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NASA NTRSDate not supplied

Acquisition of and Access to Research Omics Data

Omics data are essential for understanding the myriad and complex effects of space environments on humans. To assure maximum benefit from these kinds of data, the NASA Human Research Program Data Management Plan stipulates that human omics data should be archived within and accessed through the NASA Life Sciences Portal (NLSP). The NLSP has the capability to acquire and provision access to omics (and other kinds of) research results for individual and ad-hoc groups of subjects at the direction of institutional review boards, or other authorizing bodies or individuals, per institutional, program and investigation-specific policies and procedures. However, because some single-subject omics data, like CT scans and other kinds of large, complex biomedical data, could be used to identify heretofore unknown risks to the subject’s health, or, in certain cases, be used to identify a subject, NASA Policy Directive 7170.1 describes various policies regarding the management of and access to “research genetic testing” data, which includes many kinds of omics data. For example, NPD 7170.1 prohibits access to human research genetic data by NASA personnel who make employment decisions for the subjects from whom the data were obtained. To meet the objective of acquiring research omics data for NLSP in compliance with the policies in NPD 7170.1 and other applicable NASA policies, we designed NOMADS (the NLSP Omics Multimodal Acquisition of Data System), a new component that supports the transfer of large research data files, including research genetic testing data, using one of several different transfer mechanisms. The choice of mechanism is made by the submitter of the data, with guiding information from the system, and is likely to often be determined in large part by the nature and source location of the data. For example, for small files where the source data files are not already stored in a cloud storage system, users are likely to prefer to transfer their data to the NLSP via a web browser. Conversely, for large sets of files already organized and stored in a cloud storage system, users may opt for NOMAD’s cloud-to-cloud transfer method. All omics datasets targeted for the NASA Life Sciences Data Archive must pass a variety of quality checks to ensure data integrity and adherence to the standards defined by the LSDA Data Submission Guidelines (DSG) (see https://nlsp.nasa.gov/explore/lsdahome/datasubmit). These include requirements that data are consistent with open standards established by the omics community. Non-compliant data will not be accepted however archivists are available to advise submitters on how to revise data submissions and re-submit until compliance is achieved. Following compliance with the LSDA DSG, omics data next undergo a variety of additional quality checks to ensure the data meet omics community standards. Domain specific Omics data quality control tools and techniques are continually evolving and linked to the advancements in omics assays utilized and thus, the tools and techniques utilized by the LSDA for data quality control and validation will need to be sustained accordingly. All human omics data will be access controlled according to the policies described above, and requiring IRB approval for any additional access grants once the data are acquired (including access for analysis using the NLSP workspace tools).

Omics↗

NASA NTRS2024

Acquisition of and Access to Research Omics Data

Omics↗

NASA NTRS1994

Fiber optic probes for laser light scattering: Ground based evaluation for micgrogravity flight experimentation. Integrated coherent imaging fiber optic systems for laser light scattering and other applications

The research work presented in this report has established a new class of backscatter fiber optics probes for remote dynamic light scattering capability over a range of scattering angles from 94 degrees to 175 degrees. The fiber optic probes provide remote access to scattering systems, and can be utilized in either a noninvasive or invasive configuration. The fiber optics create an interference free data channel to inaccessible and harsh environments. Results from several studies of concentrated suspension, microemulsions, and protein systems are presented. The second part of the report describes the development of a new technology of wavefront processing within the optical fiber, that is, integrated fiber optics. Results have been very encouraging and the technology promises to have significant impact on the development of fiber optic sensors in a variety of fields ranging from environmental monitoring to optical recording, from biomedical sensing to photolithography.

