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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Temporal Analysis and Scene Change Detection in Multispectral Overhead Imagery

Scene change detection can be a tedious and time consuming process especially when concerning large geographical areas, and the process can be even more cumbersome when analyzing changes in an area over large spans of time. Developing a useful way to help analysts recognize at what points in time significant changes to a scene have occurred can allow them to better focus their efforts in characterizing events. Applications include: Facility monitoring, Construction chronology, Monitoring of vehicle/aircraft activity, Characterization of larger sequences of events. In large areas exceeding hundreds to thousands of square kilometers in size, it can be difficult localizing when scene changes have occurred. Analysts can spend hours going through imagery to try to identify new construction, monitor facility activities, monitor vehicle movement, etc. where the object of interest may only be a few square meters. Our goal is to help cut down this time by giving analysts change maps with hot spots of change, allowing them to focus on regions that have experienced actual change in time frames they're interested in. Additionally, by combining these change maps into layers within a data cube, analysts can examine the change maps from a temporal perspective, allowing events to be characterized over spans of time. By opening the data cube in an imaging software capable of separating the layers, we can analyze the change maps sequentially, allowing us to examine scene changes occurring over time. As an example, we examined overhead imagery from Planet Labs of what appears to be a parking lot on Fort Irwin over the course of a year using ENVI, a geospatial satellite imagery analysis software. Using ENVI, we generate a graph of changes over time, and notice a particular segment near the end of our analysis window where no changes are detected. Examination of the actual satellite imagery reveals that during this time span, the parking lot was empty. This could be due to facility shutdown for maintenance or upgrades, or possibly even total workforce/vehicle fleet movement. Information like this could help analysts better characterize events, as well as to help create clearer timelines in larger sequences of events. Workflow steps: - Collect multiple maps of the same AOI (Area of Interest) during a time span of interest; - Generate change maps from AOI maps; - Generate data cube from change maps. An analyst can use the data cube to help inspect an AOI for activities within a time span of interest. If an event of interest is discovered, the analyst can then refer to the maps corresponding to the appropriate dates and times in the data cube to see precisely what is transpiring. The biggest objective being worked on is improving the change detection methodology employed. We currently use PCA-EM (Principal Component Analysis with Expectation Maximization), but we are currently focusing on implementing IR-MAD (Iteratively Reweighted Multivariate Alteration Detection) to be used in conjunction with PCA-EM in an effort to decrease false positivity and noise in the change maps we generate.

42 ENGINEERING↗
DOE OSTI2018

Part 2: Forensic attribution profiling of Russian VX in food using liquid chromatography-mass spectrometry

This work is part two of a three-part series in this issue of a Sweden-United States collaborative effort towards the understanding of the chemical attribution signatures of Russian VX (VR) in synthesized samples and complex food matrices. We describe the sourcing of VR present in food based on chemical analysis of attribution signatures by liquid chromatography-tandem mass spectrometry (LC-MS/MS) combined with multivariate data analysis. Analytical data was acquired from seven different foods spiked with VR batches that were synthesized via six different routes in two separate laboratories. The synthesis products were spiked at a lethal dose into seven food matrices: water, orange juice, apple purée, baby food, pea purée, liquid eggs and hot dog. After acetonitrile sample extraction, the samples were analyzed by LC-MS/MS operated in MRM mode. A multivariate statistical calibration model was built on the chemical attribution profiles from 118 VR spiked food samples. Using the model, an external test-set of the six synthesis routes employed for VR production was correctly identified with no observable major impact of the food matrices to the classification. The overall performance of the statistical models was found to be exceptional (94%) for the test set samples retrospectively classified to their synthesis routes.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1975

A method of using cluster analysis to study statistical dependence in multivariate data

A technique is presented that uses both cluster analysis and a Monte Carlo significance test of clusters to discover associations between variables in multidimensional data. The method is applied to an example of a noisy function in three-dimensional space, to a sample from a mixture of three bivariate normal distributions, and to the well-known Fisher's Iris data.

