrier in the normal stomach and their expression maintains the normal physiological processes, cellular integrity and morphology of the gastric wall. However, the regulation of stomach lineage specific genes in gastric cancer (GC) is far less clear. In the present study, we sought to investigate the role and regulation of stomach lineage specific gene set (SLSGS) in GC. SLSGS was identified by comparing the mRNA expression profiles of normal stomach tissue with other organ tissue. The obtained SLSGS was found to be under expressed in gastric tumors. Functional annotation analysis revealed that the SLSGS was enriched for digestive function and gastric epithelial maintenance. Employing a single sample prediction method across GC mRNA expression profiles identified the under expression of SLSGS in proliferative type and invasive type gastric tumors compared to the metabolic type gastric tumors. Integrative pathway activation prediction analysis revealed a close association between estrogen-α signaling and SLSGS expression pattern in GC. Elevated expression of SLSGS in GC is associated with an overall increase in the survival of GC patients. In conclusion, our results highlight that estrogen mediated regulation of SLSGS in gastric tumor is a molecular predictor of metabolic type GC and prognostic factor in GC.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Activation of IRE1α-XBP1 pathway induces cell proliferation and invasion in colorectal carcinoma

Cell proliferation and tumor metastasis are considered as the main reasons for death in colorectal carcinoma (CRC). IRE1α-XBP1 pathway is the most conserved UPR pathways, which are activated during ER stress caused by the accumulation of unfolded or misfolded protein in the lumen of ER. Here, we demonstrated the critical role of IRE1α-XBP1 pathway and underlying molecular mechanism in cell proliferation and tumor metastasis in CRC. By the use of tissue microarray analysis of samples from 119 patients with CRC, IRE1α was determined to be an independent predictor of overall survival as higher expression of IRE1α in CRC patients showed lower survival rates (p = 0.0041). RNA interference and ectopic expression of IRE1α were applied to determine the molecular effects of IRE1α in CRC cells. The silencing of IRE1α inhibited the proliferation and blocked the invasion of CRC cells in vitro, while ectopic expression of IRE1α in turn promoted cell proliferation and invasion. IRE1α-XBP1 pathway regulated the mitosis of CRC cells through the directly binding of XBP1s to Cyclin D1 promoter to activate Cyclin D1 expression. Our results reveal that IRE1α-XBP1 pathway plays an important role in tumor progression and epithelial-to-mesenchymal transition (EMT), and IRE1α could be employed as a novel prognostic marker and a promising therapeutic target for CRC. - Highlights: • IRE1 was determined to be an independent predictor of overall survival in CRC patient. • IRE1-XBP1 pathway promoted CRC cell proliferation through regulating Cyclin D1 expression. • IRE1-XBP1 pathway played important role in EMT of CRC cells.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Downregulation of caveolin-1 increased EGFR-TKIs sensitivity in lung adenocarcinoma cell line with EGFR mutation

Highlights: • Knockdown of Cav-1 enhanced EGFR-TKIs sensitivity in PC-9 cells. • Knockdown of Cav-1 inhibited biological behavior of PC-9 cells. • Cav-1 functions as a predictor for poor efficacy of EGFR-TKIs in human lung adenocarcinoma. Although epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), including gefitinib and erlotinib, have shown notable effects in lung adenocarcinoma patients harboring EGFR mutations, there are significant differences between individual patients in the degree of benefits provided by EGFR-TKIs. Some evidence supports a role for caveolin-1 (Cav-1) in modulating drug sensitivity. This study aimed to investigate whether Cav-1 plays an important role in sensitivity to EGFR-TKIs in lung adenocarcinoma cells. Downregulation of Cav-1 in PC-9 cells were performed to investigate changes in sensitivity to EGFR-TKIs in vitro and in vivo. Knockdown of Cav-1 dramatically enhanced sensitivity to EGFR-TKIs by down-regulating phosphorylation of EGFR. These results suggest that Cav-1 may be a predictor of the poor efficacy of EGFR-TKIs treatment in lung adenocarcinoma with EGFR mutations.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Two-stage ignition behavior and octane sensitivity of toluene reference fuels as gasoline surrogate