Dhadwal, Harbans Singh↗

NASA NTRS1975

Apollo Soyuz, mission evaluation report

The Apollo Soyuz mission was the first manned space flight to be conducted jointly by two nations - the United States and the Union of Soviet Socialist Republics. The primary purpose of the mission was to test systems for rendezvous and docking of manned spacecraft that would be suitable for use as a standard international system, and to demonstrate crew transfer between spacecraft. The secondary purpose was to conduct a program of scientific and applications experimentation. With minor modifications, the Apollo and Soyuz spacecraft were like those flown on previous missions. However, a new module was built specifically for this mission - the docking module. It served as an airlock for crew transfer and as a structural base for the docking mechanism that interfaced with a similar mechanism on the Soyuz orbital module. The postflight evaluation of the performance of the docking system and docking module, as well as the overall performance of the Apollo spacecraft and experiments is presented. In addition, the mission is evaluated from the viewpoints of the flight crew, ground support operations, and biomedical operations. Descriptions of the docking mechanism, docking module, crew equipment and experiment hardware are given.

Source record↗

NASA NTRS2004

A Novel Biomedical Device Utilizing Light Emitting Nano-Structures

This paper will discuss the development of a novel biomedical detection device that will be used to detect microorganisms with the use of infrared fluorochrome polymers attached to antibodies in fluids such as water. The fluorochrome polymers emit light in the near inferred region (NIR), approximately 805 nm, when excited by an NIR laser at 778 nm. The device could remarkably change the way laboratory testing is done today. The testing process is usually performed on a time scale of days while our device will be able to detect microorganisms in minutes. This type of time efficient analysis is ideal for use aboard the International Space Station and the Space Shuttle (ISS/SS) and has many useful commercial applications, for instance at a water treatment plant and food processing plants. With more research and experimentation the testing might also one day be used to detect bacteria and viruses in complex fluids such as blood, which would revolutionize blood analysis as it is performed today. My contribution to the project has been to develop a process which will allow an antibody/fluorescent dye pair to be conjugated to a specific bacteria or virus and than to to be separated from a sample body of water for detection. The antibody being used in this experiment is anti beta galactosidase and its complement enzyme is beta galactosidase, a non harmful derivative of E. Coli. The anti beta galactosidase has been conjugated to the fluorochrome polymer, IRDye800, which emits at approximately 806 nm. The dye when excited by the NIR laser emits a signal which is detected by a spectrometer and then is read by state of the art computer software. The state-of-the-art process includes incubating the anti beta galactosidase and beta galactosidase in a phosphate buffer solution in a test tube, allowing the antibody to bind to specific sites on the enzyme. After the antibody is bound to the enzyme, it is centrifuged in specific filters that will allow free antibody to wash away and leave the antibody-enzyme complexes on top in solution for testing and analysis. This solution is pipetted into a cuvette, a special plastic test tube, which will then be excited by the laser. The signal read will tell US that an antibody is present and since it is bound to the enzyme, that the bacteria is also present.

Varaljay, Vanessa A.↗

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 NTRS2016

Recommended Methods for Monitoring Skeletal Health in Astronauts to Distinguish Specific Effects of Prolonged Spaceflight

NASA uses areal bone mineral density (aBMD) by dual-energy X-ray absorptiometry (DXA) to monitor skeletal health in astronauts after typical 180-day spaceflights. The osteoporosis field and NASA, however, recognize the insufficiency of DXA aBMD as a sole surrogate for fracture risk. This is an even greater concern for NASA as it attempts to expand fracture risk assessment in astronauts, given the complicated nature of spaceflight-induced bone changes and the fact that multiple 1-year missions are planned. In the past decade, emerging analyses for additional surrogates have been tested in clinical trials; the potential use of these technologies to monitor the biomechanical integrity of the astronaut skeleton will be presented. OVERVIEW: An advisory panel of osteoporosis policy-makers provided NASA with an evidence-based assessment of astronaut biomedical and research data. The panel concluded that spaceflight and terrestrial bone loss have significant differences and certain factors may predispose astronauts to premature fractures. Based on these concerns, a proposed surveillance program is presented which a) uses Quantitative Computed Tomography (QCT) scans of the hip to monitor the recovery of spaceflight-induced deficits in trabecular BMD by 2 years after return, b) develops Finite Element Models [FEM] of QCT data to evaluate spaceflight effect on calculated hip bone strength and c) generates Trabecular Bone Score [TBS] from serial DXA scans of the lumbar spine to evaluate the effect of age, spaceflight and countermeasures on this novel index of bone microarchitecture. SIGNIFICANCE: DXA aBMD is a widely-applied, evidence-based predictor for fractures but not applicable as a fracture surrogate for premenopausal females and males <50 years. Its inability to detect structural parameters is a limitation for assessing changes in bone integrity with and without countermeasures. Collective use of aBMD, TBS, QCT, and FEM analysis for astronaut surveillance could accommodate NASA's aggressive schedule for risk definition and inform a NASA-developed model which assesses the probability of overloading bones during mechanically-loaded mission tasks and possibly for physical activities after return to Earth.

