Results for “human identification” — Page 28

NASA NTRS2017

Stratospheric Intrusion-Influenced Ozone Air Quality Exceedences Investigated in MERRA-2

Ozone near the surface is harmful to human health and is a result of the photochemical reaction with both man-made and natural precursor pollutant sources. Therefore, in order to reduce near surface ozone concentrations, communities must reduce anthropogenic pollution sources. However, the injection of stratospheric ozone into the troposphere, known as a stratospheric intrusion, can also lead to concentrations of ground-level ozone exceeding air quality standards. Stratospheric intrusions are dynamical atmospheric features, however, these intrusions have been misrepresented in models and reanalyses until recently, as the features of a stratospheric intrusion are best identified in horizontal resolutions of approximately 50 km or smaller. NASA's Modern-Era Retrospective Analysis for Research and Applications Version-2 (MERRA-2) reanalysis is a publicly-available high-resolution dataset (~50 km) with assimilated ozone that characterizes stratospheric ozone on the same spatiotemporal resolution as the meteorology. We show that stratospheric intrusions that impact surface air quality are well represented in the MERRA-2 reanalysis. This is demonstrated through a case study analysis of stratospheric intrusion events which were identified by the United States Environmental Protection Agency (EPA) to impact surface ozone air quality in spring 2012 in Colorado. The stratospheric intrusions are identified in MERRA-2 by the folding of the dynamical tropopause under the jet stream and subsequent isentropic descent of dry, O3-rich stratospheric air towards the surface where ozone air quality exceedences were observed. The MERRA-2 reanalysis can support air quality agencies for more rapid identification of the impact of stratospheric air on ground-level ozone.

Knowland, K. Emma↗

NASA NTRS2001

Analysis and visualization of single-trial event-related potentials

In this study, a linear decomposition technique, independent component analysis (ICA), is applied to single-trial multichannel EEG data from event-related potential (ERP) experiments. Spatial filters derived by ICA blindly separate the input data into a sum of temporally independent and spatially fixed components arising from distinct or overlapping brain or extra-brain sources. Both the data and their decomposition are displayed using a new visualization tool, the "ERP image," that can clearly characterize single-trial variations in the amplitudes and latencies of evoked responses, particularly when sorted by a relevant behavioral or physiological variable. These tools were used to analyze data from a visual selective attention experiment on 28 control subjects plus 22 neurological patients whose EEG records were heavily contaminated with blink and other eye-movement artifacts. Results show that ICA can separate artifactual, stimulus-locked, response-locked, and non-event-related background EEG activities into separate components, a taxonomy not obtained from conventional signal averaging approaches. This method allows: (1) removal of pervasive artifacts of all types from single-trial EEG records, (2) identification and segregation of stimulus- and response-locked EEG components, (3) examination of differences in single-trial responses, and (4) separation of temporally distinct but spatially overlapping EEG oscillatory activities with distinct relationships to task events. The proposed methods also allow the interaction between ERPs and the ongoing EEG to be investigated directly. We studied the between-subject component stability of ICA decomposition of single-trial EEG epochs by clustering components with similar scalp maps and activation power spectra. Components accounting for blinks, eye movements, temporal muscle activity, event-related potentials, and event-modulated alpha activities were largely replicated across subjects. Applying ICA and ERP image visualization to the analysis of sets of single trials from event-related EEG (or MEG) experiments can increase the information available from ERP (or ERF) data. Copyright 2001 Wiley-Liss, Inc.

Non-NASA Center↗

NASA NTRS1994

Locating buildings in aerial photos

Algorithms and techniques for use in the identification and location of large buildings in digitized copies of aerial photographs are developed and tested. The building data would be used in the simulation of objects located in the vicinity of an airport that may be detected by aircraft radar. Two distinct approaches are considered. Most building footprints are rectangular in form. The first approach studied is to search for right-angled corners that characterize rectangular objects and then to connect these corners to complete the building. This problem is difficult because many nonbuilding objects, such as street corners, parking lots, and ballparks often have well defined corners which are often difficult to distinguish from rooftops. Furthermore, rooftops come in a number of shapes, sizes, shadings, and textures which also limit the discrimination task. The strategy used linear sequences of different samples to detect straight edge segments at multiple angles and to determine when these segments meet at approximately right-angles with respect to each other. This technique is effective in locating corners. The test image used has a fairly rectangular block pattern oriented about thirty degrees clockwise from a vertical alignment, and the overall measurement data reflect this. However, this technique does not discriminate between buildings and other objects at an operationally suitable rate. In addition, since multiple paths are tested for each image pixel, this is a time consuming task. The process can be speeded up by preprocessing the image to locate the more optimal sampling paths. The second approach is to rely on a human operator to identify and select the building objects and then to have the computer determine the outline and location of the selected structures. When presented with a copy of a digitized aerial photograph, the operator uses a mouse and cursor to select a target building. After a button on the mouse is pressed, with the cursor fully within the perimeter of the building, the program scans from the position of the cursor to a perimeter position where a shift in grayscale is detected. Once at the perimeter, the process traces along it, around the building, until it eventually returns to the perimeter starting point. Spatial resolution limits cause the perimeter trace to be somewhat course so that a line straightening algorithm is employed. One result is that the building corner positions become more distinctly defined.

