Search NASA⌕ Search

SEARCH · Search NASA

Results for “dl”

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 19 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2023

DL-TODA: A Deep Learning Tool for Omics Data Analysis

Metagenomics is a technique for genome-wide profiling of microbiomes; this technique generates billions of DNA sequences called reads. Given the multiplication of metagenomic projects, computational tools are necessary to enable the efficient and accurate classification of metagenomic reads without needing to construct a reference database. The program DL-TODA presented here aims to classify metagenomic reads using a deep learning model trained on over 3000 bacterial species. A convolutional neural network architecture originally designed for computer vision was applied for the modeling of species-specific features. Using synthetic testing data simulated with 2454 genomes from 639 species, DL-TODA was shown to classify nearly 75% of the reads with high confidence. The classification accuracy of DL-TODA was over 0.98 at taxonomic ranks above the genus level, making it comparable with Kraken2 and Centrifuge, two state-of-the-art taxonomic classification tools. DL-TODA also achieved an accuracy of 0.97 at the species level, which is higher than 0.93 by Kraken2 and 0.85 by Centrifuge on the same test set. Application of DL-TODA to the human oral and cropland soil metagenomes further demonstrated its use in analyzing microbiomes from diverse environments. Compared to Centrifuge and Kraken2, DL-TODA predicted distinct relative abundance rankings and is less biased toward a single taxon.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2024

DL_POLY Quantum 2.0: A modular general-purpose software for advanced path integral simulations

DL_POLY Quantum 2.0, a vastly expanded software based on DL_POLY Classic 1.10, is a highly parallelized computational suite written in FORTRAN77 with a modular structure for incorporating nuclear quantum effects into large-scale/long-time molecular dynamics simulations. This is achieved by presenting users with a wide selection of state-of-the-art dynamics methods that utilize the isomorphism between a classical ring polymer and Feynman’s path integral formalism of quantum mechanics. Here, the flexible and user-friendly input/output handling system allows the control of methodology, integration schemes, and thermostatting. DL_POLY Quantum is equipped with a module specifically assigned for calculating correlation functions and printing out the values for sought-after quantities, such as dipole moments and center-of-mass velocities, with packaged tools for calculating infrared absorption spectra and diffusion coefficients.

36 MATERIALS SCIENCE↗
DOE OSTI2022

Doppler Lidar (DL) Instrument Handbook

The Doppler lidar (DL) is an active remote sensing instrument that provides range- and time-resolved measurements of radial velocity and attenuated backscatter. The principle of operation is similar to radar in that pulses of energy are transmitted into the atmosphere; the energy scattered back to the transceiver is collected and measured as a time-resolved signal. From the time delay between each outgoing transmitted pulse and the backscattered signal, the distance to the scatterer is inferred. The radial or line-of-sight velocity of the scatterers is determined from the Doppler frequency shift of the backscattered radiation. The DL uses a heterodyne detection technique in which the return signal is mixed with a reference laser beam (i.e., local oscillator) of known frequency. An onboard signal processing computer then determines the Doppler frequency shift from the spectra of the heterodyne signal. The energy content of the Doppler spectra can also be used to determine attenuated backscatter.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2017

Optimization of Poly(DL-Lactic Acid) Degradation and Evaluation of Biological Re-polymerization

Poly(DL-lactic acid) or PLA is a biodegradable polymer. It has received much attention since it plays an important role in resolving the global warming problem. The protease produced by Actinomadura keratinilytica strain T16-1 was previously reported as having PLA depolymerase potential and being applicable to PLA biodegradation, which was used in this work. Therefore, this research demonstrates the important basic knowledge on the biological degradation process by the crude PLA-degrading enzyme from strain T16-1. Its re-polymerization was evaluated. The optimization of PLA degradation by statistical methods based on central composite design was determined. Approximately 6700 mg/l PLA powder was degraded by the crude enzyme under optimized conditions: an initial enzyme activity of 200 U/ml, incubated at 60 °C for 24 h released 6843 mg/l lactic acid with 82% conversion, which was similar to the commercial enzyme proteinase K (81%). The degradable products were re-polymerized repeatedly by using commercial lipase as a catalyst under a nitrogen atmosphere for 6 h. A PLA oligomer was achieved with a molecular weight of 378 Da (n = 5). This is the first report to demonstrate the high efficiency of the enzyme to degrade 100% of PLA powder and to show the biological recycling process of PLA, which is promising for the treatment and utilization of biodegradable plastic wastes in the future.

