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NASA NTRSDate not supplied

Impact of Pacific Ocean Heatwaves on Phytoplankton Community Composition

Since 2013, marine heatwaves have become recurrent throughout the equatorial and northeastern Pacific Ocean and are expected to increase in intensity relative to historic norms. Among the ecological ramifications associated with these high temperature anomalies are increased mortality of higher trophic organisms such as marine mammals and seabirds, which are likely triggered by changes in the composition of phytoplankton, the base of the marine trophic food web. Here, we assimilated satellite ocean color data into an ocean biogeochemical model to describe changes in the abundance of phytoplankton functional types (PFTs) during the last decade’s (2010s) warm anomalies in the equatorial and northeastern Pacific Ocean. We find important changes associated with the “Blob” warm anomaly in the Gulf of Alaska, where reduced silica supply led to a switch in community composition from diatoms to dinoflagellates, resulting in an increase in surface ocean chlorophyll during the Summer–Fall of 2014. A more dramatic change was observed in the equatorial Pacific, where the extreme warm conditions of the 2016 El Niño resulted in a major decline of about 40% in surface chlorophyll, which was associated with a nearly total collapse in diatoms.

Lionel A Quintero↗
NASA NTRS2023

New Evidence on the Origin of Solar Wind Microstreams/Switchbacks

Microstreams are fluctuations in the solar wind speed and density associated with polarity-reversing folds in the magnetic field (also denoted switchbacks). Despite their long heritage, the origin of these microstreams/switchbacks remains poorly understood. For the first time, we investigated periodicities in microstreams during Parker Solar Probe (PSP) Encounter 10 to understand their origin. Our analysis was focused on the inbound corotation interval on 2021 November 19–21, while the spacecraft dove toward a small area within a coronal hole (CH). Solar Dynamics Observatory remote-sensing observations provide rich context for understanding the PSP in situ data. Extreme ultraviolet images from the Atmospheric Imaging Assembly reveal numerous recurrent jets occurring within the region that was magnetically connected to PSP during intervals that contained microstreams. The periods derived from the fluctuating radial velocities in the microstreams (approximately 3, 5, 10, and 20 minutes) are consistent with the periods measured in the emission intensity of the jetlets at the base of the CH plumes, as well as in larger coronal jets and in the plume fine structures. Helioseismic and Magnetic Imager magnetograms reveal the presence of myriad embedded bipoles, which are known sources of reconnection-driven jets on all scales. Simultaneous enhancements in the PSP proton flux and ionic ( 3 He, 4 He, Fe, O) composition during the microstreams further support the connection with jetlets and jets. In keeping with prior observational and numerical studies of impulsive coronal activity, we conclude that quasiperiodic jets generated by interchange/ breakout reconnection at CH bright points and plume bases are the most likely sources of the microstreams/ switchbacks observed in the solar wind.

Pankaj Kumar↗
NASA NTRS2023

LunaNet Position, Navigation, and Timing Services and Signal, Enabling the Future of Lunar Exploration

