Search NASA⌕ Search

SEARCH · Search NASA

Results for “biological databases”

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 199 records · Page 11
All sourcesNASA NTRSDOE OSTI
NASA NTRS2017

Systemic Microgravity Response: Utilizing GeneLab to Develop Hypotheses for Spaceflight Risks

Biological risks associated with microgravity is a major concern for space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. The overall goal of our work is to identify potential master regulators responsible for such responses to microgravity conditions. To do this we utilized the NASA GeneLab database which contains a wide array of omics experiments, including data from: 1) different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); 2) different tissues; and 3) different types of assays that measure epigenetic, transcriptional, and protein expression changes. We have performed meta-analysis identifying potential master regulators involved with systemic responses to microgravity. The analysis used 7 different murine and rat data sets, examining the following tissues: liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius). Using a systems biology approach, we were able to determine that p53 and immune related pathways appear central to pan-tissue microgravity responses. Evidence for a universal response in the form of consistency of change across tissues in regulatory pathways was observed in both STS and ISS experiments with varying durations; while degree of change in expression of these master regulators varied across species and strain, some change in these master regulators was universally observed. Interestingly, certain skeletal muscle (gastrocnemius and soleus) show an overall down-regulation in these genes, while in other types (extensor digitorum longus, tibialis anterior and quadriceps) they are up-regulated, suggesting certain muscle tissues may be compensating for atrophy responses caused by microgravity. Studying these organtissue-specific perturbations in molecular signaling networks, we demonstrate the value of GeneLab in characterizing potential master regulators associated with biological risks for spaceflight.

Microgravity↗
NASA NTRS2012

NASA Thesaurus: Hierarchical Listing with Definitions; Rotated Term Display - Volumes 1 and 2

The NASA Thesaurus contains the authorized subject terms by which the documents in the NASA STI Databases are indexed and retrieved. The scope of this controlled vocabulary includes not only aerospace engineering, but all supporting areas of engineering and physics, the natural space sciences (astronomy, astrophysics, planetary science), Earth sciences, and to some extent, the biological sciences. Volume 1 - Hierarchical Listing With Definitions contains over 18,400 subject terms, 4,300 definitions, and more than 4,500 USE cross references. The Hierarchical Listing presents full hierarchical structure for each term along with 'related term' lists, and can serve as an orthographic authority. Volume 2 - Rotated Term Display is a ready-reference tool which provides over 52,700 additional 'access points' to the thesaurus terminology. It contains the postable and nonpostable terms found in the Hierarchical Listing arranged in a KWIC (key-word-in-context) index.

Source record↗
NASA NTRSDate not supplied

Common Scientific and Technological Interests Between Astrobiology and Space Biology

The disciplines of astrobiology (AB) and space biology (SB) clearly have common interests, however they have not been pursued jointly. SB and AB are inextricably linked, both intellectually and technologically. They can now be effectively linked operationally. Cross-cutting joint collaborations will enhance innovation and increase cost effectiveness. Session topics include joint science questions, technologies, instrumentation, and missions. Examples include life detection, overlapping planetary protection concerns, biofilms, radiation, hyper- and hypogravity, applications of artificial intelligence and machine learning, interoperable databases, facilities (i.e., spacecraft, lunar surface efforts, simulation chambers, analog sites, etc.), training opportunities, and other topics relevant to AB and SB joint ventures. We welcome contributions on this very broad topical area to facilitate cross-fertilization of these disciplines that are of great importance to NASA.

astrobiology↗
NASA NTRS2012

NASA Thesaurus Data File

The NASA Thesaurus contains the authorized NASA subject terms used to index and retrieve materials in the NASA Aeronautics and Space Database (NA&SD) and NASA Technical Reports Server (NTRS). The scope of this controlled vocabulary includes not only aerospace engineering, but all supporting areas of engineering and physics, the natural space sciences (astronomy, astrophysics, planetary science), Earth sciences, and the biological sciences. The NASA Thesaurus Data File contains all valid terms and hierarchical relationships, USE references, and related terms in machine-readable form. The Data File is available in the following formats: RDF/SKOS, RDF/OWL, ZThes-1.0, and CSV/TXT.

