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NASA NTRS2008

Joint Test Plan for Gas Dynamic Spray Technology Demonstration

Air Force Space Command (AFSPC) and NASA have similar missions, facilities, and structures located in similar harsh environments. Both are responsible for a number of facilities/structures with metallic structural and non-structural components in highly and moderately corrosive environments. Regardless of the corrosivity of the environment, all metals require periodic maintenance activity to guard against the insidious effects of corrosion and thus ensure that structures meet or exceed design or performance life. The standard practice for protecting metallic substrates in atmospheric environments is the use of an applied coating system. Current coating systems used across AFSPC and NASA contain volatile organic compounds (VOCs) and hazardous air pollutants (HAPs). These coatings are subject to environmental regulations at the Federal and State levels that limit their usage. In addition, these coatings often cannot withstand the high temperatures and exhaust that may be experienced by AFSPC and NASA structures. In response to these concerns, AFSPC and NASA have approved the use of thermal spray coatings (TSCs). Thermal spray coatings are extremely durable and environmentally friendly coating alternatives, but utilize large cumbersome equipment for application that make the coatings difficult and time consuming to repair. Other concerns include difficulties coating complex geometries and the cost of equipment, training, and materials. Gas Dynamic Spray (GDS) technology (also known as Cold Spray) will be evaluated as a smaller, more maneuverable repair method as well as for areas where thermal spray techniques are not as effective. The technology can result in reduced maintenance and thus reduced hazardous materials/wastes associated with current processes. Thermal spray and GDS coatings also have no VOCs and are environmentally preferable coatings. To achieve a condition suitable for the application of a coating system, including GDS coatings, the substrate must undergo some type of surface preparation and/or depainting operation to ensure adhesion of the new coating system. The GDS unit selected for demonstration has a powder feeding system that can be used for surface preparation or coating application. The surface preparation feature will also be examined. The primary objective of this effort is to demonstrate GDS technology as a repair method for TSCs. The project will also determine the optimal GDS coating thickness for acceptable performance. Successful completion of this project will result in approval of GDS technology as a repair method for TSCs at AFSPC and NASA installations and will improve corrosion protection at critical systems, facilitate easier maintenance activity, extend maintenance cycles, eliminate flight hardware contamination, and reduce the amount of hazardous waste generated.

Lewis, Pattie↗

NASA NTRS2012

Affordability Approaches for Human Space Exploration

The design and development of historical NASA Programs (Apollo, Shuttle and International Space Station), have been based on pre-agreed missions which included specific pre-defined destinations (e.g., the Moon and low Earth orbit). Due to more constrained budget profiles, and the desire to have a more flexible architecture for Mission capture as it is affordable, NASA is working toward a set of Programs that are capability based, rather than mission and/or destination specific. This means designing for a performance capability that can be applied to a specific human exploration mission/destination later (sometime years later). This approach does support developing systems to flatter budgets over time, however, it also poses the challenge of how to accomplish this effectively while maintaining a trained workforce, extensive manufacturing, test and launch facilities, and ensuring mission success ranging from Low Earth Orbit to asteroid destinations. NASA Marshall Space Flight Center (MSFC) in support of Exploration Systems Directorate (ESD) in Washington, DC has been developing approaches to track affordability across multiple Programs. The first step is to ensure a common definition of affordability: the discipline to bear cost in meeting a budget with margin over the life of the program. The second step is to infuse responsibility and accountability for affordability into all levels of the implementing organization since affordability is no single person s job; it is everyone s job. The third step is to use existing data to identify common affordability elements organized by configuration (vehicle/facility), cost, schedule, and risk. The fourth step is to analyze and trend this affordability data using an affordability dashboard to provide status, measures, and trends for ESD and Program level of affordability tracking. This paper will provide examples of how regular application of this approach supports affordable and therefore sustainable human space exploration architecture.

