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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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All sourcesNASA NTRSDOE OSTI
NASA NTRS1989

A mathematical model of physiological processes and its application to the study of aging

The behavior of a physiological system which, after displacement, returns by homeostatic mechanisms to its original condition can be described by a simple differential equation in which the "recovery time" is a parameter. Two such systems, which influence one another, can be linked mathematically by the use of "coupling" or "feedback" coefficients. These concepts are the basis for many mathematical models of physiological behavior, and we describe the general nature of such models. Next, we introduce the concept of a "fatal limit" for the displacement of a physiological system, and show how measures of such limits can be included in mathematical models. We show how the numerical values of such limits depend on the values of other system parameters, i.e., recovery times and coupling coefficients, and suggest ways of measuring all these parameters experimentally, for example by monitoring changes induced by X-irradiation. Next, we discuss age-related changes in these parameters, and show how the parameters of mortality statistics, such as the famous Gompertz parameters, can be derived from experimentally measurable changes. Concepts of onset-of-aging, critical or fatal limits, equilibrium value (homeostasis), recovery times and coupling constants are involved. Illustrations are given using published data from mouse and rat populations. We believe that this method of deriving survival patterns from model that is experimentally testable is unique.

Aging/physiology↗
NASA NTRS2004

Effects of heavy ions on visual function and electrophysiology of rodents: the ALTEA-MICE project

ALTEA-MICE will supplement the ALTEA project on astronauts and provide information on the functional visual impairment possibly induced by heavy ions during prolonged operations in microgravity. Goals of ALTEA-MICE are: (1) to investigate the effects of heavy ions on the visual system of normal and mutant mice with retinal defects; (2) to define reliable experimental conditions for space research; and (3) to develop animal models to study the physiological consequences of space travels on humans. Remotely controlled mouse setup, applied electrophysiological recording methods, remote particle monitoring, and experimental procedures were developed and tested. The project has proved feasible under laboratory-controlled conditions comparable in important aspects to those of astronauts' exposure to particle in space. Experiments are performed at the Brookhaven National Laboratories [BNL] (Upton, NY, USA) and the Gesellschaft fur Schwerionenforschung mbH [GSI]/Biophysik (Darmstadt, FRG) to identify possible electrophysiological changes and/or activation of protective mechanisms in response to pulsed radiation. Offline data analyses are in progress and observations are still anecdotal. Electrophysiological changes after pulsed radiation are within the limits of spontaneous variability under anesthesia, with only indirect evidence of possible retinal/cortical responses. Immunostaining showed changes (e.g. increased expression of FGF2 protein in the outer nuclear layer) suggesting a retinal stress reaction to high-energy particles of potential relevance in space. c2004 COSPAR. Published by Elsevier Ltd. All rights reserved.

NASA Center JSC↗
NASA NTRS2012

Motion-Capture-Enabled Software for Gestural Control of 3D Models

Current state-of-the-art systems use general-purpose input devices such as a keyboard, mouse, or joystick that map to tasks in unintuitive ways. This software enables a person to control intuitively the position, size, and orientation of synthetic objects in a 3D virtual environment. It makes possible the simultaneous control of the 3D position, scale, and orientation of 3D objects using natural gestures. Enabling the control of 3D objects using a commercial motion-capture system allows for natural mapping of the many degrees of freedom of the human body to the manipulation of the 3D objects. It reduces training time for this kind of task, and eliminates the need to create an expensive, special-purpose controller.

Norris, Jeffrey S.↗
NASA NTRS1994

Kinetics model for initiation and promotion for describing tumor prevalence from HZE radiation

A kinetics model for cellular repair and misrepair for multiple radiation-induced lesions (mutation-inactivation) is coupled to a two-mutation model of initiation and promotion in tissue to provide a parametric description of tumor prevalence in the Harderian gland in a mouse. Dose-response curves are described for gamma-rays and relativistic ions. The effects of nuclear fragmentation are also considered for high-energy proton and alpha particle exposures The model described provides a parametric description of age-dependent cancer induction for a wide range of radiation fields. We also consider the two hypotheses that radiation acts either solely as an initiator or as both initiator and promoter and make model calculations for fractionation exposures from gamma-rays and relativistic Fe ions. For fractionated Fe exposures, an inverse dose-rate effect is provided by a promotion hypothesis using a mutation rate for promotion typical of single-gene mutations.