The International Space Exploration Coordination Group established in 2018 the 3rd edition of the Global Exploration Roadmap (ISECG, 2018) that aims to achieve Mars human surface activities and identifies the exploration of the Moon as a critical intermediate step. A supplement covering updates on surface exploration scenarios was released in 2020 (ISECG, 2020). The Artemis Accords (NASA Artemis, 2020), first signed in October 2020, now includes over two dozen nations, in an agreement on the principles for best practices, including interoperability. September 2022 introduced the National Aeronautics and Space Administration’s (NASA) Moon to Mars Objectives highlighting recurring tenets of collaboration with international and industry partners and interoperability, along with infrastructure objectives for Position, Navigation, and Timing (PNT). The successful Artemis 1 mission paved the way to the ambitious plans to establish a sustainable human presence on the Moon. Just a few months after Artemis 1 launch (NASA, 2022), iSpace HAKUTO-R Mission1 (iSpace, 2022) launched, being the first-ever commercial mission, launched by a commercial launch service provider, aiming to land on the lunar surface. The NASA Artemis programme plans initial crewed landings and traverses in 2025, supported by the Lunar Gateway, followed by regular launches to build the lunar systems (NASA Artemis Plan, 2020), (NASA, 2022). NASA’s contracts with Commercial Lunar Payload Services (CLPS, (NASA, n.d.)) to deliver science and technology to the Moon, with launches starting in November 2023. The European Space Agency (ESA) Argonaut (ESA Argonaut, 2022) programme plans to have recurrent missions to bring payloads to the lunar surface, supporting lunar exploration. These are just a few examples of planned missions that will target the Earth’s natural satellite in the next decade, with forecasts of tens of missions per year (NSR, 2022), (Euroconsult, 2020). The large number of missions and the complexity of landing and operating are expected to demand a change of paradigm from the current Earth-based communication and navigation services. In recent years, several agencies have proposed to deploy cislunar communication and navigation services to support lunar missions (NASA LCRNS, 2022), (ESA Moonlight, 2022), (JAXA, 2022)). All these proposals seek to deploy service-providing satellites in lunar orbit to ease the user missions’ operations. The PNT services objective is to support all types of lunar users (e.g.: orbiters, landers, ascent vehicles, and surface crew and rovers). At the same time, NASA and ESA initiated an effort to define a common framework to ensure interoperability among different service providers: the LunaNet framework. The LunaNet Interoperability Specification (NASA and ESA, 2023) covers communication, PNT, and auxiliary services, by establishing a common set of requirements to ensure interoperability. This conference contribution will present the LunaNet PNT services, focusing on the Lunar Augmented Navigation Service (LANS) that resembles the Global Navigation Satellite System (GNSS) concept on Earth: constellations of satellites broadcasting a radio navigation signal synchronized to a common reference clock, with augmentations to accommodate users’ needs in an environment away from Earth. This includes a description of the high-level LANS concept and the basic principles defined to ensure interoperability. In addition, it will describe the common S-band PNT Augmented Forward Signal (AFS) and common messages to be adopted for compliance with the LunaNet framework, and the justification of the selected approach.

LunaNet↗
NASA NTRS2023

Stiffness and Fatigue Life Estimator for Polymer Composite Laminates Using Machine Learning

Machine learning (ML) models are increasingly being used in many engineering fields due to the advancements in ML algorithms and availability of high-speed computing power. One of the most popular ML class of models is artificial neural networks (ANN). ML is increasingly being used in the design and analysis of composite materials and structures, specifically in the constitutive modeling of composite materials with the focus on greatly accelerating multiscale analyses of composite materials and structures through development of surrogate models. Towards that end, Python-based neural nets have been developed to predict initial stiffness and fatigue life of an eight-ply symmetric polymer matrix composite laminate. Two types of neural networks, a Multilayer Perceptron (MLP) and a Recurrent Neural Network (RNN), have been established. Results show that both neural net type algorithms can provide an excellent estimate of initial laminate stiffness as well as fatigue life of eight-ply symmetric polymer matrix composite laminates (PMCs). RNNs are better able to capture the shape of the fatigue curve of a laminate. The resulting tool and GUI can be very useful for system level studies to obtain an estimate of desired properties and life of PMC composite laminates. Further, the associated surrogate models can also be used in composite multiscale analyses to replace the actual physics-based calculations at lower scales and thereby significantly increase the computational efficiency of such analyses and thus make micromechanics-based multiscale analyses a viable industrial tool for large scale structural problems.

multiscale analysis↗
NASA NTRS2023

Lunanet Position, Navigation, and Timing Services and Signals, Enabling the Future of Lunar Exploration