Source record↗
DOE OSTI2024

RNA language models predict mutations that improve RNA function

Structured RNA lies at the heart of many central biological processes, from gene expression to catalysis. RNA structure prediction is not yet possible due to a lack of high-quality reference data associated with organismal phenotypes that could inform RNA function. We present GARNET (Gtdb Acquired RNa with Environmental Temperatures), a new database for RNA structural and functional analysis anchored to the Genome Taxonomy Database (GTDB). GARNET links RNA sequences to experimental and predicted optimal growth temperatures of GTDB reference organisms. Using GARNET, we develop sequence- and structure-aware RNA generative models, with overlapping triplet tokenization providing optimal encoding for a GPT-like model. Leveraging hyperthermophilic RNAs in GARNET and these RNA generative models, we identify mutations in ribosomal RNA that confer increased thermostability to the Escherichia coli ribosome. The GTDB-derived data and deep learning models presented here provide a foundation for understanding the connections between RNA sequence, structure, and function.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

Space Flown Rodent Liver RNA Sequencing Data for Machine Learning in Space Biology Research

High-throughput nucleic acid sequencing (DNA-seq, RNA-seq) has become widespread in biomedical research due to the growing availability and affordability of these assays. Data analysis has been accelerated in recent years by the adoption of artificial intelligence (AI) and machine learning (ML) techniques by biomedical researchers. In space biology research, RNAseq datasets from space-flown experimental samples are critical for characterizing the gene expression aberrations associated with exposure to spaceflight stressors. However, space biological experiments tend to be very low sample size, so identifying proper AI/ML algorithms for sequencing data analysis is an ongoing challenge since these algorithms typically require large sample size. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML”, focused on creating datasets meant for three main applications: 1) scientific benchmarking, which finds the best algorithm for a specific problem; 2) application benchmarking, which measures algorithm performance against a set of parameters; and 3) system benchmarking, which evaluates performance of hardware and software architecture. These scientific benchmarks consist of an AI-ready dataset and a reference implementation on a specific scientific question. In this work, we focused on generating standardized datasets to allow the scientific community to benchmark AI/ML algorithms in the domain of space biology. We present here a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data as a collaboration between the NASA AI4LS (Artificial Intelligence for Life Sciences) working group. and NASA’s SMD. This dataset consists of space-flown and ground control mouse liver found in the NASA GeneLab omics database. However, to amplify the small sample number (n=112 samples) for ML purposes, we employ Gaussian noise and a generative adversarial network to extend this dataset to 6,000 synthetic samples, matching the original gene expression characteristics.

James Casaletto↗
NASA NTRS2001

Vibrational Spectroscopy and Astrobiology

Role of vibrational spectroscopy in solving problems related to astrobiology will be discussed. Vibrational (infrared) spectroscopy is a very sensitive tool for identifying molecules. Theoretical approach used in this work is based on direct computation of anharmonic vibrational frequencies and intensities from electronic structure codes. One of the applications of this computational technique is possible identification of biological building blocks (amino acids, small peptides, DNA bases) in the interstellar medium (ISM). Identifying small biological molecules in the ISM is very important from the point of view of origin of life. Hybrid (quantum mechanics/molecular mechanics) theoretical techniques will be discussed that may allow to obtain accurate vibrational spectra of biomolecular building blocks and to create a database of spectroscopic signatures that can assist observations of these molecules in space. Another application of the direct computational spectroscopy technique is to help to design and analyze experimental observations of ice surfaces of one of the Jupiter's moons, Europa, that possibly contains hydrated salts. The presence of hydrated salts on the surface can be an indication of a subsurface ocean and the possible existence of life forms inhabiting such an ocean.

