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At least 1,117 records · Page 62
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

MOSAIC: a joint modeling methodology for combined circadian and non-circadian analysis of multi-omics data

Abstract Motivation Circadian rhythms are approximately 24-h endogenous cycles that control many biological functions. To identify these rhythms, biological samples are taken over circadian time and analyzed using a single omics type, such as transcriptomics or proteomics. By comparing data from these single omics approaches, it has been shown that transcriptional rhythms are not necessarily conserved at the protein level, implying extensive circadian post-transcriptional regulation. However, as proteomics methods are known to be noisier than transcriptomic methods, this suggests that previously identified arrhythmic proteins with rhythmic transcripts could have been missed due to noise and may not be due to post-transcriptional regulation. Results To determine if one can use information from less-noisy transcriptomic data to inform rhythms in more-noisy proteomic data, and thus more accurately identify rhythms in the proteome, we have created the Multi-Omics Selection with Amplitude Independent Criteria (MOSAIC) application. MOSAIC combines model selection and joint modeling of multiple omics types to recover significant circadian and non-circadian trends. Using both synthetic data and proteomic data from Neurospora crassa, we showed that MOSAIC accurately recovers circadian rhythms at higher rates in not only the proteome but the transcriptome as well, outperforming existing methods for rhythm identification. In addition, by quantifying non-circadian trends in addition to circadian trends in data, our methodology allowed for the recognition of the diversity of circadian regulation as compared to non-circadian regulation. Availability and implementation MOSAIC’s full interface is available at https://github.com/delosh653/MOSAIC. An R package for this functionality, mosaic.find, can be downloaded at https://CRAN.R-project.org/package=mosaic.find. Supplementary information Supplementary data are available at Bioinformatics online.

De los Santos, Hannah↗
NASA NTRS1991

Impact melts in the MAC88105 lunar meteorite - Inferences for the lunar magma ocean hypothesis and the diversity of basaltic impact melts

The MAC88105 lunar meteorite, as represented by thin section 78, contains three major types of impact melt breccias. The most abundant type is clast-laden, fine-grained, and rich in Al2O3 (28 wt pct); these clasts constitute most of the meteorite. Their abundance and aluminous nature indicate that the MAC88105 source area was very aluminous. This is consistent with formation of the primordial lunar crust from a global magma ocean. The second type of impact melt is represented by only one clast in 78. It has a basaltic bulk composition similar to many other lunar impact melts, but is significantly richer in P2O5 than most and has a much lower MgO/(MgO + FeO). The third impact-melt type resembles a prominent melt group at Apollo 16, but has lower MgO/(MgO + FeO). These data show that basaltic impact melts are compositionally diverse. Dating samples of the Al-rich impact melts and the new types of basaltic impact melts from this meteorite can test the idea that the Moon suffered a terminal cataclysm 3.9 Ga ago.

Taylor, G. J.↗
NASA NTRS2020

High-Resolution UV/Optical/IR Imaging of Jupiter in 2016–2019

Imaging observations of Jupiter with high spatial resolution were acquired beginning in 2016, with a cadence of 53 days to coincide with atmospheric observations of the Juno spacecraft during each perijove pass. The Wide Field Camera 3 (WFC3) aboard the Hubble Space Telescope (HST) collected Jupiter images from 236 to 925 nm in 14 filters. The Near-Infrared Imager (NIRI) at Gemini North imaged Jovian thermal emission using a lucky-imaging approach (co-adding the sharpest frames taken from a sequence of short exposures), using the M' filter at 4.7 μm. We discuss the data acquisition and processing and an archive collection that contains the processed WFC3 and NIRI data (doi:10.17909/T94T1H). Zonal winds remain steady over time at most latitudes, but significant evolution of the wind profile near 24°N in 2016 and near 15°S in 2017 was linked with convective superstorm eruptions. Persistent mesoscale waves were seen throughout the 2016–2019 period. We link groups of lightning flashes observed by the Juno team with water clouds in a large convective plume near 15°S and in cyclones near 35°N–55°N. Thermal infrared maps at the 10.8 micron wavelength obtained at the Very Large Telescope show consistent high brightness temperature anomalies, despite a diversity of aerosol properties seen in the HST data. Both WFC3 and NIRI imaging reveal depleted aerosols consistent with downwelling around the periphery of the 15°S storm, which was also observed by the Atacama Large Millimeter/submillimeter Array. NIRI imaging of the Great Red Spot shows that locally reduced cloud opacity is responsible for dark features within the vortex. The HST data maps multiple concentric polar hoods of high-latitude hazes.

