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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

Combining RNA-SEQ Datasets from NASA GENELAB: An Evaluation of Correction Methods

Background: Conducting space biology experiments aboard the International Space Station, particularly those utilizing complex model organisms like mice, is expensive and difficult due to limited crew availability, hardware, and space. As a result, sample numbers from these studies are low, reducing the statistical power of any one experiment. Aggregating spaceflight datasets serves as a method to increase sample numbers, allowing for novel insights through bioinformatic analysis of ‘omics data from merged datasets. However, aggregating datasets can introduce unwanted variation including 1) differences in sample handling, processing, and sequencing platforms between datasets (technical variation) as well as 2) differences in experimental design between datasets. Methods: In the present study, NASA GeneLab-hosted RNAseq datasets from mouse liver tissues were used to evaluate several statistical methods to correct for this unwanted variation through two approaches, reference-based and standard. The following correction algorithms were applied with (reference-based) and/or without (standard) considering Universal Mouse RNA Reference samples: ComBat and ComBat_seq from the SVA package, the median polish, empirical Bayes, and ANOVA-based algorithms from the MBatch package, and negative binomial regression normalization in the DESeq2 package. For each approach, after the correction algorithm was applied, differential gene expression (DGE) analysis of flight and ground control samples was performed with the combined data. The robustness of each tool was evaluated using BatchQC to determine statistical differences between datasets before and after correction, Principal Component Analysis to evaluate global gene expression in samples before and after correction, and by comparing DGE analysis of individual datasets and combined datasets before and after correction. Results: The results showed that the reference-based approach introduced several additional (and likely artificial) differentially expressed genes when compared with the respective standard approach. Conclusions: Of the methods tested, standard ComBat_seq and DESeq2 were identified as the most robust correction methods for combining spaceflight mouse liver RNAseq datasets hosted on GeneLab.

Finsam Samson↗
NASA NTRS2000

Contractile reserve and intracellular calcium regulation in mouse myocytes from normal and hypertrophied failing hearts

Mouse myocyte contractility and the changes induced by pressure overload are not fully understood. We studied contractile reserve in isolated left ventricular myocytes from mice with ascending aortic stenosis (AS) during compensatory hypertrophy (4-week AS) and the later stage of early failure (7-week AS) and from control mice. Myocyte contraction and [Ca(2+)](i) transients with fluo-3 were measured simultaneously. At baseline (0.5 Hz, 1.5 mmol/L [Ca(2+)](o), 25 degrees C), the amplitude of myocyte shortening and peak-systolic [Ca(2+)](i) in 7-week AS were not different from those of controls, whereas contraction, relaxation, and the decline of [Ca(2+)](i) transients were slower. In response to the challenge of high [Ca(2+)](o), fractional cell shortening was severely depressed with reduced peak-systolic [Ca(2+)](i) in 7-week AS compared with controls. In response to rapid pacing stimulation, cell shortening and peak-systolic [Ca(2+)](i) increased in controls, but this response was depressed in 7-week AS. In contrast, the responses to both challenge with high [Ca(2+)](o) and rapid pacing in 4-week AS were similar to those of controls. Although protein levels of Na(+)-Ca(2+) exchanger were increased in both 4-week and 7-week AS, the ratio of SR Ca(2+)-ATPase to phospholamban protein levels was depressed in 7-week AS compared with controls but not in 4-week AS. This was associated with an impaired capacity to increase sarcoplasmic reticulum Ca(2+) load during high work states in 7-week AS myocytes. In hypertrophied failing mouse myocytes, depressed contractile reserve is related to an impaired augmentation of systolic [Ca(2+)](i) and SR Ca(2+) load and simulates findings in human failing myocytes.