Cucinotta, Francis A.↗
NASA NTRSDate not supplied

Benchmark Models for Classification of Radiation Type Induced in Immune Cells

NASA Biological and Physical Sciences and the Science Mission Directorate have published a benchmark dataset of mouse immune cells subjected to radiation-induced DNA damage. The dataset comprises ML-ready microscopic imagery of said cells, including labels indicating radiation type and dose. The machine learning team at NASA Interagency Implementation and Advanced Concept Team (IMPACT) created multiple benchmark models. Initially, we conducted a preliminary analysis using thresholding. The algorithm used thresholds on average brightness of the available images to classify them into their respective radiation type. We also tested machine learning approaches. Convolutional Neural Networks (CNN) emerged as the best-performing model. This poster presents the benchmark scores obtained by the models.

Vishal Perekadan↗
NASA NTRS2021

Topical: Stem Cell-Based Tissue Regenerative Health in Space

The maintenance of healthy adult tissues in mammals requires a complex homeostasis of molecular, cellular, tissue, and metabolic processes which are fundamentally different from the development and aging processes that bookend life. Cellular homeostasis in the adult requires molecular maintenance and repair of non-dividing cells such as cardiomyocytes and neurons, but also stem cell-based tissue regeneration via direct replacement of cell loss, such as in the blood, immune system, bone, skin, liver, intestine, and other tissues. Because stem cell-based tissue regenerative health requires constant proliferation and differentiation of stem cell progenitors in the bone marrow, and other adult stem cell niches, it is uniquely sensitive to the stresses of spaceflight including exposure to space radiation and mechanical unloading in microgravity. A key central hypothesis in this field is that those spaceflight stress factors can have profound negative effects on long-term tissue regenerative health mediated by adult stem cells, and that unmitigated, they may lead to premature tissue aging and functional failure. Specifically, it is thought that mechanical unloading due to lack of weight-bearing in space reduces mitogenic mechanotransduction necessary to promote adult stem cell proliferation and differentiation, and that space radiation can also lead to activation of cell cycle arrest mechanisms, further reducing adult stem cell proliferation. These hypotheses are being tested in low earth orbit(LEO)using a variety of cellular and whole organism tissue model systems, suggesting that spaceflight consistently interferes with stem cell tissue regenerative processes such as in mammalian embryoid bodies, regenerating newt tails, and mouse bone marrow hematopoietic and osteoprogenitor cells. Furthermore, potential molecular mechanisms integrating both space radiation and mechanical disuse via oxidative stress and the cell cycle inhibitor Cdkn1aare now under study using single cell (scRNAseq) expressome analysis of bone marrow osteoprogenitors, both under stretch loading, and spaceflight conditions including in various mouse transgenic null backgrounds relevant to these mechanisms. Future work in the area of tissue regenerative health in space for the coming decade should seek to understand the responses the various tissue regenerative stem cell niches in humans and relevant model organisms, and how they respond to long-term exposure to the space environment. Special emphases of future work should be on how regenerative deficits in whole-organism stem cell niches may lead to tissue degeneration and premature aging, and on the long-term proliferation and differentiation of stem cell derived tissue organoid models in the deep space environment outside of LEO.

Eduardo A C Almeida↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), has typically limited machine learning (ML) in space studies and further study of radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNAseq) data from 6 mouse liver GeneLab datasets (GLDS) with a total of 113 spaceflight and ground-control samples to determine top features relevant to spaceflight including the effect of radiation exposure. Data was normalized within each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. The top MRMR features were used to predict spaceflight vs. ground-control samples using a Random Forest (RF) classifier with 5-fold cross validation (CV). The ML-based gene sets were further compared against differential gene expression results from individual GLDS. CV training using the top 100 MRMR genes show averages of 86% accuracy and 0.95 AUC value on the validation set over 5 folds (Figure 1A). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 811 or 68 DEGs overlapping between at least 2 or 3 studies, respectively (Figure 1B). Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism. Set analysis between the MRMR features and the DEGs showed 60 or 8 genes overlapping with at least 1 or 2 studies, respectively. MRMR feature selection and ensemble ML methods (e.g. RF) improve performance relative to a Naïve Bayes classifier when NGS data sets are analyzed. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise ratio. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from RNASeq analysis. Non-intersecting sets introduce opportunity to explore spaceflight relevant genes and implementing ML methods across existing NGS datasets may overcome sample size limitations. ML coupled with existing analytical methods enhances understanding of disease by revealing common underlying pathways across datasets.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning (ML) Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRSDate not supplied