Michael H. Wong↗
DOE OSTI2021

Optimal dimensionality selection for independent component analysis of transcriptomic data

Independent component analysis is an unsupervised machine learning algorithm that separates a set of mixed signals into a set of statistically independent source signals. Applied to high-quality gene expression datasets, independent component analysis effectively reveals both the source signals of the transcriptome as co-regulated gene sets, and the activity levels of the underlying regulators across diverse experimental conditions. Two major variables that affect the final gene sets are the diversity of the expression profiles contained in the underlying data, and the user-defined number of independent components, or dimensionality, to compute. Availability of high-quality transcriptomic datasets has grown exponentially as high-throughput technologies have advanced; however, optimal dimensionality selection remains an open question. We computed independent components across a range of dimensionalities for four gene expression datasets with varying dimensions (both in terms of number of genes and number of samples). We computed the correlation between independent components across different dimensionalities to understand how the overall structure evolves as the number of user-defined components increases. We then measured how well the resulting gene clusters reflected known regulatory mechanisms, and developed a set of metrics to assess the accuracy of the decomposition at a given dimension. We found that over-decomposition results in many independent components dominated by a single gene, whereas under-decomposition results in independent components that poorly capture the known regulatory structure. From these results, we developed a new method, called OptICA, for finding the optimal dimensionality that controls for both over- and under-decomposition. Specifically, OptICA selects the highest dimension that produces a low number of components that are dominated by a single gene. We show that OptICA outperforms two previously proposed methods for selecting the number of independent components across four transcriptomic databases of varying sizes. OptICA avoids both over-decomposition and under-decomposition of transcriptomic datasets resulting in the best representation of the organism’s underlying transcriptional regulatory network.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS1993

How diverse is the asteroid belt?

For approximately twenty years, many different asteroid taxonomies, which used many different observational data sets, have been developed to try to group asteroids into classes that contain members with similar spectral characteristics. However, to understand the structure of the asteroid belt, the resulting classes are only useful if they are grouping together asteroids with somewhat similar mineralogies and thermal histories. Until recently, these taxonomies have focused on spectral reflectance data from 0.3 to 1.1 microns and visual albedo. But in the last five years, observational data sets (e.g., 0.8 to 2.5 microns spectra, CCD spectra, 3 microns spectra, and radar albedos) for a small number of asteroids were compiled that can give a better mineralogical interpretation, but whose use in asteroid taxonomy was relatively limited. Analyses of these 'supplementary' data sets show that most asteroid classes contain members with different compositions and/or thermal histories. To understand the diversity of the asteroid belt, the number of objects with these observations must be expanded and used in the next generation of taxonomies.

Burbine, Thomas H.↗
DOE OSTI2024

The Galaxy platform for accessible, reproducible, and collaborative data analyses: 2024 update

