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 937 records · Page 52
All sourcesNASA NTRSDOE OSTI
DOE OSTI2022

Use of Machine Learning on PMU Data for Transmission System Fault Analysis

Synchrophasor technology has been used for monitoring, control, and protection of bulk power system for over 10 years. Deployment of phasor measurement units (PMUs) in the USA power system has surpassed 3000 units installed in the transmission substations as stand-alone intelligent electronic devices (IEDs) or as a software add-on to other devices such as digital protective relays (DPRs) or digital fault recorders (DFRs). By now, thousands of terabytes of PMU data may have been captured and stored by various transmission system operators (TSOs) and independent system operators (ISOs). This creates an opportunity to deploy advanced machine learning (ML) techniques to detect and classify faults recorded by PMUs automatically to be used by the system operators for rapid, critical decision-making when manual analysis of the past or unfolding events is not feasible. In this paper we offer a brief background on how the automated fault analysis may be done using DPR and/or DFR data, and compare some of the legacy approaches to the new ML approaches in the context of the system-wide PMU recordings. We then offer insights from developing practical ML solutions that have been applied on field recordings captured by close to 450 PMUs from all three US interconnections (Western, Eastern and ERCOT) over two years (2016-2017). We identify and illustrate ML challenges we addressed: inaccurate data, data with scarce and temporally imprecise fault labels, data recorded by PMUs sparsely located at substations resulting in the fault records taken afar from the ends of the faulted lines, data containing only positive sequence values, and data taken at different voltage levels. We then illustrate the ML model results for fault analysis under different application scenarios. The novelty of this study is not only in the design, implementation, and performance analysis of the ML algorithms, but also in the use of advanced fault modelling and simulation approaches to improve the training results when developing supervised ML models for fault detection and classification. Extensive simulations of faults were conducted on a 14-bus power system to create a training dataset with over 1400 accurately labelled faults. This dataset was applied to enhance the accuracy of fault detection and classification of machine learning-based models trained with small number of labelled faults in large datasets recorded in the grid interconnections ranging from 5,000 to 70,000 buses.

Synchrophasors, Machine Learning, Fault Analysis, ↗
DOE OSTI2022

Contextual Active Online Model Selection with Expert Advice

How can we collect the most useful labels to learn a model selection policy, when presented with arbitrary heterogeneous data streams? In this paper, we formulate this task as a contextual active model selection problem, where at each round the learner receives an unlabeled data point along with a context. The goal is to output the best model for any given context without obtaining an excessive amount of labels. In particular, we focus on the task of selecting pre-trained classifiers, and propose a contextual active model selection algorithm (CAMS), which relies on a novel uncertainty sampling query criterion defined on a given policy class for adaptive model selection. In comparison to prior art, our algorithm does not assume a globally optimal model. We provide rigorous theoretical analysis for the regret and query complexity under both adversarial and stochastic settings. Our experiments on several benchmark classification datasets demonstrate the algorithm’s effectiveness in terms of both regret and query complexity. Notably, to achieve the same accuracy, CAMS incurs less than 10% of the label cost when compared to the best online model selection baselines on CIFAR10.

Liu, Xuefeng↗
DOE OSTI2022

Methods for detecting and determining levels of monolignol ester conjugates incorporated into lignin and compounds relating thereto

Methods of detecting and, optionally, determining a level of incorporation of monolignol ester conjugates into lignin. The methods include derivatizing lignin to acylate at least a portion of free phenolic and aliphatic hydroxyls and to halogenate at least a portion of benzylic alcohols present in the lignin to yield derivatized lignin, treating the derivatized lignin with a reducing agent to cleave at least a portion of the derivatized lignin to yield lignin cleavage products, acetylating at least a portion of free hydroxyl groups in the lignin cleavage products with a labeled acetylation agent to yield labeled lignin fragments, and detecting the labeled lignin fragments.

09 BIOMASS FUELS↗
DOE OSTI2025

Systems and methods for binary code analysis

Human-readable (HR) code may be derived from a binary. The HR code may be configured to have statistical properties suitable for machine-learned (ML) translation. The HR code may comprise source code, intermediate code, assembly code, or the like. A machine-learned translator may be configured to translate the HR code into labels comprising semantic information pertaining to respective functions of the binary, such as a function name, role, or the like. Execution of the binary may be blocked in response to translating the HR code to a label associated with malware, such as cryptocurrency mining malware or the like. Conversely, the binary may be permitted to proceed to execution in response to determining that the translation is free from labels indicative of malware.

