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Aerosol sampling with next-generation sequencing was used to characterize microbial communities in a cafeteria and medical facility waiting room in Los Alamos, New Mexico, USA. We detected sequences from human, bacteria, archaea, fungi, other eukaryotes, and viruses, providing insights into the diversity of the aerosol microbiome.
The landmark detection of both gravitational waves (GWs) and electromagnetic (EM) radiation from the binary neutron star merger GW170817 has spurred efforts to streamline the follow-up of GW alerts in current and future observing runs of ground-based GW detectors. Within this context, the radio band of the EM spectrum presents unique challenges. Sensitive radio facilities capable of detecting the faint radio afterglow seen in GW170817, and with sufficient angular resolution, have small fields of view compared to typical GW localization areas. Additionally, theoretical models predict that the radio emission from binary neutron star mergers can evolve over weeks to years, necessitating long-term monitoring to probe the physics of the various postmerger ejecta components. These constraints, combined with limited radio observing resources, make the development of more coordinated follow-up strategies essential—especially as the next generation of GW detectors promises a dramatic increase in detection rates. Here, we present RADAR, a framework designed to address these challenges by promoting community-driven information sharing, federated data analysis, and system resilience, while integrating AI methods for both GW signal identification and radio data aggregation. We show that it is possible to preserve data rights while sharing models that can help design and/or update follow-up strategies. We demonstrate our approach through a case study of GW170817, and discuss future directions for refinement and broader application.
A major focus of the commercial aviation community is discovery of unknown safety events in flight operations data. Data-driven unsupervised anomaly detection methods are better at capturing unknown safety events compared to rule-based methods which only look for known violations. However, not all statistical anomalies that are discovered by these unsupervised anomaly detection methods are operationally significant (e.g., represent a safety concern). Subject Matter Experts (SMEs) have to spend significant time reviewing these statistical anomalies individually to identify a few operationally significant ones. In this paper we propose an active learning algorithm that incorporates SME feedback in the form of rationales to build a classifier that can distinguish between uninteresting and operationally significant anomalies. Experimental evaluation on real aviation data shows that our approach improves detection of operationally significant events by as much as 75% compared to the state-of-the-art. The learnt classifier also generalizes well to additional validation data sets.
The Geostationary Operational Environmental Satellite -16 (GOES-16) Geostationary Lightning Mapper (GLM) is evaluated for many months during the Post Launch Product Test (PLPT) phase in order to ensure that optimal products are available for both the operational forecasting and broader scientific research communities. The emphasis of the PLPT phase is to validate the GLM performance (i.e., lightning flash detection efficiency, geolocation and time-stamp accuracy) using an extensive network of independent ground-based, in-situ, and space-based reference lightning detection systems. However, another essential aspect of the PLPT phase is to obtain benchmarks of the GLM lightning optical amplitude, so that any long-term degradation in the nadir-staring GLM camera system can be realized and quantitatively assessed. This is accomplished in a straight-forward manner by collecting a very large sample of lightning flashes across many geographical regions in the GLM field-of-view so that statistically meaningful benchmarks of lightning optical amplitude (i.e., optical energy in units of femto-joules per flash) are obtained. The benchmarking is particularly important to follow-on studies that will attempt to incorporate the flash optical energy product into new derived products (e.g., energy-weighted lightning "jump" warning algorithms, and lightning nitrogen oxides production estimates).
An essential factor for remote sensing data products in impacting decision making is latency, or the time between earth observation and data are available to users. In many applications areas, latency plays an important or even decisive role where low latency earth observations help people to make timely, data-based decisions. Within the open and free NASA resources, NASA’s Land, Atmosphere Near real-time Capability for Earth Observing System (EOS) (LANCE) supports users interested in monitoring a wide variety of natural and man-made phenomena using near real-time (NRT) data products that are made available much quicker than routine processing allows. The combination of all available LANCE satellite products provides global coverage at multiple times per day, which makes it possible to meet user needs in various areas of applications including water resources, agriculture, air quality, wildland fire and many other disasters monitoring and management. As one of the prime users of LANCE, NASA’s Earth Science Applied Sciences Program promotes the use of LANCE data products to demonstrate applications in decision making and facilitates the end-user feedback to the science team to improve data products. One of the most critical applications that we expect data to be processed as close to the user as possible is wildland file response and management. User feedback indicates that LANCE NRT fire data products within 3 hours latency would meet the needs of the wildland fire community. Other examples of earth science application areas for which low latency is particularly important include detecting volcanic eruptions, early warning of disasters, tracking extreme weather events, and monitoring air quality.