Chaban, Galina M.↗
NASA NTRS2001

Molecular motors and their functions in plants

Molecular motors that hydrolyze ATP and use the derived energy to generate force are involved in a variety of diverse cellular functions. Genetic, biochemical, and cellular localization data have implicated motors in a variety of functions such as vesicle and organelle transport, cytoskeleton dynamics, morphogenesis, polarized growth, cell movements, spindle formation, chromosome movement, nuclear fusion, and signal transduction. In non-plant systems three families of molecular motors (kinesins, dyneins, and myosins) have been well characterized. These motors use microtubules (in the case of kinesines and dyneins) or actin filaments (in the case of myosins) as tracks to transport cargo materials intracellularly. During the last decade tremendous progress has been made in understanding the structure and function of various motors in animals. These studies are yielding interesting insights into the functions of molecular motors and the origin of different families of motors. Furthermore, the paradigm that motors bind cargo and move along cytoskeletal tracks does not explain the functions of some of the motors. Relatively little is known about the molecular motors and their roles in plants. In recent years, by using biochemical, cell biological, molecular, and genetic approaches a few molecular motors have been isolated and characterized from plants. These studies indicate that some of the motors in plants have novel features and regulatory mechanisms. The role of molecular motors in plant cell division, cell expansion, cytoplasmic streaming, cell-to-cell communication, membrane trafficking, and morphogenesis is beginning to be understood. Analyses of the Arabidopsis genome sequence database (51% of genome) with conserved motor domains of kinesin and myosin families indicates the presence of a large number (about 40) of molecular motors and the functions of many of these motors remain to be discovered. It is likely that many more motors with novel regulatory mechanisms that perform plant-specific functions are yet to be discovered. Although the identification of motors in plants, especially in Arabidopsis, is progressing at a rapid pace because of the ongoing plant genome sequencing projects, only a few plant motors have been characterized in any detail. Elucidation of function and regulation of this multitude of motors in a given species is going to be a challenging and exciting area of research in plant cell biology. Structural features of some plant motors suggest calcium, through calmodulin, is likely to play a key role in regulating the function of both microtubule- and actin-based motors in plants.

Non-NASA Center↗
NASA NTRSDate not supplied

LSKnowledge: Nexus for Transformative Scientific Discoveries and Enhanced Information Retrieval in NASA Life Sciences Portal

We stand at the brink of an extraordinary transformation in the field of AI, driven by the convergence of generative AI and semantic technologies (e.g., knowledge graphs). This fusion holds immense potential and could redefine the future of scientific exploration, particularly in the realm of life sciences research. In this context, we shed light on the pivotal roles that Large Language Models (LLMs) and semantic technologies will play in advancing research, unearthing and comprehending life sciences information through innovative approaches, and empowering researchers to extract insights from NASA's extensive Life Sciences Data Archive. Within the NASA Life Sciences Portal (NLSP), the integration of LLMs and semantic technologies unlocks several advanced capabilities. First and foremost, it equips scientists with sophisticated tools to manage the ever-expanding wealth of scientific literature and data. Furthermore, it facilitates the creation of knowledge graphs that visually represent intricate relationships among biological entities, enabling comprehensive systems-level analysis. Additionally, the fusion of generative AI (including LLMs) and semantic technology can significantly benefit NASA's life sciences research by enhancing information retrieval and hypothesis generation. These tools enhance natural language understanding, facilitating knowledge discovery within NLSP. The overarching vision is to establish a cohesive knowledge ecosystem within NLSP, harnessing the power of LLMs and semantic technologies to synthesize and cross-reference data from diverse missions, disciplines, and research domains. This holistic approach ultimately deepens our understanding of how space environments impact life sciences data. To advance this initiative, we have launched LSKnowledge, aimed at enhancing the information retrieval capabilities of NLSP. In the short term, our primary goal is to develop a robust semantic search system. This system will empower HRP (Human Research Program) researchers to navigate NLSP data repositories more efficiently and precisely, catalyzing the process of hypothesis formation and scientific breakthroughs. To achieve this, we have employed pre-trained LLMs as part of a semantic search tool that can rank and highlight the most relevant records for user queries. To assess the tool's performance, we have curated a set of approximately 200 queries from subject matter experts (SMEs) and manually ranked the top records retrieved by both the current search system and the new semantic search, using SME judgments as the gold standard for relevancy. Herein, we present the results of our comparative analysis and illustrate how these findings have informed the fine-tuning of the system for enhanced performance. In the long term, our objectives include 1) retrieving publicly available information and integrating it with NLSP data to provide more precise answers to user queries, and 2) incorporating non-textual information from the NLSP database into our approach. In conclusion, the fusion of LLMs and semantic technologies within NLSP represents a pioneering stride towards reshaping the landscape of scientific discovery. This synergy not only equips researchers with powerful tools to navigate the burgeoning sea of information but also facilitates a deeper understanding of complex biological relationships, all while accelerating hypothesis generation and knowledge discovery. Through our initiative, LSKnowledge, we are committed to continually refining and expanding these capabilities, with the aim of not only enhancing information retrieval but also integrating diverse data sources to provide more precise insights. In the grand vision, NLSP strives to become the cornerstone of a comprehensive knowledge ecosystem, unraveling the enigmatic intricacies of life sciences phenomena in the context of space environments.

