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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 883 records · Page 49
All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Identification and molecular cloning of novel transcripts of the human kallikrein-related peptidase 10 (KLK10) gene using next-generation sequencing

Tissue kallikrein and kallikrein-related peptidases (KLKs) form the largest group of serine proteases in the human genome, sharing many structural and functional characteristics. Multiple alternative transcripts have been reported for the most human KLK genes, while many of them are aberrantly expressed in various malignancies, thus possessing significant prognostic and/or diagnostic value. Alternative splicing of cancer-related genes is a common cellular mechanism accounting for cancer cell transcriptome complexity, as it affects cell cycle control, proliferation, apoptosis, invasion, and metastasis. In this study, we describe the identification and molecular cloning of eight novel transcripts of the human KLK10 gene using 3′ rapid amplification of cDNA ends (3′ RACE) and next-generation sequencing (NGS), as well as their expression analysis in a wide panel of cell lines, originating from several distinct cancerous and normal tissues. Bioinformatic analysis revealed that the novel KLK10 transcripts contain new alternative splicing events between already annotated exons as well as novel exons. In addition, investigation of their expression profile in a wide panel of cell lines was performed with nested RT-PCR using variant-specific pairs of primers. Since many KLK mRNA transcripts possess clinical value, these newly discovered alternatively spliced KLK10 transcripts appear as new potential biomarkers for diagnostic and/or prognostic purposes or as targets for therapeutic strategies. - Highlights: • NGS was used to identify novel transcripts of the human KLK10 gene. • 8 novel KLK10 transcripts were identified. • A novel 3′UTR was detected and characterized. • The expression profiles of all 8 novel KLK10 transcripts were identified.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Identification of a DYRK1A-mediated phosphorylation site within the nuclear localization sequence of the hedgehog transcription factor GLI1

GLI1 is a key downstream transcription effector of the Hedgehog (Hh) signaling pathway that is involved in promoting cell growth, differentiation and tissue patterning in embryonic development. GLI1 over-activation and its nuclear localization has also been linked to the increased aggressiveness of a number of cancers. It has previously been demonstrated that DYRK1A (dual-specificity tyrosine-regulated kinase 1A) can phosphorylate GLI1 and promote GLI1 nuclear localization and its transcriptional activity. Utilizing recombinant human GLI1 and DYRK1A proteins and phospho-peptide mass spectrometry, we demonstrated that GLI1 is phosphorylated by DYRK1A at Ser408, a phospho-site that falls within the putative nuclear localization sequence (NLS) of GLI1, suggesting a possible mechanistic role in modulating its translocation. Further, we showed that the Ser408 site on GLI1 was not phosphorylated in the presence of the selective DYRK1A inhibitor harmine. The data described herein provide the first identification of a DYRK1A-mediated site of phosphorylation on GLI1 within its NLS and may serve as a valuable mechanism for further understanding Hh signaling modulation. - Highlights: • We describe a novel direct DYRK1A phosphorylation site on GLI1 at Ser408. • Harmine a selective DYRK1A inhibitor, blocks DYRK1A-mediated GLI1 phosphorylation. • The Ser408 phospho-site is within the GLI1 nuclear localization sequence (380–420). • This phosphorylation site provides a potential anti-Hh therapeutic target.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Identification and expression pattern analysis of BoMYB51 involved in indolic glucosinolate biosynthesis from broccoli (Brassica oleracea var. italica)

Highlights: • Overexpression of BoMYB51 in Arabidopsis increases indolic glucosinolate production by upregulating biosynthetic genes. • Overexpression of BoMYB51 in Arabidopsis results in enhanced flagellin22 (Flg22) induced callose deposition. • BoMYB51 is upregulated by SA, JA, ABA and ET. GA represses BoMYB51 expression in true leaves, but not in cotyledons. • BoMYB51 is induced by both biotic and abiotic stresses. Glucosinolates are a class of amino acid-derived specialized metabolites characteristic of the Brassicales order. Trp derived indolic glucosinolates are essential for the effective plant defense responses to a wide range of pathogens and herbivores. In Arabidopsis, MYB51 is the key transcription factor positively regulates indolic glucosinolate production by activating certain biosynthetic genes. In this study, we report the isolation and identification of a MYB51 from broccoli designated as BoMYB51. Overexpression of BoMYB51 in Arabidopsis increased indolic glucosinolate production by upregulating biosynthetic genes and resulted in enhanced flagellin22 (Flg22) induced callose deposition. The spatial expression pattern and responsive expression of BoMYB51 to several hormones and stress treatments were investigated by expressing the β-glucuronidase (GUS) reporter gene driven by BoMYB51 promotor in Arabidopsis and quantitative real-time PCR analysis in broccoli. Our study provides information on molecular characteristics of BoMYB51 and possible physiological process BoMYB51 may involve.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Identification of human immunodeficiency virus type-1 Gag-TSG101 interaction inhibitors by high-throughput screening

