Results for “Physiological Features” — Page 4

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 NTRS1996

Evaluation of transit-time and electromagnetic flow measurement in a chronically instrumented nonhuman primate model

The Physiology Research Branch at Brooks AFB conducts both human and nonhuman primate experiments to determine the effects of microgravity and hypergravity on the cardiovascular system and to identify the particular mechanisms that invoke these responses. Primary investigative efforts in our nonhuman primate model require the determination of total peripheral resistance, systemic arterial compliance, and pressure-volume loop characteristics. These calculations require beat-to-beat measurement of aortic flow. This study evaluated accuracy, linearity, biocompatability, and anatomical features of commercially available electromagnetic (EMF) and transit-time flow measurement techniques. Five rhesus monkeys were instrumented with either EMF (3 subjects) or transit-time (2 subjects) flow sensors encircling the proximal ascending aorta. Cardiac outputs computed from these transducers taken over ranges of 0.5 to 2.0 L/min were compared to values obtained using thermodilution. In vivo experiments demonstrated that the EMF probe produced an average error of 15% (r = .896) and 8.6% average linearity per reading, and the transit-time flow probe produced an average error of 6% (r = .955) and 5.3% average linearity per reading. Postoperative performance and biocompatability of the probes were maintained throughout the study. The transit-time sensors provided the advantages of greater accuracy, smaller size, and lighter weight than the EMF probes. In conclusion, the characteristic features and performance of the transit-time sensors were superior to those of the EMF sensors in this study.

Non-NASA Center↗

NASA NTRS1995

Image Discrimination Models With Stochastic Channel Selection

Many models of human image processing feature a large fixed number of channels representing cortical units varying in spatial position (visual field direction and eccentricity) and spatial frequency (radial frequency and orientation). The values of these parameters are usually sampled at fixed values selected to ensure adequate overlap considering the bandwidth and/or spread parameters, which are usually fixed. Even high levels of overlap does not always ensure that the performance of the model will vary smoothly with image translation or scale changes. Physiological measurements of bandwidth and/or spread parameters result in a broad distribution of estimated parameter values and the prediction of some psychophysical results are facilitated by the assumption that these parameters also take on a range of values. Selecting a sample of channels from a continuum of channels rather than using a fixed set can make model performance vary smoothly with changes in image position, scale, and orientation. It also facilitates the addition of spatial inhomogeneity, nonlinear feature channels, and focus of attention to channel models.

Ahumada, Albert J., Jr.↗

NASA NTRSDate not supplied

The NASA Open Science Data Repository: Biomedical Data, Analysis Tools, and Informatic Collaborations

Increased biomedical risks and challenges associated with deep space missions require knowledge discovery, health countermeasures, and biomedical support capabilities. Maximally open-access and reusable data is needed by developers, scientists, and engineers to develop these systems. The NASA Open Science Data Repository (OSDR) is a maximally open access and FAIR database (ie., findable, accessible, interoperable, and reusable), and meets various scientific, technical, and operational needs. It offers users and submitters the ability to upload, download, search, share, analyze, cite, and visualize data across ‘omics, physiological, phenotypic, payload, hardware, behavioral, bioimaging, video, and environmental monitoring telemetry datasets. OSDR is an expanded database, based upon the successes of NASA GeneLab. OSDR has >460 studies with datasets covering model organisms to 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 with raw files relating to the epitranscriptome, and a new request feature is live in OSDR (with a backend review process established) developed from industry norms. OSDR is collecting and curating biomedical human data from a new sub-orbital research flight and is open to more space life science/biomedical submissions from the international and commercial sectors. OSDR also recently began a collaboration with the European Space Agency (ESA) to collect and curate >200 terabytes of human and model organism data. The OSDR submission portal is designed to ingest and curate ~25 ‘omics and ~50 physiological-phenotypic-imaging assay data types. Tools available for OSDR users include: 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, and 3) a Multi-study visualization tool which enables users to look across and combine ‘omics datasets. There are ~600 volunteer OSDR Analysis Working Group (AWG) members providing feedback on scientific data/metadata standards and collaborating to mine-reuse OSDR in research. OSDR/GeneLab has enabled ~60 publications reusing data as of October 2023.