Current approaches to improve the efficiency of Spark-Ignition (SI) gasoline engines have been focusing on turbocharging, increasing the compression ratio, and pursuing advanced low-temperature combustion concepts. In order to maximize these strategies, it is important to optimize the knock resistance of the fuel, and therefore knowledge of the sensitivity of the ignition process under a wide range of engine operating conditions is required. Octane sensitivity (OS), which is defined as the difference between Research Octane Number (RON) and Motored Octane Number (MON), has been introduced to represent how fuel's ignition reactivity changes relative to the primary reference fuels (n-heptane/iso-octane) within RON/MON conditions. Previous works have indicated that OS is intimately related to low temperature reactivity of the fuel, which can be revealed as two-stage heat release characteristics during an ignition event. Prompted by these findings, in this paper, we investigate the relationship between two-stage ignition behavior and OS, using chemical kinetic simulations of 24 Toluene Reference Fuels (TRFs)/ethanol blends. TRFs are ternary mixtures of n-heptane/iso-octane/toluene, which is capable of capturing aromatic content and positive values of OS of real gasoline fuels. Simulation results show that fuels with weak or no two-stage ignition behavior tend to have high OS, due to their lack of Negative Temperature Coefficient (NTC) effect and high sensitivity in ignition delay time. Leveraging such observations, we develop a correlation between two-stage behavior and OS as an OS prediction method. Two metrics that represent the strength of the two-stage ignition behavior are proposed and used as OS predictors, which are Low Temperature Heat Release percentage (LTHR%) and Heat Release Rate at the end of first stage (HRR inf ) calculated from a simple kinetic simulation. Regression analysis shows a clear trend between decreases in the proposed two-stage behavior metrics and increases in the value of OS of the fuel. We also test the new metric (LTHR%) using simulation results of 0-D reactors with imposed pressure time histories obtained from engine experiments, as well as using different TRF kinetic mechanisms. Here, the results demonstrate the effectiveness of the metric as a representation of the two-stage ignition behavior in practical combustion systems, highlighting the importance of the proposed relationship, and its potential as a simple and effective OS predictor.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2019

9Cr steel visualization and predictive modeling

The goals of this work were to develop tools to visualize and characterize materials information, to explore new alloy compositions and/or processing, to better predict tensile strength and other mechanical properties. The 9–12 wt% Cr (9Cr) steel test data were compiled from several sources. About 3000 records with 30 predictor variables represent 82 unique steel alloy compositions, variations of thermo-mechanical processing steps and temperatures (homogenization, normalization and tempering cycles), test conditions and outcomes. Detailed data processing steps such as visualization and exploratory analysis, including univariate and bivariate analysis, different clustering techniques used to segment the data, statistical post-hoc analysis to verify significance of the findings, feature engineering to identify predictors of importance, and predictive modeling with cross-validation were performed. The outcome of analysis at each step was reviewed in the context of the domain knowledge of this class of steel, to see if there were underlying physical mechanisms that explain statistical relationships. Data analytics techniques and their parameters were fine-tuned to facilitate interpretation of the results as aligned with the insights from domain experts. Here, the ensemble predictive modeling using Random Forest regressor and post-hoc means comparison corroborated domain knowledge on the role of Co which is known to increase strength of steel alloys through solid-solution strengthening and to affect diffusion of alloying elements and precipitates. Alloys with Co content of 0.7–8 wt% had significantly higher mean strength than the ones without (while having Cr locked within 10–11 wt% range in either group and keeping other compositional element ratios fixed). End products of the computational techniques exploration are presented as the tools that can be used in iterative workflow of materials development and testing.

