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DOE OSTI2018

An automated framework for NMR chemical shift calculations of small organic molecules

When using nuclear magnetic resonance (NMR) to assist in chemical identification in complex samples, researchers commonly rely on databases for chemical shift spectra. However, authentic standards are typically depended upon to build libraries experimentally. Considering complex biological samples, such as blood and soil, the entirety of NMR spectra required for all possible compounds would be infeasible to ascertain due to limitations of available standards and experimental processing time. As an alternative, we introduce the in silico Chemical Library Engine (ISiCLE) NMR chemical shift module to accurately and automatically calculate NMR chemical shifts of small organic molecules through use of quantum chemical calculations. ISiCLE performs density functional theory (DFT)-based calculations for predicting chemical properties - specifically NMR chemical shifts in this manuscript - via the open source, high-performance omputational chemistry software, NWChem. ISiCLE calculates the NMR chemical shifts of sets of molecules using any available combination of DFT method, solvent, and NMR-active nuclei, using both user-selected reference compounds and/or linear regression methods. Calculated NMR chemical shifts are provided to the user for each molecule, along with comparisons with respect to a number of metrics commonly used in the literature. Here, we demonstrate ISiCLE using a set of 312 molecules, ranging in size up to 90 carbon atoms. For each, calculation of NMR chemical shifts have been performed with 8 different levels of DFT theory, and with solvation effects using the implicit solvent Conductor-like Screening Model (COSMO). The DFT method dependence of the calculated chemical shifts have been systematically investigated through benchmarking and subsequently compared to experimental data available in the literature. Furthermore, ISiCLE has been applied to a set of 80 methylcyclohexane conformers, combined via Boltzmann weighting and compared to experimental values. We demonstrate that our protocol shows promise in the automation of chemical shift calculations and, ultimately, the expansion of chemical shift libraries.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2022

Development of the uncertainty quantification toolkit's python interface and surrogate construction tutorial

The uncertainty quantification toolkit (UQTk) is a collection of c++ libraries that assess the confidence of numerical models. Surrogate approximations, often polynomial chaos expansions (PCEs), lessen the computational cost of these assessments. I developed a Python interface in UQTk for regression and Bayesian compressive sensing to add to the existing Galerkin projection method. These methods receive an object containing the polynomial basis information and NumPy arrays of sample points, call c++ methods, and return the PCE coefficients in a NumPy array. To demonstrate these methods, I wrote a tutorial in which I use them to construct surrogates for Genz functions and calculate the resulting error.

97 MATHEMATICS AND COMPUTING↗

NASA NTRS2009

DNA Repair Defects and Chromosomal Aberrations

Yields of chromosome aberrations were assessed in cells deficient in DNA doublestrand break (DSB) repair, after exposure to acute or to low-dose-rate (0.018 Gy/hr) gamma rays or acute high LET iron nuclei. We studied several cell lines including fibroblasts deficient in ATM (ataxia telangiectasia mutated; product of the gene that is mutated in ataxia telangiectasia patients) or NBS (nibrin; product of the gene mutated in the Nijmegen breakage syndrome), and gliomablastoma cells that are proficient or lacking in DNA-dependent protein kinase (DNA-PK) activity. Chromosomes were analyzed using the fluorescence in situ hybridization (FISH) chromosome painting method in cells at the first division post irradiation, and chromosome aberrations were identified as either simple exchanges (translocations and dicentrics) or complex exchanges (involving >2 breaks in 2 or more chromosomes). Gamma irradiation induced greater yields of both simple and complex exchanges in the DSB repair-defective cells than in the normal cells. The quadratic dose-response terms for both simple and complex chromosome exchanges were significantly higher for the ATM- and NBS-deficient lines than for normal fibroblasts. However, in the NBS cells the linear dose-response term was significantly higher only for simple exchanges. The large increases in the quadratic dose-response terms in these repair-defective cell lines points the importance of the functions of ATM and NBS in chromatin modifications to facilitate correct DSB repair and minimize the formation of aberrations. The differences found between ATM- and NBS-deficient cells at low doses suggest that important questions should with regard to applying observations of radiation sensitivity at high dose to low-dose exposures. For aberrations induced by iron nuclei, regression models preferred purely linear dose responses for simple exchanges and quadratic dose responses for complex exchanges. Relative biological effectiveness (RBE) factors of all of the DNA repair-defective cell lines were smaller than those of normal cells, with the DNA-PK-deficient cells having RBEs near unity. To further investigate the sensitivity differences that were observed in ATM and NBS deficient cells, chromosomal aberrations were analyzed in normal lung fibroblast cells treated with KU-55933 (a specific ATM kinase inhibitor) or Mirin (an Mre11- Rad50-Nbs1 complex inhibitor involved in activation of ATM). We also performed siRNA knockdown of these proteins. Preliminary data indicate that chromosome exchanges increase in cells treated with the specific ATM inhibitor. Possible cytogenetic signatures of acute and low dose-rate gamma irradiation in ATM or nibrin deficient and suppressed cells will be discussed.