space biology↗

NASA NTRS1990

Ames vision group research overview

A major goal of the reseach group is to develop mathematical and computational models of early human vision. These models are valuable in the prediction of human performance, in the design of visual coding schemes and displays, and in robotic vision. To date researchers have models of retinal sampling, spatial processing in visual cortex, contrast sensitivity, and motion processing. Based on their models of early human vision, researchers developed several schemes for efficient coding and compression of monochrome and color images. These are pyramid schemes that decompose the image into features that vary in location, size, orientation, and phase. To determine the perceptual fidelity of these codes, researchers developed novel human testing methods that have received considerable attention in the research community. Researchers constructed models of human visual motion processing based on physiological and psychophysical data, and have tested these models through simulation and human experiments. They also explored the application of these biological algorithms to applications in automated guidance of rotorcraft and autonomous landing of spacecraft. Researchers developed networks for inhomogeneous image sampling, for pyramid coding of images, for automatic geometrical correction of disordered samples, and for removal of motion artifacts from unstable cameras.

Watson, Andrew B.↗

NASA NTRS1998

AAH Cage Out-Link and In-Link Antenna Characterization

This final report encapsulates the accomplishments of the third year of work on an Advanced Biotelemetry System (ABTS). Overall MU/ABTS project objectives are to provide a biotelemetry system that can collect data from and send commands to an implanted biotransceiver. This system will provide for studies of rodent development in space. The system must be capable of operating in a metal animal cage environment. An important goal is the development of a small, "smart", micropower, implantable biotransceiver with eight-channel data output and single channel command input capabilities with the flexibility for easy customization for a variety of physiologic investigations. The NASA Ames/Marquette University Joint Research work has been devoted to the system design of such a new state of the art biotelemetry system, having multiple physiologic inputs, and bi-directional data transfer capabilities. This work has provided a successful prototype system that connects, by two-way radio links, an addressable biotelemetry system that provides communication between an animal biotelemeter prototype and a personal computer. The operational features of the prototype system are: (1) two-way PCM communication with implanted biotelemeter; (2) microcontroller based biotelemeter; (3) out-link: wideband FSK (60 kBaud); (4) in-link: OOK (2.4 kbaud); (5) septum antenna arrays (In/Out-Links); and (6) personal computer data interface. The important requirement of this third year's work, to demonstrate two-way communication with transmit and receive antennas inside the metal animal cage, has been successfully accomplished. The advances discussed in this report demonstrate that the AAH cage antenna system can provide Out-link and In-link capability for the ABTS bi-directional telemetry system, and can serve as a benchmark for project status. Additions and enhancements to the most recent (April 1997) prototype cage and antenna have been implemented. The implementation, testing, and documentation was accomplished at the Biotelemetry Laboratory at Marquette University with Out-Link (slot) antenna design assistance was provided.

Jeutter, Dean C.↗

NASA NTRS1998

AAH Cage Out-Link and In-Link Antenna Characterization

This final report encapsulates the accomplishments of the third year of work on an Advanced Biotelemetry System (ABTS). Overall MU/ABTS project objectives are to provide a biotelemetry system that can collect data from and send commands to an implanted biotransceiver. This system will provide for studies of rodent development in space. The system must be capable of operating in a metal animal cage environment. An important goal is the development of a small, "smart", micropower, -channel data output and single channel command implantable biotransceiver with eight input capabilities with the flexibility for easy customization for a variety of physiologic investigations. The NASA Ames/Marquette University Joint Research work has been devoted to the system design of such a new state of the art biotelemetry system, having multiple physiologic inputs, and bi-directional data transfer capabilities. This work has provided a successful prototype system that connects, by two-way radio links, an addressable biotelemetry system that provides communication between an animal biotelemeter prototype and a personal computer. The operational features of the prototype system are listed below: Two-Way PCM Communication with Implanted Biotelemeter Microcontroller Based Biotelemeter Out-Link: Wideband FSK (60 kbaud) In-Link: OOK (2.4 kbaud) Septum Antenna Arrays (In/Out-Links) Personal Computer Data Interface The important requirement of this third year's work, to demonstrate two-way communication with transmit and receive antennas inside the metal animal cage, has been successfully accomplished. The advances discussed in this report demonstrate that the AAH cage antenna system can provide Out-link and In-link capability for the ABTS bi-directional telemetry system, and can serve as a benchmark for project status.

