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All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Label Assist: Personalized Travel Models for Longitudinal Data Collection

Understanding travel behavior is crucial to transportation decarbonization. OpenPATH is an open-source mobility platform which collects and analyzes human travel behavior at the individual level. The mobile application passively senses trips and prompts users to label them. However, users find the labeling process burdensome; less than half the trips are typically labeled, making much of the data unusable in aggregate analyses of mobility patterns. Prior work has addressed the response fatigue challenge through automated mode inference using sensor data, but sensors cannot capture all aspects of travel behavior. We explore an alternative approach in which we leverage prior user input to predict travel choices in novel trips. We first explore trip clustering methods and develop a novel two-step pipeline using DBSCAN and SVMs to extract realistic geospatial clusters. We then propose two strategies to predict trip labels: (i) clustering trips and extrapolating labels for similar trips, and (ii) random forest classification. The random forest approach is able to achieve - $70-80% accuracy (purpose: 72%, mode: 79%, replaced mode: 81%). These novel approaches to trip classification allow us to increase the rate of user labeling by suggesting predicted labels to be verified by the user. Unlabeled trips can also contribute to aggregate analyses, using label predictions and their associated confidences as a substitute. While there exist other travel survey apps with the ability to infer travel choices, to our knowledge, this is the first paper to describe such a supervised system and rigorously evaluate it.

ADVANCED PROPULSION SYSTEMS,ENERGY PLANNING, POLIC↗
DOE OSTI2023

Syntheses and characterization of isotopically labeled 1,3,5-triamino-2,4,6-trinitrobenzene (TATB)

Synthesis and characterization of chemical analogues of TATB, where specific atoms in the structure have been isotopically substituted, are reported. 15 N, 2 H, and 18 O have replaced the naturally occurring isotope distributions in the amino and/or the nitro attendant sites and 13 C has replaced the carbon in the ring structure. A modified wet-amination method was used to produce the analogues, and the isotopic replacements were performed by selective choice of labeled precursors. Four 15 N-labeled compounds (N replaced in the amino and nitro positions), two deuterium-labeled compounds (hydrogens replaced on the amino groups), and one 13 C-labeled compound (C in the ring substituted) were synthesized of high isotopic and chemical purity. One partially labeled 18 O-labeled compound (O in the nitro position) was a result of incomplete labeling due to exchange reactions during synthesis. The compounds were characterized by various spectroscopic methods – mass spectrometry (MS), solid-state nuclear magnetic resonance (SS-NMR), infrared (FTIR), powder x-ray diffraction (PXRD), and differential scanning calorimetry (DSC), depending upon the substitution. In conclusion, these compounds have been critical to the efforts in understanding the decomposition pathways of TATB when exposed to abnormal thermal environments.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Radius measurement via super-resolution microscopy enables the development of a variable radii proximity labeling platform

The elucidation of protein interaction networks is critical to understanding fundamental biology as well as developing new therapeutics. Proximity labeling platforms (PLPs) are state-of-the-art technologies that enable the discovery and delineation of biomolecular networks through the identification of protein-protein interactions. These platforms work via catalytic generation of reactive probes at a biological region of interest; these probes then diffuse through solution and covalently “tag” proximal biomolecules. The physical distance that the probes diffuse determines the effective labeling radius of the PLP and is a critical parameter that influences the scale and resolution of interactome mapping. As such, by expanding the degrees of labeling resolution offered by PLPs, it is possible to better capture the various size scales of interactomes. At present, however, there is little quantitative understanding of the labeling radii of different PLPs. Here, we report the development of a superresolution microscopy-based assay for the direct quantification of PLP labeling radii. Using this assay, we provide direct extracellular measurements of the labeling radii of state-of-the-art antibody-targeted PLPs, including the peroxidase-based phenoxy radical platform (269 ± 41 nm) and the high-resolution iridium-catalyzed µMap technology (54 ± 12 nm). Last, we apply these insights to the development of a molecular diffusion-based approach to tuning PLP resolution and introduce a new aryl-azide-based µMap platform with an intermediate labeling radius (80 ± 28 nm).

