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DOE OSTI2024

Nanoscale visualization and spectral fingerprints of the charge order in ScV 6 Sn 6 distinct from other kagome metals

Charge density waves (CDWs) in kagome metals have been tied to many exotic phenomena. Here, using spectroscopic-imaging scanning tunneling microscopy and angle-resolved photoemission spectroscopy, we study the charge order in kagome metal ScV 6 Sn 6 . The similarity of electronic band structures of ScV 6 Sn 6 and TbV 6 Sn6 (where charge ordering is absent) suggests that charge ordering in ScV 6 Sn 6 is unlikely to be primarily driven by Fermi surface nesting of the Van Hove singularities. In contrast to the CDW state of cousin kagome metals, we find no evidence supporting rotation symmetry breaking. Differential conductance dI/dV spectra show a partial gap Δ 1 CO ≈ 20 meV at the Fermi level. Interestingly, dI/dV maps reveal that charge modulations exhibit an abrupt phase shift as a function of energy at energy much higher than Δ 1 CO , which we attribute to another spectral gap. Our experiments reveal a distinctive nature of the charge order in ScV 6 Sn 6 with fundamental differences compared to other kagome metals.

36 MATERIALS SCIENCE↗

DOE OSTI2017

A 3D-printed microbial cell culture platform with in situ PEGDA hydrogel barriers for differential substrate delivery

Additive manufacturing, or 3D-printing techniques have recently begun to enable simpler, faster, and cheaper production of millifluidic devices at resolutions approaching 100–200 μ m. At this resolution, cell culture devices can be constructed that more accurately replicate natural environments compared with conventional culturing techniques. A number of microfluidics researchers have begun incorporating additive manufacturing into their work, using 3D-printed devices in a wide array of chemical, fluidic, and even some biological applications. In this work, we describe a 3D-printed cell culture platform and demonstrate its use in culturing Pseudomonas putida KT2440 bacteria for 44 h under a differential substrate gradient. Polyethylene glycol diacrylate (PEGDA) hydrogel barriers are patterned in situ within a 3D-printed channel. Transport of the toluidine blue tracer dye through the hydrogel barriers is characterized. Nutrients and oxygen were delivered to cells in the culture region by diffusion through the PEGDA hydrogel barriers from adjacent media or saline perfusion channels. Expression of green fluorescent protein by P. putida KT2440 enabled real time visualization of cell density within the 3D-printed channel, and demonstrated cells were actively expressing protein over the course of the experiment. Cells were observed clustering near hydrogel barrier boundaries where fresh substrate and oxygen were being delivered via diffusive transport, but cells were unable to penetrate the barrier. The device described here provides a versatile and easy to implement platform for cell culture in readily controlled gradient microenvironments. By adjusting device geometry and hydrogel properties, this platform could be further customized for a wide variety of biological applications.

42 ENGINEERING↗

DOE OSTI2015

SU-E-J-153: Reconstructing 4D Cone Beam CT Images for Clinical QA of Lung SABR Treatments

Purpose: To verify that the planned Primary Target Volume (PTV) and Internal Gross Tumor Volume (IGTV) fully enclose a moving lung tumor volume as visualized on a pre-SABR treatment verification 4D Cone Beam CT. Methods: Daily 3DCBCT image sets were acquired immediately prior to treatment for 10 SABR lung patients using the on-board imaging system integrated into a Varian TrueBeam (v1.6: no 4DCBCT module available). Respiratory information was acquired during the scan using the Varian RPM system. The CBCT projections were sorted into 8 bins offline, both by breathing phase and amplitude, using in-house software. An iterative algorithm based on total variation minimization, implemented in the open source reconstruction toolkit (RTK), was used to reconstruct the binned projections into 4DCBCT images. The relative tumor motion was quantified by tracking the centroid of the tumor volume from each 4DCBCT image. Following CT-CBCT registration, the planning CT volumes were compared to the location of the CBCT tumor volume as it moves along its breathing trajectory. An overlap metric quantified the ability of the planned PTV and IGTV to contain the tumor volume at treatment. Results: The 4DCBCT reconstructed images visibly show the tumor motion. The mean overlap between the planned PTV (IGTV) and the 4DCBCT tumor volumes was 100% (94%), with an uncertainty of 5% from the 4DCBCT tumor volume contours. Examination of the tumor motion and overlap metric verify that the IGTV drawn at the planning stage is a good representation of the tumor location at treatment. Conclusion: It is difficult to compare GTV volumes from a 4DCBCT and a planning CT due to image quality differences. However, it was possible to conclude the GTV remained within the PTV 100% of the time thus giving the treatment staff confidence that SABR lung treatements are being delivered accurately.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2022