Vasadi, Lukas J.↗

NASA NTRS2002

Second International Conference on Near-Field Optical Analysis: Photodynamic Therapy and Photobiology Effects

The International NASA/DARPA Photobiology Conference held at the Johnson Space Center in Houston/TX demonstrated where low level laser therapy (LLLT), respectively low intensity light activated biostimulation (LILAB) and nanotechnological applications employing photobiomodulation techniques will presumably go in the next ten years. The conference was a continuation of the 1st International Conference on Nearfield Optical Analysis organized by Andrei Sommer (ENSOMA Lab, University of Ulm, Germany) in November 2000 at Castle Reisenburg, Germany, which started with a group of ten scientists from eight different countries. The 1st conference was co-sponsored by the American Chemical Society to evaluate the molecular mechanism of accelerated and normal wound healing processes. The 2nd conference was co-sponsored by DARPA, NASA-JSC and the Medical College of Wisconsin. Despite the short time between events, the 2nd conference hosted 40 international experts form universities, research institutes, agencies and the industry. The materials published here are expected to become milestones forming a novel platform in biomedical photobiology. The multidisciplinary group of researchers focused on LLLT/LILAB-applications under extreme conditions expected to have beneficial effects particularly in space, on submarines, and under severe battlefield conditions. The group also focused on novel technologies with possibilities allowing investigating the interaction of light with biological systems, molecular mechanisms of wound healing, bone regeneration, nerve regeneration, pain modulation, as well as biomineralization and biofilm formulation processes induced by nanobacteria.

Bulgher, Debra L.↗

NASA NTRS2001

Compact Microscope Imaging System Developed

The Compact Microscope Imaging System (CMIS) is a diagnostic tool with intelligent controls for use in space, industrial, medical, and security applications. The CMIS can be used in situ with a minimum amount of user intervention. This system, which was developed at the NASA Glenn Research Center, can scan, find areas of interest, focus, and acquire images automatically. Large numbers of multiple cell experiments require microscopy for in situ observations; this is only feasible with compact microscope systems. CMIS is a miniature machine vision system that combines intelligent image processing with remote control capabilities. The software also has a user-friendly interface that can be used independently of the hardware for post-experiment analysis. CMIS has potential commercial uses in the automated online inspection of precision parts, medical imaging, security industry (examination of currency in automated teller machines and fingerprint identification in secure entry locks), environmental industry (automated examination of soil/water samples), biomedical field (automated blood/cell analysis), and microscopy community. CMIS will improve research in several ways: It will expand the capabilities of MSD experiments utilizing microscope technology. It may be used in lunar and Martian experiments (Rover Robot). Because of its reduced size, it will enable experiments that were not feasible previously. It may be incorporated into existing shuttle orbiter and space station experiments, including glove-box-sized experiments as well as ground-based experiments.

