Peru: Health & Air Quality - Leveraging Earth Observations and Health Data to Map Outbreak Risk and Inform Public Health Interventions for Zoonotic Disease Prevention
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Jennifer Rogers↗
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Peru Health and Air Quality II: Leveraging Earth Observations and Health Data to Map Outbreak Risk and Inform Public Health Interventions for Zoonotic Disease Prevention
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Jennifer Rogers↗
Leveraging Earth Observations and Health Data to Map Outbreak Risk and Inform Public Health Interventions for Zoonotic Disease Prevention
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Ellen Delgado Florian↗
Prodromal disease: Immune responses of the host macrophage system to humoral factors
A composite is presented of nine studies, each yielding information contributing toward an understanding of methods designed to detect disease during the prodromal stages. The data further point to new areas of study that might be useful in early diagnoses. Five of the none experiments were done in mice. Four of these involved acute infectious disease states and one involved a chronic autoimmune type disease. Of the numerous perimeters studied of the acute diseases, the uptake of H3- thymidine by peripheral blood lymphocytes appeared to yield the earliest indication of disease. This test was not useful in studying the chronic disease state. Four of the nine studies involved application of diagnostic technics to human disease. A normal baseline for H3-thymidine incorporation by human lymphocytes was determined. A subject with severe combined immunodeficiency disease was studied. A human volunteer study was done using Influenza A live attenuated vaccine. Finally, a human volunteer study of subjects infected with Influenza A was done.
Criswell, B. S.↗
Microbial Risk Assessment
Historically, microbiological spaceflight requirements have been established in a subjective manner based upon expert opinion of both environmental and clinical monitoring results and the incidence of disease. The limited amount of data, especially from long-duration missions, has created very conservative requirements based primarily on the concentration of microorganisms. Periodic reevaluations of new data from later missions have allowed some relaxation of these stringent requirements. However, the requirements remain very conservative and subjective in nature, and the risk of crew illness due to infectious microorganisms is not well defined. The use of modeling techniques for microbial risk has been applied in the food and potable water industries and has exceptional potential for spaceflight applications. From a productivity standpoint, this type of modeling can (1) decrease unnecessary costs and resource usage and (2) prevent inadequate or inappropriate data for health assessment. In addition, a quantitative model has several advantages for risk management and communication. By identifying the variable components of the model and the knowledge associated with each component, this type of modeling can: (1) Systematically identify and close knowledge gaps, (2) Systematically identify acceptable and unacceptable risks, (3) Improve communication with stakeholders as to the reasons for resource use, and (4) Facilitate external scientific approval of the NASA requirements. The modeling of microbial risk involves the evaluation of several key factors including hazard identification, crew exposure assessment, dose-response assessment, and risk characterization. Many of these factors are similar to conditions found on Earth; however, the spaceflight environment is very specialized as the inhabitants live in a small, semi-closed environment that is often dependent on regenerative life support systems. To further complicate modeling efforts, microbial dose-response characteristics may be affected by a potentially dysfunctional crew immune system during a mission. In addition, microbial virulence has been shown to change under certain conditions during spaceflight, further complicating dose-response characterization. An initial study of the applicability of microbial risk assessment techniques was performed using Crew Health Care System (CHeCS) operational data from the International Space Station potable water systems. The risk of infection from potable water was selected as the flight systems and microbial ecology are well defined. This initial study confirmed the feasibility of using microbial risk assessment modeling for spaceflight systems. While no immediate threat was detected, the study identified several medically significant microorganisms that could pose a health risk if uncontrolled. The study also identified several specific knowledge gaps in making a risk assessment and noted that filling these knowledge gaps is essential as the risk estimates may change by orders of magnitude depending on the answers. The current phase of the microbial risk assessment studies focuses on the dose-response relationship of specific infectious agents, focusing on Salmonella enterica Typhimurium, Pseudomonas spp., and Escherichia coli, as their evaluation will provide a better baseline for determining the overall hazard characterization. The organisms were chosen as they either have been isolated on spacecraft or have an identified route of infection during a mission. The characterization will utilize dose-response models selected either from the peer-reviewed literature and/or by using statistical approaches. Development of these modeling and risk assessment techniques will help to optimize flight requirements and to protect the safety, health, and performance of the crew.