Galaxy (https://galaxyproject.org) is deployed globally, predominantly through free-to-use services, supporting user-driven research that broadens in scope each year. Users are attracted to public Galaxy services by platform stability, tool and reference dataset diversity, training, support and integration, which enables complex, reproducible, shareable data analysis. Applying the principles of user experience design (UXD), has driven improvements in accessibility, tool discoverability through Galaxy Labs/subdomains, and a redesigned Galaxy ToolShed. Galaxy tool capabilities are progressing in two strategic directions: integrating general purpose graphical processing units (GPGPU) access for cutting-edge methods, and licensed tool support. Engagement with global research consortia is being increased by developing more workflows in Galaxy and by resourcing the public Galaxy services to run them. The Galaxy Training Network (GTN) portfolio has grown in both size, and accessibility, through learning paths and direct integration with Galaxy tools that feature in training courses. Code development continues in line with the Galaxy Project roadmap, with improvements to job scheduling and the user interface. Environmental impact assessment is also helping engage users and developers, reminding them of their role in sustainability, by displaying estimated CO 2 emissions generated by each Galaxy job.

97 MATHEMATICS AND COMPUTING↗
NASA NTRS2002

The Intelligent Data Understanding Element of NASA's Intelligent Systems Program

Within the NASA Intelligent Systems Program, the Intelligent Data Understanding (IDU) element develops techniques for transforming data into scientific understanding. Automating such tools is critical for space science, space-based earth science, and planetary exploration with onboard scientific data analysis. Intelligent data understanding (IDU) is about extracting meaning from large, diverse science and engineering databases, via autonomous techniques that transform very large datasets into understanding. The earth science community in particular needs new tools for analyzing multi-formatted and geographically distributed datasets and for identifying cause-effect relationships in the complex data. Research within the IDU program element seeks to automate data analysis tasks so that humans can focus on creative hypothesis generation and knowledge synthesis. It may also enable NASA space missions in which autonomous agents must generate knowledge and take actions, and missions where limited bandwidth permits transmission of only the most interesting scientific observations, summaries, and conclusions. Twenty-seven research projects are-currently funded.

Coughlan, Joseph C.↗
DOE OSTI2026

Optimal Methods for Estimating Cactus Pear Biomass Using Cladode Dimensions of Morphologically Diverse Accessions

Current allometric methods for photosynthetic-stem (cladode) plants, such as cactus pear (Opuntia spp.), require refinement to be used in field settings in which diverse accessions are grown. We analysed cladode dimensional data using 14 accessions representing four species and two hybrids to quantify statistically significant morphological differences among accessions and derived cross-accession models to approximate cladode fresh weight. A Box model using cladode dimensions (e.g., length, width, thickness and diameter) and factorial combinations of these measures (e.g., length*width*thickness*diameter vs. fresh weight) resulted in the highest coefficient of determination (R 2 = 0.95 general fit) across all accessions for estimating fresh weight along with parsimony estimates using the Schwarz–Bayes Criterion (SBC), which assesses the most consistent performance on individual accessions. A Fitting-box modelling approach used the measured cladode area captured using ImageJ (R 2 = 0.93 general fit). Lastly, an Elliptical model used an elliptical approximation for the measured area and performed well over all accessions (R 2 = 0.94 general fit) while avoiding extensive manual measurements. These models meet or exceed the performance of previously published approaches when applied across morphologically diverse accessions, providing efficient tools for nondestructive estimation of cactus pear biomass under the conditions tested.

Opuntia↗
NASA NTRS1981

User requirements for NASA data base management systems. Part 1: Oceanographic discipline

Generic oceanographic user requirements were collected and analyzed for use in developing a general multipurpose data base management system for future missions of the Office of Space and Terrestrial Applications (OSTA) of NASA. The collection of user requirements involved; studying the state-of-the-art technology in data base management systems; analyzing the results of related studies; formulating a viable and diverse list of scientists to be interviewed; developing a presentation format and materials; and interviewing oceanographic data users. More effective data management systems are needed to handle the increasing influx of data.

