Cybernetics and space biology
Cybernetics applied to space biology and medicine through use of mathematical models, biological controls, and statistical dynamics
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Cybernetics applied to space biology and medicine through use of mathematical models, biological controls, and statistical dynamics
The Space Biology Research Associate Program for the 21st Century provided a unique opportunity to train individuals to conduct biological research in hypo- and hyper-gravity, and to conduct ground-based research. This grant was developed to maximize the potential for Space Biology as an emerging discipline and to train a cadre of space biologists. The field of gravitational and space biology is rapidly growing at the future of the field is reflected in the quality and education of its personnel. Our chief objective was to train and develop these scientists rapidly and in a cost effective manner. The program began on June 1, 1980 with funding to support several Research Associates each year. 113 awards, plus 1 from an independently supported minority component were made for the Research Associates program. The program was changed from a one year award with a possibility for renewal to a two year award. In 1999, the decision was made by NASA to discontinue the program due to development of new priorities for funding. This grant was discontinued because of the move of the Program Director to a new institution; a new grant was provided to that new institution to allow completion of the training of the remaining 2 research associates in 1999. After 1999, the program will be discontinued.
The Space Biology Research Associate Program for the 21st Century provided a unique opportunity to train individuals to conduct biological research in hypo- and hyper-gravity, and to conduct ground-based research. This grant was developed to maximize the potential for Space Biology as an emerging discipline and to train a cadre of space biologists. The field of gravitational and space biology is rapidly growing at the future of the field is reflected in the quality and education of its personnel. Our chief objective was to train and develop these scientists rapidly and in a cost effective model.
Volume IV is devoted to examining the medical and associated organizational measures used to maintain the health of space crews and to support their performance before, during, and after space flight. These measures, collectively known as the medical flight support system, are important contributors to the safety and success of space flight. The contributions of space hardware and the spacecraft environment to flight safety and mission success are covered in previous volumes of the Space Biology and Medicine series. In Volume IV, we address means of improving the reliability of people who are required to function in the unfamiliar environment of space flight as well as the importance of those who support the crew. Please note that the extensive collaboration between Russian and American teams for this volume of work resulted in a timeframe of publication longer than originally anticipated. Therefore, new research or insights may have emerged since the authors composed their chapters and references. This volume includes a list of authors' names and addresses should readers seek specifics on new information. At least three groups of factors act to perturb human physiological homeostasis during space flight. All have significant influence on health, psychological, and emotional status, tolerance, and work capacity. The first and most important of these factors is weightlessness, the most specific and radical change in the ambient environment; it causes a variety of functional and structural changes in human physiology. The second group of factors precludes the constraints associated with living in the sealed, confined environment of spacecraft. Although these factors are not unique to space flight, the limitations they entail in terms of an uncomfortable environment can diminish the well-being and performance of crewmembers in space. The third group of factors includes the occupational and social factors associated with the difficult, critical nature of the crewmembers' work: the risks involved in space flight, changes in circadian rhythms, and intragroup interactions. The physical and emotional stress and fatigue that develop under these conditions also can disturb human health and performance. In addition to these factors, the risk also exists that crewmembers will develop various illnesses during flight. The risk of illness is no less during space flight than on Earth, and may actually be greater for some classes of diseases.
Biological systems react in complex ways to the stressors of spaceflight, and the data capturing these relationships is concomitantly high-dimensional and complex. Deep learning and machine learning approaches are increasingly popular as an analytical approach for space biosciences, due to their ability to model complex relationships in complex data. However, such approaches often require large datasets and extensive computational resources. New approaches that minimize data sizes and computational power needed to leverage machine learning, and resources that make these approaches accessible, are needed to increase accessibility and adoption of machine learning in the space biosciences. Transfer learning, in which a pretrained model of broad utility is trained on a large dataset, and subsequently reused on downstream applications for which data is more limited, is one approach to minimizing data and computational intensity of deep learning applications. This transfer learning approach results in more performant models in high-dimensional, low-sample-size settings such as space biology, as compared to training models on limited data from scratch. This presentation will outline efforts to generate pretrained models for the space biology community, and highlight transfer learning applications modeling microbial antibiotic resistance during spaceflight. Finally, in order to increase accessibility of these models and tools, as well as others, for the broader space biology community, we present a modeling and analysis platform facilitating machine learning applications in space biology. This platform streamlines machine learning training and analysis in a notebook format, facilitates download and use of space biology data from the NASA GeneLab database, and can be utilized on NASA-hosted servers or downloaded and hosted locally. This effort, as part of the AI4LS (Artificial Intelligence for Life in Space) working group, will increase accessibility, feasibility, and performance of machine learning approaches for the space biology community.
