Search NASA⌕ Search

SEARCH · Search NASA

Results for “LABELLING”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 127 records · Page 7
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Comparative Study of Novel Fluorescent Cyanine Nucleotides: Hybridization Analysis of Labeled PCR Products Using a Biochip

This study investigated the synthesis and substrate properties of Cy5-labeled dUTP derivatives with different substituents, linkers between the dye unit and pyrimidine heterocycle and fluorophore charges. Fluorescently labeled nucleoside triphosphates were studied as substrates using multiplex PCR with Taq and Vent (exo-) DNA polymerases, the typical representatives of the A and B polymerase families. The efficiency of nucleotide incorporation during PCR was assessed with a multi-parameter hybridization analysis using a diagnostic DNA microarray. The hybridization analysis indirectly estimates the incorporation efficiency of dye-labeled nucleotides in multiplex PCR. Our results demonstrated higher efficiencies of substrates with electrically neutral dyes than electropositive and electronegative Cy5 residues.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

ResNet and CycleGAN for pulse shape discrimination of He-4 detector pulses: Recovering pulses conventional algorithms fail to label unanimously

Pulse shape discrimination (PSD) capable detectors, such as He-4, that respond to neutron and gamma-ray 7 interactions have a threshold deposited energy value below which n/γ discrimination vanishes when using 8 conventional PSD algorithms. Recent attempts in applying supervised learning based artificial neural 9 networks for PSD use the pulses in the separated regions to train the networks so they can be used to classify 10 another set of separated pulses. In doing so, pulses previously indistinguishable are not recovered for 11 classification, which would have increased the number of neutron and gamma-ray pulses that could be used 12 for further analysis. Assuming the reason why conventional PSD algorithms have unseparated regions is 13 because the parameter space of the algorithms fail to capture the intrinsic (but subtle) distinguishing 14 behavior of some of the neutron and gamma-ray pulses, a cycle-consistent generative adversarial network 15 (CycleGAN) was trained to amplify those differences and extract well separated neutron and gamma-ray 16 clusters. Results show that, once the network is trained with pulses from separated and unseparated regions, 17 it was able to transform the pulses in the unseparated region to improve the PSD. Subsequent n/γ 18 classification was performed using deep residual network (ResNet) that takes pulses with 512 data points 19 as an input. Two different ResNets were explored – simple ResNet and modified ResNet which takes 20 segmented pulse inputs in the first layer and the corresponding time axis values in the last hidden layer. 21 The later approach enables the network to extract time correlated pulse features to enhance its ability to 22 capture the pulse behaviors relevant for PSD. Although it achieves slightly lower accuracy, 99.41% versus 23 99.89%, based on simply counting the number of correct n/γ labels assigned, compared to the simple 24 ResNet, the modified ResNets architecture was able to decreases the cross-entropy loss function by half, 25 which implies that the correct n/γ labels assigned are less likely to be accidental. PSD parameter 26 distributions based on n/γ classification by ResNet before and after transforming unseparated pulses using 27 CycleGAN show that by enhancing the separation between neutrons and gamma-rays, the transformation 28 helps improve the performance of classifier networks that are trained using labeled dataset. The 29 enhancement of neutron and gamma-ray separation by the CycleGAN increased the PSD figure of merit 30 (FOM) by up to 70% in some regions. Here, the results show that, if a given detector achieves clear separation 31 between neutron and gamma-ray pulses in any energy region, such neural network approaches can help 32 lower the energy threshold for the separation and increasing the number of neutron and gamma-ray pulses 33 that can be used for further analysis.

4He↗
DOE OSTI2018

Coatomer subunit beta 2 (COPB2), identified by label-free quantitative proteomics, regulates cell proliferation and apoptosis in human prostate carcinoma cells