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

Dl-3-n-butylphthalide promotes neurite outgrowth of primary cortical neurons by Sonic Hedgehog signaling via upregulating Gap43

Highlights: • NBP promoted neurite outgrowth in immature primary cortical neurons. • NBP increased axon and dendrite extension in mature primary cortical neurons. • NBP promoted neuritogenesis and neuronal plasticity via Shh signaling pathway. • The Shh signaling pathway increased Gap43 expression in primary cortical neuron. • NBP promoted neurite outgrowth by Shh signaling via upregulating Gap43 expression. Neurite outgrowth is the basis for wiring during the development of the nervous system. Dl-3-n-butylphthalide (NBP) has been recognized as a promising treatment to improve behavioral, neurological and cognitive outcomes in ischemic stroke. However, little is known about the effect and mechanism of NBP on the neurite outgrowth. In this study, we used different methods to investigate the potential effects of NBP on the neurite extension and plasticity of immature and mature primary cortical neurons and explored the underlying mechanisms. Our results demonstrated that in immature and mature cortical neurons, NBP promoted the neurite length and intersections, increased neuritic arborization, elevated numbers of neurite branch and terminal points and improved neurite complexity and plasticity of neuronal development processes. Besides, our data revealed that NBP promoted neurite extension and branching partly by activating Shh signaling pathway via increasing Gap43 expression both in immature and mature primary cortical neurons. The present study provided new insights into the contribution of NBP in neuronal plasticity and unveiled a novel pathway to induce Gap43 expression in primary cortical neurons.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2013

Structural basis for type VI secreted peptidoglycan dl-endopeptidase function, specificity and neutralization in Serratia marcescens

Crystal structures of type VI secretion system-associated immunity proteins, a peptidoglycan endopeptidase and a complex of the endopeptidase and its cognate immunity protein are reported together with assays of endopeptidase activity and functional assessment. Some Gram-negative bacteria target their competitors by exploiting the type VI secretion system to extrude toxic effector proteins. To prevent self-harm, these bacteria also produce highly specific immunity proteins that neutralize these antagonistic effectors. Here, the peptidoglycan endopeptidase specificity of two type VI secretion-system-associated effectors from Serratia marcescens is characterized. These small secreted proteins, Ssp1 and Ssp2, cleave between γ-d-glutamic acid and l-meso-diaminopimelic acid with different specificities. Ssp2 degrades the acceptor part of cross-linked tetratetrapeptides. Ssp1 displays greater promiscuity and cleaves monomeric tripeptides, tetrapeptides and pentapeptides and dimeric tetratetra and tetrapenta muropeptides on both the acceptor and donor strands. Functional assays confirm the identity of a catalytic cysteine in these endopeptidases and crystal structures provide information on the structure–activity relationships of Ssp1 and, by comparison, of related effectors. Functional assays also reveal that neutralization of these effectors by their cognate immunity proteins, which are called resistance-associated proteins (Raps), contributes an essential role to cell fitness. The structures of two immunity proteins, Rap1a and Rap2a, responsible for the neutralization of Ssp1 and Ssp2-like endopeptidases, respectively, revealed two distinct folds, with that of Rap1a not having previously been observed. The structure of the Ssp1–Rap1a complex revealed a tightly bound heteromeric assembly with two effector molecules flanking a Rap1a dimer. A highly effective steric block of the Ssp1 active site forms the basis of effector neutralization. Comparisons with Ssp2–Rap2a orthologues suggest that the specificity of these immunity proteins for neutralizing effectors is fold-dependent and that in cases where the fold is conserved sequence differences contribute to the specificity of effector–immunity protein interactions.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2023

ORNL_AISD_DL-HLgap

This dataset provides supplementary molecular dataset of Deep Learning Workflow for the Inverse Design of Molecules with Specific Optoelectronic Properties. The dataset comprises three main directories such as GDB-9_dataset, Low_HL_Gap_dataset, and High_HL_Gap_dataset which individually has csv files, smiles_txt files, pdb files and xyz files containing information of molecular structures, properties and coordinates generated from deep learning workflow using generative model, surrogate model and DFTB calculation results. GDB-9_dataset contains the molecular data extracted from the original GDB-9 dataset with additional data of DFTB HL gap, surrogate HL gap and molecular property analysis. (the number of atoms, aromaticity and double bond equivalent) Low_HL_Gap_dataset and High_HL_Gap_dataset contains series of dataset for different generations with further split to train and test dataset that were obtained from the iterative workflow described in the manuscript. Additional directory Chemiscope_visualization in Low_HL_Gap_dataset directory contains compressed json files to visualize molecules using chemiscope.org page or application to help readers examine generated molecules.