One of the major objectives of any orbital space research platform is to provide a quiescent low gravity, preferably a zero gravity environment, to perform fundamental as well as applied research. However, small disturbances exist onboard any low earth orbital research platform. The impact of these disturbances must be taken into account by space research scientists during their research planning, design and data analysis in order to avoid confounding factors in their science results. The reduced gravity environment of an orbiting research platform in low earth orbit is a complex phenomenon. Many factors, among others, such as experiment operations, equipment operation, life support systems and crew activity (if it is a crewed platform), aerodynamic drag, gravity gradient, rotational effects as well as the vehicle structural resonance frequencies (structural modes) contribute to form the overall reduced gravity environment in which space research is performed. The contribution of these small disturbances or accelerations is precisely why the environment is NOT a zero gravity environment, but a reduced acceleration environment. This paper does not discuss other factors such as radiation, electromagnetic interference, thermal and pressure gradient changes, acoustic and CO2 build-up to name a few that affect the space research environment as well, but it focuses solely on the magnitude of the acceleration level found on orbiting research laboratory used by research scientists to conduct space research. For ease of analysis this paper divides the frequency spectrum relevant to most of the space research disciplines into three regimes: a) quasi-steady, b) vibratory and c) transient. The International Space Station is used as an example to illustrate the point. The paper discusses the impact of these three regimes on space biology research and results from space flown experiments are used to illustrate the potential negative impact of these disturbances (accelerations) on space biology research.
The purpose of the Search for Extraterrestrial Intelligence (SETI) Institute is to conduct and promote research related activities regarding the search for extraterrestrial life, particularly intelligent life. Such research encompasses the broad discipline of 'Life in the Universe', including all scientific and technological aspects of astronomy and the planetary sciences, chemical evolution, the origin of life, biological evolution, and cultural evolution. The primary purpose was to provide funding for the Principal Investigator to collaborate with the personnel of the SETI Institute and the NASA-Ames Research center in order to plan and develop space biology research on and in connection with Space Station Freedom; to promote cooperation with the international partners in the space station; to conduct a study on the use of biosensors in space biology research and life support system operation; and to promote space biology research through the initiation of an annual publication 'Advances in Space Biology and Medicine'.
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.
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.
Increased biomedical risks associated with deep space crewed missions (cis-Lunar, Mars transit/surface) require development of health countermeasures, novel ecosystem support, risk modeling, and fundamental space biological knowledge discovery. Molecular-omics, physiological-phenotypic-behavioral, and environmental-radiation telemetry data from space biological and health studies are needed for reuse by scientists to address these tasks. The data as well as space-relevant biospecimens are being made more findable, accessible, interoperable, and reusable through NASA’s Open Science Data Repository (OSDR). This new OSDR umbrella grouping includes NASA GeneLab, the NASA Ames Life Sciences Data Archive (ALSDA), and the NASA Biological Institutional Scientific Collection. The OSDR system design appropriately handles metadata and processed-tabular results from ALSDA studies collected from space experiments. But raw and processed ALSDA bioimage and video datasets require an expansion of OSDR’s data architecture to handle ingestion, curation, and egress. The academic-industry bioimaging field saw a scientific renaissance in the past several years through leveraging open-source software, international collaborations, machine learning, and other open science/programming approaches. As crewed missions and more biological experiments are on the deep space horizon, OSDR is embracing data stewardship through listening to feedback from subject matter experts and designing an expanded architecture which is appropriate for NASA’s goals to enable analysis and reuse of bioimaging and video data for the public science community.Discovery Through Image and Video Data Sharing
The broad fields of space biology and astrobiology share much in common in terms of science questions, approaches, and goals. However, historical circumstances and funding agency practices have frequently resulted in a wide separation between the two related areas. Is this a good thing? We believe that it is not, and that much is to be gained in each field from sharing ideas, resources, and perhaps projects between investigators traditionally working in one discipline or the other. Some of the strengths that the Space Biology community offers include sophistication and experience in flying experiments on space missions. In turn, Astrobiology has focused heavily on ground-based and field research. Challenging physical and chemical conditions experienced in space and on other planets partially overlap, and much can be gleaned from the body of work of each community along these topical lines. A combination of these areas of expertise and experience could result in major advances to all involved. When possible, avoiding having to reinvent methods or approaches already used by a sister community can result in greater efficiencies of resource use. We will discuss some case studies where we believe there are significant overlaps including adaptation to a variety of environmental stresses, extremophiles as potential flight organisms, microfluidics as applied to planetary environment simulations, and others.
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.
The last decade has witnessed the beginning of man's exploration of the Universe with an entirely new potentiality-manned and instrumented space vehicle capable of bringing observations closer to celestial bodies than had been deemed possible a few years ago. How far into the Universe this exploration will ultimately lead is in the hands of future generations. However, the solar system is already highly instrumented, with data being recorded hourly both directly and indirectly. A vast amount of information is accumulating, the resolution of which will take painstaking effort for many years. In short, mankind has achieved familiarity with the Sun and its planets in a comparatively short time.
