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All sourcesNASA NTRSDOE OSTI
NASA NTRSDate not supplied

The Integrated Impact of Diet on Human Immune Response, the Gut Microbiota, and Nutritional Status During Adaptation to Spaceflight

Long-duration spaceflight impacts human physiology, including well documented immune system dysregulation. Diet, the microbiome, and immune system function are interlinked, but diet is the only one of these factors that we have the ability to easily, and significantly, alter on Earth or during flight. As we better understand dietary impacts on physiology, we may then improve the spaceflight diet to improve crew health and potentially reduce spaceflight-associated physiological decrements. Increasing the consumption of fruits and vegetables and bioactive compounds (e.g., omega-3 fatty acids, lycopene, flavonoids) and therefore enhancing overall nutritional intake from the nominal shelf-stable, fully-processed, space food system is expected to serve as a countermeasure to detrimental impacts to human physiology, including dysregulation in immunological profiles, the taxonomic profile of the gut microbiota, and nutritional status during spaceflight. In this study, first we sought to determine the effect of the nominal shelf-stable spaceflight diet compared to an "enhanced" shelf-stable spaceflight diet on human biochemistry, immunology, and the microbiome in a ground-based, simulated space mission. The ground analog portion of this study was conducted in the NASA Human Exploration Research Analog (HERA) Campaign 4 missions, which consisted of four 45-day missions with closed chamber confinement and realistic mission simulation to study effects on crew health and performance. As reported previously, analyses indicate beneficial associations between diet and markers of nutritional status, stress, the microbiome, and cognitive performance. Intake and beneficial associations varied by subject. This data will be used as a ground-based control for spaceflight, where the spaceflight environment (e.g., radiation, microgravity) will have additional impacts and the potential to evaluate effects of the diet will be greater. The second phase of this study is to occur on the International Space Station, where it is currently being implemented. The test plan is similar to that used in the HERA missions. The enhanced diet is intended to provide 25% of the crews’ diet with foods rich in omega-3 fatty acids, lycopene, and flavonoids, along with more fruits and vegetables in general (the other 75% of the diet will be obtained from standard and crew preference items available on the ISS). Biological samples (blood, urine, stool, and saliva) are being collected from participants at selected time points before, during, and after the mission. Data collection also includes dietary intake recording and body mass measurement. Currently, 6 of 9 planned astronauts have completed data collection. Analysis of immune markers, latent herpes virus reactivation, the taxonomic and metatranscriptomic profile of the gut microbiome, and nutritional status biomarkers and biochemical metabolites will occur in batch to minimize sample handling variations. Mixed models statistical analyses will be used, incorporating random effects to account for repeated measures within individuals to assess the impact of diet on physiological outcomes. We expect this study to provide evidence of beneficial impact of this enhanced diet on crew health and adaptation to spaceflight. These data will aid in evidence-based mass-risk trades for food system design and development of targeted dietary interventions for future exploration-class space missions.

Grace L. Douglas↗
NASA NTRS1972

Path selection system simulation and evaluation for a Martian roving vehicle

The simulation and evaluation of proposed path selection systems for an autonomous Martian roving vehicle was developed. The package incorporates a number of realistic features, such as the simulation of random effects due to vehicle bounce and sensor-reading uncertainty, to increase the reliability of the results. Qualitative and quantitative evaluation criteria were established. The performance of three different path selection systems was evaluated to determine the effectiveness of the simulation package, and to form some preliminary conclusions regarding the tradeoffs involved in a path selection system design.

Boheim, S. L.↗
NASA NTRS1974

Path selection system development and evaluation for a Martian roving vehicle

A path selection system evaluation test procedure has been developed to enhance the analysis capability of an existing digital computer simulation package. The procedure investigates the obstacle avoidance ability of a path selection system on a sequence of test terrains with and without random effects. Using the standard test procedure a proposed mid-range sensor system has been evaluated and recommendations directed at improving the performance of the system have been made. In addition, the initial development and evaluation of a short range sensor system has been undertaken.

