Damhnait Gleeson

Low temperature S 0 biomineralization at a supraglacial spring system in the Canadian High Arctic

Elemental sulfur (S(0) ) is deposited each summer onto surface ice at Borup Fiord pass on Ellesmere Island, Canada, when high concentrations of aqueous H(2) S are discharged from a supraglacial spring system. 16S rRNA gene clone libraries generated from sulfur deposits were dominated by β-Proteobacteria, particularly Ralstonia sp. Sulfur-cycling micro-organisms such as Thiomicrospira sp., and ε-Proteobacteria such as Sulfuricurvales and Sulfurovumales spp. were also abundant. Concurrent cultivation experiments isolated psychrophilic, sulfide-oxidizing consortia, which produce S(0) in opposing gradients of Na(2) S and oxygen. 16S rRNA gene analyses of sulfur precipitated in gradient tubes show stable sulfur-biomineralizing consortia dominated by Marinobacter sp. in association with Shewanella, Loktanella, Rubrobacter, Flavobacterium, and Sphingomonas spp. Organisms closely related to cultivars appear in environmental 16S rRNA clone libraries; none currently known to oxidize sulfide. Once consortia were simplified to Marinobacter and Flavobacteria spp. through dilution-to-extinction and agar removal, sulfur biomineralization continued. Shewanella, Loktanella, Sphingomonas, and Devosia spp. were also isolated on heterotrophic media, but none produced S(0) alone when reintroduced to Na(2) S gradient tubes. Tubes inoculated with a Marinobacter and Shewanella spp. co-culture did show sulfur biomineralization, suggesting that Marinobacter may be the key sulfide oxidizer in laboratory experiments. Light, florescence and scanning electron microscopy of mineral aggregates produced in Marinobacter experiments revealed abundant cells, with filaments and sheaths variably mineralized with extracellular submicron sulfur grains; similar biomineralization was not observed in abiotic controls. Detailed characterization of mineral products associated with low temperature microbial sulfur-cycling may provide biosignatures relevant to future exploration of Europa and Mars.

Biosignature Detection at an Arctic Analog to Europa

The compelling evidence for an ocean beneath the ice shell of Europa makes it a high priority for astrobiological investigations. Future missions to the icy surface of this moon will query the plausibly sulfur-rich materials for potential indications of the presence of life carried to the surface by mobile ice or partial melt. However, the potential for generation and preservation of biosignatures under cold, sulfur-rich conditions has not previously been investigated, as there have not been suitable environments on Earth to study. Here, we describe the characterization of a range of biosignatures within potentially analogous sulfur deposits from the surface of an Arctic glacier at Borup Fiord Pass to evaluate whether evidence for microbial activities is produced and preserved within these deposits. Optical and electron microscopy revealed microorganisms and extracellular materials. Elemental sulfur (S⁰), the dominant mineralogy within field samples, is present as rhombic and needle-shaped mineral grains and spherical mineral aggregates, commonly observed in association with extracellular polymeric substances. Orthorhombic α-sulfur represents the stable form of S⁰, whereas the monoclinic (needle-shaped) γ-sulfur form rosickyite is metastable and has previously been associated with sulfide-oxidizing microbial communities. Scanning transmission electron microscopy showed mineral deposition on cellular and extracellular materials in the form of submicron-sized, needle-shaped crystals. X-ray diffraction measurements supply supporting evidence for the presence of a minor component of rosickyite. Infrared spectroscopy revealed parts-per-million level organics in the Borup sulfur deposits and organic functional groups diagnostic of biomolecules such as proteins and fatty acids. Organic components are below the detection limit for Raman spectra, which were dominated by sulfur peaks. These combined investigations indicate that sulfur mineral deposits may contain identifiable biosignatures that can be stabilized and preserved under low-temperature conditions. Borup Fiord Pass represents a useful testing ground for instruments and techniques relevant to future astrobiological exploration at Europa.

Autonomous Spectral Discovery and Mapping Onboard the EO-1 Spacecraft

Imaging spectrometers are valuable instruments for space exploration, but their large data volumes limit the number of scenes that can be downlinked. Missions could improve science yield by acquiring surplus images and analyzing them onboard the spacecraft. This onboard analysis could generate surficial maps, summarizing scenes in a bandwidth-efficient manner to indicate data cubes that warrant a complete downlink. Additionally, onboard analysis could detect targets of opportunity and trigger immediate automated follow-up measurements by the spacecraft. Here, we report a first step toward these goals with demonstrations of fully automatic hyperspectral scene analysis, feature discovery, and mapping onboard the Earth Observing One (EO-1) spacecraft. We describe a series of overflights in which the spacecraft analyzes a scene and produces summary maps along with lists of salient features for prioritized downlink. The onboard system uses a superpixel endmember detection approach to identify compositionally distinctive features in each image. This procedure suits the limited computing resources of the EO-1 flight processor. It requires very little advance information about the anticipated spectral features, but the resulting surface composition maps agree well with canonical human interpretations. Identical spacecraft commands detect outlier spectral features in multiple scenarios having different constituents and imaging conditions.

