Search NASA⌕ Search

SEARCH · Search NASA

Results for “deep”

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 145 records · Page 8
All sourcesNASA NTRSDOE OSTI
DOE OSTI2016

Contributions of immunoaffinity chromatography to deep proteome profiling of human biofluids

Human biofluids, especially blood plasma or serum, hold great potential as the sources of potential biomarkers for various diseases; however, the enormous dynamic range of protein concentrations in biofluids represents a significant analytical challenge to detect promising low-abundance protein biomarkers. Over the last decade, various immunoaffinity chromatographic methods have been developed and routinely applied for separating low-abundance proteins from the high and moderate-abundance proteins, thus enabling more effective detection of low-abundance proteins. Herein, we review the advances of immunoaffinity separation methods and their contributions to the proteomics applications of different human biofluids. The limitations and future perspective of immunoaffinity separation methods are also discussed.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2017

Stability of iron-bearing carbonates in the deep Earth’s interior

The presence of carbonates in inclusions in diamonds coming from depths exceeding 670 km are obvious evidence that carbonates exist in the Earth’s lower mantle. However, their range of stability, crystal structures and the thermodynamic conditions of the decarbonation processes remain poorly constrained. We investigate the behaviour of pure iron carbonate at pressures over 100 GPa and temperatures over 2,500 K using single-crystal X-ray diffraction and Mossbauer spectroscopy in laser-heated diamond anvil cells. On heating to temperatures of the Earth’s geotherm at pressures to B 50 GPa FeCO 3 partially dissociates to form various iron oxides. Furthermore, at higher pressures FeCO 3 forms two new structures— tetrairon(III) orthocarbonate Fe$3+\atop{4}$C 3 O 12 and diiron(II) diiron(III) tetracarbonate Fe$2+\atop{2}$ Fe$3+\atop{2}$C 4 O 13 , both phases containing CO 4 tetrahedra. Fe 4 C 4 O 13 is stable at conditions along the entire geotherm to depths of at least 2,500 km, thus demonstrating that self-oxidation-reduction reactions can preserve carbonates in the Earth’s lower mantle.

58 GEOSCIENCES↗
DOE OSTI2018

Nanodroplet processing platform for deep and quantitative proteome profiling of 10–100 mammalian cells

Nanoscale or single cell technologies are critical for biomedical applications. However, current mass spectrometry (MS)-based proteomic approaches require samples comprising a minimum of thousands of cells to provide in-depth profiling. Here, we report the development of a nanoPOTS (Nanodroplet Processing in One pot for Trace Samples) platform as a major advance in overall sensitivity. NanoPOTS dramatically enhances the efficiency and recovery of sample processing by downscaling processing volumes to <200 nL to minimize surface losses. When combined with ultrasensitive LC-MS, nanoPOTS allows identification of ~1500 to ~3,000 proteins from ~10 to ~140 cells, respectively. By incorporating the Match Between Runs algorithm of MaxQuant, >3000 proteins were consistently identified from as few as 10 cells. Furthermore, we demonstrate robust quantification of ~2400 proteins from single human pancreatic islet thin sections from type 1 diabetic and control donors, illustrating the application of nanoPOTS for spatially resolved proteome measurements from clinical tissues.

Environmental Molecular Sciences Laboratory↗
DOE OSTI2020

Optimizing automatic morphological classification of galaxies with machine learning and deep learning using Dark Energy Survey imaging

There are several supervised machine learning methods used for the application of automated morphological classification of galaxies; however, there has not yet been a clear comparison of these different methods using imaging data, or an investigation for maximizing their effectiveness. We carry out a comparison between several common machine learning methods for galaxy classification [Convolutional Neural Network (CNN), K-nearest neighbour, logistic regression, Support Vector Machine, Random Forest, and Neural Networks] by using Dark Energy Survey (DES) data combined with visual classifications from the Galaxy Zoo 1 project (GZ1). Our goal is to determine the optimal machine learning methods when using imaging data for galaxy classification. We show that CNN is the most successful method of these ten methods in our study. Using a sample of ~2800 galaxies with visual classification from GZ1, we reach an accuracy of ~0.99 for the morphological classification of ellipticals and spirals. The further investigation of the galaxies that have a different ML and visual classification but with high predicted probabilities in our CNN usually reveals the incorrect classification provided by GZ1. We further find the galaxies having a low probability of being either spirals or ellipticals are visually lenticulars (S0), demonstrating that supervised learning is able to rediscover that this class of galaxy is distinct from both ellipticals and spirals. We confirm that ~2.5 percent galaxies are misclassified by GZ1 in our study. After correcting these galaxies’ labels, we improve our CNN performance to an average accuracy of over 0.99 (accuracy of 0.994 is our best result).

79 ASTRONOMY AND ASTROPHYSICS↗
DOE OSTI2018

Deep SOAR follow-up photometry of two Milky Way outer-halo companions discovered with Dark Energy Survey

We report the discovery of a new star cluster, DES 3, in the constellation of Indus, and deeper observations of the previously identified satellite DES J0222.7−5217 (Eridanus III). DES 3 was detected as a stellar overdensity in first-year Dark Energy Survey data, and confirmed with deeper photometry from the 4.1-m Southern Astrophysical Research (SOAR) telescope. The new system was detected with a relatively high significance and appears in the DES images as a compact concentration of faint blue point sources. We determine that DES 3 is located at a heliocentric distance of ≃76.2 kpc and it is dominated by an old (≃9.8 Gyr) and metal-poor ([Fe/H] ≃ −1.84) population. While the age and metallicity values of DES 3 are comparable to typical globular clusters (objects with a high stellar density, stellar mass of ∼10^5 M_⊙ and luminosity ∼ −7.3), its half-light radius (r_h ∼ 6.87 pc) and luminosity (M_V ∼ −1.7) are more indicative of faint star cluster. Based on the angular size, DES 3, with a value of r_h ∼ 0$${^{\prime}_{.}}$$31, is among the smallest faint star clusters known to date. Furthermore, using deeper imaging of DES J0222.7−5217 taken with the SOAR telescope, we update structural parameters and perform the first isochrone modelling. Our analysis yields the first age (≃12.6 Gyr) and metallicity ([Fe/H] ≃ −2.01) estimates for this object. The half-light radius (r_h ≃ 11.24 pc) and luminosity (M_V ≃ −2.4) of DES J0222.7−5217 suggest that it is likely a faint star cluster. The discovery of DES 3 indicates that the census of stellar systems in the Milky Way is still far from complete, and demonstrates the power of modern wide-field imaging surveys to improve our knowledge of the Galaxy’s satellite population.

79 ASTRONOMY AND ASTROPHYSICS↗