Jeutter, Dean C.↗

NASA NTRS1995

Neurodynamic oscillators

Oscillation of electrical activity has been found in many nervous systems, from invertebrates to vertebrates including man. There exists experimental evidence of very simple circuits with the capability of oscillation. Neurons with intrinsic oscillation have been found and also neural circuits where oscillation is a property of the network. These two types of oscillations coexist in many instances. It is nowadays hypothesized that behind synchronization and oscillation there is a system of coupled oscillators responsible for activities that range from locomotion and feature binding in vision to control of sleep and circadian rhythms. The huge knowledge that has been acquired on oscillators from the times of Lord Rayleigh has made the simulation of neural oscillators a very active endeavor. This has been enhanced with more recent physiological findings about small neural circuits by means of intracellular and extracellular recordings as well as imaging methods. The future of this interdisciplinary field looks very promising; some researchers are going into quantum mechanics with the idea of trying to provide a quantum description of the brain. In this work we describe some simulations using neuron models by means of which we form simple neural networks that have the capability of oscillation. We analyze the oscillatory activity with root locus method, cross-correlation histograms, and phase planes. In the more complicated neural network models there is the possibility of chaotic oscillatory activity and we study that by means of Lyapunov exponents. The companion paper shows an example of that kind.

Espinosa, Ismael↗

NASA NTRS2019

Social Isolation Impacts Select Responses to Simulated Weightlessness

The rodent hindlimb unloading (HU) model was initially developed to simulate the cephalad fluid shift and musculoskeletal disuse in astronauts. Since then, the HU model has been applied to explore how other systems (e.g. immune, cardiovascular and CNS) respond to weightlessness. Most HU studies are performed with singly-housed animals, although social isolation also can substantially impact behavior and physiology, and therefore may confound HU experimental results. We hypothesized that relative to social housing, single housing exacerbates HU-induced dysfunction in select organ systems. We refined the standard NASA-Ames HU model to accommodate social housing in HU pairs, retaining advantageous features of traditional housing but using commercial off-the-shelf components to facilitate adoption by others. We conducted a 30 day HU experiment with adult, female C57Bl6/NJ mice that were either singly or socially housed. HU animals in both single and social HU housing displayed expected musculoskeletal deficits compared to housing matched, normally loaded (NL) controls. However, select immune, HPA axis, and CNS responses were differentially impacted by the HU social environment relative to NL controls. HU reduced % CD4+ T cells in singly-housed, but not socially-housed mice. Surprisingly, HU increased adrenal gland mass in socially-housed but not singly-housed mice, while social isolation increased adrenal gland mass in NL controls. HU also increased plasma corticosterone levels (day 30) in both singly and socially-housed mice. Thus, the social environment altered select adrenal and immune, but not musculoskeletal, responses to simulated weightlessness. We refine our original hypothesis since our results show combined stressors can mask, not only exacerbate, tissue responses to HU. These findings further expand the utility of the HU model for studying possible combined effects of the various spaceflight stressors.