Life Sciences↗
NASA NTRS2017

Systemic Microgravity Response: Utilizing GeneLab to Develop Hypotheses for Spaceflight Risks

Biological risks associated with microgravity are a major concern for long-term space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. To perform our analysis, we utilized the NASA GeneLab database which is a publicly available repository containing a wide array of omics results from experiments conducted with: i) with different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); ii) a variety of tissues; and 3) assays that measure epigenetic, transcriptional, and protein expression changes. Meta-analysis of the transcriptomic data from 7 different murine and rat data sets, examining tissues such as liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius) revealed for the first time, the existence of potential master regulators coordinating systemic responses to microgravity in rodents. We identified p53, TGF1 and immune related pathways as the highly prevalent pan-tissue signaling pathways that are affected by microgravity. Some variability in the degree of change in their expression across species, strain and time of flight was also observed. Interestingly, while certain skeletal muscle (gastrocnemius and soleus) exhibited an overall down-regulation of these genes, some other muscle types such as the extensor digitorum longus, tibialis anterior and quadriceps, showed an up-regulated expression, indicative of potential compensatory mechanisms to prevent microgravity-induced atrophy. Key genes isolated by unbiased systems analyses displayed a major overlap between tissue types and flight conditions and established TGF1 to be the most connected gene across all data sets. Finally, a set of microgravity responsive miRNA signature was identified and based on their predicted functional state and subsequent impact on health, a theoretical health risk score was calculated. The genes and miRNAs identified from our analyses can be targeted for future research involving efficient countermeasure design. Our study thus exemplifies the utility of GeneLab data repository to aid in the process of performing novel hypothesis based spaceflight research aimed at elucidating the global impact of environmental stressors at multiple biological scales.

GeneLab↗
NASA NTRS2017

Systemic Response to Microgravity: Utilizing GeneLab Datasets to Identify Molecular Targets for Future Hypotheses-Driven Spaceflight Studies

