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NASA NTRS2024

Optimizing Sample Collection and Accessibility through the Biospecimen and Tissue Sharing Collection (BTSC) Program

The Space Radiation Element (SRE) of the Human Research Program (HRP) is dedicated to establishing a robust biospecimen and tissue sharing collection (BTSC) program that enhances sample collection, tracking, access, distribution, and usability, with the goal of maximizing scientific return. By leveraging biospecimens and tissues from previous experiments, HRP effectively achieves its scientific objectives in characterizing and mitigating the human health impacts of spaceflight while optimizing resource utilization. To further improve the usability and accessibility of the current biospecimen archive, the project aims to expand upon NASA's existing resources and institutional knowledge, ensuring ongoing modernization. To facilitate seamless navigation of the program's workflow, an educational series on the BTSC program is provided to Principal Investigators (PIs). This comprehensive series equips PIs with crucial information on submitting their inventory via the BTSC Metadata Intake Form, ultimately leading to the public availability of their data on NASA's Life Science Portal (NLSP). Covering various aspects such as metadata submission instructions and backend processes for transferring metadata to the Laboratory Information Management System (LIMS), the series incorporates guidance from NASA's Biological Institutional Scientific Collection (NBISC) and Ames Life Sciences Data Archive (ALSDA). The BTSC program represents a significant stride towards enhancing the usability and accessibility of biospecimens for space research. By enabling NASA to deepen its understanding of the health implications of long-term spaceflight, this initiative plays a pivotal role in ensuring the safety and well-being of astronauts.

Shelita Renee Augustus↗

NASA NTRS2016

Satellite-Based Spatiotemporal Trends in PM2.5 Concentrations: China 2004-2013

Three decades of rapid economic development is causing severe and widespread PM2.5(particulate matter (is) less than 2.5 ) pollution in China. However, research on the health impacts of PM2.5 exposure has been hindered by limited historical PM2.5 concentration data. We estimated ambient PM2.5 concentrations from 2004 to 2013 in China at 0.1 deg resolution using the most recent satellite data and evaluated model performance with available ground observations. We developed a two-stage spatial statistical model using the Moderate Resolution Imaging Spectroradiometer (MODIS) Collection 6 aerosol optical depth (AOD) and assimilated meteorology, land use data, and PM2.5 concentrations from China's recently established ground monitoring network. An inverse variance weighting (IVW) approach was developed to combine MODIS Dark Target and Deep Blue AOD to optimize data coverage. We evaluated model predicted PM2.5 concentrations from 2004 to early 2014 using ground observations. The overall model cross-validation R(sup 2) and relative prediction error were 0.79 and 35.6%, respectively. Validation beyond the model year (2013) indicated that it accurately predicted PM(sub 2.5) concentrations with little bias at the monthly (R(sup 2) = 0.73), regression slope = 0.91) and seasonal (R(sup 2) = 0.79), regression slope = 0.92) levels. Seasonal variations revealed that winter was the most polluted season and that summer was the cleanest season. Analysis of predicted PM2.5 levels showed a mean annual increase of 1.97 micro-g/cu cm between 2004 and 2007 and a decrease of 0.46 micro-g/cu cm between 2008 and 2013. Our satellite-driven model can provide reliable historical PM2.5 estimates in China at a resolution comparable to those used in epidemiologic studies on the health effects of long-term PM2.5 exposure in North America. This data source can potentially advance research on PM2.5 health effects in China.

satellite remote sensing↗

NASA NTRS2019

NASA GeneLab Platform Utilized for Space Radiation Dosimetry Biological Response Compared to Radiation Ground Studies