Highlights: • Development of HTS system based on ELISA targeting Gag-TSG101 interaction. • Screening of 9600 compounds identified 11 compounds inhibiting Gag-TSG101 binding. • Hit compounds, HSM-9 and HSM-10 potently reduced HIV-1 replication. The interaction between viral protein Gag and cellular protein tumor susceptibility gene 101 (TSG101) is a crucial step in the HIV-1 replication cycle. This interaction initiates the viral assembly/budding via the cellular endosomal sorting complexes required for transport (ESCRT) pathway, making it a potential target for antiviral therapy. Here we developed a simple, robust, and reliable high-throughput screening (HTS) system based on enzyme-linked immunosorbent assay (ELISA) to identify compounds that inhibit HIV-1 replication by targeting Gag-TSG101 interaction. Through screening of the 9600-compound library using the established HTS system, several hit compounds, which inhibited Gag-TSG101 interaction, were identified. Subsequent assays revealed two hit compounds, HSM-9 and HSM-10, which have antiviral activity against CD4{sup +} T cell-tropic NL4-3 and macrophage-tropic JR-CSF HIV-1 strains. These results suggest that our established HTS system is an indispensable tool for the identification of HIV-1 Gag-TSG101 interaction inhibitors.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Identification and characterization of a novel phosphoregulatory site on cyclin-dependent kinase 5

Highlights: • Identification of a novel phosphosite on Cyclin-dependent kinase 5 (CDK5). • Elucidation of the mechanism by which the phosphosite negatively regulates CDK5 activity. • Potential role for this phosphoevent in regulating cellular proliferation-migration dichotomy. Cyclin-dependent kinase 5 (CDK5) is a serine/threonine kinase essential for embryonic development whose overactivation has been implicated in several pathologies including neurodegeneration, cancer cell metastasis and type II diabetes. Therefore, it is important to investigate molecular mechanism(s) that mediate regulation of CDK5 activity. Here we identify and characterize a novel phosphoregulatory site on CDK5. Our mass spectrometry analysis identified seven putative phosphorylation sites on CDK5. Using phosphomimetic and non-phosphorylatable mutants, we determined that phosphorylation of S47, one of the identified sites, renders the kinase catalytically inactive. The inactivation of the kinase due to the phosphomimetic change at S47 results from inhibition of its interaction with its cognate activator, p35. We connect the effect of this regulatory event to a cellular phenotype by showing that the S47D CDK5 mutant inhibits cell migration and promotes cell proliferation. Together, these results have uncovered a potential physiological mechanism to regulate CDK5 activity. The evolutionary placement of a phosphorylatable residue (S/T) at this position not only in CDK5 but also in the majority of other CDK family members suggests that this phosphosite may represent a shared regulatory mechanism across the CDK family.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2018

Identification of peptide based B-cell epitopes in Zika virus NS1

Highlights: • B-cell epitope mapping in ZIKV NS1. • Identification of the ZIKV NS1 specific antibody binding site. • Tool for design of diagnostics and structure-based vaccine against ZIKV. Zika virus (ZIKV), a mosquito-borne flavivirus that has recently emerged globally, poses a major threat to public health. To control this emerging disease, accurate diagnostics are required for monitoring current ZIKV outbreaks. Owing to the high nucleotide sequence similarity and cross-reactivity of ZIKV with other members of the Flaviviridae family, discrimination from other flavivirus infections is often difficult in endemic areas. ZIKV NS1 induces major virus-specific antibodies and is therefore utilized as a serological marker for ZIKV diagnosis. To identify ZIKV specific epitopes for clinical application, 33 NS1 peptides that are 15–30 amino acid in length covering whole NS1 were synthesized and analyzed linear B-cell epitopes with 38 human serum samples (20 ZIKV-positive and 18 ZIKV-negative). As a result of screening, eight epitope regions were identified. In particular, the Z8 and Z14 peptides located in the β-ladder surface region showed higher levels of binding activity in ZIKV-positive sera without cross-reactivity to other flaviviruses. These identified sensitive and specific epitopes provide a tool for design of diagnostics and structure-based vaccine antigens for ZIKV infection.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2017