Biological risks associated with microgravity are a major concern for long-term space travel. Although determination of risk has been a focus for NASA research, data examining systemic (i.e., multi- or pan-tissue) responses to space flight are sparse. To perform our analysis, we utilized the NASA GeneLab database which is a publicly available repository containing a wide array of omics results from experiments conducted with: i) with different flight conditions (space shuttle (STS) missions vs. International Space Station (ISS); ii) a variety of tissues; and 3) assays that measure epigenetic, transcriptional, and protein expression changes. Meta-analysis of the transcriptomic data from 7 different murine and rat data sets, examining tissues such as liver, kidney, adrenal gland, thymus, mammary gland, skin, and skeletal muscle (soleus, extensor digitorum longus, tibialis anterior, quadriceps, and gastrocnemius) revealed for the first time, the existence of potential master regulators coordinating systemic responses to microgravity in rodents. We identified p53, TGF(beta)1 and immune related pathways as the highly prevalent pan-tissue signaling pathways that are affected by microgravity. Some variability in the degree of change in their expression across species, strain and time of flight was also observed. Interestingly, while certain skeletal muscle (gastrocnemius and soleus) exhibited an overall down-regulation of these genes, some other muscle types such as the extensor digitorum longus, tibialis anterior and quadriceps, showed an up-regulated expression, indicative of potential compensatory mechanisms to prevent microgravity-induced atrophy. Key genes isolated by unbiased systems analyses displayed a major overlap between tissue types and flight conditions and established TGF(beta)1 to be the most connected gene across all data sets. Finally, a set of microgravity responsive miRNA signature was identified and based on their predicted functional state and subsequent impact on health, a theoretical health risk score was calculated. The genes and miRNAs identified from our analyses can be targeted for future research involving efficient countermeasure design. Our study thus exemplifies the utility of GeneLab data repository to aid in the process of performing novel hypothesis based spaceflight research aimed at elucidating the global impact of environmental stressors at multiple biological scales.

GeneLab↗
NASA NTRSDate not supplied

Expanding Repository Data Available For Sharing And Knowledge Discovery

Some of the hardest space biology and space health challenges require data-intensive, bioinformatic, meta-analytical, and computer-assisted research approaches. These challenges include examining interdisciplinary space life science research across experiments and across interacting spaceflight hazards (radiation, altered gravity, confinement, hostile-closed environments, distance-duration from Earth). The approaches to confront these challenges involve mining multiple datasets simultaneously from various hierarchical organizations of biological complexity, all while concurrently evaluating how experimental design factors affect endpoints of standard assays. To enable this field, it is essential that principal investigators (PIs) submit data in a structure so it can be maximally re-used. The purpose of the NASA Ames Life Sciences Data Archive (ALSDA) is to collect, curate, and make publicly available all non-human space-relevant biological data. ALSDA must also ensure data are open-access, and maximally findable, accessible, interoperable, and reusable (FAIR). The scope of ALSDA data collected and submitted by PIs include subject and study design metadata, assay metadata parameters, raw and processed assay data, assay imagery/video, and subject-experienced mission data telemetry (radiation, temperature, humidity, acoustics, vibrations, etc.). ALSDA recently integrated into a collaborative group of Open Science projects to facilitate a suite of new tools and workflows that will improve data submission, accessibility, and reusability by implementing digital data submission agreements, and adopting the data management system originally developed by NASA GeneLab. ALSDA intends to bring current biological repository data and all future collected data into this new scientific data reuse reality. This new suite of tools will enable ALSDA to deploy a science curation system using scientific assay configurations for the data submission portal. It will capture essential assay parameters according to established standards in each sub-field within biology. The submission portal expedites data collection by enhancing ease of PI data submission, providing a user interface and specificity for which data is to be submitted. Data submissions can be brought into cutting-edge informatic analysis portals to enable mining of physiological, behavioral, biochemical, and imaging datasets in conjunction with ‘omics-level datasets. As ALSDA datasets are submitted, curated, and published (e.g., micro-computed tomography, histology, pulse oximetry, serum metabolites, magnetic resonance imaging, intraocular pressure, novel object recognition, etc.), the merging together of spaceflight data along this multi-hierarchical complexity of biology will enable informatics and data-intensive approaches resulting in knowledge discoveries across missions, space hazards, and biological disciplines.

