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Bibliographies on ethylene oxide sterilant, aerospace medicine, environmental science, exobiology, physical biology, planetary quarantine, and bioscience communications
The nature of biological issues which can be addressed during long-term space missions is briefly discussed. These issues include structure, from cell to organ to organism; function, the regulation of systems such as immunology, neural sciences, and behavior; and reproduction and development.
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Biological systems analysis and biodynamic modelling of physiological and biological interrelationships in human body and mammals
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.
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.
With the advent of sophisticated space facilities we discuss the overall nature of some biological questions that can be addressed. We point out the need for broad participation by the biological community, the necessary facilities, and some unique requirements.
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An attempt is made to describe the scope of scientific areas that comprise the current field of exobiology in the United States. From investigations of astrophysical phenomena that deal with the birth of stars and planetary systems to questions of molecular biology involving phylogenetic relationships among organisms, from attempts to simulate the synthesis of biological precursor molecules in the chemistry laboratory to making measurements of the organic constituents of Titan's atmosphere, these researches all converge toward a common objective--answering the question of how life came about in the universe.
There is a critical need for better access and visualization of spaceflight environmental telemetry and mission hardware data from sensors including relative humidity, carbon dioxide, oxygen, radiation, airflow, temperature, acceleration, and acoustics. Under the stewardship of the Ames Life Sciences Data Archive (ALSDA) and GeneLab, an effort is underway to consolidate, normalize and provide accessibility of archived mission environmental data and hardware information, with the purpose of providing important context to biological data. This effort is necessary to provide scientific context of its impact upon biological and biomedical data from spaceflight missions and experiments (genomic, metagenomic, gene expression, proteomic, metabolomic, physiological, phenomics, behavioral; tabular, imaging, video). Environmental spaceflight data is derived from dozens of sources, with various formats, and in the past year a pipeline is in development to collect, curate and present this data efficiently. In the upcoming year, a new Data Visualization Portal will utilize the standardized pipeline data to provide easy user access to compare parameters and environmental conditions between missions, locations, subjects, and durations. Environmental and hardware data enables broad accessibility and analytics, without the need for advanced data informatic expertise. Familiarity with the capabilities and limitations of a variety of existing hardware/tools is a strength that could be applied to creation of improved hardware for future ecosystems on the Moon and Mars. The intention is to make biological and environmental telemetry data maximally open-access and FAIR (findable, accessible, interoperable, reusable) for data mining-informatic approaches to support knowledge discovery necessary for low Earth orbit, cis-Lunar, Mars transit, and Mars surface missions.
This viewgraph presentation reviews the state of science research on the International Space Station (ISS). The shuttle and other missions that have delivered science research facilities to the ISS are shown. The different research facilities provided by both NASA and partner organizations available for use and future facilities are reviewed. The science that has been already completed is discussed. The research facilitates the Vision for Space Exploration, in Human Life Sciences, Biological Sciences, Materials Science, Fluids Science, Combustion Science, and all other sciences. The ISS Focus for NASA involves: Astronaut health and countermeasure, development to protect crews from the space environment during long duration voyages, Testing research and technology developments for future exploration missions, Developing and validating operational procedures for long-duration space missions. The ISS Medical Project (ISSMP) address both space systems and human systems. ISSMP has been developed to maximize the utilization of ISS to obtain solutions to the human health and performance problems and the associated mission risks of exploration class missions. Including complete programmatic review with medical operations (space medicine/flight surgeons) to identify: (1) evidence base on risks (2) gap analysis.
Advances in space exploration have evolved in lockstep with key technology advances in diverse fields such as materials science, biological science, and engineering risk management. Research in these areas, where structure and physical processes come together, can proceed rapidly in part due to sophisticated ground-based analytical tools that help re-searchers develop technologies and engineering processes that push frontiers of human space exploration. Electron microscopes (EM) are an example of such a workhorse tool, lending a unique blend of strong optical scattering, high native resolution, large depth of focus, and spectroscopy via characteristic X-ray emission, providing exquisite high-magnification structural imaging and chemical analysis. Ground-based EM’s have been essential in NASA research for many years. In particular, in mineralogy and petrology, EM is used to understand the origin and evolution of the solar system, particularly rocky bodies. In microbiology, EM has helped visualize the architecture of tissues and cells. In engineering/materials science, EM has been used to characterize particulate debris in air and water samples, determine pore sizes in ceramics/catalysts, understand the nature of fibers, determine composition and morphology of new and existing materials, and characterize micro-textures of vapor deposited films. EM is highly effective at investigating a wide variety of nanoscale materials/biomaterials at the core of many of NASA’s inquiries. Despite exquisite optical performance and versatility, EM’s are traditionally large, heavy, and have high power consumption. They are also expensive so they tend to be housed at universities and large research institutions, or at major industrial laboratory sites with support staff, supplies, and skilled operators. Since most organizations cannot support their own EM, samples are often sent to these large institutions and service centers to be imaged, at great expense and of-ten with delay of weeks to months for complex analyses. Complexity, high cost, and maintenance associated with collecting EM image data has until now severely limited fields in which EM is used. Making EM accessible outside constrained terrestrial laboratory environments will bring EM’s performance and versatility to a much broader range of scientific and engineering endeavors, including in space.
