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NASA NTRSDate not supplied

Biological Space Radiation Countermeasures to Enable Long Duration Exploration Missions

NASA’s career radiation limit for astronauts is 600 mSv. Currently planned missions beyond low Earth orbit (LEO) will expose crew to at least double that amount of radiation (for Mars missions). Therefore, to enable long duration exploration, countermeasures need to be deployed to reduce the long-term health outcomes of space radiation exposure. Limitations to the ability of spacecraft to shield against the high energy charged particles of the space radiation environment necessitate alternative methods to reduce overall space radiation risk for carcinogenesis. Recent successes in the arenas of Acute Radiation Syndrome (ARS) and clinical radiotherapy have demonstrated the efficacy of compound-based/biologicals in reducing the detrimental long term health outcomes associated with space radiation exposure. In recent years, the Space Radiation Element has funded the investigation of several such compounds including Avasopasem Manganese, CDDO-Me, Metformin, and γ-tocotrienol. The demonstrated efficacy of these compounds in reducing carcinogenesis, central nervous system, and cardiovascular disease risks demonstrate the need for, and potential benefit of, a robust countermeasure identification and development program with an initial starting suite of compounds available to validate others. The authors would also like to present highlights of a recent Space Radiation Element sponsored issue of Life Sciences in Space Research entitled “Breaking the Limit” on this specific topic.

Space Radiation↗
NASA NTRS2016

GeneLab: Scientific Partnerships and an Open-Access Database to Maximize Usage of Omics Data from Space Biology Experiments

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASAs premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.

spaceflight↗
NASA NTRS2016

Genelab: Scientific Partnerships and an Open-Access Database to Maximize Usage of Omics Data from Space Biology Experiments

NASA's mission includes expanding our understanding of biological systems to improve life on Earth and to enable long-duration human exploration of space. The GeneLab Data System (GLDS) is NASA's premier open-access omics data platform for biological experiments. GLDS houses standards-compliant, high-throughput sequencing and other omics data from spaceflight-relevant experiments. The GeneLab project at NASA-Ames Research Center is developing the database, and also partnering with spaceflight projects through sharing or augmentation of experiment samples to expand omics analyses on precious spaceflight samples. The partnerships ensure that the maximum amount of data is garnered from spaceflight experiments and made publically available as rapidly as possible via the GLDS. GLDS Version 1.0, went online in April 2015. Software updates and new data releases occur at least quarterly. As of October 2016, the GLDS contains 80 datasets and has search and download capabilities. Version 2.0 is slated for release in September of 2017 and will have expanded, integrated search capabilities leveraging other public omics databases (NCBI GEO, PRIDE, MG-RAST). Future versions in this multi-phase project will provide a collaborative platform for omics data analysis. Data from experiments that explore the biological effects of the spaceflight environment on a wide variety of model organisms are housed in the GLDS including data from rodents, invertebrates, plants and microbes. Human datasets are currently limited to those with anonymized data (e.g., from cultured cell lines). GeneLab ensures prompt release and open access to high-throughput genomics, transcriptomics, proteomics, and metabolomics data from spaceflight and ground-based simulations of microgravity, radiation or other space environment factors. The data are meticulously curated to assure that accurate experimental and sample processing metadata are included with each data set. GLDS download volumes indicate strong interest of the scientific community in these data. To date GeneLab has partnered with multiple experiments including two plant (Arabidopsis thaliana) experiments, two mice experiments, and several microbe experiments. GeneLab optimized protocols in the rodent partnerships for maximum yield of RNA, DNA and protein from tissues harvested and preserved during the SpaceX-4 mission, as well as from tissues from mice that were frozen intact during spaceflight and later dissected on the ground. Analysis of GeneLab data will contribute fundamental knowledge of how the space environment affects biological systems, and as well as yield terrestrial benefits resulting from mitigation strategies to prevent effects observed during exposure to space environments.

bioinformatics↗
NASA NTRSDate not supplied

Microbial Optical Data Processing: A Key Step in the Metabolic Assessment of Lunar Explorer Instrument for Space Biology Applications (LEIA) and Biosentinel’s Payload Data