Holladay, Jon↗

NASA NTRS2016

Pre-Mission Input Requirements to Enable Successful Sample Collection by A Remote Field/EVA Team

The FINESSE (Field Investigations to Enable Solar System Science and Exploration) team, part of the Solar System Exploration Virtual Institute (SSERVI), is a field-based research program aimed at generating strategic knowledge in preparation for human and robotic exploration of the Moon, near-Earth asteroids, Phobos and Deimos, and beyond. In contract to other technology-driven NASA analog studies, The FINESSE WCIS activity is science-focused and, moreover, is sampling-focused with the explicit intent to return the best samples for geochronology studies in the laboratory. We used the FINESSE field excursion to the West Clearwater Lake Impact structure (WCIS) as an opportunity to test factors related to sampling decisions. We examined the in situ sample characterization and real-time decision-making process of the astronauts, with a guiding hypothesis that pre-mission training that included detailed background information on the analytical fate of a sample would better enable future astronauts to select samples that would best meet science requirements. We conducted three tests of this hypothesis over several days in the field. Our investigation was designed to document processes, tools and procedures for crew sampling of planetary targets. This was not meant to be a blind, controlled test of crew efficacy, but rather an effort to explicitly recognize the relevant variables that enter into sampling protocol and to be able to develop recommendations for crew and backroom training in future endeavors.

FINESSE↗

NASA NTRS2020

Southern Colorado Disasters: Using NASA Observations to Map Aspen Extent and Recovery Due to Wildfire

Quaking aspen (Populus tremuloides) is an important species for wildlife, watershed health, and ecosystem resilience across its range. Heavy ungulate browsing and factors influenced by a changing climate including seasonal temperature changes and moisture deficit have led to reduced post-fire aspen regeneration rates in southern Colorado. This project partnered with Trinchera Ranch and the Colorado State Forest Service to estimate aspen recovery after the Spring Creek Fire, which ignited in June of 2018. The Southern Colorado Disasters team utilized field measurements and satellite imagery from Landsat Operational Land Imager (OLI), Sentinel-2 MultiSpectral Instrument (MSI), and the Shuttle Radar Topography Mission (SRTM) to train and run several random forest models that detect pre- and post-fire aspen extent. Ocular sampling of over 500 points on high-resolution pre-fire and post-fire images identified percentage aspen cover in 30 x 30-meter grid cells. This process provided training data for regression models, which were able to detect aspen across the landscape for both time periods using multiple remote sensing vegetation health indices. In addition, landscape suitability for aspen regeneration was modeled to provide a guide for managers on where to monitor for aspen regeneration post-fire.

DEVELOP Project Summary↗

NASA NTRS2020

Southern Colorado Disasters: Using NASA Earth Observations to Map Aspen Extent and Recovery Due to Wildfire

DEVELOP Technical Paper↗

NASA NTRSDate not supplied

Mass Inferencing Model Creation and Deployment to the RASSOR Lunar Excavation Robot

The Regolith Advanced Surface Systems Operations Robot (RASSOR) Excavator is a teleoperated mobile robotic platform with a unique space regolith excavation capability. The Intelligent Capabilities Enhanced RASSOR research project developed functionality for inferencing regolith mass ingested during RASSOR operation, enhancing RASSOR’s ability to successfully complete ISRU missions. To teleoperate or run autonomously, it is crucial for the quantity of regolith mass ingested by RASSOR to be available as a system state for efficient operation. For example, during autonomous operation, RASSOR should navigate and move to a processing plant to offload the collected regolith when the drums are full; without knowledge of how much mass is in the drums, this type of high-level planning is not possible. Four distinct modeling approaches were employed in developing a mass inferencing approach that could work on RASSOR. All take in system states, such as arm/drum positions, velocities, currents, voltages, and robot pose, and output a mass prediction for each set of the robot’s bucket drums.1) A neural network model that takes a vector of normalized system states; 2) A model that uses the integrated power consumption of an arm-raise (normalized by velocity); 3) A model that uses average drum current over a variable length interval of the drum disengaged from the surface; and 4) A real-time estimation model that aggregates excavation drum current. The developed models run in real time, outputting predictions for the front and rear drums, timestamp of the last prediction, and total mass in RASSOR’s drums. Further testing is required to validate the arm-raise model (2), though initial tests indicate reasonable performance (<10% mean error) on the hardware. The linear fit of average drum-current model (3) had a front value of r^2=0.99 and a rear value of r^2=0.98 on the validation dataset. This model currently has the best performance on unseen data. The real time model (4) is still in development, though initial results on a small subset of the training data show that it has high accuracy in predicting the increase in mass during excavation. Though work remains to be done with deploying a high-fidelity model to the physical system that makes predictions with error below the desired threshold, the modular architecture for model development allows quick adjustment of parameters to increase model fidelity. This architecture can also be adapted to use lunar excavation data to create models that are reflective of RASSOR’s dynamics when operating on the lunar surface. The results are promising as it has been shown that models can be developed that accurately estimate excavated regolith mass.