Hada, Megumi↗

DOE OSTI2015

Imaging spectroscopy algorithms for mapping canopy foliar chemical and morphological traits and their uncertainties

A major goal of remote sensing is the development of generalizable algorithms to repeatedly and accurately map ecosystem properties across space and time. Imaging spectroscopy has great potential to map vegetation traits that cannot be retrieved from broadband spectral data, but rarely have such methods been tested across broad regions. Here we illustrate a general approach for estimating key foliar chemical and morphological traits through space and time using NASA's Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-Classic). We apply partial least squares regression (PLSR) to data from 237 field plots within 51 images acquired between 2008 and 2011. Using a series of 500 randomized 50/50 subsets of the original data, we generated spatially explicit maps of seven traits (leaf mass per area (M area ), percentage nitrogen, carbon, fiber, lignin, and cellulose, and isotopic nitrogen concentration, δ 15 N) as well as pixel-wise uncertainties in their estimates based on error propagation in the analytical methods. Both Marea and %N PLSR models had a R 2 > 0.85. Root mean square errors (RMSEs) for both variables were less than 9% of the range of data. Fiber and lignin were predicted with R 2 > 0.65 and carbon and cellulose with R 2 > 0.45. Although R 2 of %C and cellulose were lower than Marea and %N, the measured variability of these constituents (especially %C) was also lower, and their RMSE values were beneath 12% of the range in overall variability. Model performance for δ 15 N was the lowest (R 2 = 0.48, RMSE = 0.95‰), but within 15% of the observed range. The resulting maps of chemical and morphological traits, together with their overall uncertainties, represent a first-of-its-kind approach for examining the spatiotemporal patterns of forest functioning and nutrient cycling across a broad range of temperate and sub-boreal ecosystems. These results offer an alternative to categorical maps of functional or physiognomic types by providing non-discrete maps (i.e., on a continuum) of traits that define those functional types. A key contribution of this work is the ability to assign retrieval uncertainties by pixel, a requirement to enable assimilation of these data products into ecosystem modeling frameworks to constrain carbon and nutrient cycling projections.

54 ENVIRONMENTAL SCIENCES↗

DOE OSTI2022

Universal machine learning framework for defect predictions in zinc blende semiconductors

Our article introduces a universal predictive framework for point defect formation energies and charge transition levels in a wide chemical space of zinc blende semiconductors and possible impurity atoms selected from across the periodic table. This framework was developed by leveraging high-throughput quantum mechanical simulations benchmarked using some experimental data from the literature, as well as machine learning (ML)-based regressions techniques that map unique materials descriptors to computed defect properties and yield optimized and generalizable models. Furthermore, the power and utility of these models is revealed through quick predictions for thousands of new defects and screening of low-energy impurities, which may tune the equilibrium conductivity in the semiconductor. This work presents, to our knowledge, the largest density functional theory (DFT) dataset of defect properties in semiconductors and the largest DFT+ML-based screening of point defects in semiconductors to date.