Highlights: • Our paper offered an available method to identified and confirm some differential expression of genes in prostate cancer. • Our paper provided us a common method to research the function of gene for prostate cancer. • Our paper helps us to identify a novel cancer related gene to explain for development of novel targeted drugs. Label-free quantitative proteomics has broad applications in the identification of differentially expressed proteins. Here, we applied this method to identify differentially expressed proteins (such as coatomer subunit beta 2 [COPB2]) and evaluated the functions and molecular mechanisms of these proteins in prostate cancer (PCA) cell proliferation. Proteins extracted from surgically resected PCA tissues and adjacent tissues of 3 patients were analyzed by label-free quantitative proteomics. The target protein was confirmed by bioinformatics and GEO dataset analyses. To investigate the role of the target protein in PCA, we used lentivirus-mediated small-interfering RNA (siRNA) to knockdown protein expression in the prostate carcinoma cell line, CWR22RV1 cells and assessed gene and protein expression by reverse transcription quantitative polymerase chain reaction and western blotting. CCK8 and colony formation assays were conducted to evaluate cell proliferation. Cell cycle distributions and apoptosis were assayed by flow cytometry. We selected the differentiation-related protein COPB2 as our target protein based on the results of label-free quantitative proteomics. High expression of COPB2 was found in PCA tissue and was related to poor overall survival based on a public dataset. Cell proliferation was significantly inhibited in COPB2-knockdown CWR22RV1 cells, as demonstrated by CCK8 and colony formation assays. Additionally, the apoptosis rate and percentage of cells in the G{sub 1} phase were increased in COPB2-knockdown cells compared with those in control cells. CDK2, CDK4, and cyclin D1 were downregulated, whereas p21 Waf1/Cip1 and p27 Kip1 were upregulated, affecting the cell cycle signaling pathway. COPB2 significantly promoted CWR22RV1 cell proliferation through the cell cycle signaling pathway. Thus, silencing of COPB2 may have therapeutic applications in PCA.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Metabolic profiling associated with autophagy of human placenta-derived mesenchymal stem cells by chemical isotope labeling LC−MS

Autophagy has been reported to have a pivotal role in maintaining stemness, regulating immunomodulation and enhancing the survival of mesenchymal stem cells (MSCs). However, the effect of autophagy on MSC metabolism is largely unknown. Here, we report a workflow for examining the impact of autophagy on human placenta-derived MSC (hPMSC) metabolome profiling with chemical isotope labeling (CIL) LC−MS. Rapamycin or 3-methyladenine was successfully used to induce or inhibit autophagy, respectively. Then, {sup 12}C- and {sup 13}C-dansylation labeling LC−MS were used to profile the amine/phenol submetabolome. A total of 935 peak pairs were detected and 50 metabolites were positively identified using the dansylation metabolite standards library, and 669 metabolites were putatively identified based on an accurate mass match in metabolome databases. {sup 12}C/{sup 13}C-p-dimethylaminophenacyl bromide labeling LC−MS was used to analyze the carboxylic acid submetabolome; 4736 peak pairs were detected, among which 33 metabolites were positively identified in the dimethylaminophenacyl metabolite standards library, and 3007 metabolites were putatively identified. PCA/OPLS-DA analysis combined with volcano plots and Venn diagrams was used to determine the significant metabolites. Metabolites pathway analysis demonstrated that hPMSCs appeared to generate more ornithine with the arginine and proline metabolism pathway and utilized more pantothenic acid to synthesize acetyl-CoA in the beta-alanine metabolism pathway when autophagy was activated. Meanwhile, acetyl-CoA conversion to fatty acids led to accumulation in the fatty acid biosynthesis pathway. In contrast, when autophagy was suppressed, a reduction in metabolites demonstrated weakened metabolic activity in these metabolic pathways. Our research provides a more comprehensive understanding of hPMSC metabolism associated with autophagy.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Plasma Protein Turnover Rates in Rats Using Stable Isotope Labeling, Global Proteomics, and Activity-Based Protein Profiling

Protein turnover is important for general health on cellular and organism scales providing a strategy to replace old, damaged, or dysfunctional proteins. Protein turnover also informs of biomarker kinetics, as a better understanding of synthesis and degradation of proteins increases the clinical utility of biomarkers. Here, turnover rates of plasma proteins in rats were measured in vivo using a pulse-chase stable isotope labeling experiment. During the pulse, rats (n=5) were fed 13C6-labeled lysine (“heavy”) feed for 23 days to label proteins. During the chase, feed was changed to an unlabeled equivalent feed (“light”), and blood was repeatedly sampled from rats over 10 time points for 28 days. Plasma samples were digested with trypsin, and analyzed with liquid chromatography-tandem mass spectrometry (LC-MS/MS). MaxQuant was used to identify peptides and proteins, and quantify heavy:light lysine ratios. A system of ordinary differential equations was used to calculate protein turnover rates. Using this approach, 273 proteins were identified, and turnover rates were quantified for 157 plasma proteins with half-lives ranging 0.3-103 days. For the ~70 most abundant proteins, variability in turnover rates among rats was low (median coefficient of variation: 0.09). Activity-based protein profiling was applied to pooled plasma samples to enrich serine hydrolases using a fluorophosphonate (FP2) activity-based probe. This enrichment resulted in turnover rates for an additional 17 proteins. This study is the first to measure global plasma protein turnover rates in rats in vivo, measure variability of protein turnover rates in any animal model, and utilize activity-based protein profiling for enhancing measurements of targeted, low-abundant proteins, such as those commonly used as biomarkers. Measured protein turnover rates will be important for understanding of the role of protein turnover in cellular and organism health as well as increasing the utility of protein biomarkers through better understanding of processes governing biomarker kinetics.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2019