A series of virtual workshops was held during June 2020 to seek ways to integrate the efforts of the astrobiology and space biology research communities under a broad umbrella of space life sciences. The overall goal was to help inspire creativity that will guide us towards new synergistic ideas complementing these existing disciplines that are of such importance to NASA. Workshop participants aspired to: (1) Exploit synergies across the biological sciences at NASA, (2) Foster research, enabling technology, and mission concepts that support commonalities in space biology, astrobiology, synthetic biology, planetary protection, and relevant human health, performance, and habitation concerns, (3) Envision the development of an “Arc of Biology in Space” to encompass this multi-faceted joint research community. The focused objective of the workshop series was to explore and demonstrate how the integration of astrobiology and space biology could be achieved, identify strengths and weaknesses in the current state of the art, and recognize where our greatest challenges lay. Specifically, we seek to: (1) Establish a scientific framework for an integrated life sciences effort, (2) Pioneer discovery by creating unique opportunities in the fundamental biological sciences, (3) Explore novel combinations of existing technologies across the relevant disciplines, (4) Invent new technologies and applications in space life sciences, and (5) Creatively increase access to spaceflight, emerging and novel technologies, Earth analogs, and simulated natural and spaceflight environments. The community aims for a broad arc of biological competence in the context of space and planetary science, spaceflight, and habitation. We will present dominant themes and innovative ideas that resulted from this interchange of relevant communities.
A series of virtual workshops was held during June 2020 to seek ways to integrate the efforts of the astrobiology and space biology research communities under a broad umbrella of space life sciences. The overall goal was to help inspire creativity that will guide us towards new synergistic ideas complementing these existing disciplines that are of such importance to NASA. Workshop participants aspired to: (1) Exploit synergies across the biological sciences at NASA, (2) Foster research, enabling technology, and mission concepts that support commonalities in space biology, astrobiology, synthetic biology, planetary protection, and relevant human health, performance, and habitation concerns, (3) Envision the development of an “Arc of Biology in Space” to encompass this multi-faceted joint research community. The focused objective of the workshop series was to explore and demonstrate how the integration of astrobiology and space biology could be achieved, identify strengths and weaknesses in the current state of the art, and recognize where our greatest challenges lay. Specifically, we seek to: (1) Establish a scientific framework for an integrated life sciences effort, (2) Pioneer discovery by creating unique opportunities in the fundamental biological sciences, (3) Explore novel combinations of existing technologies across the relevant disciplines, (4) Invent new technologies and applications in space life sciences, and (5) Creatively increase access to spaceflight, emerging and novel technologies, Earth analogs, and simulated natural and spaceflight environments. The community aims for a broad arc of biological competence in the context of space and planetary science, spaceflight, and habitation. We will present dominant themes and innovative ideas that resulted from this interchange of relevant communities.
To identify an appropriate AI/ML approach for a specific problem, the best practice is to measure algorithm performance through the benchmarking process. A scientific benchmark consists of an AI-ready dataset and a reference implementation on a specific scientific question. The NASA Science Mission Directorate (SMD) has started the “Benchmark Initiative for AI/ML to create scientific benchmark datasets in three 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. Currently, there are no standardized datasets available to benchmark AI/ML algorithms in the domain of space biology. In this work, we constructed two AI/ML-ready biological datasets from experiments in space-flown mice: cellular imaging and RNA-seq. First, radiation-exposed immune cells harbor DNA damage foci that can be fluorescently marked to visualize the amount of damage following exposure to ionizing radiation. However, such large datasets are difficult to analyze visually, due to imaging inconsistencies and human bias, and classical image processing approaches can fail on imaging artifacts. AI/ML are therefore exciting alternative, providing the speed of machines and the accuracy of humans. We have made this dataset available at https://registry.opendata.aws/bps_microscopy/. Second, 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. However, most sequencing datasets suffer from high dimensionality and low sample count. In this work, we used a generative adversarial network to synthesize a standardized, AI-ready, publicly available benchmark dataset for space biology RNA-seq data with sufficient space-flown and ground control mouse liver samples from NASA GeneLab. This dataset is available at https://registry.opendata.aws/bps_rnaseq/. These datasets are now fully open the Space Biology community to test their favorite AI/ML approaches.
The optimum hardware miniaturization level with the lowest cost impact for space biology hardware was determined. Space biology hardware and/or components/subassemblies/assemblies which are the most likely candidates for application of miniaturization are to be defined and relative cost impacts of such miniaturization are to be analyzed. A mathematical or statistical analysis method with the capability to support development of parametric cost analysis impacts for levels of production design miniaturization are provided.