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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Towards Automated Transcription of Label Text from Pinned Insect Collections

We present a computer vision system that can transcribe the text on tiny printed labels stacked beneath pinned insects (as found in museum collections). The approach uses multiple views of each label because the labels are often occluded by the pin, the insect specimen, and other labels. Our approach handles occlusion and the extreme viewing angles required to image the stacked labels. Automated image analysis identifies the lines of text and then aligns and rectifies the images. Combining the aligned and rectified images from multiple viewpoints enables us to create a composite image that can be read using optical character recognition tools (OCR) to extract the text. We provide experimental demonstration using both museum specimens and experimental test labels

Agarwal, Nitin↗
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↗
DOE OSTI2020

Multiperspective Automotive Labeling

Supervised machine learning techniques inherently rely on datasets to be trained. With image datasets traditionally being annotated by humans, many advancements in image annotation tools have been made to ensure creation of rich datasets with accurate labels. Nevertheless, users still find it challenging to create and use their own datasets with labels that reflect their problem domain. We propose a streamlined labeling process that aligns multiperspective images and allows a transition from a labeled perspective to other perspectives. The main goal of this work is to reduce the human effort required for labeling vehicle images under favorable conditions where the image perspectives are correlated and one or more perspectives are known. A case study is described and analyzed to show the effectiveness of the process, as well as constraints and limitations when applied to other cases.

training dataset, multiperspective images, supervi↗
DOE OSTI2022

Direction-optimizing Label Propagation Framework for Structure Detection in Graphs: Design, Implementation, and Experimental Analysis

Label Propagation is not only a well-known machine learning algorithm for classification but also an effective method for discovering communities and connected components in networks. We propose a new Direction-optimizing Label Propagation Algorithm (DOLPA) framework that enhances the performance of the standard Label Propagation Algorithm (LPA), increases its scalability, and extends its versatility and application scope. As a central feature, the DOLPA framework relies on the use of frontiers and alternates between label push and label pull operations to attain high performance. It is formulated in such a way that the same basic algorithm can be used for finding communities or connected components in graphs by only changing the objective function used. Additionally, DOLPA has parameters for tuning the processing order of vertices in a graph to reduce the number of edges visited and improve the quality of solution obtained. We present the design and implementation of the enhanced algorithm as well as our shared-memory parallelization of it using OpenMP. We also present an extensive experimental evaluation of our implementations using the LFR benchmark and real-world networks drawn from various domains. Compared with an implementation of LPA for community detection available in a widely used network analysis software, we achieve at most five times the F-Score while maintaining similar runtime for graphs with overlapping communities. We also compare DOLPA against an implementation of the Louvain method for community detection using the same LFR-graphs and show that DOLPA achieves about three times the F-Score at just 10% of the runtime. For connected component decomposition, our algorithm achieves orders of magnitude speedups over the basic LP-based algorithm on large-diameter graphs, up to 13.2× speedup over the Shiloach-Vishkin algorithm, and up to 1.6× speedup over Afforest on an Intel Xeon processor using 40 threads.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2017

Fluorescent Labeling of Protein Using Blue-Emitting 8-Amino-BODIPY Derivatives

8-Amino-BODIPY (boron-dipyrromethane) dyes show bright blue fluorescence. Disclosed here are synthesis and characterization of the photophysical properties of a series of functionalized 8-Amino-BODIPY (BP1–4) for protein labeling. The compact structure and solvent-insensitive absorption property of the dye are desirable features for protein labeling. For the model protein, bovine serum albumin (BSA), the labeling proceeds under mild condition via amide bond formation or thiol-ene conjugation with maintaining the bright blue fluorescence. The chromatography and mass spectroscopy analysis clearly support the labeling of the BODIPY dye on the BSA. The protein labeling with blue-emitting BODIPY would be applicable for studying protein dynamics and fluorescence resonance energy transfer (FRET) with intrinsic biomolecules.