Uniformly 15N-Labeled Recombinant Ricin A-chain as an Internal Retention Time Standard for Increased Confidence in Forensic Identification of Ricin by Untargeted Nanoflow Liquid Chromatography-Tandem Mass Spectrometry

Ricin, a toxic protein from the castor plant, is of forensic and biosecurity interest because of its high toxicity and common occurrence in crimes and attempted crimes. Qualitative methods to detect ricin are therefore needed. Untargeted liquid chromatography-tandem mass spectrometry (LC-MS/MS) proteomics methods are well-suited because of their high speci-ficity. Specificity in LC-MS/MS comes from both the LC and MS components. However, modern untargeted proteomics methods often use nanoflow LC, which has less reproducible retention times than standard-flow LC, making it challenging to use retention time as a point of identification in a forensic assay. We address this challenge by using retention times rela-tive to a standard, namely uniformly 15N–labeled ricin A-chain produced recombinantly in a bacterial expression system. This material, added as an internal standard prior to trypsin digestion, produces a stable isotope labeled standard for every ricin tryptic peptide in the sample. We show that the MS signals for 15N and natural isotopic abundance ricin peptides are distinct, with mass shifts that correspond to the numbers of nitrogen atoms in each peptide or fragment. We also show that, as expected, labeled and unlabeled peptides coelute, with relative retention time differences of less than 0.2%.

ricin, mass spectrometry, isotope, nitrogen-15, re↗
DOE OSTI2025

Using Multiple Isotope-Labeled Infrared Spectra for the Structural Characterization of an Intrinsically Disordered Peptide

Intrinsically disordered proteins (IDPs) rapidly interconvert between conformers, requiring an ensemble description. This complicates their experimental characterization, and force field limitations pose challenges for their simulation. Here, in this work, we use isotope-labeled and unlabeled infrared (IR) spectra to reweight simulated ensembles of the elastin-like peptide GVGVPGVG, a paradigmatic disordered peptide. By comparing the results obtained with different spectra, we explicitly show that the weights are underdetermined by the ensemble averaged data. We identify which labels and frequency regions maximize structural information while minimizing sensitivity to simulation error and show that these regions report on whether the peptide makes specific interactions. Our work shows the importance of incorporating simulations and simulated spectra at the planning stages of isotope-labeled IR experiments and more generally provides a framework for interpreting IR data for IDPs.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Facile One-Pot Nanoproteomics for Label-Free Proteome Profiling of 50–1000 Mammalian Cells

Recent advances in sample preparation enable label-free MS-based proteome profiling of small numbers of mammalian cells. However, specific devices are often required to downscale sample processing volume from the standard 50-200 µL to sub-µL for effective nanoproteomics, which greatly impedes the implementation of current nanoproteomics methods by broad proteomics research community. Here we report a facile one-pot nanoproteomics method termed SOPs-MS (Surfactant-assisted One-Pot sample processing at the standard volume coupled with MS) for convenient proteome profiling of 50-1000 mammalian cells. Building upon our recent development of SOP-MS for label-free single-cell proteomics at low µL volume (Commun Bio 2021, 4, 265), we have systematically evaluated its processing volume at 10-200 µL using 100 human cells for robust reproducible nanoproteomic analysis. The processing volume of 50 µL which is in the range of volume for standard proteomics sample preparation, has been selected for easy sample handling with benchtop micropipette. Using the commonly accessible LC-MS platform, SOPs-MS allows for reliable label-free quantification of ~1200-2700 protein groups from 50-1000 MCF10A cells. When applied to small subpopulations of mouse colon crypt cells, SOPs-MS can reveal distinct protein signatures between any two subpopulation cells with identification of ~1500-2500 protein groups for each subpopulation. SOPs-MS may pave the way for routine deep proteome profiling of small numbers of cells as well as low-input samples.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2017

Covalent Protein Labeling and Improved Single-Molecule Optical Properties of Aqueous CdSe/CdS Quantum Dots