Anderson, Matthew W.↗
DOE OSTI2021

Pyrolysis temperature and soil depth interactions determine PyC turnover and induced soil organic carbon priming

Pyrogenic organic carbon (PyC) is a complex, heterogeneous class of thermally altered organic substrates, but its dynamics and how its behavior changes with soil depth remain poorly understood. We conducted a laboratory incubation study to investigate the interactive effects of pyrolysis temperature and soil depth on the turnover of PyC compared to its precursor wood and native SOC (NSOC). We incubated dual-labeled (13C and 15N) jack pine pyrogenic organic matter produced at 300 °C (PyC300), 450 °C (PyC450), and their precursor pine wood in a fine-loamy, mixed-conifer forest soil for 745 days. A mixture of surface (0–10 cm) and subsurface (50–70 cm) forest soils, with and without labeled biomass were incubated in the dark at 55% soil water field capacity and 25 °C. Total 13C from PyC and wood mineralized as 13C-CO2 (as % of C added to soil) declined with an increase in pyrolysis temperature as follows: 54 ± 7.7% for wood, 3.1 ± 0.2% for PyC300, and 0.94 ± 0.08% for PyC450. Furthermore, after 2 years, soil depth interacted with pyrolysis temperature to affect C turnover, with total wood C losses significantly declining from 70.6% in surface soils to 37.5% in subsurface soil, while total losses of PyC300 and PyC450 were unaffected by differences between surface and subsurface soils. Wood induced negative priming (i.e., decreased mineralization rates) in surface soil at days 3 and 60, while PyC300 induced positive priming (i.e., increased mineralization rates) in subsurface soil at day 60. After 2 years, unlabeled NSOC losses increased from 9.2 ± 0.8% of NSOC in unamended treatments to 16.5 ± = 2.6% of NSOC with PyC450 additions. Our results suggest that PyC pyrolyzed at a given temperature can mineralize at similar rates between soil depths, and high amounts of PyC450 in subsurface soils can stimulate NSOC losses. These findings indicate that soil depth imposes critical controls on PyC dynamics belowground.

13C-labeling↗
DOE OSTI2018

Investigation on the upconversion luminescence in Ho3+/Yb3+ co-doped Ba3Sc4O9 phosphor

Highlights: • The optical properties of novel Ba{sub 3}Y{sub 4}O{sub 9}:Ho{sup 3+}/Yb{sup 3+} phosphor were investigated. • The luminescence color could be tuned from green to yellow-green light. • The energy transfer mechanisms of UC luminescence were investigated in detail. • The phosphor can be a good candidate for green UC material in display fields and fluorescent labeling. - Abstract: A series of Ba{sub 3}Sc{sub 4}O{sub 9}:Ho{sup 3+}/Yb{sup 3+} phosphors were synthesized by a high-temperature solid-state reaction method. The phase purity and upconversion luminescence were characterized by X-ray diffraction and photoluminescence spectroscopy. The sintering temperature and doping concentrations were optimized to obtain the strongest emission intensity. Under 980 nm laser diode excitation, the green (545 nm, {sup 5}F{sub 4}/{sup 5}S{sub 2} → {sup 5}I{sub 8}) and red (665 nm, {sup 5}F{sub 5} → {sup 5}I{sub 8}) emissions were observed. The cross-relaxation and back-energy-transfer processes play an important role to adjust the emission intensity ratio of Green/Red at different dopant concentrations. Based on the energy level diagrams, the energy transfer mechanisms are investigated in detail according to the analysis of pump power dependence and luminescence decay curves. The upconversion luminescence properties of the phosphor were evaluated and the results show good color monochromaticity and relatively high upconversion luminescence intensity. The Ba{sub 3}Sc{sub 4}O{sub 9}:Ho{sup 3+}/Yb{sup 3+} can be a good candidate for green upconversion material in display fields and fluorescent labeling.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2020

The Ontologies Community of Practice: A CGIAR Initiative for Big Data in Agrifood Systems