Green, James S.↗

NASA NTRS2014

Exploration Systems Development (ESD) Approach to Enterprise Risk Management

The National Aeronautics and Space Administration (NASA) Exploration Systems Development (ESD) Division has implemented an innovative approach to Enterprise Risk Management under a unique governance structure and streamlined integration model. ESD's mission is to design and build the capability to extend human existence to deep space. The Enterprise consists of three Programs: Space Launch System (SLS), Orion, and Ground Systems Development and Operations (GSDO). The SLS is a rocket and launch system that will be capable of powering humans, habitats, and support systems to deep space. Orion will be the first spacecraft in history capable of taking humans to multiple destinations within deep space. GSDO is modernizing Kennedy's spaceport to launch spacecraft built and designed by both NASA and private industry. ESD's approach to Enterprise Risk Management is commensurate with affordability and a streamlined management philosophy. ESD Enterprise Risk Management leverages off of the primary mechanisms for integration within the Enterprise. The Enterprise integration approach emphasizes delegation of authority to manage and execute the majority of cross-program activities and products to the individual Programs, while maintaining the overall responsibility for all cross-program activities at the Division. The intent of the ESD Enterprise Risk Management approach is to improve risk communication, to avoid replication and/or contradictory strategies, and to minimize overhead process burden. This is accomplished by the facilitation and integration of risk information within ESD. The ESD Division risks, Orion risks, SLS risks, and GSDO risks are owned and managed by the applicable Program. When the Programs have shared risks with multiple consequences, they are jointly owned and managed. When a risk is associated with the integrated system that involves more than one Program in condition, consequence, or mitigation plan, it is considered an Exploration Systems Integration (ESI) Risk. An ESI risk may require visibility and risk handling by multiple organizations. The Integrated Risk Working Group (IRWG) is a small team of Risk experts that are responsible for collaborating and communicating best practices. In addition, the forum facilitates proper integration of risks across the Enterprise. The IRWG uses a Continuous Risk Management approach for facilitating the identification, analysis, planning, tracking, and controlling of ESI Risks. The ESD Division, Programs, and Integrated Task Teams identify ESI Risks. The IRWG maintains a set of metrics for understanding Enterprise Risk process and the overall Risk Posture. The team is also actively involved in the modeling of risk for Enterprise Performance Management. With the Enterprise being constrained in Schedule and Budget, and with significant technical complexity, the appropriate use of Risk Management techniques is crucial to the success of the Enterprise. The IRWG achieves this through the modified approach, providing a forum for collaboration on risks that cross boundaries between the separate entities.

Bauder, Stephen P.↗

NASA NTRS2020

Medical Data Architecture Prototype Development - Summary of Recent Work and Proposed Ideas for Upcoming Work

The Medical Data Architecture (MDA) project supports the Exploration Medical Capability (ExMC) risk to minimize or reduce the risk of adverse health outcomes and decrements in performance due to in-flight medical capabilities on human exploration missions. To mitigate this risk, the ExMC MDA project addresses the technical limitations identified in ExMC Gap Med 07: We do not have the capability to comprehensively process medically-relevant information to support medical operations during exploration missions, and in ExMC Gap Med 10: We do not have the capability to provide computed medical decision support during exploration missions. These gaps recognize the need for a comprehensive medical data management system and the accompanying computational support to provide autonomous medical care during long duration exploration missions. As the MDA matures—including the capability to comprehensively process and discover medically-relevant information to support medical operations during exploration missions—project focus will shift to maturing and extending the MDA platform to enable clinical decision support and real-time guidance. To date, the MDA foundational architecture has recommended exploration medical system Level of Care IV requirements through a series of test bed prototype developments and analog demonstrations. The next stage in the development will focus on more autonomous clinical decision making necessary to address challenges in executing a self-contained medical system that enables health care both with and without assistance from ground support. A thorough understanding of current state of medical decision support systems, advanced machine learning algorithms and vast and varied data sources is required. The development of a clinical decision support for exploration missions (Level of Care V) roadmap is needed: one that assesses of current state of the art of clinical decision support systems (CDSS), interoperability issues, identification of challenges in health and performance monitoring, obtaining and processing information from biosensors, knowledge and data management, data integration and fusion, and advanced algorithm development. This roadmap must also include rapid prototype development in the areas of data processing, advanced analysis and prediction of medical events, and treatment based on medically relevant information processing and evidence-based best practices. In this presentation, an overview of the relevant issues and the beginning framework of a Level of Care V CDSS development roadmap will be provided.