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

Crowdsourced reliable labeling of safety-rule violations on images of complex construction scenes for advanced vision-based workplace safety

Construction workplace hazard detection requires engineers to analyze scenes manually against many safety rules, which is time-consuming, labor-intensive, and error-prone. Computer vision algorithms are yet to achieve reliable discrimination of anomalous and benign object relations underpinning safety violation detections. Recently developed deep learning-based computer vision algorithms need tens of thousands of images, including labels of the safety rules violated, in order to train deep-learning networks for acquiring spatiotemporal reasoning capacity in complex workplaces. Such training processes need human experts to label images and indicate whether the relationship between the worker, resource, and equipment in the scenes violate spatiotemporal arrangement rules for safe and productive operations. False alarms in those manual labels (labeling no-violation images as having violations) can significantly mislead the machine learning process and result in computer vision models that produce inaccurate hazard detections. Compared with false alarms, another type of mislabels, false negatives (labeling images having violations as “no violations”), seem to have fewer impacts on the reliability of the trained computer vision models.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Spin label EPR suggests the presence of cholesterol rich domains in cultured insect cell membranes

Highlights: • Membranes of cultured insect cells were studied with spin label EPR. • Two kinds of spin labels (SL) with different chemical affinity were used. • EPR of SL chemically akin to cholesterol indicates its uneven distribution in intact cells. • Evidence of cholesterol enriched nanodomains in intact cell membranes. • Cholesterol depletion erases the EPR evidence of cell membrane domains. Different spin labels were incorporated to the membranes of cultured insect UFL-AG-286 cells in order to characterize their physical properties by Electron Paramagnetic Resonance spectroscopy (EPR). The spectrum of the spin label 12-SASL incorporated to cell membranes was similar as those obtained in membrane model systems composed of eggPC/cholesterol. However, the spectrum of the spin label CSL, chemically related to cholesterol, was drastically different in the two systems. Interestingly, when cell cholesterol content was reduced using methyl beta cyclodextrin, an EPR spectrum similar to those of model membranes was obtained. The analysis of these experiments suggests the existence of cholesterol rich regions in UFL-AG-286 cell membranes.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Targeted cancer cell ablation in mice by an α-particle-emitting astatine-211-labeled antibody against major histocompatibility complex class I chain-related protein A and B

Highlights: • MICA/B is expressed in several human cancer cells. • We generated an α-particle-emitting {sup 211}At-labelled antibody targeting MICA/B. • {sup 211}At-labelled antibody specifically killed human cancer cells expressing MICA/B. • {sup 211}At-labelled antibody targeting MICA/B showed anti-tumor activity in vivo. Major histocompatibility complex class I chain-related protein A and B (MICA/B) are ligands of the immune receptor, natural-killer group 2 member D. MICA/B expression is often found in several types of cancer but is restricted in normal tissues. Here, we show that an α-particle emitting astatine-211 ({sup 211}At)-labeled antibody targeting MICA/B ({sup 211}At-anti MICA/B Ab) efficiently ablates cancer cells in vitro and in vivo. We generated {sup 211}At-anti MICA/B Ab, an anti-MICA/B antibody conjugated with a highly cytotoxic α-particle emitting radionuclide {sup 211}At. {sup 211}At-anti MICA/B Ab binds to human osteosarcoma SaOS2 and U2OS cells that exhibit high levels of MICA/B expression and efficiently kills those cells in vitro. Biodistribution analysis using xenograft mouse models of HCT116 p53{sup −/−} positive for MICA/B expression, showed increased {sup 211}At in the xenografts for up to 22 h after injection as time proceeded. A single dose of {sup 211}At-anti MICA/B Ab (1 MBq) showed significant reduction in the tumor growth rate of HCT116 p53{sup −/−} xenografts compared to {sup 211}At-labeled mouse IgG (1 MBq) at 21 days after injection. No body weight loss and erythrocytopenia was evident in mice that received {sup 211}At-anti MICA/B. Leukocytopenia and thrombocytopenia were observed within a week after {sup 211}At-anti MICA/B injection, but counts of red blood cells and platelets were recovered to control levels at about 3–4 weeks after injection. Taken together, these data strongly demonstrate that targeted α-particle therapy using {sup 211}At-anti-MICA/B Ab emitting highly cytotoxic α-particles is a potential new therapeutic option for several types of cancer.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

HPLC-Parallel accelerator and molecular mass spectrometry analysis of 14 C-labeled amino acids

