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NASA NTRSDate not supplied

Transcriptomic Analysis of Irradiated Mouse Retina Following Readaptation

Rodent models are used as analogs for studying the effects of spaceflight. NASA GeneLab provides access to relevant omics datasets generated from spaceflight and ground-based experiments allowing for additional retrospective analysis. In this study, we used GeneLab’s GLDS-203, a dataset generated by researchers at Loma Linda University to study the impact of prolonged unloading and/or low-dose radiation on mouse retina. We analyzed transcriptomics data from retina of mice irradiated with gamma-rays for 21 days followed by 7 days, 1 month, or 4 months of readaptation. We obtained raw gene counts from GeneLab and performed differential gene expression analysis after data normalization. For each of the three timepoints, we performed differential expression analysis to compare transcriptional profiles for retina from irradiated vs. non-irradiated (controls) mice, all exposed to gravity. We observed the highest number of differentially expressed genes at 7 days, followed by 1 month and 4 months. Enrichment analysis showed top pathways (adjusted p-value < 0.05) were related to transport along microtubule and photoreceptor cell development in the 7-day readaptation group. Fewer significantly enriched pathways were observed for the 1-month group and included mRNA metabolic processes and neuron differentiation. No significantly enriched pathways were found in the 4-month group. The Gene Ontology biological processes common between the 7 days and 1-month groups include visual perception, synapse organization, and perception of light stimulus. This analysis is part of a larger effort to characterize the molecular mechanisms involved in retinal readaptation following radiation exposure. Future analyses will include other related retina datasets in GeneLab repository to assess whether gene expression patterns are consistent across different study cohorts.

Prachi Kothiyal↗
NASA NTRS2007

Revised Energy Spectra for Primary Elements, H - Si, above 50 GeV from the ATIC-2 Science Flight

The Advanced Thin Ionization Calorimeter (ATIC) long duration balloon experiment had a successful science flight accumulating 18 days of data (12/02 - 1/03) during a single circumnavigation in Antarctica. ATIC measures the energy spectra of elements from H to Fe in primary cosmic rays using a fully active Bismuth Germanate calorimeter preceded by a carbon target, with embedded scintillator hodoscopes, and a silicon matrix charge detector at the top. Preliminary results from ATIC have been reported in previous conferences. The revised results reported here are derived from a new analysis of the data with improved charge resolution, lower background and revised energy calibration. The raw energy deposit spectra are de-convolved into primary energy spectra and extrapolated to the top of the atmosphere. We compare these revised results to previous data and comment upon the astrophysical interpretation of the results.

Wefel, J. P.↗
NASA NTRS2004

Joint-probability Analysis of the Natural Variability of Tropical Oceanic Precipitation

Data projects pertaining to KWAJEX are described.Data sets delivered to the Goddard Distributed Active Archive Center (DAAC): 1) Kwajalein Experiment (KWAJEX) S-band calibrated, quality-controlled radar data, 1221 1 files of 3D volume data and 6832 files of 2D low-level reflectivity. 2) Raw and quality-control- processed versions of University of Washington Joss-Waldvogel disdrometer measurements obtained during KWAJEX. 3) A time series of synoptic-scale gif images of the Geostationary Meteorological Satellite (GMS) IR data for the KWAJEX period. The GMS satellite data set for the KWAJEX period was obtained from the University of Wisconsin and reprocessed into format amenable for comparison with radar data.Aircraft microphysics flight-leg definitions for all aircraft and all missions during KWAJEX were completed to facilitate microphysics data processing.