Fujimoto, B.↗
NASA NTRS1975

ATS-6 - Preliminary results from the 13/18-GHz COMSAT Propagation Experiment

The 13/18-GHz COMSAT Propagation Experiment (CPE) is reviewed, the data acquisition and processing are discussed, and samples of preliminary results are presented. The need for measurements of both hydrometeor-induced attenuation statistics and diversity effectiveness is brought out. The facilitation of the experiment - CPE dual frequency and diversity site location, the CPE ground transmit terminals, the CPE transponder on Applications Technology Satellite-6 (ATS-6), and the CPE receive and data acquisition system - is briefly examined. The on-line preprocessing of the received signal is reviewed, followed by a discussion of the off-line processing of this database to remove signal fluctuations not due to hydrometeors. Finally, samples of the results of first-level analysis of the resultant data for the 18-GHz diversity site near Boston, Mass., and for the dual frequency 13/18-GHz site near Detroit, Mich., are presented and discussed.

Hyde, G.↗
DOE OSTI2020

Ectomycorrhizal fungal diversity interacts with soil nutrients to predict plant growth despite weak plant-soil feedbacks

Background and aims Plant-soil feedbacks are the result of multiple abiotic and biotic mechanisms. However, few studies have addressed how feedbacks vary based on abiotic context or attempted to identify microbiota responsible for feedbacks. In this work, we investigated whether plant-soil feedbacks of an ectomycorrhizal tree (Quercus macrocarpa) varied based on soil nutrient status and whether fungal community composition and diversity could explain feedback patterns. Furthermore, we inoculated Q. macrocarpa seedlings with field-sampled soils taken from five soil origins – including heterospecific and conspecific trees and an old field – which were profiled using fungal DNA metabarcoding.Results Despite finding that soils associated with different hosts harbored distinct fungal communities and that fungal communities were predictive of plant growth, we did not find any significant plant-soil feedbacks regardless of fertilization status. We did find that the growth promotive effect associated with ectomycorrhizal OTU diversity was weakened with fertilization, suggesting context-dependent relations between plant growth and a guild of fungal mutualists. Our results demonstrate that the host-specific accumulation of functionally important soil microbes is not always sufficient to drive observable plant-soil feedbacks. Our data provide support for a role for ECM fungal diversity in mediating plant growth responses, though it is unclear whether this effect is direct or indirect.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2004

SAMZ: Satellite-Derived Management Zones

The term "satellite-derived management zones" (SAMZ) denotes agricultural management zones that are subdivisions of large fields and that are derived from images of the fields acquired by instruments aboard Earth orbiting satellites during approximately the past 15 years. "SAMZ" also denotes the methodology and the software that implements the methodology for creating such zones. The SAMZ approach is one of several products of continuing efforts to realize a concept of precision agriculture, which involves optimal variations in seeding, in application of chemicals, and in irrigation, plus decisions to farm or not to farm certain portions of fields, all in an effort to maximize profitability in view of spatial and temporal variations in the growth and health of crops and in the chemical and physical conditions of soils. As used here, "management zone" signifies, more precisely, a subdivision of a field within which the crop production behavior is regarded as homogeneous. From the perspective of precision agriculture, management zones are the smallest subdivisions between which the seeding, application of chemicals, and other management parameters are to be varied. In the SAMZ approach, the main sources of data are the archives of satellite imagery that have been collected over the years for diverse purposes. One of the main advantages afforded by the SAMZ approach is that the data in these archives can be reused for purposes of precision agriculture at low cost. De facto, these archives contain information on all sources of variability within a field, including weather, crop types, crop management, soil types, and water drainage patterns. The SAMZ methodology involves the establishment of a Web-based interface based on an algorithm that generates management zones automatically and quickly from archival satellite image data in response to requests from farmers. A farmer can make a request by either uploading data describing a field boundary to the Web site or else drawing the boundary on a reference image. Hence, a farmer can start to engage in precision farming shortly after gaining access to the Web site, without need for incurring the high costs of conventional precision-agriculture data-collection practices that include collecting soil samples, mapping electrical conductivity of soil, and compiling multi-year crop-yield data. Given the boundary of a field, a SAMZ server computes the zones within the field in a three-stage process. In the first stage, a vector-valued image of the field is constructed by assembling, from the archives, the equivalent of a stack of the available images of the field (see figure). In the second stage, the vector-valued image is analyzed by use of a wavelet transform that detects spatial variations considered significant for precision farming while suppressing small-scale heterogeneities that are regarded as insignificant. In the third stage, a segmentation algorithm assembles the zones from smaller regions that have been identified in the wavelet analysis.