McDowell, Mark↗

NASA NTRS1991

NASA spinoffs to bioengineering and medicine

Through the active transfer of technology, the National Aeronautics and Space Administration (NASA) Technology Utilization (TU) Program assists private companies, associations, and government agencies to make effective use of NASA's technological resources to improve U.S. economic competitiveness and to provide societal benefit. Aerospace technology from areas such as digital image processing, space medicine and biology, microelectronics, optics and electrooptics, and ultrasonic imaging have found many secondary applications in medicine. Examples of technology spinoffs are briefly discussed to illustrate the benefits realized through adaptation of aerospace technology to solve health care problems. Successful implementation of new technologies increasingly requires the collaboration of industry, universities, and government, and the TU Program serves as the liaison to establish such collaborations with NASA. NASA technology is an important resource to support the development of new medical products and techniques that will further advance the quality of health care available in the U.S. and worldwide.

Biomedical Engineering/instrumentation/trends↗

NASA NTRS1993

Psychophysiological investigations of the biomedical problems of manned spaceflight

The Final Report on psychophysiological investigations of the biomedical problems of manned spaceflight is presented. In the first project, statistical analyses of human autonomic data were performed. The objectives were the following: to establish a relational data base containing human psychophysiological data obtained from Shuttle flight experiments and ground-based research over a 20 year period; to enable multi-user access and retrieval of these data for subsequent analyses and for possible inclusion in the proposed Life Sciences Data Archive; and to enable/conduct statistical analyses across several experiments on large subject populations which can thereby provide definitive answers to questions on human autonomic and behavioral responses and adaptation to environmental stressors on Earth and in space. The second project studied motion sickness. The objectives were: to test/develop hardware and procedures to be incorporated into preflight training of crewmembers of Spacelab-J; and to examine spin-off applications of AFT. The third project studied orthostatic intolerance. The objective was to test the feasibility of applying autogenic-feedback training as a potential treatment for postflight orthostatic intolerance.

Source record↗

NASA NTRS2023

Application of Bioengineering in Space Exploration - Sleep Study System

Bioengineering has spread roots far and wide among the needs of space exploration. The field creates many of paths to generate information from a variety of sources. One such source is the astronauts on the International Space Station (ISS). As of now the investigation is focused on the quality of sleep of crew members during a long duration flight. Our team is investing time into a piece of hardware’s comfort, waste, and operability. The hardware was not originally designed for space; therefore, the device must be modified to function at a optimal level in space. The design not made for space has generated some ailments, such as discomfort and decreased mobility. A function of the internship was to produce designs that would manage the wires to increase mobility and comfort. The wires excess slack was reduced by the part that was designed. The piece was adjustable, so the patient may adjust the system to their comfort and space compatible material was used. This product will help control the direction and length of the wires, thereby aiding in comforting the patient. This multifaceted design may be used in future for all other wire management for wearable biomedical sensor on orbit.

Biomedical↗

NASA NTRSDate not supplied

Towards an Aviation Large Language Model by Fine-tuning and Evaluating Transformers