Krihak, M.↗

NASA NTRS1997

Effect of low-dose scopolamine on autonomic control of the heart

Background: In low doses, scopolamine paradoxically enhances parasympathetic outflow to the heart. The mechanisms which mediate this action are not fully understood. Moreover, there are conflicting data regarding the potential role of sympathetic activity. This study in 17 healthy individuals was designed to characterize the influence of low dose transdermal scopolamine on the gain of the baroreflex and respiratory heart rate reflex and to determine the role of sympathetic activity. Methods: The effect of scopolamine was analyzed in the time and frequency domain by computing heart rate variability indices. The gains of the respiratory heart rate reflex and the baroreflex were estimated simultaneously by means of a cardiovascular system identification approach using an optimized autoregressive moving average algorithm. Measurements were repeated in the upright posture to assess the influence of enhanced sympathetic activity. In six subjects ambulatory ECGs were recorded to determine whether there are diurnal variations of the effect of scopolamine. Results: Scopolamine enhances vagal modulation of heart rate through both the respiratory-heart rate reflex and the baroreflex, as the gains of both were augmented by the drug in the supine and in the upright postures. Conclusions: Scopolamine increases parasympathetic cardiac control by augmenting the gain of the respiratory-heart rate and baroreflex. This action is not attenuated in the upright posture when sympathetic tone is increased.

NASA Discipline Regulatory Physiology↗

NASA NTRSDate not supplied

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2023

Using Machine Learning to Infer Material Properties of Debris Fragments from X-ray Images in the DebriSat Project

The DebriSat project is a collaboration effort with the NASA Orbital Debris Program Office, the U.S. Space Force Space Systems Command Center, The Aerospace Corporation, and the University of Florida. To date, over 200,000 fragments from this ground-based, hypervelocity impact experiment have been collected, and processing is underway to determine their physical characteristics, such as material, shape, color, characteristic length, and average cross-sectional area. The x-ray process is primarily used to identify the location of the fragments and estimated size for extraction, so that these physical characteristics can be assessed. This paper proposes a machine learning-based approach to characterize materials from x-ray images of debris fragments embedded in soft-catch foam used in the DebriSat project. The novel methodology discussed in this paper will highlight the use of x-ray imagery data to characterize these fragments without extraction or a human-in-the-loop. Both supervised and unsupervised machine learning techniques are utilized with this approach to infer the physical parameters of the fragments embedded in the soft-catch foam panels used in the impact experiment based on x-ray images of the foam panels. Additionally, 3D reconstructions of the extracted fragments are created with images taken from two different angles using the structure from motion (SfM) method. The characteristic lengths and shape from the 3D reconstruction, alongside the physical characteristics of the debris, are used in the inference of the material type. To develop and test the approach, a dataset of x-ray images of debris fragments of varying sizes and materials is collected. Supervised learning methods such as convolutional neural networks (CNNs), support vector machines (SVM), decision trees, and random forest classifiers are used due to the high-dimensional feature spaces of the debris and nonlinear decision boundaries for material categorization. Given the limited pre-labeled data of embedded debris materials smaller than 10 mm, unsupervised machine learning techniques such as clustering algorithms and autoencoders are used, in addition to supervised learning methods. The clustering algorithms group similar fragments together based on their physical properties, and autoencoders reduce the dimensionality of the x ray images and extract relevant features. The performance of the proposed approach's is analyzed using a range of statistical methods, including confusion matrices, receiver operating characteristic curves, and precision-recall curves. The results are compared with those obtained using a baseline approach that relies on manual identification and classification of debris fragments. To evaluate the effectiveness of different machine learning methods, statistical tests such as t-tests, ANOVA, and cross-validation are performed, comparing the performance of CNNs, SVMs, clustering algorithms, and autoencoders. Additional analysis needs to be conducted to identify any sources of bias or variability that may affect the results, such as variations in imaging conditions or fragmentation patterns. Other topics explored are limitations, refinements, and the potential use of semi-supervised learning techniques, such as self-training to label unlabeled datasets and co-training using x-ray images taken from two different angles as two different models.