Ott, C. M.↗
An interoperable data architecture for data exchange in a biomedical research network
This paper discusses the approach taken to develop a prototype data architecture for the discovery and validation of disease biomarkers within a biomedical research network.
data↗
Applied Uses of MERRA Reanalyses: Current and Future Prospects
During the initial planning of the Modern Era Retrospective-analysis for Research and Applications (MERRA), the project was envisioned to become a tool for applied sciences and decision makers, as well as weather and climate research. MERRA was the first reanalysis to provide data at 1 hourly frequencies. After discussions with wind energy users, 50m above the surface winds were included in the data collection to begin to represent the environment at the turbine height. MERRA-2 was the first reanalysis to include interactive and assimilated aerosols. From these, PM 1, 2.5 and 10 records have been computed and contribute to the climate and health sector. The US Center for Disease Control is accessing MERRA-2 data for comparison along with their health data. MERRA-2 Extreme Indices have been computed from the high frequency data (e.g. precipitation maxima, heat waves and fire weather). These have been used to characterize the changing extremes in the United States and around the globe as well as the weather associated with the extremes. The next-generation, MERRA-21C, includes increased resolution (25km) and continues to provide innovative Earth system data. For example, a boundary layer collection will provide constant height level data starting at 100m, continuing up to 4000m. This will provide detailed information about the boundary layer processes that greatly affect the biosphere. Chemical tracers for constituents are included in the system. The ensemble data assimilation is run with 32 members, and the variance for critical variables are stored. We will explore the use of this variance as one measure of uncertainty relevant for applied uses, which has not been included in any MERRA reanalysis. We will discuss use case scenarios developed to engage with the energy and financial sectors, to better understand their needs for climate data. Prospects for further downscaling of reanalyses will also be explored.
Michael Bosilovich↗
Influence of Microgravity on Bacterial Pathogen Virulence and Immune Cell Function—Relevance for Infectious Disease Risk During Spaceflight
Spaceflight has measurable impacts on astronaut immune profiles as well as the virulence patterns of bacterial pathogens. Data with respect to human immunity indicate diminished T and NK cell function, altered cytokine profiles, persistent inflammation, and latent herpesvirus reactivation. Furthermore, evaluation of International Space Station (ISS) crewmembers gives evidence of compromised immunity, including atypical allergy, infectious disease, and dermatitis. Data with respect to certain human bacterial pathogens suggest modified virulence that may be enhanced. It is therefore critical to examine this interaction of immune dysregulation and increased microbial virulence and whether it might synergistically increase the risk of infectious disease to crew members. The goal of this study is to use modeled microgravity to study the impact of medically significant ISS bacteria that may have altered virulence on the immune response of the host. This study consists of two primary aims to assess this relationship. First, immune cells will be collected from healthy test subjects and cultured in static or in modeled microgravity conditions together with either control pathogens or with microgravity conditioned pathogens that were grown in bioreactors. Second, immune cells will be collected from astronauts before, during, and after flight and co-cultured with the control or the microgravity conditioned bacteria. Three pathogens were selected for this investigation: Pseudomonas aeruginosa , Salmonella enterica serovar Enteritidis and Burkholderia cepacia . Previously, the optimal cell to bacteria ratios that produce the greatest immune cell responses have been derived for these three bacteria. Cellular activation, as determined by the induction of cell surface activation markers and cytokine profiles, will be measured. Interactions between cells and bacteria will be assessed using fluorescent and electron microscopy techniques. This study will provide critical information to help understand how microgravity alters microbial virulence and the associated infectious disease risk to crewmembers during spaceflight missions. Over the past year, the Immunology and Microbiology laboratories at NASA Johnson Space Center have collaborated to process the astronaut subject samples to complete the last year of the flight portion of this study. This included the completion of four astronaut subjects full mission sampling sets. Sample processing included innate and adaptive cell flow cytometry as well as analysis of cytokine concentrations in the supernatant. The ground control segment of the study will take place during FY25 which involves parallel infections run under static and clinostat conditions. The acquired data sets over this 3-year study are now being analyzed to provide a comprehensive set of results and conclusions that will contribute to a more effective risk assessment for astronauts during spaceflight regarding this host-pathogen context.
