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NASA NTRS2016

Taxi-Out Time Prediction for Departures at Charlotte Airport Using Machine Learning Techniques

Predicting the taxi-out times of departures accurately is important for improving airport efficiency and takeoff time predictability. In this paper, we attempt to apply machine learning techniques to actual traffic data at Charlotte Douglas International Airport for taxi-out time prediction. To find the key factors affecting aircraft taxi times, surface surveillance data is first analyzed. From this data analysis, several variables, including terminal concourse, spot, runway, departure fix and weight class, are selected for taxi time prediction. Then, various machine learning methods such as linear regression, support vector machines, k-nearest neighbors, random forest, and neural networks model are applied to actual flight data. Different traffic flow and weather conditions at Charlotte airport are also taken into account for more accurate prediction. The taxi-out time prediction results show that linear regression and random forest techniques can provide the most accurate prediction in terms of root-mean-square errors. We also discuss the operational complexity and uncertainties that make it difficult to predict the taxi times accurately.

Safe and efficient surface operations↗

NASA NTRS2018

Grasp Assist Device with Automatic Mode Control Logic

A system includes a glove, sensors, actuator assemblies, and controller. The sensors include load sensors which measure an actual grasping force and attitude sensors which determine a glove attitude. The actuator assembly provides a grasp assist force to the glove. Respective locations of work cells in the work environment and permitted work tasks for each work cell are programmed into the controller. The controller detects the glove location and attitude. A work task is selected by the controller for the location. The controller calculates a required grasp assist force using measured actual grasping forces from the load sensors. The required grasp assist force is applied via the glove using the actuator assembly to thereby assist the operator in performing the identified work task.

Davis, Donald R.↗

NASA NTRS2018

Antarctic Martian Meteorites at Johnson Space Center

This past year marked the 40th anniversary of the first Martian meteorite found in Antarctica by the ANSMET Antarctic Search for Meteorites) program, ALH 77005. Since then, an additional 14 Martian meteorites have been found by the ANSMET program making for a total of 15 Martian meteorites in the U. S. Antarctic meteorite collection at Johnson Space Center (JSC). Of the 15 meteorites, some have been paired so the 15 meteorites actually represent a total of approximately 9 separate samples. The first Martian meteorite found by ANSMET was ALH 77005 (482.500 g), a lherzolitic shergottite. When collected, this meteorite was split as a part of the joint expedition with the National Institute of Polar Research (NIPR) Japan. Originally classified as an "achondrite-unique", it was re-classified as a Martian lherzolitic shergottite in 1982. This meteorite has been allocated to 137 scientists for research and there are 180.934 g remaining at JSC. Two years later, one of the most significant Martian meteorites of the collection at JSC was found at Elephant Moraine, EET 79001 (7942.000 g), a shergottite. This meteorite is the largest in the Martian collection at JSC and was the largest stony meteorite sample collected during the 1979 season. In addition to its size, this meteorite is of particular interest because it contains a linear contact separating two different igneous lithologies, basaltic and olivine-phyric. EET 79001 has glass inclusions that contain noble gas and nitrogen compositions that are proportionally identical to the Martian atmosphere, as measured by the Viking spacecraft. This discovery helped scientists to identify where the "SNC" meteorite suite had originated, and that we actually possessed Martian samples. This meteorite has been allocated to 205 scientists for research and 5,298.435 g of sample is available.

Funk, R. C.↗

NASA NTRS2018

A Prognostic Launch Vehicle Probability of Failure Assessment Methodology for Conceptual Systems Predicated on Human Causal Factors