36 MATERIALS SCIENCE↗
DOE OSTI2026

Leveraging large language models to address data scarcity in machine learning for graphene synthesis

Machine learning in experimental materials science faces significant challenges due to the scarcity of data, which are costly and time-consuming to generate, particularly when relying on in-house experiments. Literature data mining offers a potential solution but introduces issues like mixed data quality, inconsistent formats, and non-uniform reporting of synthesis parameters, resulting in partially missing and heterogeneous features across the dataset. Here, we propose data imputation and feature engineering methods that employ pre-trained large language models (LLMs) to enhance machine learning performance on scarce, heterogeneous datasets, demonstrated on graphene CVD synthesis data and the ML-HydPARK hydrogen storage dataset. GPT models perform data imputation via tailored prompting and semantic normalization of inconsistently reported features through embeddings, for example, to harmonize the complex nomenclature of CVD substrates. Beyond yielding more diverse and richer feature representations than traditional methods such as K-nearest neighbors (KNN) and Multivariate Imputation by Chained Equations (MICE), LLM-based data imputation is evaluated against dataset characteristics and prompting strategies. We vary the level of autonomy granted to the LLM, from generic prompting that leverages pre-trained knowledge for autonomous data generation to data-informed prompting that constrains outputs using target-specific information, and demonstrate which level of autonomy yields superior imputation performance across datasets and feature types. The proposed data engineering methods markedly improve downstream performance; for example, in graphene layer number classification using a support vector machine (SVM), binary accuracy increases from 39% to 65% and ternary accuracy from 52% to 72%. Fine-tuning experiments on both datasets show that combining our proposed LLM-based data imputation and feature encoding methods with numerical machine learning predictors outperforms standalone fine-tuned LLM predictors in data-scarce settings. The proposed strategies emphasize data enhancement techniques rather than refining learning architectures or regularizing loss functions, offering a broadly applicable framework for improving machine learning performance on scarce, inhomogeneous datasets.

Chemical vapor deposition↗
DOE OSTI2015

Prediction of the visual impact of motorways using GIS

Large scale transportation projects can adversely affect the visual perception of environmental quality and require adequate visual impact assessment. In this study, we investigated the effects of the characteristics of the road project and the character of the existing landscape on the perceived visual impact of motorways, and developed a GIS-based prediction model based on the findings. An online survey using computer-visualised scenes of different motorway and landscape scenarios was carried out to obtain perception-based judgements on the visual impact. Motorway scenarios simulated included the baseline scenario without road, original motorway, motorways with timber noise barriers, transparent noise barriers and tree screen; different landscape scenarios were created by changing land cover of buildings and trees in three distance zones. The landscape content of each scene was measured in GIS. The result shows that presence of a motorway especially with the timber barrier significantly decreases the visual quality of the view. The resulted visual impact tends to be lower where it is less visually pleasant with more buildings in the view, and can be slightly reduced by the visual absorption effect of the scattered trees between the motorway and the viewpoint. Based on the survey result, eleven predictors were identified for the visual impact prediction model which was applied in GIS to generate maps of visual impact of motorways in different scenarios. The proposed prediction model can be used to achieve efficient and reliable assessment of visual impact of motorways. - Highlights: • Motorways induce significant visual impact especially with timber noise barriers. • Visual impact is negatively correlated with amount of buildings in the view. • Visual impact is positively correlated with percentage of trees in the view. • Perception-based motorway visual impact prediction model using mapped predictors • Predicted visual impacts in different scenarios are mapped in GIS.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2012

Transformation of Southern California's Residential Photovoltaics Market Through Third-Party Ownership

Third-party photovoltaics (PV) ownership is a rapidly growing market trend, where commercial companies own and operate customer-sited PV systems and lease PV equipment or sell PV electricity to the building occupant. Third-party PV companies can reduce or eliminate up-front adoption costs, reduce technology risk and complexity by monitoring system performance, and can repackage the PV value proposition by showing cost savings in the first month of ownership rather than payback times on the order of a decade. We find that the entrance of third-party business models in southern California residential PV markets has enticed a new demographic to adopt PV systems that is more highly correlated to younger, less affluent, and less educated populations than the demographics correlated to purchasing PV systems. By enticing new demographics to adopt PV, we find that third-party PV products are likely increasing total PV demand rather than gaining market share entirely at the expense of existing customer owned PV demand. We also find that mean population demographics are good predictors of third-party and customer owned PV adoption, and mean voting trends on California carbon policy (Proposition 23) are poor predictors of PV adoption.