Borucki, W. J.↗
NASA NTRS2009

Regression Model Optimization for the Analysis of Experimental Data

A candidate math model search algorithm was developed at Ames Research Center that determines a recommended math model for the multivariate regression analysis of experimental data. The search algorithm is applicable to classical regression analysis problems as well as wind tunnel strain gage balance calibration analysis applications. The algorithm compares the predictive capability of different regression models using the standard deviation of the PRESS residuals of the responses as a search metric. This search metric is minimized during the search. Singular value decomposition is used during the search to reject math models that lead to a singular solution of the regression analysis problem. Two threshold dependent constraints are also applied. The first constraint rejects math models with insignificant terms. The second constraint rejects math models with near-linear dependencies between terms. The math term hierarchy rule may also be applied as an optional constraint during or after the candidate math model search. The final term selection of the recommended math model depends on the regressor and response values of the data set, the user s function class combination choice, the user s constraint selections, and the result of the search metric minimization. A frequently used regression analysis example from the literature is used to illustrate the application of the search algorithm to experimental data.

Ulbrich, N.↗
NASA NTRS2021

Salvaging Data Records with Missing Data: Data Imputation using the Multivariate t Distribution

When doing multivariate data analysis, one commonobstacle is the presence of incomplete observations, i.e., observationsfor which one or more key fields are blank. Missing datais often countered by deleting entire observations that containmissing data. The negative effects of deleting entire observationsare multiple: deleting observations reduces sample size andcan also result in biased inferences even if data is missing atrandom. In addition, knowledge contained within incompleteobservations is knowledge lost when they are deleted– and theeffort spent collecting that knowledge is effort wasted. Data imputationmethods, or methods of statistically “filling-in” missingdata, can help combat small sample sizes by using the existinginformation in partially complete observations with the end goalof producing less biased and higher confidence inferences. Whena sample from a multivariate normal population is only partiallycomplete, and the missing data meets appropriate assumptions(missing at random), robust data imputation of the missing datacan be implemented with monotone data augmentation (MDA)using the multivariate t distribution.Missing data imputation is applied to data from the NASA InstrumentCost Model (NICM) using the MDA algorithm underthe assumption of having a multivariate t distribution with fixeddegrees of freedom. A sensitivity analysis to the degrees offreedom parameter is presented to demonstrate robustness ofthe multivariate t distribution when dealing with small samplesas compared to the multivariate normal distribution.

DiNicola, Michael↗
NASA NTRS1985

Proceedings of the Third Annual Symposium on Mathematical Pattern Recognition and Image Analysis

Topics addressed include: multivariate spline method; normal mixture analysis applied to remote sensing; image data analysis; classifications in spatially correlated environments; probability density functions; graphical nonparametric methods; subpixel registration analysis; hypothesis integration in image understanding systems; rectification of satellite scanner imagery; spatial variation in remotely sensed images; smooth multidimensional interpolation; and optimal frequency domain textural edge detection filters.

Guseman, L. F., Jr.↗
NASA NTRS1995

A multiparametric analysis of the Einstein sample of early-type galaxies. 2: Galaxy formation history and properties of the interstellar medium

