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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

Gaps labeling theorem for the bubble-diamond self-similar graphs

Abstract Motivated by the appearance of fractals in several areas of physics, especially in solid state physics and the physics of aperiodic order, and in other sciences, including the quantum information theory, we present a detailed spectral analysis for a new class of fractal-type diamond graphs, referred to as bubble-diamond graphs, and provide a gap-labeling theorem in the sense of Bellissard for the corresponding probabilistic graph Laplacians using the technique of spectral decimation. Labeling the gaps in the Cantor set by the normalized eigenvalue counting function, also known as the integrated density of states, we describe the gap labels as orbits of a second dynamical system that reflects the branching parameter of the bubble construction and the decimation structure. The spectrum of the natural Laplacian on limit graphs is shown generically to be pure point supported on a Cantor set, though one particular graph has a mixture of pure point and singularly continuous components.

Physics↗
DOE OSTI2023

SpeckleNN: a unified embedding for real-time speckle pattern classification in X-ray single-particle imaging with limited labeled examples

With X-ray free-electron lasers (XFELs), it is possible to determine the three-dimensional structure of noncrystalline nanoscale particles using X-ray single-particle imaging (SPI) techniques at room temperature. Classifying SPI scattering patterns, or `speckles', to extract single-hits that are needed for real-time vetoing and three-dimensional reconstruction poses a challenge for high-data-rate facilities like the European XFEL and LCLS-II-HE. Here, we introduce SpeckleNN, a unified embedding model for real-time speckle pattern classification with limited labeled examples that can scale linearly with dataset size. Trained with twin neural networks, SpeckleNN maps speckle patterns to a unified embedding vector space, where similarity is measured by Euclidean distance. We highlight its few-shot classification capability on new never-seen samples and its robust performance despite having only tens of labels per classification category even in the presence of substantial missing detector areas. Without the need for excessive manual labeling or even a full detector image, our classification method offers a great solution for real-time high-throughput SPI experiments.

47 OTHER INSTRUMENTATION↗
DOE OSTI2017

When Labels Fall Short: Property Graph Simulation via Blending of Network Structure and Vertex Attributes

Property graphs can be used to represent heterogeneous networks with attributed vertices and edges. Given one property graph, simulating another graph with same or greater size with identical statistical properties with respect to the attributes and connectivity is critical for privacy preservation and benchmarking purposes. In this work we tackle the problem of capturing the statistical dependence of the edge connectivity on the vertex labels and using the same distribution to regenerate property graphs of the same or expanded size in a scalable manner. However, accurate simulation becomes a challenge when the attributes do not completely explain the network structure. We propose the Property Graph Model (PGM) approach that uses an attribute (or label) augmentation strategy to mitigate the problem and preserve the graph connectivity as measured via degree distribution, vertex label distributions and edge connectivity. Our proposed algorithm is scalable with a linear complexity in the number of edges in the target graph. We illustrate the efficacy of the PGM approach in regenerating and expanding the datasets by leveraging two distinct illustrations.

Visweswara Sathanur, Arun↗
DOE OSTI2026

Deep-ultraviolet ptychographic pocket-scope (DART): mesoscale lensless molecular imaging with label-free spectroscopic contrast

The mesoscale characterization of biological specimens has traditionally required compromises between resolution, field-of-view, depth-of-field, and molecular specificity, with most approaches relying on external labels. Here we present the Deep-ultrAviolet ptychogRaphic pockeT-scope (DART), a handheld platform that transforms label-free molecular imaging through intrinsic deep-ultraviolet spectroscopic contrast. By leveraging biomolecules’ natural absorption fingerprints and combining them with lensless ptychographic microscopy, DART resolves down to 308-nm linewidths across centimeter-scale areas while maintaining millimeter-scale depth-of-field. The system’s virtual error-bin methodology effectively eliminates artifacts from limited temporal coherence and other optical imperfections, enabling high-fidelity molecular imaging without lenses. Through differential spectroscopic imaging at deep-ultraviolet wavelengths, DART quantitatively maps nucleic acid and protein distributions with femtogram sensitivity, providing an intrinsic basis for explainable virtual staining. We demonstrate DART’s capabilities through imaging of tissue sections, cytopathology specimens, blood cells, and neural populations, revealing detailed molecular contrast without external labels. The combination of high-resolution molecular mapping and broad mesoscale imaging in a portable platform opens new possibilities from rapid clinical diagnostics, tissue analysis, to biological characterization in space exploration.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2025