The BioSensor payload platform on BioSentinel and LEIA autonomously collects optical data from microbial model organisms in liquid culture. The BioSensor is designed to monitor metabolic activity using absorbance measurements of cell density and alamarBlue, a readily available colorimetric redox indicator dye. BioSentinel, a pioneering NASA CubeSat, uses yeast to study deep space radiation. LEIA investigates radiation and lunar gravity response. The experimental setup includes 16 wells equipped with three LEDs (570, 630, and 850 nm) and their corresponding photodetectors. One well is a calibration control without biology while the rest have desiccated cultures. Autonomous rehydration initiates the experiment. Data from the BioSensor are received from the flight and ground units, enabling comparison to uncover location-based metabolic rate variations. This study presents a Python Jupyter notebook developed for efficient data processing of multiple CSV files containing date and time columns, temperature, and well illumination data. It offers a user-friendly interface while maintaining computational power, automatically recognizing and iteratively processing data files in a user-input path. A Hampel filter with a short window eliminates outlier artifacts from sensor dropout. Because absorbance is a relative measurement, conversion from raw illumination requires defining a “blank” value, so the first data points are averaged to provide the necessary denominator. A cube-root function correction mitigates undesired drift caused by air pockets during the fluidic card filling phase, maintaining optical path length consistency. Beer-Lambert's law is applied to further convert absorbance values to cell and dye form concentrations, the desired science parameters. The processed data are saved and visualized as SVG plots. Future plans include extracting specific science parameters from the processed data like growth rate and metabolic rate, and identification of features corresponding to metabolic and phenotypic shifts such as starvation, shifts from aerobic to anaerobic growth, and osmotic stresses.

Space biology↗
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 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 NTRS1992

The rationale for fundamental research in space biology - Introduction and background

An overview is presented of the concept and development of SSF and the unique opportunities offered by SSF to take advantage of the greater space, the increased power, and especially the long duration of the station for a cascade of innovative experiments in fundamental science. It is emphasized that this space environment will provide new dimensions for approaching some of the most challenging problems still facing modern biology.

Halstead, T. W.↗
NASA NTRSDate not supplied

RadLab: A Comprehensive Database and Graphical and Programming Interfaces for Biologically Relevant Space Radiation Data

RadLab, a new component of the NASA Open Science Data Repository (OSDR), is a platform built upon a database of radiation data relevant to space biology. RadLab provides visual and programmatic interfaces for interrogation of its database, as well as a submission process for inclusion of data from investigators. The RadLab application programming interface (API) implements a request syntax enabling users to retrieve data filtered by various combinations of parameters (detector type, location, direction, timespan, etc), which are delivered in machine-readable text formats, ready to be ingested by downstream analysis pipelines; while the graphical user interface (GUI) provides easy means to iteratively modify query parameters and incorporates a number of standard analyses and visualizations (time series plots, geospatial visualizations, detector comparison). Investigators from many countries, including US, Russia, Japan, Canada, the Czech Republic, Germany, Hungary, and Italy, have committed to provide data from their instruments located on the ISS; RadLab will also include data from other spacecraft in LEO (e.g., the Space Shuttle, the Mir space station), BLEO (e. g. BioSentinel, Mars Orbiter, among others), and on other celestial bodies (e. g. Chang’e 4, Curiosity). The first release of RadLab has been made available to the public. Once fully operational, RadLab will provide a comprehensive and ever-growing compendium of space radiation data, facilitating straightforward access to multiple types of readings and enabling space biology researchers to perform intercomparisons of detectors and to determine the radiation environment of research missions, both via programmatic retrieval of these data and via the graphical analysis toolkit; as well as a user-friendly submission portal for ingesting data from space agencies and research institutions. Radiation scientists will be able to use RadLab to gain a deeper understanding of the space radiation environment for future human space exploration. The RadLab Working Group has been formed to foster close collaborations among data contributors and users, to identify data sources, to put in place standards for data normalization, to guide the development of features of the analysis toolkit, to establish the use of RadLab in space radiation biology research, and eventually to provide a forum for discussing relevant research issues that can take advantage of RadLab's capabilities.

radiation↗
NASA NTRSDate not supplied

Metabolic Vessel for Impedance Spectroscopy and Electrochemistry (MVISE): The Ground Mapping Unit for the Lunar Explorer Instrument for Space Biology Applications (LEIA)​