Heterogeneous and multidisciplinary data generated by research on sustainable global agriculture and agrifood systems requires quality data labeling or annotation in order to be interoperable. As recommended by the FAIR principles, data, labels, and metadata must use controlled vocabularies and ontologies that are popular in the knowledge domain and commonly used by the community. Despite the existence of robust ontologies in the Life Sciences, there is currently no comprehensive full set of ontologies recommended for data annotation across agricultural research disciplines. In this paper, we discuss the added value of the Ontologies Community of Practice (CoP) of the CGIAR Platform for Big Data in Agriculture for harnessing relevant expertise in ontology development and identifying innovative solutions that support quality data annotation. The Ontologies CoP stimulates knowledge sharing among stakeholders, such as researchers, data managers, domain experts, experts in ontology design, and platform development teams.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
DOE OSTI2018

Stimulation of anaerobic organic matter decomposition by subsurface organic N addition in tundra soils

We present that increasing nitrogen (N) availability in Arctic soils could stimulate the growth of both plants and microorganisms by relieving the constraints of nutrient limitation. It was hypothesized that organic N addition to anoxic tundra soil would increase CH 4 production by stimulating the fermentation of labile substrates, which is considered the rate-limiting step in anaerobic C mineralization. We tested this hypothesis through both field and lab-based experiments. In the field experiment, we injected a solution of 13 C- and 15 N-labeled glutamate 35 cm belowground at a site near Nome on the Seward Peninsula, Alaska, and observed the resulting changes in porewater geochemistry and dissolved greenhouse gas concentrations. The concentration of free glutamate declined rapidly within hours of injection, and the 15 N label was recovered almost exclusively as dissolved organic N within 62 h. These results indicate rapid microbial assimilation of the added N and transformation into novel organic compounds. We observed increasing concentrations of dissolved CH 4 and Fe(II), indicating rapid stimulation of methanogenesis and Fe(III) reduction. Low molecular weight organic acids such as acetate and propionate accumulated despite increasing consumption through anaerobic C mineralization. A laboratory soil column flow experiment using active layer soil collected from the same site further supported these findings. Glutamate recovery was low compared to a conservative bromide tracer, but concentrations of NO 3 - and NH 4 + remained low, consistent with microbial uptake of the added N. Similar to the field experiment, we observed both increasing Fe(II) and organic acid concentrations. Lastly, together, these results support our hypothesis of increased fermentation in response to organic N addition and suggest that increasing N availability could accelerate CH 4 production in tundra soils.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2021

Belowground allocation and dynamics of recently fixed plant carbon in a California annual grassland

Plant roots and the organisms that surround them are a primary source for stabilized soil organic carbon (SOC). While grassland soils have a large capacity to store organic carbon (C), few field-based studies have quantified the amount of plant-fixed C that moves into soil and persists belowground over multiple years. Yet this characteristic of the soil C cycle is critical to C storage, soil water holding capacity, nutrient provisions, and the management of soil health. Here, we tracked the fate of plant-fixed C following a five-day 13 CO 2 labeling of a Northern California annual grassland, measuring C pools starting at the end of the labeling period, at three days, four weeks, six months, one year, and two years. Soil organic carbon was fractionated using a density-based approach to separate the free-light fraction (FLF), occluded-light fraction (OLF), and heavy fraction (HF). Using isotope ratio mass spectrometry, we measured 13 C enrichment and total C content for plant shoots, roots, soil, soil dissolved organic carbon (DOC), and the FLF, OLF, and HF. The chemical nature of C in the HF was further analyzed by solid state 13 C nuclear magnetic resonance (NMR) spectroscopy.

13C-NMR↗
DOE OSTI2017

Metabolite Spectral Accuracy on Orbitraps

Orbitraps are high-resolution ion-trap mass spectrometers that are widely used in metabolomics. While the mass accuracy and resolving power of orbitraps have been extensively documented, their spectral accuracy, i.e., accuracy in measuring the abundances of isotopic peaks, remains less studied. In analyzing spectra of unlabeled metabolites, we discovered a systematic under representation of heavier natural isotopic species, especially for high molecular weight metabolites (~20% under-measurement of [M + 1]/[M + 0] ratio at m/z 600). We hypothesize that these discrepancies arise for metabolites far from the lower limit of the mass scan range, due to the weaker containment in the C-trap that results in suboptimal trajectories inside the Orbitrap analyzer. Consistent with this, spectral fidelity was restored by dividing the mass scan range (initially 75 m/z to 1000 m/z) into two scan events, one for lower molecular weight and the other for higher molecular weight metabolites. Having thus obtained accurate mass spectra at high resolution, we found that natural isotope correction for high-resolution labeling data requires more sophisticated algorithms than typically employed: the correction algorithm must take into account whether isotopologues with the same nominal mass are resolved. We present an algorithm and associated open-source code, named AccuCor, for this purpose. Together, these improvements in instrument parameters and natural isotope correction enable more accurate measurement of metabolite labeling and thus metabolic flux.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2020