Identification of quinazoline based inhibitors of IRAK4 for the treatment of inflammation

Interleukin-1 receptor associated kinase 4 (IRAK4) has been implicated in IL-1R and TLR based signaling. Therefore selective inhibition of the kinase activity of this protein represents an attractive target for the treatment of inflammatory diseases. Medicinal chemistry optimization of high throughput screening (HTS) hits with the help of structure based drug design led to the identification of orally-bioavailable quinazoline based IRAK4 inhibitors with excellent pharmacokinetic profile and kinase selectivity. These highly selective IRAK4 compounds show activity in vivo via oral dosing in a TLR7 driven model of inflammation.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Rapid identification of neutralizing antibodies against SARS-CoV-2 variants by mRNA display

The increasing prevalence of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants with the ability to escape existing humoral protection conferred by previous infection and/or immunization necessitates the discovery of broadly reactive neutralizing antibodies (nAbs). Utilizing mRNA display, we identify a set of antibodies against SARS-CoV-2 spike (S) proteins and characterize the structures of nAbs that recognize epitopes in the S1 subunit of the S glycoprotein. These structural studies reveal distinct binding modes for several antibodies, including the targeting of rare cryptic epitopes in the receptor-binding domain (RBD) of S that interact with angiotensin-converting enzyme 2 (ACE2) to initiate infection, as well as the S1 subdomain 1. Further, we engineer a potent ACE2-blocking nAb to sustain binding to S RBD with the E484K and L452R substitutions found in multiple SARS-CoV-2 variants. We demonstrate that mRNA display is an approach for the rapid identification of nAbs that can be used in combination to combat emerging SARS-CoV-2 variants.

SARS-CoV-2↗
DOE OSTI2021

Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision

Highlights: • A method is presented to detect, locate, quantify, and visualize dense microcracks in HPFRCC. • Quantification of dense microcracks is realized using deep learning method for the first time. • The presented method uses deep learning models that are trained using a realistic dataset size. • The presented method has a high computation efficiency for identifying and quantifying cracks. • The presented method provides crack width with errors up to 50 μm and a R{sup 2} value of 0.984. High-performance fiber-reinforced cementitious composites (HPFRCCs) feature high mechanical strengths, crack resistance, and durability. Under excessive loading, HPFRCCs demonstrate dense microcracks that are difficult to identify using existing methods. This study presents a computer vision method for identification, quantification, and visualization of microcracks in HPFRCCs based on deep learning. The presented method integrates multiple deep learning models and computer vision techniques in a hierarchical architecture. The crack pattern (e.g., number, width, and spacing of cracks) are automatically determined from pictures without human intervention. This study shows that the presented method achieves an accuracy of 0.992 for crack detection and an accuracy finer than 50 μm (R{sup 2} > 0.984) for quantification of crack width when deep learning models are trained using only 200 pictures of HPFRCCs and 200 pictures of conventional concrete with incorporation of data augmentation. The presented method is expected to be also applicable to other materials featuring complex cracks.