Non-NASA Center↗
NASA NTRS1988

Design of automation tools for management of descent traffic

The design of an automated air traffic control system based on a hierarchy of advisory tools for controllers is described. Compatibility of the tools with the human controller, a key objective of the design, is achieved by a judicious selection of tasks to be automated and careful attention to the design of the controller system interface. The design comprises three interconnected subsystems referred to as the Traffic Management Advisor, the Descent Advisor, and the Final Approach Spacing Tool. Each of these subsystems provides a collection of tools for specific controller positions and tasks. This paper focuses primarily on the Descent Advisor which provides automation tools for managing descent traffic. The algorithms, automation modes, and graphical interfaces incorporated in the design are described. Information generated by the Descent Advisor tools is integrated into a plan view traffic display consisting of a high-resolution color monitor. Estimated arrival times of aircraft are presented graphically on a time line, which is also used interactively in combination with a mouse input device to select and schedule arrival times. Other graphical markers indicate the location of the fuel-optimum top-of-descent point and the predicted separation distances of aircraft at a designated time-control point. Computer generated advisories provide speed and descent clearances which the controller can issue to aircraft to help them arrive at the feeder gate at the scheduled times or with specified separation distances. Two types of horizontal guidance modes, selectable by the controller, provide markers for managing the horizontal flightpaths of aircraft under various conditions. The entire system consisting of descent advisor algorithm, a library of aircraft performance models, national airspace system data bases, and interactive display software has been implemented on a workstation made by Sun Microsystems, Inc. It is planned to use this configuration in operational evaluations at an en route center.

Erzberger, Heinz↗
NASA NTRS2023

T-Cell Shenanigans: The Impact of MHC Pathway & Lipid Metabolism Genes on T-Cell Differentiation in the Thymus

Astronaut health and proper immune function are key to the success and sustainability of long term missions in space. While previous studies have observed adaptive immune dysfunction such as diminished WBC counts and cytokine production during spaceflight, the underlying mechanisms behind why immune function worsens are poorly understood. Using transcriptomic data analyzed from mouse thymus tissues available from the GLDS-421 experiment on the Rodent Research-9 mission, we used the GeneLab standardized RNA-Seq pipeline and identified four genes that were largely dysregulated (p<0.05): lipid metabolic genes Hpgd and Pgr and cell cycle regulator genes Cenpe and Kif11. We analyzed how these genes regulate the MHC-TCR interaction, and used this understanding to propose a novel mechanism for alterations to T-cell differentiation. In our experimental methodology, we propose to use 36 transgenic CD4-CD8 mice and 12 wild-type mice from the Jackson Lab to create a microgravity-simulating hindlimb unloading model and test our hypothesis. By silencing the aforementioned genes using custom siRNA based primers, we will use flow cytometry and RNA-Seq to quantify Treg counts in comparison to naive CD4+ cells, Alamar Blue assay for observing the metabolic activity in T-cells, and Enzyme-Linked Immunosorbent Assay (ELISA) to observe the general immune response in terms of T-cell differentiation. We hope this methodology will improve our knowledge of MHC-TCR interaction, contribute to a better understanding of the mechanisms of autoimmune diseases such as lupus, and improve the success rate of newer cancer treatments such as CAR-T cell therapy.

GL4HS↗
NASA NTRS1993

A microbased shared virtual world prototype

Virtual reality (VR) allows sensory immersion and interaction with a computer-generated environment. The user adopts a physical interface with the computer, through Input/Output devices such as a head-mounted display, data glove, mouse, keyboard, or monitor, to experience an alternate universe. What this means is that the computer generates an environment which, in its ultimate extension, becomes indistinguishable from the real world. 'Imagine a wraparound television with three-dimensional programs, including three-dimensional sound, and solid objects that you can pick up and manipulate, even feel with your fingers and hands.... 'Imagine that you are the creator as well as the consumer of your artificial experience, with the power to use a gesture or word to remold the world you see and hear and feel. That part is not fiction... three-dimensional computer graphics, input/output devices, computer models that constitute a VR system make it possible, today, to immerse yourself in an artificial world and to reach in and reshape it.' Our research's goal was to propose a feasibility experiment in the construction of a networked virtual reality system, making use of current personal computer (PC) technology. The prototype was built using Borland C compiler, running on an IBM 486 33 MHz and a 386 33 MHz. Each game currently is represented as an IPX client on a non-dedicated Novell server. We initially posed the two questions: (1) Is there a need for networked virtual reality? (2) In what ways can the technology be made available to the most people possible?