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1989

Specific photoaffinity labeling of two plasma membrane polypeptides with an azido auxin

Plasma membrane vesicles were isolated from zucchini (Cucurbita pepo) hypocotyl tissue by aqueous phase partitioning and assessed for homogeneity by the use of membrane-specific enzyme assays. The highly pure (ca. 95%) plasma membrane vesicles maintained a pH differential across the membrane and accumulated a tritiated azido analogue of 3-indoleacetic acid (IAA), 5-azido-[7-3H]IAA ([3H]N3IAA), in a manner similar to the accumulation of [3H]IAA. The association of the [3H]N3IAA with membrane vesicles was saturable and subject to competition by IAA and auxin analogues. Auxin-binding proteins were photoaffinity labeled by addition of [3H]N3IAA to plasma membrane vesicles prior to exposure to UV light (15 sec; 300 nm) and detected by subsequent NaDodSO4/PAGE and fluorography. When the reaction temperature was lowered to -196 degrees C, high-specific-activity labeling of a 40-kDa and a 42-kDa polypeptide was observed. Triton X-100 (0.1%) increased the specific activity of labeling and reduced the background, which suggests that the labeled polypeptides are intrinsic membrane proteins. The labeled polypeptides are of low abundance, as expected for auxin receptors. Further, the addition of IAA and auxin analogues to the photoaffinity reaction mixture resulted in reduced labeling that was qualitatively similar to their effects on the accumulation of radiolabeled IAA in membrane vesicles. Collectively, these results suggest that the radiolabeled polypeptides are auxin receptors. The covalent nature of the label should facilitate purification and further characterization of the receptors.

NASA Discipline Number 29-20↗
DOE OSTI2021

An Open Combinatorial Diffraction Dataset Including Consensus Human and Machine Learning Labels with Quantified Uncertainty for Training New Machine Learning Models

Modern machine learning and autonomous experimentation schemes in materials science rely on accurate analysis of the data ingested by these models. Unfortunately, accurate analysis of the underlying data can be difficult, even for domain experts, complicating the training of the models intended to drive experiments. This is especially true when the goal is to identify the presence of weak signatures in diffraction or spectroscopic datasets. In this work, we examine a set of as-obtained diffraction data that track the phase transition from monoclinic to tetragonal in a Nb-doped VO2 film as a function of temperature and dopant concentration. We then task a set of domain experts and a set of machine learning experts with identifying which phase is present in each diffraction pattern manually and algorithmically, respectively; in both cases, the labels can vary dramatically, especially at the phase boundaries. We use the mode of the labels and the Shannon entropy as a method to capture, preserve and propagate consensus labels and their variance. Further we use the expert labels as a benchmark and demonstrate the use of Shannon entropy weighted scoring to test the performance of machine learning generated labels. Finally, we propose a material data challenge centered around generating improved labeling algorithms. This real-world dataset curated with expert labels can act as test bed for new algorithms. The raw data, annotations and code used in this study are all available online at data.gov and the interested reader is encouraged to replicate and improve the existing models

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Classifying handedness in chiral nanomaterials using label error robust deep learning

Abstract High-throughput scanning electron microscopy (SEM) coupled with classification using neural networks is an ideal method to determine the morphological handedness of large populations of chiral nanoparticles. Automated labeling removes the time-consuming manual labeling of training data, but introduces label error, and subsequently classification error in the trained neural network. Here, we evaluate methods to minimize classification error when training from automated labels of SEM datasets of chiral Tellurium nanoparticles. Using the mirror relationship between images of opposite handed particles, we artificially create populations of varying label error. We analyze the impact of label error rate and training method on the classification error of neural networks on an ideal dataset and on a practical dataset. Of the three training methods considered, we find that a pretraining approach yields the most accurate results across label error rates on ideal datasets, where size and other morphological variables are held constant, but that a co-teaching approach performs the best in practical application.