Transcriptomics-based Machine Learning Analysis Predicts Space-Exposed Murine Livers

Limited sample sizes, high data dimensionality, and sensitivity to technical and biological variability of next generation sequencing (NGS), typically limits machine learning (ML) approaches in spaceflight studies that include radiation effects. However, pooling smaller studies while addressing intra- and inter-study variabilities allows for ML predictive modeling. Here, integration methods were applied to whole transcriptome shotgun sequencing (RNA-seq) data from six mouse liver GeneLab datasets (GLDS) (n ranging from 6 to 39 samples) from with a total of 81 spaceflight and ground-control samples to determine top features (i.e. genes) relevant to spaceflight including the effect of radiation exposure. RNASeq counts were normalized for each study, then merged and scaled across all datasets. Data dimensionality was reduced using a minimum redundancy maximum relevance (MRMR) methodology. Redundancy and relevance were computed using the Pearson correlation and F-statistic, respectively. The top 100 MRMR features were used to predict spaceflight vs. ground-control samples using Random Forest (RF), Support Vector Machine (SVM), and Linear Discriminant Analysis (LDA) classifiers with 5-fold cross validation (CV). Principal component analysis (PCA) on the complete feature set versus the MRMR features shows separation between spaceflight samples and ground controls (Figure 1A). The ML-based gene sets were compared against differential gene expression results obtained with DESeq2 from individual GLDS. Using all features or randomly sampled subsets at matching set sizes with MRMR, a maximum classifier accuracy of 69% was shown on the test set over 5 folds. For all classifiers, CV training using at least the top 30 MRMR genes show minimum 89% accuracy and 0.95 AUC value on the test set over 5 folds (Figure 1B). Baseline set analysis on differentially expressed genes (DEGs) identified using padj ≤ 0.05 show 295 DEGs that overlap at least two studies and 13 DEGs that overlap three studies (Figure 1C). Set analysis between the top 100 MRMR features and the DEGs showed 47 genes that overlap at least one study and 24 genes that overlap two studies. Over-representation analysis showed overlapping biological processes related to fatty acid and lipid metabolism which may indicate these processes in the response to spaceflight stressors. MRMR feature selection for the selected ML methods improve performance relative to a classifier built on all features or randomly sampled subsets. Permutation feature importance within the decorrelated MRMR features showed concordance in feature ranking between ML methods. A challenge of applying ML methods across heterogeneous NGS data is accounting for signal:noise. Here, signal validation across studies was shown by intersecting sets between top MRMR genes and DEGs from DESeq2 analysis. Non-intersecting sets introduce opportunity to explore genes relevant to differentiating space flight exposed groups and implementing ML methods across existing NGS datasets may overcome sample size limitations.

Machine Learning↗
NASA NTRS2016

Analysis of Adult Female Mouse (Mus musculus) Group Behavior on the International Space Station (ISS)

As interest in long duration effects of space habitation increases, understanding the behavior of model organisms living within the habitats engineered to fly them is vital for designing, validating, and interpreting future spaceflight studies. A handful of papers have previously reported behavior of mice and rats in the weightless environment of space. The Rodent Research Hardware and Operations Validation (Rodent Research-1; RR1) utilized the Rodent Habitat (RH) developed at NASA Ames Research Center to fly mice on the ISS (International Space Station). Ten adult (16-week-old) female C57BL/6 mice were launched on September 21st, 2014 in an unmanned Dragon Capsule, and spent 37 days in microgravity. Here we report group behavioral phenotypes of the RR1 Flight (FLT) and environment-matched Ground Control (GC) mice in the Rodent Habitat (RH) during this long-duration flight. Video was recorded for 33 days on the ISS, permitting daily assessments of overall health and well-being of the mice, and providing a valuable repository for detailed behavioral analysis. We previously reported that, as compared to GC mice, RR1 FLT mice exhibited the same range of behaviors, including eating, drinking, exploration, self- and allo-grooming, and social interactions at similar or greater levels of occurrence. Overall activity was greater in FLT as compared to GC mice, with spontaneous ambulatory behavior, including organized 'circling' or 'race-tracking' behavior that emerged within the first few days of flight following a common developmental sequence, and comprised the primary dark cycle activity persisting throughout the remainder of the experiment. Participation by individual mice increased dramatically over the course of the flight. Here we present a detailed analysis of 'race-tracking' behavior in which we quantified: (1) Complete lap rotations by individual mice; (2) Numbers of collisions between circling mice; (3) Lap directionality; and (4) Recruitment of mice into a group phenotype. This analysis contributes to the first NASA long-duration study of rodent behavior, providing evidence for the emergence of a distinctive, organized group behavior unique to the weightless space environment.