Pitts, Gerald↗
DOE OSTI2025

Visualizing and analyzing 3D biomolecular structures using Mol* at RCSB.org: Influenza A H5N1 virus proteome case study

The easiest and often most useful way to work with experimentally determined or computationally predicted structures of biomolecules is by viewing their three-dimensional (3D) shapes using a molecular visualization tool. Mol* was collaboratively developed by RCSB Protein Data Bank (RCSB PDB, RCSB.org) and Protein Data Bank in Europe (PDBe, PDBe.org) as an open-source, web-based, 3D visualization software suite for examination and analyses of biostructures. It is capable of displaying atomic coordinates and related experimental data of biomolecular structures together with a variety of annotations, facilitating basic and applied research, training, education, and information dissemination. Across RCSB.org, the RCSB PDB research-focused web portal, Mol* has been implemented to support single-mouse-click atomic-level visualization of biomolecules (e.g., proteins, nucleic acids, carbohydrates) with bound cofactors, small-molecule ligands, ions, water molecules, or other macromolecules. RCSB.org Mol* can seamlessly display 3D structures from various sources, allowing structure interrogation, superimposition, and comparison. Using influenza A H5N1 virus as a topical case study of an important pathogen, we exemplify how Mol* has been embedded within various RCSB.org tools—allowing users to view polymer sequence and structure-based annotations integrated from trusted bioinformatics data resources, assess patterns and trends in groups of structures, and view structures of any size and compositional complexity. In addition to being linked to every experimentally determined biostructure and Computed Structure Model made available at RCSB.org, Standalone Mol* is freely available for visualizing any atomic-level or multi-scale biostructure at rcsb.org/3d-view.

3D biostructure↗
NASA NTRS2002

Biological and genetic properties of the p53 null preneoplastic mammary epithelium

The absence of the tumor suppressor gene p53 confers an increased tumorigenic risk for mammary epithelial cells. In this report, we describe the biological and genetic properties of the p53 null preneoplastic mouse mammary epithelium in a p53 wild-type environment. Mammary epithelium from p53 null mice was transplanted serially into the cleared mammary fat pads of p53 wild-type BALB/c female to develop stable outgrowth lines. The outgrowth lines were transplanted for 10 generations. The outgrowths were ductal in morphology and progressed through ductal hyperplasia and ductal carcinoma in situ before invasive cancer. The preneoplastic outgrowth lines were immortal and exhibited activated telomerase activity. They are estrogen and progesterone receptor-positive, and aneuploid, and had various levels of tumorigenic potential. The biological and genetic properties of these lines are distinct from those found in most hyperplastic alveolar outgrowth lines, the form of mammary preneoplasia occurring in most traditional models of murine mammary tumorigenesis. These results indicate that the preneoplastic cell populations found in this genetically engineered model are similar in biological properties to a subset of precurser lesions found in human breast cancer and provide a unique model to identify secondary events critical for tumorigenicity and invasiveness.

NASA Discipline Radiation Health↗
NASA NTRS1989

Structural zooming research and development of an interactive computer graphical interface for stress analysis of cracks

Engineering problems sometimes involve the numerical solution of boundary value problems over domains containing geometric feature with widely varying scales. Often, a detailed solution is required at one or more of these features. Small details in large structures may have profound effects upon global performance. Conversely, large-scale conditions may effect local performance. Many man-hours and CPU-hours are currently spent in modeling such problems. With the structural zooming technique, it is now possible to design an integrated program which allows the analyst to interactively focus upon a small region of interest, to modify the local geometry, and then to obtain highly accurate responses in that region which reflect both the properties of the overall structure and the local detail. A boundary integral equation analysis program, called BOAST, was recently developed for the stress analysis of cracks. This program can accurately analyze two-dimensional linear elastic fracture mechanics problems with far less computational effort than existing finite element codes. An interactive computer graphical interface to BOAST was written. The graphical interface would have several requirements: it would be menu-driven, with mouse input; all aspects of input would be entered graphically; the results of a BOAST analysis would be displayed pictorially but also the user would be able to probe interactively to get numerical values of displacement and stress at desired locations within the analysis domain; the entire procedure would be integrated into a single, easy to use package; and it would be written using calls to the graphic package called HOOPS. The program is nearing completion. All of the preprocessing features are working satisfactorily and were debugged. The postprocessing features are under development, and rudimentary postprocessing should be available by the end of the summer. The program was developed and run on a VAX workstation, and must be ported to the SUN workstation. This activity is currently underway.