Tahimic, Candice↗

NASA NTRS1989

Regulation of nitrogen uptake and assimilation: Effects of nitrogen source, root-zone pH, and aerial CO2 concentration on growth and productivity of soybeans

An important feature of controlled-environment crop production systems such as those to be used for life support of crews during space exploration is the efficient utilization of nitrogen supplies. Making decisions about the best sources of these supplies requires research into the relationship between nitrogen source and the physiological processes which regulate vegetative and reproductive plant growth. Work done in four areas within this research objective is reported: (1) experiments on the effects of root-zone pH on preferential utilization of NO3(-) versus NH4(+) nitrogen; (2) investigation of processes at the whole-plant level that regulate nitrogen uptake; (3) studies of the effects of atmospheric CO2 and NO3(-) supply on the growth of soybeans; and (4) examination of the role of NO3(-) uptake in enhancement of root respiration.

Raper, C. D.↗

NASA NTRS2016

Psychophysiological Sensing and State Classification for Attention Management in Commercial Aviation

Attention-related human performance limiting states (AHPLS) can cause pilots to lose airplane state awareness (ASA), and their detection is important to improving commercial aviation safety. The Commercial Aviation Safety Team found that the majority of recent international commercial aviation accidents attributable to loss of control inflight involved flight crew loss of airplane state awareness, and that distraction of various forms was involved in all of them. Research on AHPLS, including channelized attention, diverted attention, startle / surprise, and confirmation bias, has been recommended in a Safety Enhancement (SE) entitled "Training for Attention Management." To accomplish the detection of such cognitive and psychophysiological states, a broad suite of sensors has been implemented to simultaneously measure their physiological markers during high fidelity flight simulation human subject studies. Pilot participants were asked to perform benchmark tasks and experimental flight scenarios designed to induce AHPLS. Pattern classification was employed to distinguish the AHPLS induced by the benchmark tasks. Unimodal classification using pre-processed electroencephalography (EEG) signals as input features to extreme gradient boosting, random forest and deep neural network multiclass classifiers was implemented. Multi-modal classification using galvanic skin response (GSR) in addition to the same EEG signals and using the same types of classifiers produced increased accuracy with respect to the unimodal case (90 percent vs. 86 percent), although only via the deep neural network classifier. These initial results are a first step toward the goal of demonstrating simultaneous real time classification of multiple states using multiple sensing modalities in high-fidelity flight simulators. This detection is intended to support and inform training methods under development to mitigate the loss of ASA and thus reduce accidents and incidents.

Harrivel, Angela R.↗

NASA NTRS1973

Investigations of respiratory control systems simulation

The Grodins' respiratory control model was investigated and it was determined that the following modifications were necessary before the model would be adaptable for current research efforts: (1) the controller equation must be modified to allow for integration of the respiratory system model with other physiological systems; (2) the system must be more closely correlated to the salient physiological functionings; (3) the respiratory frequency and the heart rate should be expanded to illustrate other physiological relationships and dependencies; and (4) the model should be adapted to particular individuals through a better defined set of initial parameter values in addition to relating these parameter values to the desired environmental conditions. Several of Milhorn's respiratory control models were also investigated in hopes of using some of their features as modifications for Grodins' model.

Gallagher, R. R.↗

NASA NTRS1997

Long-Duration Space Flight

Session WA1 includes short reports concerning: (1) Medical and Physiological Studies During 438-Day Space Flights: (2) Human Performance During a 14 Month Space Mission: (3) Homeostasis in Long-Term Microgravity Conditions; (4) Strategy of Preservation of Health of Cosmonauts in Prolonged and Superprolonged Space Flights; (5) Rehabilitation of Cosmonauts Health Following Long-Term Space Missions; and (6) Perfect Cosmonauts: Some Features of Bio-Portrait.