We have conducted bivariate and multivariate statistical analysis of data measuring the integrated luminosity, shape, and potential depth of the Einstein sample of early-type galaxies (presented by Fabbiano et al. 1992). We find significant correlations between the X-ray properties and the axial ratios (a/b) of our sample, such that the roundest systems tend to have the highest L(sub x) and L(sub x)/L(sub B). The most radio-loud objects are also the roundest. We confirm the assertion of Bender et al. (1989) that galaxies with high L(sub x) are boxy (have negative a(sub 4)). Both a/b and a(sub 4) are correlated with L(sub B), but not with IRAS 12 um and 100 um luminosities. There are strong correlations between L(sub x), Mg(sub 2), and sigma(sub nu) in the sense that those systems with the deepest potential wells have the highest L(sub x) and Mg(sub 2). Thus the depth of the potential well appears to govern both the ability to reatin an ISM at the present epoch and to retain the enriched ejecta of early star formation bursts. Both L(sub x)/L(sub B) and L(sub 6) (the 6 cm radio luminosity) show threshold effects with sigma(sub nu) exhibiting sharp increases at log sigma(sub nu) approximately = 2.2. Finally, there is clearly an interrelationship between the various stellar and structural parameters: The scatter in the bivariate relationships between the shape parameters (a/b and a(sub 4)) and the depth parameter sigma(sub nu) is a function of abundance in the sense that, for a given a(sub 4) or a/b, the systems with the highest sigma(sub nu) also have the highest Mg(sub 2). Furthermore, for a constant sigma(sun nu), disky galaxies tend to have higher Mg(sub 2) than boxy ones. Alternatively, for a given abundance, boxy ellipticals tend to be more massive than disky ellipticals. One possibility is that early-type galaxies of a given mass, originating from mergers (boxy ellipticals), have lower abundances than 'primordial' (disky) early-type galaxies. Another is that disky inner isophotes are due not to primordial dissipation collapse, but to either the self-gravitating inner disks of captured spirals or the dissipational collapse of new disk structures from the premerger ISM. The high measured nuclear Mg(sub 2) values would thus be due to enrichment from secondary bursts of star formation triggered by the merging event.

Eskridge, Paul B.↗
DOE OSTI2013

Risk of Cerebrovascular Events in Elderly Patients After Radiation Therapy Versus Surgery for Early-Stage Glottic Cancer

Purpose: Comprehensive neck radiation therapy (RT) has been shown to increase cerebrovascular disease (CVD) risk in advanced-stage head-and-neck cancer. We assessed whether more limited neck RT used for early-stage (T1-T2 N0) glottic cancer is associated with increased CVD risk, using the Surveillance, Epidemiology, and End Results (SEER)-Medicare linked database. Methods and Materials: We identified patients ≥66 years of age with early-stage glottic laryngeal cancer from SEER diagnosed from 1992 to 2007. Patients treated with combined surgery and RT were excluded. Medicare CPT codes for carotid interventions, Medicare ICD-9 codes for cerebrovascular events, and SEER data for stroke as the cause of death were collected. Similarly, Medicare CPT and ICD-9 codes for peripheral vascular disease (PVD) were assessed to serve as an internal control between treatment groups. Results: A total of 1413 assessable patients (RT, n=1055; surgery, n=358) were analyzed. The actuarial 10-year risk of CVD was 56.5% (95% confidence interval 51.5%-61.5%) for the RT cohort versus 48.7% (41.1%-56.3%) in the surgery cohort (P=.27). The actuarial 10-year risk of PVD did not differ between the RT (52.7% [48.1%-57.3%]) and surgery cohorts (52.6% [45.2%-60.0%]) (P=.89). Univariate analysis showed an increased association of CVD with more recent diagnosis (P=.001) and increasing age (P=.001). On multivariate Cox analysis, increasing age (P<.001) and recent diagnosis (P=.002) remained significantly associated with a higher CVD risk, whereas the association of RT and CVD remained not statistically significant (HR=1.11 [0.91-1.37,] P=.31). Conclusions: Elderly patients with early-stage laryngeal cancer have a high burden of cerebrovascular events after surgical management or RT. RT and surgery are associated with comparable risk for subsequent CVD development after treatment in elderly patients.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2018

1 H NMR-based metabolomics of antimalarial plant species traditionally used by Vha-Venda people in Limpopo Province, South Africa and isolation of antiplasmodial compounds