Gerstle, Walter↗
DOE OSTI2024

Combinatorial transcription factor binding encodes cis -regulatory wiring of mouse forebrain GABAergic neurogenesis

Transcription factors (TFs) bind combinatorially to cis-regulatory elements, orchestrating transcriptional programs. Although studies of chromatin state and chromosomal interactions have demonstrated dynamic neurodevelopmental cis-regulatory landscapes, parallel understanding of TF interactions lags. To elucidate combinatorial TF binding driving mouse basal ganglia development, we integrated chromatin immunoprecipitation sequencing (ChIP-seq) for twelve TFs, H3K4me3-associated enhancer-promoter interactions, chromatin and gene expression data, and functional enhancer assays. We identified sets of putative regulatory elements with shared TF binding (TF-pRE modules) that orchestrate distinct processes of GABAergic neurogenesis and suppress other cell fates. The majority of pREs were bound by one or two TFs; however, a small proportion were extensively bound. These sequences had exceptional evolutionary conservation and motif density, complex chromosomal interactions, and activity as in vivo enhancers. Our results provide insights into the combinatorial TF-pRE interactions that activate and repress expression programs during telencephalon neurogenesis and demonstrate the value of TF binding toward modeling developmental transcriptional wiring.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRSDate not supplied

An Open-Science Approach to Address Individual Response to Simulated GCR In Genetically Diverse Populations of Mice and Humans

This project addresses the challenge of understanding and predicting individual radiation sensitivity by integrating genetics, demographics and biomarker characteristics across species (mice and humans). We hypothesize that ex vivo DNA repair response to GCR components is a central determinant of cancer risk from space radiation and can serve as a biomarker of radiation risk in combination with genetics. Automated image quantification of 53BP1+ radiation-induced foci (RIF) during the first 4-48 h post-irradiation was performed as a function of dose and LET in non-immortalized primary skin fibroblasts derived from 76 mice across 15 strains (5 inbred reference strains and 10 collaborative-cross strains) exposed to X rays (0.1, 1 and 4 Gy), 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100sq. μm), as well as in peripheral blood mononuclear cells (PBMCs) from 768 healthy donors (matched ethnicity, 50/50 male/female, 18-70 years old) exposed to gamma rays (0.1 and 1 Gy), 350 MeV/n 28Si, 350 MeV/n 40Ar and 600 MeV/n 56Fe (1.1 and 3 particles/100sq. μm). A genome-wide association study (GWAS) was performed on the mouse strains between DNA damage responses to space radiation and single nucleotide polymorphisms (SNPs). We found SNPs, which were significantly associated to the RIF phenotype, mapped to genes and pathways that are functionally linked to health hazards for deep space exploration (e.g. carcinogenesis, nervous system damage and immune dysfunction). Some of these SNPs were located within protein coding regions, potentially interfering with protein functions and providing promising genetic targets for countermeasures. We also found correlations between both spontaneous and radiation-induced DNA damage and SNPs mapped to pathways associated with cellular metabolism. GWAS is undergoing for the human data. All data have been made available via the NASA Space Biology Open-Science database (genelab.nasa.gov) and we will discuss how various genomic and transcriptomic datasets can be accessed for modeling and integrated using machine learning methods for discovering new radiation biology.