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

Clear-air Vertical Air Motion Within the Marine Boundary Layer

The Doppler lidars (DL) at the ARM observatories operate at 1.5-micrometer wavelength and record backscatter and mean Doppler velocity. The DL present at the ARM ENA site records these quantities at a 1-second temporal and 30-meter range resolution. Reported here are daily files containing gridded values of noise-filtered and calibrated backscatter, and noise-filtered mean Doppler velocity. In addition we also report the noise-filtered and calibrated gradient of backscatter that is used to derive estimates of cloud base height. All of the variables are reported at a uniform 5-second temporal resolution and 30-m range resolution. The instrument reported raw backscatter was first corrected for telescope focus function, filtered for noise, and then calibrated by exploiting the constant lidar ratio exhibited during nonprecipitating stratocumulus cloud conditions. Hence, the reported backscatter can be used to derive aerosol and hydrometeor properties. Daily files are reported for the summer Intensive Operational Period (IOP) of the Aerosol, and Cloud and Experiments in the Eastern North Atlantic (ACE-ENA) field campaign. Similar files can be produced for any DL at ARM observatories. Please find below the header of the NetCDF files. double time (time) ; time:Long_Name = "Time" ; time:units = "Seconds from 1970-1-1 0:00:00" ; double height (height) ; height:Long_Name = "Height" ; height:units = "meters" ; double dl_el (time) ; dl_el:Long_Name = "elevation" ; dl_el:units = "degrees"; double dl_beta (time, height) ; dl_beta:Long_Name = "Noise Filtered Calibrated Backscatter" ; dl_beta:units = "1 / (m * Sr)" ; double dl_velo (time, height); dl_velo:Long_Name = "Mean Doppler Velocity" ; dl_velo:units = "m/s" ; double dl_att (time, height) ; dl_att:Long_Name = "Gradient of Backscatter" ; dl_att:units = "1 / (m ^ 2 * Sr)" ; double base (time) ; base:Long_Name = "Cloud base height" ; base:units = "meters" ;

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2016

Planck intermediate results: XXIX. All-sky dust modelling with Planck , IRAS, and WISE observations

In this paper, we present all-sky modelling of the high resolution Planck, IRAS, and WISE infrared (IR) observations using the physical dust model presented by Draine & Li in 2007 (DL, ApJ, 657, 810). We study the performance and results of this model, and discuss implications for future dust modelling. The present work extends the DL dust modelling carried out on nearby galaxies using Herschel and Spitzer data to Galactic dust emission. We employ the DL dust model to generate maps of the dust mass surface density Σ Md , the dust optical extinction A V , and the starlight intensity heating the bulk of the dust, parametrized by U min . The DL model reproduces the observed spectral energy distribution (SED) satisfactorily over most of the sky, with small deviations in the inner Galactic disk and in low ecliptic latitude areas, presumably due to zodiacal light contamination. In the Andromeda galaxy (M31), the present dust mass estimates agree remarkably well (within 10%) with DL estimates based on independent Spitzer and Herschel data. We compare the DL optical extinction A V for the diffuse interstellar medium (ISM) with optical estimates for approximately 2 × 10 5 quasi-stellar objects (QSOs) observed inthe Sloan Digital Sky Survey (SDSS). The DL A V estimates are larger than those determined towards QSOs by a factor of about 2, which depends on U min . The DL fitting parameter U min , effectively determined by the wavelength where the SED peaks, appears to trace variations in the far-IR opacity of the dust grains per unit A V , and not only in the starlight intensity. These results show that some of the physical assumptions of the DL model will need to be revised. To circumvent the model deficiency, we propose an empirical renormalization of the DL A V estimate, dependent of U min , which compensates for the systematic differences found with QSO observations. This renormalization, made to match the A V estimates towards QSOs, also brings into agreement the DL A V estimates with those derived for molecular clouds from the near-IR colours of stars in the 2 micron all sky survey (2MASS). The DL model and the QSOs data are also used to compress the spectral information in the Planck and IRAS observations for the diffuse ISM to a family of 20 SEDs normalized per A V , parameterized by U min , which may be used to test and empirically calibrate dust models. Finally, the family of SEDs and the maps generated with the DL model are made public in the Planck Legacy Archive.