Natural disasters are a major cause of power outages, primarily due to their damage to power line infrastructure. As a result, the current state of power maintenance, with a heavy reliance on manual labor, is in trouble. Its lack of speed and autonomy must be addressed to ensure power security for households and institutions worldwide. This innovative approach explores how High-Speed Unmanned Aerial Vehicle (HSUAV) technology can inspect power line infrastructure rapidly in response to natural disaster scenarios. The HSUAV technology will be accompanied by enhanced sensory equipment including light detection and ranging (LiDAR), thermal imaging, and electromagnetic field (EMF) navigation. It would also be supplemented by innovative forms of machine learning models and recharging systems to efficiently detect anomalies in power line infrastructure. The proposed system, ENLIGHTEN, can help restore power in affected communities after a devastating natural disaster, greatly improving relief efforts. In the future, ENLIGHTEN can be expanded beyond the United States and shared worldwide, effectively mitigating the consequences on a larger scale.
As we approach the James Webb Space Telescope (JWST) era, several studies have emerged that aim to (1) characterize how the instruments will perform and (2) determine what atmospheric spectral features could theoretically be detected using transmission and emission spectroscopy. To some degree, all these studies have relied on modeling of JWST's theoretical instrument noise. With under two years left until launch, it is imperative that the exoplanet community begins to digest and integrate these studies into their observing plans, as well as think about how to leverage the Hubble Space Telescope (HST) to optimize JWST observations. To encourage this and to allow all members of the community access to JWST & HST noise simulations, we present here an open-source Python package and online interface for creating observation simulations of all observatory-supported timeseries spectroscopy modes. This noise simulator, called PandExo, relies on some aspects of Space Telescope Science Institute's Exposure Time Calculator, Pandeia. We describe PandExo and the formalism for computing noise sources for JWST. Then we benchmark PandExoʼs performance against each instrument team's independently written noise simulator for JWST, and previous observations for HST. We find that PandExo is within 10% agreement for HST/WFC3 and for all JWST instruments.
In this report, we describe an experiment conducted at Kennedy Space Center in the biomass production chamber (BPC) using soybean plants for purification and processing of human hygiene water. Specifically, we tested whether it was possible to detect changes in the root-associated bacterial assemblage of the plants and ultimately to identify the specific microorganism(s) which differed when plants were exposed to hygiene water and other hydroponic media. Plants were grown in hydroponics media corresponding to four different treatments: control (Hoagland's solution), artificial gray water (Hoagland's+surfactant), filtered gray water collected from human subjects on site, and unfiltered gray water. Differences in rhizosphere microbial populations in all experimental treatments were observed when compared to the control treatment using both community level physiological profiles (BIOLOG) and molecular fingerprinting of 16S rRNA genes by terminal restriction fragment length polymorphism analysis (TRFLP). Furthermore, screening of a clonal library of 16S rRNA genes by TRFLP yielded nearly full length SSU genes associated with the various treatments. Most 16S rRNA genes were affiliated with the Klebsiella, Pseudomonas, Variovorax, Burkholderia, Bordetella and Isosphaera groups. This molecular approach demonstrated the ability to rapidly detect and identify microorganisms unique to experimental treatments and provides a means to fingerprint microbial communities in the biosystems being developed at NASA for optimizing advanced life support operations.
Absolute gas counting (AGC) was applied to two gas blends of 85Kr in argon-methane (P10) counting gas to establish a high-precision specific activity (Bq/cm3) reference value for characterizing 85Kr detection efficiency for groundwater age dating measurements. The AGC or length-compensated technique has been utilized by the metrology community for decades and is an accepted method for developing radioactive gas standards. The AGC capability at Pacific Northwest National Laboratory (PNNL) uses a set of nine unequal-length proportional counters with precisely-measured internal volumes, and a gas loading system with high-precision pressure and temperature sensors. A series of AGC measurements were collected at multiple pressures to determine the inverse pressure relationship (1/P) for 85Kr and define a wall-effect correction that accounts for events decaying into the detector wall and not depositing sufficient energy in the gas to be detected. In addition to the wall-effect, two additional corrections were evaluated and are discussed in detail. Specifically, the threshold effect which accounts for events deposited below the analysis threshold and a detection efficiency as a function of detector volume effect that was observed during analysis. A robust uncertainty model was developed using the Guide to the expression of Uncertainty in Measurements (GUM) approach. The combination of carefully scrutinized correction factors, precise measurements of pressure, temperature and detector volume, and robust counting statistics resulted in the determination of high-precision specific activity values with 0.50% or less total combined uncertainty for two Kr-in-P10 reference gas standards (KP10) that will enable new groundwater age-dating measurements at PNNL.