Saik Anam Siam↗

NASA NTRS2006

Satellite Detection of Ebola River Hemorrhagic Fever Epidemics Trigger Events

Ebola hemorrhagic fever, named after the Ebola River in Central Africa, first appeared in June 1976, during an outbreak in Nzara and Maridi, Sudan. In September 1976, a separate outbreak was recognized in Yambuku, Democratic Republic of the Congo (DRC). One fatal case was identified in Tandala, DRC, in June 1977, followed by another outbreak in Nzara, Sudan, in July 1979. Ebola hemorrhagic fever outbreaks results in a very high mortality of patients who contract the disease: from 50 to 80% of infected people perish from this highly virulent disease. Death is gruesome, with those afflicted bleeding to death from massive hemorrhaging of organs and capillaries. The disease was not identified again until the end of 1994, when three outbreaks occurred almost simultaneously in Africa. In October, an outbreak was identified in a chimpanzee community studied by primatologists in Tal, Cote d'lvoire, with one human infection. The following month, multiple cases were reported in northeast Gabon in the gold panning camps of Mekouka, Andock, and Minkebe. Later that same month, the putative index case of the 1995 Kikwit, DRC, outbreak was exposed through an unknown mechanism while working in a charcoal pit. In Gabon, two additional outbreaks were reported in February and JuIy,1996, respectively, in Mayibout II, a village 40 km south of the original outbreak in the gold panning camps, and a logging camp between Ovan and Koumameyong, near Booue. The largest Ebola hemorrhagic fever epidemic occurred in Gulu District, Uganda from August 2000 to January 2001. In December 2001, Ebola reappeared in the Ogooue-lvindo Province, Gabon with extension into Mbomo District, The Republic of the Congo lasting until July 2002. Since 2002 there have been several outbreaks of Ebola hemorrhagic fever in Gabon and adjacent areas of Congo. Of interest is the seasonal context and occasional temporal clustering of Ebola hemorrhagic fever outbreaks. Near simultaneous appearances of Ebola epidemics in Nzara, Sudan and Yambuku, DRC in 1976 occurred within two months of each other in two geographic locations separated by hundreds of kilometers involving two separate viral strains (Sudan and Zaire EBO strains). The outbreaks of Tal, Cote d'lvoire; Mekouka, Gabon; and Kikwit, DRC in late 1994 also occurred within months of each other in three different geographic regions involving two different viral strains (Cote d'lvoire and Zaire EBO strains). Fifteen years passed between the 1976-9 and 1994-6 temporal clusters of Ebola cases without identification of additional cases.

Tucker, Compton J.↗

NASA NTRS2018

Assessing the Completeness of Occupational Exposure Data in the Lifetime Surveillance of Astronaut Health

INTRODUCTION: Longitudinal analysis on how spaceflight affects human health requires significant amounts of data. Missing data, especially if missing in a non-random fashion, could be a significant challenge to the success and validity of ongoing occupational surveillance and research. Astronaut occupational health data have been collected since 1959 in various formats and as part of several flight programs. As a result of changing methodologies over this span, epidemiologists in the NASA Lifetime Surveillance of Astronaut Health (LSAH) project regularly compile data sets with important exposure or outcome data missing. METHODS: NASA medical records of astronauts participating in voluntary annual LSAH examinations were reviewed and compiled to develop Individual Exposure Profiles (IEP) for each astronaut. These data were supplemented by an interview. If the interview yielded medically relevant information absent from the medical record, that information was considered an update. The IEPs were analyzed to identify trends regarding the characteristics of astronauts who provided updates and what kinds of information were consistently being updated. RESULTS: To date, 190 astronauts have participated in the IEP project. Medical information was updated for 119 individuals during these interviews. The astronauts' likelihood of updating their record upon interview was not significantly related to their spaceflight experience, era of active spaceflight, or duration of longest spaceflight. The most commonly updated categories of medical information were issues encountered during spaceflights, including CO2 symptoms, vision changes, back pain, headaches, and space motion sickness. DISCUSSION: The most commonly updated categories correspond to areas where LSAH has ongoing analysis efforts and therefore do not appear to have been reported at random. This presentation will address identification of missing astronaut health data and trends, forward work identified by the IEP project and how this information may be used for future LSAH data gap analyses.