Development of Multiplexed Immuno-N-Terminomics to Reveal the Landscape of Proteolytic Processing in Early Embryogenesis of Drosophila melanogaster

Protein expression levels are regulated through both translation and degradation mechanisms. Levels of degradation intermediates, i.e., partially degraded proteins, cannot be distinguished from those of intact proteins by global proteomics analysis, which quantify total protein abundance levels. This study aimed to develop a tool for assessing the aspects of degradation regulation via proteolytic processing through a new multiplexed N-terminomics method involving selective isobaric labeling of protein N-termini and immunoaffinity capture of the labeled N-terminal peptides. Our method allows for not only identification or proteolytic cleavage sites, but also highly multiplexed quantification of proteolytic processing. In this paper, we profiled a number of potential cleavage sites by signal peptidase and provided experimental confirmation of predicted cleavage sites of signal peptide. Furthermore, the present method uniquely represents the landscape of proteomic proteolytic processing rate during early embryogenesis in Drosophila melanogaster, revealing the underlying mechanism of stringent decay regulation of zygotically expressed proteins during early stages of embryogenesis.

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

Quantitative Comparison of Dye and Ultrasmall Fluorescent Silica Core–Shell Nanoparticle Probes for Optical Super-Resolution Imaging of Model Block Copolymer Thin Film Surfaces

In recent years, high-resolution optical imaging in the far field has provided opportunities for alternative approaches to nanocharacterization traditionally dominated by electron and scanning probe microscopies. Here in this paper, we report the optical super-resolution imaging of model block copolymer (BCP) thin film surface nanostructures through stochastic optical reconstruction microscopy (STORM). We compare a set of surface-functionalized fluorescent core–shell silica nanoparticles encapsulating two different organic dyes, Cy3 and Cy5, with the corresponding free dyes in STORM. Using various click-type chemistries, these probes are covalently attached to the surface of specific blocks of BCP thin films, enabling selective block labeling and optical visualization. We demonstrate that the enhanced brightness of these particle probes offers distinct advantages over conventional dye labeling, outperforming one of the best STORM dyes available (Cy5).

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

Characterizing cellular mechanical phenotypes with mechano-node-pore sensing

The mechanical properties of cells change with their differentiation, chronological age, and malignant progression. Consequently, these properties may be useful label-free biomarkers of various functional or clinically relevant cell states. Here, we demonstrate mechano-node-pore sensing (mechano-NPS), a multi-parametric single-cell-analysis method that utilizes a four-terminal measurement of the current across a microfluidic channel to quantify simultaneously cell diameter, resistance to compressive deformation, transverse deformation under constant strain, and recovery time after deformation. We define a new parameter, the whole-cell deformability index (wCDI), which provides a quantitative mechanical metric of the resistance to compressive deformation that can be used to discriminate among different cell types. The wCDI and the transverse deformation under constant strain show malignant MCF-7 and A549 cell lines are mechanically distinct from non-malignant, MCF-10A and BEAS-2B cell lines, and distinguishes between cells treated or untreated with cytoskeleton-perturbing small molecules. We categorize cell recovery time, ΔTr, as instantaneous (ΔTr ~ 0 ms), transient (ΔTr≤40 ms), or prolonged (ΔTr440 ms), and show that the composition of recovery types, which is a consequence of changes in cytoskeletal organization, correlates with cellular transformation. Through the wCDI and cell-recovery time, mechano-NPS discriminates between sub-lineages of normal primary human mammary epithelial cells with accuracy comparable to flow cytometry, but without antibody labeling. Mechano-NPS identifies mechanical phenotypes that distinguishes lineage, chronological age, and stage of malignant progression in human epithelial cells.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A streamlined tandem tip-based workflow for sensitive nanoscale phosphoproteomics