Gamma-Ray Imaging to Monitor Accumulating Holdup

Over time, nuclear materials can accumulate in process equipment and keeping track of such material is of interest to many stakeholder communities including safeguards, process monitoring, criticality safety, and radiological safety. Passive gamma-ray imaging provides a visual and quantitative tool that can be applied to this problem, allowing one to determine the amount of material present from the images. In this work we emulate the accumulation of highly enriched uranium (HEU) in a large rectangular stainless-steel duct by stacking 5, 27 × 50 cm2 cards, each of which has approximately 11 g of 235U in oxide form distributed across its surface, behind each other. A series of images were collected using a coded-aperture gamma-ray imager, starting with a single card, adding a second card, etc. Long integrations were collected to allow evaluating the performance of the approach as a function of integration time. The images are analyzed using first principles to determine the amount of material in each image. The results are compared to the known amount of material in each configuration, and as a function of time. The analysis includes a first-principles error propagation and to further evaluate the uncertainty, a boot-strap sampling approach is also applied to the data. The work offers a proof of concept for unattended monitoring of uranium holdup with a quantitative gamma-ray imaging technique.

Ziock, Klaus-Peter↗

DOE OSTI2019

Tracking materials science data lineage to manage millions of materials experiments and analyses

Abstract In an era of rapid advancement of algorithms that extract knowledge from data, data and metadata management are increasingly critical to research success. In materials science, there are few examples of experimental databases that contain many different types of information, and compared with other disciplines, the database sizes are relatively small. Underlying these issues are the challenges in managing and linking data across disparate synthesis and characterization experiments, which we address with the development of a lightweight data management framework that is generally applicable for experimental science and beyond. Five years of managing experiments with this system has yielded the Materials Experiment and Analysis Database (MEAD) that contains raw data and metadata from millions of materials synthesis and characterization experiments, as well as the analysis and distillation of that data into property and performance metrics via software in an accompanying open source repository. The unprecedented quantity and diversity of experimental data are searchable by experiment and analysis attributes generated by both researchers and data processing software. The search web interface allows users to visualize their search results and download zipped packages of data with full annotations of their lineage. The enormity of the data provides substantial challenges and opportunities for incorporating data science in the physical sciences, and MEAD’s data and algorithm management framework will foster increased incorporation of automation and autonomous discovery in materials and chemistry research.

Soedarmadji, Edwin↗

DOE OSTI2015

WE-EF-210-06: Ultrasound 2D Strain Measurement of Radiation-Induced Toxicity: Phantom and Ex Vivo Experiments

Purpose: Radiation-induced fibrosis is a common long-term complication affecting many patients following cancer radiotherapy. Standard clinical assessment of subcutaneous fibrosis is subjective and often limited to visual inspection and palpation. Ultrasound strain imaging describes the compressibility (elasticity) of biological tissues. This study’s purpose is to develop a quantitative ultrasound strain imaging that can consistently and accurately characterize radiation-induce fibrosis. Methods: In this study, we propose a 2D strain imaging method based on deformable image registration. A combined affine and B-spline transformation model is used to calculate the displacement of tissue between pre-stress and post-stress B-mode image sequences. The 2D displacement is estimated through a hybrid image similarity measure metric, which is a combination of the normalized mutual information (NMI) and normalized sum-of-squared-differences (NSSD). And 2D strain is obtained from the gradient of the local displacement. We conducted phantom experiments under various compressions and compared the performance of our proposed method with the standard cross-correlation (CC)- based method using the signal-to-noise (SNR) and contrast-to-noise (CNS) ratios. In addition, we conducted ex-vivo beef muscle experiment to further validate the proposed method. Results: For phantom study, the SNR and CNS values of the proposed method were significantly higher than those calculated from the CC-based method under different strains. The SNR and CNR increased by a factor of 1.9 and 2.7 comparing to the CC-based method. For the ex-vivo experiment, the CC-based method failed to work due to large deformation (6.7%), while our proposed method could accurately detect the stiffness change. Conclusion: We have developed a 2D strain imaging technique based on the deformable image registration, validated its accuracy and feasibility with phantom and ex-vivo data. This 2D ultrasound strain imaging technology may be valuable as physicians try to eliminate radiation-induce fibrosis and improve the therapeutic ratio of cancer radiotherapy. This research is supported in part by DOD PCRP Award W81XWH-13-1-0269, and National Cancer Institute (NCI) Grant CA114313.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2012

NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism’s genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotypeexpressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to the set of phenotype-biased subgraphs output by an exact maximally-biased subgraph enumeration algorithm ( MBS-Enum ). The code (NIBBS and the module to visualize the identified subsystems) is available at http:// freescience.org/cs/NIBBS.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗

DOE OSTI2018

Characterizing Photometric Flicker. Handheld Meters

Temporal light modulation (TLM) is the variation of light output as a function of time and its presence can have adverse effects on health and visual performance (e.g., increased occurrence of migraines, reduced visual-task performance). TLM has been the subject of recent articles primarily associated with flicker in LED lighting. Lighting system characteristics that can affect flicker vary by technology; examples include filament thickness for incandescent sources, phosphor persistence for fluorescent and coated metal halide sources, and circuit designs for electronically ballasted or driven sources. The characterization of flicker, especially in the field, is important to ensure adequate living and working conditions. Today, various types of flicker meters are available, including high-speed and precise benchtop models for laboratory environments and portable handheld meters that can range from simple smartphone applications in an existing smart device to scientific grade meters that have capabilities beyond flicker measurements. In this report, the performance of various handheld meters capable of measuring flicker in the field is compared to a reference benchtop meter chosen based on its performance in the initial round of meter comparisons performed by Pacific Northwest National Laboratory.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗

DOE OSTI2021

Initial tests of large format sensors for the ATLAS ITk strip tracker

For the construction of the Inner Tracker (ITk) as part of the phase-II upgrade programme of the ATLAS detector for the High-Luminosity (HL) LHC, batches of Long Strip (LS) and Short Strip (SS) n + -in-p type micro-strip sensors have been produced by Hamamatsu Photonics and Infineon. The full size sensors measure approximately 98 × 98 mm 2 and are designed and engineered for tolerance against the 9.7 × 10 14 1 MeV n eq /cm 2 fluence expected at the HL-LHC, including a safety factor of 1.5. Each sensor has 2 or 4 columns of 1280 individual channels arranged at 75.5 μ m horizontal pitch. To ensure the sensors comply with their specifications, a Quality Control (QC) procedure has been implemented, comprising measurements on every individual sensor as well as on a sample basis. Every sensor is subjected to an initial visual inspection, after which the full surface of the sensor is captured with very high resolution by an automated camera setup. Non-contact metrology is performed to obtain the sensor surface profile. Electrical measurements establishing the reverse bias leakage current and depletion voltage are then conducted automatically. Sample sensors from every batch are subjected to 40 h of leakage stability checks in controlled atmosphere, and tests on every channel measuring leakage current, coupling capacitance and bias resistance are done. The recorded results are uploaded to a production database following data quality checks. In this paper, QC test validation data and the compiled results for the first batches of production grade sensors are presented. The QC protocol was validated, and the first production sensors were confirmed to be within specification. The results are compared to those from the previous generation of prototype sensors.

