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All sourcesNASA NTRSDOE OSTI
DOE OSTI2024

High-resolution ion mobility based on traveling wave structures for lossless ion manipulation resolves hidden lipid features

Abstract High-resolution ion mobility (resolving power > 200) coupled with mass spectrometry (MS) is a powerful analytical tool for resolving isobars and isomers in complex samples. High-resolution ion mobility is capable of discerning additional structurally distinct features, which are not observed with conventional resolving power ion mobility (IM, resolving power ~ 50) techniques such as traveling wave IM and drift tube ion mobility (DTIM). DTIM in particular is considered to be the “gold standard” IM technique since collision cross section (CCS) values are directly obtained through a first-principles relationship, whereas traveling wave IM techniques require an additional calibration strategy to determine accurate CCS values. In this study, we aim to evaluate the separation capabilities of a traveling wave ion mobility structures for lossless ion manipulation platform integrated with mass spectrometry analysis (SLIM IM-MS) for both lipid isomer standards and complex lipid samples. A cross-platform investigation of seven subclass-specific lipid extracts examined by both DTIM-MS and SLIM IM-MS showed additional features were observed for all lipid extracts when examined under high resolving power IM conditions, with the number of CCS-aligned features that resolve into additional peaks from DTIM-MS to SLIM IM-MS analysis varying between 5 and 50%, depending on the specific lipid sub-class investigated. Lipid CCS values are obtained from SLIM IM ( TW(SLIM) CCS) through a two-step calibration procedure to align these measurements to within 2% average bias to reference values obtained via DTIM ( DT CCS). A total of 225 lipid features from seven lipid extracts are subsequently identified in the high resolving power IM analysis by a combination of accurate mass-to-charge, CCS, retention time, and linear mobility-mass correlations to curate a high-resolution IM lipid structural atlas. These results emphasize the high isomeric complexity present in lipidomic samples and underscore the need for multiple analytical stages of separation operated at high resolution. Graphical abstract

Reardon, Allison R. (ORCID:0000000165830134)↗
DOE OSTI2022

ROI-Finder : machine learning to guide region-of-interest scanning for X-ray fluorescence microscopy

The microscopy research at the Bionanoprobe (currently at beamline 9-ID and later 2-ID after APS-U) of Argonne National Laboratory focuses on applying synchrotron X-ray fluorescence (XRF) techniques to obtain trace elemental mappings of cryogenic biological samples to gain insights about their role in critical biological activities. The elemental mappings and the morphological aspects of the biological samples, in this instance, the bacterium Escherichia coli ( E. Coli ), also serve as label-free biological fingerprints to identify E. coli cells that have been treated differently. The key limitations of achieving good identification performance are the extraction of cells from raw XRF measurements via binary conversion, definition of features, noise floor and proportion of cells treated differently in the measurement. Automating cell extraction from raw XRF measurements across different types of chemical treatment and the implementation of machine-learning models to distinguish cells from the background and their differing treatments are described. Principal components are calculated from domain knowledge specific features and clustered to distinguish healthy and poisoned cells from the background without manual annotation. The cells are ranked via fuzzy clustering to recommend regions of interest for automated experimentation. The effects of dwell time and the amount of data required on the usability of the software are also discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2015

TU-F-CAMPUS-J-04: Impact of Voxel Anisotropy On Statistic Texture Features of Oncologic PET: A Simulation Study