The LargE Area burst Polarimeter (LEAP) will investigate the nature of gamma-ray burst jets by making via the first high-fidelity polarization and spectroscopy measurements of the prompt gamma-ray emission from a large sample of gamma-ray bursts (GRBs). LEAP is a proposed International Space Station (ISS) payload with a three-year mission designed to answer the following science questions. Are the jet magnetic fields randomly oriented or are their directions ordered? Are the jets dominated by matter or magnetic fields? Is the energy dissipated within the jet by internal shocks or by magnetic reconnection? Is the non-thermal emission mechanism synchrotron radiation, and what portion of the signal is of thermal photospheric origin? LEAP's baseline mission requires observation of at least 65 GRBs with a sensitivity defined by a minimum detectable polarization (MDP) of 30%. The current LEAP design is expected to trigger on approximately 400 GRBs, with about 86 of those having an MDP <30%. LEAP will enable rapid community follow-up to better understand GRBs and their environments. The LEAP design enables a broad range of secondary science while achieving its baseline mission. During overlap between LIGO's A+ configuration, approximately 3 joint GW/GRB detections per year are expected with LEAP. LEAP will also be sensitive to magnetar bursts, which have recently been associated with Fast Radio Bursts and will potentially measure polarization for bright individual bursts or stacked collections of bursts. LEAP will extend pulse flux and spin frequency histories for accreting pulsars with a sensitivity similar to Fermi GBM, and will potentially measure polarization for their brightest outbursts. The LEAP mission is scheduled during the declining phase of Solar Cycle 25, during which many intense flares are likely to occur; LEAP will make the most sensitive measurements to date of solar flare polarization. LEAP will open a new window into the nature of the most energetic phenomena in the universe with gamma-ray polarization.
The NREL Hydrogen Sensor Laboratory was commissioned in 2010 as a resource for sensor developers, end-users, and regulatory agencies within the national and international hydrogen community. The Laboratory continues to provide as its core capability the unbiased verification of hydrogen sensor performance to assure sensor availability and their proper use. However, the mission and strategy of the NREL Sensor Laboratory has evolved to meet the needs of the growing hydrogen market. The Sensor Laboratory program has expanded to support research in conventional and alternative detection methods as hydrogen use expands to large-scale markets as envisioned by the DOE National Clean Hydrogen Strategy and Roadmap. Current research encompasses advanced methods of hydrogen leak detection including stand-off and wide area monitoring approaches for large scale and distributed applications. In addition to safety applications, low-level detection strategies to support the potential environmental impacts of hydrogen and hydrogen product losses along the value chain are being explored. Many of these applications utilize detection strategies that supplement and may supplant the use of traditional point sensors. The latest results of the hydrogen detection strategy research at NREL will be presented.
This paper describes the results of a set of experiments and analyses conducted to evaluate the capability of small unmanned aircraft systems (sUAS) to spot nascent fires in the Great Dismal Swamp (GDS) National Wildlife Refuge. This work is the result of a partnership between the National Aeronautics and Space Administration and the US Fish and Wildlife service specifically to investigate sUAS usage for fire-spotting. The objectives of the current effort were to: 1) Determine suitability and utility of low-cost Small Unmanned Aircraft Systems (sUAS) to detect nascent fires at GDS; 2) Identify and assess the necessary National Airspace System (NAS) integration issues; and 3) Provide information to GDS and the community on system requirements and concepts-of-operation (CONOPS) for conducting fire detection/support mission in the National Airspace and (4) Identify potential applications of intelligent autonomy that would enable or benefit this high-value mission. In addition, data on the ability of various low-cost sensors to detect smoke plumes and fire hot spots was generated during the experiments as well as identifying a path towards a future practical mission utility by using sUAS in beyond visual-line-of-sight operation in the National Airspace System (NAS).
Until recently, the simultaneous detection and evaluation of multiple Space Transportation System (STS) element compartments for propellant leakage was not possible during the External Tank (ET) pre-pressurization (pre-press) phase of propellant loading; late in the loading sequence (T-3 min to T-0). There exist launch commit criteria (LCCS) designed to protect against leaks resulting in flammable mixtures in the intertank during the functional life of the ET; start of propellant loading through ET-Orbiter separation. However thus far, leak protection had relied on the premise that if a propellant supply system leak were to exist, it would be a leak of fixed area present at the start of propellant loading (dumb leak mode) and such a leak could be detected earlier in the loading sequence than the pre-press phase. No measures had ever been implemented which would protect against leaks potentially developing late in the loading sequence (smart leak mode), The STS community had baselined this situation as an accepted risk their decision supported by exhaustive hardware acceptance criteria and a history of many successful launches. With the recent development of improvements to the hazardous gas detection system (HGDS), an opportunity arose to monitor the intertank compartment for hazardous gas concentrations beyond the T-3 minute mark. In this timeframe the propellant supply system is pressurized for flight and protection was sought against potential leaks generated by this pressurization process. The challenge at hand was to develop LCCs in this timeframe that were not too conservative to unnecessarily prohibit a launch, yet not too liberal to endanger crew safety and mission success. Since essentially no measurement history existed in this timeframe, the LCCs would have to rely on analysis alone. At NASA's direction, Lockheed Martin Michoud Space Systems (LMMSS) developed requirements that would protect against a smart leak during pre-press. A smart leak protection strategy is noteworthy from an STS main propulsion system (NWS) community perspective. This paper presents the resultant requirements as well as the technical and program issues involved with establishing hazardous gas LCCs for the ET intertank compartment.