Source record↗
NASA NTRS2005

Satellite-Derived Management Zones

The term "satellite-derived management zones" (SAMZ) denotes agricultural management zones that are subdivisions of large fields and that are derived from images of the fields acquired by instruments aboard Earth-orbiting satellites during approximately the past 15 years. "SAMZ" also denotes the methodology and the software that implements the methodology for creating such zones. The SAMZ approach is one of several products of continuing efforts to realize a concept of precision agriculture, which involves optimal variations in seeding, in application of chemicals, and in irrigation, plus decisions to farm or not to farm certain portions of fields, all in an effort to maximize profitability in view of spatial and temporal variations in the growth and health of crops, and in the chemical and physical conditions of soils. As used here, "management zone" signifies, more precisely, a subdivision of a field within which the crop-production behavior is regarded as homogeneous. From the perspective of precision agriculture, management zones are the smallest subdivisions between which the seeding, application of chemicals, and other management parameters are to be varied. In the SAMZ approach, the main sources of data are the archives of satellite imagery that have been collected over the years for diverse purposes. One of the main advantages afforded by the SAMZ approach is that the data in these archives can be reused for purposes of precision agriculture at low cost. De facto, these archives contain information on all sources of variability within a field, including weather, crop types, crop management, soil types, and water drainage patterns. The SAMZ methodology involves the establishment of a Web-based interface based on an algorithm that generates management zones automatically and quickly from archival satellite image data in response to requests from farmers. A farmer can make a request by either uploading data describing a field boundary to the Web site or else drawing the boundary on a reference image. Hence, a farmer can start to engage in precision farming shortly after gaining access to the Web site, without the need for incurring the high costs of conventional precision-agriculture data-collection practices that include collecting soil samples, mapping electrical conductivity of soil, and compiling multiyear crop-yield data. Given the boundary of a field, a SAMZ server computes the zones within the field in a three-stage process. In the first stage, a vector-valued image of the field is constructed by assembling, from the archives, the equivalent of a stack of the available images of the field (see figure). In the second stage, the vector-valued image is analyzed by use of a wavelet transform that detects spatial variations considered significant for precision farming while suppressing small-scale heterogeneities that are regarded as insignificant. In the third stage, a segmentation algorithm assembles the zones from smaller regions that have been identified in the wavelet analysis.

Lepoutre, Damien↗
NASA NTRS2023

GLORIA - A Globally Representative Hyperspectral In Situ Dataset for Optical Sensing of Water Quality

The development of algorithms for remote sensing of water quality (RSWQ) requires a large amount of in situ data to account for the bio-geo-optical diversity of inland and coastal waters. The GLObal Reflectance community dataset for Imaging and optical sensing of Aquatic environments (GLORIA) includes 7,572 curated hyperspectral remote sensing reflectance measurements at 1 nm intervals within the 350 to 900 nm wavelength range. In addition, at least one co-located water quality measurement of chlorophyll a , total suspended solids, absorption by dissolved substances, and Secchi depth, is provided. The data were contributed by researchers affiliated with 59 institutions worldwide and come from 450 different water bodies, making GLORIA the de-facto state of knowledge of in situ coastal and inland aquatic optical diversity. Each measurement is documented with comprehensive methodological details, allowing users to evaluate fitness-for-purpose, and providing a reference for practitioners planning similar measurements. We provide open and free access to this dataset with the goal of enabling scientific and technological advancement towards operational regional and global RSWQ monitoring.