Sieker, Jeremy↗

NASA NTRS2024

iGCE and MitoFlyght Spaceflight Missions: Unraveling Oxidative Stress Responses in Space

Thriving In DEep Space (TIDES) initiative aims to comprehensively understand how hostile environments such as the Moon and Mars affect human physiology. Here we present two NASA-selected spaceflight experiments under the TIDES portfolio, iGCE (Integrated Gravity Continuum Experiment) and MitoFlyght (Mitochondrial Investigation of Oxidative Stress in Flies). We hypothesize that exposure to spaceflight conditions induces oxidative stress responses that negatively impact physiology. The iGCE mission employs two well-established spaceflight models, Drosophila melanogaster and C.elegans, using Redwire’s Multi-use Variable-g Platform (MVP) hardware to assess changes in cardiac, muscle, and nervous systems across five different gravities: Hypergravity (2g), Earth (1g), Mars (0.37g), Moon (0.16g), and microgravity (ug). This mission focuses on uncovering alterations in protein homeostasis, autophagy, and mitochondrial function conserved across species. In the MitoFlyght mission to the ISS, Drosophila will be housed in the Vented Fly Box (VFB). This mission evaluates the oxidative stress response and autophagic pathway in muscle, heart, and nervous system. Additionally, we will (a) use the genetic mutant, Tor7/P to test whether increased autophagy is beneficial or a maladaptive response to the spaceflight stressors, and (b) utilize fly lines with tissue-specific expression (neuronal, muscle, and cardiac) of an antioxidant gene, SOD2 (superoxide dismutase) as a potential countermeasure. Data from these missions will be compared with previous LEO-based datasets to identify shared signatures. Furthermore, cross-species analysis of the transcriptomic data from other invertebrate and vertebrate spaceflight studies will help determine evolutionarily conserved pathways perturbed by space stressors. Overall, both these missions aim to provide crucial insights into the mechanisms underlying oxidative stress responses, synaptic changes, and heart and muscle deficits, facilitating the identification of diagnostic and therapeutic targets to mitigate the adverse health effects of long-duration space habitation. Ultimately, this research will enhance our ability to thrive in deep space and inform future missions.

Janani Iyer↗

NASA NTRS1999

Three-Dimensional, Transgenic Cell Models to Quantify Space Genotoxic Effects

The space environment contains radiation and chemical agents known to be mutagenic and carcinogenic to humans. Additionally, microgravity is a complicating factor that may modify or synergize induced genotoxic effects. Most in vitro models fail to use human cells (making risk extrapolation to humans more difficult), overlook the dynamic effect of tissue intercellular interactions on genotoxic damage, and lack the sensitivity required to measure low-dose effects. Currently a need exists for a model test system that simulates cellular interactions present in tissue, and can be used to quantify genotoxic damage induced by low levels of radiation and chemicals, and extrapolate assessed risk to humans. A state-of-the-art, three-dimensional, multicellular tissue equivalent cell culture model will be presented. It consists of mammalian cells genetically engineered to contain multiple copies of defined target genes for genotoxic assessment,. NASA-designed bioreactors were used to coculture mammalian cells into spheroids, The cells used were human mammary epithelial cells (H184135) and Stratagene's (Austin, Texas) Big Blue(TM) Rat 2 lambda fibroblasts. The fibroblasts were genetically engineered to contain -a high-density target gene for mutagenesis (60 copies of lacl/LacZ per cell). Tissue equivalent spheroids were routinely produced by inoculation of 2 to 7 X 10(exp 5) fibroblasts with Cytodex 3 beads (150 micrometers in diameter). at a 20:1 cell:bead ratio, into 50-ml HARV bioreactors (Synthecon, Inc.). Fibroblasts were cultured for 5 days, an equivalent number of epithelial cells added, and the fibroblast/epithelial cell coculture continued for 21 days. Three-dimensional spheroids with diameters ranging from 400 to 600 micrometers were obtained. Histological and immunohistochemical Characterization revealed i) both cell types present in the spheroids, with fibroblasts located primarily in the center, surrounded by epithelial cells; ii) synthesis of extracellular matrix; and iii,, mitotic cells located throughout the spheroids. Spheroidal integrity and cell viability were retained for the 30-day test period after removal of spheroids from the bioreactor. Potential utility of this three-dimensional, transgenic model for genotoxicity was initially assessed by exposure of spheroids to 0-2 Gy neon at dose rates of 0.3 to 1.5 Gy/min (National Institute of Radiological Sciences, Chiba, Japan). Quantification of mutation at the lacl gene revealed a linear dose response for mutation induction. Limited sequencing analysis of mutant clones revealed higher frequencies of deletions and multiple base sequence changes with increasing dose. These results suggest that our three-dimensional, transgenic model is applicable to a wide variety of studies involving the quantification, identification, and characterization of genotoxicity incurred in space and on Earth. This model uniquely allows investigation of the interaction of relevant factors, namely cell-to-cell interactions and the mechanistic interaction of microgravity with radiation insults and DNA repair. Using this three-dimensional model will allow us to obtain dual genotoxic information (i.e., mutation rate plus chromosome aberration data) from the same system so that one endpoint can be used to reference the other, thereby increasing the fidelity of the data set. Moreover, the tissue-equivalent nature of the three-dimensional model provides high confidence for relevance of risk assessment, i.e., the establishment of quality factors directly applicable to the microgravity environment.