The BioSensor payload on the upcoming LEIA platform aboard a CLPS lander will carry yeast to the moon to study response to radiation and lunar gravity. The LEIA BioSensor is designed to monitor metabolic activity using absorbance in conjunction with alamarBlue for measuring colorimetric changes as proxy measurement for redox potential. The science data returned from small spacecraft mission modules like LEIA is limited as it relies solely on optical measurements, necessitating a corresponding ground mapping unit that is equipped with multiple electrochemical sensors for accurate mapping of the optical data and operates fully automatically. This technology development work discusses the extensive design and optimization efforts put into the ground mapping unit, MVISE. MVISE is a custom designed, 3D-printed vessel with an agitation system, with six different electrochemical sensor probes, and ports for sample collection and a pressure release valve. The sensors provide real-time data, with dry absorbance measurements aligning with LEIA flight hardware and wet measurements demonstrating invasive sensor design enabling a comparison between the two setups. The 3D printer resin was tested for mechanical robustness, biocompatibility, and resistance to autoclave sterilization. The inner walls were coated with food-grade epoxy, ensuring a smooth finish to prevent microbial lodging and dye staining. The MVISE has successfully passed a week-long leak test and is now undergoing active biology tests. The MVISE prototype will be prepared for radiation tests, with three identical units being tested for varying radiation levels and culture compositions at the NASA Space Radiation Laboratory in November 2024. The integrated sensor approach proposed in this work will enable the accurate mapping of the BioSentinel/LEIA optical flight data to six sensor parameters on the ground unit for better science data return and will enable the first effort to evaluate classical biochemical sensor measurements by comparing and contrasting their responses.

Chinmayee Govinda Raj↗
NASA NTRS1980

Bulgarian contributions to the development of space biology and medicine

Several aspects of aerospace medicine are discussed. Particular attention is given to the following: the effects of anoxia; the effects of positive radial acceleration; and the effects of various degrees of athletic conditioning and drugs on the tolerance of space flight factors.

Serafimov, K.↗
NASA NTRS2005

Development as adaptation: a paradigm for gravitational and space biology

Adaptation is a central precept of biology; it provides a framework for identifying functional significance. We equate mammalian development with adaptation, by viewing the developmental sequence as a series of adaptations to a stereotyped sequence of habitats. In this way development is adaptation. The Norway rat is used as a mammalian model, and the sequence of habitats that is used to define its adaptive-developmental sequence is (a) the uterus, (b) the mother's body, (c) the huddle, and (d) the coterie of pups as they gain independence. Then, within this framework and in relation to each of the habitats, we consider problems of organismal responses to altered gravitational forces (micro-g to hyper-g), especially those encountered during space flight and centrifugation. This approach enables a clearer identification of simple "effects" and active "responses" with respect to gravity. It focuses our attention on functional systems and brings to the fore the manner in which experience shapes somatic adaptation. We argue that this basic developmental approach is not only central to basic issues in gravitational biology, but that it provides a natural tool for understanding the underlying processes that are vital to astronaut health and well-being during long duration flights that will involve adaptation to space flight conditions and eventual re-adaptation to Earth's gravity.

Non-NASA Center↗
NASA NTRS2014

The Forgetful Professor and the Space Biology Adventure

This video was created as one of the products of the 2013 ISS Faculty Fellows Summer Program. Our High School science teacher faculty fellows developed this video as an elementary/middle school education component. The video shows a forgetful professor who is trying to remember something, and along the journey she learns more about the space station, space station related plant science, and the Kennedy Space Center. She learns about the Veggie hardware, LED lighting for plant growth, the rotating garden concept, and generally about space exploration and the space station. Lastly she learns about the space shuttle Atlantis.

Massa, Gioia D.↗
NASA NTRS1991

Advances in space biology and medicine. Vol. 1

Topics discussed include the effects of prolonged spaceflights on the human body; skeletal responses to spaceflight; gravity effects on reproduction, development, and aging; neurovestibular physiology in fish; and gravity perception and circumnutation in plants. Attention is also given to the development of higher plants under altered gravitational conditions; the techniques, findings, and theory concerning gravity effects on single cells; protein crystal growth in space; and facilities for animal research in space.

Bonting, Sjoerd L.↗
NASA NTRS1983

The scanning electron microscope as a tool in space biology

Normal erythrocytes are disc-shaped and are referred to here descriptively as discocytes. Several morphologically variant forms occur nomally but in rather small amounts, usually less than one percent of total. It has been shown though, that spiculed variant forms referred to as echinocytes are generated in significant amounts at zero g. Normal red cells have been stressed in vitro in an effort to duplicate the observed discocyte-echinocyte transformation at zero g. The significance of this transformation to extended stay in space and some of the plausible reasons for this transformation are discussed.

Barrett, R. A.↗