73 NUCLEAR PHYSICS AND RADIATION PHYSICS↗

DOE OSTI2022

Direct Multimodal Nanoscale Visualization of Early Phosphorus-Based Antiwear Tribofilm Formation

Understanding the mechanism of antiwear (AW) tribofilm formation and how to tune surface chemistry to control functionality is essential for the development of the next generation of oil lubricants. In particular, understanding and optimizing early AW tribofilm formation can increase the energy efficiency of mechanical systems. However, the mechanism for how these films form is not well understood. The majority of prior work has focused on analyzing only end-of-test surfaces long after the film has formed. In this work, we develop an in situ multimodal chemical imaging methodology to directly visualize the early formation of AW films on steel surfaces. We investigate an oil formulation containing a phosphorus-based additive commonly used to protect surfaces from wear and fatigue processes in machine elements, such as gears, bearings, and sliding contacts. Using nanoscale multimodal chemical imaging on combined platforms of atomic force microscopy (AFM) coupled directly with in situ nano-infrared (nano-IR) spectroscopy, and further combined ex situ with time-of-flight secondary ion mass spectrometry (ToF-SIMS), we demonstrate a direct correlation between changes in friction and local surface chemistry. In these experiments, the AFM probe acts as a single asperity contact to generate the tribofilm as well as a tool to analyze it in situ as it is forming. To verify our in situ measurements, we compare these results to the ex situ ToF-SIMS of macroscale block-on-ring tribometer-formed samples. We report the understanding gained here on how AW films form and how film properties can be modified by tuning the chemistry of the additives will facilitate developing transmission fluids to meet increasing demands for vehicle performance and efficiency.

36 MATERIALS SCIENCE↗

DOE OSTI2020

In situ characterization of foreign object damage (FOD) in environmental-barrier-coated silicon carbide (SiC) ceramic

Environmental barrier coatings (EBCs) protect advanced ceramics and ceramic matrix composites (CMCs) from oxidation and corrosion in gas turbine engine environments. Foreign object damage (FOD), where debris impact the protective coatings, is a critical hazard which limits the turbine durability. Despite previous efforts to understand FOD in EBCs, a detailed understanding of the fundamental transient damage mechanisms is still lacking. In the current work, the real-time FOD behavior of a Mullite / Silicon EBC was visualized via a dynamic synchrotron X-ray source in phase contrast imaging (PCI) configuration. Prior to the in situ FOD experiments, the microstructure and composition of the coating were respectively characterized using a scanning electron microscope (SEM) and X-ray diffraction (XRD). The variation in the properties of the debris was modeled by ~ 1.5 mm diameter Partially Stabilized Zirconia (PSZ) and Silicon Nitride (Si 3 N 4 ) spheres. A modified light-gas gun setup, synchronized with the X-ray beam, was used to propel the projectiles at velocities ranging between 300 and 355 m·s -1 . Coated samples were impacted under a fully backed support configuration and at normal incidence. Coating penetration and delamination, as well as projectile deformation at the bond coat resulted for FOD by PSZ spheres. Comparatively, projectile fracture, with subsequent rebound of fragments, as well as complete coating penetration and delamination at the substrate interface occurred for FOD by Si 3 N 4 spheres. It should be noted that only cone cracking was observed for FOD by PSZ spheres while back surface cracking was present for both projectile types. Qualitative understanding of the driving forces for the observed damage mechanisms were provided.

36 MATERIALS SCIENCE↗

DOE OSTI2021

Comparing perceptions of a dimmable LED lighting system between a real space and a virtual reality display

Over the last several decades, designers have used digital screens to view images of real and simulated spaces and make critical design decisions. Screen technology has improved during this time, as technologies like OLED have replaced legacy displays (CRT, plasma, and LCD). These new screens provide a higher pixel resolution, luminous output and contrast ratio. Immersive head-mounted displays now allow designers to view immersive images, and recent developments in real-time rendering have encouraged the uptake of virtual reality (VR) head-mounted displays in mainstream practice and design education. This paper presents an experiment on lighting perception using a series of LED lighting conditions in a real space and a virtual representation of those conditions captured using a 360° high-dynamic-range camera and presented on an HTC Vive Pro HMD. Fifty-three participants were asked to rate each lighting condition by viewing it in a real space (n = 30) or via immersive HDR photographs displayed in a VR HMD (n = 23). The results show that ratings of visual comfort, pleasantness, evenness, contrast and glare are similar between the HTC Vive Pro HMD and our real space when evaluating well-lit scenes, but significant differences emerge in dim and highly contrasted scenes for a number of rating scales.