Effective phosphoproteome of nanoscale sample analysis remains a daunting task, primarily due to significant sample loss associated with non-specific surface adsorption during enrichment of low stoichiometric phosphopeptide. We develop a tandem tip phosphoproteomics sample preparation method that is capable of sample cleanup and enrichment without additional sample transfer, and its integration with our recently developed SOP (Surfactant-assisted One-Pot sample preparation) and iBASIL (improved Boosting to Amplify Signal with Isobaric Labeling) approaches provides a streamlined workflow enabling sensitive, high-throughput nanoscale phosphoproteome measurements. This approach significantly reduces both sample loss and processing time, allowing the identification of >3000 (>9500) phosphopeptides from 1 (10) µg of cell lysate using the label-free method without a spectral library. It also enables precise quantification of ~600 phosphopeptides from 100 sorted cells (single-cell level input for the enriched phosphopeptides) and ~700 phosphopeptides from human spleen tissue voxels with a spatial resolution of 200 µm (equivalent to ~100 cells) in a high-throughput manner. The new workflow opens avenues for phosphoproteome profiling of mass-limited samples at the low nanogram level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

Daylength variation affects growth, photosynthesis, leaf metabolism, partitioning, and metabolic fluxes

Abstract Daylength, a seasonal and latitudinal variable, exerts a substantial impact on plant growth. However, the relationship between daylength and growth is nonproportional, suggesting the existence of adaptive mechanisms. Thus, our study aimed to comprehensively investigate the adaptive strategies employed by plants in response to daylength variation. We grew false flax (Camelina sativa) plants, a model oilseed crop, under long-day (LD) and short-day (SD) conditions and used growth measurements, gas exchange measurements, and isotopic labeling techniques, including 13C, 14C, and 2H2O, to determine responses to different daylengths. Our findings revealed that daylength influences various growth parameters, photosynthetic physiology, carbon partitioning, metabolic fluxes, and metabolite levels. SD plants employed diverse mechanisms to compensate for reduced CO2 fixation in the shorter photoperiod. These mechanisms included enhanced photosynthetic rates and reduced respiration in the light (RL), leading to increased shoot investment. Additionally, SD plants exhibited reduced rates of the glucose 6-phosphate (G6P) shunt and greater partitioning of sugars into starch, thereby sustaining carbon availability during the longer night. Isotopic labeling results further demonstrated substantial alterations in the partitioning of amino acids and TCA cycle intermediates between rapidly and slowly turning over pools. Overall, the results point to multiple developmental, physiological, and metabolic ways in which plants adapt to different daylengths to maintain growth.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2026

Curation and Dissemination of Complex Multi-Modal Datasets for Radiation Detection, Localization, and Tracking

The PANDAWN sensor network in Chicago, IL, is a state-of-the-art testbed for networked, multi-modal sensing. It integrates AI/data science methods into its operation, from data acquisition to automated data labeling and curation workflows. The curation and dissemination of diverse multi-modal datasets will enable the development of new radiological/nuclear (R/N) detection, localization, and tracking algorithms and methods relevant across the nonproliferation mission space. This article first introduces the PANDAWN sensor network and the features that make it stand out from previous multi-modal data acquisition efforts. We then review the various data streams acquired on the PANDAWN nodes and present the implementation of an automated data curation pipeline that includes the labeling of radiation and contextual data streams. Here, we finally provide a short overview of different studies that leveraged the curated datasets.

Data curation↗
DOE OSTI2021

Improve Learning from Crowds via Generative Augmentation

Crowdsourcing provides an efficient label collection schema for supervised machine learning. However, to control annotation cost, each instance in the crowdsourced data is typically annotated by a small number of annotators. This creates a sparsity issue and limits the quality of machine learning models trained on such data. In this paper, we study how to handle sparsity in crowdsourced data using data augmentation. Specifically, we propose to directly learn a classifier by augmenting the raw sparse annotations. We implement two principles of high-quality augmentation using Generative Adversarial Networks: 1) the generated annotations should follow the distribution of authentic ones, which is measured by a discriminator; 2) the generated annotations should have high mutual information with the ground-truth labels, which is measured by an auxiliary network. Extensive experiments and comparisons against an array of state-of-the-art learning from crowds methods on three real-world datasets proved the effectiveness of our data augmentation framework. It shows the potential of our algorithm for low-budget crowdsourcing in general.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