life science↗
NASA NTRSDate not supplied

GeneLab for High Schools – Bioinformatic Training For Students And Educators

Modern biological sciences are increasingly based on high-throughput molecular techniques, including genomics, transcriptomics, and proteomics. NASA’s GeneLab program has collected extensive data from ‘omics’ studies, curated them into an accessible platform and provided data analysis/visualization tools to facilitate the generation of new hypotheses and research directions. GeneLab for High Schools (GL4HS), launched in 2017, has endeavored to utilize this database and provide tools for students to understand and analyze omics datasets whilst also learning about spaceflight research. The GL4HS program ran in person at Ames from 2017-2019 and has run virtually since 2020. Each year fifteen high school students are trained to analyze and interpret GeneLab transcriptomic data. Additionally, in the last several years we have expanded our “teacher training program” to include 10 teachers total in an effort to enable this program to be utilized in classrooms across the USA. Teachers also join the NASA GeneLab Education Working Group (EWG) enabling support as they implement custom GL4HS modules into their classrooms. The GL4HS program consists of three main components – (1) core learning modules, (2) networking and teamwork, and (3) an independent learning project. Students are also taught critical networking and science communication skills facilitating their ability to ‘sell their science’ in innovative and creative ways. This program has enabled students to learn about biology in space and to have a glimpse into the world of research for the first time. Many of the students in this program shared that the course was transformative to their perception about biological sciences and how it linked to other areas of STEM. The ultimate and long-term goal of GL4HS is to expand the program to multiple locations thereby facilitating the reach of NASA Space Biology beyond NASA-centric regions.

GeneLab↗
NASA NTRS1992

Materials dispersion and biodynamics project research

The Materials Dispersion and Biodynamics Project (MDBP) focuses on dispersion and mixing of various biological materials and the dynamics of cell-to-cell communication and intracellular molecular trafficking in microgravity. Research activities encompass biomedical applications, basic cell biology, biotechnology (products from cells), protein crystal development, ecological life support systems (involving algae and bacteria), drug delivery (microencapsulation), biofilm deposition by living organisms, and hardware development to support living cells on Space Station Freedom (SSF). Project goals are to expand the existing microgravity science database through experiments on sounding rockets, the Shuttle, and COMET program orbiters and to evolve,through current database acquisition and feasibility testing, to more mature and larger-scale commercial operations on SSF. Maximized utilization of SSF for these science applications will mean that service companies will have a role in providing equipment for use by a number of different customers. An example of a potential forerunner of such a service for SSF is the Materials Dispersion Apparatus (MDA) 'mini lab' of Instrumentation Technology Associates, Inc. (ITA) in use on the Shuttle for the Commercial MDAITA Experiments (CMIX) Project. The MDA wells provide the capability for a number of investigators to perform mixing and bioprocessing experiments in space. In the area of human adaptation to microgravity, a significant database has been obtained over the past three decades. Some low-g effects are similar to Earth-based disorders (anemia, osteoporosis, neuromuscular diseases, and immune system disorders). As new information targets potential profit-making processes, services and products from microgravity, commercial space ventures are expected to expand accordingly. Cooperative CCDS research in the above mentioned areas is essential for maturing SSF biotechnology and to ensure U.S. leadership in space technology. Currently, the MDBP conducts collaborative research with investigators at the Rockefeller University, National Cancer Institute, and the Universities of California, Arizona, and Alabama in Birmingham. The growing database from these collaborations provides fundamental information applicable to development of cell products, manipulation of immune cell response, bone cell growth and mineralization, and other processes altered by low-gravity. Contacts with biotechnology and biopharmaceutical companies are being increased to reach uninformed potential SSF users, provide access through the CMDS to interested users for feasibility studies, and to continue active involvement of current participants. We encourage and actively seek participation of private sector companies, and university and government researchers interested in biopharmaceuticals, hardware development and fundamental research in microgravity.