36 MATERIALS SCIENCE↗
DOE OSTI2019

Structure-Guided Identification of Resistance Breaking Antimalarial N -Myristoyltransferase Inhibitors

The attachment of myristate to the N-terminal glycine of certain proteins is largely a co-translational modification catalyzed by N-myristoyltransferase (NMT), and involved in protein membrane-localization. Pathogen NMT is a validated therapeutic target in numerous infectious diseases including malaria. In Plasmodium falciparum , NMT substrates are important in essential processes including parasite gliding motility and host cell invasion. Here, we generated parasites resistant to a particular NMT inhibitor series and show that resistance in an in vitro parasite growth assay is mediated by a single amino acid substitution in the NMT substrate-binding pocket. The basis of resistance was validated and analyzed with a structure-guided approach using crystallography, in combination with enzyme activity, stability, and surface plasmon resonance assays, allowing identification of another inhibitor series unaffected by this substitution. We suggest that resistance studies incorporated early in the drug development process help selection of drug combinations to impede rapid evolution of parasite resistance.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2022

Comparison of Machine Learning and Deep Learning for View Identification from Cardiac Magnetic Resonance Images

Background: Artificial intelligence is increasingly utilized to aid in the interpretation of cardiac magnetic resonance (CMR) studies. One of the first steps is the identification of the imaging plane depicted, which can be achieved by both deep learning (DL) and classical machine learning (ML) techniques without user input. We aimed to compare the accuracy of ML and DL for CMR view classification and to identify potential pitfalls during training and testing of the algorithms. Methods: To train our DL and ML algorithms, we first established datasets by retrospectively selecting 200 CMR cases. The models were trained using two different cohorts (passively and actively curated) and applied data augmentation to enhance training. Once trained, the models were validated on an external dataset, consisting of 20 cases acquired at another center. We then compared accuracy metrics and applied class activation mapping (CAM) to visualize DL model performance. Results: The DL and ML models trained with the passively-curated CMR cohort were 99.1% and 99.3% accurate on the validation set, respectively. However, when tested on the CMR cases with complex anatomy, both models performed poorly. After training and testing our models again on all 200 cases (active cohort), validation on the external dataset resulted in 95% and 90% accuracy, respectively. The CAM analysis depicted heat maps that demonstrated the importance of carefully curating the datasets to be used for training. Conclusions: Both DL and ML models can accurately classify CMR images, but DL outperformed ML when classifying images with complex heart anatomy.

artificial intelligence↗
DOE OSTI2023

GRIDS-Net: Inverse shape design and identification of scatterers via geometric regularization and physics-embedded deep learning

This study presents a deep learning based methodology for both remote sensing and design of acoustic scatterers. The ability to determine the shape of a scatterer, either in the context of material design or sensing, plays a critical role in many practical engineering problems. This class of inverse problems is extremely challenging due to their high-dimensional, nonlinear, and ill-posed nature. To overcome these technical hurdles, we introduce a geometric regularization approach for deep neural networks (DNN) based on non-uniform rational B-splines (NURBS) and capable of predicting complex 2D scatterer geometries in a parsimonious dimensional representation. Then, this geometric regularization is combined with physics-embedded learning and integrated within a robust convolutional autoencoder (CAE) architecture to accurately predict the shape of 2D scatterers in the context of identification and inverse design problems. Further, an extensive numerical study is presented in order to showcase the remarkable ability of this approach to handle complex scatterer geometries while generating physically-consistent acoustic fields. The study also assesses and contrasts the role played by the (weakly) embedded physics in the convergence of the DNN predictions to a physically consistent inverse design.

42 ENGINEERING↗
DOE OSTI2024

tLaSDI: Thermodynamics-informed latent space dynamics identification

Here we propose a latent space dynamics identification method, namely tLaSDI, that embeds the first and second principles of thermodynamics. The latent variables are learned through an autoencoder as a nonlinear dimension reduction model. The latent dynamics are constructed by a neural network-based model that precisely preserves certain structures for the thermodynamic laws through the GENERIC formalism. An abstract error estimate is established, which provides a new loss formulation involving the Jacobian computation of autoencoder. The autoencoder and the latent dynamics are simultaneously trained to minimize the new loss. Computational examples demonstrate the effectiveness of tLaSDI, which exhibits robust generalization ability, even in extrapolation. In addition, an intriguing correlation is empirically observed between a quantity from tLaSDI in the latent space and the behaviors of the full-state solution.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2022

Graph-component approach to defect identification in large atomistic simulations

In this work, the graph-theoretical concept of connected components is employed to extract the evolution of defect configurations in a polycrystalline aluminum structure containing ~8.3 million atoms. This graph-component approach is applied to reveal details of defect formation, transport, and transformation in the polycrystalline Al under large shear deformation. Building upon standard nearest neighbor analysis, graph theory and associated tools are used to reduce the multi-million-atom system into discrete component subgraphs that represent distinct structural defects. This method allows the automated identification, characterization, and tracking of defective regions within large volumes of data representing atomic-scale processes. Such analysis elucidates relationships between external stimuli, such as strain, and defect distributions, which have a large influence on material properties. The Graph Analytics for Large Atomistic Simulations (GALAS) codebase that implements this analysis, together with user guidance, is openly available at https://github.com/pnnl/galas.