Sylvain V Costes↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields, in part due to a culture of open data sharing and reuse. AI/ML methodology is well-suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Inexperienced researchers can produce models that perform poorly outside of the training dataset. Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Casaletto↗
NASA NTRS2001

Modest increased sensitivity to radiation oncogenesis in ATM heterozygous versus wild-type mammalian cells

Subpopulations that are genetically predisposed to radiation-induced cancer could have significant public health consequences. Individuals homozygous for null mutations at the ataxia telangiectasia gene are indeed highly radiosensitive, but their numbers are very small. Ataxia Telangiectasia heterozygotes (1-2% of the population) have been associated with somewhat increased radiosensitivity for some end points, but none directly related to carcinogenesis. Here, intralitter comparisons between wild-type mouse embryo fibroblasts and mouse embryo fibroblasts carrying ataxia telangiectasia mutated (ATM) null mutation indicate that the heterozygous cells are more sensitive to radiation oncogenesis than their normal, litter-matched, counterparts. From these data we suggest that Ataxia Telangiectasia heterozygotes could indeed represent a societally-significant radiosensitive human subpopulation.

Non-NASA Center↗
NASA NTRS2004

EPIC Study of Two Enigmatic Sources: The Mouse and SNR 359.1-0.5

The objective of the original proposal was to observe the Mouse pulsar wind nebula (associated with PSR J1744-2958) and the nearby supernova remnant G359.1-0.5, where the pulsar was probably born, with the XMM-Newton observatory to study the properties of these objects. SNR G359.1-0.5 was accepted as a Category C target and has not been observed. The Mouse was observed on April 27,2003 for 52 ks. The image analysis has shown that the Mouse is extended in the East-West direction, possibly along the direction of the pulsar's proper motion. The spectrum of this pulsar wind nebula can be described as an absorbed power law with the photon index GAMMA = 1.9 plus or minus 0.1, effective hydrogen column density n(sub H) = (2.6 plus or minus 0.1) x 10(exp 22) per square centimeter, and flux F = 1.8 x 10(exp -11) erg per square centimeter per second in the 1-10 keV energy range. Based on the n(sub H) value, the distance to the source is about 5 kpc, which results in the luminosity 3.7 x 10(exp 34) erg per second. We conclude that PSR J1744-2958 and the Mouse are not physically associated with G359.1-0.5, which lies at a larger distance. In addition to the Mouse, we also detected two Low-Mass X-ray Binaries, SLX 1744-299 and 1744-300, in the EPIC MOS and PN fields of view. The latter of these objects showed a Type I X-ray burst during our observation, with a rise time of 5 s and decay time of 60 s. A very strong pileup during the burst made the analysis of the burst properties unreliable. The spectral analysis of the persistent radiation from SLX 1744-299 and 1744-300 yields the hydrogen column densities of 3.2 plus or minus 0.1 and (3.6 plus or minus 0.2) x 10(exp 22) per square centimeter, respectively, which suggests that the sources are close to the Galactic center (d = 8-9 kpc). The spectra can be reasonably well fitted with a blackbody plus thin disk model, with the blackbody temperatures of 1.7 plus or minus 0.2 and 1.8 plus or minus 0.2 keV, respectively.

Pavlov, George↗
NASA NTRS2020

Fisher's Peak Ecological Forecasting: Mapping Biomass to Inform Conservation Planning of a Future State Park in Southern Colorado

Fisher’s Peak is a 77.5 km2 property southeast of Trinidad, Colorado that is planned to become Colorado’s newest state park. The area has experienced limited anthropogenic disturbance and is home to an abundance of unique habitats and species. A rapid, approximately 900 m change in elevation over the extent of the area nurtures a variety of plants and animals, including the endangered New Mexico meadow jumping mouse. In 2019, the State of Colorado obtained Fisher’s Peak with plans to make it Colorado’s second largest state park. A diverse group of collaborators, including the Colorado State Forest Service and The Nature Conservancy, worked closely to design the state park to maximize recreation opportunity while conserving the property’s rich habitats and biodiversity. The Fisher’s Peak Ecological Forecasting Team utilized Light Detection and Ranging (LiDAR) surveys, in situ forest inventory data, and Earth observations from Landsat8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Sentinel-2 Multispectral Instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) and the Shuttle Radar Topography Mission (SRTM) to quantify and map biomass over the extent of the study area. The results from modeling biomass had an out-of-bag root mean square error of 55 Mg/ha and an R2 of 12. The resulting map indicates areas where carbon storage on the property is high, informing decision-making processes for future park development. While more in situ training data may improve modeling capacity for biomass in the Fisher’s Peak area, this work represents a feasible attempt to better understand biomass distribution using earth observation.