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2022

Deep learning to estimate permeability using geophysical data

Time-lapse electrical resistivity tomography (ERT) is a popular geophysical method to estimate three-dimensional (3D) permeability fields from electrical potential difference measurements. Traditional inversion and data assimilation methods are used to ingest this ERT data into hydrogeophysical models to estimate permeability. Due to ill-posedness and the curse of dimensionality, existing inversion strategies provide poor estimates and low resolution of the 3D permeability field. Recent advances in deep learning provide us with powerful algorithms to overcome this challenge. This paper presents a deep learning (DL) framework to estimate the 3D subsurface permeability from time-lapse ERT data. To test the feasibility of the proposed framework, we train DL-enabled inverse models on simulation data. Each measurement in both synthetic and field data is standardized by removing the mean and scaling the time-series to unit variance. This pre-processing step is necessary to bring simulation data closer to field observations. Subsurface process models based on hydrogeophysics are used to generate this synthetic data. Training performed on limited simulation data resulted in the DL model over-fitting. An advanced data augmentation based on mixup is implemented to generate additional training samples to overcome this issue. This mixup technique creates weakly labeled (low-fidelity) samples from strongly labeled (high-fidelity) data. The weakly labeled training data is then used to develop DL-enabled inverse models and reduce over-fitting. As both time-lapse ERT (1133048 features/realization) and 3D permeability (585453 features/realization) data samples are from a high-dimensional space, principal component analysis (PCA) is employed to reduce dimensionality. Encoded ERT and encoded permeability are generated using the trained PCA estimators. A deep neural network is then trained to map the encoded ERT to encoded permeability. This mixup training and unsupervised learning allowed us to build a fast and reasonably accurate DL-based inverse model under limited simulation data. Results show that proposed weak supervised learning can capture salient spatial features in the 3D permeability field. Quantitatively, the average mean squared error (in terms of the natural log) on the strongly labeled training, validation, and test datasets is less than 0.5. The R 2 -score (global metric) is greater than 0.75, and the percent error in each cell (local metric) is less than 10%. Finally, an added benefit in terms of computational cost is that the proposed DL-based inverse model is at least O(10 4 ) times faster than running a forward model once it is trained. Data generation, DL model training, and hyperparameter tuning to identify optimal neural network architectures utilized high-performance computing resources while the DL inference is performed on a standard laptop. Approximately, O(10 5 ) processor hours are used for generating data and DL tuning and training. We acknowledge that the data generation and DL model development are expensive. But once a DL model is trained, it can be re-used for inversion rapidly for the given system, with set physics and domain. Note that traditional inversion may require multiple forward model simulations (e.g., in the order of 10 to 1000), which are very expensive. This computational savings ≈ O(10 5 ) – O(10 7 )) makes the proposed DL-based inverse model attractive for subsurface imaging and real-time ERT monitoring applications due to fast and yet reasonably accurate estimations of permeability field.

58 GEOSCIENCES↗
DOE OSTI2014

Elastic properties, sp³ fraction, and Raman scattering in low and high pressure synthesized diamond-like boron rich carbides

Dense BC{sub x} phases with high boron concentration are predicted to be metastable, superhard, and conductors or superconductors depending on boron concentration. However, up to this point, diamond-like boron rich carbides BC{sub x} (dl-BC{sub x}) phases have been thought obtainable only through high pressure and high temperature treatment, necessitating small specimen volume. Here, we use electron energy loss spectroscopy combined with transmission electron microscopy, Raman spectroscopy, surface Brillouin scattering, laser ultrasonics (LU) technique, and analysis of elastic properties to demonstrate that low pressure synthesis (chemical vapor deposition) of BC{sub x} phases may also lead to the creation of diamond-like boron rich carbides. The elastic properties of the dl-BC{sub x} phases depend on the carbon sp²versus sp³ content, which decreases with increasing boron concentration, while the boron bonds determine the shape of the Raman spectra of the dl-BC{sub x} after high pressure-high temperature treatment. Using the estimation of the density value based on the sp³ fraction, the shear modulus μ of dl-BC₄, containing 10% carbon atoms with sp³ bonds, and dl-B₃C₂, containing 38% carbon atoms with sp³ bonds, were found to be μ = 19.3 GPa and μ = 170 GPa, respectively. The presented experimental data also imply that boron atoms lead to a creation of sp³ bonds during the deposition processes.

75 CONDENSED MATTER PHYSICS, SUPERCONDUCTIVITY AND↗
DOE OSTI2016

Effect of heavy metal layer thickness on spin-orbit torque and current-induced switching in Hf|CoFeB|MgO structures

We study the heavy metal layer thickness dependence of the current-induced spin-orbit torque (SOT) in perpendicularly magnetized Hf|CoFeB|MgO multilayer structures. The damping-like (DL) current-induced SOT is determined by vector anomalous Hall effect measurements. A non-monotonic behavior in the DL-SOT is found as a function of the thickness of the heavy-metal layer. The sign of the DL-SOT changes with increasing the thickness of the Hf layer in the trilayer structure. As a result, in the current-driven magnetization switching, the preferred direction of switching for a given current direction changes when the Hf thickness is increased above ∼7 nm. Although there might be a couple of reasons for this unexpected behavior in DL-SOT, such as the roughness in the interfaces and/or impurity based electric potential in the heavy metal, one can deduce a roughness dependence sign reversal in DL-SOT in our trilayer structure.

71 CLASSICAL AND QUANTUM MECHANICS, GENERAL PHYSIC↗