Yuter, Sandra E.↗
NASA NTRS1997

Attributes of GRB Pulses: Analysis of BATSE TTE Data

Our extensive program of modeling GRB profiles is aimed at elucidating the physical processes responsible for the burst itself, as well as possible extrinsic phenomena (e.g. time dilation) as discussed in another paper in this Symposium (Norris et al., "GRB PROFILES AS COSMIC PROBES"). We have developed special methods to extract the wealth of short time-scale information contained in the BATSE time-tag event (TTE) data. Our algorithm yields a piecewise-constant representation of the light curve -- using only the raw photon arrival times, and based on Bayesian change-point methods. This representation in effect lets the data determine the bin size and location, and avoids unwanted effects due to arbitrary choices of the bin parameters. We have determined widths, separations, and amplitudes of pulses contained in the bursts, without invoking a specific pulse model. The effect of cosmic time dilation can be easily seen in a direct plot of amplitude vs. time scale for individual pulses, without the need to lump the data into a small number of brightness classes. We are also performing noise equalization on these data (to reduce a well-known bias of pulse width as a function of signal-to-noise ratio), as well as fits of parametric pulse-shape models -- including explicit energy dependence of the pulse parameters. Such refinements are expected to improve the quality and physical significance of these results.

Norris, J. P.↗
NASA NTRS2010

Photometric Analysis in the Kepler Science Operations Center Pipeline

We describe the Photometric Analysis (PA) software component and its context in the Kepler Science Operations Center (SOC) Science Processing Pipeline. The primary tasks of this module are to compute the photometric flux and photocenters (centroids) for over 160,000 long cadence (~thirty minute) and 512 short cadence (~one minute) stellar targets from the calibrated pixels in their respective apertures. We discuss science algorithms for long and short cadence PA: cosmic ray cleaning; background estimation and removal; aperture photometry; and flux-weighted centroiding. We discuss the end-to-end propagation of uncertainties for the science algorithms. Finally, we present examples of photometric apertures, raw flux light curves, and centroid time series from Kepler flight data. PA light curves, centroid time series, and barycentric timestamp corrections are exported to the Multi-mission Archive at Space Telescope [Science Institute] (MAST) and are made available to the general public in accordance with the NASA/Kepler data release policy.

photocenter↗
NASA NTRS2019

Predictive Modeling for Differential Diagnosis and Mortality Risk Assessment

The prevalence of electronic health record (EHR) systems has brought prodigious biomedical informatics opportunity. Automated machine learning methods can effectively utilize such data and have become common tools for healthcare predictive modeling. Researches in medical informatics have explored the potential of deep learning and classical models in emergent care scenarios. In particular, predicting differential diagnoses for admissions have proven useful in decreasing unnecessary lab tests and improving inpatient triage decision-making. Moreover, identification of high-risk patients for in-hospital mortality is vitally important to maximize allocation of medical resources.The Medical Information Mart for Intensive Care (MIMIC-III) database, containing de-identified critical care inpatient was used in our study. This data set captures hospital patient laboratory measurements, pharmacologic prescriptions, diagnostic data and procedure event recordings. When considering adult patients and discounting admissions with ICU length of stay less than 24 hours, there were 37,787 unique admissions and 30,414 total patients. We examined the top 25 most prevalent ICD-9 group-level disease specificities in MIMIC-III using a multi-label classification model. In-hospital mortality was modeled as binary classification with 4,155 (13%) adult patients that expired, of which 3,138 (75.5%) were in the ICU setting. The metrics AUC, F1 score, sensitivity and specificity values calculated for each disease label measured prediction performance.The usage of ICD-9 group codes reduced feature dimension from 14,567 to 942 and greatly improved distribution of patient diagnostic categories. Disease temporal patterns were captured by considering the most frequently sampled 6 vital signs and 13 laboratory values. Missing data were imputed at each time-stamp. Time-series raw hourly average values were converted into 5 summary features (mean, standard deviation, number of observations, min & max values). Patient demographic variables such as age, gender, marital status and ethnicity were also factored into the modeling. Choi et al showed that contextual embedding of medical data, diagnostic and procedural codes alone can predict future diagnoses with sensitivity as high as 0.79. We utilized an embedding technique called word2vec which allowed sparse representations of medical history to be transformed into dense word vectors. The mappings captured contextual information by treating each admission as a sentence and learning the most likely neighboring words in a sliding window fashion. Binary and multi-label classification was achieved via collapse models, which do not consider temporal information, as well as recurrent neural networks with regularization, Softmax output layer activation together with categorical cross-entropy as the loss function.