Create an improved method to calculate reliability of a conceptual launch vehicle system prior to fabrication by using historic data of actual root causes of failures. While failures have unique "proximate causes", there are typically a finite amount of common "root causes". Heretofore launch vehicle reliability evaluation typically hardware-centric statistical analyses, while most root causes of failures are been shown to be human-centric. A method based on human-centric root causes can be used to quantify reliability assessments and focus proposed actions to mitigate problems. Existing methods have been optimistic in their projections of launch vehicle reliability compared to actuals. Hypothesis: reliability of a conceptual launch vehicle can be more accurately evaluated based on a rational, probabilistic approach using past failure assessment teams' findings predicated on human-centric causes."Human Reliability Analysis Methods Selection Guidance for NASA"Chandler F.T., et al., NASA HQ/OSMA study group, July 2006. Outside HRA experts from academia, other federal labs, and the private sector. 50 system reliability methods considered, fourteen selected for further study, four finally selected as best suited for human spaceflight. Probabilistic Risk Analysis (PRA) + Human Reliability Analysis (HRA) enabled incorporating effects and probabilities of human errors. While four down-selected methods deemed appropriate for failure assessment, it did not appear that these methods could be concisely applied to perform major system-wide assessment of probability of failure of a conceptual design without becoming unwieldy."Engineering a Safer World", Detailed, comprehensive study external to NASA Leveson N. G., MIT, 2011.Systems-Theoretic Accident Model and Processes (STAMP). All-encompassing accident model based on systems theory analyzed accidents after they occurred and created approaches to prevent occurrence in developing systems not focused on failure prevention per se, but rather reducing hazards by influencing human behavior through use of constraints, hierarchical control structures, and process models to improve system safetySystem Theoretic Process Analysis (STPA) addresses predictive part of problem (a "hazard analysis"). Includes all causal factors identified in STAMP: "...design errors, software flaws, component interaction accidents, cognitively complex human decision-making errors, and social organizational and management factors contributing to accidents" can guide design process rather than require it to exist before-hand did not appear capable of concise application for system-wide assessment of probability of failure of a conceptual design without becoming unwieldy.

Williams, Craig H.↗

NASA NTRS2018

Ammonia Vent of the External Active Thermal Control System (EATCS) Radiator #3 Flow Path #2 on the International Space Station (ISS)

The External Active Thermal Control System (EATCS) provides cooling for all pressurized modules and the main Power Distribution Electronics (PDE) on the International Space Station (ISS). There are 2 EATCS loops (Loop A and Loop B) which includes 3 deployable radiators. Each deployable radiator contains 2 flow paths to provide heat rejection.Telemetry monitoring identified a coolant (liquid ammonia) leak in EATCS Loop B. Robotic External Leak Locator (RELL) scans found higher concentrations of vaporous ammonia near the EATCS Loop B Radiator #3 Flow Path #2. On May 3, 2017, the EATCS Loop B Radiator #3 Flow Path #2 was isolated and vented. As of the data to date, the ammonia leak has ceased. The purpose of this presentation is to discuss the analysis for venting the EATCS Loop B Radiator #3 Flow Path #2. Venting analysis is performed to determine the worst case time to empty the flow path and maximum thrusts imposed on the ISS. Flight controllers and engineers in the Mission Control Center (MCC) uses this data to develop operational procedures and perform the vent safely. It was predicted that the worst case time to empty the EATCS Loop B Radiator #3 Flow Path #2 was ~ 60 minutes. The predicted maximum thrusts were ~ 11 lbf (49 N) at the start of the vent and ~10 lbf (45 N) after the system reaches saturation.The vent was successfully performed and took ~ 20 minutes to empty the EATCS Loop B Radiator #3 Flow Path #2. Using telemetry from the day of the vent, analysis determined the time to empty the EATCS Loop B Radiator #3 Flow Path #2 would be ~13 minutes. The predictive analysis used worst case inputs and assumptions which bounded the actual results. Telemetry is not available to correlate actual thrust with the predicted maximum thrusts. However, by using Russian Thrusters for ISS attitude control, attitude control telemetry indicated the flight attitude was maintained.

Cowan, Darnell T.↗

NASA NTRS2018

Gene Expression of Pathogens in Simulated Microgravity

Extended exposure to radiation and microgravity in space has been linked to astronauts developing chronic diseases upon returning to Earth. The Gram-negative pathogen Serratia marcescens has been shown to potentially cause significant infections in humans and in insect models on Earth. Our recent findings also showed that S. marcescens shows an increase in virulence after a short period of growth in the spaceflight environment, which raises initiatives to find the correlation between space environment and the increased virulence. Because we know that the health of astronauts is immunocompromised in space, it is possible that the combination of increased bacterial virulence and the weakened immune system will cause astronauts to be more susceptible to chronic diseases in extended spaceflight. With 75% of human disease genes being conserved in the fruit fly Drosophila melanogaster, these insects act as an ideal model organism to study the human immune system. The high accessibility, low cost, high rate of reproductivity, and short lifespans of D. melanogaster facilitate efficient, high-quality research that seeks to understand altered virulence of this opportunistic pathogen. In this ground-based study, we will use a rotating wall vessel apparatus to simulate microgravity and determine how pathogenicity changes by evaluating differences in gene expression for S. marcescens between bacteria grown in simulated microgravity conditions and controls. We will compare the results of our findings to gene expression patterns in actual spaceflight samples of S. marcescens grown on the ISS (International Space Station) during a recent validation mission, to see if there are common mechanisms across our simulated microgravity and actual spaceflight microgravity samples that both show increased virulence in the fruit fly. With extended space travel in the foreseeable future, understanding how human physiology will be affected by these different factors will help mitigate risks and deaths.

