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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
DOE OSTI2017

Context-aided analysis of community evolution in networks

Here, we are interested in detecting and analyzing global changes in dynamic networks (networks that evolve with time). More precisely, we consider changes in the activity distribution within the network, in terms of density (ie, edge existence) and intensity (ie, edge weight). Detecting change in local properties, as well as individual measurements or metrics, has been well studied and often reduces to traditional statistical process control. In contrast, detecting change in larger scale structure of the network is more challenging and not as well understood. We address this problem by proposing a framework for detecting change in network structure based on separate pieces: a probabilistic model for partitioning nodes by their behavior, a label-unswitching heuristic, and an approach to change detection for sequences of complex objects. We examine the performance of one instantiation of such a framework using mostly previously available pieces. The dataset we use for these investigations is the publicly available New York City Taxi and Limousine Commission dataset covering all taxi trips in New York City since 2009. Using it, we investigate the evolution of an ensemble of networks under different spatiotemporal resolutions. We identify the community structure by fitting a weighted stochastic block model. In conclusion, we offer insights on different node ranking and clustering methods, their ability to capture the rhythm of life in the Big Apple, and their potential usefulness in highlighting changes in the underlying network structure.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2018

Use of an ion mobility spectrometer for detecting uranium compounds

The safeguards community currently lacks a method to rapidly determine the chemical form of radioactive and non-radioactive compounds in real time during inspection activities. Chemical speciation identification can provide important information on both the types of materials that are collected during environmental sampling and can inform inspectors as to where to focus efforts during inspections or complementary access visits. Ion Mobility Spectrometry (IMS) is an established field technique for the detection of explosives, narcotics, and other organic compounds. More recently, electrospray ionization (ESI) has been used to introduce inorganic compounds to IMS instruments for analysis. These techniques have shown the ability to supply chemical information about the compounds being analyzed. Although these laboratory based instruments use a liquid-based injection system, there is evidence in the literature of unaltered and intact pharmaceutical tablets being volatilized and ionized in open atmosphere using heat and a Ni-63 source. Lastly, this work determined that a commercial-off-the-shelf (COTS) IMS could be used for the identification of solid uranium compounds directly after sampling using a COTS sample swipe.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
DOE OSTI2019

Detailed Statistical Models of Host-Based Data for Detection of Malicious Activity

The cybersecurity research community has focused primarily on the analysis and automation of intrusion detection systems by examining network traffic behaviors. Expanding on this expertise, advanced cyber defense analysis is turning to host-based data to use in research and development to produce the next generation network defense tools. The ability to perform deep packet inspection of network traffic is increasingly harder with most boundary network traffic moving to HTTPS. Additionally, network data alone does not provide a full picture of end-to-end activity. These are some of the reasons that necessitate looking at other data sources such as host data. We outline our investigation into the processing, formatting, and storing of the data along with the preliminary results from our exploratory data analysis. In writing this report, it is our goal to aid in guiding future research by providing foundational understanding for an area of cybersecurity that is rich with a variety of complex, categorical, and sparse data, with a strong human influence component. Including suggestions for guiding potential directions for future research.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2024

Autonomous Anomaly Detection for MPC Forecasts of HVAC Systems in Residential Communities

The use of residential heating, ventilation, and air conditioning (HVAC) to shift peak demand or provide ancillary services is a potential solution in the presence of older grids and distributed renewables. However, to ensure the efficient use of devices, utilities need to accurately forecast the load and adopt error correction schemes when necessary. While significant theoretical research exists in the area of predictive control of HVAC, little experimental evidence exists. The lack of experimental data in turn causes researchers to be unprepared for unsystematic errors which emerge due to the higher complexity of the data generating process. This study offers an anomaly detection methodology that uses unsupervised machine learning algorithms to detect and isolate these errors with different forecast error ranges. The results of anomaly detection procedure can then be used for error correction and would eventually help develop better predictive controllers. The methodology is tested using real world data from a smart neighborhood that currently operates in Atlanta. GA.