The Vha-Venda people living in rural areas of Limpopo Province of South Africa regularly use traditional plant-based medicines to treat malaria. In our earlier publication, twenty indigenous plant species used to treat malaria or its symptoms by Vha-Venda people were evaluated for antiplasmodial activity. The main objective of the current paper was to assess the robustness of NMR-based metabolomics in discriminating classes of secondary compounds that are responsible for the observed antimalarial activity and the isolation of antiplasmodial compounds. Twenty dichloromethane extracts were reconstituted in CDCl 3 , subjected to 1 H NMR-based metabolomic analysis on a Varian 600 MHz spectrometer and the acquired 1 H NMR spectra were then evaluated collectively using multivariate data analysis (MDA). Principal Component Analysis (PCA) and Orthogonal Projections to Latent Structures–Discriminant Analysis (OPLS-DA) were used to ‘globally’ discern antiplasmodial profiles. A contribution plot was then generated from the OPLS-DA scoring plot in an attempt to determine the classes of compounds that are responsible for the observed grouping. Further phytochemical analyses were conducted on the lipophilic extracts of Tabernaemontana elegans and Vangueria infausta subsp. infausta. These best candidates were fractionated, purified and their isolated compounds identified based on conventional chromatographic and spectroscopic techniques. The PCA did not separate the acquired profiles according to the detected antiplasmodial bioactivity. Application of a supervised OPLS-DA on the 1 H NMR profiles resulted in a discrimination pattern that could be correlated to the observed antimalarial bioactivity. A contribution plot generated from the OPLS-DA scoring plot illustrated the classes of compounds responsible for the observed grouping. Prominent peaks were observed in the aromatic, sugar-based/N-containing and aliphatic spectral regions of the contribution plot. Two known indole alkaloids were isolated from T. elegans, and identified as tabernaemontanine (IC 50 = 12.0±0.8 µM) and dregamine (IC 50 = 62.0±2.4 µM). Friedelin (IC 50 = 7.20±0.5 µM) and morindolide (IC 50 = 107.1±0.6 µM) were isolated from V. infausta subsp. infausta. This is the first report of the rare iridoid lactone, morindolide's antimalarial activity. While these two compounds have been previously identified, this is the first account of their occurrence in the genus Vangueria. The study illustrated the potential of NMR-based metabolomics in discriminating classes of compounds that may be attributed to antiplasmodial activity. Finally and additionally, the study demonstrated the potential of discovering novel antiplasmodial scaffolds from medicinal plants and the rationale for the bioprospecting antimalarial plant species used by Vha-Venda people.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Expression and prognostic role of orphan receptor GPR110 in glioma

Glioma is the most common type of malignancy in the central nervous system, which has a poor prognosis due to its rapid progression and diffuse invasion. Identification of novel biomarkers for glioma would be invaluable for studying disease mechanism and improving prognosis. Orphan G protein-coupled receptor 110 (GPR110) belongs to the subfamily VI of adhesion GPCR. The knowledge of the ligand, signaling pathway or physiology function of GPR110 is poorly elucidated. The potential role of GPR110 as an oncogene in mouse has been recently reported by mutagenesis screen. However, its expression and role in human glioma hasn't been identified. Here in the current study, we initially explored the RNA and protein expression of GPR110 in patients with glioma. Statistical analysis proved that GPR110 was highly expressed in some patients, which was correlated with advanced disease stages. Furthermore, univariate and multivariate analyses revealed its role as an independent prognostic biomarker for the overall survival of glioma patients. Interestingly, cellular studies showed that overexpression or knockdown of GPR110 in U87 cells didn't affect cell proliferation and migration. However, the invasion of U87 cells was significantly enhanced by GPR110-overepxression, while inhibited by GPR110-knockdown. The detailed mechanisms remain further investigation although our results suggested the possible participation of STAT3 instead of ERK in the GPR110 signaling pathways. - Highlights: • GPR110 was highly expressed in glioma than normal brain tissues. • High GPR110 indicates poor overall survival of glioma patients. • GPR110 enhance cell invasion without affecting cell proliferation and migration. • GPR110 inhibit the activation of STAT3, but not ERK protein.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Decoding the Mechanisms of Phase Transitions from In Situ Microscopy Observations

Abstract Analysis of the temperature‐ and stimulus‐dependent imaging data toward elucidation of the physical transformations is an ubiquitous problem in multiple fields. Here, temperature‐induced phase transition in BaTiO 3 is explored using the machine learning analysis of domain morphologies visualized via variable‐temperature scanning transmission electron microscopy (STEM) imaging data. This approach is based on the multivariate statistical analysis of the time or temperature dependence of the statistical descriptors of the system, derived in turn from the categorical classification of observed domain structures or projection on the continuous parameter space of the feature extraction‐dimensionality reduction transform. The proposed workflow offers a powerful tool for the exploration of the dynamic data based on the statistics of image representation as a function of the external control variable to visualize the transformation pathways during phase transitions and chemical reactions. This can include the mesoscopic STEM data as demonstrated here, but also optical, chemical imaging, etc., data. It can further be extended to the higher dimensional spaces, for example, analysis of the combinatorial libraries of materials compositions.