spaceflight analogue↗

NASA NTRS2018

End-to-End Solution for Data Customization with NASA's Earthdata Search

The goal of NASA's Earthdata Search End-to-End Services workflow is to take the pain and headache out of searching for data and getting that data back in a format that is usable with only that data that is relevant for you. For too long scientists have had to jump through endless hoops, use tools that only offer specific data or specific services, and perform any number of other non-science tasks just to get started on their actual project. Earthdata Search leverages the Common Metadata Repository's (CMR) newly implemented Unified Metadata Models for Services and Variables as well as a new service broker to expose and seamlessly integrate a collection's service capabilities and variables into an intuitive user interface. Using the new End-to-End Services workflow, scientists will be able to quickly see what data is available to be customized, what customization options are available, and actually perform those customizations on the data all within Earthdata Search, regardless of who the data provider is. This talk will demonstrate the simple workflow that will be available to end users and also give an overview covering how the workflow is enabled by the metadata stored within the CMR. (https://search.earthdata.nasa.gov/)

Reese, Mark↗

NASA NTRS2018

Required Time of Arrival as a Control Mechanism to Mitigate Uncertainty in Arrival Traffic Demand Management

The objective of this study is to explore the use of Required Time of Arrival (RTA) capability on the flight deck as a control mechanism on arrival traffic management to improve traffic delivery accuracy by mitigating the effect of traffic demand uncertainty. The uncertainties are caused by various factors, such as departure error due to the difference between scheduled departure and the actual take-off time. A simulation study was conducted using the Multi Aircraft Control System (MACS) software, a comprehensive research platform developed in the Airspace Operations Laboratory (AOL) at NASA Ames Research Center. The Crossing Time (CT) performance (i.e. the difference between target crossing time and actual crossing time) of the RTA for uncertainty mitigation during cruise phase was evaluated under the influence of varying two main factors: wind severity (heavy wind vs. mild wind), and wind error (1 hour, 2 hours, and 5 hours wind forecast errors). To examine the CT performance improvement made by the RTA, the comparison to the CT of the aircraft that were not assigned with RTA (Non-RTA) under the influence of the selected factors was also made. The Newark Liberty International Airport (EWR) was chosen for this study. A total 66 inbound traffic to the EWR (34 of them were airborne when the simulation was initiated, 32 were pre-departures at that time) was simulated, where the pre-scripted departure error was assigned to each pre-departure (61 conform to their Expected Departure Clearance Time, which is +-300 seconds of their scheduled departure time). The results of the study show that the delivery accuracy improvement can be achieved by assigning RTA, regardless of the influence of the selected two factors (the wind severity and the wind information inaccuracy). Across all wind variances, 66.9 (265 out of 396) of the CT performance of the RTA assigned aircraft was within +- 60 seconds (i.e. target tolerance range) and 88.9 (352 out of 396) aircraft met +-300 seconds marginal tolerance range, while only 33.6 (133 out of 396) of the Non-RTA assigned aircrafts CT performance achieved the target tolerance range and 75.5 (299 out of 396) stayed within the marginal. Examination of the impact of different error sources i.e. departure error, wind severity, and wind error suggest that although large departure errors can significantly impact the CT performance, the impacts of wind severity and errors were modest relative the targeted +- 60 second conformance range.

required time of arrival (RTA)↗

NASA NTRS2019

Cellular Statistical Models of Broken Cloud Fields. Part IV: Effects of Pixel Size on Idealized Satellite Observations