Lebakula, Viswadeep↗
DOE OSTI2020

Groundwater Depth Overrides Tree-Species Effects on the Structure of Soil Microbial Communities Involved in Nitrogen Cycling in Plantation Forests

Microbial communities found in soil ecosystems play important roles in decomposing organic materials and recycling nutrients. A clear understanding on how biotic and abiotic factors influence the microbial community and its functional role in ecosystems is fundamental to terrestrial biogeochemistry and plant production. The purpose of this study was to investigate microbial communities and functional genes involved in nitrogen cycling as a function of groundwater depth (deep and shallow), tree species (pine and eucalypt), and season (spring and fall). Soil fungal, bacterial, and archaeal communities were determined by length heterogeneity polymerase chain reaction (LH-PCR). Soil ammonia oxidation archaeal (AOA) amoA gene, ammonia oxidation bacterial (AOB) amoA gene, nitrite oxidoreductase nrxA gene, and denitrifying bacterial narG, nirK, nirS, and nosZ genes were further studied using PCR and denaturing gradient gel electrophoresis (DGGE). Soil fungal and bacterial communities remained similar between tree species and groundwater depths, respectively, regardless of season. Soil archaeal communities remained similar between tree species but differed between groundwater depths in the spring only. Archaeal amoA for nitrification and bacterial nirK and nosZ genes for denitrification were detected in DGGE, whereas bacterial amoA and nrxA for nitrification and bacterial narG and nirS genes for denitrification were undetectable. The detected nitrification and denitrification communities varied significantly with groundwater depth. There was no significant difference of nitrifying archaeal amoA or denitrifying nirK communities between different tree species regardless of season. The seasonal difference in microbial communities and functional genes involved in nitrogen cycling suggests microorganisms exhibit seasonal dynamics that likely impact relative rates of nitrification and denitrification.

54 ENVIRONMENTAL SCIENCES↗
NASA NTRS2019

Does Seed Sanitization Affect the Plant Rhizosphere Microbiome and Its Ability to Compete with the Human Associated Pathogen, E. coli on Salad Crops?

Cultivation of crops in controlled environmental agricultural systems may limit microbial colonization and reduce diversity of the microbial communities. Practices like seed and growth medium sanitization may further impact microbial communities in the mature plant and the plant’s capacity to limit the growth of pathogens through competition. As humans expand their travels to space, understanding plant growth, health, and development in closed environments will be critical to the success of producing a safe, supplemental food source for astronauts. To determine the persistence of a potential human pathogen in plant growth and development, sanitized and unsanitized seeds from, mizuna (Brassica rapa var japonica) and red romaine lettuce (Lactuca sativa cultivar ‘Outredgeous’), were inoculated with Escherichia coli, ATCC 21445, germinated under simulated International Space Station (ISS) environmental conditions and harvested every 7 days until maturity. The persistence of E. coli in the rhizosphere was determined by plating on selective media, real time PCR (Polymerase Chain Reaction) and community sequencing of the rhizosphere communities. E. coli was detected in the crops’ roots and leaves for several weeks post germination. At day 28, plants from sanitized seeds had significantly higher counts of E. coli on the roots than those from unsanitized seeds. E. coli was also detected on a few uninoculated plants indicating airborne cross contamination among plants in the same growth chamber and suggesting an influence of the natural microbiome on human pathogen survival and persistence in leafy greens. Sequencing analysis revealed variations in composition and diversity between the communities. Understanding the microbial community of the rhizospheric microbiome is only the first step in determining the relationships between plants. Additional studies to include genotypic and phenotypic variations in the plants should be considered to determine if the natural microbes in the rhizosphere may contribute to the health and therefore, safety of the edible plants.