Valleti, Sai Mani Prudhvi↗
DOE OSTI2018

Exploring the effects of climatic variables on monthly precipitation variation using a continuous wavelet-based multiscale entropy approach

Highlights: • Developed a wavelet transform entropy approach to study precipitation. • Multiscale entropy values were the basis for precipitation regionalization. • Various entropy-based bands were introduced to segregate the CWME values for each cluster. • The relationship between changes of CWME and mean monthly precipitation were investigated. • Central rain gauges of each cluster were used for further analysis. Understanding precipitation on a regional basis is an important component of water resources planning and management. The present study outlines a methodology based on continuous wavelet transform (CWT) and multiscale entropy (CWME), combined with self-organizing map (SOM) and k-means clustering techniques, to measure and analyze the complexity of precipitation. Historical monthly precipitation data from 1960 to 2010 at 31 rain gauges across Iran were preprocessed by CWT. The multi-resolution CWT approach segregated the major features of the original precipitation series by unfolding the structure of the time series which was often ambiguous. The entropy concept was then applied to components obtained from CWT to measure dispersion, uncertainty, disorder, and diversification of subcomponents. Based on different validity indices, k-means clustering captured homogenous areas more accurately, and additional analysis was performed based on the outcome of this approach. The 31 rain gauges in this study were clustered into 6 groups, each one having a unique CWME pattern across different time scales. The results of clustering showed that hydrologic similarity (multiscale variation of precipitation) was not based on geographic contiguity. According to the pattern of entropy across the scales, each cluster was assigned an entropy signature that provided an estimation of the entropy pattern of precipitation data in each cluster. Based on the pattern of mean CWME for each cluster, a characteristic signature was assigned, which provided an estimation of the CWME of a cluster across scales of 1–2, 3–8, and 9–13 months relative to other stations. The validity of the homogeneous clusters demonstrated the usefulness of the proposed approach to regionalize precipitation. Further analysis based on wavelet coherence (WTC) was performed by selecting central rain gauges in each cluster and analyzing against temperature, wind, Multivariate ENSO index (MEI), and East Atlantic (EA) and North Atlantic Oscillation (NAO), indeces. The results revealed that all climatic features except NAO influenced precipitation in Iran during the 1960–2010 period.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2024

NCAPH drives breast cancer progression and identifies a gene signature that predicts luminal a tumour recurrence

Luminal A tumours generally have a favourable prognosis but possess the highest 10-year recurrence risk among breast cancers. Additionally, a quarter of the recurrence cases occur within 5 years post-diagnosis. Identifying such patients is crucial as long-term relapsers could benefit from extended hormone therapy, while early relapsers might require more aggressive treatment. We conducted a study to explore non-structural chromosome maintenance condensin I complex subunit H’s (NCAPH) role in luminal A breast cancer pathogenesis, both in vitro and in vivo, aiming to identify an intratumoural gene expression signature, with a focus on elevated NCAPH levels, as a potential marker for unfavourable progression. Our analysis included transgenic mouse models overexpressing NCAPH and a genetically diverse mouse cohort generated by backcrossing. A least absolute shrinkage and selection operator (LASSO) multivariate regression analysis was performed on transcripts associated with elevated intratumoural NCAPH levels. We found that NCAPH contributes to adverse luminal A breast cancer progression. The intratumoural gene expression signature associated with elevated NCAPH levels emerged as a potential risk identifier. Transgenic mice overexpressing NCAPH developed breast tumours with extended latency, and in Mouse Mammary Tumor Virus (MMTV)-NCAPH ErbB2 double-transgenic mice, luminal tumours showed increased aggressiveness. High intratumoural Ncaph levels correlated with worse breast cancer outcome and subpar chemotherapy response. A 10-gene risk score, termed Gene Signature for Luminal A 10 (GSLA10), was derived from the LASSO analysis, correlating with adverse luminal A breast cancer progression. The GSLA10 signature outperformed the Oncotype DX signature in discerning tumours with unfavourable outcomes, previously categorised as luminal A by Prediction Analysis of Microarray 50 (PAM50) across three independent human cohorts. This new signature holds promise for identifying luminal A tumour patients with adverse prognosis, aiding in the development of personalised treatment strategies to significantly improve patient outcomes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2012