Behavior in Space↗
NASA NTRS1992

Effect of microgravity and mechanical stimulation on the in vitro mineralization and resorption of fetal mouse long bones (7-IML-1)

Mechanical forces play an important role in the differentiation, growth, and remodeling of skeletal tissues. An increase in the normal loading pattern of the skeleton leads to an increase in bone mass. An overall decrease in the functional load exerted on the skeleton produces mineral loss and osteoporosis. However, the responses of the skeletal tissue cells to various loading conditions are still largely unresolved, as is the mechanism of the cellular response to changed mechanical environment. Using an in vitro approach, we hope to avoid some problems encountered in the use of in vivo animal and man models, which have been extensively used in the past. In a number of experiments we have demonstrated that 16 and 17 day old fetal mouse long bone rudiments (metatarsalia), cultured in a liquid culture medium, are very suitable to study mineralization and resorption, respectively. We have also demonstrated that under hydrostatic compression, mineralization is increased while resorption is decreased. Culture of long bone rudiments under noncompressed control conditions can be regarded as a situation of partial unloading, showing some phenomena of a disuse situation. Under microgravity conditions, responses of osteoblasts and chondrocytes (involved in mineralization) and osteoclasts (involved in mineral resorption), to culture with and without compression, may be much more outspoken. This will have advantages for the study and the interpretation of the role of cellular events in the process of mineralization and resorption of developing skeletal tissues under various loading conditions. The BONES Experiment is carried out in four type I/O and four type I/E containers. Various aspects of the investigation are discussed.

Veldhuijzen, J. Paul↗
NASA NTRS2005

Development and Function of the Mouse Vestibular System in the Absence of Gravity Perception

The hypothesis that was tested in this research was that the absence of gravity perception, such as would occur in space, would affect the development and function of the vestibular and central nervous systems. Further, we postulated that these effects would be more significant at specific stages of post-natal development of the animal. We also proposed the use of molecular genetic approaches that would provide important information as to the hierarchy of gene function during the development and subsequent function of the vestibular system. The tilted (tlt) mutant mouse has been characterized as lacking the ability to provide sensory input to the gravity receptors. The tlt/tlt mutant mice were a particularly attractive model for the study of vestibular function since the primary defect was limited to the receptor part of the vestibular system, and there were no detectable abnormal phenotypes in other organ systems. The goal of the proposed studies was to assess immediate and delayed effects of the lack of gravity perception on the vestibular system. Particular attention was paid to characterizing primarily affected periods of vestibular morphogenesis, and to identifying downstream genetic pathways that are altered in the CNS of the tlt/tlt mutant mouse. The specific aims were: (1) to characterize the postnatal morphogenesis of the CNS in the tlt mutant mouse, using detailed morphometric analysis of isolated vestibular ganglia and brain tissue at different stages of postnatal development and assessment of apoptotic cell death; (2) to examine the expression of selected genes implicated by mutational analysis to be important in vestibular development or function by in situ hybridization or immunohistochemistry in the mutant mice; and (3) to identify other genes involved in vestibular development and function, using differential cloning strategies to isolate genes whose expression is changed in the mutant versus normal vestibular system.