DEVELOP Tech Paper↗
NASA NTRS2011

Software Displays Data on Active Regions of the Sun

The Solar Active Region Display System is a computer program that generates, in near real time, a graphical display of parameters indicative of the spatial and temporal variations of activity on the Sun. These parameters include histories and distributions of solar flares, active region growth, coronal mass ejections, size, and magnetic configuration. By presenting solar-activity data in graphical form, this program accelerates, facilitates, and partly automates what had previously been a time-consuming mental process of interpretation of solar-activity data presented in tabular and textual formats. Intended for original use in predicting space weather in order to minimize the exposure of astronauts to ionizing radiation, the program might also be useful on Earth for predicting solar-wind-induced ionospheric effects, electric currents, and potentials that could affect radio-communication systems, navigation systems, pipelines, and long electric-power lines. Raw data for the display are obtained automatically from the Space Environment Center (SEC) of the National Oceanic and Atmospheric Administration (NOAA). Other data must be obtained from the NOAA SEC by verbal communication and entered manually. The Solar Active Region Display System automatically accounts for the latitude dependence of the rate of rotation of the Sun, by use of a mathematical model that is corrected with NOAA SEC active-region position data once every 24 hours. The display includes the date, time, and an image of the Sun in H light overlaid with latitude and longitude coordinate lines, dots that mark locations of active regions identified by NOAA, identifying numbers assigned by NOAA to such regions, and solar-region visual summary (SRVS) indicators associated with some of the active regions. Each SRVS indicator is a small pie chart containing five equal sectors, each of which is color-coded to provide a semiquantitative indication of the degree of hazard posed by one aspect of the activity at the indicated location. The five aspects in question are the history of solar flares, the history of coronal mass ejections, the growth or decay of activity, the overall size, and the magnetic configuration. Mouse-clicking on an active-region-marking dot, SRVS indicator, or NOAA region number causes the program to generate a solar-region summary table (SRT) for the active region in question. The SRT contains additional quantitative and qualitative data, beyond those contained in the SRVS: These data include the solar coordinates of the region, the area of the region and its change in area during the past 24 hours, the change in the number of sunspots in the region during the past 24 hours, the magnetic configuration, and the types, dates, and times of the most recent flare and coronal mass ejection.

Golightly, Mike↗
NASA NTRS2020

Fisher's Peak Ecological Forecasting - Mapping Biomass to Inform Conservation Planning of a Future State Park in Southern Colorado

Fisher’s Peak is a 77.5 km2 property southeast of Trinidad, Colorado that is planned to become Colorado’s newest state park. The area has experienced limited anthropogenic disturbance and is home to an abundance of unique habitats and species. A rapid, approximately 900 m change in elevation over the extent of the area nurtures a variety of plants and animals, including the endangered New Mexico meadow jumping mouse. In 2019, the State of Colorado obtained Fisher’s Peak with plans to make it Colorado’s second largest state park. A diverse group of collaborators, including the Colorado State Forest Service and The Nature Conservancy, worked closely to design the state park to maximize recreation opportunity while conserving the property’s rich habitats and biodiversity. The Fisher’s Peak Ecological Forecasting Team utilized Light Detection and Ranging (LiDAR) surveys, in situ forest inventory data, and Earth observations from Landsat 8 Operational Land Imager (OLI), Sentinel-1 C-band Synthetic Aperture Radar (C-SAR), Sentinel-2 Multispectral Instrument (MSI), Advanced Land Observing Satellite 2 (ALOS-2) Phased Array type L-band Synthetic Aperture Radar (PALSAR-2) and the Shuttle Radar Topography Mission (SRTM) to quantify and map biomass over the extent of the study area. The results from modeling biomass had an out-of-bag root mean square error of 55 Mg/ha and an R2 of 12. The resulting map indicates areas where carbon storage on the property is high, informing decision-making processes for future park development. While more in situ training data may improve modeling capacity for biomass in the Fisher’s Peak area, this work represents a feasible attempt to better understand biomass distribution using earth observation