Purpose: Texture metrics extracted from oncologic PET have been investigated with respect to their usefulness as definitive indicants for prognosis in a variety of cancer. Metric calculation is often based on cubic voxels. Most commonly used PET scanners, however, produce rectangular voxels, which may change texture metrics. The objective of this study was to examine the variability of PET texture feature metrics resulting from voxel anisotropy. Methods: Sinograms of NEMA NU-2 phantom for 18F-FDG were simulated using the ASIM simulation tool. The obtained projection data was reconstructed (3D-OSEM) on grids of cubic and rectangular voxels, producing PET images of resolution of 2.73x2.73x3.27mm3 and 3.27x3.27x3.27mm3, respectively. An interpolated dataset obtained from resampling the rectangular voxel data for isotropic voxel dimension (3.27mm) was also considered. For each image dataset, 28 texture parameters based on grey-level co-occurrence matrices (GLCOM), intensity histograms (GLIH), neighborhood difference matrices (GLNDM), and zone size matrices (GLZSM) were evaluated within lesions of diameter of 33, 28, 22, and 17mm. Results: In reference to the isotopic image data, texture features appearing on the rectangular voxel data varied with a range of -34-10% for GLCOM based, -31-39% for GLIH based, -80 -161% for GLNDM based, and −6–45% for GLZSM based while varied with a range of -35-23% for GLCOM based, -27-35% for GLIH based, -65-86% for GLNDM based, and -22 -18% for GLZSM based for the interpolated image data. For the anisotropic data, GLNDM-cplx exhibited the largest extent of variation (161%) while GLZSM-zp showed the least (<1%). As to the interpolated data, GLNDM-busy varied the most (86%) while GLIH-engy varied the least (<1%). Conclusion: Variability of texture appearance on oncologic PET with respect to voxel representation is substantial and feature-dependent. It necessitates consideration of standardized voxel representation for inter-institution studies attempting to validate prognostic values of PET texture features in cancer treatment.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2016

Morpheus Spectral Counter: A computational tool for label-free quantitative mass spectrometry using the Morpheus search engine

Label-free quantitative MS based on the Normalized Spectral Abundance Factor (NSAF) has emerged as a straightforward and robust method to determine the relative abundance of individual proteins within complex mixtures. Here, we present Morpheus Spectral Counter (MSpC) as the first computational tool that directly calculates NSAF values from output obtained from Morpheus, a fast, open-source, peptide-MS/MS matching engine compatible with high-resolution accurate-mass instruments. NSAF has distinct advantages over other MS-based quantification methods, including a higher dynamic range as compared to isobaric tags, no requirement to align and re-extract MS1 peaks, and increased speed. MSpC features an easy to use graphic user interface that additionally calculates both distributed and unique NSAF values to permit analyses of both protein families and isoforms/proteoforms. MSpC determinations of protein concentration were linear over several orders of magnitude based on the analysis of several high-mass accuracy datasets either obtained from PRIDE or generated with total cell extracts spiked with purified Arabidopsis 20S proteasomes. Lastly, the MSpC software was developed in C# and is open sourced under a permissive license with the code made available at http://dcgemperline.github.io/Morpheus_SpC/.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2015

Optimal well placement and brine extraction for pressure management during CO 2 sequestration

Large-scale pressure increases resulting from carbon dioxide (CO 2 ) injection in the subsurface can potentially impact caprock integrity, induce reactivation of critically stressed faults, and drive CO 2 or brine through conductive features into shallow groundwater. Pressure management involving the extraction of native fluids from storage formations can be used to reduce such pressure increases. However, dealing with large volumes of extracted brine can be technically challenging and expensive. Selection of optimal well locations and pumping rates are critical for maximizing CO 2 storage and minimizing brine extraction during geologic CO 2 sequestration (GCS). Robust and efficient computerized algorithms combining reservoir models and optimization methods are needed to make proper decisions on well placement and pumping rates. This work presents a constrained differential evolution (CDE) algorithm for solving global optimization problems involving pressure management of GCS projects. Application of the CDE optimization methodology was demonstrated for a hypothetical CO 2 storage scenario in a deep sandstone reservoir in the Southern San Joaquin Basin in California, USA. Industrial-scale storage of CO 2 would generate significant pressure buildup in this formation, which in turn would raise concerns about induced seismicity due to presence of multiple faults surrounding the injection site. Through the CDE optimization algorithm coupled to a vertically-averaged reservoir simulator, we successfully estimated optimal solutions for brine extraction wells in the reservoir that would limit the local pressure along the faults to a prescribed threshold. Multiple realizations of the reservoir permeability field were created to understand the impact of reservoir heterogeneity on optimization results. Our results indicate that the reservoir slope and heterogeneity have significant impact on optimum extraction rates. Reservoir heterogeneity is also a significant factor for extraction well locations, suggesting that in practice decisions about extraction well placement through optimization should be made at later project stages when data from a few years of CO 2 injection have allowed iterative updating and refining of the reservoir forward models. Although the study focused on optimization of brine extraction, the CDE optimization methodology presented in this paper has also potential to solve other complex optimization problems related to GCS, such as increasing storage efficiency by enhancing injectivity and capillary and dissolution trapping.