Wolgemuth, Debra J.↗
NASA NTRS2002

Size distribution of retrovirally marked lineages matches prediction from population measurements of cell cycle behavior

Mechanisms that regulate neuron production in the developing mouse neocortex were examined by using a retroviral lineage marking method to determine the sizes of the lineages remaining in the proliferating population of the ventricular zone during the period of neuron production. The distribution of clade sizes obtained experimentally in four different injection-survival paradigms (E11-E13, E11-E14, E11-E15, and E12-E15) from a total of over 500 labeled lineages was compared with that obtained from three models in which the average behavior of the proliferating population [i.e., the proportion of cells remaining in the proliferative population (P) vs. that exiting the proliferative population (Q)] was quantitatively related to lineage size distribution. In model 1, different proportions of asymmetric, symmetric terminal, and symmetric nonterminal cell divisions coexisted during the entire developmental period. In model 2, the developmental period was divided into two epochs: During the first, asymmetric and symmetric nonterminal cell divisions occurred, but, during the second, asymmetric and symmetric terminal cell divisions occurred. In model 3, the shifts in P and Q are accounted for by changes in the proportions of the two types of symmetric cell divisions without the inclusion of any asymmetric cell divisions. The results obtained from the retroviral experiments were well accounted for by model 1 but not by model 2 or 3. These findings demonstrate that: 1) asymmetric and both types of symmetric cell divisions coexist during the entire period of neurogenesis in the mouse, 2) neuron production is regulated in the proliferative population by the independent decisions of the two daughter cells to reenter S phase, and 3) neurons are produced by both asymmetric and symmetric terminal cell divisions. In addition, the findings mean that cell death and/or tangential movements of cells in the proliferative population occur at only a low rate and that there are no proliferating lineages "reserved" to make particular laminae or cell types. Copyright 2002 Wiley-Liss, Inc.

NASA Discipline Developmental Biology↗
NASA NTRS2023

Applicability of the Nasa Galactic Cosmic Ray Simulator for Mice, Rats, and Minipigs

Space radiation poses multiple health risks to astronauts including cancer, cardiovascular disease, and damage to the central nervous system. Radiation from galactic cosmic rays (GCR) is the main source of these risks for missions beyond low Earth orbit to the moon and beyond. A galactic cosmic ray simulator (GCRsim) was developed at the NASA Space Radiation Laboratory to better understand and mitigate these risks by simulating the complex mixed field of the GCR environment at a ground-based facility. The GCRsim delivers a radiation field to cell and animal models in a laboratory setting that is comparable to the shielded radiation environment within internal organs of astronauts in deep space missions. Previous verification studies using Monte Carlo simulations with mouse (Digimouse) and rat (Digirat) digital phantoms showed that the GCRsim yields acceptable dose homogeneity within the internal organs of these animals. Spectral characteristics of the intended space radiation environment are also accurately reproduced at radiosensitive sites in both phantoms. In this work, similar Monte Carlo simulations were performed in a minipig model (Digipig) to show the applicability and limitations of the current GCRsim for larger animal model systems. The results showed dose homogeneity in internal organs of the three animal models with GCRsim irradiation. While slightly lower average doses were seen in the minipig compared to the external beam dose, the individual voxel doses in slices of the phantoms were found to be within 6%–8% of the average voxel dose, establishing that the GCRsim can still provide a relatively homogeneous irradiation within larger animals. Furthermore, simulated dose and fluence spectral results across the relevant linear energy transfer (LET) range agreed well with the reference field in the most relevant regions of the spectra, further verifying that the GCRsim beam represents the reference field in larger animals.

Galactic cosmic rays↗
NASA NTRS1999

NSBRI Radiation Effects: Carcinogenesis in Sprague-Dawley Rats Irradiated with Iron Ions, Protons, or Photons

Our ability to confidently develop appropriate countermeasures for radiations in space in terms of shielding and design of a spacecraft, the mission scenario, or chemoprevention is severely limited by the uncertainties in both the risk itself and the change in that risk with intervention. Despite the fact that the risk of carcinogenesis from exposures of personnel to radiations on long-term missions is considered one of the worst hazards in space, only a limited amount of in-vivo data exist for tumor induction from exposures to protons or energetic heavy ions (HZEs) at lower doses. The most extensive work remains the landmark study. for tumor development in the harderian gland of the mouse. The objective of this study is to characterize the level of risk for tumor induction in another relevant animal model. Subsequent experiments are designed to test the hypothesis that the level of risk can be reduced by pharmaceutical intervention in the promoting and progressing stages of the disease rather than in the initiating stage. The work presented here results from a cooperative effort on the part of investigators from two projects of the Radiation-Effects Team of the National Space Biomedical Research Institute (NSBRI). The collaborating projects are the Core Project which is investigating the risk of carcinogenesis in Sprague-Dawley rats and the Chemoprevention Project which is investigating the ability of Tamoxifen to reduce the number of malignant tumors in the irradiated animals. Research at the cellular and subcellular levels is being conducted in two other projects of the Radiation-Effects Team, Cytogenetics with J. R. Williams as Principal Investigator and Mutations from Repeated DNA Sequences. Results for these other projects also are being presented at this Workshop.