36 MATERIALS SCIENCE↗
DOE OSTI2025

A machine-learning-driven data labeling pipeline for scientific analysis in MLExchange

This study introduces a novel labeling pipeline to accelerate the labeling process of scientific data sets by using artificial intelligence (AI)-guided tagging techniques. This pipeline includes a set of interconnected web-based graphical user interfaces (GUIs), where Data Clinic and MLCoach enable the preparation of machine learning (ML) models for data reduction and classification, respectively, while Label Maker is used for label assignment. Throughout this pipeline, data can be accessed through a direct connection to a file system or through Tiled for access through Hypertext Transfer Protocol (HTTP). Our experimental results present three use cases where this labeling pipeline has been instrumental for the study of large X-ray scattering data sets in the area of pattern recognition, the remote analysis of resonant soft X-ray scattering data and the fine-tuning process of foundation models. These use cases highlight the labeling capabilities of this pipeline, including the ability to label large data sets in a short period of time, to perform remote data analysis while minimizing data movement and to enhance the fine-tuning process of complex ML models with human involvement.

Chavez, Tanny (ORCID:0000000193172896)↗
NASA NTRS1999

Textural-Contextual Labeling and Metadata Generation for Remote Sensing Applications

Despite the extensive research and the advent of several new information technologies in the last three decades, machine labeling of ground categories using remotely sensed data has not become a routine process. Considerable amount of human intervention is needed to achieve a level of acceptable labeling accuracy. A number of fundamental reasons may explain why machine labeling has not become automatic. In addition, there may be shortcomings in the methodology for labeling ground categories. The spatial information of a pixel, whether textural or contextual, relates a pixel to its surroundings. This information should be utilized to improve the performance of machine labeling of ground categories. Landsat-4 Thematic Mapper (TM) data taken in July 1982 over an area in the vicinity of Washington, D.C. are used in this study. On-line texture extraction by neural networks may not be the most efficient way to incorporate textural information into the labeling process. Texture features are pre-computed from cooccurrence matrices and then combined with a pixel's spectral and contextual information as the input to a neural network. The improvement in labeling accuracy with spatial information included is significant. The prospect of automatic generation of metadata consisting of ground categories, textural and contextual information is discussed.

Kiang, Richard K.↗
NASA NTRS2016

Implementing Connected Component Labeling as a User Defined Operator for SciDB

We have implemented a flexible User Defined Operator (UDO) for labeling connected components of a binary mask expressed as an array in SciDB, a parallel distributed database management system based on the array data model. This UDO is able to process very large multidimensional arrays by exploiting SciDB's memory management mechanism that efficiently manipulates arrays whose memory requirements far exceed available physical memory. The UDO takes as primary inputs a binary mask array and a binary stencil array that specifies the connectivity of a given cell to its neighbors. The UDO returns an array of the same shape as the input mask array with each foreground cell containing the label of the component it belongs to. By default, dimensions are treated as non-periodic, but the UDO also accepts optional input parameters to specify periodicity in any of the array dimensions. The UDO requires four stages to completely label connected components. In the first stage, labels are computed for each subarray or chunk of the mask array in parallel across SciDB instances using the weighted quick union (WQU) with half-path compression algorithm. In the second stage, labels around chunk boundaries from the first stage are stored in a temporary SciDB array that is then replicated across all SciDB instances. Equivalences are resolved by again applying the WQU algorithm to these boundary labels. In the third stage, relabeling is done for each chunk using the resolved equivalences. In the fourth stage, the resolved labels, which so far are "flattened" coordinates of the original binary mask array, are renamed with sequential integers for legibility. The UDO is demonstrated on a 3-D mask of O(1011) elements, with O(108) foreground cells and O(106) connected components. The operator completes in 19 minutes using 84 SciDB instances.