Electron microscopes (EM’s), are workhorse tools serving diverse fields such as materials science, biological science, and engineering. Scanning EM’s (SEM’s) in particular enable high magnification study and pinpoint chemical analyses of structures down to the nanoscale by providing a powerful blend of strong optical scattering, high native resolution, large depth of focus, and energy-dispersive X-ray spectroscopy (EDS). Mochii is the world’s smallest production electron microscope, scheduled to travel to the International Space Station (ISS) this spring where it will serve as an ISS National Laboratory (ISSNL) microgravity facility on successful demonstration. We previously reported on progress preparing Mochii for space flight, in particular flight integration verifications and science application testing. These included standard integration testing such as electromagnetic interference and flight vibration, and extend to unique functional testing such as magnetic susceptibility and extreme analog environment testing under the sea. Presently, Mochii payload flight hardware has completed testing and was handed over to NASA’s ISS payload processing facility in Houston. It will make its way to the the east coast for launch currently scheduled on Space-X CRS-20 for Mission increment 62 in March 2020.
Biological systems analysis for developing dynamic physiological models
The NASA Biological Institutional Science Collection (NBISC) has amassed a collection of valuable space biology samples spanning from early Space Shuttle missions to recent missions on the International Space Station (ISS). However, the full potential of this archive has not been realized, with many samples having been stored for decades without being re-accessed. Given the pace of analytical advancement since NBISC began accumulating samples, we initiated a pilot study to reveal additional patterns that may have been missed during the original investigations and provide technical robustness and scientific value for a cost-effective sequencing approach. We selected 84 mouse and rat samples spanning four separate Space Shuttle, ISS and ground-analogue studies with a focus on muscle, spleen and thymus tissues to allow identification of important changes related to musculoskeletal unloading and immune function. High quality RNA from these tissues was extracted and used to generate transcriptional profiling data using a cost-effective Tag-seq approach and immediately released it on GeneLab. The released datasets included gastrocnemius tissues from rats during the 1991 Space Shuttle mission (GLDS-422) and a ground analogue study in 2019 (GLDS-418), and mice from the 2014 ISS mission (GLDS-419). Also released were two thymus datasets, one from rats flown on the 1993 Space Shuttle (GLDS-423) and another from mice flown on the 2017 ISS (GLDS-421) missions, along with one mouse spleen dataset from a 2017 ISS mission (GLDS-420). These data will serve as a pilot of a much larger and comprehensive study which would generate similar data from thousands of NBISC samples, enabling the discovery and validation of molecular networks influenced by space conditions.
The Life Sciences Space Biology (LSSB) research will explore the effect of microgravity on humans, including the physiological, clinical, and sociological implications of space flight and the readaptations upon return to earth. Physiological anomalies from past U.S. space flights will be used in planning the LSSB project.The planning effort integrates science and engineering. Other goals of the LSSB project include the provision of macroscopic view of the earth's biosphere, and the development of spinoff technology for application on earth.
Biological and Environmental Research (BER) conducts climate science research activities under three programs: the Atmospheric Radiation Measurement (ARM) user facility, the Atmospheric System Research (ASR) program, ad the Earth and Environmental Systems Modeling (EESM) program. This document provides key findings and recommendations from the Biological and Environmental Research Advisory Committee (BERAC) Climate Science Working Group.
The goal of the Gas-Grain Simulation Facility project is to provide a microgravity laboratory to facilitate research relevant to exobiology (the study of the origin and evolution of life in the universe). Such a facility will also be useful in other areas of study important to NASA including planetary science, biology, atmospheric science, astrophysics, chemistry, and physics. To achieve this goal, the project will develop and support the GGSF, a modular facility-class payload planned for inclusion on Space Station Freedom. The GGSF will consist of the following: an experiment chamber(s) supported by subsystems that provide chamber environment regulation and monitoring capabilities; sample generation, injection, positioning, and retrieval capabilities; and computer control, data acquisition, and housekeeping capabilities. The facility will also provide analytical tools such as light-scattering measurement systems, aerosol size-spectrum measurement devices, and optical imaging systems.