Ionizing radiation from Galactic Cosmic Rays (GCR) is one of the major risk factors factor that will impact health of astronauts on extended missions outside the protective effects of the Earth's magnetic field. Currently there are gaps in our knowledge of the health risks associated with chronic low dose, low dose rate ionizing radiation, specifically ions associated with high (H) atomic number (Z) and energy (E). The NASA GeneLab project (genelab.nasa.gov) aims to provide a detailed library of Omics datasets associated with biological samples exposed to HZE. The GeneLab Data System (GLDS) includes datasets from both spaceflight and ground-based studies, a majority of which involved exposure to ionizing radiation. Recently GeneLab has also assessed radiation dosimetry data with omics datasets associated with samples flown to the International Space Station (ISS). The combination of the detailed information on radiation exposure for ground-based studies and curated dosimetry information for spaceflight experiments allows GeneLab to be the first comprehensive Omics database for space related research from which an investigator can generate hypotheses to direct future experiments utilizing both ground and space biological radiation data. We demonstrate the usefulness of these datasets by analyzing multiple GeneLab datasets associated with both radiation ground-based studies and spaceflight studies. The radiation ground based studies we analyzed includes both in vivo and in vitro work with a range ions from protons to iron particles with doses from 0.1Gy to 2Gy. These datasets were compared to both in vivo and in vitro datasets from samples flown to the ISS and on shorter shuttle missions with total doses of 0.1 mGy to 30 mGys. From this analysis we were able to associate distinct biological signatures associating specific ions to specific biological response to radiation exposure in space. For example, we discovered radiation biological response related to cardiovascular effects from proton ground studies are the dominating response for samples related to cardiovascular effects on the ISS. With this work we will provide a summary of how different ions will impact different biological response in space and how this can be used in future studies to assess optimal ground experiments to simulate space radiation.

Beheshti, Afshin↗

NASA NTRS1977

Comparative assessment of orbital and terrestrial central power plants

Recent studies of the space power system (SPS) are integrated into a total social cost framework developed for terrestrial central electric power systems. Total social costs include the projection of commercial economics to the time frame of interest as well as the federal research, development and demonstration (RD&D) costs, the health impacts, the resources required, the environmental impacts and other social costs. The SPS system is limited to transporting all materials from the earth's surface to geosynchronous orbit. Only silicon photovoltaic is considered as the SPS energy conversion technique. Costs and impacts of the LWR are considered as a reference for nuclear systems, and the low BTU coal gasification with combined cycle gas and steam turbines is considered as a reference for a fossil central electric plant. The ground solar systems considered are solar thermal using the central receiver approach with thermal storage, and solar photovoltaic using the silicon cell with battery storage.

Caputo, R.↗

NASA NTRS1978

Carbon fiber study. A compilation of an intergovernmental committee study

Carbon fibers have opened a wealth of new structural engineering and consumer product opportunities. The potential benefits are higher strength, and lighter weight military and commercial products, which portend energy savings and increases safety. Unfortunately, these benefits are not realized without risk. Inadvertent fiber release, during manufacture or by destruction of the resin binder in fire, is the major hazard associated with composites. The carbon (or graphite) fibers are finer than human hair and if released into the air, they can be easily transported by winds or currents. In contact with electrical devices, they can create resistive loading, short circuits, and arcing, resulting in stoppages or destruction. Their health impact is not fully known and requires careful research and analysis before any firm conclusions can be reached; however, based upon current available information, they are primarily an irritant to the eyes and skin, like fiberglass, rather than carcinogenic or destructive to lung tissue. Major manufacturers are aware of the unique problems associated with these materials and have successfully applied controls to avoid the inplant problems.

Source record↗

NASA NTRS2003

Radiation Protection Studies of International Space Station Extravehicular Activity Space Suits