58 GEOSCIENCES↗
DOE OSTI2021

A Deep Gravity model for mobility flows generation

The movements of individuals within and among cities influence critical aspects of our society, such as well-being, the spreading of epidemics, and the quality of the environment. When information about mobility flows is not available for a particular region of interest, we must rely on mathematical models to generate them. In this work, we propose Deep Gravity, an effective model to generate flow probabilities that exploits many features (e.g., land use, road network, transport, food, health facilities) extracted from voluntary geographic data, and uses deep neural networks to discover non-linear relationships between those features and mobility flows. Our experiments, conducted on mobility flows in England, Italy, and New York State, show that Deep Gravity achieves a significant increase in performance, especially in densely populated regions of interest, with respect to the classic gravity model and models that do not use deep neural networks or geographic data. Deep Gravity has good generalization capability, generating realistic flows also for geographic areas for which there is no data availability for training. Finally, we show how flows generated by Deep Gravity may be explained in terms of the geographic features and highlight crucial differences among the three considered countries interpreting the model’s prediction with explainable AI techniques.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Extracting Chemical Information from XPS Spectra: A Perspective

Important mechanisms that lead to features, often complex, in X-ray photoelectron spectroscopy (XPS) spectra are defined and described. It is shown that there is much information in an XPS spectrum that can be obtained by examining these features rather than examining only the shifts of main peaks between different materials. These mechanisms are presented with a focus on describing the underlying chemical and physical phenomena responsible for features of the XPS and on showing how these XPS features can be related to the properties and electronic structure of the material studied. While it is necessary to consider certain quantum mechanical rules, the mathematical formalism is not discussed. However, a general awareness of multiplet splittings, which are a result of angular momentum coupling combined with ligand field and spin-orbit splittings, and of covalent mixings in the metal-ligand bond of oxides is essential to properly interpret the significance of XPS features. A conceptual framework of shake excitation from bonding to anti-bonding orbitals is introduced to provide an understanding of the significance of XPS satellites. While the coupling of theory and measurement is required to extract quantitative information from XPS, it may be possible to obtain useful qualitative information directly from features of the XPS spectra provided that one takes into account more than only shifts of the XPS binding energies.

Bagus, Paul S.↗
DOE OSTI2022

A Novel Fault Diagnosis Approach for Rolling Bearing Based on CWT and Adaptive Sparse Representation

Extraction and enhancement of weak impulse signature is the key of rolling bearing fault prognostics in which case the features are often weak and covered by noise. Tunable Q-factor wavelet transform (TQWT), as an emerging wavelet construction theory developed in a frequency domain explicitly, has the advantages of matching with the specific oscillation behavior of signal components. In this article, an adaptive sparse representation (ASR) method is proposed, which integrates the sparse code shrinkage (SCS) and parameter optimization into TQWT. However, direct application of ASR is difficult to extract fault signatures at the early stage or low-speed operation due to weak fault symptoms and background noise. A novel fault diagnosis strategy based on continuous wavelet transform (CWT) and ASR is investigated. CWT owns significant advantages on multiscale subdivision and weak signal detection. The results of simulated and experimental vibration signal analyses verify the effectiveness of the proposed method in accurately extracting weak impulse features from the noise environment.

Yuan, Xing↗
DOE OSTI2017

Automated Morphological and Morphometric Analysis of Mass Spectrometry Imaging Data: Application to Biomarker Discovery

Mass spectrometry imaging datasets are mostly analyzed in terms of average intensity in regions of interest. However, biological tissues have different morphologies with several sizes, shapes, and structures. The important biological information, contained in this highly heterogeneous cellular organization, could be hidden by analyzing the average intensities. Finding an analytical process of morphology would help to find such information, describe tissue model, and support identification of biomarkers. This study describes an informatics approach for the extraction and identification of mass spectrometry image features and its application to sample analysis and modeling. For the proof of concept, two different tissue types (healthy kidney and CT-26 xenograft tumor tissues) were imaged and analyzed. A mouse kidney model and tumor model were generated using morphometric – number of objects and total surface – information. The morphometric information was used to identify m/z that have a heterogeneous distribution. It seems to be a worthwhile pursuit as clonal heterogeneity in a tumor is of clinical relevance. This study provides a new approach to find biomarker or support tissue classification with more information. .