Khodadad, Christina L. M.↗
DOE OSTI2024

Metrics and Methods for Radiation Detection Algorithm Characterization for Nuclear/Radiological Source Search

This report presents a series of recommendations for data to train and evaluate radiation detection algorithms and performance metrics to evaluate these algorithms. These recommendations were formed through a community consensus approach through the Detection Radiation Algorithms Group (DRAG), a multi-institution collaboration spanning eight Department of Energy laboratories and John Hopkins Applied Physics Laboratory. This report includes recommendations on background data variability, and metrics to quantify variability, sources and shielding configurations to include in data collection campaigns and detector response variability. In addition, this report describes several anomaly detection and identification algorithms and recommends metrics to report their performance. Finally, this report ends with a discussion on machine learning algorithms.

46 INSTRUMENTATION RELATED TO NUCLEAR SCIENCE AND ↗
NASA NTRS1996

Algal Accessory Pigment Detection Using AVIRIS Image-Derived Spectral Radiance Data

Visual and derivative analyses of AVIRIS spectral data can be used to detect algal accessory pigments in aquatic communities. This capability extends the use of remote sensing for the study of aquatic ecosystems by allowing detection of taxonomically significant pigment signatures which yield information about the type of algae present. Such information allows remote sensing-based assessment of aquatic ecosystem health, as in the detection of nuisance blooms of cyanobacteria or toxic blooms of dinoflagellates. Remote sensing of aquatic systems has traditionally focused on quantification of chlorophyll a, a photoreactive (and light-harvesting) pigment which is common to all algae as well as cyanobacteria (bluegreen algae). Due to the ubiquitousness of this pigment within algae, chl a is routinely measured to estimate algal biomass both during ground-truthing and using various airborne or satellite based sensors, including AVIRIS. Within the remote sensing and aquatic sciences communities, ongoing research has been performed to detect algal accessory pigments for assessment of algal population composition. This research is based on the fact that many algal accessory pigments are taxonomically significant, and all are spectrally unique. Aquatic scientists have been refining pigment analysis techniques, primarily high performance liquid chromatography, or HPLC, to detect specific pigments as a time-saving alternative to individual algal cell identifications and counts. Remote sensing scientists are investigating the use of pigment signatures to construct pigment libraries analogous to mineral spectral libraries used in geological remote sensing applications. The accessory pigment approach has been used successfully in remote sensing using data from the Thematic Mapper, low-altitude, multiple channel scanners, field spectroradiometers and the AVIRIS hyperspectral scanner. Due to spectral and spatial resolution capabilities, AVIRIS is the sensor of choice for such studies. We present here our results on detection of algal accessory pigments using AVIRIS data.

Richardson, Laurie L.↗
NASA NTRS1998

Lidar Measurements of Tropospheric Wind Profiles with the Double Edge Technique

Research has established the importance of global tropospheric wind measurements for large scale improvements in numerical weather prediction. In addition, global wind measurements provide data that are fundamental to the understanding and prediction of global climate change. These tasks are closely linked with the goals of the NASA Earth Science Enterprise and Global Climate Change programs. NASA Goddard has been actively involved in the development of direct detection Doppler lidar methods and technologies to meet the wind observing needs of the atmospheric science community. A variety of direct detection Doppler wind lidar measurements have recently been reported indicating the growing interest in this area. Our program at Goddard has concentrated on the development of the edge technique for lidar wind measurements. Implementations of the edge technique using either the aerosol or molecular backscatter for the Doppler wind measurement have been described. The basic principles have been verified in lab and atmospheric lidar wind experiments. The lidar measurements were obtained with an aerosol edge technique lidar operating at 1064 nm. These measurements demonstrated high spatial resolution (22 m) and high velocity sensitivity (rms variances of 0.1 m/s) in the planetary boundary layer (PBL). The aerosol backscatter is typically high in the PBL and the effects of the molecular backscatter can often be neglected. However, as was discussed in the original edge technique paper, the molecular contribution to the signal is significant above the boundary layer and a correction for the effects of molecular backscatter is required to make wind measurements. In addition, the molecular signal is a dominant source of noise in regions where the molecular to aerosol ratio is large since the energy monitor channel used in the single edge technique measures the sum of the aerosol and molecular signals. To extend the operation of the edge technique into the free troposphere we have developed a variation of the edge technique called the double edge technique. In this paper a ground based aerosol double edge lidar is described and the first measurements of wind profiles in the free troposphere obtained with this lidar will be presented.