Immunology↗
MAPS of Cancer
Our goal was to produce an interactive visualization from a mathematical model that successfully predicts metastases from head and neck cancer. We met this goal early in the project. The visualization is available for the public to view. Our work appears to fill a need for more information about this deadly disease. The idea of this project was to make an easily interpretable visualization based on what we call "functional maps" of disease. A functional map is a graphic summary of medical data, where distances between parts of the body are determined by the probability of disease, not by anatomical distances. Functional maps often beat little resemblance to anatomical maps, but they can be used to predict the spread of disease. The idea of modeling the spread of disease in an abstract multidimensional space is difficult for many people. Our goal was to make the important predictions easy to see. NASA must face this problem frequently: how to help laypersons and professionals see important trends in abstract, complex data. We took advantage of concepts perfected in NASA's graphics libraries. As an analogy, consider a functional map of early America. Suppose we choose travel times, rather than miles, as our measures of inter-city distances. For Abraham Lincoln, travel times would have been the more meaningful measure of separation between cities. In such a map New Orleans would be close to Memphis because of the Mississippi River. St. Louis would be close to Portland because of the Oregon Trail. Oklahoma City would be far from Little Rock because of the Cheyenne. Such a map would look puzzling to those of us who have always seen physical maps, but the functional map would be more useful in predicting the probabilities of inter-site transit. Continuing the analogy, we could predict the spread of social diseases such as gambling along the rivers and cattle rustling along the trails. We could simply print the functional map of America, but it would be more interesting to show meaningful patterns of dispersal. We had previously published the functional map of the head and neck, but it was difficult to explain to either patients or surgeons because that view of our body did not resemble anatomy. This discrepancy between functional and physical maps is just a mathematical restatement of the well-known fact that some diseases, such as head and neck cancer, spread in complex patterns, not always to the next nearest site. We had discovered that a computer could re-arrange anatomy so that this particular disease spreads to the next nearest site. The functional map explains over 95% of the metastases in 1400 patients. In a sense, we had graphed what our body "looks like" to a tumor. The tumor readily travels between adjacent areas in the functional map. The functional map is a succinct visual display of trends that are not easily appreciated in tables of probabilities.
Gray, Lincoln↗
A Spatiotemporal Indexing Approach for Efficient Processing of Big Array-Based Climate Data with MapReduce
Climate observations and model simulations are producing vast amounts of array-based spatiotemporal data. Efficient processing of these data is essential for assessing global challenges such as climate change, natural disasters, and diseases. This is challenging not only because of the large data volume, but also because of the intrinsic high-dimensional nature of geoscience data. To tackle this challenge, we propose a spatiotemporal indexing approach to efficiently manage and process big climate data with MapReduce in a highly scalable environment. Using this approach, big climate data are directly stored in a Hadoop Distributed File System in its original, native file format. A spatiotemporal index is built to bridge the logical array-based data model and the physical data layout, which enables fast data retrieval when performing spatiotemporal queries. Based on the index, a data-partitioning algorithm is applied to enable MapReduce to achieve high data locality, as well as balancing the workload. The proposed indexing approach is evaluated using the National Aeronautics and Space Administration (NASA) Modern-Era Retrospective Analysis for Research and Applications (MERRA) climate reanalysis dataset. The experimental results show that the index can significantly accelerate querying and processing (10 speedup compared to the baseline test using the same computing cluster), while keeping the index-to-data ratio small (0.0328). The applicability of the indexing approach is demonstrated by a climate anomaly detection deployed on a NASA Hadoop cluster. This approach is also able to support efficient processing of general array-based spatiotemporal data in various geoscience domains without special configuration on a Hadoop cluster.