remote sensing of water quality↗
DOE OSTI2017

A Generalized Framework for Modeling Next Generation 911 Implementations

This document summarizes the current state of Sandia 911 modeling capabilities and then addresses key aspects of Next Generation 911 (NG911) architectures for expansion of existing models. Analysis of three NG911 implementations was used to inform heuristics ,associated key data requirements, and assumptions needed to capture NG911 architectures in the existing models. Modeling of NG911 necessitates careful consideration of its complexity and the diversity of implementations. Draft heuristics for constructing NG911 models are pres ented based on the analysis along with a summary of current challenges and ways to improve future NG911 modeling efforts. We found that NG911 relies on E nhanced 911 (E911) assets such as 911 selective routers to route calls originating from traditional tel ephony service which are a majority of 911 calls. We also found that the diversity and transitional nature of NG911 implementations necessitates significant and frequent data collection to ensure that adequate model s are available for crisis action support.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

A Heterogeneity-Aware Task Scheduler for Spark

Big data processing systems such as Spark are employed in an increasing number of diverse applications-such as machine learning, graph computation, and scientific computing-each with dynamic and different resource needs. These applications increasingly run on heterogeneous hardware, e.g., with out-of-core accelerators. However, big data platforms do not factor in the multi-dimensional heterogeneity of applications and hardware. This leads to a fundamental mismatch between the application and hardware characteristics, and the resource scheduling adopted in big data platforms. For example, Hadoop and Spark consider only data locality when assigning tasks to nodes, and typically disregard the hardware capabilities and suitability to specific application requirements. In this paper, we present RUPAM, a heterogeneity-aware task scheduling system for big data platforms, which considers both task-level resource characteristics and underlying hardware characteristics, as well as preserves data locality. RUPAM adopts a simple yet effective heuristic to decide the dominant scheduling factor (e.g., CPU, memory, or I/O), given a task in a particular stage. Our experiments show that RUPAM is able to improve the performance of representative applications by up to 62.3% compared to the standard Spark scheduler.

Computer Science↗
DOE OSTI2017

Raw diffraction data preservation and reuse: Overview, update on practicalities and metadata requirements

A topical review is presented of the rapidly developing interest in and storage options for the preservation and reuse of raw data within the scientific domain of the IUCr and its Commissions, each of which operates within a great diversity of instrumentation. A résumé is included of the case for raw diffraction data deposition. An overall context is set by highlighting the initiatives of science policy makers towards an `Open Science' model within which crystallographers will increasingly work in the future; this will bring new funding opportunities but also new codes of procedure within open science frameworks. Skills education and training for crystallographers will need to be expanded. Overall, there are now the means and the organization for the preservation of raw crystallographic diffraction data via different types of archive, such as at universities, discipline-specific repositories (Integrated Resource for Reproducibility in Macromolecular Crystallography, Structural Biology Data Grid), general public data repositories (Zenodo, ResearchGate) and centralized neutron and X-ray facilities. Formulation of improved metadata descriptors for the raw data types of each of the IUCr Commissions is in progress; some detailed examples are provided. Lastly, a number of specific case studies are presented, including an example research thread that provides complete open access to raw data.

96 KNOWLEDGE MANAGEMENT AND PRESERVATION↗
NASA NTRS2019

NASA GES DISC Giovanni: Current and Future

Giovanni (Geospatial Interactive Online Visualization and Analysis Infrastructure), developed by the NASA Goddard Earth Sciences Data and Information Services Center (GES DISC), has established a reputation among NASA users for easy access, analysis, and visualization of NASA Earth science data. Currently, Giovanni supports over 1900 variables in eight disciplinary areas. Like any other enterprise application, Giovanni faces big data challenges, such as servicing increasingly large data volumes and more complex data types, while at the same time addressing the demands of a more diverse user community, e.g., placing requests for long-term time series from multiple spatially and temporally dense data records. I will present how Giovanni has been evolving from an on-premises, monolithic software application towards a cloud-enabled implementation to address these challenges.

Analytics↗