Gentry, Bruce M.↗
DOE OSTI2018

A Scalable HPC Insider Threat Monitoring System

Cyber Equations, Inc. is reporting on the results of its DOE SBIR Phase I project for the development of a Scalable HPC Insider Threat Monitoring System. This final report to the Department of Energy for Task DE-FOA-0001618 covers the period from 02/21/2017 to 02/20/2018 and summarizes the larger research accomplishments, which can be divided in theoretical and laboratory experiments. One of the many challenges for the U.S. Department of Energy (DOE) is the support of the high-performance computing (HPC) centers while providing quality information services, strategic vision, leadership, technical expertise, and capabilities necessary to enable the DOE to deliver effective, efficient, economical, and secure HPC services. In order to realize a secure DOE laboratory enterprise, DOE facilities operations must counter a wide range of cyber insider threats, from unsophisticated adversaries up to advanced, nation state sponsored actors. The DOE laboratories also presents an enormous insider attack surface of an ever-growing number of computers, networks, and devices all running a myriad of software. In addition, the DOE laboratories are connected to a variety of external organizations which multiplies the possibility of insider threat. During this SBIR project Cyber Equations developed a strategic combination of supervised and unsupervised machine learning techniques to control scale and classify whether entities, such as hosts, actors, or documents, are showing suspicious insider behavior. We conducted experiments using real cyber data to show that changes in observable behavior (e.g., changes in outbound traffic patterns) are indicative of attacks executing (e.g., insiders exfiltrating data, bots calling home, or an attack spreading). The features and methods we use, have been used in the anomaly detection research and development community for a significant amount of time and robust implementations are openly available. K-Means clustering, Decision Trees, and Support Vector Machine based classifiers are readily available through the R language and open source H20 machine learning framework. The Cyber Equations approach is novel in its use of these classifiers combined with Hadoop based parallel processing technology. The five stages of processing used by CyberInsider improve scalability and reduce the high number of false positives normally associated with network anomaly detection. In summary, the Cyber Equations has successfully accomplished all of its Phase I tasks and demonstrated the feasibility of the Insider Threat Monitoring System.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2013