Sensos Smart Label Performance Summary as Observed by Oak Ridge National Laboratory

The Oak Ridge National Laboratory (ORNL) team performed an evaluation of the Sensos Smart Label Gen 2.0, as shown in Figure 1, for package tracking. A long-distance round-trip shipment between Oak Ridge, Tennessee, and Seattle, Washington, was completed to assess the device’s performance in location tracking, environment sensing capabilities, alerting features, threshold options, battery life, and real-time and historical data retrieval from the “Sync” data dashboard provided by Sensos. The evaluation was conducted to gain a general understanding of the capabilities of the device. Furthermore, due to time and resource constraints, ORNL did not conduct exhaustive testing to confirm reliability, availability, or effectiveness of alerting and tracking features. On equipment arrangement, Sensos (sensos.ai) graciously agreed to provide a Sensos Smart Label Gen 2.0 device to ORNL, at no cost, for testing and evaluation purposes. ORNL conducted assessments along with other commercial off-the-shelf (COTS) tracking devices. As a courtesy, ORNL will provide Sensos with this report summarizing the observations and findings specific to the Sensos label based on the tests performed.

42 ENGINEERING↗
DOE OSTI2020

Soil Respiration and Microbial Biomass from Soil Incubations with 13C Labeled Additions

This data set provides the respiration and soil microbial biomass data from a series of short and long-term laboratory incubation experiments with 13C labeled substrates to examine how plant communities (forest vs. grassland), edaphic properties, and microbial communities influence C cycling and the long-term fate of C in soil systems. Paired forest and grassland soils from four locations were incubated with 13C labeled glucose in a short-term (144 hours) study while 13C labeled cellulose was added to soils used in the long-term (729 days) study. Soil respiration, microbial biomass, and soil 13C values were monitored throughout the study. Soils were collected from paired forest and grassland locations in the following states in the United States of America: Tennessee (TN), Ohio (OH), Iowa (IA), and Missouri (MO). This dataset contains one data file in comma-separate (*.csv) format.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Resin Assisted Capture Coupled with Isobaric Tandem Mass Tag Labeling for Multiplexed Quantification of Protein Thiol Oxidation

Reversible oxidative modifications on protein thiols have recently emerged as important mediators of cellular function. Herein we describe the detailed procedure of a quantitative redox proteomics method that utilizes resin-assisted capture (RAC) in combination with tandem mass tag (TMT) isobaric labeling and liquid chromatography-tandem mass spectrometry (LC-MS/MS) to allow multiplexed stochiometric quantification of oxidized protein thiols at the proteome level. The site-specific quantitative information on oxidized cysteine residues provides additional insight into the functional impacts of such modifications. The workflow is adaptable across many sample types including cultured cells (e.g. mammalian, prokaryotic) and whole tissues (e.g. heart, lung, muscle) which are initially lysed/homogenized with free thiols being alkylated to prevent artificial oxidation. The oxidized protein thiols are then reduced and captured by a thiol affinity resin which streamlines and simplifies the workflow steps by allowing the proceeding digestion, labeling, and washing procedures to be performed without additional transfer of proteins/peptides. Finally, labeled peptides are eluted and analyzed by LC-MS/MS to reveal comprehensive stoichiometric changes related to thiol oxidation across the entire proteome. This method greatly improves our understanding of the role of redox dependent regulation under physiological and pathophysiological states related to protein thiol oxidation.