Characterizing Tumor Heterogeneity With Functional Imaging and Quantifying High-Risk Tumor Volume for Early Prediction of Treatment Outcome: Cervical Cancer as a Model

Purpose: Treatment response in cancer has been monitored by measuring anatomic tumor volume (ATV) at various times without considering the inherent functional tumor heterogeneity known to critically influence ultimate treatment outcome: primary tumor control and survival. This study applied dynamic contrast-enhanced (DCE) functional MRI to characterize tumors' heterogeneous subregions with low DCE values, at risk for treatment failure, and to quantify the functional risk volume (FRV) for personalized early prediction of treatment outcome. Methods and Materials: DCE-MRI was performed in 102 stage IB{sub 2}-IVA cervical cancer patients to assess tumor perfusion heterogeneity before and during radiation/chemotherapy. FRV represents the total volume of tumor voxels with critically low DCE signal intensity (<2.1 compared with precontrast image, determined by previous receiver operator characteristic analysis). FRVs were correlated with treatment outcome (follow-up: 0.2-9.4, mean 6.8 years) and compared with ATVs (Mann-Whitney, Kaplan-Meier, and multivariate analyses). Results: Before and during therapy at 2-2.5 and 4-5 weeks of RT, FRVs >20, >13, and >5 cm{sup 3}, respectively, significantly predicted unfavorable 6-year primary tumor control (p = 0.003, 7.3 Multiplication-Sign 10{sup -8}, 2.0 Multiplication-Sign 10{sup -8}) and disease-specific survival (p = 1.9 Multiplication-Sign 10{sup -4}, 2.1 Multiplication-Sign 10{sup -6}, 2.5 Multiplication-Sign 10{sup -7}, respectively). The FRVs were superior to the ATVs as early predictors of outcome, and the differentiating power of FRVs increased during treatment. Discussion: Our preliminary results suggest that functional tumor heterogeneity can be characterized by DCE-MRI to quantify FRV for predicting ultimate long-term treatment outcome. FRV is a novel functional imaging heterogeneity parameter, superior to ATV, and can be clinically translated for personalized early outcome prediction before or as early as 2-5 weeks into treatment.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2014

Functional Data Analysis in NTCP Modeling: A New Method to Explore the Radiation Dose-Volume Effects

Purpose/Objective(s): To describe a novel method to explore radiation dose-volume effects. Functional data analysis is used to investigate the information contained in differential dose-volume histograms. The method is applied to the normal tissue complication probability modeling of rectal bleeding (RB) for patients irradiated in the prostatic bed by 3-dimensional conformal radiation therapy. Methods and Materials: Kernel density estimation was used to estimate the individual probability density functions from each of the 141 rectum differential dose-volume histograms. Functional principal component analysis was performed on the estimated probability density functions to explore the variation modes in the dose distribution. The functional principal components were then tested for association with RB using logistic regression adapted to functional covariates (FLR). For comparison, 3 other normal tissue complication probability models were considered: the Lyman-Kutcher-Burman model, logistic model based on standard dosimetric parameters (LM), and logistic model based on multivariate principal component analysis (PCA). Results: The incidence rate of grade ≥2 RB was 14%. V{sub 65Gy} was the most predictive factor for the LM (P=.058). The best fit for the Lyman-Kutcher-Burman model was obtained with n=0.12, m = 0.17, and TD50 = 72.6 Gy. In PCA and FLR, the components that describe the interdependence between the relative volumes exposed at intermediate and high doses were the most correlated to the complication. The FLR parameter function leads to a better understanding of the volume effect by including the treatment specificity in the delivered mechanistic information. For RB grade ≥2, patients with advanced age are significantly at risk (odds ratio, 1.123; 95% confidence interval, 1.03-1.22), and the fits of the LM, PCA, and functional principal component analysis models are significantly improved by including this clinical factor. Conclusion: Functional data analysis provides an attractive method for flexibly estimating the dose-volume effect for normal tissues in external radiation therapy.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2015