big data↗
Predictions of malaria vector distribution in Belize based on multispectral satellite data
Use of multispectral satellite data to predict arthropod-borne disease trouble spots is dependent on clear understandings of environmental factors that determine the presence of disease vectors. A blind test of remote sensing-based predictions for the spatial distribution of a malaria vector, Anopheles pseudopunctipennis, was conducted as a follow-up to two years of studies on vector-environmental relationships in Belize. Four of eight sites that were predicted to be high probability locations for presence of An. pseudopunctipennis were positive and all low probability sites (0 of 12) were negative. The absence of An. pseudopunctipennis at four high probability locations probably reflects the low densities that seem to characterize field populations of this species, i.e., the population densities were below the threshold of our sampling effort. Another important malaria vector, An. darlingi, was also present at all high probability sites and absent at all low probability sites. Anopheles darlingi, like An. pseudopunctipennis, is a riverine species. Prior to these collections at ecologically defined locations, this species was last detected in Belize in 1946.
Non-NASA Center↗
Biophysics Representation of the Two-Hit Model of Alzheimer's Disease for the Exploration of Late CNS Risks from Space Radiation
A concern for long-term space travel outside the Earth s magnetic field is the late effects to the central nervous system (CNS) from galactic cosmic ray (GCR) or solar particle events (SPE). Human epidemiology data is severely limited for making CNS risk estimates and it is not clear such effects occur following low LET exposures. We are developing systems biology models based on biological information on specific diseases, and experimental data for proton and heavy ion radiation. A two-hit model of Alzheimer s disease (AD) has been proposed by Zhu et al.(1), which is the framework of our model. Of importance is that over 50% of the US population over the age of 75-y have mild to severe forms of AD. Therefore we recommend that risk assessment for a potential AD risk from space radiation should focus on the projection of an earlier age of onset of AD and the prevention of this possible acceleration through countermeasures. In the two-hit model, oxidative stress and aberrant cell cycle-related abnormalities leading to amyloid-beta plaques and neurofibrillary tangles are necessary and invariant steps in AD. We have formulated a stochastic cell kinetics model of the two-hit AD model. In our model a population of neuronal cells is allowed to undergo renewal through neurogenesis and is susceptible to oxidative stress or cell cycle abnormalities with age-specific accumulation of damage. Baseline rates are fitted to AD population data for specific ages, gender, and for persons with an apolipoprotein 4 allele. We then explore how low LET or heavy ions may increase either of the two-hits or neurogenesis either through persistent oxidative stress, direct mutation, or through changes to the micro-environment, and suggest possible ways to develop accurate quantitative estimates of these processes for predicting AD risks following long-term space travel.
Cucinotta, Francis A.↗
Biomarkers of exposure and dose: state of the art
Biomarkers provide methods to measure changes in biological systems and to relate them to environmental insults and disease processes. Biomarkers can be classified as markers of exposure and dose, markers of sensitivity, and markers of disease. It is important that the differences and applications of the various types of biomarkers be clearly understood. The military is primarily interested in early biomarkers of exposure and dose that do not require high levels of sensitivity but can be used to rapidly triage war fighters under combat or terrorist conditions and determine which, if any, require medical attention. Biomarkers of long-term radiation risk represent the second area of interest for the military. Biomarkers of risk require high sensitivity and specificity for the disease and insult but do not require rapid data turnaround. Biomarkers will help provide information for quick command decisions in the field, characterise long-term troop risks and identify early stages of radiation-induced diseases. This information provides major positive reassurances about individual exposures and risk that will minimise the physical and psychological impact of wartime radiation exposures.