Lewis, Marian L.↗
NASA NTRS2006

NASA Tech Briefs, October 2006

Topics covered include: Protein Sensors Based on Optical Ring Resonators; Phase Sensor for Aligning a Segmented Telescope Mirror; Control Software for Advanced Video Guidance Sensor; Generating Control Commands From Gestures Sensed by EMG; Multiple-Flat-Panel System Displays Multidimensional Data; 3D X-Ray Luggage-Screening System; Probe Station and Near-Field Scanner for Testing Antennas; Photodetector Arrays for Multicolor Visible/Infrared Imaging; Semiconductor Bolometers Give Background-Limited Performance; Multichannel X-Band Dielectric-Resonator Oscillator; Automatic Alignment of Displacement-Measuring Interferometer; Earth Observing System Data Gateway; Power User Interface; Mercury Shopping Cart Interface; Cassini Archive Tracking System; Architecture Adaptive Computing Environment; Computing Fault Displacements from Surface Deformations; Oxygen-Permeable, Hydrophobic Membranes of Silanized alpha-Al2O3; SiC Composite Turbine Vanes; Retaining Device for the Interior Structure of a Spacecraft Payload; Tool for Torquing Circular Electrical-Connector Collars; System for Continuous Deaeration of Hydraulic Oil; Solar-Powered Cooler and Heater for an Automobile Interior; Improved Oxygen-Beam Texturing of Glucose-Monitoring Optics; Tool for Two Types of Friction Stir Welding; Stationary Apparatus Would Apply Forces of Walking to Feet; Instrument Would Detect and Collect Biological Aerosols; Boundary Condition for Modeling Semiconductor Nanostructures; Miniature Distillation Column for Producing LOX From Air; Even Illumination from Fiber-Optic-Coupled Laser Diodes; Optically Driven Deformable Mirrors; Algorithm for Automated Detection of Edges of Clouds; Exploiting Quantum Resonance to Solve Combinatorial Problems; Hybrid Terrain Database; On Release of Microbe-Laden Particles from Mars Landers; A Concept for Run-Time Support of the Chapel Language; Thermoelectric Inhomogeneities in (Ag(sub 1-y)SbTe2)(sub x)(PbTe)(sub 1-x); and Spacecraft Escape Capsule.

Source record↗
NASA NTRSDate not supplied

The GeneLab Buffet: A Bioinformatic MATRIX of MANGO and TOAST

The GeneLab data repository provides an unparalleled resource for exploring how spaceflight affects organisms with omics-level insights. However, two major interlinked challenges to capitalizing on the information within these data are their vast breadth and the often-specialized expertise that has been required in the past for their analysis. How do you compare responses within and between studies, especially if you are a non-bioinformatics specialist? This presentation will discuss how Space Biology data can be accessed using software to help provide these data resources to address research questions and generate new hypotheses. The presentation will cover a wide range of the available space life science tools but will focus on TOAST, MANGO, the MATRIX, RadBioApp and other interactive relational databases (https://genelab.nasa.gov/external-vis-apps). These exploration environments have been developed to search the GeneLab data repository for new insights that inform how model organisms respond to microgravity, radiation and other factors associated with spaceflight. The presentation will be interactive, and participants will have the opportunity to ask questions and learn more about the data viz and modeling tools that are available to them.

AstroBotany↗
DOE OSTI2024

Building a framework to genetically characterize “feather spots” and understand demographic impacts of solar energy sites on migratory bird populations