Accelerator mass spectrometry (AMS) is the method of choice for quantitation of low amounts of 14 C-labeled biomolecules. Despite exquisite sensitivity, an important limitation of AMS is its inability to provide structural information about the analyte. This limitation is not critical when the labeled compounds are well-characterized prior to AMS analysis. However, analyte identity is important in other experiments where, for example, a compound is metabolized and the structures of its metabolites are not known. We previously described a moving wire interface that enables direct AMS measurement of liquid sample in the form of discrete drops or HPLC eluent without the need for individual fraction collection, termed liquid sample-AMS (LS-AMS). Here, we now report the coupling of LS-AMS with a molecular mass spectrometer, providing parallel accelerator and molecular mass spectrometry (PAMMS) detection of analytes separated by liquid chromatography. The repeatability of the method was examined by performing repeated injections of 14 C-labeled tryptophan, and relative standard deviations of the 14 C peak areas were ≤10.57% after applying a normalization factor based on a standard. Five 14 C-labeled amino acids were separated and detected to provide simultaneous quantitative AMS and structural MS data, and AMS results were compared with solid sample-AMS (SS-AMS) data using Bland-Altman plots. To demonstrate the utility of the workflow, yeast cells were grown in a medium with 14 C-labeled tryptophan. The cell extracts were analyzed by PAMMS, and 14 C was detected in tryptophan and its metabolite kynurenine.

38 RADIATION CHEMISTRY, RADIOCHEMISTRY, AND NUCLEA↗
DOE OSTI2022

An automated liquid jet for fluorescence dosimetry and microsecond radiolytic labeling of proteins

X-ray radiolytic labeling uses broadband X-rays for in situ hydroxyl radical labeling to map protein interactions and conformation. High flux density beams are essential to overcome radical scavengers. However, conventional sample delivery environments, such as capillary flow, limit the use of a fully unattenuated focused broadband beam. An alternative is to use a liquid jet, and we have previously demonstrated that use of this form of sample delivery can increase labeling by tenfold at an unfocused X-ray source. Here we report the first use of a liquid jet for automated inline quantitative fluorescence dosage characterization and sample exposure at a high flux density microfocused synchrotron beamline. Our approach enables exposure times in single-digit microseconds while retaining a high level of side-chain labeling. This development significantly boosts the method’s overall effectiveness and efficiency, generates high-quality data, and opens up the arena for high throughput and ultrafast time-resolved in situ hydroxyl radical labeling.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Clinical pharmacology information in regulatory submissions and labeling: A comparative analysis of orphan and non-orphan drugs approved by the FDA

Clinical pharmacology is an integral discipline supporting the development, regulatory evaluation, and clinical use of drugs for the treatment of both common and rare diseases. Here, we evaluated the recommendations and information available from select clinical pharmacology studies in the therapeutic product labeling of new molecular entities (NMEs) approved from 2017 to 2019 for both common and rare diseases. A total of 151 NMEs, including 72 orphan and 79 non-orphan drugs, were analyzed for recommendations and information available related to food–drug interaction, drug–drug interaction, renal impairment, hepatic impairment, QT assessment, and human radiolabeled mass balance studies using data collected from the original labeling and other regulatory documents. The analysis showed no statistically significant difference in the recommendations between orphan and non-orphan drugs except for renal impairment related recommendations in section 8 of the labeling. Although not significant, fewer hepatic impairment labeling recommendations were available for orphan drugs when compared with non-orphan drugs. At the time of initial approval, 79 postmarketing requirements (PMRs) and postmarketing commitments (PMCs) for 33 orphan drugs and 39 PMRs and PMCs for 19 non-orphan drugs were established; with most difference observed for drug–drug interaction, hepatic impairment, and QT assessment. Overall, although there was a trend for more labeling recommendations and fewer postmarketing studies and clinical trials for non-orphan drugs, there appeared to be no substantial differences in how these select clinical pharmacology studies are leveraged during the development and approval of orphan and non-orphan drugs.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2022

RiverPIXELS: paired Landsat images and expert-labeled sediment and water pixels for a selection of rivers v1.0

RiverPIXELS contains GeoTIFFs of hand-labeled water and sediment pixels from Landsat images containing rivers. Each of the 104 labeled patches contains 256 x 256 Landsat pixels (30 meter resolution). Our aim in releasing RiverPIXELS is to provide an "off-the-shelf" training and testing dataset for building machine-learned models to automatically identify rivers from multispectral imagery. While a number of trained models and/or surface water products already exist, RiverPIXELS aims for pixel-level accuracy in order to precisely identify river boundaries in particular. Our selection of rivers is heavily Arctic, but we include tropical and temperate rivers as well. Patches are provided for the Colville (7), Indigirka (6), Kolyma (4), Ucayali (54), Waitaki (21), and Yana (12) Rivers. For each patch, all surface water pixels are labeled (1) and all in-channel sediment pixels are labeled (2). Sediments not in-channel are considered part of the land (0) class. RiverPIXELS also includes paired surface water data from the Global Surface Water dataset that may be useful as additional features in machine learning models. Each patch therefore contains four aligned GeoTIFFs: labeled, landsat, gswmo, and gswocc.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2018