In the fourth part of our “Cellular Statistical Models of Broken Cloud Fields” series we use the binary Markov processes framework for quantitative investigation of the effects of low resolution of idealized satellite observations on the statistics of the retrieved cloud masks. We assume that the cloud fields are Markovian and are characterized by the “actual” cloud fraction (CF) and scale length. We use two different models of observations: a simple discrete-point sampling and a more realistic “pixel” protocol. The latter is characterized by a state attribution function (SAF) which has the meaning of the probability that the pixel with a certain CF is declared cloudy in the observed cloud mask. The stochasticity of the SAF means that the cloud/clear attribution is not ideal and can be affected by external or unknown factors. We show that the observed cloud masks can be accurately described as Markov chains of pixels and use the master-matrix formalism (introduced in Part III of the series) for analytical computation of their parameters: the “observed” CF and scale length. This procedure allows us to establish a quantitative relationship (which is pixel-size dependent) between the actual and the observed cloud-field statistics. The feasibility of restoring the former from the latter is considered. The adequacy of our analytical approach to idealized observations is evaluated using numerical simulations. Comparison of the observed parameters of the simulated datasets with their theoretical expectations showed an agreement within 0.005 for the CF, while for the scale length it is within 1% in the sampling case and within 4% in the pixel case.

Mikhail D. Alexandrov↗

NASA NTRS2015

Strategic Time-Based Metering that Assures Separation for Integrated Operations in a Terminal Airspace

This paper reports an algorithm for strategic time-based metering of air traffic arriving and departing from a large ( tens of nautical miles) area (called here, the commitment region) around an airport or metroplex. The algorithm assures separation continuously in time and avoids a dictation of intent to an aircraft crew. This is accomplished by allowing an aircraft (specifically, its Flight Management System) to specify, and commit to, an intended route and ground speed profile along that route within the commitment region, and by supplying the time at which to enter the region to the aircraft crew. The airspace that comprises the commitment region need not be confined to the terminal airspace and can include some of the enroute space: the size and shape of the commitment region are parameters in the algorithm. An exact formula for including speed profile uncertainty in the algorithm is provided. The algorithm is applied to a number of data sets recorded during actual air traffic operations in the Southern California TRACON in July of 2014 and the Atlanta TRACON in November and December of 2013. The results of the numerical simulations indicate that the algorithm succeeds at keeping the aircraft separated, but introduces, in its current implementation, more separation than that observed in actual operations. This excess separation can be reduced by modeling more accurately the Visual Flight Rules separation practices, a direction for future research.

air traffic management↗

NASA NTRS2017

Evaluation of the Simple Safe Site Selection (S4) Hazard Detection Algorithm Using Helicopter Field Test Data

Small scale terrain hazards, such as rocks, slopes, and craters, can pose significant risk to landing spacecraft and rover or payload deployment. Onboard Hazard Detection and Avoidance (HDA) systems scan and analyze the landing area for these hazards in real time during descent, and divert the spacecraft to the safest touchdown site. The computationally efficient Simple Safe Site Selection (S4) algorithm combined with a flash LIDAR is an HDA system geared towards small robotic spacecraft. Rather than creating and analyzing a digital elevation map (DEM) from potentially many overlapping range images, S4 operates directly on a single flash LIDAR image. Extending prior work that has analyzed S4 performance for Mars landing using extensive simulations, this paper evaluates S4 performance using actual flash LIDAR images of an artificial hazard field acquired during a 2014 helicopter field test in Death Valley, CA. In particular, we describe LIDAR characterization and calibration, creation of ground truth elevation and safety maps, creation of ground truth sensor poses, actual S4 algorithm processing, and performance analysis. The results show that the safety cost images produced by S4 are remarkably close to the ground truth safety map (computed offline by an HDA algorithm developed under the Autonomous Landing and Hazard Avoidance (ALHAT) project) at significantly reduced computational cost, confirming S4 as a viable candidate algorithm for onboard spacecraft HDA.

Luna, Michael E.↗

NASA NTRS2019

A Ground Footprint Eccentricity Model For Asteroid Airbursts

Uncertainties in early observations of potentially hazardous asteroids result in preliminary impact corridors that can stretch across large portions of the Earth’s surface. At this early stage of detection, the corridor width and potential for damage are typically estimated using techniques from nuclear weapons research. These estimates often employ spherical blast assumptions resulting in a constant width impact corridor. In actuality, however, the ground damage footprint of obliquely entering asteroids is generally roughly elliptical or “butterfly” shaped, with the major axis extending in the cross range direction and the minor axis aligned with ground-track of the meteoroid. Since actual ground footprints for oblique entries may have aspect ratios greater than two or three, the assumption of a circular blast may significantly underestimate the area of the impact swath and the at-risk population. This work develops an engineering model that can be used to quickly estimate the eccentricity of the ground footprint as a function of local impact parameters. This yields vastly improved local estimates of the corridor width and can significantly enhance the accuracy of risk analysis.