This publication describes recent investigations that evaluate radiation shielding characteristics of NASA's and the Russian Space Agency's space suits. The introduction describes the suits and presents goals of several experiments performed with them. The first chapter provides background information about the dynamic radiation environment experienced at ISS and summarized radiation health and protection requirements for activities in low Earth orbit. Supporting studies report the development and application of a computer model of the EMU space suit and the difficulty of shielding EVA crewmembers from high-energy reentrant electrons, a previously unevaluated component of the space radiation environment. Chapters 2 through 6 describe experiments that evaluate the space suits' radiation shielding characteristics. Chapter 7 describes a study of the potential radiological health impact on EVA crewmembers of two virtually unexamined environmental sources of high-energy electrons-reentrant trapped electrons and atmospheric albedo or "splash" electrons. The radiological consequences of those sources have not been evaluated previously and, under closer scrutiny. A detailed computational model of the shielding distribution provided by components of the NASA astronauts' EMU is being developed for exposure evaluation studies. The model is introduced in Chapters 8 and 9 and used in Chapter 10 to investigate how trapped particle anisotropy impacts female organ doses during EVA. Chapter 11 presents a review of issues related to estimating skin cancer risk form space radiation. The final chapter contains conclusions about the protective qualities of the suit brought to light form these studies, as well as recommendations for future operational radiation protection.

Cucinotta, Francis A.↗

NASA NTRS2009

The Human in Space: Lesson from ISS

This viewgraph presentation reviews the lessons learned from manned space flight on the International Space Station. The contents include: 1) Overview of space flight effects on crewmembers; 2) General overview of immune system; 3) How does space flight alter immune system? 4) What factors associated with space flight inteact with crewmember immune function and impact health risks? 5) What is the current understanding of space flight effects on the immune system? and 6) Why should NASA be interested in immunology? Why is it significant?

Sams, Clarence F.↗

NASA NTRS2009

Integration of Airborne Aerosol Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health Decision Support Systems

The residual signal indicates that the pollen event may influence the seasonal signal to an extent that would allow detection, given accurate QA filtering and BRDF corrections. MODIS daily reflectances increased during the pollen season. The DREAM model (PREAM) was successfully modified for use with pollen and may provide 24-36 hour running pollen forecasts. Publicly available pollen forecasts are linked to general weather patterns and roughly-known species phenologies. These are too coarse for timely health interventions. PREAM addresses this key data gap so that targeting intervention measures can be determined temporally and geospatially. The New Mexico Department of Health (NMDOH) as part of its Environmental Public Health Tracking Network (EPHTN) would use PREAM a tool for alerting the public in advance of pollen bursts to intervene and reduce the health impact on asthma populations at risk.

Luvall, Jeffrey C.↗

NASA NTRS2010

Occupational Surveillance for Spaceflight Exposures

This slide presentation reviews the importance of longterm occupational health surveillance of astronauts after exposure to the possible hazards of spaceflight. Because there is not much information about long term effects of spaceflight on human health, it is important to identify some of the possible results of exposure to the many possible factors that can influence longterm health impacts. This surveillance also allows for NASA to meet the obligation to care for the astronauts for their lifetime.

Tarver, William J.↗

NASA NTRS2011

Integration of Airborne Aerosol Prediction Systems and Vegetation Phenology to Track Pollen for Asthma Alerts in Public Health Decision Support Systems

This slide presentation reviews the study that used a model to forecast pollen to assist in warning for asthma populations. Using MODIS daily reflectances to input to a model, PREAM, adapted from the Dust REgional Atmospheric Modeling (DREAM) system, a product of predicted pollen is produced. Using the pollen from Juniper the PREAM model was shown to be an assist in alerting the public of pollen bursts, and reduce the health impact on asthma populations.

Luvall, Jeffrey C.↗

NASA NTRS2012

Plasma Cytokine Levels During Long-Duration Spaceflight

Determine the in-flight status of immunity, physiological stress, viral immunity/reactivation. Specific measurements include leukocyte distribution, T cell function, cytokine production profiles (mRNA, intracellular, secreted, plasma), virus-specific T cell number/function, latent herpesvirus reactivation, stress hormone levels. Determine the clinical risk related to immune dysregulation for exploration class spaceflight, as well as an appropriate monitoring strategy for spaceflight-associated immune dysfunction, that could be used for the evaluation of countermeasures. Specific Study Objectives: Determine the nutritional status of astronauts before, during, and after spaceflight ensure adequate intake of energy, protein, and vitamins during missions. The Clinical Nutritional Status Assessment measures dietary intake, body composition, protein, bone, iron, mineral, vitamin, and antioxidant status (60 total analytes). Currently, it is a medical requirement for U.S. crewmembers on-board the ISS. The results of data analysis are used both to understand the connections between nutrition and human health during space flight, and to develop effective dietary strategies to reduce adverse health impacts (including bone loss, loss of important vitamins and minerals, and increased genetic damage from radiation).