US Army collaboration↗
NASA NTRS1987

Mining the IUE archive

The IUE archives are described as well as their applications and possible future developments. The data for each image is archived as a set usually containing a raw image, a processed image, and one or more extracted spectra. A cumulative plot of the number of images which have been retrieved from the IUE archives is provided, indicating that interest in the data continues to grow in all of the astronomical communities served.

Giaretta, David↗
NASA NTRS1978

Remote sensing of Gulf Stream using GEOS-3 radar altimeter

Radar altimeter measurements from the GEOS-3 satellite to the ocean surface indicated the presence of expected geostrophic height differences across the the Gulf Stream. Dynamic sea surface heights were found by both editing and filtering the raw sea surface heights and then referencing these processed data to a 5 minute x 5 minute geoid. Any trend between the processed data and the geoid was removed by subtracting out a linear fit to the residuals in the open ocean. The mean current velocity of 107 + or - 29 cm/sec calculated from the dynamic heights for all orbits corresponded with velocities obtained from hydrographic methods. Also, dynamic topographic maps were produced for August, September, and October 1975. Results pointed out limitations in the accuracy of the geoid, height anomaly deteriorations due to filtering, and lack of dense time and space distribution of measurements.

Leitao, C. D.↗
NASA NTRS1989

IRAS LRS spectra of extended objects: The Crab nebula

The spectra of extended sources from the raw Infrared Astronomy Satellite (IRAS) Low Resolution Spectrometer (LRS) data, is recovered by means of a nonlinear deconvolution technique built around a maximum entropy algorithm. The results are applied to the Crab Nebula. The deconvolved spectrum of the Crab shows significant departures from a blackbody spectrum, with emission features present at 10.2, 11.3, 12.3 and 12.8 microns.

Lynch, D. K.↗
NASA NTRS2019

Diurnal Cycles in SST: Coupled Data Assimilation and Future Observational Requirements

Most operational centers are developing coupled (atmosphere-ocean) data assimilation systems as an alternative to uncoupled counterparts (atmosphere- or ocean-only). The Sea Surface Temperature (SST) is one of the key variables that tightly connects the atmosphere and ocean states and also air-sea fluxes. However, current prototype coupled data assimilation systems rely on external (L4) gridded SST or along-track (L3 or L2) SST retrievals as observed data or relaxation field. But in reality, SST are measurements are available from sparse in-situ network of ships, moorings, and buoys; all of them combined together are far less than those from satellites. However, satellites do not directly measure temperature, and inferring SST from satellite measured radiances requires a radiative transfer model, its calibration and also bias correction.The NASA Global Modeling and Assimilation Office (GMAO) is developing a coupled data assimilation system which assimilates SST directly from the raw observations, i.e., satellite radiances and in-situ observations. The methodology to directly assimilate radiances for SST became operational in Jan, 2017 in the GMAO's near-real time Weather Analysis and Prediction System. There were many modifications to the GMAO system in order to implement SST assimilation, most of which generally improved the predictability of the system. In order to maintain and further improve this system, we advocate for the availability of a microwave satellite radiometer in future beyond the currently operational GPM- GMI and AMSR-2 missions. For improved modeling of the near-surface temperature, salinity and mixing processes, we suggest adding more than one temperature sensor and salinity sensors to the drifting buoy network.

Akella, Santha↗
NASA NTRS1993

Spectral decomposition of AVIRIS data

A set of techniques is presented that uses only information contained within a raw Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) scene to estimate and to remove additive components such as multiple scattering and instrument dark current. Multiplicative components (instrument gain, topographic modulation of brightness, atmospheric transmission) can then be normalized, permitting enhancement, extraction, and identification of relative reflectance information related to surface composition and mineralogy. The technique for derivation of additive-component spectra from a raw AVIRIS scene is an adaption of the 'regression intersection method' of Crippen. This method uses two surface units that are spatially extensive, and located in rugged terrain. For a given wavelength pair, subtraction of the derived additive component from individual band values will remove topography in both regions in a band/band ratio image. Normalization of all spectra in the scene to the average scene spectrum then results in cancellation of multiplicative components and production of a relative-reflectance scene. The resulting AVIRIS product contains relative-reflectance features due to mineral absorption that depart from the average spectrum. These features commonly are extremely weak and difficult to recognize, but they can be enhanced by using two simple 3-D image-processing tools. The validity of these techniques will be demonstrated by comparisons between relative-reflectance AVIRIS spectra and those derived by using JPL standard calibrations. The AVIRIS data used in this analysis were acquired over the Kelso Dunes area (34 deg 55' N, 115 deg 43' W) of the eastern Mojave Desert, CA (in 1987) and the Upheaval Dome area (38 deg 27' N, 109 deg 55' W) of the Canyonlands National Park, UT (in 1991).