Microbial Characterization of Halite and Groundwater Samples from the WIPP

This report summarizes the progress made in characterizing the microbial community structures within the WIPP repository, and in surrounding groundwaters. Through cultivation and DNA-based identification, the potential activity of these organisms is being inferred, thus leading to a better understanding of their impact on WIPP performance. WIPP halite constitutes the near-field microbial environment. Microbial activity in this setting is predicted by WIPP performance assessment (PA) to proceed from aerobic respiration, through nitrate reduction, to focus on sulfate reduction. The role of methanogenesis in the WIPP remains unclear, due to both energetic constraints imposed by a high-salt environment and substrate selectivity, so this role is no longer considered in PA. Members of the three biological domains — Bacteria, Archaea, and Eukarya (in this case, Fungi) — have been found associated with WIPP halite. Thus far, their activity has been limited to aerobic respiration. Archaea identified in WIPP halite thus far fall exclusively within the family Halobacteriaceae. These include Halobacterium noricense, a Halorubrum-like species, and a Halopenitus-like species. Bacterial signatures associated with WIPP halite include members of the phylum Proteobacteria — Halomonas, Pelomonas, Limnobacter, and Chromohalobacter — but only the latter has been isolated. Also detected and cultivated were Salinicoccus and Nesterenkonia spp. Three fungal species were isolated from halite (Cladosporium, Phoma, and Engyodontium). Although these were most likely introduced into the WIPP as contaminants from above-ground, their survival and potential role in the WIPP (e.g., cellulose degradation) is under investigation. WIPP groundwaters comprise the far-field microbial environment. Bacteria cultivated and identified from the overlying Culebra and nearby borehole groundwater are capable of aerobic respiration, denitrification, fermentation, metal reduction, and sulfate reduction, and are distributed across many different phyla. Their structural and metabolic diversity is dependent upon the ionic strength of the sampled groundwater, with a decrease in both at higher strength. Two of the Bacteria found in groundwater were also found in WIPP halite (Chromohalobacter sp. and Virgibacillus sp.). Archaea identified in groundwater include Halococcus saccharolyticus, Haloferax sp., and Natrinema sp. The differences in the microbial communities detected thus far in halite and groundwater suggest that there will be significant differences in the associated metabolic potential of the near- and far-field environments. Whereas the near-field is dominated by Archaea with more limited metabolic capabilities, the far-field is dominated by Bacteria with extremely broad capabilities. The authors wish to thank Russ Patterson and Dan Ferguson (DOE-CBFO) for their support, and Washington TRU Solutions for access to sampling. All research was performed by the Los Alamos National Laboratory Actinide Chemistry & Repository Science team at the Carlsbad Environmental and Monitoring Research Center, managed by New Mexico State University (NMSU) and located in Carlsbad, NM. Funding for this research is through the Waste Isolation Pilot Plant project, managed by the Department of Energy—Carlsbad Field Office.

54 ENVIRONMENTAL SCIENCES↗
DOE OSTI2020

Taxon-specific microbial growth and mortality patterns reveal distinct temporal population responses to rewetting in a California grassland soil

Abstract Microbial activity increases after rewetting dry soil, resulting in a pulse of carbon mineralization and nutrient availability. The biogeochemical responses to wet-up are reasonably well understood and known to be microbially mediated. Yet, the population level dynamics, and the resulting changes in microbial community patterns, are not well understood as ecological phenomena. Here, we used sequencing of 16S rRNA genes coupled with heavy water (H218O) DNA quantitative stable isotope probing to estimate population-specific rates of growth and mortality in response to a simulated wet-up event in a California annual grassland soil. Bacterial growth and mortality responded rapidly to wet-up, within 3 h, and continued throughout the 168 h incubation, with patterns of sequential growth observed at the phylum level. Of the 37 phyla detected in the prewet community, growth was found in 18 phyla while mortality was measured in 26 phyla. Rapid growth and mortality rates were measurable within 3 h of wet-up but had contrasting characteristics; growth at 3 h was dominated by select taxa within the Proteobacteria and Firmicutes, whereas mortality was taxonomically widespread. Furthermore, across the community, mortality exhibited density-independence, consistent with the indiscriminate shock resulting from dry-down and wet-up, whereas growth was density-dependent, consistent with control by competition or predation. Total aggregated growth across the community was highly correlated with total soil CO2 production. Together, these results illustrate how previously “invisible” population responses can translate quantitatively to emergent observations of ecosystem-scale biogeochemistry.

58 GEOSCIENCES↗
DOE OSTI2016

Datasets for radiation network algorithm development and testing

Domestic Nuclear Detection Office s (DNDO) Intelligence Radiation Sensors Systems (IRSS) program supported the development of networks of commercial-off-the-shelf (COTS) radiation counters for detecting, localizing, and identifying low-level radiation sources. Under this program, a series of indoor and outdoor tests were conducted with multiple source strengths and types, different background profiles, and various types of source and detector movements. Following the tests, network algorithms were replayed in various re-constructed scenarios using sub-networks. These measurements and algorithm traces together provide a rich collection of highly valuable datasets for testing the current and next generation radiation network algorithms, including the ones (to be) developed by broader R&D communities such as distributed detection, information fusion, and sensor networks. From this multiple TeraByte IRSS database, we distilled out and packaged the first batch of canonical datasets for public release. They include measurements from ten indoor and two outdoor tests which represent increasingly challenging baseline scenarios for robustly testing radiation network algorithms.