rassor↗

NASA NTRS2004

Subsonic Aircraft With Regression and Neural-Network Approximators Designed

At the NASA Glenn Research Center, NASA Langley Research Center's Flight Optimization System (FLOPS) and the design optimization testbed COMETBOARDS with regression and neural-network-analysis approximators have been coupled to obtain a preliminary aircraft design methodology. For a subsonic aircraft, the optimal design, that is the airframe-engine combination, is obtained by the simulation. The aircraft is powered by two high-bypass-ratio engines with a nominal thrust of about 35,000 lbf. It is to carry 150 passengers at a cruise speed of Mach 0.8 over a range of 3000 n mi and to operate on a 6000-ft runway. The aircraft design utilized a neural network and a regression-approximations-based analysis tool, along with a multioptimizer cascade algorithm that uses sequential linear programming, sequential quadratic programming, the method of feasible directions, and then sequential quadratic programming again. Optimal aircraft weight versus the number of design iterations is shown. The central processing unit (CPU) time to solution is given. It is shown that the regression-method-based analyzer exhibited a smoother convergence pattern than the FLOPS code. The optimum weight obtained by the approximation technique and the FLOPS code differed by 1.3 percent. Prediction by the approximation technique exhibited no error for the aircraft wing area and turbine entry temperature, whereas it was within 2 percent for most other parameters. Cascade strategy was required by FLOPS as well as the approximators. The regression method had a tendency to hug the data points, whereas the neural network exhibited a propensity to follow a mean path. The performance of the neural network and regression methods was considered adequate. It was at about the same level for small, standard, and large models with redundancy ratios (defined as the number of input-output pairs to the number of unknown coefficients) of 14, 28, and 57, respectively. In an SGI octane workstation (Silicon Graphics, Inc., Mountainview, CA), the regression training required a fraction of a CPU second, whereas neural network training was between 1 and 9 min, as given. For a single analysis cycle, the 3-sec CPU time required by the FLOPS code was reduced to milliseconds by the approximators. For design calculations, the time with the FLOPS code was 34 min. It was reduced to 2 sec with the regression method and to 4 min by the neural network technique. The performance of the regression and neural network methods was found to be satisfactory for the analysis and design optimization of the subsonic aircraft.

Patnaik, Surya N.↗

NASA NTRS1993

Training for life science experiments in space at the NASA Ames Research Center

As this country prepares for exploration to other planets, the need to understand the affects of long duration exposure to microgravity is evident. The National Aeronautics and Space Administration (NASA) Ames Research Center's Space Life Sciences Payloads Office is responsible for a number of non-human life sciences payloads on NASA's Space Shuttle's Spacelab. Included in this responsibility is the training of those individuals who will be conducting the experiments during flight, the astronauts. Preparing a crew to conduct such experiments requires training protocols that build on simple tasks. Once a defined degree of performance proficiency is met for each task, these tasks are combined to increase the complexity of the activities. As tasks are combined into in-flight operations, they are subjected to time constraints and the crew enhances their skills through repetition. The science objectives must be completely understood by the crew and are critical to the overall training program. Completion of the in-flight activities is proof of success. Because the crew is exposed to the background of early research and plans for post-flight analyses, they have a vested interest in the flight activities. The salient features of this training approach is that it allows for flexibility in implementation, consideration of individual differences, and a greater ability to retain experiment information. This training approach offers another effective alternative training tool to existing methodologies.