Campbell, R. S.↗
NASA NTRS1993

Probabilistic material degradation under high temperature, fatigue, and creep

A methodology has been developed and embodied in two computer codes for quantitatively characterizing the material strength degradation of aerospace propulsion system structural components that are subjected to various random effects over the course of their service lives. The codes, PROMISS and PROMISC, constitute a material-resistance model that is used in the NESSUS aerospace structural-reliability program. NESSUS addresses the service life-reducing effects of high temperature, mechanical fatigue, and creep.

Boyce, L.↗
NASA NTRS1996

Gravitational Effects on Closed-Cellular-Foam Microstructure

Polyurethane foam has been produced in low gravity for the first time. The cause and distribution of different void or pore sizes are elucidated from direct comparison of unit-gravity and low-gravity samples. Low gravity is found to increase the pore roundness by 17% and reduce the void size by 50%. The standard deviation for pores becomes narrower (a more homogeneous foam is produced) in low gravity. Both a Gaussian and a Weibull model fail to describe the statistical distribution of void areas, and hence the governing dynamics do not combine small voids in either a uniform or a dependent fashion to make larger voids. Instead, the void areas follow an exponential law, which effectively randomizes the production of void sizes in a nondependent fashion consistent more with single nucleation than with multiple or combining events.

Noever, David A.↗
NASA NTRS2008

Using an Informative Missing Data Model to Predict the Ability to Assess Recovery of Balance Control after Spaceflight

Astronauts show degraded balance control immediately after spaceflight. To assess this change, astronauts' ability to maintain a fixed stance under several challenging stimuli on a movable platform is quantified by "equilibrium" scores (EQs) on a scale of 0 to 100, where 100 represents perfect control (sway angle of 0) and 0 represents data loss where no sway angle is observed because the subject has to be restrained from falling. By comparing post- to pre-flight EQs for actual astronauts vs. controls, we built a classifier for deciding when an astronaut has recovered. Future diagnostic performance depends both on the sampling distribution of the classifier as well as the distribution of its input data. Taking this into consideration, we constructed a predictive ROC by simulation after modeling P(EQ = 0) in terms of a latent EQ-like beta-distributed random variable with random effects.

Feiveson, Alan H.↗
NASA NTRS2009

Reaction Wheel Disturbance Model Extraction Software - RWDMES

The RWDMES is a tool for modeling the disturbances imparted on spacecraft by spinning reaction wheels. Reaction wheels are usually the largest disturbance source on a precision pointing spacecraft, and can be the dominating source of pointing error. Accurate knowledge of the disturbance environment is critical to accurate prediction of the pointing performance. In the past, it has been difficult to extract an accurate wheel disturbance model since the forcing mechanisms are difficult to model physically, and the forcing amplitudes are filtered by the dynamics of the reaction wheel. RWDMES captures the wheel-induced disturbances using a hybrid physical/empirical model that is extracted directly from measured forcing data. The empirical models capture the tonal forces that occur at harmonics of the spin rate, and the broadband forces that arise from random effects. The empirical forcing functions are filtered by a physical model of the wheel structure that includes spin-rate-dependent moments (gyroscopic terms). The resulting hybrid model creates a highly accurate prediction of wheel-induced forces. It accounts for variation in disturbance frequency, as well as the shifts in structural amplification by the whirl modes, as the spin rate changes. This software provides a point-and-click environment for producing accurate models with minimal user effort. Where conventional approaches may take weeks to produce a model of variable quality, RWDMES can create a demonstrably high accuracy model in two hours. The software consists of a graphical user interface (GUI) that enables the user to specify all analysis parameters, to evaluate analysis results and to iteratively refine the model. Underlying algorithms automatically extract disturbance harmonics, initialize and tune harmonic models, and initialize and tune broadband noise models. The component steps are described in the RWDMES user s guide and include: converting time domain data to waterfall PSDs (power spectral densities); converting PSDs to order analysis data; extracting harmonics; initializing and simultaneously tuning a harmonic model and a wheel structural model; initializing and tuning a broadband model; and verifying the harmonic/broadband/structural model against the measurement data. Functional operation is through a MATLAB GUI that loads test data, performs the various analyses, plots evaluation data for assessment and refinement of analysis parameters, and exports the data to documentation or downstream analysis code. The harmonic models are defined as specified functions of frequency, typically speed-squared. The reaction wheel structural model is realized as mass, damping, and stiffness matrices (typically from a finite element analysis package) with the addition of a gyroscopic forcing matrix. The broadband noise model is realized as a set of speed-dependent filters. The tuning of the combined model is performed using nonlinear least squares techniques. RWDMES is implemented as a MATLAB toolbox comprising the Fit Manager for performing the model extraction, Data Manager for managing input data and output models, the Gyro Manager for modifying wheel structural models, and the Harmonic Editor for evaluating and tuning harmonic models. This software was validated using data from Goodrich E wheels, and from GSFC Lunar Reconnaissance Orbiter (LRO) wheels. The validation testing proved that RWDMES has the capability to extract accurate disturbance models from flight reaction wheels with minimal user effort.

