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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 37 records · Page 2
All sourcesNASA NTRSDOE OSTI
NASA NTRS2007

Biomedical Results of ISS Expeditions 1-12

A viewgraph presentation on biomedical data from International Space Station (ISS) Expeditions 1-12 is shown. The topics include: 1) ISS Expeditions 1-12; 2) Biomedical Data; 3) Physiological Assessments; 4) Bone Mineral Density; 5) Bone Mineral Density Recovery; 6) Orthostatic Tolerance; 7) Postural Stability Set of Sensory Organ Test 6; 8) Performance Assessment; 9) Aerobic Capacity of the Astronaut Corps; 10) Pre-flight Aerobic Fitness of ISS Astronauts; 11) In-flight and Post-flight Aerobic Capacity of the Astronaut Corps; and 12) ISS Functional Fitness Expeditions 1-12.

Fogarty, Jennifer↗
NASA NTRS2007

An Evidence-based Approach to Developing a Management Strategy for Medical Contingencies on the Lunar Surface: The NASA/Haughton-Mars Project (HMP) 2006 Lunar Medical Contingency Simulation at Devon Island

The lunar architecture for future sortie and outpost missions will require humans to serve on the lunar surface considerably longer than the Apollo moon missions. Although the Apollo crewmembers sustained few injuries during their brief lunar surface activity, injuries did occur and are a concern for the longer lunar stays. Interestingly, lunar medical contingency plans were not developed during Apollo. In order to develop an evidence-base for handling a medical contingency on the lunar surface, a simulation using the moon-Mars analog environment at Devon Island, Nunavut, high Canadian Arctic was conducted. Objectives of this study included developing an effective management strategy for dealing with an incapacitated crewmember on the lunar surface, establishing audio/visual and biomedical data connectivity to multiple centers, testing rescue/extraction hardware and procedures, and evaluating in suit increased oxygen consumption. Methods: A review of the Apollo lunar surface activities and personal communications with Apollo lunar crewmembers provided the knowledge base of plausible scenarios that could potentially injure an astronaut during a lunar extravehicular activity (EVA). Objectives were established to demonstrate stabilization and transfer of an injured crewmember and communication with ground controllers at multiple mission control centers. Results: The project objectives were successfully achieved during the simulation. Among these objectives were extraction from a sloped terrain by a two-member crew in a 1 g analog environment, establishing real-time communication to multiple centers, providing biomedical data to flight controllers and crewmembers, and establishing a medical diagnosis and treatment plan from a remote site. Discussion: The simulation provided evidence for the types of equipment and methods for performing extraction of an injured crewmember from a sloped terrain. Additionally, the necessary communications infrastructure to connect multiple centers worldwide was established from a remote site. The surface crewmembers were confronted with a number of unexpected scenarios including environmental, communications, EVA suit, and navigation challenges during the course of the simulation which provided insight into the challenges of carrying out a medical contingency in an austere environment. The knowledge gained from completing the objectives will be incorporated into the exploration medical requirements involving an incapacitated astronaut on the lunar surface.

Scheuring, R. A.↗
NASA NTRS1969

Biomedical bulk data processing program

Analog-to-digital computer accepts physiological flight data as three basic analog input signals - the ECG signal, the flowmeter signal which is a respiration monitor, and the accelerometer signal which measures the normal-g-load on the subject.

Source record↗
NASA NTRS1976

Physiologic and anti-G suit performance data from YF-16 flight tests

Biomedical data were collected during high-G portions of 11 YF-16 test flights. Test pilots monitored revealed increased respiratory rate and volume, decreased tidal volume, and increased heart rate at higher G levels, with one pilot exhibiting various cardiac arrhythmias. Anti-G suit inflation and pressurization lags varied inversely with G-onset rate, and suit pressurization slope was near the design value.

Gillingham, K. K.↗
NASA NTRS1974

Method and data evaluation at NASA endocrine laboratory

The biomedical data of the astronauts on Skylab 3 were analyzed to evaluate the univariate statistical methods for comparing endocrine series experiments in relation to other medical experiments. It was found that an information storage and retrieval system was needed to facilitate statistical analyses.

