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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS1977

The gas chromatographic resolution of DL-isovaline

Isovaline is of cosmological interest because it is one of the 12 non-protein amino acids which have been isolated from the Murchison meteorite, and unlike the other chiral amino acids in this meteorite, it has no alpha-hydrogen at its asymmetric center and hence cannot racemize by the customary alpha-hydrogen-dependent mechanisms which engender racemization in ordinary amino acids. Experiments were conducted in which a .01 M solution of N-TFA-DL-isovalyl-L-leucine isopropyl ester in nitromethane was injected into the capillary column of a gas chromatograph coupled to a digital electronic integrator-recorder. Efflux times and integrated peak area percents are shown next to each diastereometer peak.

Flores, J. J.↗
NASA NTRS1999

Effect of osteoblastic culture conditions on the structure of poly(DL-lactic-co-glycolic acid) foam scaffolds

Poly(DL-lactic-co-glycolic acid) (PLGA) foams are an osteoconductive support that holds promise for the development of bone tissue in vitro and implantation into orthopedic defects. Because it is desirable that foams maintain their shape and size, we examined a variety of foams cultured in vitro with osteoblastic cells. Foams were prepared with different porosities and pore sizes by the method of solvent casting/porogen leaching using 80, 85, and 90 wt% NaCl sieved with particle sizes of 150-300 and 300-500 microm and characterized by mercury intrusion porosimetry. Foams seeded with cells were found to have volumes after 7 days in static culture that decreased with increasing porosity: the least porous exhibited no change in volume while the most porous foams decreased by 39 +/- 10%. In addition, a correlation was observed between decreasing foam volume after 7 days in culture and decreasing internal surface area of the foams prior to seeding. Furthermore, foams prepared with the 300-500 microm porogen had lower porosities, greater mean wall thicknesses between adjacent pores, and larger volumes after 7 days in culture than those prepared with the smaller porogen. Two culture conditions for maintaining cells, static and agitated (in a rotary vessel), were found to have similar influences on foam size, cell density, and osteoblastic function for 7 and 14 days in culture. Finally, we examined unseeded foams in aqueous solutions of pH 3.0, 5.0, and 7.4 and found no significant decrease in foam size with degradation. This study demonstrates that adherent osteoblastic cells may collapse very porous PLGA foams prepared by solvent casting/particulate leaching: a potentially undesirable property for repair of orthopedic defects.

NASA Program Fundamental Space Biology↗
NASA NTRS1997

Ectopic bone formation by marrow stromal osteoblast transplantation using poly(DL-lactic-co-glycolic acid) foams implanted into the rat mesentery

Porous biodegradable poly(DL-lactic-co-glycolic acid) foams were seeded with rat marrow stromal cells and implanted into the rat mesentery to investigate in vivo bone formation at an ectopic site. Cells were seeded at a density of 6.83 x 10(5) cells/cm2 onto polymer foams having pore sizes ranging from either 150 to 300 to 710 microns and cultured for 7 days in vitro prior to implantation. The polymer/cell constructs were harvested after 1, 7, 28, or 49 days in vivo and processed for histology and gel permeation chromatography. Visual observation of hematoxylin and eosin-stained sections and von Kossa-stained sections revealed the formation of mineralized bonelike tissue in the constructs within 7 days postimplantation. Ingrowth of vascular tissue was also found adjacent to the islands of bone, supplying the necessary metabolic requirements to the newly formed tissue. Mineralization and bone tissue formation were investigated by histomorphometry. The average penetration depth of mineralized tissue in the construct ranged from 190 +/- 50 microns for foams with 500-710-microns pores to 370 +/- 160 microns for foams with 150-300-microns pores after 49 days in vivo. The mineralized bone volume per surface area and total bone volume per surface area had maximal values of 0.28 +/- 0.21 mm (500-710-microns pore size, day 28) and 0.038 +/- 0.024 mm (150-300-microns, day 28), respectively. As much as 11% of the foam volume penetrated by bone tissue was filled with mineralized tissue. No significant trends over time were observed for any of the measured values (penetration depth, bone volume/surface area, or percent mineralized bone volume). These results suggest the feasibility of bone formation by osteoblast transplantation in an orthotopic site where not only bone formation from transplanted cells but also ingrowth from adjacent bone may occur.

NASA Discipline Developmental Biology↗
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↗