Blaurock, Carl↗
NASA NTRS2012

Assessing the Association Between Asthma and Air Quality in the Presence of Wildfires

Asthma hospital/emergency room (patient) data are used as the foundation for creating a health outcome indicator of human response to environmental air quality. Daily U.S. Environmental Protection Agency (EPA) Air Quality System (AQS) fine particulates (PM2.5) ground data and the U.S. National Aeronautical Space Administration (NASA) MODIS aerosol optical depth (AOD) data were acquired and processed for years of 2007 and 2008. Figure 1 shows the PM2.5 annual mean composite of all the 2007 B-spline daily surfaces. Initial models for predicting the number of weekly asthma cases within a Florida county has focused on environmental variables. Weekly maximums of PM2.5, relative humidity, and the proportions of the county with smoke and fire were the environmental variables included in the model. Cosine and sine functions of time were used to account for seasonality in asthma cases. Counties were considered to be random effects, thereby adjusting for differences in socio ]demographics and other factors. The 2007 predictions for Miami ]Dade county when using B ]splines PM2.5 are displayed in Figures 2.

Young, L. J.↗
NASA NTRS2013

Modeling Longitudinal Data Containing Non-Normal Within Subject Errors

The mission of the National Aeronautics and Space Administration’s (NASA) human research program is to advance safe human spaceflight. This involves conducting experiments, collecting data, and analyzing data. The data are longitudinal and result from a relatively few number of subjects; typically 10 – 20. A longitudinal study refers to an investigation where participant outcomes and possibly treatments are collected at multiple follow-up times. Standard statistical designs such as mean regression with random effects and mixed–effects regression are inadequate for such data because the population is typically not approximately normally distributed. Hence, more advanced data analysis methods are necessary. This research focuses on four such methods for longitudinal data analysis: the recently proposed linear quantile mixed models (lqmm) by Geraci and Bottai (2013), quantile regression, multilevel mixed–effects linear regression, and robust regression. This research also provides computational algorithms for longitudinal data that scientists can directly use for human spaceflight and other longitudinal data applications, then presents statistical evidence that verifies which method is best for specific situations. This advances the study of longitudinal data in a broad range of applications including applications in the sciences, technology, engineering and mathematics fields.

Feiveson, Alan↗
NASA NTRS2011

Space-Time Data Fusion

Space-time Data Fusion (STDF) is a methodology for combing heterogeneous remote sensing data to optimally estimate the true values of a geophysical field of interest, and obtain uncertainties for those estimates. The input data sets may have different observing characteristics including different footprints, spatial resolutions and fields of view, orbit cycles, biases, and noise characteristics. Despite these differences all observed data can be linked to the underlying field, and therefore the each other, by a statistical model. Differences in footprints and other geometric characteristics are accounted for by parameterizing pixel-level remote sensing observations as spatial integrals of true field values lying within pixel boundaries, plus measurement error. Both spatial and temporal correlations in the true field and in the observations are estimated and incorporated through the use of a space-time random effects (STRE) model. Once the models parameters are estimated, we use it to derive expressions for optimal (minimum mean squared error and unbiased) estimates of the true field at any arbitrary location of interest, computed from the observations. Standard errors of these estimates are also produced, allowing confidence intervals to be constructed. The procedure is carried out on a fine spatial grid to approximate a continuous field. We demonstrate STDF by applying it to the problem of estimating CO2 concentration in the lower-atmosphere using data from the Atmospheric Infrared Sounder (AIRS) and the Japanese Greenhouse Gasses Observing Satellite (GOSAT) over one year for the continental US.