36 MATERIALS SCIENCE↗
DOE OSTI2020

The good, the bad, and the ugly: Data-driven load profile discord identification in a large building portfolio

Reducing the overall energy consumption and associated greenhouse gas emissions in the building sector is essential for meeting our future sustainability goals. Recently, smart energy metering facilities have been deployed to enable monitoring of energy consumption data with hourly or subhourly temporal resolution. This unprecedented data collection has created various opportunities for advanced data analytics involving load profiles (e.g., building energy benchmarking programs, building-to-grid integration, and calibration of urban-scale energy models). These applications often need preprocessing steps to detect daily load profile discords, such as: 1) outliers due to system malfunctions (the bad) and 2) irregular energy consumption patterns, such as those resulting from holidays (the ugly) compared to normal consumption patterns (the good). However, current preprocessing methods predominantly focus on filtering using statistical threshold values, which fail to capture the contextual discords of daily profiles. In addition, discord detection algorithms in building research are often aimed at finding individual building-level discords, which are not suitable at a large scale. Thus, here, we develop a method for automated load profile discord identification (ALDI) in a large portfolio of buildings (more than 100 buildings). Specifically, ALDI 1) uses the matrix profile (MP) method to quantify the similarities of daily subsequences in time series meter data, 2) compares daily MP values with typical-day MP distributions using the Kolmogorov-Smirnov test, and 3) identifies daily load profile discords in a large building portfolio. We evaluate ALDI using the metering data of both an academic campus and a residential neighborhood. Our results demonstrate that ALDI efficiently discovers measurement errors by system malfunctions and low energy consumption days in the academic campus portfolio, and it detects unique load shape patterns likely driven by occupant behavior and extreme weather conditions in the residential neighborhood.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2026

Machine learning-enhanced hybrid modeling approach for better identification of a building thermal network model and improved prediction

The gray-box modeling approach, which uses a semi-physical thermal network model, has been widely used in building prediction applications, such as model predictive control (MPC). However, unmeasured disturbances, such as occupants, lighting, and in/exfiltration loads, make it challenging to apply this approach to practical buildings. In this word, we propose a hybrid modeling approach that integrates the gray-box model with a model for unmeasured disturbance. After reviewing several system identification approaches, we systematically designed the unmeasured disturbance model with a model selection process based on statistical tests to make it robust. We generated data based on the building model calibrated by real operational data and then trained the hybrid model for two different weather conditions. The hybrid model approach demonstrates an RMSE reduction of approximately 0.2–0.9 °C and 0.3–2 °C on 1-day ahead temperature prediction compared to the Conventional approach for mild (Berkeley, CA) and cold (Chicago, IL) climates, respectively. In addition, this approach was applied to experimental data obtained from the laboratory building to be used for the MPC application, showing superior prediction performances.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
DOE OSTI2022

Multi-area parameter error identification for large power systems

Power grid model parameters may contain errors due to various reasons. Detecting and correcting parameter errors typically requires significant computational effort due to the size and complexity of the parameter database. While the normalized Lagrange multiplier (NLM) method can effectively detect, identify and correct parameter errors, its computational burden could rapidly grow with increasing system size. This paper addresses this issue by proposing a multi-area parameter error identification method. Each area has its own outlier detection tool for detecting the incorrect parameters and measurements within the area. On the other hand, due to the reduced redundancy at area boundaries, parameter errors on branches incident to boundary buses may not be detected. Such errors are subsequently detected by a coordination level estimator completing the system-wide parameter detection procedure. In conclusion, performance of the developed method is demonstrated using the IEEE 118-bus and 2000-bus Texas synthetic systems.

24 POWER TRANSMISSION AND DISTRIBUTION↗