A 89 Zr-labeled lipoplex nanosystem for image-guided gene delivery: design, evaluation of stability and in vivo behavior

Background: With the advances in radiopharmaceutical research, the development of image-guided therapy has become a major interest. While the development of theranostic nanotherapeutics is frequently associated with cancer chemotherapy, phototherapy and radiotherapy, there is little information available on the in vivo monitoring of gene delivery systems and the application of image-guided approach in gene therapy. The goal of this work was to determine the in vivo behavior of DNA delivery nanosystems - based on cationic gemini surfactants – designed for image-guided gene therapy. We tested the feasibility of monitoring tumor accumulation of gene delivery nanoparticles by positron emission tomography. Methods: To be able to conjugate radiotracers to the nanoparticles, a deferoxamine-modified gemini surfactant was synthesized, DNA-containing lipoplex nanoparticles were formulated, and radiolabeled with Zirconium-89 ( 89 Zr). The pharmacokinetics and biodistribution of 89 Zr labeled surfactant and 89 Zr labeled nanoparticles were monitored in mice by microPET/CT imaging and ex vivo gamma counting. Results: Modification of the nanoparticles with deferoxamine did not alter their physicochemical properties. The radiolabeled nanoparticles (labeling efficiency of 95±3%) were stable in PBS and serum. The biological half-life of the 89Zr labeled nanoparticles was significantly higher compared to 89Zr labeled surfactant. As expected, the nanoparticles had significantly higher liver accumulation than the radiolabeled surfactant alone and lower kidney accumulation. Tumor uptake was detected at 2 hours post injection and decreased throughout the 3-day monitoring. Conclusion: We propose that radiolabeling DNA delivery lipoplex nanosystems is a promising approach for the design and optimization of image-guided nanomedicines, especially in the context of cancer gene therapy.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

Isotopically labelled inorganic carbon delivered to algalcultures via bubbler bottle

This protocol describes a method for delivering labelled inorganic carbon as 13 CO 2 to algal cultures by bubbling air through a solution of H 13 CO 3 - , and then into the culture. We developed this method to deliver label to cultures grown under continuous bubbling with air, without the use of 13 CO 2 labelled gas and the necessary equipment to mix labelled gas with air at near-atmospheric levels. Bubbling precludes the more common approach of adding H 13 CO 3 - label directly to the media, because dissolved HCO 3 - is in equilibrium with atmospheric CO 2 . Thus, excess HCO 3 - added to a solution will leave the solution as CO 2 gas as it equilibrates. Bubbling rapidly accelerates this equilibration which is typically diffusion limited. The method described here takes advantage of this aspect of carbonate chemistry, and uses a solution of H 13 CO 3 - —which is less expensive and more convenient than 13 CO 2 gas—to generate a flux of 13 CO 2 that can be bubbled into a culture.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Using 14 C-acetate Pulse-chase Labeling to Study Fatty Acid and Glycerolipid Metabolism in Plant Leaves

Lipids metabolism is comprised of networks of reactions occurred in different subcellular compartments. Isotopic labeling is a good way to track the transformations and movements of metabolites without perturbing overall cellular metabolism. Fatty acids, the building blocks of membrane lipids and storage triacylglycerols, are synthesized in plastids. The immediate precursor for fatty acid synthesis is acetyl-CoA. Exogenous acetate is rapidly incorporated into fatty acids in leaves and isolated plastids because it can diffuse freely through cellular membranes, enter the plastid where it is rapidly metabolized to acetyl-CoA. Therefore, isotope-labeled acetate is often used as a tracer for the investigation of fatty acid synthesis and complex lipid metabolism in plants and other organisms. The basic principle of isotope labeling and its recent technical advances have been reviewed (Allen et al., 2015). The present protocol describes the use of 14C-labeled acetate to determine rates of fatty acid synthesis and degradation and to track the metabolism of glycerolipids in leaves. This method, which is often referred to as acetate pulse-chase labeling, has been widely used to probe various aspects of lipid metabolism (Allen et al., 2015), including the role of autophagy in membrane lipid turnover (Fan et al., 2019) and the interplay between lipid and starch metabolism pathways (Yu et al., 2018).