UDO↗
DOE OSTI2025

A simple and highly efficient protocol for 13 C-labeling of plant cell wall for structural and quantitative analyses via solid-state nuclear magnetic resonance

Plant cell walls are made of a complex network of interacting polymers that play a critical role in plant development and responses to environmental changes. Thus, improving plant biomass and fitness requires the elucidation of the structural organization of plant cell walls in their native environment. The 13 C-based multi-dimensional solid-state nuclear magnetic resonance (ssNMR) has been instrumental in revealing the structural information of plant cell walls through 2D and 3D correlation spectral analyses. However, the requirement of enriching plants with 13 C limits the applicability of this method. To our knowledge, there is only a very limited set of methods currently available that achieve high levels of 13 C-labeling of plant materials using 13 CO 2 , and most of them require large amounts of 13 CO 2 in larger growth chambers. In this study, a simplified protocol for 13C-labeling of plant materials is introduced that allows ca 60% labeling of the cell walls, as quantified by comparison with commercially labeled samples. This level of 13 C-enrichment is sufficient for all conventional 2D and 3D correlation ssNMR experiments for detailed analysis of plant cell wall structure. The protocol is based on a convenient and easy setup to supply both 13 C-labeled glucose and 13 CO 2 using a vacuum-desiccator. The protocol does not require large amounts of 13 CO 2 . This study shows that our 13 C-labeling of plant materials can make the accessibility to ssNMR technique easy and affordable. The derived high-resolution 2D and 3D correlation spectra are used to extract structural information of plant cell walls. This helps to better understand the influence of polysaccharide-polysaccharide interaction on plant performance and allows for a more precise parametrization of plant cell wall models.

09 BIOMASS FUELS↗
DOE OSTI2022

Tag you're it: Application of stable isotope labeling and LC-MS to identify the precursors of specialized metabolites in plants

Untargeted liquid chromatography/mass spectrometry (LC-MS) can contribute a comprehensive and unbiased picture of the metabolic space of plants. These data can be used to quantify natural metabolite variation for genome wide association studies, to compare global metabolic responses from environmental or genetic perturbations, and to identify previously undescribed metabolites in Nature. A major limitation with untargeted metabolomics is the classification and identification of the thousands of metabolite features that can be detected in a single analytical run. Isotopic labeling improves the informational value of these datasets by categorizing metabolites as being derived from specific upstream precursors and/or to known metabolic pathways. When a 13 C-labeled precursor is fed to either a plant or tissue, the downstream metabolites produced from it have a higher m/z value than the molecules in the pre-existing pool, generating an m/z peak pair that can be specifically identified within the MS data. In this paper, we outline methods and principles to consider when supplementing untargeted MS data with isotopic labeling, including how to choose the appropriate isotopic label, grow and feed plant tissues to maximize label uptake and incorporation into derivatives, optimize LC-MS methods, and interpret the resulting labeling data. Although the focus here is on annotation of amino acid-derived metabolites using LC-MS, we anticipate that the methods are generally adaptable to other precursors, plant species, and chromatographic approaches.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Endogenous Production and Vibrational Analysis of Heavy-Isotope-Labeled Peptides from Cyanobacteria

Stable isotope labeling is an extremely useful tool for characterizing the structure, tracing the metabolism, and imaging the distribution of natural products in living organisms using mass-sensitive measurement techniques. In this study, a cyanobacterium was cultured in 15 N/ 13 C-enriched media to endogenously produce labeled, bioactive oligopeptides. The extent of heavy isotope incorporation in these peptides was determined with LC–MS, while the overall extent of heavy isotope incorporation in whole cells was studied with nanoSIMS and AFM-IR. Up to 98 % heavy isotope incorporation was observed in labeled cells. Three of the most abundant peptides, microcystin-LR (MCLR), cyanopeptolin-A (CYPA), and aerucyclamide-A (ACAA), were isolated and further studied with Raman and FTIR spectroscopies and DFT calculations. This revealed several IR and Raman active vibrations associated with functional groups not common in ribosomal peptides, like diene, ester, thiazole, thiazoline, and oxazoline groups, which could be suitable for future vibrational imaging studies. More broadly, this study outlines a simple and relatively inexpensive method for producing heavy-labeled natural products. Finally, manipulating the bacterial culture conditions by the addition of specific types and amounts of heavy-labeled nutrients provides an efficient means of producing heavy-labeled natural products for mass-sensitive imaging studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