Review, Tutorial↗
NASA's Current Earth Science Program
NASA's Earth science program is a scientific endeavor whose goal is to provide long-term understanding of the Earth as an integrated system of land, water, air and life. A highly developed scientific knowledge of the Earth system is necessary to understand how the environment affects humanity, and how humanity may be affecting the environment. The remote sensing technologies used to gather the global environmental data used in such research also have numerous practical applications. Current applications of remote sensing data demonstrate their practical benefits in areas such as the monitoring of crop conditions and yields, natural disasters and forest fires; hazardous waste clean up; and tracking of vector-borne diseases. The long-term availability of environmental data is essential for the continuity of important research and applications efforts. NASA's Earth observation program has undergone many changes in the recent past.
Charles, Leslie Bermann↗
Using NASA Environmental Data to Enhance Public Health Decision Making
The Universities Space Research Association at the NASA Marshall Space Flight Center is collaborating with the University of Alabama at Birmingham (UAB) School of Public Health and the Centers for Disease Control and Prevention (CDC) to address issues of environmental health and enhance public health decision making by utilizing NASA remotely sensed data and products. The objectives of this collaboration are to develop high-quality spatial data sets of environmental variables, and deliver the data sets and associated analyses to local, state and federal end-user groups. These data can be linked spatially and temporally to public health data, such as mortality and disease morbidity, for further analysis and decision making. Three daily environmental data sets have been developed for the conterminous U.S. on different spatial resolutions for the time period 2003-2008: (1) spatial surfaces of estimated fine particulate matter (PM2.5) exposures on a 10-km grid utilizing the US Environmental Protection Agency (EPA) ground observations and NASA s MODerate-resolution Imaging Spectroradiometer (MODIS) data; (2) a 1-km grid of Land Surface Temperature (LST) using MODIS data; and (3) a 12-km grid of daily Solar Insolation (SI) and maximum and minimum air temperature using the North American Land Data Assimilation System (NLDAS) forcing data. These environmental data sets will be linked with public health data from the UAB REasons for Geographic And Racial Differences in Stroke (REGARDS) national cohort study to determine whether exposures to these environmental risk factors are related to cognitive decline and other health outcomes. These environmental datasets and public health linkage analyses will be made available to public health professionals, researchers and the general public through the CDC Wide-ranging Online Data for Epidemiologic Research (WONDER) system and through peer reviewed publications. To date, two of the data sets have been released to the public in CDC WONDER, Daily Air Temperature and Heat Index for years 1979-2010, and Daily Fine Particulate Matter (PM2.5) air quality measures for years 2003-2008. These data in CDC WONDER can be aggregated to the county-level, state-level, or regional-level as per users need and downloaded in tabular, graphical, and map formats. The summary statistical output are available to web and app developers via the WONDER Application Programming Interface (API). The linkage of these data with the CDC WONDER system provides a significant addition to CDC WONDER, allowing public health researchers and policy makers to better include environmental exposure data in the context of other health data available in CDC WONDER online system. It also substantially expands public access to NASA environmental data, making their use by a wide range of decision makers feasible.
Al-Hamdan, Mohammad↗
A project to evaluate moisture stress in corn and soybean areas of western and southwestern Minnesota
Remote sensing techniques, particularly LANDSAT data, were used to assess soil moisture stress through crop signature in southwestern Minnesota. Related objectives were: localization of droughty, well drained, and poorly drained soils; detection of stress from hail, wind, and disease damage; and the use of remote sensing data for agricultural management. Since the amount and distribution of precipitation were adequate during the 1977 and 1978 growing seasons, no significant stress occurred. Crop conditions were very favorable. As a result, crop signatures were too uniform to reflect soilscape variations and crop condition changes. In 1979 precipitation was again adequate to excess, particularly in June and August. In some cases, poorly drained sites especially, stress conditions developed as a result of excess of water and could be identified on color infrared photographs.