Gonda, S. R.↗

NASA NTRSDate not supplied

Analyzing the Subjectivity of Hole-Type Image Quality Indicators for Radiography

Hole-type penetrameters used as image quality indicators (IQIs) for radiography have an inherent degree of subjectivity to their interpretation. The 1T (one times the thickness of the penetrameter) hole is so small, it can be difficult to distinguish from noise. It is suspected that an operator’s knowledge of the true location of the 1T hole may subconsciously influence a false positive identification of the 1T hole when in fact it cannot be discerned. In the case of computed radiography (CR), the size of the phosphor particles may lead to a noise pattern with features on the scale of the 1T hole. Per NASA-STD-5009, the 1T hole must be detected in order to achieve adequate sensitivity. This is based on the historical understanding that this sensitivity will enable detection of the minimum detectable flaw sizes listed in the standard. It’s important to understand if 1T sensitivity is being achieved, and the associated risk if not. This study sought to determine the true detectability of 1T-sized holes in aluminum and Inconel by creating and inspecting a set of penetrameters with randomly placed holes. Enough holes and vacant zones were created to enable a full probability of detection study with 90% detectability, 95% confidence. Testing is ongoing, but preliminary results have shown poor detectability. The detection rate is slightly better for Inconel than aluminum, slightly better using a micro-focus vs. mini-focus tube, and definitively better for film than CR. One of the key questions of this study is whether historical requirements for film are applicable for CR, and these initial findings suggest they may not be. There is also a requirement in the NASA standard for the minimum contrast-to-noise ratio of the hole. The results have shown that this numerical threshold does not correlate well with visual detection. This raises questions about the true nature of detection, in an age of image processing vs. human judgement. As the results indicate that the detection of 1T holes is unreliable, the next challenge will be determining what sensitivity is really achieved, and what is needed.

Erin Lanigan↗

NASA NTRSDate not supplied

Analyzing the Subjectivity of Hole-Type Image Quality Indicators for Radiography

Hole-type penetrameters used as image quality indicators (IQIs) for radiography have an inherent degree of subjectivity to their interpretation. The 1T (one times the thickness of the penetrameter) hole is so small, it can be difficult to distinguish from noise. It is suspected that an operator’s knowledge of the true location of the 1T hole may subconsciously influence a false positive identification of the 1T hole when in fact it cannot be discerned. In the case of computed radiography (CR), the size of the phosphor particles may lead to a noise pattern with features on the scale of the 1T hole. Per NASA-STD-5009, the 1T hole must be detected in order to achieve adequate sensitivity. This is based on the historical understanding that this sensitivity will enable detection of the minimum detectable flaw sizes listed in the standard. It’s important to understand if 1T sensitivity is being achieved, and the associated risk if not. This study sought to determine the true detectability of 1T-sized holes in aluminum and Inconel by creating and inspecting a set of penetrameters with randomly placed holes. Enough holes and vacant zones were created to enable a full probability of detection study with 90% detectability, 95% confidence. Testing is ongoing, but preliminary results have shown poor detectability. The detection rate is slightly better for Inconel than aluminum, slightly better using a micro-focus vs. mini-focus tube, and definitively better for film than CR. One of the key questions of this study is whether historical requirements for film are applicable for CR, and these initial findings suggest they may not be. There is also a requirement in the NASA standard for the minimum contrast-to-noise ratio of the hole. The results have shown that this numerical threshold does not correlate well with visual detection. This raises questions about the true nature of detection, in an age of image processing vs. human judgement. As the results indicate that the detection of 1T holes is unreliable, the next challenge will be determining what sensitivity is really achieved, and what is needed.