In the aviation domain, there are many applications for machine learning and artificial intelligence tools that utilize natural language. For example, there is a desire to know the commonalities in written safety reports such as voluntary post incidents reports or aerial wildfire operations reports to better understand the risks present. Another use-case is the possibility of extracting airspace procedures and constraints currently written in documents such as Letters of Agreement. These applications can benefit from the use of state-of-the-art natural language processing techniques when adapted to the language/phraseology specific to the aviation domain. This paper evaluates the viability of adaptation of NLP tools to the aviation domain by fine-tuning transformer based models using aviation data sets. In 2018, a novel language model based on neural units (also called transformers) was created and became known as “Bidirectional Encoder Representations from Transformers” or BERT. This architecture combined with large amounts of English training data and innovative semi-supervised training tasks set the standard for what would later emerge as Large Language Models. The performance of these models was further improved by hyperparameter tuning and refinement of the semi-supervised training task and resulted in “Robustly Optimized BERT Pre-training Approach through hyperparameter tuning” or RoBERTa models. These pre-trained Large Language Models proved to be useful for a wide variety of natural language processing tasks such as text classification and question answering through a process called fine-tuning. The transformer architecture with pre-trained weights served as the basis with the last few layers replaced with layers fine-tuned to perform a new task e.g., a layer that provides a label for the entire input text. This process of fine-tuning can also be used to adapt the models to new domains; e.g., BioBERT started with the pre-trained BERT model and was completed by additional fine-tuning and training on biomedical documents. Transformer-based architectures can also be used to create rich representations of text called embeddings which can serve as the input to other machine learning models. This allows simpler algorithms such as logistic regression to use context-rich representations of the text while still remaining quick to train and evaluate. In the world of aviation, there is a growing demand for natural language processing and understanding but the domain presents unique challenges. Due to the technical content (and specialized language) of most aviation documents, fine-tuning pre-trained Large Language Models to specific tasks has not met the benchmark on natural language processing tasks set by simpler models trained from scratch on the data. To address this deficiency, this paper evaluates the improvements from fine-tuning a Large Language Model on a large set of aviation documents using the original semi-supervised training tasks before performing specific natural language tasks. In fine-tuning, a domain-specific dataset is used on the original training task but with the pre-trained Large Language Model instead of starting from a random initialization. This approach allows the model to be adapted to the specific domain language without discarding the information gained from training on general English data. This paper utilized two major dataset types to train and assess the RoBERTa fine-tuning performance. The first are 7,057 Letters of Agreement which are Federal Aviation Administration (FAA) documents that formalize airspace operations across the national airspace system. They contain many examples of ‘aviation English’ using domain specific terminology and phrasing which serves as a representative basis to perform the semi-supervised fine-tuning. The second type is the 494 document classification labels to be used for evaluation. This down-stream evaluation aims to show the performance of the fine-tuned model, better understand how much data is needed for an effective fine-tuning, and how fine-tuning can be adapted for different applications in-the domain. After semi-supervised training, evaluation begins by encoding the documents for classification using the fine-tuned RoBERTa model. Then a logistic regression classifier is trained to label the document type and compared against our ground truth labels. This currently leads to a 82.8% accuracy on 10-fold cross validation showing improvement over baseline RoBERTa which achieved 81.0%. We plan to measure the improvements on additional tasks and it is expected that these improvements will lead to more robust models that can tackle the natural language processing challenges present in aviation datasets.

ATM↗

NASA NTRS1978

Medical Image Analysis Facility

To improve the quality of photos sent to Earth by unmanned spacecraft. NASA's Jet Propulsion Laboratory (JPL) developed a computerized image enhancement process that brings out detail not visible in the basic photo. JPL is now applying this technology to biomedical research in its Medical lrnage Analysis Facility, which employs computer enhancement techniques to analyze x-ray films of internal organs, such as the heart and lung. A major objective is study of the effects of I stress on persons with heart disease. In animal tests, computerized image processing is being used to study coronary artery lesions and the degree to which they reduce arterial blood flow when stress is applied. The photos illustrate the enhancement process. The upper picture is an x-ray photo in which the artery (dotted line) is barely discernible; in the post-enhancement photo at right, the whole artery and the lesions along its wall are clearly visible. The Medical lrnage Analysis Facility offers a faster means of studying the effects of complex coronary lesions in humans, and the research now being conducted on animals is expected to have important application to diagnosis and treatment of human coronary disease. Other uses of the facility's image processing capability include analysis of muscle biopsy and pap smear specimens, and study of the microscopic structure of fibroprotein in the human lung. Working with JPL on experiments are NASA's Ames Research Center, the University of Southern California School of Medicine, and Rancho Los Amigos Hospital, Downey, California.