Crucian, Brian E.↗

NASA NTRS2012

Hybrid Filter Membrane

Cabin environmental control is an important issue for a successful Moon mission. Due to the unique environment of the Moon, lunar dust control is one of the main problems that significantly diminishes the air quality inside spacecraft cabins. Therefore, this innovation was motivated by NASA s need to minimize the negative health impact that air-suspended lunar dust particles have on astronauts in spacecraft cabins. It is based on fabrication of a hybrid filter comprising nanofiber nonwoven layers coated on porous polymer membranes with uniform cylindrical pores. This design results in a high-efficiency gas particulate filter with low pressure drop and the ability to be easily regenerated to restore filtration performance. A hybrid filter was developed consisting of a porous membrane with uniform, micron-sized, cylindrical pore channels coated with a thin nanofiber layer. Compared to conventional filter media such as a high-efficiency particulate air (HEPA) filter, this filter is designed to provide high particle efficiency, low pressure drop, and the ability to be regenerated. These membranes have well-defined micron-sized pores and can be used independently as air filters with discreet particle size cut-off, or coated with nanofiber layers for filtration of ultrafine nanoscale particles. The filter consists of a thin design intended to facilitate filter regeneration by localized air pulsing. The two main features of this invention are the concept of combining a micro-engineered straight-pore membrane with nanofibers. The micro-engineered straight pore membrane can be prepared with extremely high precision. Because the resulting membrane pores are straight and not tortuous like those found in conventional filters, the pressure drop across the filter is significantly reduced. The nanofiber layer is applied as a very thin coating to enhance filtration efficiency for fine nanoscale particles. Additionally, the thin nanofiber coating is designed to promote capture of dust particles on the filter surface and to facilitate dust removal with pulse or back airflow.

Laicer, Castro↗

NASA NTRS2018

Challenges in Clinical Management of Radiation-Induced Illnesses in Exploration Spaceflight

Historical solar particle events (SPEs) provide context for some understanding of acute radiation exposure risk to astronauts traveling outside of low Earth orbit. Modeling of potential doses delivered to exploration crewmembers anticipates limited radiation-induced health impacts, including prodromal symptoms of nausea, emesis, and fatigue, but suggests that more severe clinical manifestations are unlikely. Recent large animal-model research in space-analogs closely mimicking SPEs has identified coagulopathic events independent of the hematopoietic sequelae of higher radiation doses, similar in manifestation to disseminated intravascular coagulation (DIC). We explored the challenges of clinical management of radiation-related clinical manifestations, using currently accepted modeling techniques and anticipated physiological sequelae, to identify medical capabilities needed to successfully manage SPE-induced radiation illnesses during exploration spaceflight.

Blue, Rebecca↗

NASA NTRS2018

NASA GeneLab Project: Bridging Space Radiation Omics with Ground Studies

Accurate assessment of risk factors for long-term space missions is critical for human space exploration: therefore it is essential to have a detailed understanding of the biological effects on humans living and working in deep space. Ionizing radiation from Galactic Cosmic Rays (GCR) is one of the major risk factors factor that will impact health of astronauts on extended missions outside the protective effects of the Earth's magnetic field. Currently there are gaps in our knowledge of the health risks associated with chronic low dose, low dose rate ionizing radiation, specifically ions associated with high (H) atomic number (Z) and energy (E). The GeneLab project (genelab.nasa.gov) aims to provide a detailed library of Omics datasets associated with biological samples exposed to HZE. The GeneLab Data System (GLDS) currently includes datasets from both spaceflight and ground-based studies, a majority of which involve exposure to ionizing radiation. In addition to detailed information for ground-based studies, we are in the process of adding detailed, curated dosimetry information for spaceflight missions. GeneLab is the first comprehensive Omics database for space related research from which an investigator can generate hypotheses to direct future experiments utilizing both ground and space biological radiation data. In addition to previously acquired data, the GLDS is continually expanding as Omics related data are generated by the space life sciences community. Here we provide a brief summary of space radiation related data available at GeneLab.