Rodrigues, Annette T.↗

NASA NTRS2007

Impact of MODIS High-Resolution Sea-Surface Temperatures on WRF Forecasts at NWS Miami, FL

Over the past few years,studies at the Short-term Prediction Research and Transition (SPoRT) Center have suggested that the use of Moderate Resolution Imaging Spectroradiometer (MODIS) composite sea-surface temperature (SST) products in regional weather forecast models can have a significant positive impact on short-term numerical weather prediction in coastal regions. The recent paper by LaCasse et al. (2007, Monthly Weather Review) highlights lower atmospheric differences in regional numerical simulations over the Florida offshore waters using 2-km SST composites derived from the MODIS instrument aboard the polar-orbiting Aqua and Terra Earth Observing System satellites. To help quantify the value of this impact on NWS Weather Forecast Offices (WFOs), the SPoRT Center and the NWS WFO at Miami, FL (MIA) are collaborating on a project to investigate the impact of using the high-resolution MODIS SST fields within the Weather Research and Forecasting (WRF) prediction system. The scientific hypothesis being tested is: More accurate specification of the lower-boundary forcing within WRF will result in improved land/sea fluxes and hence, more accurate evolution of coastal mesoscale circulations and the associated sensible weather elements. The NWS MIA is currently running the WRF system in real-time to support daily forecast operations, using the National Centers for Environmental Prediction Nonhydrostatic Mesoscale Model dynamical core within the NWS Science and Training Resource Center's Environmental Modeling System (EMS) software; The EMS is a standalone modeling system capable of downloading the necessary daily datasets, and initializing, running and displaying WRF forecasts in the NWS Advanced Weather Interactive Processing System (AWIPS) with little intervention required by forecasters. Twenty-seven hour forecasts are run daily with start times of 0300,0900, 1500, and 2100 UTC on a domain with 4-km grid spacing covering the southern half of Florida and the far western portions of the Bahamas, the Florida Keys, the Straights of Florida, and adjacent waters of the Gulf of Mexico and Atlantic Ocean. Each model run is initialized using the Local Analysis and Prediction System (LAPS) analyses available in AWIPS, invoking the diabatic. "hot-start" capability. In this WRF model "hot-start", the LAPS-analyzed cloud and precipitation features are converted into model microphysics fields with enhanced vertical velocity profiles, effectively reducing the model spin-up time required to predict precipitation systems. The SSTs are initialized with the NCEP Real-Time Global (RTG) analyses at l/12 degree resolution (approx. 9 km); however, the RTG product does not exhibit fine-scale details consistent with its grid resolution. SPoRT is conducting parallel WRF EMS runs identical to the operational runs at NWS MIA in every respect except for the use of MODIS SST composites in place of the RTG product as the initial and boundary conditions over water. The MODIS SST composites for initializing the SPoRT WRF runs are generated on a 2-km grid four times daily at 0400, 0700, 1600, and 1900 UTC, based on the times of the overhead passes of the Aqua and Terra satellites. The incorporation of the MODIS SST composites into the SPoRTWRF runs is staggered such that the 0400UTC composite initializes the 0900 UTC WRF, the 0700 UTC composite initializes the 1500 UTC WRF, the 1600 UTC composite initializes the 2100 UTC WRF, and the 1900 UTC composite initializes the 0300 UTC WRF. A comparison of the SPoRT and Miami forecasts is underway in 2007, and includes quantitative verification of near-surface temperature, dewpoint, and wind forecasts at surface observation locations. In addition, particular days of interest are being analyzed to determine the impact of the MODIS SST data on the development and evolution of predicted sea/land-breeze circulations, clouds, and precipitation. This paper will present verification results comparing the NWS MIA forecasts the SPoRT experimental WRF forecasts, and highlight any substantial differences noted in the predicted mesoscale phenomena.

Case, Jonathan L.↗

NASA NTRS2002

Training, transfer, and retention of three-dimensional spatial memory in virtual environments

Human orientation requires one to remember and visualize spatial arrangements of landmarks from different perspectives. Astronauts have reported difficulties remembering relationships between environmental landmarks when imagined in arbitrary 3D orientations. The present study investigated the effects of strategy training on humans' 1) ability to infer their orientation from landmarks presented ahead and below, 2) performance when subsequently learning a different array, and 3) retention of configurational knowledge over time. On the first experiment day, 24 subjects were tested in a virtual cubic chamber in which a picture of an animal was drawn on each wall. Through trial-by-trial exposures, they had to memorize the spatial relationships among the six pictures around them and learn to predict the direction to a specific picture when facing any view direction, and in any roll orientation. Half of the subjects ("strategy group") were taught methods for remembering picture groupings, while the remainder received no such training ("control group"). After learning one picture array, the procedure was repeated in a second. Accuracy (% correct) and response time learning curves were measured. Performance for the second array and configurational memory of both arrays were also retested 1, 7, and 30 days later. Results showed that subjects "learned how to learn" this generic 3D spatial memory task regardless of their relative orientation to the environment, that ability and configurational knowledge was retained for at least a month, that figure rotation ability and field independence correlate with performance, and that teaching subjects specific strategies in advance significantly improves performance. Training astronauts to perform a similar generic 3D spatial memory task, and suggesting strategies in advance, may help them orient in three dimensions.