Rao, Nageswara S↗
NASA NTRS2007

Mission Risk Reduction Regulatory Change Management

NASA Headquarters Environmental Management Division supports NASA's mission to pioneer the future in space exploration, scientific discovery, and aeronautics research by integrating environmental considerations into programs and projects early-on, thereby proactively reducing NASA's exposure to institutional, programmatic and operational risk. As part of this effort, NASA established the Principal Center for Regulatory Risk Analysis and Communication (RRAC PC) as a resource for detecting, analyzing, and communicating environmental regulatory risks to the NASA stakeholder community. The RRAC PC focuses on detecting emerging environmental regulations and other operational change drivers that may pose risks to NASA programs and facilities, and effectively communicating the potential risks. For example, regulatory change may restrict how and where certain activities or operations may be conducted. Regulatory change can also directly affect the ability to use certain materials by mandating a production phase-out or restricting usage applications of certain materials. Regulatory change can result in significant adverse impacts to NASA programs and facilities due to NASA's stringent performance requirements for materials and components related to human-rated space vehicles. Even if a regulation does not directly affect NASA operations, U.S. and international regulations can pose program risks indirectly through requirements levied on manufacturers and vendors of components and materials. For example, manufacturers can change their formulations to comply with new regulatory requirements. Such changes can require time-consuming and costly requalification certification for use in human spaceflight programs. The RRAC PC has implemented a system for proactively managing regulatory change to minimize potential adverse impacts to NASA programs and facilities. This presentation highlights the process utilized by the RRACPC to communicate regulatory change and the associated potential risks within NASA, as well as the process for communicating and cooperating with other government agencies and industry partners, both domestic and international, to ensure mission success.

Scroggins, Sharon↗
DOE OSTI2020

GADRAS Batch Inject Tool User Guide

Gamma Detector Response and Analysis Software (GADRAS) is used by the radiation detection and emergency response community to perform modeling and spectral analysis for gamma detector systems. Built into GADRAS is the ability to define a detector, geometry, background characteristics and source composition to generate synthetic spectra for drills and exercises (injects). Consequence Management is currently in development of a sample result data simulator tool in which a deposition model is probed for source conditions at moments in time and locations in space. These values are used to generate realistic sample results for use in drills and exercises. In addition to sample results, there is a need to simulate the actual spectra that would be observed in the field by downlooking HPGe instruments given a deposition activity. This way, the FRMAC Gamma Spectroscopist can practice their process of generating quantified results from spectra on realistic data as well. Recognizing the decades of work done in GADRAS to accurately generate synthetic spectra, this team decided to build a link between the new simulator and GADRAS to generate these spectra quickly and easily. The simulator tool will generate a file that specifies the name of the spectra, its location, date/time of measurement, duration of measurement, height off the ground, and the deposition activity and age for every radionuclide in the simulation. Then, a new tool within the Inject Tab of GADRAS was developed to read in this file given a detector selection and generate In-Situ spectra for each row in the file in any file format the user chooses. This way, simulation cell staff can take these files and then upload them to the appropriate data system (RAMS or RadResponder) for use during drills and exercises. An advanced feature of this tool allows for generating any spectra given an appropriate model and mapping of source to model layer in the batch inject tool. This way, spectra from field sample counts, mobile laboratories, or even fixed laboratories can be generated in bulk given an estimate of the radioactivity concentration or total radioactivity in an import file. This expands the capabilities of this tool a great deal and will make it a more useful tool for CM and others to help estimate detector response for nearly any situation. This user guide will explain the steps needed to perform a batch inject file generation.