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2024

A Practical Solver for Scalar Data Topological Simplification

This paper presents a practical approach for the optimization of topological simplification, a central pre-processing step for the analysis and visualization of scalar data. Given an input scalar field f and a set of “signal” persistence pairs to maintain, our approaches produces an output field g that is close to f and which optimizes (i) the cancellation of “non-signal” pairs, while (ii) preserving the “signal” pairs. In contrast to pre-existing simplification algorithms, our approach is not restricted to persistence pairs involving extrema and can thus address a larger class of topological features, in particular saddle pairs in three-dimensional scalar data. Our approach leverages recent generic persistence optimization frameworks and extends them with tailored accelerations specific to the problem of topological simplification. Extensive experiments report substantial accelerations over these frameworks, thereby making topological simplification optimization practical for real-life datasets. Our approach enables a direct visualization and analysis of the topologically simplified data, e.g., via isosurfaces of simplified topology (fewer components and handles). We apply our approach to the extraction of prominent filament structures in three-dimensional data. Specifically, we show that our pre-simplification of the data leads to practical improvements over standard topological techniques for removing filament loops. Here, we also show how our approach can be used to repair genus defects in surface processing. Finally, we provide a C++ implementation for reproducibility purposes.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2021

UCGretina geant4 simulation of the GRETINA Gamma-Ray Energy Tracking Array

UCGretina, a geant4 simulation of the GRETINA gamma-ray tracking array of highly-segmented high-purity germanium detectors is described. We have developed a model of the array, in particular of the Quad Module and the capsules, that gives good agreement between simulated and measured photopeak efficiencies over a broad range of gamma-ray energies and reproduces the shape of the measured Compton continuum. Both of these features are needed in order to accurately extract gamma-ray yields from spectra collected in in-beam gamma-ray spectroscopy measurements with beams traveling at v / c ≳0.3 at the National Superconducting Cyclotron Laboratory and the Facility for Rare Isotope Beams. In the process of developing the model, we determined that millimeter-scale layers of passive germanium surrounding the active volumes of the simulated crystals must be included in order to reproduce measured photopeak efficiencies. In conclusion, we adopted a simple model of effective passive layers and developed heuristic methods of determining passive-layer thicknesses by comparison of simulations and measurements for a single crystal and for the full array. Prospects for future development of the model are discussed.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2026

PyHydroGeophysX: An extensible open-source platform for integrating hydrological models with geophysical measurements

Hydrological models and geophysical measurements are widely used tools for understanding subsurface hydrological processes relevant to water resource management, yet they typically remain disconnected due to technical barriers. We present PyHydroGeophysX, an open-source Python platform bridging this gap by providing standardized interfaces between hydrological modeling software (MODFLOW, ParFlow) and geophysical simulation tools (PyGIMLi, SimPEG). The platform implements bidirectional workflows: translating hydrological outputs into simulated geophysical responses through petrophysical models, and extracting hydrological information from geophysical inversions. Key features include bidirectional workflow modules, configurable petrophysical models, time-lapse inversion with temporal regularization, parallel computing, and mesh utilities for property transfer between geophysical and hydrological grids. The modular architecture of PyHydroGeophysX enables researchers to incorporate additional models and methods, fostering broader adoption of integrated hydrogeophysical approaches. The software is freely available on GitHub and is intended for researchers and practitioners working at the intersection of hydrology and geophysics.

Hydrogeophysics↗
DOE OSTI2020

A method for mapping submicron-scale crystallographic order/disorder applied to human tooth enamel