The lack of data on the impact of utility-scale solar facilities on avian species and populations adds to the cost of siting and operation. As much as 32 percent of the avian biological material (feathers and carcasses) recovered from solar facilities remain unidentified, because they often take the form of “feather spots”. Feather spots are remains of impacted animals that can be separated into two broad categories: 1) those remains that may be visually identified to a species, or 2) those that cannot be visually identified to a species due to degradation from the environment and/or scavenger activity (listed as “unknown”). Even when feather spots can be identified to species, they cannot be visually assigned to particular breeding populations. In some cases, it is unknown whether multiple feather spots represent single or multiple individuals. This project’s objectives were to: 1. Use a developed, genetic-based technique to identify and determine the species, population of origin, and number of individuals found in feather spots recovered from solar facilities. 2. Implement collected data and resulting analyses to develop a publicly accessible web-based decision-making tool that can be used by the solar industry, regulators and other stakeholders to inform siting, mitigation, and conservation management efforts. 3. Establish a not-for-profit fee-for-service center at UCLA to ensure collection and identification of feather spots continue after the project period of performance. During the Project Period, we proposed to establish a pipeline for collecting, transporting, and storing of avian biological material collected at solar facilities and the collection and identification of feather spots to species and individual. We proposed the development of a genetic-based framework that would recover viable DNA from feather spots, amplify this DNA (i.e., make millions of copies of the original DNA), and use it to match the resulting sequences to a national database of known species of birds. The result would be the identification of feathers spots that were previously unidentified, and the incorporation of these samples into a larger database that included all samples recovered from solar facilities. The resulting report (below) details the result of this work and its alignment with proposed activities. We proposed the use of the data collected to assess the comparative risk to specific species or populations of species from solar facilities. For some species, we have already identified genomic markers of specific breeding populations and developed “genoscapes,” maps of unique genetic variation across the full breeding range of a species. We used these (previously and newly developed) genoscapes to probabilistically link a feather spot to the specific breeding populations from which it originated (assignment probabilities range from 75%-100% depending on species and population groups). For those species without genoscapes, we developed a vulnerability and susceptibility estimate that determines the relative local and regional risk to populations that are in geographic proximity to solar facilities, using citizen science data (Breeding Bird Survey (BBS) and eBird). These two feather spot processing pipelines (see Figure 1 below) provide quantitative estimates as to the numbers of individuals from a given population of origin that are affected by solar facilities, and ultimately can reduce costs to the consumer by reducing the industry costs associated with mitigation and siting strategies for future solar energy development.

14 SOLAR ENERGY↗
DOE OSTI2024

Simulating water dynamics related to pedogenesis across space and time: Implications for four-dimensional digital soil mapping

Digital soil mapping (DSM) relies on machine-learning and geostatistics to represent soil property observations across space. DSM techniques are powerful but often empirical, being limited to the quality and density of point samples. Water dynamics are closely related to soil variability, and the physics that govern water movement are well known. Hydrological properties can hence be simulated by physical models through space and time, unveiling key characteristics about soils. We propose the use of hydrologic models to map soils across the surface (2D), depth (1D), and time (1D)–which provides a 4D approach to digital soil mapping (4DSM). The Distributed Hydrology Soil Vegetation Model (DHSVM) was applied to a watershed currently under pasture. Moisture sensors and wells were installed at different depths in the watershed on summit, sideslope and toeslope positions to validate the model. DHSVM simulations of soil moisture distribution and depth to saturation were performed during the hydrological year (October 2008-September 2009). Clusters of similar pixels based on soil moisture values were determined using Dynamic Time Warping (DTW) to align temporal data and K-means. Clustering was performed both seasonally and for the entire year. Temporal patterns simulated by DHSVM matched measurements given by moisture sensors and wells. Seasonal clusters differed from the annual cluster. Distinct clusters were observed for each season and with depth, showing that spatiotemporal soil variability is lost when statically assessing soils. Spatiotemporal clusters corroborated field observations of fragipan occurrence not explicitly spatially mapped by Soil Survey Geographic Database (SSURGO). If a connection can be made between water and soils, static and dynamic soil variability can be predicted using physically based hydrologic models. Hydrologic models can benefit soil mapping by enabling reliable 4D simulation of water dynamics, which are fundamental to soil variability and soil classification and directly relate to biological, physical and chemical soil processes not captured by typical soil sampling protocols.

54 ENVIRONMENTAL SCIENCES↗