Non-NASA Center↗

NASA NTRS1986

Interaction between workload and training - Converging evidence from psychophysiology and performance measurement

Traditionally, the domains of mental workload and training have been studied in isolation even though they share many common characteristics. The present paper integrates findings from psychophysiological and performance-based studies which address both of these issues. By augmenting traditional indices of skill acquisition such as RMS error scores, reaction times, and accuracy measures with psychophysiological measures such as the event-related brain potential, it is shown that workload effects can be assessed throughout the training process. More specifically, it is argued that the development of skill and the effects of workload on the human operator can be modeled within the framework of resource theories of attentional allocation. Also described is how converging evidence from psychophysiological and behavioral studies can be used to examine subtle changes in operator strategies during training.

Kramer, Arthur F.↗

NASA NTRS1991

Blood Volume: Its Adaptation to Endurance Training

Expansion of blood volume (hypervolemia) has been well documented in both cross-sectional and longitudinal studies as a consequence of endurance exercise training. Plasma volume expansion can account for nearly all of the exercise-induced hypervolemia up to 2-4 wk; after this time expansion may be distributed equally between plasma and red cell volumes. The exercise stimulus for hypervolemia has both thermal and nonthermal components that increase total circulating plasma levels of electrolytes and proteins. Although protein and fluid shifts from the extravascular to intravascular space may provide a mechanism for rapid hypervolemia immediately after exercise, evidence supports the notion that chronic hypervolemia associated with exercise training represents a net expansion of total body water and solutes. This net increase of body fluids with exercise training is associated with increased water intake and decreased urine volume output. The mechanism of reduced urine output appears to be increased renal tubular reabsorption of sodium through a more sensitive aldosterone action in man. Exercise training-induced hypervolemia appears to be universal among most animal species, although the mechanisms may be quite different. The hypervolemia may provide advantages of greater body fluid for heat dissipation and thermoregulatory stability as well as larger vascular volume and filling pressure for greater cardiac stroke volume and lower heart rates during exercise.

Convertino, Victor A.↗

NASA NTRSDate not supplied

A Community for Polar Early Career Researchers Engaging With the Interagency Arctic Research Policy Committee

The Interagency Arctic Research Policy Committee (IARPC) is tasked with implementation of the Arctic Research Plan (ARP) by coordinating representatives from federal and state agencies, academic, industry, and Indigenous partners with interests in Arctic research. IARPC is organized into several collaboration teams and discipline-specific communities of practice that interface on the IARPC Collaborations website. The Early Career Community of Practice (ECCoP) is one such community that was established to promote the research of early career researchers (ECR) and increase ECR engagement with the ARP, program managers, collaboration teams, and communities of practice. The ECCoP has grown to develop regular newsletters promoting recent ECR research publications ad sharing training, job, funding, and collaboration opportunities relevant to community members at all life stages that self-identify as ECRs. Within IARPC, the ECCoP continues to promote ECR participation in monthly community of practice meetings and webinars and to report on ECR efforts that contribute to deliverables of the ARP Biennial Implementation Plan. Recent activity of the ECCoP includes developing an annual survey of the ECR cohort on IARPC to follow change in demographics over time and to identify community needs to improve diversity and inclusion in the polar research space and to address gaps in training needs for ECRs.