Genelab↗

NASA NTRS2019

Assessing Uncertainties of a Geophysical Approach to Estimate Surface Fine Particulate Matter Distributions from Satellite-Observed Aerosol Optical Depth

Health impact analyses are increasingly tapping the broad spatial coverage of satellite aerosol optical depth (AOD) products to estimate human exposure to fine particulate matter (PM2.5). We use a forward geophysical approach to derive ground-level PM2.5 distributions from satellite AOD at 1 km2(exp) resolution for 2011 over the northeastern US by applying relationships between surface PM2.5 and column AOD (calculated offline from speciated mass distributions) from a regional air quality model (CMAQ; 12×12 km2(exp) horizontal resolution). Seasonal average satellite-derived PM2.5 reveals more spatial detail and best captures observed surface PM2.5 levels during summer. At the daily scale, however, satellite-derived PM2.5 is not only subject to measurement uncertainties from satellite instruments, but more importantly to uncertainties in the relationship between surface PM2.5 and column AOD. Using 11 ground-based AOD measurements within 10 km of surface PM2.5 monitors, we show that uncertainties in modeled PM2.5∕AOD can explain more than 70 % of the spatial and temporal variance in the total uncertainty in daily satellite-derived PM2.5 evaluated at PM2.5 monitors. This finding implies that a successful geophysical approach to deriving daily PM2.5 from satellite AOD requires model skill at capturing day-to-day variations in PM2.5∕AOD relationships. Overall, we estimate that uncertainties in the modeled PM2.5∕AOD lead to an error of 11 µg m−3(exp) in daily satellite-derived PM2.5, and uncertainties in satellite AOD lead to an error of 8 µg m−3(exp). Using multi-platform ground, airborne, and radiosonde measurements, we show that uncertainties of modeled PM2.5∕AOD are mainly driven by model uncertainties in aerosol column mass and speciation, while model representation of relative humidity and aerosol vertical profile shape contributes some systematic biases. The parameterization of aerosol optical properties, which determines the mass extinction efficiency, also contributes to random uncertainty, with the size distribution being the largest source of uncertainty and hygroscopicity of inorganic salt the second largest. Future efforts to reduce uncertainty in geophysical approaches to derive surface PM2.5 from satellite AOD would thus benefit from improving model representation of aerosol vertical distribution and aerosol optical properties, to narrow uncertainty in satellite-derived PM2.5.

aerosol optical depth↗

NASA NTRSDate not supplied

Composition and Evolution of Southeast Asian Biomass Burning Trace Gas Emissions during a September 2019 Borneo Burning Event

Biomass burning in Southeast Asia is a major pollutant source contributing to poor air quality throughout the region. Thus, understanding these emissions is critical for predicting and mitigating their health impacts. While there have been many studies reporting ground-based and satellite measurements, regional scale measurements to tie the two together have not been common in this region. The 2019 Cloud, Aerosol and Monsoon Processes Philippines Experiment (CAMP2Ex) field campaign examined Southeast Asian regional sources and their effects on aerosol/cloud interactions using a combination of airborne, shipborne, and ground-based measurements. On 16 September, a major biomass burning event primarily located on Borneo was sampled via aircraft in the near-field over the Sulu Sea, while on 17 September, the aged plume from the event was sampled again east of the Philippines over the Philippine Sea. We discuss the emissions from this event, using tracer-tracer ratios (e.g. MCE, NOy/CO), HYSPLIT back trajectories, and HSRL-2 lidar retrievals to differentiate sources and provide insight into the composition and chemical evolution of the plume. This case study provides a valuable perspective into the accuracy of emission inventories as well as a rare opportunity for satellite validation.