Greenhouse Gases Observing Satellite (GOSAT)↗
NASA NTRS2016

Estimating Model Prediction Error: Should You Treat Predictions as Fixed or Random?

Crop models are important tools for impact assessment of climate change, as well as for exploring management options under current climate. It is essential to evaluate the uncertainty associated with predictions of these models. We compare two criteria of prediction error; MSEP fixed, which evaluates mean squared error of prediction for a model with fixed structure, parameters and inputs, and MSEP uncertain( X), which evaluates mean squared error averaged over the distributions of model structure, inputs and parameters. Comparison of model outputs with data can be used to estimate the former. The latter has a squared bias term, which can be estimated using hindcasts, and a model variance term, which can be estimated from a simulation experiment. The separate contributions to MSEP uncertain (X) can be estimated using a random effects ANOVA. It is argued that MSEP uncertain (X) is the more informative uncertainty criterion, because it is specific to each prediction situation.

model structure uncertainty↗
NASA NTRS2016

Modeling Forest Biomass and Growth: Coupling Long-Term Inventory and Lidar Data

Combining spatially-explicit long-term forest inventory and remotely sensed information from Light Detection and Ranging (LiDAR) datasets through statistical models can be a powerful tool for predicting and mapping above-ground biomass (AGB) at a range of geographic scales. We present and examine a novel modeling approach to improve prediction of AGB and estimate AGB growth using LiDAR data. The proposed model accommodates temporal misalignment between field measurements and remotely sensed data-a problem pervasive in such settings-by including multiple time-indexed measurements at plot locations to estimate AGB growth. We pursue a Bayesian modeling framework that allows for appropriately complex parameter associations and uncertainty propagation through to prediction. Specifically, we identify a space-varying coefficients model to predict and map AGB and its associated growth simultaneously. The proposed model is assessed using LiDAR data acquired from NASA Goddard's LiDAR, Hyper-spectral & Thermal imager and field inventory data from the Penobscot Experimental Forest in Bradley, Maine. The proposed model outperformed the time-invariant counterpart models in predictive performance as indicated by a substantial reduction in root mean squared error. The proposed model adequately accounts for temporal misalignment through the estimation of forest AGB growth and accommodates residual spatial dependence. Results from this analysis suggest that future AGB models informed using remotely sensed data, such as LiDAR, may be improved by adapting traditional modeling frameworks to account for temporal misalignment and spatial dependence using random effects.

Babcock, Chad↗
NASA NTRS2017

Daily Kilometer-Scale MODIS Satellite Maps of PM2.5 Describe Wintertime Episodes

The San Joaquin Valley (SJV) suffers from severe health-endangering episodes of PM2.5 aerosol loadings in wintertime; episodes last approximately 5 days and differ in geographical distribution and composition. PM2.5 stations are scattered; consequently the use of remote sensing to map variable regional patterns of these varying respirable aerosol concentrations is desirable. High-precision AOT retrievals can capture column particulate loading. However,PM2.5 mapping is challenging due to several reasons: particularly thin mixed layers (ML) and thus relatively low aerosol optical thickness (AOT) close to current measurement limits, variable and a typical composition of the aerosols, and complex surface bidirectional reflectance. However, the West does present some advantages in analysis. Air basins are isolated from long-distance transport, and experience predominant strong meteorological subsidence. Thus these Western basin regions have fewer problematic cases of overriding aerosol layers detached from the surface. To counter such local overriding, Chu et al. have described an approach for the Eastern US, and He et al have described a synoptic classification approach useful in Shanghai. The Bay Area Air Quality Management District (BAAQMD) expands our experience with the use of AOT, with lower PM2.5 and several isolated sub-basins. We have prepared daily maps of episodes in each region. We present also a sequence of increasingly detailed statistical models, AOT initially appears to contribute little information; however, inclusion of weather information reveals its utility. Lyapustin and Wang's MultiAngle Implementation of Atmospheric Correction (MAIAC) retrieval for AOT provided the most useful operational remote sensing information for these regions. It provides high (1-km) spatial resolution maps and a high percentage of availability. Empirical regression methods have found that random effects regression models (aka mixed effects models, ME) employing AOT provide good estimates of ground PM2.5 concentrations.Here, we attempt to extend these methods and evaluate the usefulness of AOT with greater physical analysis, based on DISCOVER-AQ4 experience.