The International Space Exploration Coordination Group established in 2018 the 3rd edition of the Global Exploration Roadmap (ISECG, 2018) that aims to achieve Mars human surface activities and identifies the exploration of the Moon as a critical intermediate step. A supplement covering updates on surface exploration scenarios was released in 2020 (ISECG, 2020). The Artemis Accords (NASA Artemis, 2020), first signed in October 2020, now includes over two dozen nations, in an agreement on the principles for best practices, including interoperability. In September 2022 the National Aeronautics and Space Administration (NASA) introduced the Moon to Mars Objectives highlighting recurring tenets of collaboration with international and industry partners and interoperability, along with infrastructure objectives for Position, Navigation, and Timing (PNT). The successful Artemis 1 mission paved the way to the ambitious plans to establish a sustainable human presence on the Moon. Just a few months after Artemis 1 launch (NASA, 2022), iSpace HAKUTO-R Mission1 (iSpace, 2022) launched, being the first-ever mission launched by a commercial launch service provider aiming to land on the lunar surface. The NASA Artemis program plans initial crewed landings and surface traverses in 2025, supported by the Lunar Gateway. Regular launches will follow to build the lunar systems for a sustained presence as presented in the Artemis Plan (NASA Artemis Plan, 2020), (NASA, 2022). NASA’s contracts with commercial providers through the Commercial Lunar Payload Services program (CLPS, (NASA, n.d.)) will deliver science and technology demonstration missions to the Moon starting in November 2023. The European Space Agency (ESA) Argonaut (ESA Argonaut, 2022) program plans to have recurrent missions to bring payloads to the lunar surface, supporting lunar exploration. These are just a few examples of planned missions that will target Earth’s natural satellite in the next decade, with forecasts of tens of missions per year (NSR, 2022), (Euroconsult, 2020). The large number of missions and the complexity of landing and operating are expected to demand a change of paradigm from the current Earth-based communication and navigation services, that may be combined with onboard sensors. In recent years, several agencies have proposed to deploy cislunar communication and navigation services to support lunar missions (NASA LCRNS, 2022), (ESA Moonlight, 2022), (JAXA, 2022)). All these proposals seek to deploy service-providing satellites in lunar orbit to ease the user missions’ operations. The PNT services objective is to support all types of lunar users (e.g.: orbiters, landers, ascent vehicles, surface crew, rovers, and deployed science payloads). At the same time, NASA and ESA initiated an effort to define a common framework to ensure interoperability among different service providers: the LunaNet framework. The LunaNet Interoperability Specification (NASA and ESA, 2023) covers communication, PNT, and auxiliary services, by establishing a common set of requirements to ensure interoperability. This conference contribution will present the LunaNet PNT services, focusing on the Lunar Augmented Navigation Service (LANS) that would be provided by a system that resembles the Global Navigation Satellite System (GNSS) concept on Earth: constellations of satellites broadcasting a radio navigation signal synchronized to a common reference clock, with augmentations to accommodate users’ needs in an environment away from Earth. This paper includes a description of the high-level LANS concept, and the basic principles defined to ensure interoperability. In addition, it will describe the common S-band PNT Augmented Forward Signal (AFS) and common messages to be adopted for compliance with the LunaNet framework, and the justification of the selected approach.

LunaNet↗
NASA NTRS2023

Self-Similar Outflows at the Source of the Fast Solar Wind: A Smoking Gun of Multiscale Impulsive Reconnection?

We present results of a quantitative analysis of structured plasma outflows above a polar coronal hole observed by the Atmospheric Imaging Assembly (AIA) on board the Solar Dynamics Observatory (SDO) spacecraft. In a 6 hr interval of continuous high-cadence SDO/AIA images, we identified more than 2300 episodes of small-scale plasma flows in the polar corona. The mean upward flow speed measured by the surfing transform technique is estimated to be 122 ± 34 km s−1, which is comparable to the local sound speed. The typical recurrence period of the flow episodes is 10–30 minutes, and the mean duration and transverse size of each episode are about 3–5 minutes and 3–4 Mm, respectively. The largest identifiable episodes last for tens of minutes and reach widths up to 40 Mm. For the first time, we demonstrate that the polar coronal-hole outflows obey a family of power-law probability distributions characteristic of impulsive interchange magnetic reconnection. Turbulent photospheric driving may play a crucial role in releasing magnetically confined plasma onto open field. The estimated occurrence rate of the detected self-similar coronal outflows is sufficient for them to make a dominant contribution to the fast-wind mass and energy fluxes and to account for the wind's small-scale structure.