61 RADIATION PROTECTION AND DOSIMETRY↗
DOE OSTI2018

A Surveillance Infrastructure for Malaria Analytics: Provisioning Data Access and Preservation of Interoperability

Background: According to the World Health Organization, malaria surveillance is weakest in countries and regions with the highest malaria burden. A core obstacle is that the data required to perform malaria surveillance are fragmented in multiple data silos distributed across geographic regions. Furthermore, consistent integrated malaria data sources are few, and a low degree of interoperability exists between them. As a result, it is difficult to identify disease trends and to plan for effective interventions. Objective: We propose the Semantics, Interoperability, and Evolution for Malaria Analytics (SIEMA) platform for use in malaria surveillance based on semantic data federation. Using this approach, it is possible to access distributed data, extend and preserve interoperability between multiple dynamic distributed malaria sources, and facilitate detection of system changes that can interrupt mission-critical global surveillance activities. Methods: We used Semantic Automated Discovery and Integration (SADI) Semantic Web Services to enable data access and improve interoperability, and the graphical user interface-enabled semantic query engine HYDRA to implement the target queries typical of malaria programs. We implemented a custom algorithm to detect changes to community-developed terminologies, data sources, and services that are core to SIEMA. This algorithm reports to a dashboard. Valet SADI is used to mitigate the impact of changes by rebuilding affected services. Results: We developed a prototype surveillance and change management platform from a combination of third-party tools, community-developed terminologies, and custom algorithms. We illustrated a methodology and core infrastructure to facilitate interoperable access to distributed data sources using SADI Semantic Web services. This degree of access makes it possible to implement complex queries needed by our user community with minimal technical skill. We implemented a dashboard that reports on terminology changes that can render the services inactive, jeopardizing system interoperability. Using this information, end users can control and reactively rebuild services to preserve interoperability and minimize service downtime. Conclusions: We introduce a framework suitable for use in malaria surveillance that supports the creation of flexible surveillance queries across distributed data resources. The platform provides interoperable access to target data sources, is domain agnostic, and with updates to core terminological resources is readily transferable to other surveillance activities. A dashboard enables users to review changes to the infrastructure and invoke system updates. The platform significantly extends the range of functionalities offered by malaria information systems, beyond the state-of-the-art.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2019

Putative Mixotrophic Nitrifying-Denitrifying Gammaproteobacteria Implicated in Nitrogen Cycling Within the Ammonia/Oxygen Transition Zone of an Oil Sands Pit Lake

Anthropogenically-impacted environments offer the opportunity to discover novel microbial species and metabolisms, which may be undetectable in natural systems. Here, a combined metagenomic and geochemical study in Base Mine Lake, Alberta, Canada, which is the only oil sands end pit lake to date, revealed that nitrification was performed by members from Nitrosomonadaceae, Chloroflexi and unclassified Gammaproteobacteria "MBAE14." While Nitrosomonadaceae and Chloroflexi groups were relatively abundant in the upper oxygenated zones, MBAE14 dominated the hypoxic hypolimnetic zones (approximately 30% of total microbial communities); MBAE14 was not detected in the underlying anoxic tailings. Replication rate analyses indicate that MBAE14 grew in metalimnetic and hypolimnetic water cap regions, most actively at the metalimnetic, ammonia/oxygen transition zone consistent with it putatively conducting nitrification. Detailed genomic analyses of MBAE14 evidenced both ammonia oxidation and denitrification into dinitrogen capabilities. However, the absence of known CO 2 -fixation genes suggests a heterotrophic denitrifying metabolism. Functional marker genes of ammonia oxidation ( amo and hao ) in the MBAE14 genome are homologous with those conserved in autotrophic nitrifiers, but not with those of known heterotrophic nitrifiers. We propose that this novel MBAE14 inhabits the specific ammonia-rich, oxygen and labile organic matter-limited conditions occurring in Base Mine Lake which selectively favors mixotrophic coupled nitrifier denitrification metabolism. Our results highlight the opportunities to better constrain biogeochemical cycles from the application of metagenomics to engineered systems associated with extractive resource sectors.

59 BASIC BIOLOGICAL SCIENCES↗