Chatfield, Robert B.↗
NASA NTRS2021

A Systematic Review and Meta-analysis of the Potential Non-human Animal Reservoirs and Arthropod Vectors of the Mayaro Virus

Improving our understanding of Mayaro virus (MAYV) ecology is critical to guide surveillance and risk assessment. We conducted a PRISMA-adherent systematic review of the published and grey literature to identify potential arthropod vectors and non-human animal reservoirs of MAYV. We searched PubMed/MEDLINE, Embase, Web of Science, SciELO and grey-literature sources including PAHO databases and dissertation repositories. Studies were included if they assessed MAYV virological/immunological measured occurrence in field-caught, domestic, or sentinel animals or in field-caught arthropods. We conducted an animal seroprevalence meta-analysis using a random effects model. We compiled granular georeferenced maps of non-human MAYV occurrence and graded the quality of the studies using a customized framework. Overall, 57 studies were eligible out of 1523 screened, published between the years 1961 and 2020. Seventeen studies reported MAYV positivity in wild mammals, birds, or reptiles and five studies reported MAYV positivity in domestic animals. MAYV positivity was reported in 12 orders of wild-caught vertebrates, most frequently in the orders Charadriiformes and Primate. Sixteen studies detected MAYV in wild-caught mosquito genera including Haemagogus, Aedes, Culex, Psorophora, Coquillettidia, and Sabethes. Vertebrate animals or arthropods with MAYV were detected in Brazil, Panama, Peru, French Guiana, Colombia, Trinidad, Venezuela, Argentina, and Paraguay. Among non-human vertebrates, the Primate order had the highest pooled seroprevalence

Mayaro virus↗
NASA NTRSDate not supplied

Defining Change Thresholds: What Change Is Outside Typical Sources of Variation?

Researchers often have a difficult time defining meaningful thresholds for change. We sometimes identify subtle changes but what amount of change is beyond typical sources of variation? This is especially complicated when trying to understand new disease pathogenesis like the constellation of eye changes leading to Spaceflight-associated Neuro-ocular Syndrome (SANS). To support decision makers in defining minimal meaningful change, we used a Bayesian hierarchical model to estimate innate sources of variability such as natural day to day variation. Healthy subjects were recruited and imaged with MRI, OCT, and US on separate days and measured by several technicians. Models were developed specifying random effects for the sources of variation – between left and right eyes, within-individuals over time, between raters, and finally between individuals. This allowed us to find the posterior distribution for the total typical variation, within an eye, which we use to define a threshold where change beyond typical sources of variation is likely. This threshold is now used as our earliest indicator of systematic increase in Total Retinal Thickness (a precursor to optic disc edema).

Millennia Young↗
NASA NTRS1986

Effective medium approximation for effective propagation constant calculation in a dense random medium

The effective medium approximation (EMA) formalism developed for scalar wave calculations in solid state physics is generalized to electromagnetic wave scattering in a dense random medium. Results are applied to compute the effective propagation constant in a dense medium involving discrete spherical scatterers. When compared with a common quasicrystalline approximation (QCA), it is found that EMA accounts for backward scattering and the effect of correlation among three scatterers which are not available in QCA. It is also found that there is not much difference in the calculated normalized phase velocity between the use of these two approximations. However, there is a significant difference in the computed effective loss tangent in a nonabsorptive random medium. The computed effective loss tangent using EMA and measurements from a snow medium are compared, showing good agreement.

Zhu, P. Y.↗
NASA NTRS1992

Effective diffusion equation in a random velocity field

The effects are studied of assumed random velocity fields on diffusion in a binary fluid. Random velocity fields can result, for example, from the high-frequency components of residual accelerations onboard spacecraft (often called g-jitter). An effective diffusion equation is derived for an average concentration which includes spatial and temporal correlations induced by the fluctuating velocity fields assumed to be Gaussianly distributed. The resulting equation becomes nonlocal, and if correlations between different components of the velocity field exist, it is also anisotropic. The simple limiting case of short correlation times is discussed and an effective diffusivity is obtained which reflects the enhanced mixing caused by the velocity fields. The results obtained in the limit of short correlation times are valid even if the probability distribution of the velocity field is not Gaussian.

Vinals, Jorge↗