Gaddis, Lisa↗
NASA NTRS1994

Lunar mineral feedstocks from rocks and soils: X-ray digital imaging in resource evaluation

The rocks and soils of the Moon provide raw materials essential to the successful establishment of a lunar base. Efficient exploitation of these resources requires accurate characterization of mineral abundances, sizes/shapes, and association of 'ore' and 'gangue' phases, as well as the technology to generate high-yield/high-grade feedstocks. Only recently have x-ray mapping and digital imaging techniques been applied to lunar resource evaluation. The topics covered include inherent differences between lunar basalts and soils and quantitative comparison of rock-derived and soil-derived ilmenite concentrates. It is concluded that x-ray digital-imaging characterization of lunar raw materials provides a quantitative comparison that is unattainable by traditional petrographic techniques. These data are necessary for accurately determining mineral distributions of soil and crushed rock material. Application of these techniques will provide an important link to choosing the best raw material for mineral beneficiation.

Chambers, John G.↗
NASA NTRS2019

Developing a Machine-Learning-Based Processing Framework for Twitter and Other Crowdsourced Data

Crowdsourced data streams such as Twitter and other social media are important sources of real-time and historical global information for Earth science applications. At the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), we have been exploring the Twitter data stream for its potential in augmenting the validation program of NASA's Global Precipitation Measurement (GPM) mission. To realize this potential, we need to increase the information density and enhance the quality of filtered precipitation tweets. We have implemented various components of a machine learning (ML)-based processing infrastructure for crowdsourced data that outputs, in this instance, useful and usable information derived from precipitation tweets. We have test enriched the Twitter stream with higher quality active tweets from those knowingly contributing to our effort and from existing crowdsourced programs (e.g., mPING, CoCoRaHS). We have experimented with various algorithms for processing tweets, including Naà ve Bayes, Convolutional Neural Network (CNN), Hierarchical Attention Network (HAN), and semi-supervised learning (with tri-training). Our current work focuses on (1) automated review of Earth science-related publications to determine relationships between discipline research needs and ML algorithms; (2) investigating Sequential Generative Adversarial Network (SeqGAN) for processing precipitation tweets for anomaly detection; and (3) managing crowdsourced data in a way that is compatible with existing NASA satellite data archives and using the data for ML applications. Key results include (1) network visualization of NLP-processed publications in various Earth science disciplines; (2) difference between GPM-linked, generated tweets and collected actual tweets that is small for GPM-determined light to moderate rain cases and high for GPM-determined heavy rain cases; and (3) identification of MongoDB for storing raw tweets and Zarr format for gridded tweets (compatible with GPM data). Our results have taken us a step closer to an operational ML-based tweet processing infrastructure and have already demonstrated that tweet-derived precipitation information is potentially useful for validation of Earth science satellite data.