Source record↗

NASA NTRS1998

Space Discovery: Teaching with Space. Evaluation: Summer, Fall 1998 Programs

This is the final report of the 1998 NASA-sponsored evaluation of the effectiveness of the United States Space Foundation's five-day Space Discovery Standard Graduate Course (Living and Working in Space), the five-day Space Discovery Advanced Graduate Course (Advanced Technology and Biomedical Research), the five-day introductory course Aviation and Space Basics all conducted during the summer of 1998, and the Teaching with Space two-day Inservice program. The purpose of the program is to motivate and equip K- 12 teachers to use proven student-attracting space and technology concepts to support standard curriculum. These programs support the America 2000 National Educational Goals, encouraging more students to stay in school, increase in competence, and have a better opportunity to be attracted to math and science. The 1998 research program continues the comprehensive evaluation begun in 1992, this year studying five summer five-day sessions and five Inservice programs offered during the Fall of 1998 in California, Colorado, New York, and Virginia. A comprehensive research design by Dr. Robert Ewell of Creative Solutions and Dr. Darwyn Linder of Arizona State University evaluated the effectiveness of various areas of the program and its applicability on diverse groups. Preliminary research methodology was a set of survey instruments administered after the courses, and another to be sent in April-4-5 months following the last inservice involved in this study. This year, we have departed from this evaluation design in two ways. First, the five-day programs used NASA's new EDCATS on-line system and associated survey rather than the Linder/Ewell instruments. The Inservice programs were evaluated using the previously developed survey adapted for Inservice programs. Second, we did not do a follow-on survey of the teachers after they had been in the field as we have done in the past. Therefore, this evaluation captures only the reactions of the teachers to the programs immediately after the instruction. Although EDCATS is designed for teachers to enter their data logged onto the appropriate internet web site, most surveys were completed on a printed copy and entered into the EDCATS system by USSF personnel. The Aviation and Space Basics class were taken to a computer lab where they responded to the survey on-line. Data from the Inservice surveys were manually entered into a computer spreadsheet program by US Space Foundation personnel and processed using the statistical program SPSS for Windows. The raw data and copy of the EDCATS survey for the five-day programs are in Appendix 1. The raw data and copy of the Inservice survey are in Appendix 2. Comments from both programs are in Appendices 3 and 4, respectively.

Ewell, Bob↗

NASA NTRS2006

Novel Concept for LSS Based on Advanced Microalgal Biotechnologies

One of the key issues for successful human space exploration is biomedical life support in hostile space and planetary environments that otherwise cannot sustain life. Bioregenerative life support systems (LSS) are one of the options for atmospheric regeneration. To date, no bioregenerative LSS has shown capability for 100% air regeneration. Nor have these LSS been robust enough to simultaneously provide a regenerable complete food source. In contrast to microalgae, traditional plant approaches, e.g. wheat and lettuce, are lacking essential amino acids, vitamins, and micronutrients. Moreover, the rate of photosynthesis by microalgae significantly exceeds that of high plants. Nevertheless, the employment of microalgae in LSS technology was restricted, until recently, due to high water demands. Also the per person requirement of a 40L volume of microalgae in a photobioreactor, to provide daily O2 production, made an algae-based approach less attractive. By employing a vertically stacked membrane bioreactor, coupled with a solar tracker and photon-delivery system, a lightweight air revitalization system for space based applications, with minimal water requirements, can be developed. Our preliminary estimations suggest that a membrane bioreactor, 8m3 in volume, comprised of 80m2 (twenty 2m x 2m membranes, each spaced 10 cm apart), and a total of 70L of water could produce 2.7 kg of dried microalgal biomass that would supply the energy and essential amino acid requirements, as well as producing sufficient O2 for the daily needs of a 15 member crew. Research on the biochemical content of edible blue-green alga Spirulina (Arthrospira) platensis shows a wide spectrum of stable Spirulina mutants with an enhanced content of amino acids, -carotene, and phycobilliprotein c-phycocyanin. Feeding animals suffering from radiation-induced lesions, c-phycocyanin, extracted from strain 27G, led to a correction in the decrement of dehydrogenase activity and energy-rich phosphate levels, as well as improved antioxidant defense and pyruvate levels, compared to untreated animals. Experimental anemia in rats was corrected by feeding Spirulina platensis strains 198B and 27G, (with an enhanced content of methionine, phycobiliproteins and carotenoids). Spirulina was recently shown by Ananyev et al, 2005, to be an oxygenic organism with the highest level of photosystem II activity (O2 production). We propose therefore to develop a design for membrane-based photoreactors for Lunar and Mars exploration habitat LSS, for the cultivation of genetically modified strains of Spirulina to scrub CO2 and supply astronauts with O2, protein, vitamins, and immunostimulators.