Tooth enamel, the outermost layer of human teeth, is a complex, hierarchically structured biocomposite. The details of this structure are important in multiple human health contexts, from understanding the progression of dental caries (tooth decay) to understanding the process of amelogenesis and related developmental defects. Enamel is composed primarily of long, nanoscale crystallites of hydroxyapatite that are bundled by the thousands to form micron-scale rods. Studies with transmission electron microscopy show the relationships between small groups of crystallites and X-ray diffraction characterize averages over many rods, but the direct measurement of variations in local crystallographic structure across and between enamel rods has been missing. Here, we describe a synchrotron X-ray-based experimental approach and a novel analysis method developed to address this gap in knowledge. A ~500-nm-wide beam of monochromatic X-rays in conjunction with a sample section only 1 μm in thickness enables 2D diffraction patterns to be collected from small well-separated volumes within the enamel microstructure but still probes enough crystallites (~300 per pattern) to extract population-level statistics on crystallographic features like lattice parameter, crystallite size, and orientation distributions. Furthermore, the development of a quantitative metric to characterize relative order and disorder based on the azimuthal autocorrelation of diffracted intensity enables these crystallographic measurements to be correlated with their location within the enamel microstructure (e.g., between rod and interrod regions). These methods represent a step forward in the characterization of human enamel and will elucidate the variation of the crystallographic structure across and between enamel rods for the first time.

crystallographic order/disorder↗
DOE OSTI2014

Interactive XCMS Online: Simplifying Advanced Metabolomic Data Processing and Subsequent Statistical Analyses

XCMS Online (xcmsonline.scripps.edu) is a cloud-based informatic platform designed to process and visualize mass-spectrometry-based, untargeted metabolomic data. Initially, the platform was developed for two-group comparisons to match the independent, “control” versus “disease” experimental design. Here, we introduce an enhanced XCMS Online interface that enables users to perform dependent (paired) two-group comparisons, meta-analysis, and multigroup comparisons, with comprehensive statistical output and interactive visualization tools. Newly incorporated statistical tests cover a wide array of univariate analyses. Multigroup comparison allows for the identification of differentially expressed metabolite features across multiple classes of data while higher order meta-analysis facilitates the identification of shared metabolic patterns across multiple two-group comparisons. Given the complexity of these data sets, we have developed an interactive platform where users can monitor the statistical output of univariate (cloud plots) and multivariate (PCA plots) data analysis in real time by adjusting the threshold and range of various parameters. On the interactive cloud plot, metabolite features can be filtered out by their significance level (p-value), fold change, mass-to-charge ratio, retention time, and intensity. The variation pattern of each feature can be visualized on both extracted-ion chromatograms and box plots. The interactive principal component analysis includes scores, loadings, and scree plots that can be adjusted depending on scaling criteria. The utility of XCMS functionalities is demonstrated through the metabolomic analysis of bacterial stress response and the comparison of lymphoblastic leukemia cell lines.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2015

THE DARK MATTER PROFILE OF THE MILKY WAY: A NON-PARAMETRIC RECONSTRUCTION

We present the results of a new, non-parametric method to reconstruct the Galactic dark matter profile directly from observations. Using the latest kinematic data to track the total gravitational potential and the observed distribution of stars and gas to set the baryonic component, we infer the dark matter contribution to the circular velocity across the Galaxy. The radial derivative of this dynamical contribution is then estimated to extract the dark matter profile. The innovative feature of our approach is that it makes no assumption on the functional form or shape of the profile, thus allowing for a clean determination with no theoretical bias. We illustrate the power of the method by constraining the spherical dark matter profile between 2.5 and 25 kpc away from the Galactic center. The results show that the proposed method, free of widely used assumptions, can already be applied to pinpoint the dark matter distribution in the Milky Way with competitive accuracy, and paves the way for future developments.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

IL-Net: Using Expert Knowledge to Guide the Design of Furcated Neural Networks

Deep neural networks (DNN) excel at extracting patterns. Through representation learning and automated feature engineering on large datasets, such models have been highly successful in computer vision and natural language applications. Designing optimal network architectures from a principled or rational approach however has been less than successful, with the best successful approaches utilizing an additional machine learning algorithm to tune the network hyperparameters. However, in many technical fields, there exist established domain knowledge and understanding about the subject matter. In this work, we develop a novel furcated neural network architecture that utilizes domain knowledge as high-level design principles of the network. We demonstrate proof-of-concept by developing IL-Net, a furcated network for predicting the properties of ionic liquids, which is a class of complex multi-chemicals entities. Compared to existing state-of-the-art approaches, we show that furcated networks can improve model accuracy by approximately 20-35%, without using additional labeled data. Lastly, we distill two key design principles for furcated networks that can be adapted to other domains.

Sakloth, Khushmeen↗