Johnston, D. A.↗
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 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 NTRS1995

Life Sciences Data Archive Scientific Development

The Life Sciences Data Archive will provide scientists, managers and the general public with access to biomedical data collected before, during and after spaceflight. These data are often irreplaceable and represent a major resource from the space program. For these data to be useful, however, they must be presented with enough supporting information, description and detail so that an interested scientist can understand how, when and why the data were collected. The goal of this contract was to provide a scientific consultant to the archival effort at the NASA-Johnson Space Center. This consultant (Jay C. Buckey, Jr., M.D.) is a scientist, who was a co-investigator on both the Spacelab Life Sciences-1 and Spacelab Life Sciences-2 flights. In addition he was an alternate payload specialist for the Spacelab Life Sciences-2 flight. In this role he trained on all the experiments on the flight and so was familiar with the protocols, hardware and goals of all the experiments on the flight. Many of these experiments were flown on both SLS-1 and SLS-2. This background was useful for the archive, since the first mission to be archived was Spacelab Life Sciences-1. Dr. Buckey worked directly with the archive effort to ensure that the parameters, scientific descriptions, protocols and data sets were accurate and useful.

Buckey, Jay C., Jr.↗
NASA NTRS2014

The Importance of Data Visualization: Incorporating Storytelling into the Scientific Presentation

From its inception in 2000, one of the primary tasks of the Biomedical Data Reduction Analysis (BDRA) group has been translation of large amounts of data into information that is relevant to the audience receiving it. BDRA helps translate data into an integrated model that supports both operational and research activities. This data integrated model and subsequent visual data presentations have contributed to BDRA's success in delivering the message (i.e., the story) that its customers have needed to communicate. This success has led to additional collaborations among groups that had previously not felt they had much in common until they worked together to develop solutions in an integrated fashion. As more emphasis is placed on working with "big data" and on showing how NASA's efforts contribute to the greater good of the American people and of the world, it becomes imperative to visualize the story of our data to communicate the greater message we need to share. METHODS To create and expand its data integrated model, BDRA has incorporated data from many different collaborating partner labs and other sources. Data are compiled from the repositories of the Lifetime Surveillance of Astronaut Health and the Life Sciences Data Archive, and from the individual laboratories at Johnson Space Center that support collection of data from medical testing, environmental monitoring, and countermeasures, as designated in the Medical Requirements Integration Documents. Ongoing communication with the participating collaborators is maintained to ensure that the message and story of the data are retained as data are translated into information and visual data presentations are delivered in different venues and to different audiences. RESULTS We will describe the importance of storytelling through an integrated model and of subsequent data visualizations in today's scientific presentations and discuss the collaborative methods used. We will illustrate the discussion with examples of graphs from BDRA's past work supporting operations and/or research efforts.

Babiak-Vazquez, A.↗
NASA NTRSDate not supplied

Spaceflight Environmental-Telemetry Data for Biological Science

There is a critical need for better access and visualization of spaceflight environmental telemetry and mission hardware data from sensors including relative humidity, carbon dioxide, oxygen, radiation, airflow, temperature, acceleration, and acoustics. Under the stewardship of the Ames Life Sciences Data Archive (ALSDA) and GeneLab, an effort is underway to consolidate, normalize and provide accessibility of archived mission environmental data and hardware information, with the purpose of providing important context to biological data. This effort is necessary to provide scientific context of its impact upon biological and biomedical data from spaceflight missions and experiments (genomic, metagenomic, gene expression, proteomic, metabolomic, physiological, phenomics, behavioral; tabular, imaging, video). Environmental spaceflight data is derived from dozens of sources, with various formats, and in the past year a pipeline is in development to collect, curate and present this data efficiently. In the upcoming year, a new Data Visualization Portal will utilize the standardized pipeline data to provide easy user access to compare parameters and environmental conditions between missions, locations, subjects, and durations. Environmental and hardware data enables broad accessibility and analytics, without the need for advanced data informatic expertise. Familiarity with the capabilities and limitations of a variety of existing hardware/tools is a strength that could be applied to creation of improved hardware for future ecosystems on the Moon and Mars. The intention is to make biological and environmental telemetry data maximally open-access and FAIR (findable, accessible, interoperable, reusable) for data mining-informatic approaches to support knowledge discovery necessary for low Earth orbit, cis-Lunar, Mars transit, and Mars surface missions.