Aftosmis, Michael J.↗

NASA NTRS2019

Systemic Alterations with Spaceflight Associated Health Risks Originating from Both Circulating miRNAs and Mitochondrial Biology

The many known health risks currently associated with space travel include increased risk of cardiovascular disease, cancer, central nervous system related diseases, muscle degeneration, and changes with host-gut microbiome interactions that can have profound impact with these and other health risks. The majority of the risk from space travel stem of the two components of the space environment which are microgravity and radiation. Two specific systemic effects have been uncovered by us to impact the body as a whole due to the space environment. One factor is related from our earlier work (Beheshti et al, PLOS One, 2018), we predicted that there is a systemic component of the host that causes general increased health risks due to spaceflight driven by a circulating microRNA (miRNA) signature consisting of 13 miRNAs that directly regulates both p53 and TGF1. MiRNAs are small non-coding RNA molecules with a negative and post-transcriptional regulation on gene expression) are increasingly recognized as major systemic regulators of responses to stressors, including microgravity, oxidative stress, and DNA damage. In addition, due to the size and stability of miRNAs, it is known that miRNAs can circulate throughout the body and have been found in the majority of the bodily fluids including blood, urine, saliva, and tears. Here, we start to dissect the actual impact of this miRNA signature on both the radiation and microgravity components and prove that this miRNA signature actually exists in the circulation of a host. The other systemic factor we uncovered was the impact the mitochondria on the whole body due to spaceflight. We hypothesize that spaceflight may promote a physiologic response driven by systemic mitochondria pathways leading to metabolic disorder stemming from the liver and directly impacting other organs and tissues. A systems biology method was implemented utilizing GeneLab datasets that involved in vitro experiments performed at the low Earth orbit, in vivo experiments involving mice flown to space, and finally human physiological data from astronauts. A comprehensive multi-omics approach was implemented which involved correlating transcriptomic analysis with proteomics, metabolomics, and methylation analysis. This approach led us to confirm our hypothesis that a systemic mitochondrial driven response is responsible for increasing potential health risk and is conserved from the in vitro studies, to the in vivo studies, and finally confirmed in astronauts.

Radiation↗

NASA NTRS2019

ATD-2 TFDM Terminal Publication Service (TTP)

Learn the many benefits of the newest SWIM feed from the surface system. This will discuss current options to onboard with this feed, allow participants to ask questions and provide some examples of how the data can be used. Service Includes Flight Data Individual flight updates containing flight identifiers, targeted times, actual times, runway, parking gate, spot, departure fix (predicted, assigned, actual as appropriate), flight states, and more. Airport Information Airport configurations, airport and runway rates, ramp closures, runway closures, taxiway closures. Traffic Management Restrictions Call for Release programs departure MIT/MINIT restrictions, departure stop/ground stop programs. Along with list of impacted flights for each. Flight Delay Airport and runway delay by arrival, departure, and total. Operational Metrics Metrics on airport throughput and individual flight metrics. Surface Metering Program SMP start / end times, metering constraint type / details, updates to existing programs, TMAT compliance window, departure queue length, and more.

Gorman, Shawn M.↗

NASA NTRS2019

Fast-Time Simulation for Evaluating the Impact of Estimated Flight Ready Time Uncertainty on Surface Metering

NASA has been developing and demonstrating new concepts and technologies for Integrated Arrival, Departure, and Surface (IADS) traffic management capabilities under the Airspace Technology Demonstration 2 (ATD-2) project. One of the IADS capabilities in the ATD-2 project is surface metering enabled by a tactical surface scheduler to provide controllers with the pushback advisories for departures at gates. The tactical surface scheduler uses the estimated flight ready times provided by airlines, called Earliest Off-Block Times (EOBTs), as input to calculate the target off-block times for pushback advisories. However, the EOBTs are often inaccurate and deviate from the actual flight ready times, which may reduce the benefits of surface metering. In this paper, a linear regression model is developed to model the EOBT uncertainty distribution over time based on actual EOBT data collected at Charlotte airport. This EOBT model is integrated with a tactical surface scheduler and a fast-time simulation tool. To evaluate the impact of the EOBT accuracy on airport surface operations, fast-time simulations are implemented for selected traffic scenarios under different levels of modelled EOBT accuracy. The simulation results show that the EOBT uncertainty affects several performance metrics related to the surface metering, such as gate hold, taxi time reduction, and target takeoff time predictability, which in turn influences ATD-2's scheduler performance.