Rockcastle, S.↗

DOE OSTI2022

Neural net modeling of equilibria in NSTX-U

Neural networks (NNs) offer a path towards synthesizing and interpreting data on faster timescales than traditional physics-informed computational models. In this work we develop two NNs relevant to equilibrium and shape control modeling, which are part of a suite of tools being developed for the National Spherical Torus Experiment-Upgrade for fast prediction, optimization, and visualization of plasma scenarios. The networks include Eqnet, a free-boundary equilibrium solver trained on the EFIT01 (Equilibrium FITtting 01) reconstruction algorithm, and Pertnet, which is trained on the Gspert code and predicts the non-rigid plasma response, a nonlinear term that arises in shape control modeling. The NNs are trained with different combinations of inputs and outputs in order to offer flexibility in use cases. In particular, Eqnet can use magnetic diagnostics as inputs and act as an EFIT-like reconstruction algorithm, or, by using pressure and current profile information the NN can act as a forward Grad–Shafranov equilibrium solver. This forward-mode version is envisioned to be implemented in the suite of tools for simulation of plasma scenarios. The reconstruction-mode version gives some performance improvements compared to the online reconstruction code real-time EFIT, especially when vessel eddy currents are significant. Here, we report strong performance for all NNs indicating that the models could reliably be used within closed-loop simulations or other applications. Some limitations are discussed.

70 PLASMA PHYSICS AND FUSION TECHNOLOGY↗

DOE OSTI2017

Chroma Shift and Gamut Shape: Going Beyond Average Color Fidelity and Gamut Area

Though sometimes referred to as a two-measure system for evaluating color rendition, IES TM-30-15 includes other key components that go beyond the high-level average values IES Rf and IES Rg. This article focuses on the Color Vector Graphic and Local Chroma Shift (IES Rcs,hj), discussing the calculation methods for these evaluation tools and providing context for the interpretation of the values. It also presents alternatives for quantifying the same characteristics, which can inform the development of future measures. The Color Vector Graphic (CVG) is a visual representation of hue and chroma shifts across different hues. It quickly communicates complex information about how object colors will be rendered by a light source, although it is difficult or impossible to use for writing a specification. CVGs demonstrate that increases in chroma for certain hues sometimes means a decrease in chroma for other hues, and illustrates hue shifts for intermediate colors. The combination of shifts over different hues can be referred to as gamut shape. Complementing this information are the IES Rcs,hj values, which quantify the average relative chroma shift for samples in each of the 16 hue-angle bins (j) specified in IES TM-30-15. Unlike measures of average color fidelity and gamut area, gamut shape and hue-specific chroma shift are new concepts with no directly-comparable historical references. It will be critical to incorporate the Color Vector Graphic and Local Chroma Shift values into practice because they capture information about color rendition that is impossible to describe with average measures (such as CIE Ra, IES Rf, or IES Rg), but that is critical to understanding human evaluations of color quality in architectural environments.

Chroma Shift↗

DOE OSTI2022

Time Sequence Machine Learning-Based Data Intrusion Detection for Smart Voltage Source Converter-Enabled Power Grid

Smart inverters of distributed energy resources can enable cloud computing, condition monitoring, result visualization, remote control, and peer-to-peer energy trading in advanced power systems. However, the advent of data injection attacks in the communication architecture can alter measurement characteristics of power grids and have devastating consequences. In this article, we propose a time sequence machine learning-based anomaly detection methodology for detecting cyber intrusion into control signal setpoints and dc voltage signal measurement bias of the voltage source converter (VSC) in wind generators. We first investigated the effects of four types of denial of service, tampering signal, and stealthy-type data intrusion attacks on smart VSCs and overall wind farms. We then proposed a novel time sequence machine learning-based intrusion detection framework that can be implemented to detect different cyberattacks in the VSCs. The performance of the proposed framework has been compared with that of autoencoder and clustering-based intrusion detection framework. The proposed framework was validated by using the IEEE 39 bus power system in the presence of four wind farms in different locations. Using several metrics for intrusion detection performance, we validated the effectiveness of the proposed framework.