Katy Smith↗

NASA NTRS1992

An on-orbit viewpoint of life sciences research

As a Payload Specialist and a life science researcher, I want to present several issues that impact life science research in space. During early space station operations, life science and other experiments will be conducted in a time-critical manner and there will be the added duties of both space shuttle and space station systems operation (and the concomittent training overhead). Life sciences research is different from other science research done in space because the crew is involved both as an operator and as a subject. There is a need for pre- and post-flight data collection as well as in flight data collection. It is imperative that the life science researcher incorporate the crew members into their team early enough in the training cycle to fully explain the science and to make the crew aware of the importance and sensitivities of the experiment. During the pre-flight phase, the crew is incredibly busy with a myriad of duties. Therefore, it is difficult to get 'pristine' subjects for the baseline data collection. There are also circadian shifts, travel, and late nights to confound the data. During this time it is imperative that the researcher develop, along with the crew, a realistic estimate of crew-time required for their experiment. In flight issues that affect the researcher are the additional activities of the crew, the stresses inherent in space flight, and the difficulty of getting early in-flight data. During SSF activities, the first day or two will be taken up with rendezvous and docking. Other issues are the small number of subjects on any given flight, the importance of complete and concise procedures, and the vagaries of on-board data collection. Post flight, the crew is tired and experiences a 'relaxation.' This along with circadian shifts and rapid re-adaptation to 1-g make immediate post-flight data collection difficult. Finally, the blending of operational medicine and research can result in either competition for resources (crew time, etc.) or influence on the physiological state of the crew. However, the unique opportunity to conduct research in an environment that cannot be duplicated on Earth outweighs the 'challenges' that exist for space life researchers.

Lichtenberg, Byron K.↗

NASA NTRS2017

A-Train Datalist - A New GES DISC Service to Allow One-Stop Shopping for A-Train Data

The currently available services at the Goddard Earth Sciences Data Information Services Center (GES DISC) only allow users to select variables from a single data set at a time. Because entire variables from a data set are often displayed, user selection of variables of interest can be overwhelming. At the American Geophysical Union (AGU) 2016 Fall Meeting, GES DISC unveiled a new service called Datalist: a collection of predefined or user-defined data variables from one or more archived data sets. Our science support team has been curating Datalists and providing added value to the user community.Originally known as Afternoon Constellation, A-Train includes six currently on polar-orbiting Earth observation satellites: OCO-2, GCOM-W1, Aqua, CALIPSO, CloudSat, and Aura, which travel a few minutes apart from each other. This constellation arrangement has enabled coordinated science observations further forming comprehensive pictures of Earth weather and climate that are readily for use in crucial studies such as climate change.GES DISC Datalists are based on the software architecture of the new GES DISC website (also unveiled at the AGU 2016 Fall Meeting). The GES DISC science support team has created a Datalist to support the A-Train Data Depot (ATDD). Using pre-defined Datalist should hopefully save users significant effort in their data searches.

A-Train data ordering↗

NASA NTRS2013

Kinect Engineering with Learning (KEWL)

According to a Nielsen survey at the time of this reporting, 41% of all households have a game console. This is one market in which NASA has been absent from education and outreach efforts. Kinect Engineering with Learning (KEWL) is made to enter into that market and bring NASA education and outreach to a very familiar venue. KEWL creates an education and outreach experience that is more participatory, both in a school and museum environment. KEWL is a set of applications that runs on an Xbox 360 using the Kinect controller used for education and outreach. These applications currently include: Train R2, a visual simulation of Robonaut 2 that allows students to control a virtual R2 in a game environment; Drive R2, an interface using the Xbox 360 and Kinect controller that allows students to control the real R2 using the methods they learned playing Train R2; ISS experience, a visual tour of the interior of the International Space Station where students use their body to fly through the virtual ISS; Gravity Ball, a simulation of throwing balls in the gravity of different planets; Solar Array repair, a simulation of the simplified STS-121 solar array repair mission; and PlaySpace, a Mars/Moon application that allows students to experience different aspects of Mars/Moon. Users can "fly through" the ISS using their body, allowing an experience similar to what an astronaut would have on orbit. In PlaySpace, users can fly over the surface of Mars and view surface data obtained by Mars rovers. Users of Train R2 and Drive R2 can experience what it is like to control a robot over a distance with a time delay, simulating the time delay that would occur between ground control and an on-orbit robot. The initial ISS experiences were built using parts of code from the NASA Enigma software. The models used in these experiences were also from the Integrated Graphics Operations and Analysis Lab model database. The PlaySpace experience incorporates surface data obtained from NASA rovers and satellites and was built by NASA JPL.