RAC, TMT, thiol redox proteomics, cysteine, PTM st↗
DOE OSTI2020

Sign-OPT: A Query-Efficient Hard-label Adversarial Attack

We study the most practical problem setup for evaluating adversarial robustness of a machine learning system with limited access: the hard-label black-box attack setting for generating adversarial examples, where limited model queries are allowed and only the decision is provided to a queried data input. Several algorithms have been proposed for this problem but they typically require huge amount (>20,000) of queries for attacking one example. Among them, one of the state-of-the-art approaches (Cheng et al., 2019) showed that hard-label attack can be modeled as an optimization problem where the objective function can be evaluated by binary search with additional model queries, thereby a zeroth order optimization algorithm can be applied. In this paper, we adopt the same optimization formulation but propose to directly estimate the sign of gradient at any direction instead of the gradient itself, which enjoys the benefit of single query. Using this single query oracle for retrieving sign of directional derivative, we develop a novel query-efficient Sign-OPT approach for hard-label black-box attack. We provide a convergence analysis of the new algorithm and conduct experiments on several models on MNIST, CIFAR-10 and ImageNet. We find that Sign-OPT attack consistently requires 5X to 10X fewer queries when compared to the current state-of-the-art approaches, and usually converges to an adversarial example with smaller perturbation.

Cheng, Minhao↗
DOE OSTI2019

Failure Analysis on Multivariate Time-series Data given Uncertain Labels

Machine failure analysis and detection is critical to today’s industrial society. Given a number of failure events on multivariate temporal data, the ability to identify 1) the discriminative patterns prior to the events and 2) the most relevant features to the failure has practical use for early warning and root cause analysis. However, since these patterns are not necessarily adjacent to the onset of failure in time, faulty labels are often with uncertainty, which makes traditional supervised detection methods inapplicable. To address the label uncertainty and learn the complicated correlation in multivariate time series, we design Failure Analysis on Multivariate Time-series Data (MAMT) that jointly selects the most failure-relevant features and time-instances by a novel dynamic and directional label diffusion process. Extensive experiments demonstrate that MAMT is more effective while more efficient than popular baselines.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Sulfoxide-Based Isobaric Labelling Reagent for Accurate Quantitative Mass Spectrometry

Modern proteomics requires reagents for exact quantification of peptides in complex mixtures. Peptide labelling is most typically achieved with isobaric tags that consist of a balancer and a reporter part that separate in the gas phase. An ingenious distribution of stable isotopes offers multiple reagents with identical molecular weight but a different mass of the reporter groups, allowing relative quantification of multiple samples in one measurement. In this work, we discuss a new isobaric labelling reagent, where the balancer and the reporter are linked by a sulfoxide group, which, based on the sulfoxide pyrolysis, leads to easy and asymmetric cleavage at low fragmentation energy. The fragmentation of our new design is significantly improved, yielding more intense complementary ion signals, allowing complementary ion cluster analysis as well.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Labeling of Phosphatidylinositol Lipid Products in Cells through Metabolic Engineering by Using a Clickable myo -Inositol Probe

Phosphatidylinositol (PI) lipids control critical biological processes, so aberrant biosynthesis often leads to disease. As a result, the capability to track the production and localization of these compounds in cells is vital for elucidating their complex roles. In this paper, we report the design, synthesis, and application of clickable myo -inositol probe 1 a for bioorthogonal labeling of PI products. To validate this platform, we initially conducted PI synthase assays to show that 1 a inhibits PI production in vitro. Fluorescence microscopy experiments next showed probe-dependent imaging in T-24 human bladder cancer and Candida albicans cells. Growth studies in the latter showed that replacement of myo -inositol with probe 1 a led to an enhancement in cell growth. Lastly, fluorescence-based TLC analysis and mass spectrometry experiments support the labeling of PI lipids. This approach provides a promising means for tracking the complex biosynthesis and trafficking of these lipids in cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2017

Combinatorial Labeling Method for Improving Peptide Fragmentation in Mass Spectrometry