Improving onboard analysis of Hyperion images by filtering mislabeled training data examples

This paper presents PWEM, a technique for detecting class label noise in training data. PWEM detects mislabeled examples by assigning to each training example a probability that its label is correct. PWEM calculates this probability by clustering examples from pairs of classes together and analyzing the distribution of labels within each cluster to derive the probability of each labels correctness. We discuss how one can use the probabilities output by PWEM to filter, mitigate, or correct mislabeled training examples. We then provide an in-depth discussion of how we applied PWEM to a sulfur detector that labels pixels from Hyperion images of the Borup-Fiord pass in Northern Canada. PWEM assigned a large number of the sulfur training examples low probabilities, indicating severe mislabeling within the sulfur class. The filtering of those low confidence examples resulted in a cleaner training set and improved the median false positive rate of the classifier by at least 29%.

Onboard SVM analysis of Hyperion data to detect sulfur deposits in Arctic regions

Onboard classification of remote sensing data can permit autonomous, intelligent scheduling decisions without ground interaction. In this study, we observe the sulfur-rich Borup-Fiord glacial springs in Canada with the Hyperion instrument aboard the EO-1 spacecraft. This system offers an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of sulfur compounds associated with the activity of microbial life. However, those results were severely limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. We also explore nonlinear SVM kernels as an extension of our onboard capability.

Onboard detection of natural sulfur on a glacier via a SVM and Hyperion data

Onboard classification of remote sensing data is of general interest given that it can be used as a trigger to initiate alarms, data download, additional higher-resolution scans, or more frequent scans of an area without ground interaction. In our case, we study the sulfur-rich Borup-Fiord glacial springs in Canada utilizing the Hyperion instrument aboard the EO-1 spacecraft. This system consists of naturally occurring sulfur-rich springs emerging from glacial ice, which are a known environment for microbial life. The biological activity of the spring is associated with sulfur compounds that can be detected remotely via spectral analysis. This system may offer an analog to far more exotic locales such as Europa where remote sensing of biogenic indicators is of considerable interest. Unfortunately, spacecraft processing power and memory is severely limited which places strong constraints on the algorithms available. Previous work has been performed in the generation and execution of an onboard SVM (support vector machine) classifier to autonomously identify the presence of sulfur compounds associated with the activity of microbial life. However, those results were limited in the number of positive examples available to be labeled. In this paper we extend the sample size from 1 to 7 example scenes between 2006 and 2008, corresponding to a change from 18 to 235 positive labels. Of key interest is our assessment of the classifiers behavior on non-sulfur-bearing imagery far from the training region. Selection of the most relevant spectral bands and parameters for the SVM are also explored.

Surface Sulfur Detection via Remote Sensing and Onboard Classification

Orbital remote sensing provides a powerful way to efficiently survey targets such as the Earth and other planets and moons for features of interest. One such feature of astrobiological relevance is the presence of surface sulfur deposits. These deposits have been observed to be associated with microbial activity at the Borup Fiord glacial springs in Canada, a location that may provide an analogue to other icy environments such as Europa. This article evaluates automated classifiers for detecting sulfur in remote sensing observations by the hyperion spectrometer on the EO-1 spacecraft. We determined that a data-driven machine learning solution was needed because the sulfur could not be detected by simply matching observations to sulfur lab spectra. We also evaluated several methods (manual and automated) for identifying the most relevant attributes (spectral wavelengths) needed for successful sulfur detection. Our findings include (1) the Borup Fiord sulfur deposits were best modeled as containing two sub-populations: sulfur on ice and sulfur on rock; (2) as expected, classifiers using Gaussian kernels outperformed those based on linear kernels, and should be adopted when onboard computational constraints permit; and (3) Recursive Feature Elimination selected sensible and effective features for use in the computationally constrained environment onboard EO-1. This study helped guide the selection of algorithm parameters and configuration for the classification system currently operational on EO-1. Finally, we discuss implications for a similar onboard classification system for a future Europa orbiter.

Concept for a new frontiers mission to Ganymede: A Planetary Science Summer School study

As part of the NASA Planetary Science Summer School 2010, the Ganymede Interior, Surface and Magnetosphere Observer (GISMO) team developed a robotic mission to Ganymede, one of Jupiters icy moons. This process included the formulation of the science objectives and the selection of a payload tailored to meet these goals. The team then designed a mission architecture aimed toward achieving the science objectives. Using a sequence of 14 flybys of Ganymede, the vehicle would use a simple, staged operation of the science payload. This timeline allows for a simplified design, with relatively low risk and cost. Principle challenges included the finite power available to the vehicle, along with a limited data downlink rate. Otherwise, this preliminary design would meet all mission requirements, as determined by the science goals, and within the allocated cost cap.