Phosphorothioate-Based Site-Specific Labeling of Large RNAs for Structural and Dynamic Studies

Pulsed electron-electron double resonance (PELDOR) spectroscopy, X-ray scattering interferometry (XSI), and single-molecule Forster resonance energy transfer (smFRET) are molecular rulers that provide inter- or intramolecular pair-wise distance distributions in the nanometer range, thus being ideally suitable for structural and dynamic studies of biomolecules including RNAs. The prerequisite for such applications requires site-specific labeling of biomolecules with spin labels, gold nanoparticles, and fluorescent tags, respectively. Recently, site-specific labeling of large RNAs has been achieved by a combination of transcription of an expanded genetic alphabet containing A-T/G-C base pairs and NaM-TPT3 unnatural base pair (UBP) with posttranscriptional modifications at UBP bases by click chemistry or amine-NHS ester reactions. However, due to the bulky sizes of functional groups or labeling probes used, such strategies might cause structural perturbation and decrease the accuracy of distance measurements. Here, we synthesize an a-thiophosphorylated variant of rTPT3TP (rTPT3aS), which allows for post-transcriptional site-specific labeling of large RNAs at the internal a-phosphate backbone via maleimide-modified probes. Subsequent PELDOR, XSI, and smFRET measurements result in narrower distance distributions than labeling at the TPT3 base. The presented strategy provides a new route to empower the molecular rulers for structural and dynamic studies of large RNA and its complex.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS1976

Studies related to the development of the Viking 1975 labeled release experiment

The labeled release life detection experiment on the Viking 1975 Mars mission is based on the concept that microorganisms will metabolize radioactive organic substrates in a nutrient medium and release radioactive carbon dioxide. Several experiments, using laboratory equipment, were carried out to evaluate various aspects of the concept. Results indicate: (1) label is released by sterilization-treated soil, (2) substantial quantities of label are retained in solution under basic conditions, (3) the substrate used, as well as position of label in the molecule, affect release of label, (4) label release is depressed by radiolytic decomposition of substrates, and (5) About 100,000 organisms are required to produce a detectable response. These results, suggest additional areas for testing, add to the data base for interpretation of flight results, and have significance for broader application of this technique for assessing microbial activity.

Devincenzi, D. L.↗
NASA NTRS2008

Gold Nanoparticle Labels Amplify Ellipsometric Signals

The ellipsometric method reported in the immediately preceding article was developed in conjunction with a method of using gold nanoparticles as labels on biomolecules that one seeks to detect. The purpose of the labeling is to exploit the optical properties of the gold nanoparticles in order to amplify the measurable ellipsometric effects and thereby to enable ultrasensitive detection of the labeled biomolecules without need to develop more-complex ellipsometric instrumentation. The colorimetric, polarization, light-scattering, and other optical properties of nanoparticles depend on their sizes and shapes. In the present method, these size-and-shape-dependent properties are used to magnify the polarization of scattered light and the diattenuation and retardance of signals derived from ellipsometry. The size-and-shape-dependent optical properties of the nanoparticles make it possible to interrogate the nanoparticles by use of light of various wavelengths, as appropriate, to optimally detect particles of a specific type at high sensitivity. Hence, by incorporating gold nanoparticles bound to biomolecules as primary or secondary labels, the performance of ellipsometry as a means of detecting the biomolecules can be improved. The use of gold nanoparticles as labels in ellipsometry has been found to afford sensitivity that equals or exceeds the sensitivity achieved by use of fluorescence-based methods. Potential applications for ellipsometric detection of gold nanoparticle-labeled biomolecules include monitoring molecules of interest in biological samples, in-vitro diagnostics, process monitoring, general environmental monitoring, and detection of biohazards.