jets↗
NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden C. Szeto↗
NASA NTRSDate not supplied

Comparison Study of Machine Learning Techniques to Predict Flight Energy Consumption for Advanced Air Mobility

This paper addresses the need to predict the flight energy consumption of aerial vehicles in the presence of wind using machine learning techniques. The presented work is critical to achieving sustainable and efficient operations for Advanced Air Mobility (AAM) and to evaluating the readiness of the ground-supporting energy infrastructure, e.g., electric grid and AAM portals. The flight energy consumption is described using the "energy per meter" (EPM) metric. We present a comparison study of influential machine learning techniques in predicting EPM using real-world flight test data. We presented new results of using the Decision Tree, Random Forest, and linear regression techniques, along with our previous results using the Recurrent Neural Network and Feed Forward Neural Network techniques. The comparison results show that the Linear Regression method outperforms other methods on the basis of the Mean Squared Error and error variance.

Machine Learning↗
NASA NTRSDate not supplied

Classification of Notices to Airmen using Natural Language Processing

This paper establishes the feasibility of using Natural Language Processing (NLP) to classify NOTAMs or Notices to Airmen – a pilot messaging framework to gather real-time situational awareness. Present day air mobility operations heavily rely on NOTAMs. However, pilots often have difficulty interpreting NOTAMs due to the sheer volume of inapplicable messages and unclear abbreviations. Using NLP, the presented study analyzes the accuracy of classifying NOTAMs and, thereby, the efficiency of generating actionable interpretations in real time. To this effect, efficacies of four NLP neural network architectures were analyzed, including three Recurrent Neural Networks (RNNs) with GloVe, Word2Vec, and FastText word embeddings, and one trained Bi-Directional Encoder Representations from Transformers (BERT) model. The four neural networks were trained and evaluated on three open-source datasets of varying text lengths, vocabularies, and grammars, taken from e-commerce product descriptions, social media tweets, and unstructured descriptions for data and analytics services on open data marketplaces such as NASA’s Data and Reasoning Fabric (DRF) platform. This provided cross-analysis of each neural network architecture’s performance per text type. The best performing architecture, BERT, was then fine-tuned on a collection of open-source NOTAM data. Post-training, a real-time NOTAM classification service was implemented to draw inference on new NOTAMs using the trained model, which demonstrated close to 99% accuracy in classification. This modular classification service is envisioned to be integrated with a data and analytics delivery platform, such as the DRF, thus availing real-time contextualization of NOTAMs to air mobility clients, humans, and machines for enhanced decision making.

Aiden Szeto↗
NASA NTRSDate not supplied

Impact of Pacific Ocean Heatwaves on Phytoplankton Community Composition

Since 2013, marine heatwaves have become recurrent throughout the equatorial and northeastern Pacific Ocean and are expected to increase in intensity relative to historic norms. Among the ecological ramifications associated with these high temperature anoma- lies are increased mortality of higher trophic organisms such as marine mammals and sea- birds, which are likely triggered by changes in the composition of phytoplankton, the base of the marine trophic food web. Here, we assimilated satellite ocean color data into an ocean biogeochemical model to describe changes in the abundance of phytoplankton functional types (PFTs) during the last decade’s (2010s) warm anomalies in the equatorial and northeastern Pacific Ocean. We find important changes associated with the “Blob” warm anomaly in the Gulf of Alaska, where reduced silica supply led to a switch in community composition from diatoms to dinoflagellates, resulting in an increase in surface ocean chlorophyll during the Summer–Fall of 2014. A more dramatic change was observed in the equatorial Pacific, where the extreme warm conditions of the 2016 El Niño resulted in a major decline of about 40% in surface chlorophyll, which was associated with a nearly total collapse in diatoms.