Teng, William↗
NASA NTRS2000

Constraints on Martian Soil Composition as Inferred from Viking XRFS and Pathfinder APXS and IMP Data

With the successful operation of the Alpha Proton X-Ray Spectrometer (APXS) during 1997's Mars Pathfinder (MPF) mission, geochemistry data are now available from three sites on Mars. APXS raw spectra for six soils and five rocks have been converted to compositional abundances. The Viking Lander X-Ray Fluorescence Spectrometer (XRFS) successfully measured elemental abundances of nine soils at Viking 1 and eight soils at Viking 2. Although the three landing sites are located in different parts of Mars, the soils exhibit broad similarities, with an iron-rich chemistry similar to that of palagonite. However, the Pathfinder sods show some significant differences from Viking soils, notably an enrichment in silica and depletion in sulfur. The XRFS samples consisted of near-surface and deep (up to 22 cm) soils acquired by a collector head at the cod of a retractable boom. It was possible to collect and analyze pebbles as large a 2 cm, but only sod, some in the form of consolidated clods, was sampled. In contrast, the APXS measured materials in situ. This resulted in MPF "rock" analyses that probably had a significant dust component and, as explored here, "soil" analyses that may have contained a rocky component We examine several possibilities to explain these differences and other attributes of the APXS and XRFS data sets: 1) The APXS soil measurements actually sampled a mixture of Viking-like soil and small bits of high-silica, low-sulfur rock, 2) The soils were derived from high-silica rocks mixed with a minor component of globally-homogenized dust; these soils are chemically distinct and have a separate geologic history from the Viking soils. 3) The weathering environment was different at the Pathfinder landing site compared to the Viking sites, and 4) Uncertainties in the XRFS and APXS measurements result in reported elemental abundances different than those that are actually present We show that none of the possibilities can be discounted, but that an MPF soil distinct in composition from Viking sods is best supported by the available data.

Bridges, N. T.↗
NASA NTRSDate not supplied

Enabling Space Biological Knowledge Discovery Through Image and Video Data Sharing

Increased biomedical risks associated with deep space crewed missions (cis-Lunar, Mars transit/surface) require development of health countermeasures, novel ecosystem support, risk modeling, and fundamental space biological knowledge discovery. Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from space biological and health studies are needed for reuse by scientists to address these tasks. The data as well as space-relevant biospecimens are being made more findable, accessible, interoperable, and reusable through NASA’s Open Science Data Repository (OSDR). This new OSDR umbrella grouping includes NASA GeneLab, the NASA Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection. The OSDR system design appropriately handles metadata and processed-tabular results from ALSDA studies collected from space experiments. But raw and processed ALSDA bioimage and video datasets require an expansion of OSDR’s data architecture to handle ingestion, curation, and egress. The academic-industry bioimaging field saw a scientific renaissance in the past several years through leveraging open-source software, international collaborations, machine learning, and other open science/programming approaches. As crewed missions and more biological experiments are on the deep space horizon, OSDR is embracing data stewardship through listening to feedback from subject matter experts and designing an expanded architecture which is appropriate for NASA’s goals to enable analysis and reuse of bioimaging and video data for the public science community.Discovery Through Image and Video Data Sharing

space biology↗
NASA NTRS1973

The minitrack tracking function description, volume 1

The treatment of tracking data by the Minitrack system is described from the transmission of the nominal 136-MHz radio beacon energy from a satellite and the reception of this signal by the interferometer network through the ultimate derivation of the direction cosines (the angular coordinates of the vector from the tracking station to the spacecraft) as a function of time. Descriptions of some of the lesser-known functions operating on the system, such as the computer preprocessing program, are included. A large part of the report is devoted to the preprocessor, which provides for the data compression, smoothing, calibration correction, and ambiguity resolution of the raw interferometer phase tracking measurements teletyped from each of the worldwide Minitrack tracking stations to the central computer facility at Goddard Space Flight Center. An extensive bibliography of Minitrack hardware and theory is presented.

Englar, T. S., Jr.↗
NASA NTRS1980

A miniature high frequency sphere probe

A spherical probe having five surface mounted silicon pressure sensors is described. The probe is 0.2 inches in diameter yielding a frequency response above 30 kHz. A scheme is described for retrieving from the five pressure signals the stagnation and static pressures, Mach number and flow angles in two planes. The calibrations forming the base for this procedure, obtained from steady state tests, are given in the form of polynomials in the two flow angles. A method for compensating the silicon pressure sensors for thermal effects is given. Typical data obtained in the MIT Blowdown Compressor are presented in raw form and after reduction to flow angles, Mach number and stagnation pressure.

Kerrebrock, J. L.↗