Dicello, J. F.↗
NASA NTRS2011

Hurricane Data Analysis Tool

In order to facilitate Earth science data access, the NASA Goddard Earth Sciences Data Information Services Center (GES DISC) has developed a web prototype, the Hurricane Data Analysis Tool (HDAT; URL: http://disc.gsfc.nasa.gov/HDAT), to allow users to conduct online visualization and analysis of several remote sensing and model datasets for educational activities and studies of tropical cyclones and other weather phenomena. With a web browser and few mouse clicks, users can have a full access to terabytes of data and generate 2-D or time-series plots and animation without downloading any software and data. HDAT includes data from the NASA Tropical Rainfall Measuring Mission (TRMM), the NASA Quick Scatterometer(QuikSCAT) and NECP Reanalysis, and the NCEP/CPC half-hourly, 4-km Global (60 N - 60 S) IR Dataset. The GES DISC archives TRMM data. The daily global rainfall product derived from the 3-hourly multi-satellite precipitation product (3B42 V6) is available in HDAT. The TRMM Microwave Imager (TMI) sea surface temperature from the Remote Sensing Systems is in HDAT as well. The NASA QuikSCAT ocean surface wind and the NCEP Reanalysis provide ocean surface and atmospheric conditions, respectively. The global merged IR product, also known as, the NCEP/CPC half-hourly, 4-km Global (60 N -60 S) IR Dataset, is one of TRMM ancillary datasets. They are globally-merged pixel-resolution IR brightness temperature data (equivalent blackbody temperatures), merged from all available geostationary satellites (GOES-8/10, METEOSAT-7/5 & GMS). The GES DISC has collected over 10 years of the data beginning from February of 2000. This high temporal resolution (every 30 minutes) dataset not only provides additional background information to TRMM and other satellite missions, but also allows observing a wide range of meteorological phenomena from space, such as, hurricanes, typhoons, tropical cyclones, mesoscale convection system, etc. Basic functions include selection of area of interest and time, single imagery, overlay of two different products, animation,a time skip capability and different image size outputs. Users can save an animation as a file (animated gif) and import it in other presentation software, such as, Microsoft PowerPoint. Since the tool can directly access the real data, more features and functionality can be added in the future.

Liu, Zhong↗
NASA NTRS2017

Mouse Behavior on ISS: The Emergence of Distinctive, Organized Group Circling Behavior Unique to Spaceflight

As interest in long duration effects of space habitation increases, understanding the behavior of model organisms living within the habitats engineered to fly them is vital for designing, validating, and interpreting future spaceflight studies. Only a handful of papers have previously reported behavior of mice and rats in the weightless environment of space (Andreev-Andrievskiy, et al., 2013; Cancedda et al., 2012; Ronca et al., 2008). The Rodent Research Hardware and Operations Validation Mission (Rodent Research-1; RR1) utilized the Rodent Habitat (RH) developed at NASA Ames Research Center to fly mice on the ISS. Ten adult (16-week-old) female C57BL6J mice were launched on September 21st, 2014 in an unmanned Dragon Capsule, and spent 37 days in flight. Here we report group behavioral phenotypes of the RR1 Flight (FLT) and environment-matched Ground Control (GC) mice in the RH during this long duration flight. Video was recorded for 34 days on the ISS, permitting daily assessments of overall health and well being of the mice, and providing a valuable repository for detailed behavioral analysis. As compared to GC mice, RR1 FLT mice exhibited the same range of behaviors, including eating, drinking, exploration, self- and allogrooming,and social interactions at similar or greater levels of occurrence. Overallactivity was greater in FLT as compared to GC mice, with spontaneous ambulatory behavior, including organized circling or race-tracking behavior that emerged within thefirst few days of flight following a common developmental sequence, comprising theprimary dark cycle activity of FLT mice. Circling participation by individual micepersisted throughout the mission. Analysis of group behavior over mission days revealed recruitment of mice into the group phenotype, coupled with decreasing numbers of collisions between circling mice. This analysis provides insights into the behavior of mice in microgravity, and clear evidence for the emergence of a distinctive,organized group behavior unique to the weightless space environment.

mice↗