Imaging Changes in Pediatric Intracranial Ependymoma Patients Treated With Proton Beam Radiation Therapy Compared to Intensity Modulated Radiation Therapy

Purpose: The clinical significance of magnetic resonance imaging (MRI) changes after radiation therapy (RT) in children with ependymoma is not well defined. We compared imaging changes following proton beam radiation therapy (PBRT) to those after photon-based intensity modulated RT (IMRT). Methods and Materials: Seventy-two patients with nonmetastatic intracranial ependymoma who received postoperative RT (37 PBRT, 35 IMRT) were analyzed retrospectively. MRI images were reviewed by 2 neuroradiologists. Results: Sixteen PBRT patients (43%) developed postradiation MRI changes at 3.8 months (median) with resolution by 6.1 months. Six IMRT patients (17%) developed changes at 5.3 months (median) with 8.3 months to resolution. Mean age at radiation was 4.4 and 6.9 years for PBRT and IMRT, respectively (P=.06). Age at diagnosis (>3 years) and time of radiation (≥3 years) was associated with fewer imaging changes on univariate analysis (odds ratio [OR]: 0.35, P=.048; OR: 0.36, P=.05). PBRT (compared to IMRT) was associated with more frequent imaging changes, both on univariate (OR: 3.68, P=.019) and multivariate (OR: 3.89, P=.024) analyses. Seven (3 IMRT, 4 PBRT) of 22 patients with changes had symptoms requiring intervention. Most patients were treated with steroids; some PBRT patients also received bevacizumab and hyperbaric oxygen therapy. None of the IMRT patients had lasting deficits, but 2 patients died from recurrent disease. Three PBRT patients had persistent neurological deficits, and 1 child died secondarily to complications from radiation necrosis. Conclusions: Postradiation MRI changes are more common with PBRT and in patients less than 3 years of age at diagnosis and treatment. It is difficult to predict causes for development of imaging changes that progress to clinical significance. These changes are usually self-limiting, but some require medical intervention, especially those involving the brainstem.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
DOE OSTI2021

Measuring Nd(III) Solution Concentration in the Presence of Interfering Er(III) and Cu(II) Ions: A Partial Least Squares Analysis of Ultraviolet–Visible Spectra

Optical spectroscopy is a powerful characterization tool with applications ranging from fundamental studies to real-time process monitoring. However, it can be difficult to apply to complex samples that contain interfering analytes which are common in processing streams. Multivariate (chemometric) analysis has been examined for providing selectivity and accuracy to the analysis of optical spectra and expanding its potential applications. Here we will discuss chemometric modeling with an in-depth comparison to more simplistic analysis approaches and outline how chemometric modeling works while exploring the limits on modeling accuracy. Understanding the limitations of the chemometric model can provide better analytical assessment regarding the accuracy and precision of the analytical result. This will be explored in the context of UV–Vis absorbance of neodymium (Nd 3+ ) in the presence of interferents, erbium (Er 3+ ) and copper (Cu 2+ ) under conditions simulating the liquid–liquid extraction approach used to recycle plutonium (Pu) and uranium (U) in used nuclear fuel worldwide. Finally, the selected chemometric model, partial least squares regression, accurately quantifies Nd 3+ with a low percentage error in the presence of interfering analytes and even under conditions that the training set does not describe.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