Venkatasubbarao, Srivatsa↗
NASA NTRS2005

Digital Equivalent Data System for XRF Labeling of Objects

A digital equivalent data system (DEDS) is a system for identifying objects by means of the x-ray fluorescence (XRF) spectra of labeling elements that are encased in or deposited on the objects. As such, a DEDS is a revolutionary new major subsystem of an XRF system. A DEDS embodies the means for converting the spectral data output of an XRF scanner to an ASCII alphanumeric or barcode label that can be used to identify (or verify the assumed or apparent identity of) an XRF-scanned object. A typical XRF spectrum of interest contains peaks at photon energies associated with specific elements on the Periodic Table (see figure). The height of each spectral peak above the local background spectral intensity is proportional to the relative abundance of the corresponding element. Alphanumeric values are assigned to the relative abundances of the elements. Hence, if an object contained labeling elements in suitably chosen proportions, an alphanumeric representation of the object could be extracted from its XRF spectrum. The mixture of labeling elements and for reading the XRF spectrum would be compatible with one of the labeling conventions now used for bar codes and binary matrix patterns (essentially, two-dimensional bar codes that resemble checkerboards). A further benefit of such compatibility is that it would enable the conversion of the XRF spectral output to a bar or matrix-coded label, if needed. In short, a process previously used only for material composition analysis has been reapplied to the world of identification. This new level of verification is now being used for "authentication."

Schramm, Harry F.↗
NASA NTRS2011

Using Ensemble Decisions and Active Selection to Improve Low-Cost Labeling for Multi-View Data

This paper seeks to improve low-cost labeling in terms of training set reliability (the fraction of correctly labeled training items) and test set performance for multi-view learning methods. Co-training is a popular multiview learning method that combines high-confidence example selection with low-cost (self) labeling. However, co-training with certain base learning algorithms significantly reduces training set reliability, causing an associated drop in prediction accuracy. We propose the use of ensemble labeling to improve reliability in such cases. We also discuss and show promising results on combining low-cost ensemble labeling with active (low-confidence) example selection. We unify these example selection and labeling strategies under collaborative learning, a family of techniques for multi-view learning that we are developing for distributed, sensor-network environments.

machine learning↗
DOE OSTI2020

Block Design with Common Reference Samples Enables Robust Large-Scale Label-Free Quantitative Proteome Profiling

Label-free quantitative proteomics has become an increasingly popular tool for profiling global protein abundances. However, one major weakness is the potential performance drift of the LC-MS platform over time, which in turn limits its utility for analyzing large-scale sample sets. To address this, in this work we introduce an experimental and data analysis scheme based on a block-design with common controls within each block for enabling LC-MS-based large-scale label-free quantification. In this scheme, a large number of samples (e.g., >100 samples) are analyzed in smaller, and more manageable blocks, minimizing instrument drift and variability within a block. Furthermore, each designated block also contains common controls (or reference) samples for normalization within and across blocks. We demonstrated the effectiveness of this method by profiling the proteome response of human macrophage THP-1 cells to 11 engineered nanomaterials (ENMs) at two different doses. A total of 116 samples were analyzed in six blocks, yielding an average coverage of 4500 proteins per sample. The data revealed consistent quantification of proteins across all six blocks, as shown by highly stable quantification of house-keeping proteins in all samples and high levels of quantification correlation among samples from different blocks. The data also demonstrated that label-free quantification is robust and accurate enough to quantify even very subtle abundance changes as well as large fold-changes without potential ratio compression as often encountered with isobaric labeling. Our streamlined workflow is easy to implement and can be readily adapted to other large cohort studies for reproducible label-free proteome quantification.

59 BASIC BIOLOGICAL SCIENCES↗