36 MATERIALS SCIENCE↗

DOE OSTI2025

SLAB: simultaneous labeling and binding affinity prediction for protein–ligand structures

Machine learning models are often used as scoring functions to predict the binding affinity of a protein–ligand complex. These models are trained with limited amounts of data with experimentally measured binding affinity values. A large number of compounds are labeled inactive through single-concentration screens without measuring binding affinities. These inactive compounds, along with the active ones, can be used to train binary classification models, while regression models are trained using compounds with binding affinities only. However, the classification and regression tasks are often handled separately, without sharing the learned feature representations. In this paper, we propose a novel model architecture that jointly performs regression and classification objectives, aiming to maximize data utilization and improve predictive performance by leveraging two complementary tasks. In our setup, the regression yields the binding affinity, whereas the classification task yields the label as active or inactive. We demonstrate our method using PDBbind, the standard 3D structure database, as well as a dataset of flavivirus protease compounds with binding affinity data. Our experiments show that the new joint training strategy improves the accuracy of the model, increasing applicability in various practical drug screening scenarios.

Biological and medical sciences↗

DOE OSTI2019

Prediction of atomization energy using graph kernel and active learning

Data-driven prediction of molecular properties presents unique challenges to the design of machine learning methods concerning data structure/dimensionality, symmetry adaption, and confidence management. In this paper, we present a kernel-based pipeline that can learn and predict the atomization energy of molecules with high accuracy. The framework employs Gaussian process regression to perform predictions based on the similarity between molecules, which is computed using the marginalized graph kernel. To apply the marginalized graph kernel, a spatial adjacency rule is first employed to convert molecules into graphs whose vertices and edges are labeled by elements and interatomic distances, respectively. We then derive formulas for the efficient evaluation of the kernel. Specific functional components for the marginalized graph kernel are proposed, while the effects of the associated hyperparameters on accuracy and predictive confidence are examined. We show that the graph kernel is particularly suitable for predicting extensive properties because its convolutional structure coincides with that of the covariance formula between sums of random variables. Using an active learning procedure, we demonstrate that the proposed method can achieve a mean absolute error of 0.62 ± 0.01 kcal/mol using as few as 2000 training samples on the QM7 dataset.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2021

Angular clustering properties of the DESI QSO target selection using DR9 Legacy Imaging Surveys

ABSTRACT The quasar target selection for the upcoming survey of the Dark Energy Spectroscopic Instrument (DESI) will be fixed for the next 5 yr. The aim of this work is to validate the quasar selection by studying the impact of imaging systematics as well as stellar and galactic contaminants, and to develop a procedure to mitigate them. Density fluctuations of quasar targets are found to be related to photometric properties such as seeing and depth of the Data Release 9 of the DESI Legacy Imaging Surveys. To model this complex relation, we explore machine learning algorithms (random forest and multilayer perceptron) as an alternative to the standard linear regression. Splitting the footprint of the Legacy Imaging Surveys into three regions according to photometric properties, we perform an independent analysis in each region, validating our method using extended Baryon Oscillation Spectroscopic Survey (eBOSS) EZ-mocks. The mitigation procedure is tested by comparing the angular correlation of the corrected target selection on each photometric region to the angular correlation function obtained using quasars from the Sloan Digital Sky Survey (SDSS) Data Release 16. With our procedure, we recover a similar level of correlation between DESI quasar targets and SDSS quasars in two-thirds of the total footprint and we show that the excess of correlation in the remaining area is due to a stellar contamination that should be removed with DESI spectroscopic data. We derive the Limber parameters in our three imaging regions and compare them to previous measurements from SDSS and the 2dF QSO Redshift Survey.

79 ASTRONOMY AND ASTROPHYSICS↗

NASA NTRS1984

A comparative look at sunspot cycles

On the basis of cycles 8 through 20, spanning about 143 years, observations of sunspot number, smoothed sunspot number, and their temporal properties were used to compute means, standard deviations, ranges, and frequency of occurrence histograms for a number of sunspot cycle parameters. The resultant schematic sunspot cycle was contrasted with the mean sunspot cycle, obtained by averaging smoothed sunspot number as a function of time, tying all cycles (8 through 20) to their minimum occurence date. A relatively good approximation of the time variation of smoothed sunspot number for a given cycle is possible if sunspot cycles are regarded in terms of being either HIGH- or LOW-R(MAX) cycles or LONG- or SHORT-PERIOD cycles, especially the latter. Linear regression analyses were performed comparing late cycle parameters with early cycle parameters and solar cycle number. The early occurring cycle parameters can be used to estimate later occurring cycle parameters with relatively good success, based on cycle 21 as an example. The sunspot cycle record clearly shows that the trend for both R(MIN) and R(MAX) was toward decreasing value between cycles 8 through 14 and toward increasing value between cycles 14 through 20. Linear regression equations were also obtained for several measures of solar activity.