Semiconductor quantum dots (QDs) have proven to be superior probes for single-molecule imaging compared to organic or genetically encoded fluorophores, but they are limited by difficulties in protein targeting, their larger size, and on-off blinking. We report compact aqueous CdSe/CdS QDs with significantly improved bioconjugation efficiency and superior single-molecule optical properties. We have synthesized covalent protein labeling ligands (i.e., SNAP tags) that are optimized for nanoparticle use, and QDs functionalized with these ligands label SNAP-tagged proteins ~10-fold more efficiently than existing SNAP ligands. Single-molecule analysis of these QDs shows 99% of time spent in the fluorescent on-state, ~4-fold higher quantum efficiency than standard CdSe/ZnS QDs, and 350 million photons detected before photobleaching. Bright signals of these QDs enable us to track the stepping movement of a kinesin motor in vitro, and the improved labeling efficiency enables tracking of single kinesins in live cells.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2025

Precision Labeling of Native Antibodies with Lock Coupling

The formation of stable protein complexes enables much of biotechnology, but even high-affinity complexes can dissociate, limiting potential applications in biomaterials, bioimaging, nanomedicine, and other protein-based technologies. Here, in this study, we describe lock coupling, a simple and selective one-step reaction between interfacial lysine and glutamate or aspartate side chains to form stable isopeptide bonds and be used for the precise labeling of native antibodies. We identify conditions in which short-lived activated esters formed by the aqueous carbodiimide EDC promote isopeptide bond formation specifically at preassociated amine-acid pairs. Indiscriminate cross-linking is minimized by formation of protein complexes before addition of catalyst, use of acidic pH to suppress exposed Lys reactivity, and limiting the aqueous stability of activated esters. For native antibody (Ab) labeling, we show that the small IgG-binding protein GB1 can be covalently attached to the Ab Fc domain and that introduction of Cys into GB1 loops allows for facile conjugation of fluorophores, micelles, or inorganic nanocrystals for imaging in live cells and animals. By varying Cys substituents and protein stoichiometry, a defined number of probes can be uniformly attached without the need for extensive purification. In live-cell confocal microscopy, labeled GB1 serves as a stable replacement for secondary Abs, enabling simple multicolor immunostaining and imaging. Lock coupling requires just a single reagent in aqueous buffer and leverages both the innate ability of proteins to form high-affinity complexes and the widespread presence of Lys-Glu/Asp pairs at their interfaces, with the potential for precision synthesis of protein-based probes for imaging, biomaterials, biophysics, and medicine.

antibody↗
DOE OSTI2021

Label-free characterization of organic nanocarriers reveals persistent single molecule cores for hydrocarbon sequestration

Self-assembled molecular nanostructures embody an enormous potential for new technologies, therapeutics, and understanding of molecular biofunctions. Their structure and function are dependent on local environments, necessitating in-situ/operando investigations for the biggest leaps in discovery and design. However, the most advanced of such investigations involve laborious labeling methods that can disrupt behavior or are not fast enough to capture stimuli-responsive phenomena. We utilize X-rays resonant with molecular bonds to demonstrate an in-situ nanoprobe that eliminates the need for labels and enables data collection times within seconds. Our analytical spectral model quantifies the structure, molecular composition, and dynamics of a copolymer micelle drug delivery platform using resonant soft X-rays. We additionally apply this technique to a hydrocarbon sequestrating polysoap micelle and discover that the critical organic-capturing domain does not coalesce upon aggregation but retains distinct single-molecule cores. This characteristic promotes its efficiency of hydrocarbon sequestration for applications like oil spill remediation and drug delivery. Such a technique enables operando, chemically sensitive investigations of any aqueous molecular nanostructure, label-free.

36 MATERIALS SCIENCE↗
DOE OSTI2025

Robust collection and processing for label-free single voxel proteomics

With advanced mass spectrometry (MS)-based proteomics, genome-scale proteome coverage can be achieved from bulk tissues. However, such bulk measurement lacks spatial resolution and obscures tissue heterogeneity, precluding proteome mapping of tissue microenvironment. Here we report an integrated $\underline{w}et$ $\underline{c}ollection$ of single microscale tissue voxels and $\underline{S}urfactant$$-assisted$ $\underline{O}ne$-$\underline{P}ot$ voxel processing method termed wcSOP for robust label-free single voxel proteomics. wcSOP capitalizes on buffer droplet-assisted wet collection of a single voxel dissected by LCM into the PCR tube cap and MS-compatible surfactant-assisted one-pot voxel processing in the collection cap. This convenient method allows reproducible label-free quantification of ~900 and ~4,600 proteins for single voxels at 20 µm × 20 µm × 10 µm (close to single cells) and 200 µm × 200 µm × 10 µm (~100 cells) from fresh frozen human spleen tissue, respectively. 100s-1000s of protein signatures were spatially resolved between spleen red and white pulp regions depending on the voxel size. Region-specific signaling pathways were enriched from single voxel proteomics data. To evaluate its broad applicability, we applied wcSOP-MS to two commonly accessible, OCT-embedded and FFPE, human archived tissues. It enabled to identify spatially resolved proteome changes and enriched pathways between diseased (breast cancer tumor or AD amyloid plaque) and adjacent normal regions. Antibody-based CODEX and IHC imaging validated label-free MS quantitation for single voxel analysis. The wcSOP-MS method paves the way for routine robust single voxel proteomics and spatial proteomics.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2025