Annotation of peptide sequence from tandem mass spectra constitutes the central step of mass spectrometry-based proteomics. Peptide mass spectra are obtained upon gas-phase fragmentation. Identification of the protein from a set of experimental peptide spectral matches is usually referred as protein inference. Occurrence and intensity of these fragment ions in the MS/MS spectra are dependent on many factors such as amino acid composition, peptide basicity, activation mode, protease, etc. Particularly, chemical derivatizations of peptides were known to alter their fragmentation. In this study, the influence of acetylation, guanidinylation, and their combination on peptide fragmentation was assessed initially on a lipase (LipA) from Bacillus subtilis followed by a bovine six protein mix digest. The dual modification resulted in improved fragment ion occurrence and intensity changes, and this resulted in the equivalent representation of b- and y-type fragment ions in an ion trap MS/MS spectrum. The improved representation has allowed us to accurately annotate the peptide sequences de novo. Dual labeling has significantly reduced the false positive protein identifications in standard bovine six peptide digest. Our study suggests that the combinatorial labeling of peptides is a useful method to validate protein identifications for high confidence protein inference. .

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2018

Quantitative monitoring of two simultaneously binding species using Label-Enhanced surface plasmon resonance

Highlights: • SPR discriminates and monitors two molecules simultaneously binding to a surface. • Accurate concentration determination of two different molecules simultaneously. • Deconstruction of a sum sensorgram into two molecular component sensorgrams. • Simultaneous measurement of binding constants of two binding molecules in a mixture. • Applicability to allosteric effects, bi-specific binders, and conformation changes. Surface plasmon resonance (SPR) is a well-established method for biomolecular interaction studies. SPR monitors the binding of molecules to a solid surface, embodied as refractive index changes close to the surface. One limitation of conventional SPR is the universal nature of the detection that results in an inability to qualitatively discriminate between different binding species. Furthermore, it is impossible to directly discriminate two species simultaneously binding to different sites on a protein, which limits the utility of SPR, for example, in the study of allosteric binders or bi-specific molecules. It is also impossible in principle to discriminate protein conformation changes from actual binding events. Here we demonstrate how Label-Enhanced SPR can be utilized to discriminate and quantitatively monitor the simultaneous binding of two different species – one dye-labeled and one unlabeled - on a standard, single-wavelength SPR instrument. This new technique increases the versatility of SPR technology by opening up application areas where the usefulness of the approach has previously been limited.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Probing labeling-induced lysosome alterations in living cells by imaging-derived mean squared displacement analysis

Lysosomes are not merely degradative organelles but play a central role in nutrient sensing, metabolism and cell-growth regulation. Our ability to study their function in living cells strictly relies on the use of lysosome-specific fluorescent probes tailored to optical microscopy applications. Still, no report thus far quantitatively analyzed the effect of labeling strategies/procedures on lysosome properties in live cells. We tackle this issue by a recently developed spatiotemporal fluctuation spectroscopy strategy that extracts structural (size) and dynamic (diffusion) properties directly from imaging, with no a-priori knowledge of the system. We highlight hitherto neglected alterations of lysosome properties upon labeling. In particular, we demonstrate that Lipofectamine reagents, used to transiently express lysosome markers fused to fluorescent proteins (FPs) (e.g. LAMP1-FP or CD63-FP), irreversibly alter the organelle structural identity, inducing a ∼2-fold increase of lysosome average size. The organelle structural identity is preserved, instead, if electroporation or Effectene are used as transfection strategies, provided that the expression levels of the recombinant protein marker are kept low. This latter condition can be achieved also by generating cell lines stably expressing the desired FP-tagged marker. Reported results call into question the interpretation of a massive amount of data collected so far using fluorescent protein markers and suggest useful guidelines for future studies.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

A Proximity Labeling Strategy Provides Insights into the Composition and Dynamics of Lipid Droplet Proteomes