Wilson, R. M.↗

NASA NTRS1990

Health monitoring system for the SSME - Program overview

This paper considers the design of a health management system (HMS) program for enhancement of the safety of SSME operations during ground tests and space missions. The fault detection techniques based on time series, nonlinear regression, and clustering algorithms were developed and were demonstrated based on data from SSME ground test failures. The fault detection algorithms of the HMS system exhibited 100 percent detection of faults and an extremely low fault alarm rate, and were robust to sensor loss. A preliminary design of hardware architecture capable of supporting real-time operation of the HMS functions is described.

Hawman, Michael W.↗

NASA NTRS1993

Flap-lag damping in hover and forward flight with a three-dimensional wake

Prediction of lag damping is difficult owing to the delicate balance of drag, induced drag and Coriolis forces in the in-plane direction. Moreover, induced drag is sensitive to dynamic wake, both shed and trailing components, and thus its prediction requires adequate unsteady-wake representation. Accordingly, rigid-blade flap-lag equations are coupled with a three-dimensional finite-state wake model; three isolated rotor configurations with three, four and five blades are treated over a range of thrust levels, Lock numbers, lag frequencies and advance ratios. The investigation includes convergence characteristics of damping with respect to the number of radial shape functions and harmonics of the wake model for multiblade modes of low frequency (less than 1/rev.) to high frequency (greater than 1/rev.). Predicted flap and lag damping levels are then compared with similar predictions with (1) rigid wake (no unsteady induced flow, (2) Loewy lift defficiency and (3) dynamic inflow. The coverage also includes correlations with the measured lag regressive-mode damping in hover and forward flight and comparisons with similar correlations with dynamic wake model are consistently higher than the predictions with the dynamic inflow model; even for the low frequency lag regressive mode, the number of wake harmonics should at least be equal to twice the number of blades.

Manjunath, A. R.↗

NASA NTRS1999

Strain Gauge Balance Uncertainty Analysis at NASA Langley: A Technical Review

This paper describes a method to determine the uncertainties of measured forces and moments from multi-component force balances used in wind tunnel tests. A multivariate regression technique is first employed to estimate the uncertainties of the six balance sensitivities and 156 interaction coefficients derived from established balance calibration procedures. These uncertainties are then employed to calculate the uncertainties of force-moment values computed from observed balance output readings obtained during tests. Confidence and prediction intervals are obtained for each computed force and moment as functions of the actual measurands. Techniques are discussed for separate estimation of balance bias and precision uncertainties.

John S Tripp↗

NASA NTRS2016

BRDF Characterization and Calibration Inter-Comparison between Terra MODIS, Aqua MODIS, and S-NPP VIIRS

The inter-comparison of reflective solar bands (RSB) between Terra MODIS, Aqua MODIS, and SNPP VIIRS is very important for assessment of each instrument's calibration and to identify calibration improvements. One of the limitations of using their ground observations for the assessment is a lack of the simultaneous nadir overpasses (SNOs) over selected pseudo-invariant targets. In addition, their measurements over a selected Earth view target have significant difference in solar and view angles, and these differences magnify the effects of Bidirectional Reflectance Distribution Function (BRDF). In this work, an inter-comparison technique using a semi-empirical BRDF model is developed for reflectance correction. BRDF characterization requires a broad coverage of solar and view angles in the measurements over selected pseudo-invariant targets. Reflectance measurements over Libya 1, 2, and 4 desert sites from both the Aqua and Terra MODIS are regressed to a BRDF model with an adjustable coefficient accounting for the calibration difference between the two instruments. The BRDF coefficients for three desert sites for MODIS bands 1 to 9 are derived and the wavelength dependencies are presented. The analysis and inter-comparison are for MODIS bands 1 to 9 and VIIRS moderate resolution radiometric bands (M bands) M1, M2, M4, M5, M7, M8, M10 and imaging bands (I bands) I1-I3. Results show that the ratios from different sites are in good agreement. The ratios between Terra and Aqua MODIS from year 2003 to 2014 are presented. The inter-comparison between MODIS and VIIRS are analyzed for year 2014.