Rust, R. H.↗
Alveolar ventilation to perfusion heterogeneity and diffusion impairment in a mathematical model of gas exchange
This study describes a two-compartment model of pulmonary gas exchange in which alveolar ventilation to perfusion (VA/Q) heterogeneity and impairment of pulmonary diffusing capacity (D) are simultaneously taken into account. The mathematical model uses as input data measurements usually obtained in the lung function laboratory. It consists of two compartments and an anatomical shunt. Each compartment receives fractions of alveolar ventilation and blood flow. Mass balance equations and integration of Fick's law of diffusion are used to compute alveolar and blood O2 and CO2 values compatible with input O2 uptake and CO2 elimination. Two applications are presented. The first is a method to partition O2 and CO2 alveolar-arterial gradients into VA/Q and D components. The technique is evaluated in data of patients with chronic obstructive pulmonary disease (COPD). The second is a theoretical analysis of the effects of blood flow variation in alveolar and blood O2 partial pressures. The results show the importance of simultaneous consideration of D to estimate VA/Q heterogeneity in patients with diffusion impairment. This factor plays an increasing role in gas alveolar-arterial gradients as severity of COPD increases. Association of VA/Q heterogeneity and D may produce an increase of O2 arterial pressure with decreasing QT which would not be observed if only D were considered. We conclude that the presented computer model is a useful tool for description and interpretation of data from COPD patients and for performing theoretical analysis of variables involved in the gas exchange process.
NASA Discipline Environmental Health↗
Controlling Malaria and Other Diseases Using Remote Sensing
Remote sensing offers the vantage of monitoring a vast area of the Earth continuously. Once developed and launched, a satellite gives years of service in collecting data from the land, the oceans, and the atmosphere. Since the 1980s, attempts have been made to relate disease occurrence with remotely sensed environmental and geophysical parameters, using data from Landsat, SPOT, AVHRR, and other satellites. With higher spatial resolution, the recent satellite sensors provide a new outlook for disease control. At sub-meter to I 10m resolution, surface types associated with disease carriers can be identified more accurately. The Ikonos panchromatic sensor with I m resolution, and the Advanced Land Imager with 1 Om resolution on the newly launched Earth Observing-1, both have displayed remarkable mapping capabilities. In addition, an entire array of geophysical parameters can now be measured or inferred from various satellites. Airborne remote sensing, with less concerns on instrument weight, size, and power consumption, also offers a low-cost alternative for regional applications. NASA/GSFC began to collaborate with the Mahidol University on malaria and filariasis control using remote sensing in late 2000. The objectives are: (1) To map the breeding sites for the major vector species; (2) To identify the potential sites for larvicide and insecticide applications; (3) To explore the linkage of vector population and transmission intensity to environmental variables; (4) To monitor the impact of climate change and human activities on vector population and transmission; and (5) To develop a predictive model for disease distribution. Field studies are being conducted in several provinces in Thailand. Data analyses will soon begin. Malaria data in South Korea are being used as surrogates for developing classification techniques. GIS has been shown to be invaluable in making the voluminous remote sensing data more readily understandable. It will be used throughout this study to clearly demonstrate the spatial relationship between the disease intensities, geophysical variables, and socioeconomic parameters. Asides from malaria and filariasis, application of remote sensing to the control of other diseases have been vigorously pursued by NASA's Environment and Health Initiative. The current program includes projects on Rift Valley fever, St. Louis encephalitis, dengue fever, ebola, African dust and diseases, meningitis, asthma, bartonellosis, cholera, and urban health concerns. Results from these projects indicate that remote sensing will play an increasingly important role in disease control in the future.
Kiang, Richard K.↗