Brown, I.↗

NASA NTRSDate not supplied

NASA Open Science Data Repository: Biomedical FAIR Data, Analysis Tools, User Communities, and Discoveries for Deep Space Missions

Increased biomedical risks and challenges associated with deep space missions require new knowledge discovery, new health countermeasures, and development of novel ecosystems, life support, crop production, and biomedical support capabilities. To meet NASA’s Moon to Mars strategic program goals for Human and Biological Sciences, findable, accessible, interoperable, reusable (FAIR), and maximally open-access data is going to be required to enable humanity to thrive in deep space. Indeed, this cornerstone perspective on FAIR and maximally open access data was also recommended in the recent 2023-2032 Decadal Survey from the National Academies of Sciences, Engineering, and Medicine. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database, and meets various scientific, technical, and operational spaceflight needs. It offers public users and submitters the ability to upload, download, search, share, analyze, and visualize data across ‘omics, physiological, phenotypic, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR includes NASA GeneLab, NASA Ames Life Sciences Data Archive, and the NASA Biological Institutional Scientific Collection. OSDR has >455 studies with datasets from model organisms and non-NASA human astronauts. There are ~12 datasets from the Inspiration 4 (I4) mission, spanning metagenomics, comprehensive metabolic panels, clonal hematopoiesis, spatial transcriptomics, proteomics, and cytokine panels. In the interest of data privacy, two I4 datasets have raw FASTQ and FASTA files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) which was developed based on industry norms. OSDR also recently began a collaboration with the European Space Agency (ESA) to scientifically curate and make available >200 terabytes of human and model organism space-relevant data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics assay data types, and ~50 physiological-phenotypic-imaging assay data types, spanning ultrasonography, micro-computed tomography, histology, morphometric photography, rebound tonometry, gait analysis, optical coherence tomography, novel object recognition, flow cytometry, and immunohistochemistry. A suite of analysis tools are available for OSDR users including: 1) an Environmental Data Application to compare radiation, CO2, relative humidity, temperature, and other telemetry across missions and subjects, 2) the RadLab database, a collaboration between NASA, ESA, the German and Italian Space Agencies, and the Bulgarian Academy of Sciences, which compiles radiation measurements relevant to human spaceflight and provides tools for accessing and manipulating the data, and 3) a Multi-study visualization tool which enables users to look across and combine GeneLab’s omics datasets across different experiments and missions. There are ~600 volunteer OSDR Analysis Working Group (AWG) members who: 1) provide feedback on scientific standards for reuse (subject and assay metadata; processing pipelines; dataset formats and uniformed structures for machine-readability), and 2) collaborate to mine-reuse OSDR data conducting scientific analysis. OSDR has enabled 60 publications as of September 2023, many directly from AWG collaborations most notably the Cell Press package in 2020. Lastly, there are at least 15 articles which mine OSDR data part of a package of ~50 articles across Nature Portfolio with research stemming from I4, the Japan Aerospace Exploration Agency, NASA Space Biology, and the NASA Human Research Program.

open access↗

NASA NTRSDate not supplied

Creating Benchmark Data for Artificial Intelligence and Machine Learning Space Biology Research

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.

James Casaletto↗