Joshua P DiGangi↗

NASA NTRS2020

Advancing Methodologies for Applying Machine Learning and Evaluating Spatiotemporal Models of Fine Particulate Matter (PM 2.5 ) Using Satellite Data Over Large Regions

Reconstructing the distribution of fine particulate matter (PM 2.5 ) in space and time, even far from ground monitoring sites, is an important exposure science contribution to epidemiologic analyses of PM 2.5 health impacts. Flexible statistical methods for prediction have demonstrated the integration of satellite observations with other predictors, yet these algorithms are susceptible to overfitting the spatiotemporal structure of the training datasets. We present a new approach for predicting PM 2.5 using machine-learning methods and evaluating prediction models for the goal of making predictions where they were not previously available. We apply extreme gradient boosting (XGBoost) modeling to predict daily PM 2.5 on a 1 x 1 km 2 resolution for a 13 state region in the Northeastern USA for the years 2000–2015 using satellite-derived aerosol optical depth and implement a recursive feature selection to develop a parsimonious model. We demonstrate excellent predictions of withheld observations but also contrast an RMSE of 3.11 μg/m 3 in our spatial cross-validation withholding nearby sites versus an overfit RMSE of 2.10 μg/m 3 using a more conventional random ten-fold splitting of the dataset. As the field of exposure science moves forward with the use of advanced machine-learning approaches for spatiotemporal modeling of air pollutants, our results show the importance of addressing data leakage in training, overfitting to spatiotemporal structure, and the impact of the predominance of ground monitoring sites in dense urban sub-networks on model evaluation. The strengths of our resultant modeling approach for exposure in epidemiologic studies of PM 2.5 include improved efficiency, parsimony, and interpretability with robust validation while still accommodating complex spatiotemporal relationships.

air pollution↗

NASA NTRS2020

Understanding and Improving Model Representation of Aerosol Optical Properties for a Chinese Haze Event Measured During KORUS-AQ

KORUS-AQ was an international cooperative air quality field study in South Korea that measured local and remote sources of air pollution affecting the Korean Peninsula during May–June 2016. Some of the largest aerosol mass concentrations were measured during a Chinese haze transport event (24 May). Air quality forecasts using the WRF-Chem model with aerosol optical depth (AOD) data assimilation captured AOD during this pollution episode but overpredicted surface particulate matter concentrations in South Korea, especially PM2.5, often by a factor of 2 or larger. Analysis revealed multiple sources of model deficiency related to the calculation of optical properties from aerosol mass that explain these discrepancies. Using in situ observations of aerosol size and composition as inputs to the optical properties calculations showed that using a low-resolution size bin representation (four bins) underestimates the efficiency with which aerosols scatter and absorb light (mass extinction efficiency). Besides using finer-resolution size bins (8–16 bins), it was also necessary to increase the refractive indices and hygroscopicity of select aerosol species within the range of values reported in the literature to achieve better consistency with measured values of the mass extinction efficiency (6.7 m2 g−1 observed average) and light-scattering enhancement factor (f(RH)) due to aerosol hygroscopic growth (2.2 observed average). Furthermore, an evaluation of the optical properties obtained using modeled aerosol properties revealed the inability of sectional and modal aerosol representations in WRF-Chem to properly reproduce the observed size distribution, with the models displaying a much wider accumulation mode. Other model deficiencies included an underestimate of organic aerosol density (1.0 g cm−3 in the model vs. observed average of 1.5 g cm−3) and an overprediction of the fractional contribution of submicron inorganic aerosols other than sulfate, ammonium, nitrate, chloride, and sodium corresponding to mostly dust (17 %–28 % modeled vs. 12 % estimated from observations). These results illustrate the complexity of achieving an accurate model representation of optical properties and provide potential solutions that are relevant to multiple disciplines and applications such as air quality forecasts, health impact assessments, climate projections, solar power forecasts, and aerosol data assimilation.

Pablo E. Saide↗