Chang, Tiejun↗

DOE OSTI2019

High spatiotemporal variability of bacterial diversity over short time scales with unique hydrochemical associations within a shallow aquifer

Understanding microbial community structure and function within the subsurface is critical to assessing overall quality and maintenance of groundwater; however, the factors that determine microbial community assembly, structure, and function in groundwater systems and their impact on water quality remains poorly understood. In this study, three shallow wells (FW301, FW303, FW305) in a non-contaminated shallow aquifer in the ENIGMA-Oak Ridge Field Research Center (Oak Ridge, TN) were sampled approximately 3 times a week over a period of three months to measure changes in groundwater geochemistry and microbial diversity. It was expected that the sampled microbial diversity from two historic field wells (FW301, FW303) would be relatively stable, while diversity from a newer well (FW305) would be less stable over time. The wells displayed some degree of hydrochemical variability over time unique to each well, with FW303 being overall the most stable well and FW301 being the most dynamic based upon dissolved oxygen, conductivity, and nitrate. Community analysis via ss-rRNA paired-end sequencing and distribution-based clustering revealed higher OTU richness, diversity, and variability in groundwater communities of FW301 than the other two wells for diversity binned over all time points. Microbial community composition of a given well was on average > 50% dissimilar to any other well at a given time (days), yet, functional gene diversity as measured with GeoChip remained relatively constant. Similarities in community structure across wells were observed with respect to the presence of 20 shared bacterial groups in all samples in all wells, although at varying levels over the tested time period. Similarity percentage (SIMPER) analysis revealed that variability in FW301 was largely attributed to low abundance, highly-transient populations, while variability in the most hydrochemically stable well (FW303) was due to fluctuations in more highly abundant and frequently present taxa. Additionally, the youngest well FW305 showed a dramatic shift in community composition towards the end of the sampling period that was not observed in the other wells, suggesting possible succession events over time. Time-series analysis using vector auto-regressive models and Granger causality showed unique relationships between richness and geochemistry over time in each well. Finally, these results indicate temporally dynamic microbial communities over short time scales, with day-to-day population shifts in local community structure influenced by available source community diversity and local groundwater hydrochemistry.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2013

ATM Polymorphisms Predict Severe Radiation Pneumonitis in Patients With Non-Small Cell Lung Cancer Treated With Definitive Radiation Therapy

Purpose: The ataxia telangiectasia mutated (ATM) gene mediates detection and repair of DNA damage. We investigated associations between ATM polymorphisms and severe radiation-induced pneumonitis (RP). Methods and Materials: We genotyped 3 potentially functional single nucleotide polymorphisms (SNPs) of ATM (rs1801516 [D1853N/5557G>A], rs189037 [-111G>A] and rs228590) in 362 patients with non-small cell lung cancer (NSCLC), who received definitive (chemo)radiation therapy. The cumulative severe RP probabilities by genotypes were evaluated using the Kaplan-Meier analysis. The associations between severe RP risk and genotypes were assessed by both logistic regression analysis and Cox proportional hazard model with time to event considered. Results: Of 362 patients (72.4% of non-Hispanic whites), 56 (15.5%) experienced grade ≥3 RP. Patients carrying ATM rs189037 AG/GG or rs228590 TT/CT genotypes or rs189037G/rs228590T/rs1801516G (G-T-G) haplotype had a lower risk of severe RP (rs189037: GG/AG vs AA, adjusted hazard ratio [HR] = 0.49, 95% confidence interval [CI], 0.29-0.83, P=.009; rs228590: TT/CT vs CC, HR=0.57, 95% CI, 0.33-0.97, P=.036; haplotype: G-T-G vs A-C-G, HR=0.52, 95% CI, 0.35-0.79, P=.002). Such positive findings remained in non-Hispanic whites. Conclusions: ATM polymorphisms may serve as biomarkers for susceptibility to severe RP in non-Hispanic whites. Large prospective studies are required to confirm our findings.