tactical surface scheduler↗

NASA NTRS2019

Fast-Time Simulation for Evaluating the Impact of Estimated Flight Ready Time Uncertainty on Surface Metering

tactical surface scheduler↗

NASA NTRS2014

Analysis of Airport Ground Delay Program Decisions Using Data Mining Techniques

Air traffic service providers have to make decisions regarding changes to air traffic flow in the event of major weather disturbances and traffic congestions to maintain safety of the system. The behavior of the air traffic management system will be more predictable if consistent decisions are made under similar traffic and weather conditions. Consistency of deciding on control action depends on the weather and traffic conditions as well as accuracy in predicting these conditions. Weather parameters (defined in terms of forecast and actual weather and traffic conditions) on different days can be used to categorize days into days with little decision consistency, days with moderate decision consistency and days with high decision consistency. Four years of traffic, weather and ground delay program decisions data at major airports in the United States are used in the analysis. This paper examines performance of different data mining methods in the three regions of decision consistency. Not surprisingly, data mining methods have the best performance in the region of most decision consistency and have the poorest performance in the region of little decision consistency. In applications where data mining methods have differing performance in differing regions, it would be more useful to characterize the region specific performance instead of characterizing performance by a single parameter. Finally, the results show no significant variation in the performance of different data mining methods for this particular problem. The fact that different mining methods show no significant variation also provides further confidence in the results of data mining methods. Work in this abstract discusses initial results. This paper describes the results in terms of both forecast and actual environmental conditions and discusses how prediction errors impact decision consistency.

Kulkarni, Deepak↗

NASA NTRS2019

Circulating miRNA Signature Predicts Health Risks Associated with Radiation and Microgravity

The many known health risks currently associated with space travel include increased risk of cardiovascular disease, cancer, central nervous system related diseases, muscle degeneration, and changes with host-gut microbiome interactions that can have profound impact with these and other health risks. The majority of the risk from space travel stem of the two components of the space environment which are microgravity and radiation. From our earlier work (Beheshti et al, PLOS One, 2018), we predicted that there is a systemic component of the host that causes general increased health risks due to spaceflight driven by a circulating microRNA (miRNA) signature consisting of 13 miRNAs that directly regulates both p53 and TGF1. MiRNAs are small non-coding RNA molecules with a negative and post-transcriptional regulation on gene expression) are increasingly recognized as major systemic regulators of responses to stressors, including microgravity, oxidative stress, and DNA damage. In addition, due to the size and stability of miRNAs, it is known that miRNAs can circulate throughout the body and have been found in the majority of the bodily fluids including blood, urine, saliva, and tears. Here, we start to dissect the actual impact of this miRNA signature on both the radiation and microgravity components and prove that this miRNA signature actually exists in the circulation of a host. To achieve this, we obtained multiple tissues including, serum, liver, and spleen and utilizing droplet digital PCR (ddPCR), we start to show how this circulating miRNA signature impacts which component of the spaceflight. The tissue was obtained from experiments performed on C57BL/6 male mice (N=10 for each condition) that were hindlimb unloaded (HU) to simulated microgravity, irradiated with 2Gy gamma (IR), HU plus IR, and control mice under normal conditions. It was shown that these miRNAs were present in the serum as predicted by the in silico prediction from our earlier predictions. The HU vs Controls show significant increases of the predicted miRNAs in the serum for more than half of the miRNA signature, with remaining miRNAs increasing comparing to the controls close to statistical significance. IR vs control mice showed increases for the miRNAs, but not has pronounced as the HU conditions. Finally, the combination of the HU+IR vs controls showed increases for the majority of the miRNA signature. The data indicates that the miRNA signature originally predicted through in silico methods is mainly associated with the microgravity component and is circulating throughout the host resulting in a systemic impact of the miRNAs on the host. These miRNAs are shown in the literature to potentially increase health risks associated with several diseases. In addition, we have begun testing the potential of utilizing antagonists to this miRNA signature to act as a potential countermeasure to mitigate radiation impact on the organism. This work demonstrates for the first time the potential of a minimally invasive novel biomarker and countermeasure that can be used to mitigate both radiation and microgravity effects.

Beheshti, Afshin↗