Erin Lanigan↗

NASA NTRS2017

Astronaut Photography of the Earth: A Long-Term Dataset for Earth Systems Research, Applications, and Education

The NASA Earth observations dataset obtained by humans in orbit using handheld film and digital cameras is freely accessible to the global community through the online searchable database at https://eol.jsc.nasa.gov, and offers a useful compliment to traditional ground-commanded sensor data. The dataset includes imagery from the NASA Mercury (1961) through present-day International Space Station (ISS) programs, and currently totals over 2.6 million individual frames. Geographic coverage of the dataset includes land and oceans areas between approximately 52 degrees North and South latitudes, but is spatially and temporally discontinuous. The photographic dataset includes some significant impediments for immediate research, applied, and educational use: commercial RGB films and camera systems with overlapping bandpasses; use of different focal length lenses, unconstrained look angles, and variable spacecraft altitudes; and no native geolocation information. Such factors led to this dataset being underutilized by the community but recent advances in automated and semi-automated image geolocation, image feature classification, and web-based services are adding new value to the astronaut-acquired imagery. A coupled ground software and on-orbit hardware system for the ISS is in development for planned deployment in mid-2017; this system will capture camera pose information for each astronaut photograph to allow automated, full georegistration of the data. The ground system component of the system is currently in use to fully georeference imagery collected in response to International Disaster Charter activations, and the auto-registration procedures are being applied to the extensive historical database of imagery to add value for research and educational purposes. In parallel, machine learning techniques are being applied to automate feature identification and classification throughout the dataset, in order to build descriptive metadata that will improve search capabilities. It is expected that these value additions will increase interest and use of the dataset by the global community.

Stefanov, William L.↗

NASA NTRS1997

An overview of the issues: physiological effects of bed rest and restricted physical activity

Reduction of exercise capacity with confinement to bed rest is well recognized. Underlying physiological mechanisms include dramatic reductions in maximal stroke volume, cardiac output, and oxygen uptake. However, bed rest by itself does not appear to contribute to cardiac dysfunction. Increased muscle fatigue is associated with reduced muscle blood flow, red cell volume, capillarization and oxidative enzymes. Loss of muscle mass and bone density may be reflected by reduced muscle strength and higher risk for injury to bones and joints. The resultant deconditioning caused by bed rest can be independent of the primary disease and physically debilitating in patients who attempt to reambulate to normal active living and working. A challenge to clinicians and health care specialists has been the identification of appropriate and effective methods to restore physical capacity of patients during or after restricted physical activity associated with prolonged bed rest. The examination of physiological responses to bed rest deconditioning and exercise training in healthy subjects has provided significant information to develop effective rehabilitation treatments. The successful application of acute exercise to enhance orthostatic stability, daily endurance exercise to maintain aerobic capacity, or specific resistance exercises to maintain musculoskeletal integrity rather than the use of surgical, pharmacological, and other medical treatments for clinical conditions has been enhanced by investigation and understanding of underlying mechanisms that distinguish physical deconditioning from the disease. This symposium presents an overview of cardiovascular and musculoskeletal deconditioning associated with reduced physical work capacity following prolonged bed rest and exercise training regimens that have proven successful in ameliorating or reversing these adverse effects.

NASA Program Space Physiology and Countermeasures↗

NASA NTRS2012

Can Terrestrial Microbes Grow on Mars?