Labels as a feature: Network homophily for systematically annotating human GPCR drug-target interactions

Machine learning has revolutionized drug discovery by enabling the exploration of vast, uncharted chemical spaces essential for discovering novel patentable drugs. Despite the critical role of human G protein-coupled receptors in FDA-approved drugs, exhaustive in-distribution drug-target interaction testing across all pairs of human G protein-coupled receptors and known drugs is rare due to significant economic and technical challenges. This often leaves off-target effects unexplored, which poses a considerable risk to drug safety. In contrast to the traditional focus on out-of-distribution exploration (drug discovery), we introduce a neighborhood-to-prediction model termed Chemical Space Neural Networks that leverages network homophily and training-free graph neural networks with labels as features. We show that Chemical Space Neural Networks’ ability to make accurate predictions strongly correlates with network homophily. Thus, labels as features strongly increase a machine learning model’s capacity to enhance in-distribution prediction accuracy, which we show by integrating labeled data during inference. We validate these advancements in a high-throughput yeast biosensing system (3773 drug-target interactions, 539 compounds, 7 human G protein-coupled receptors) to discover novel drug-target interactions for FDA-approved drugs and to expand the general understanding of how to build reliable predictors to guide experimental verification.

Hansson, Frederik G↗
DOE OSTI2019

Binding partner- and force-promoted changes in αE-catenin conformation probed by native cysteine labeling

Adherens Junctions (AJs) are cell-cell adhesion complexes that sense and propagate mechanical forces by coupling cadherins to the actin cytoskeleton via β-catenin and the F-actin binding protein αE-catenin. When subjected to mechanical force, the cadherin•catenin complex can tightly link to F-actin through αE-catenin, and also recruits the F-actin-binding protein vinculin. In this study, labeling of native cysteines combined with mass spectrometry revealed conformational changes in αE-catenin upon binding to the E-cadherin•β-catenin complex, vinculin and F-actin. A method to apply physiologically meaningful forces in solution revealed force-induced conformational changes in αE-catenin when bound to F-actin. Comparisons of wild-type αE-catenin and a mutant with enhanced vinculin affinity using cysteine labeling and isothermal titration calorimetry provide evidence for allosteric coupling of the N-terminal β-catenin-binding and the middle (M) vinculin-binding domain of αE-catenin. Cysteine labeling also revealed possible crosstalk between the actin-binding domain and the rest of the protein. The data provide insight into how binding partners and mechanical stress can regulate the conformation of full-length αE-catenin, and identify the M domain as a key transmitter of conformational changes.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

[18F]Tosyl fluoride as a versatile [18F]fluoride source for the preparation of 18F-labeled radiopharmaceuticals

Abstract Positron emission tomography (PET) is an in vivo imaging technology that utilizes positron-emitting radioisotope-labeled compounds as PET radiotracers that are commonly used in clinic and in various research areas, including oncology, cardiology, and neurology. Fluorine-18 is the most widely used PET-radionuclide and commonly produced by proton bombardment of 18 O-enriched water in a cyclotron. The [ 18 F]fluoride thus obtained generally requires processing by azeotropic drying in order to completely remove H 2 O before it can be used for nucleophilic radiofluorination. In general, the drying step is important in facilitating the radiofluorination reactions and the preparation of 18 F-labeled PET radiotracers. In this communication, we have demonstrated the feasibility of using [ 18 F]tosyl fluoride ([ 18 F]TsF) as a versatile [ 18 F]fluoride source for radiofluorination to bypass the azeotropic drying step, and we have developed a continuous flow solid-phase radiosynthesis strategy to generate [ 18 F]TsF in a form that is excellent for radiofluorination. [ 18 F]TsF shows high reactivity in radiofluorination and provides the features suitable for preparing PET radiotracers on a small scale and exploring novel radiolabeling technologies. Thus, using [ 18 F]TsF as a [ 18 F]fluoride source is a promising strategy that facilitates radiofluorination and provides a convenient and efficient solution for the preparation of 18 F-labeled radiopharmaceuticals that is well matched to the emerging trends in PET imaging technologies.

62 RADIOLOGY AND NUCLEAR MEDICINE↗
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↗