Lipid droplet (LD) functions are regulated by a complement of integral and peripheral proteins that associate with the bounding LD phospholipid monolayer. Defining the composition of the LD proteome has remained a challenge due to the presence of contaminating proteins in LD-enriched buoyant fractions. To overcome this limitation, we developed a proximity labeling strategy that exploits LD-targeted APEX2 to biotinylate LD proteins in living cells. Application of this approach to two different cell types identified the vast majority of previously validated LD proteins, excluded common contaminating proteins, and revealed new LD proteins. Moreover, quantitative analysis of LD proteome dynamics uncovered a role for endoplasmic reticulum-associated degradation in controlling the composition of the LD proteome. In conclusion, these data provide an important resource for future LD studies and demonstrate the utility of proximity labeling to study the regulation of LD proteomes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2016

SU-C-BRA-01: Interactive Auto-Segmentation for Bowel in Online Adaptive MRI-Guided Radiation Therapy by Using a Multi-Region Labeling Algorithm

Purpose: In MRI-guided online adaptive radiation therapy, re-contouring of bowel is time-consuming and can impact the overall time of patients on table. The study aims to auto-segment bowel on volumetric MR images by using an interactive multi-region labeling algorithm. Methods: 5 Patients with locally advanced pancreatic cancer underwent fractionated radiotherapy (18–25 fractions each, total 118 fractions) on an MRI-guided radiation therapy system with a 0.35 Tesla magnet and three Co-60 sources. At each fraction, a volumetric MR image of the patient was acquired when the patient was in the treatment position. An interactive two-dimensional multi-region labeling technique based on graph cut solver was applied on several typical MRI images to segment the large bowel and small bowel, followed by a shape based contour interpolation for generating entire bowel contours along all image slices. The resulted contours were compared with the physician’s manual contouring by using metrics of Dice coefficient and Hausdorff distance. Results: Image data sets from the first 5 fractions of each patient were selected (total of 25 image data sets) for the segmentation test. The algorithm segmented the large and small bowel effectively and efficiently. All bowel segments were successfully identified, auto-contoured and matched with manual contours. The time cost by the algorithm for each image slice was within 30 seconds. For large bowel, the calculated Dice coefficients and Hausdorff distances (mean±std) were 0.77±0.07 and 13.13±5.01mm, respectively; for small bowel, the corresponding metrics were 0.73±0.08and 14.15±4.72mm, respectively. Conclusion: The preliminary results demonstrated the potential of the proposed algorithm in auto-segmenting large and small bowel on low field MRI images in MRI-guided adaptive radiation therapy. Further work will be focused on improving its segmentation accuracy and lessening human interaction.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Sequential Learning with Active Partial Labeling for Building Metadata

Modern buildings are instrumented with thousands of sensing and control points. The ability to automatically extract the physical context of each point, e.g., the type, location, and relationship with other points, is the key to enabling building analytics at scale. However, this process is costly as it usually requires domain expertise with a deep understanding of the building system and its point naming scheme. In this study, we aim to reduce the human effort required for mapping sensors to their context, i.e., metadata mapping. We formulate the problem as a sequential labeling process and use the conditional random field to exploit the regular and dependent structures observed in the metadata. We develop a suite of active learning strategies to adaptively select the most informative subsequences in point names for human labeling, which significantly reduces the inputs from domain experts.We evaluated our approach on three different buildings and observed encouraging performance in metadata mapping from the proposed solution.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2019

Heavy-atom labeling of RNA by PLOR for de novo crystallographic phasing

Due to the paucity of known RNA structures, experimental phasing is crucial for obtaining three-dimensional structures of RNAs by X-ray crystallography. Covalent attachment of heavy atoms to RNAs is one of the most useful strategies to facilitate phase determination. However, this approach is limited by the inefficiency or inability to synthesize large RNAs (>60 nucleotides) site-specifically labeled with heavy atoms using traditional methods. Here, we applied our recently reported method, PLOR (position-selective labeling of RNA) to incorporate 5-iodouridine at specific positions in the adenine riboswitch RNA aptamer domain, which was then used for crystallization and subsequent de novo SAD phasing. PLOR is a powerful tool to improve the efficiency of obtaining RNA structures de novo by X-ray crystallography.

59 BASIC BIOLOGICAL SCIENCES↗