The theme for AbSciCon 2012 is "Exploring Life: Past and Present, Near and Far." The conference will address our current understanding of life - from processes at the molecular level to those which operate at planetary scales. Studying these aspects of life on Earth provides an essential platform from which to examine the potential for life on other worlds, both within our solar system and beyond. Mars exhibits a variety of extreme environments characterized by high UV and ionizing radiation flux, low pressure anoxic atmosphere, scarce or absent liquid water, extreme low temperatures, etc. The ability of terrestrial microorganisms to survive and adapt to the Mars environment has profound implications for astrobiology, planetary protection, and Mars life detection missions. At the NASA Ames Synthetic Biology Initiative, we believe that synthetic biology has the potential to revolutionize human space exploration. As such, the initiative is dedicated to applying the tools and techniques of synthetic biology to space exploration and astrobiology. Biological solutions will be invaluable for space exploration because they are not resource intensive, and they are versatile and self-renewing. An understanding of how to work with DNA in an unfavorable environment is paramount to utilizing biological tools on space missions. Furthermore, the ability to adjust life to the parameters of Mars is vital both to discovering what life on Mars might look like, and to using biological tools under such conditions. As a first step, we need an energy-efficient, low cost means of transporting, storing, and protecting genomic DNA, DNA parts, and whole microbial strains. Our goal is to develop and demonstrate viable and superior alternatives to standard DNA storage methods, which can be optimized to the conditions of space exploration, using synthetic biology as a tool. This includes protocols and kit designs for easy and repeatable DNA and strain recovery from protective storage conditions. We are constructing newly engineered genetic parts for different valuable host organisms, designed to increased long-term survival and functional retention. These methods should be applied for DNA and strain storage and transportation. In parallel, we seek inspiration from natural organisms that have developed means for survival in extreme environmental conditions. We are utilizing novel techniques for analysis of lipid biomarkers in the Antarctic Dry Valleys in order to identify resident microbes in the Antarctic soil and permafrost, as well as biomarker fossils of organisms that survived in the valleys in ages past. Through the identification of these life forms, we hope to understand and draw on new biological tools and strategies for synthetic biological applications on Mars.

Rothschild, Lynn↗

NASA NTRS2014

Pre-Flight Advanced Clothing Study

All human space missions require significant logistical mass and volume that will become an excessive burden for long duration missions beyond low Earth orbit. The current International Space Station (ISS) crew wardrobe has already evolved not only to reduce some of the logistical burden but also to address crew preference. The present study was undertaken to find ways further to reduce this logistical burden while examining human response to different types of clothes. The primary objective of the study is to measure how long people can wear the same exercise garment, depending on the type of fabric and the presence of antimicrobial treatment. The secondary objective is to assess the reasons for length of wear from perceptions of clothing characteristics, including nine ordinal scales. Cardiovascular exercise was chosen as the activity in this experiment for its profuse sweating effect and because it is considered a more severe treatment applied to the clothes than every-day usage. Study garments were exercise T-shirts and shorts purchased from various vendors. Fabric construction, fabric composition, and finishing treatment were defined as the key variables. A web-based questionnaire was used for self-reported data collection. The study was divided in three balanced experiments: a cotton-polyester-wool (CPW) T-shirts study with 61 participants, a polyester-modacrylic-polyester/cocona (PMC) T-shirts study with 40 participants, and a shorts study with 70 participants. In the CPW study, the T-shirts were made of 100% cotton, or of 100% polyester or of 100% wool, and categorized into open and tight knit constructions. In the PMC study, the T-shirts were made of 100% polyester, or of 82% modacrylic, or of 95% polyester with 5% cocona fiber, without construction distinction. The shorts were made either of 100% cotton or of 100% polyester, and were knitted or woven. Some garments were treated with Bio-Protect 500 antimicrobial finish according the experimental design. The data collected from the questionnaire included garment identification, level of exertion, duration of exercise session, number of exercise sessions, an ordinal preference scale for nine sensory elements, and reason for retiring a used garment. From the analysis of the combined CPW and PMC shirt studies, there are statistically significant differences among the mean lifetimes of various types of shirts. The exercise shirts with the longest mean lifetimes are untreated wool (600 minutes), treated cotton (526 minutes), and untreated modacrylic (515 minutes). From the combined CPW and PMC shirt studies, the most preferred material was untreated open-knit wool, which is one of the two materials that jointly were worn the longest, untreated wool, both open-knit and tight-knit. For the CP shorts study, there were no statistically significant differences in mean lifetimes of the exercise shorts at the 5% significance level due to the treatment combinations. There was therefore no justification to examine differences among levels of main effects or interactions. The preference for shorts was in this order: untreated woven polyester, untreated knitted polyester, untreated woven cotton, and treated knitted cotton.The nine preference scales were tabulated to determine the preference responses at the end of those exercise periods which were prior to the period when a garment was retired and a new garment was started. The assumption is that an unfavorable assessment of a garment leads to its retirement. The scent scale response was predominantly unfavorable at the end of the exercise period immediately prior to the exercise period when a new garment was started. Additional work on wool clothing is needed to assess if this material can be part of a crew wardrobe for long duration missions. The results of this study informed the choice of fabrics for an upcoming ISS intra-vehicular clothing study.

Orndoff, Evelyne↗