14C-acetate pulse-chase↗
DOE OSTI2020

Direction-optimizing Label Propagation and its Application for Community Detection

Label Propagation is a machine learning algorithm typically used for classification. It has also been found to be an effective method for detecting communities in networks. It has two attractive features as a community detection method: it has nearly linear runtime and it requires no \textit{a priori} community information. We propose a new Direction Optimizing Label Propagation Algorithm (DOLPA) that relies on the use of {\em frontiers} and alternates between label {\em push} and label {\em pull} operations to enhance the performance of LPA. Specifically, DOLPA has parameters for tuning the processing order of vertices in a graph. This reduces the number of edges visited and improves the quality of solution. We apply DOLPA to community detection and present the design and implementation of the algorithm as well as its shared-memory parallelization using OpenMP. Empirically, we evaluate our algorithm using synthetic graphs as well as real-world networks. Compared with the state-of-art \textit{Parallel Label Propagation} algorithm, we achieve at least two times the F-Score while reducing the runtime by 50\% for synthetic graphs with overlapping communities. We also compare DOLPA against the state-of-art parallel implementation of the Louvain method using the same graphs and show that DOLPA achieves about three times the F-Score at 10\% the runtime. On real-world graphs, we get a speedup of up to $10\times$ using 64 threads.

Liu, X↗
DOE OSTI2022

High-yield recombinant bacterial expression of 13 C-, 15 N-labeled, serine-16 phosphorylated, murine amelogenin using a modified third generation genetic code expansion protocol

Amelogenin constitutes ~ 90% of the enamel matrix in the secretory stage of amelogenesis, a still poorly understood process that results in the formation of the hardest and most mineralized tissue in vertebrates – enamel. Most biophysical research with amelogenin uses recombinant protein expressed in Escherichia coli. In addition to providing copious amounts of protein, recombinant expression allows 13 C- and 15 N-labelling for detailed structural studies using NMR spectroscopy. However, native amelogenin is phosphorylated at one position, Ser-16 in murine amelogenin, and a lingering question overshadowing decades of enamel research with recombinant amelogenin is the effect of phosphorylation on its biophysical properties. Using a modified genetic code expansion protocol we have expressed and purified uniformly 13 C-, 15 N-labeled murine amelogenin (pS16M179) with ~95% of the protein being correctly phosphorylated. Homogeneous phosphorylation was achieved using commercially available, enriched, 13 C-, 15 N-labeled media and protein expression was induced with isopropyl β-D-1-thiogalactopyranoside at 310 K. Phosphoserine incorporation was verified from one-dimensional 31P NMR spectra, comparison of 1 H- 15 N HSQC spectra, Phos-tag SDS PAGE, and mass spectrometry. Phosphorus-31 NMR spectra for pS16M179 under conditions known to trigger amelogenin self-assembly into nanospheres confirm nanosphere models with buried N-termini. Lambda phosphatase treatment of these nanospheres results in the dephosphorylation of pS16M179, confirming that smaller oligomers and monomers with exposed N-termini are in equilibrium with nanospheres. Such 13 C-, 15 N-labeling of amelogenin with accurately encoded phosphoserine incorporation will accelerate biomineralization research to understand amelogenesis and stimulate the expanded use of genetic code expansion protocols to introduce phosphorylated amino acids into proteins.

31P-NMR↗
DOE OSTI2023

High dimensional binary classification under label shift: phase transition and regularization

Label Shift has been widely believed to be harmful to the generalization performance of machine learning models. Researchers have proposed many approaches to mitigate the impact of the label shift, e.g., balancing the training data. However, these methods often consider the underparametrized regime, where the sample size is much larger than the data dimension. The research under the overparametrized regime is very limited. Here, to bridge this gap, we propose a new asymptotic analysis of the Fisher Linear Discriminant classifier for binary classification with label shift. Specifically, we prove that there exists a phase transition phenomenon: Under certain overparametrized regime, the classifier trained using imbalanced data outperforms the counterpart with reduced balanced data. Moreover, we investigate the impact of regularization to the label shift: The aforementioned phase transition vanishes as the regularization becomes strong.

binary classification↗