14 SOLAR ENERGY↗
DOE OSTI2015

Examining confounding by diet in the association between perfluoroalkyl acids and serum cholesterol in pregnancy

Background: Perfluorooctane sulfonate (PFOS) and perfluorooctanoic acid (PFOA) have consistently been associated with higher cholesterol levels in cross sectional studies. Concerns have, however, been raised about potential confounding by diet and clinical relevance. Objective: To examine the association between concentrations of PFOS and PFOA and total cholesterol in serum during pregnancy taking into considerations confounding by diet. Methods: 854 Danish women who gave birth in 1988–89 and provided a blood sample and reported their diet in week 30 of gestation. Results: Mean serum PFOS, PFOA and total cholesterol concentrations were 22.3 ng/mL, 4.1 ng/mL and 7.3 mmol/L, respectively. Maternal diet was a significant predictor of serum PFOS and PFOA concentrations. In particular intake of meat and meat products was positively associated while intake of vegetables was inversely associated (P for trend <0.01) with relative difference between the highest and lowest quartile in PFOS and PFOA concentrations ranging between 6% and 25% of mean values. After adjustment for dietary factors both PFOA and PFOS were positively and similarly associated with serum cholesterol (P for trend ≤0.01). For example, the mean increase in serum cholesterol was 0.39 mmol/L (95%CI: 0.09, 0.68) when comparing women in the highest to lowest quintile of PFOA concentrations. In comparison the mean increase in serum cholesterol was 0.61 mmol/L (95%CI: 0.17, 1.05) when comparing women in the highest to lowest quintile of saturated fat intake. Conclusion: In this study associations between PFOS and PFOA with serum cholesterol appeared unrelated to dietary intake and were similar in magnitude as the associations between saturated fat intake and serum cholesterol. - Highlights: • PFOS and PFOA have consistently been linked with raised serum cholesterol • Clinical relevance remains uncertain and confounding by diet has been suggested • The aim of this study was to address these issues in a cohort of 854 pregnant women • With detailed dietary information we found no indication of such confounding • As predictors cholesterol PFOSA and PFOA were similar to other known risk factors.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Thyroid antagonists and thyroid indicators in U.S. pregnant women in the Vanguard Study of the National Children's Study

The sodium iodide-symporter (NIS) mediates uptake of iodide into thyroid follicular cells. This key step in thyroid hormone synthesis is inhibited by perchlorate, thiocyanate (SCN) and nitrate (NO{sub 3}) anions. When these exposures occur during pregnancy the resulting decreases in thyroid hormones may adversely affect neurodevelopment of the human fetus. Our objectives were to describe and examine the relationship of these anions to the serum thyroid indicators, thyroid stimulating hormone (TSH) and free thyroxine (FT4), in third trimester women from the initial Vanguard Study of the National Children's Study (NCS); and to compare urine perchlorate results with those in pregnant women from the National Health and Nutritional Examination Survey (NHANES). Urinary perchlorate, SCN, NO{sub 3}, and iodine, serum TSH, FT4, and cotinine were measured and a food frequency questionnaire (FFQ) was administered to pregnant women enrolled in the initial Vanguard Study. We used multiple regression models of FT4 and TSH that included perchlorate equivalent concentration (PEC, which estimates combined inhibitory effects of the anions perchlorate, SCN, and NO{sub 3} on the NIS). We used multiple regression to model predictors of each urinary anion, using FFQ results, drinking water source, season of year, smoking status, and demographic characteristics. Descriptive statistics were calculated for pregnant women in NHANES 2001–2012. The geometric mean (GM) for urinary perchlorate was 4.04 µg/L, for TSH 1.46 mIU/L, and the arithmetic mean for FT4 1.11 ng/dL in 359 NCS women. In 330 women with completed FFQs, consumption of leafy greens, winter season, and Hispanic ethnicity were significant predictors of higher urinary perchlorate, which differed significantly by study site and primary drinking water source, and bottled water was associated with higher urinary perchlorate compared to filtered tap water. Leafy greens consumption was associated with higher urinary NO{sub 3} and higher urinary SCN. There was no association between urinary perchlorate or PEC and TSH or FT4, even for women with urinary iodine <100 µg/L. GM urinary perchlorate concentrations in the full sample (n=494) of third trimester NCS women (4.03 µg/L) were similar to pregnant women in NHANES (3.58 µg/L). - Highlights: • Limited biomonitoring data are available in pregnant women. • We found no effect of perchlorate and related anions on thyroid indicators in third trimester women. • Leafy green vegetables were a dietary source for perchlorate, nitrate, and thiocyanate.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2017