Source record↗

NASA NTRS2011

Expression of Enzymes that Metabolize Medications

INTRODUCTION: Increased exposure to radiation is one physiological stressor associated with spaceflight and it is feasible to conduct ground experiments using known radiation exposures. The health of the liver, especially the activity rate of its metabolic enzymes, determines the concentration of circulating drugs as well as the duration of their efficacy. While radiation is known to alter normal physiological function, how radiation affects liver metabolism of administered medications is unclear. Crew health could be affected if the actions of medications used in spaceflight deviated from expectations formed during terrestrial medication use. This study is an effort to identify liver metabolic enzymes whose expression is altered by spaceflight or by radiation exposures that mimic features of the spaceflight environment. METHODS: Using procedures approved by the Animal Care and Use Committee, mice were exposed to either 137Cs (controls, 50 mGy, 6Gy, or 50 mGy + 6Gy separated by 24 hours) or 13 days of spaceflight on STS 135. Animals were anesthetized and sacrificed at several time points (4 hours, 24 hours or 7 days) after their last radiation exposure, or within 6 hours of return to Earth for the STS 135 animals. Livers were removed immediately and flash-frozen in liquid nitrogen. Tissue was homogenized, RNA extracted, purified and quality-tested. Complementary DNA was prepared from high-quality RNA samples, and used in RT-qPCR experiments to determine relative expression of a wide variety of genes involved in general metabolism and drug metabolism. RESULTS: Results of the ground radiation exposure experiments indicated ~65 genes of the 190 tested were significantly affected by at least one of the radiation doses. Many of the affected genes are involved in the metabolism of drugs with hydrophobic or steroid-like structures, maintenance of redox homeostasis and repair of DNA damage. Most affected genes returned to near control expression levels by 7 days post-treatment. Not all recovered completely by the final time point tested: with 6 Gy exposure, metallothionein expression was 132-fold more than control at the 4 hr time point, and fell at each later time point (11-fold at 24 hrs, and 8-fold at 7 days). In contrast, there were other genes whose expression was altered and remained relatively constant through the 7 day period we tested. One examples is Cyp17a1, which showed a 4-fold elevation at 4 hrs after exposure and remained constant for 7 days after the last treatment. Spaceflight samples evaluated with similar methods and comparisons will be made between the radiation-treated groups and the spaceflight samples. CONCLUSION It seems likely that radiation exposure triggers homeostatic mechanisms, which could include alterations of gene expression. Better understanding of these pathways could aid in optimizing medications doses given to crewmembers who require treatment and eventually, to development of new countermeasures to ameliorate or prevent radiation-induced damage to cells and tissues.

Wotring, V. E.↗

NASA NTRSDate not supplied

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

J Lemery↗

NASA NTRS2024

Earth Independent Medical Operations (EIMO) Datascope: Challenges and Potential Solutions

Data flows and storage/retrieval capacity are severely constrained during missions in space and challenges will become even greater during exploration class missions. There is a need for an artificial intelligence (AI)-based clinical decision support system (CDSS) to monitor and analyze data to provide real-time consultative support for crew medical officer (CMO) decision-making. EIMO is defined as the gradual transition of medical care and decision making from terrestrial to space-based assets, enabling support of astronaut health and performance and reducing overall mission risk. While a hallmark of this paradigm shift from low-earth orbit is that on-board care will increasingly become the responsibility of the astronauts for primary management and decision making, terrestrial assets will continue to be paramount in pre-mission screening and planning, as well as prevention, health maintenance and long-term care contingencies. New capabilities and systems that enable progressively more robust and resilient systems and crews will be necessary to reduce risk and increase probability of deep space exploration mission success. An aspiration for EIMO is to develop AI-enhanced solutions for analysis of crew health & performance data and to facilitate clinical decision support for autonomous medical operations. A “system of systems” approach is envisioned whereby EIMO will deploy AI-supported natural language processing and machine learning (ML) techniques to utilize embedded reference databases and real-time data streams [input vectors] from multiple data sources. Constituent input vectors may include environmental controls, countermeasures data, behavioral data, physiologic wearables, point-of-care laboratory tests, personalized medical records, inventory trade space risk assessments, COTS medical databases, and ground support inputs. An ideal AI capability would possess trained fusion algorithms to cross reference input vectors with medical ‘knowledge’ [cultivated database] to stratify relevant data streams for predictive and actionable capabilities. In addition, EIMO will feature mobility, in that it can be accessed and can push/pull data within and between multiple vehicles/habitats. Large amounts and variable sources of data can be leveraged to diagnose, inform treatment strategies, and potentially predict medical events and performance decrements. Inclusion of advanced training tools using extended reality will enable increasingly autonomous medical care to aid a CMO when ground support is unavailable or time-delayed beyond required action window, e.g., emergent medical situations. EIMO CDSS would require very large datasets to train pre-flight and significant amounts of data are needed to support ML via in-flight CDSS operations. An additional challenge will be to find sufficient data to train a model relevant to astronaut demographics. The rapid, accelerating evolution of this field creates a propitious solution space to leverage multi-modal AI through public-private partnership(s). The status of multi-modal AI systems today would preclude their use for long duration missions as they remain unreliable and are subject to “digital hallucinations” and other errors that could pose operational risk. A federated labs structure is being considered to test and optimize data flow from the multiple input vectors leading to field testing in suitable ground/flight analogs. Critical to the success of an EIMO CDSS will be integration and interoperability and success will be defined by a system that can serve as an in-flight medical consult for the CMO providing critical support during medical contingencies. Benefits to terrestrial medicine may be significant as an outflow of the EIMO medical system, particularly for remote areas and communities lacking significant infrastructure, personnel and resources.