Goza, Sharon↗

NASA NTRS2021

Real-Time Culture-Independent Microbial Profiling Onboard the International Space Station using Nanopore Sequencing

For the past two decades, microbial monitoring of the International Space Station (ISS) has relied on culture-dependent methods that require return to Earth for analysis. This has a number of limitations, with the most significant being bias towards the detection of culturable organisms and the inherent delay between sample collection and ground-based analysis. In recent years, portable and easy-to-use molecular-based tools, such as Oxford Nanopore Technologies’ MinION™ sequencer and miniPCR bio’s miniPCR™ thermal cycler, have been validated onboard the ISS. Here, we report on the development, validation, and implementation of a swab-to-sequencer method that provides a culture-independent solution to real-time microbial profiling onboard the ISS. Method development focused on analysis of swabs collected in a low-biomass environment with limited facility resources and stringent controls on allowed processes and reagents. ISS-optimized procedures included enzymatic DNA extraction from a swab tip, bead-based purifications, altered buffers, and the use of miniPCR and the MinION. Validation was conducted through extensive ground-based assessments comparing current standard culture-dependent and newly developed culture-independent methods. Similar microbial distributions were observed between the two methods; however, as expected, the culture-independent data revealed microbial profiles with greater diversity. Protocol optimization and verification was established during NASA Extreme Environment Mission Operations (NEEMO) analog missions 21 and 22, respectively. Unique microbial profiles obtained from analog testing validated the swab-to-sequencer method in an extreme environment. Finally, four independent swab-to-sequencer experiments were conducted onboard the ISS by two crewmembers. Microorganisms identified from ISS swabs were consistent with historical culture-based data, and primarily consisted of commonly observed human-associated microbes. This simplified method has been streamlined for high ease-of-use for a non-trained crew to complete in an extreme environment, thereby enabling environmental and human health diagnostics in real-time as future missions take us beyond low-Earth orbit.

microbiology↗

NASA NTRSDate not supplied

Earth-Independent Medical Operations (EIMO) Concept of Operations

In contrast to the current crew health paradigm for low-Earth orbit and Lunar missions, which depends on real-time communication with Mission Control, deep-space exploration missions will require a significant shift in medical operations. This shift is driven by the constraints of operating at a considerable distance from Earth, such as resource limitations—lack of resupply, restricted mass, power, volume, and data—as well as communication delays and the inability to evacuate back to Earth during emergencies. To move toward a more self-reliant medical model, a strategy is needed to gradually increase space-based crew autonomy and reduce risks to mission success in the challenging environment of deep space. This transformative change, known as "Earth-Independent Medical Operations" (EIMO), explores the gradual transfer of medical care and decision-making from Earth-based support to space-based systems. The goal of this transition is to enhance astronaut health and performance while minimizing mission risks. EIMO requires the development of a medical system that integrates seamlessly with mission planning, vehicle and spacesuit design, and data architecture. This integration is crucial for building a robust medical infrastructure that not only safeguards astronaut well-being but also ensures overall mission success. The Human Research Program (HRP) Exploration Medical Capability (ExMC) Element has revised the EIMO model-based Concept of Operations (ConOps) which outlines an initial vision for EIMO. The ConOps, which is built on the stakeholders’ need, system goals, and objectives (NGOs), presents an array of in-mission scenarios that span a wide range of medical conditions demonstrating the system’s capabilities from basic to complex events. Developed by a multidisciplinary team of systems engineers, scientists, and clinicians within ExMC, the ConOps revision includes two new scenarios(Barotrauma and Self-Medical Management and Behavioral Health and Chronic Medical Care), and implementation of findings from EIMO technical interchange meetings that focused on data and training. The envisioned EIMO Medical System (MS) operates as a system of systems, gathering data from various sources such as reference databases, real-time wearable sensors, point-of-care diagnostics, and environmental controls. The MS also incorporates advanced training tools to support autonomous medical care, assisting the Crew Medical Officer (CMO) during medical events where Ground Support is either unavailable or communication-delayed beyond practicality. Furthermore, MS functions and capabilities were decomposed from the scenarios to establish foundational requirements for EIMO and traced to the NASA Spaceflight Human-System Standard(NASA-STD-3001, Volumes 1 and 2). These traces were performed to gain insights on the alignment of EIMO requirements with the NASA standard. This work serves as an initial recommendation to increase crew autonomy gradually and safely for Mars missions and future deep-space exploration.

medical system↗