Blood cadmium concentrations and environmental exposure sources in newcomer South and East Asian women in the Greater Toronto Area, Canada

Background: Immigrant women are often identified as being particularly vulnerable to environmental exposures and health effects. The availability of biomonitoring data on newcomers is limited, thus, presenting a challenge to public health practitioners in the identification of priorities for intervention. Objectives: In fulfillment of data needs, the purpose of this study was to characterize blood concentrations of cadmium (Cd) among newcomer women of reproductive age (19–45 years of age) living in the Greater Toronto Area (GTA), Canada and to assess potential sources of environmental exposures. Methods: A community-based model, engaging peer researchers from the communities of interest, was used for recruitment and follow-up purposes. Blood samples were taken from a total of 211 newcomer women from South and East Asia, representing primary, regional origins of immigrants to the GTA, and environmental exposure sources were assessed via telephone survey. Metal concentrations were measured in blood samples (diluted with 0.5% (v/v) ammonium hydroxide and 0.1% (v/v) octylphenol ethoxylate) using a quadrupole ICP-MS. Survey questions addressed a wide range of environmental exposure sources, including dietary and smoking patterns and use of nutritional supplements, herbal products and cosmetics. Results: A geometric mean (GM) blood Cd concentration of 0.39 µg/L (SD:±2.07 µg/L) was determined for study participants (min/max: <0.045 µg /L (LOD)/2.36 µg/L). Several variables including low educational attainment (Relative Ratio (RR) (adjusted)=1.50; 95% CI 1.17–1.91), milk consumption (RR (adjusted)=0.86; 95% CI 0.76–0.97), and use of zinc supplements (RR (adjusted)=0.76; 95% CI 0.64–0.95) were observed to be significantly associated with blood Cd concentrations in the adjusted regression model. The variable domains socioeconomic status (R{sup 2}{sub adj}=0.11) and country of origin (R{sup 2}{sub adj}=0.236) were the strongest predictors of blood Cd. Conclusion: Blood Cd concentrations fell below those generally considered to be of human health concern. However, negative health effects cannot be entirely excluded, especially for those that fall in the upper percentile range of the distribution, given the mounting evidence for negative health outcomes at low environmental exposure concentrations. - Highlights: • Blood Cd and exposure sources were assessed for 211 newcomer women in Toronto. • Blood Cd was slightly elevated compared to that in Canadian-born populations. • SES and country of origin were the strongest predictors of blood Cd. • Intake of essential micronutrients is a likely important modulator of blood Cd. • Identified need for more research on newcomers as a function of country of origin.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2015

Edaphic controls on soil organic carbon stocks in restored grasslands

Cultivation of undisturbed soils dramatically depletes organic carbon stocks at shallow depths, releasing a substantial quantity of stored carbon to the atmosphere. Restoration of native ecosystems can help degraded soils rebuild a portion of the depleted soil organic matter. However, the rate and magnitude of soil carbon accrual can be highly variable from site to site. Thus, a better understanding of the mechanisms controlling soil organic carbon stocks is necessary to improve predictions of soil carbon recovery. We measured soil organic carbon stocks and a suite of edaphic factors in the upper 10 cm of a series of restored tallgrass prairies representing a range of drainage conditions. Our findings suggest that factors related to soil organic matter stabilization mechanisms (texture, polyvalent cations) were key predictors of soil organic carbon, along with variables that influence plant and microbial biomass (available phosphorus, pH) and soil moisture. Exchangeable soil calcium was the strongest single predictor, explaining 74% of the variation in soil organic carbon, followed by clay content,which explained 52% of the variation. Our results demonstrate that the cumulative effects of even relatively small differences in these edaphic properties can have a large impact on soil carbon stocks when integrated over several decades.

Calcium↗