Lionel A. Arteaga↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
NASA NTRS2022

Revisiting the Ground Magnetic Field Perturbations Challenge: A Machine Learning Perspective

Forecasting ground magnetic field perturbations has been a long-standing goal of the space weather community. The availability of ground magnetic field data and its potential to be used in geomagnetically induced current studies, such as risk assessment, have resulted in several forecasting efforts over the past few decades. One particular community effort was the Geospace Environment Modeling (GEM) challenge of ground magnetic field perturbations that evaluated the predictive capacity of several empirical and first principles models at both mid- and high-latitudes in order to choose an operative model. In this work, we use three different deep learning models-a feed-forward neural network, a long short-term memory recurrent network and a convolutional neural network-to forecast the horizontal component of the ground magnetic field rate of change (dB H /dt) over 6 different ground magnetometer stations and to compare as directly as possible with the original GEM challenge. We find that, in general, the models are able to perform at similar levels to those obtained in the original challenge, although the performance depends heavily on the particular storm being evaluated. We then discuss the limitations of such a comparison on the basis that the original challenge was not designed with machine learning algorithms in mind.

Victor A. Pinto↗
NASA NTRS2024

Variability and Distribution of Nighttime Equatorial to Mid Latitude Ionospheric Irregularities and Vertical Plasma Drift Observed by FORMOSAT-5 Advanced Ionospheric Probe In-Situ Measurements from 2017 – 2020

Irregularities in ionospheric plasma distribution can result in severe scintillation and disruption to the radio frequencies utilized for satellite communications and navigation. In the low and mid latitudes, these irregularities can include Equatorial Plasma Bubbles (EPBs) and Travelling Ionospheric Disturbances (TIDs). EPBs are irregularities manifesting in low latitude nighttime ionosphere plasma density that can extend along magnetic field lines with zonal scales on the order of 100 km or less, while TIDs are propagating wave disturbances. High frequency in-situ measurements of ionospheric plasma aboard spacecraft in Low Earth Orbit (LEO) are a direct measurement of irregularities in plasma density and are therefore valuable for resolving EPB and TID occurrences, variability, and relation to other ionospheric parameters that are believed to play a driving role in the formation of such irregularities. In this study, we utilize observations taken over a three-year period between 2017 and 2020 by the Advanced Ionospheric Probe (AIP) carried aboard the FORMOSAT-5 satellite to examine the spatial, seasonal, and interannual variability of equatorial to mid latitude ionospheric irregularities and vertical ion drift during this time. AIP provides in-situ measurements of ion density and vertical ion drift in the equatorial to mid latitude ionosphere at approximately 720 km altitude with local times between 22:00 – 23:00 local time. Our global scale results resolve distinct and inter-annually recurrent seasonal patterns in the distribution of nighttime ionospheric irregularities and vertical plasma drift during this time. Elevated occurrences of ion density irregularities are resolved along the Equatorial Ionization Anomaly (EIA) latitudes, while notable occurrences with variability consistent with EPBs also observed along the low and equatorial magnetic latitudes. Zonal variability of equatorial irregularities consistent with the signatures of nonmigrating atmospheric tides are observed. It is also notable that the occurrences and geographic distribution of ion density irregularities showed a considerable level of interannual variability, especially at mid latitudes over the South Atlantic and Southern African sectors, which showed much higher levels of irregularities in 2017–––2018, compared to 2019 and 2020. In comparison, the spatial and interannual variation of the co-located vertical ion drifts were much more consistent during the years examined, indicating that the driver for the observed interannual variability in ion density irregularities cannot be attributed to the vertical ion drift at the same time and location of the observations. This highlights the need for in-situ instruments distributed across multiple satellites in different local time zones.