62 RADIOLOGY AND NUCLEAR MEDICINE↗

DOE OSTI2025

Enhancing fire emissions inventories for acute health effects studies: integrating high spatial and temporal resolution data

Daily fire progression information is crucial for public health studies that examine the relationship between population-level smoke exposures and subsequent health events. Issues with remote sensing used in fire emissions inventories (FEI) lead to the possibility of missed exposures that impact the results of acute health effects studies. This paper provides a method for improving an FEI dataset with readily available information to create a more robust dataset with daily fire progression. High temporal and spatial resolution burned area information from two FEI products are combined into a single dataset, and a linear regression model fills gaps in daily fire progression. The combined dataset provides up to 71% more PM 2.5 emissions, 69% more burned area, and 367% more fire days per year than using a single source of burned area information. The FEI combination method results in improved FEI information with no gaps in daily fire emissions estimates. The combined dataset provides a functional improvement to FEI data that can be achieved with currently available data.

63 RADIATION, THERMAL, AND OTHER ENVIRON. POLLUTAN↗

DOE OSTI2016

SU-G-BRB-03: Assessing the Sensitivity and False Positive Rate of the Integrated Quality Monitor (IQM) Large Area Ion Chamber to MLC Positioning Errors

Purpose: To examine both the IQM’s sensitivity and false positive rate to varying MLC errors. By balancing these two characteristics, an optimal tolerance value can be derived. Methods: An un-modified SBRT Liver IMRT plan containing 7 fields was randomly selected as a representative clinical case. The active MLC positions for all fields were perturbed randomly from a square distribution of varying width (±1mm to ±5mm). These unmodified and modified plans were measured multiple times each by the IQM (a large area ion chamber mounted to a TrueBeam linac head). Measurements were analyzed relative to the initial, unmodified measurement. IQM readings are analyzed as a function of control points. In order to examine sensitivity to errors along a field’s delivery, each measured field was divided into 5 groups of control points, and the maximum error in each group was recorded. Since the plans have known errors, we compared how well the IQM is able to differentiate between unmodified and error plans. ROC curves and logistic regression were used to analyze this, independent of thresholds. Results: A likelihood-ratio Chi-square test showed that the IQM could significantly predict whether a plan had MLC errors, with the exception of the beginning and ending control points. Upon further examination, we determined there was ramp-up occurring at the beginning of delivery. Once the linac AFC was tuned, the subsequent measurements (relative to a new baseline) showed significant (p <0.005) abilities to predict MLC errors. Using the area under the curve, we show the IQM’s ability to detect errors increases with increasing MLC error (Spearman’s Rho=0.8056, p<0.0001). The optimal IQM count thresholds from the ROC curves are ±3%, ±2%, and ±7% for the beginning, middle 3, and end segments, respectively. Conclusion: The IQM has proven to be able to detect not only MLC errors, but also differences in beam tuning (ramp-up). Partially supported by the Susan Scott Foundation.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2023

Predicting partner fitness based on spatial structuring in a light-driven microbial community

Microbial communities have vital roles in systems essential to human health and agriculture, such as gut and soil microbiomes, and there is growing interest in engineering designer consortia for applications in biotechnology ( e . g ., personalized probiotics, bioproduction of high-value products, biosensing). The capacity to monitor and model metabolite exchange in dynamic microbial consortia can provide foundational information important to understand the community level behaviors that emerge, a requirement for building novel consortia. Where experimental approaches for monitoring metabolic exchange are technologically challenging, computational tools can enable greater access to the fate of both chemicals and microbes within a consortium. In this study, we developed an in-silico model of a synthetic microbial consortia of sucrose-secreting Synechococcus elongatus PCC 7942 and Escherichia coli W. Our model was built on the NUFEB framework for Individual-based Modeling (IbM) and optimized for biological accuracy using experimental data. We showed that the relative level of sucrose secretion regulates not only the steady-state support for heterotrophic biomass, but also the temporal dynamics of consortia growth. In order to determine the importance of spatial organization within the consortium, we fit a regression model to spatial data and used it to accurately predict colony fitness. We found that some of the critical parameters for fitness prediction were inter-colony distance, initial biomass, induction level, and distance from the center of the simulation volume. We anticipate that the synergy between experimental and computational approaches will improve our ability to design consortia with novel function.

59 BASIC BIOLOGICAL SCIENCES↗