Medical Operations↗

NASA NTRS2005

Why is Light Text Harder to Read Than Dark Text?

Scharff and Ahumada (2002, 2003) measured text legibility for light text and dark text. For paragraph readability and letter identification, responses to light text were slower and less accurate for a given contrast. Was this polarity effect (1) an artifact of our apparatus, (2) a physiological difference in the separate pathways for positive and negative contrast or (3) the result of increased experience with dark text on light backgrounds? To rule out the apparatus-artifact hypothesis, all data were collected on one monitor. Its luminance was measured at all levels used, and the spatial effects of the monitor were reduced by pixel doubling and quadrupling (increasing the viewing distance to maintain constant angular size). Luminances of vertical and horizontal square-wave gratings were compared to assess display speed effects. They existed, even for 4-pixel-wide bars. Tests for polarity asymmetries in display speed were negative. Increased experience might develop full letter templates for dark text, while recognition of light letters is based on component features. Earlier, an observer ran all conditions at one polarity and then switched. If dark and light letters were intermixed, the observer might use component features on all trials and do worse on the dark letters, reducing the polarity effect. We varied polarity blocking (completely blocked, alternating smaller blocks, and intermixed blocks). Letter identification responses times showed polarity effects at all contrasts and display resolution levels. Observers were also more accurate with higher contrasts and more pixels per degree. Intermixed blocks increased the polarity effect by reducing performance on the light letters, but only if the randomized block occurred prior to the nonrandomized block. Perhaps observers tried to use poorly developed templates, or they did not work as hard on the more difficult items. The experience hypothesis and the physiological gain hypothesis remain viable explanations.

Scharff, Lauren V.↗

NASA NTRS1988

Science and applications on the space station: A strategic vision

The central themes relating to science and applications on the Space Station for fiscal year 1989 are discussed. Materials science research is proposed in a wide variety of subfields including protein crystal growth, metallurgy, and properties of fluids. Also proposed are the U.S. Polar Platform, an Extended Duration Crew Operations Project, and a long-range Space Biology Research Project to investigate plant and animal physiology, gravitational biology, life support systems, and exobiology. The exterior of the Space Station will provide attachment points for payloads to study subjects such as the earth and its environment, the sun, other bodies in the solar system, and cosmic objects. Examples of such attached payloads are given. They include a plasma interaction monitoring system, observation of solar features and properties, studies of particle radiation from the sun, cosmic dust collection and analysis, surveys of various cosmic and solar rays, measurements of rainfall and wind and the study of global changes on earth.

Source record↗