Microorganisms grow differently in spaceflight than they do on Earth. While numerous factors likely contribute, one ubiquitous feature of the space environment is altered gravity - but because microbes are generally considered too small to detect gravity directly, they likely experience the effects of gravity via changes in their fluid environment, primarily a reduction in convective mixing. This theory is supported by evidence that individual microbial strains experience starvation and acid stress in microgravity. If it is true, then microgravity would also have measurable effects on microbes in mixed communities, because many interspecies interactions involve the exchange of soluble metabolites through their medium (cross-feeding). Specifically, cooperative cross-feeding communities would grow more slowly in microgravity, and this effect should be reproducible in ground-based rotational culture systems (rotating wall vessels, RWVs) designed to simulate the quiescent environment of microgravity. Here we present preliminary results of our efforts to test this hypothesis using using a model system of Eschericia coli and Salmonella enterica that grow only when they can exchange methionine and acetate through the medium. We report on the development of a novel laboratory system that allows us measure growth rates and species ratios in real-time using fluorescence spectrometry as the organisms grow in RWVs. We are able to track growth rates of individual species and the co-culture continuously with high temporal resolution for several hours, a substantial improvement over prior RWV systems that require destructive sampling. We vary the fidelity of the microgravity simulation by varying the rotation rate, and find that rotation rate affects growth rates of the community more than that of individual strains, consistent with the claim that RWVs can simulate a microgravity-like quiescent environment. While more work needs to be done to probe the mechanisms of this effect, we hope that our system will ultimately allow us to generate quantitative predictions about the effects of spaceflight on organisms that are tiny but essential to sustaining long-term human space exploration.
A body of techniques that have been developed and planned for use during the Earth Radiation Budget Experiment (ERBE), the International Satellite Cloud Climatology Project (ISCCP), and related climate experiments of the 1980's are reviewed. Validation and verification methods must apply for systems of satellites. They include: (1) use of a normalization or intercalibration satellite, (2) special intensive observation areas located over ground-truth sites, and (3) monitoring of sun and earth by several satellites and/or several instruments at the same time. Since each climate application area has a hierarchy of user communities, validation techniques vary from very detailed methods to those that simply assure high relative accuracy in detecting space and time variations for climate studies. It is shown that climate experiments generally require more emphasis on long-term stability and internal consistency of satellite data sets than high absolute accuracy.
We describe the development of an easy-to-use sensor with widespread applications for detecting small molecules. The flow immunosensor can analyze discrete samples in under one minute or continuously monitor a flowing stream for the presence of specific analytes. This detection system is extremely specific, and achieves a level of sensitivity which meets or exceeds the detection limits reported for rival assays. Because the system is also compact, transportable, and automated, it has the potential to impact diverse areas. For example, the flow immunosensor has successfully detected drugs of abuse and explosives, and may well address many of the needs of the environmental community with respect to continuous monitoring for pollutants. Efforts are underway to engineer a portable device in the field.
The National Snow and Ice Data Center (NSIDC) Distributed Active Archive Center (DAAC) provides data and information on snow and ice processes, especially pertaining to interactions among snow, ice, atmosphere and ocean, in support of research on global change detection and model validation, and provides general data and information services to cryospheric and polar processes research community. The NSIDC DAAC is an integral part of the multi-agency-funded support for snow and ice data management services at NSIDC. The Moderate Resolution Imaging Spectroradiometer (MODIS) will be flown on the first Earth Observation System (EOS) platform (AM-1) in 1998. The MODIS Instrument Science Team is developing geophysical products from data collected by the MODIS instrument, including snow and ice products which will be archived and distributed by NSIDC DAAC. The MODIS snow and ice mapping algorithms will generate global snow, lake ice, and sea ice cover products on a daily basis. These products will augment the existing record of satellite-derived snow cover and sea ice products that began about 30 years ago. The characteristics of these products, their utility, and comparisons to other data set are discussed. Current developments and issues are summarized.
The goal of this program is to acquire a FUSE spectrum of the symbiotic binary RR Tel. With these data, we plan to derive improved constraints on the hot component, the nebula, and perhaps the red giant wind. Based on results from AG Dra, we should also be able to use some line detections to improve atomic parameters for high ionization emission lines. This results would benefit the general FUSE community. As of this writing, the FUSE observation of RR Tel has not been made. Because RR Tel is a very bright UV source, the FUSE team is assessing the likelihood that RR Tel will have an adverse affect on the instrument.