42 ENGINEERING↗

DOE OSTI2021

Orbits and Masses of Binaries from Speckle Interferometry at SOAR

We present results from Speckle inteferometric observations of 15 visual binaries and one double-line spectroscopic binary, carried out with the HRCam Speckle camera of the SOAR 4.1 m telescope. These systems were observed as a part of an on-going survey to characterize the binary population in the solar vicinity, out to a distance of 250 pc. We obtained orbital elements and mass sums for our sample of visual binaries. The orbits were computed using a Markov Chain Monte Carlo algorithm that delivers maximum likelihood estimates of the parameters, as well as posterior probability density functions that allow us to evaluate their uncertainty. Their periods cover a range from 5 yr to more than 500 yr; and their spectral types go from early A to mid M, implying total system masses from slightly more than 4M{sub ⊙} down to 0.2M {sub ⊙}. They are located at distances between approximately 12 and 200 pc, mostly at low Galactic latitude. For the double-line spectroscopic binary YSC8, we present the first combined astrometric/radial-velocity orbit resulting from a self-consistent fit, leading to individual component masses of 0.897 ± 0.027 M {sub ⊙} and 0.857 ± 0.026 M {sub ⊙}; and an orbital parallax of 26.61 ± 0.29 mas, which compares very well with the Gaia DR2 trigonometric parallax (26.55 ± 0.27 mas). In combination with published photometry and trigonometric parallaxes, we place our objects on an H-R diagram and discuss their evolutionary status. We also present a thorough analysis of the precision and consistency of the photometry available for them.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗

DOE OSTI2013

Adaptive Sampling Algorithms for Probabilistic Risk Assessment of Nuclear Simulations

Nuclear simulations are often computationally expensive, time-consuming, and high-dimensional with respect to the number of input parameters. Thus exploring the space of all possible simulation outcomes is infeasible using finite computing resources. During simulation-based probabilistic risk analysis, it is important to discover the relationship between a potentially large number of input parameters and the output of a simulation using as few simulation trials as possible. This is a typical context for performing adaptive sampling where a few observations are obtained from the simulation, a surrogate model is built to represent the simulation space, and new samples are selected based on the model constructed. The surrogate model is then updated based on the simulation results of the sampled points. In this way, we attempt to gain the most information possible with a small number of carefully selected sampled points, limiting the number of expensive trials needed to understand features of the simulation space. We analyze the specific use case of identifying the limit surface, i.e., the boundaries in the simulation space between system failure and system success. In this study, we explore several techniques for adaptively sampling the parameter space in order to reconstruct the limit surface. We focus on several adaptive sampling schemes. First, we seek to learn a global model of the entire simulation space using prediction models or neighborhood graphs and extract the limit surface as an iso-surface of the global model. Second, we estimate the limit surface by sampling in the neighborhood of the current estimate based on topological segmentations obtained locally. Our techniques draw inspirations from topological structure known as the Morse-Smale complex. We highlight the advantages and disadvantages of using a global prediction model versus local topological view of the simulation space, comparing several different strategies for adaptive sampling in both contexts. One of the most interesting models we propose attempt to marry the two by obtaining a coarse global representation using prediction models, and a detailed local representation based on topology. Our methods are validated on several analytical test functions as well as a small nuclear simulation dataset modeled after a simplified Pressurized Water Reactor.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2023

Molecular Hypernetworks for Exploration of Multi-Dimensional Metabolomics Data (Chyper)

Orthogonal separations of data from high-resolution mass spectrometry can provide insight into sample composition and help address the challenge of complete annotation of molecules in untargeted metabolomics. “Molecular networks” (MNs), as used, for example, in the Global Natural Products Social Molecular Networking platform, are an increasingly popular computational strategy for exploring and visualizing molecular relationships and improving annotation. MNs use graph representations to show the relationships between measured multidimensional data features. MNs also show promise for using network science algorithms to automatically identify targets for annotation candidates and to dereplicate features associated to a single molecular identity. However, more advanced methods may better represent the complexity present in samples. Our work aims to increase confidence in annotation propagation by extending molecular network methods to include “molecular hypernetworks” (MHNs), able to natively represent multiway relationships among observations supporting both human and analytical processing. In this paper we first introduce MHNs illustrated with simple examples, and demonstrate how to build them from liquid chromatography- and ion mobility spectrometry- separated MS data. We then describe a method to construct MHNs directly from existing MNs as their “clique reconstructions”, demonstrating their utility by comparing examples of previously published graph-based MNs to their respective MHNs.

59 BASIC BIOLOGICAL SCIENCES↗