Danielle K. Lopez↗
NASA NTRS2019

Maintaining Skeletal Health During the Mission to Mars

Understanding how the effects of long-duration spaceflight (~6-months) might increase fracture risk in the younger-aged, physically-fit astronaut is challenging. Most of our skeletal data have been acquired from long-duration astronauts, crewmembers who typically serve on 120-180 day missions aboard the International Space Station (ISS). Astronaut biomedical data are predominantly 2-d measurements from DXA scans because this is a required clinical test at Johnson Space Center. Data from these clinical tests, and some data from research studies, are what NASA evaluates to define a risk for fracture in astronauts, both during a mission and long-term health. To date, the agency considers the risk for fracture during spaceflight to be of high (severe) consequence but of low probability (<0.1%) while the risk for fracture in during long-term health to be of medium consequence (interventions available) and medium probability (<1%). These risks are considered acceptable. Notably, there are minimal data to suggest that postflight fractures in long-duration astronauts are directly due to spaceflight exposure. Analyses by NASA epidemiologists and by biomedical engineers suggest that postflight fracture incidence in astronauts is consistent with a physically-active terrestrial population with no exposure to spaceflight. The epidemiological data to-date may be considered insufficient (low # and younger-aged subjects, limited follow-up time) to assess a fracture risk with reliability. In the absence of fracture evidence to substantiate a risk, it may be more useful to maintain astronauts at baseline (preflight) level of skeletal health during a mission. This lecture will present data from astronauts that affirms that 1) the maintenance of skeletal health during the future 3-year Mars mission will require an anti-resorptive therapy and 2) the risk for fracture during long-term health cannot be defined by the DXA clinical test alone.

Sibonga, Jean↗
NASA NTRS1972

Biomedical technology transfer applications of NASA science and technology

The identification and solution of research and clinical problems in cardiovascular medicine which were investigated by means of biomedical data transfer are reported. The following are sample areas that were focused upon by the Stanford University Biomedical Technology Transfer Team: electrodes for hemiplegia research; vectorcardiogram computer analysis; respiration and phonation electrodes; radiotelemetry of intracranial pressure; and audiotransformation of the electrocardiographic signal. It is concluded that this biomedical technology transfer is significantly aiding present research in cardiovascular medicine.

Source record↗
NASA NTRS1979

Portable data system

Compact system for data recording, manipulation, and transmission uses readily available components. Data system originally designed for high-altitude research is used with appropriate sensors to monitor transportation systems, biomedical data, weather stations, mineral exploration equipment, and various other tasks.

Dix, M.↗
NASA NTRS2024

Artificial Intelligence Medical Support for Long-Duration Space Missions

We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible. In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.

Artificial Intelligence↗
NASA NTRS1976

Shuttle era waste management and biowaste monitoring

The acquisition of crew biomedical data has been an important task on manned space missions. The monitoring of biowastes from the crew to support water and mineral balance studies and endocrine studies has been a valuable part of this activity. This paper will present a review of waste management systems used in past programs. This past experience will be cited as to its influence on the Shuttle design. Finally, the Shuttle baseline waste management system and the proposed Shuttle biomedical measurement and sampling systems will be presented.

Sauer, R. L.↗
DOE OSTI2025

Quantum Transfer Learning to Boost Dementia Detection

Dementia is a devastating condition with profound implications for individuals, families, and healthcare systems. Early and accurate detection of dementia is critical for timely intervention and improved patient outcomes. While classical machine learning and deep learning approaches have been explored extensively for dementia prediction, these solutions often struggle with high-dimensional biomedical data and large-scale datasets, quickly reaching computational and performance limitations. To address this challenge, quantum machine learning (QML) has emerged as a promising paradigm, offering faster training and advanced pattern recognition capabilities. This work aims to demonstrate the potential of quantum transfer learning (QTL) to enhance the performance of a weak classical deep learning model applied to a binary classification task for dementia detection. Besides, we show the effect of noise on the QTL-based approach, investigating the reliability and robustness of this method. Using the OASIS 2 dataset, we show how quantum techniques can transform a suboptimal classical model into a more effective solution for biomedical image classification, highlighting their potential impact on advancing healthcare technology.

Bhowmik, Sounak [University of Tennessee, Knoxvill↗