Lauren Lad↗
NASA NTRSDate not supplied

Developing Open-Source Training Materials for AI/ML and Space Biological Sciences Using NASA Cloud-Based Data

Artificial Intelligence (AI) and Machine Learning (ML) has gained significant traction in the biological and biomedical research fields in the last two decades, in part thanks to an increasing culture of open data sharing and reuse. Due to its capability for identifying complex relationships and patterns, AI/ML methodology is particularly well suited to recognize and predict biological patterns from high-dimensional next-generation sequencing data (e.g. whole genome sequencing, transcriptomic sequencing), as well as from biological or medical imaging data (e.g. microscopy, computed tomography, ultrasound, magnetic resonance imaging, radiography). These methodologies hold particular promise for space biosciences research and automated space health monitoring systems. However, there are many key considerations for properly training, validating, and testing a machine learning model in biological research or clinical application. Even with the positive culture of Open Science and data sharing, inexperienced researchers working quickly without proper checks can produce models that perform poorly outside of the immediate training dataset. Lessons learned from biological AI/ML research indicate that Open Science principles such as data sharing and open-source code must go hand-in-hand with publicly available, high-quality training curricula in best practices, with modules centered on real-life scientific use cases and data so future AI/ML practitioners gain experience on real problems. Here we present the development of open-source training materials for AI/ML and space biosciences, as part of the NASA Transform to Open Science Training (TOPST) initiative. We develop 4 independent training programs, focused on the following topics: 1) Fundamentals of Machine Learning and Space Biosciences Domain, 2) Open Science, Artificial Intelligence, and Ethical Best Practices for Data Sharing and Analysis, 3) Using AI/ML Classification to Identify Gene Networks Affected By Space Exposure in Mouse Liver, and 4) Using Neural Networks to Find DNA Damage Patterns in Immune Cells after Radiation. All programs leverage cloud-based NASA biological datasets. The curriculum we present will enable worldwide access to training in AI/ML and scientific analysis.

James Andrew Casaletto↗
NASA NTRS1986

TAE Plus: A conceptual view of TAE in the space station era

The use of the Transportable Applications Executive (TAE) for prototyping user interfaces has been the prime force behind the new TAE research and development work. The Data Systems Technology Division at GSFC is developing prototypes of user interfaces for different functions involved in the operation, analysis and data communication of space station payloads. TAE is a valuable prototyping tool because it enables a developer to build an entire application user interface model and run it without writing a single line of application code. One force driving new development is the need to update TAE's user interface to support the latest interactive graphic device technology. The current TAE, TAE Classic, uses interface techniques designed for an 80x24 character monochrome alphanumeric terminal, but does not effectively utilize features such as windowing, graphics, color, and selection devices available on newer workstations. To meet our needs, development of a TAE Plus began in FY-86 and involves augmenting TAE with three different sets of tools: a user interface toolkit; an application toolkit; and run-time service subroutines. A phased approach is being used to develop TAE Plus. In the first phase, we have met the needs of the user community and provided some support for rapid prototyping by developing a TAE Facelift, which adds an enhanced TAE interface (with windowing, mouse interaction, pull-down menus, etc.) to a select set of graphic workstations. The TAE Facelift allows many new concepts to be tested quickly for feedback and performance. In the second phase, a fully-integrated user interface management system, TAE Plus, will be built that supports the separation of interface from application, with the concomitant ability to prototype and rapidly change interfaces. This robust functionality will support, in an integrated manner, an application's development cycle from the prototype step through to the fully operational system.

Szczur, Martha↗