ionosphere↗
NASA NTRSDate not supplied

Predictive Workload Model for Air Traffic Controllers during UAM Operations

The effect of airspace factors on air traffic controller (ATC) workload has been an active area of study for almost three decades due to the importance of safety considerations necessary to design and maintain operations. Existing literature has examined several traffic-related (e.g., number of aircraft under control, loss of separation) contributors to ATC workload and proposed mathematical functions to best describe controller response. However, future air traffic continues to increase in complexity with the introduction of urban air mobility (UAM) – or the transportation of humans and cargo using electric vertical takeoff and landing (eVTOL) aircraft. UAM aims to alleviate congestion for existing ground transportation systems and improve mobility within urban centers and other high-demand locations. This shift in the traditional airspace paradigm necessitates an evolved understanding of model use and development for ATC workload prediction. This study aimed to develop an ATC workload forecasting model based on human-in-the-loop (HITL) simulation data for UAM operations at large airports. Data collected from the HITL simulation served as the training and testing data for a Long Short-Term Memory recurrent neural network and enabled time-series forecasting of ATC workload from traffic characteristics. Results demonstrated the potential of LSTM models for forecasting ATC workload 40 minutes into the future and highlighted important considerations for future development.

predictive model↗
NASA NTRSDate not supplied

State of Wildfires

Fire is an essential component of ecosystems and acts as key driver of biogeochemical cycling with impacts on vegetation structure and composition, soil conditions, and climate feedbacks. Fire behavior and effects vary based on the types of fuel, fuel dryness, and frequency of ignition. Due to changing climate and land use patterns, fire danger is increasing in many regions globally, and fires are having increasingly devastating impacts on human health, infrastructure, and ecosystem services. Recurrent fires help to determine the distribution of trees and grasses, and overall fuel load, which inform the behavior of future fires. Process-based models can be used to capture multi-scale impacts at the forest stand-level up to the landscape level, and across minutes to centuries, but must capture variation in fire behavior and intensity as a function of the fuel and climate, from high intensity forest crown fires within boreal regions to low intensity rapid grass fires of the tropics. Fire model development demonstrates our ability to capture large scale fire influenced biogeography and vegetation distribution at the earth system scale through vegetation traits and fire feedbacks. At the individual stand scale the importance of interactions and feedbacks between above and below ground process is essential to capturing the vegetation dynamics across boreal forest systems. Improving the mechanics of including plant physiology and specifically live fuel moisture content is the next step to advancing the capability of process based models to inform fire research. Advances in the testing and creation of a mechanistic live fuel moisture model demonstrate the foundation for future live fuel dynamics research that can be informed by field and remote sensing information. Improved remote sensing, technological and modeling capabilities support a more comprehensive and cohesive fire response that will be better equipped to overcome current barriers and anticipate the new reality of fires in a warming world.

wildfires↗
NASA NTRS2001

Short-term hemodynamic performance of the mitral Carpentier-Edwards PERIMOUNT pericardial valve. Carpentier-Edwards PERIMOUNT Investigators

BACKGROUND: Although long-term durability data exist, little data are available concerning the hemodynamic performance of the Carpentier-Edwards PERIMOUNT pericardial valve in the mitral position. METHODS: Sixty-nine patients who were implanted with mitral PERIMOUNT valves at seven international centers between January 1996 and February 1997 consented to participate in a short-term echocardiography follow-up. Echocardiographs were collected at a mean of 600+/-133 days after implantation (range, 110 to 889 days); all underwent blinded core lab analysis. RESULTS: At follow-up, peak gradients were 9.09+/-3.43 mm Hg (mean, 4.36+/-1.79 mm Hg) and varied inversely with valve size (p < 0.05). The effective orifice areas were 2.5+/-0.6 cm2 and tended to increase with valve size (p = 0.08). Trace mitral regurgitation (MR) was common (n = 48), 9 patients had mild MR, 1 had moderate MR, none had severe MR. All MR was central (n = 55) or indeterminate (n = 3). No paravalvular leaks were observed. Mitral regurgitation flow areas were 3.4+/-2.8 cm2 and were without significant volumes. CONCLUSIONS: In this multicenter study, these mitral valves are associated with trace, although physiologically insignificant, central MR. Despite known echocardiographic limitations, the PERIMOUNT mitral valves exhibit similar hemodynamics to other prosthetic valves.

Clinical Trial↗
NASA NTRS1998

Prognostic significance of electrical alternans versus signal averaged electrocardiography in predicting the outcome of electrophysiological testing and arrhythmia-free survival

OBJECTIVE: To investigate the accuracy of signal averaged electrocardiography (SAECG) and measurement of microvolt level T wave alternans as predictors of susceptibility to ventricular arrhythmias. DESIGN: Analysis of new data from a previously published prospective investigation. SETTING: Electrophysiology laboratory of a major referral hospital. PATIENTS AND INTERVENTIONS: 43 patients, not on class I or class III antiarrhythmic drug treatment, undergoing invasive electrophysiological testing had SAECG and T wave alternans measurements. The SAECG was considered positive in the presence of one (SAECG-I) or two (SAECG-II) of three standard criteria. T wave alternans was considered positive if the alternans ratio exceeded 3.0. MAIN OUTCOME MEASURES: Inducibility of sustained ventricular tachycardia or fibrillation during electrophysiological testing, and 20 month arrhythmia-free survival. RESULTS: The accuracy of T wave alternans in predicting the outcome of electrophysiological testing was 84% (p < 0.0001). Neither SAECG-I (accuracy 60%; p < 0.29) nor SAECG-II (accuracy 71%; p < 0.10) was a statistically significant predictor of electrophysiological testing. SAECG, T wave alternans, electrophysiological testing, and follow up data were available in 36 patients while not on class I or III antiarrhythmic agents. The accuracy of T wave alternans in predicting the outcome of arrhythmia-free survival was 86% (p < 0.030). Neither SAECG-I (accuracy 65%; p < 0.21) nor SAECG-II (accuracy 71%; p < 0.48) was a statistically significant predictor of arrhythmia-free survival. CONCLUSIONS: T wave alternans was a highly significant predictor of the outcome of electrophysiological testing and arrhythmia-free survival, while SAECG was not a statistically significant predictor. Although these results need to be confirmed in prospective clinical studies, they suggest that T wave alternans may serve as a non-invasive probe for screening high risk populations for malignant ventricular arrhythmias.

Non-NASA Center↗
NASA NTRS1997

Antarctic isolation: immune and viral studies

Stressful environmental conditions are a major determinant of immune reactivity. This effect is pronounced in Australian National Antarctic Research Expedition populations exposed to prolonged periods of isolation in the Antarctic. Alterations of T cell function, including depression of cutaneous delayed-type hypersensitivity responses and a peak 48.9% reduction of T cell proliferation to the mitogen phytohaemagglutinin, were documented during a 9-month period of isolation. T cell dysfunction was mediated by changes within the peripheral blood mononuclear cell compartment, including a paradoxical atypical monocytosis associated with altered production of inflammatory cytokines. There was a striking reduction in the production by peripheral blood mononuclear cells of the predominant pro-inflammatory monokine TNF-alpha and changes were also detected in the production of IL-1, IL-2, IL-6, IL-1ra and IL-10. Prolonged Antarctic isolation is also associated with altered latent herpesvirus homeostasis, including increased herpesvirus shedding and expansion of the polyclonal latent Epstein-Barr virus-infected B cell population. These findings have important long-term health implications.

NASA Discipline Environmental Health↗