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Applying a bio-optical model designed to identify the mixotrophic dinoflagellate Noctiluca miliaris to MODIS-Aqua revealed (1) patterns in its spatial distribution not previously seen (including its appearance in places not previously sampled), and (2) the surprising disassociation of total chlorophyll biomass with the presence of N. miliaris.
A composition comprising thermoset polymer, shape memory polymer to facilitate macro scale damage closure, and a thermoplastic polymer for molecular scale healing is disclosed; the composition has the ability to resolve structural defects by a bio-mimetic close-then heal process. In use, the shape memory polymer serves to bring surfaces of a structural defect into approximation, whereafter use of the thermoplastic polymer for molecular scale healing allowed for movement of the thermoplastic polymer into the defect and thus obtain molecular scale healing. The thermoplastic can be fibers, particles or spheres which are used by heating to a level at or above the thermoplastic's melting point, then cooling of the composition below the melting temperature of the thermoplastic. Compositions of the invention have the ability to not only close macroscopic defects, but also to do so repeatedly even if another wound/damage occurs in a previously healed/repaired area.
This project, performed in support of the NASA GRC Space Academy summer program, sought to develop an open-source workflow methodology that segmented medical image data, created a 3D model from the segmented data, and prepared the model for finite-element analysis. In an initial step, a technological survey evaluated the performance of various existing open-source software that claim to perform these tasks. However, the survey concluded that no single software exhibited the wide array of functionality required for the potential NASA application in the area of bone, muscle and bio fluidic studies. As a result, development of a series of Python scripts provided the bridging mechanism to address the shortcomings of the available open source tools. The implementation of the VTK library provided the most quick and effective means of segmenting regions of interest from the medical images; it allowed for the export of a 3D model by using the marching cubes algorithm to build a surface mesh. To facilitate the development of the model domain from this extracted information required a surface mesh to be processed in the open-source software packages Blender and Gmsh. The Preview program of the FEBio suite proved to be sufficient for volume filling the model with an unstructured mesh and preparing boundaries specifications for finite element analysis. To fully allow FEM modeling, an in house developed Python script allowed assignment of material properties on an element by element basis by performing a weighted interpolation of voxel intensity of the parent medical image correlated to published information of image intensity to material properties, such as ash density. A graphical user interface combined the Python scripts and other software into a user friendly interface. The work using Python scripts provides a potential alternative to expensive commercial software and inadequate, limited open-source freeware programs for the creation of 3D computational models. More work will be needed to validate this approach in creating finite-element models.
Coastal benthic marine ecosystems, such as coral reefs, seagrass beds, and kelp forests are highly productive as well as ecologically and commercially important resources. These systems are vulnerable to degraded water quality due to coastal development, terrestrial run-off, and harmful algal blooms. Measurements of these features are important for understanding linkages with land-based sources of pollution and impacts to coastal ecosystems. Challenges for accurate remote sensing of coastal benthic (shallow water) ecosystems and water quality are complicated by atmospheric scattering/absorption (approximately 80+% of the signal), sun glint from the sea surface, and water column scattering (e.g., turbidity). Further, sensor challenges related to signal to noise (SNR) over optically dark targets as well as insufficient radiometric calibration thwart the value of coastal remotely-sensed data. Atmospheric correction of satellite and airborne remotely-sensed radiance data is crucial for deriving accurate water-leaving radiance in coastal waters. C-HORSE seeks to optimize coastal remote sensing measurements by using a novel airborne instrument suite that will bridge calibration, validation, and research capabilities of bio-optical measurements from the sea to the high altitude remote sensing platform. The primary goal of C-HORSE is to facilitate enhanced optical observations of coastal ecosystems using state of the art portable microradiometers with 19 targeted spectral channels and flight planning to optimize measurements further supporting current and future remote sensing missions.
Aerosol types measured during the Ship-Aircraft Bio-Optical Research (SABOR) experiment are related to GEOS-Chem model chemical composition. The application for this procedure to link model chemical components to aerosol type is desirable for understanding aerosol evolution over time. The Mahalanobis distance (DM) statistic is used to cluster model groupings of five chemical components (organic carbon, black carbon, sea salt, dust and sulfate) in a way analogous to the methods used by Burton et al. [2012] and Russell et al. [2014]. First, model-to-measurement evaluation is performed by collocating vertically resolved aerosol extinction from SABOR High Spectral Resolution LiDAR (HSRL) to the GEOS-Chem nested high-resolution data. Comparisons of modeled-to-measured aerosol extinction are shown to be within 35% +/- 14%. Second, the model chemical components are calculation into five variables to calculate the DM and cluster means and covariances for each HSRL-retrieved aerosol type. The layer variables from the model are aerosol optical depth (AOD) ratios of (i) sea salt and (ii) dust to total AOD, mass ratios of (iii) total carbon (i.e. sum of organic and black carbon) to the sum of total carbon and sulfate (iv) organic carbon to black carbon, and (v) the natural log of the aerosol-to-molecular extinction ratio. Third, the layer variables and at most five out of twenty SABOR flights are used to form the pre-specified clusters for calculating DM and to assign an aerosol type. After determining the pre-specified clusters, model aerosol types are produced for the entire vertically resolved GEOS-Chem nested domain over the United States and the model chemical component distributions relating to each type are recorded. Resulting aerosol types are Dust/Dusty Mix, Maritime, Smoke, Urban and Fresh Smoke (separated into 'dark' and 'light' by a threshold of the organic to black carbon ratio). Model-calculated DM not belonging to a specific type (i.e. not meeting a threshold probability) is termed an outlier and those DM values that can belong to multiple types (i.e. showing weak probability of belonging to a specific cluster) are termed as Overlap. MODIS active fires are overlaid on the model domain to qualitatively evaluate the model-predicted Smoke aerosol types.
The RAZOR (trademark) EX, a quantitative Polymerase Chain Reaction (qPCR) instrument, is a portable, ruggedized unit that was designed for the Department of Defense (DoD) with its reagent chemistries traceable to a Small Business Innovation Research (SBIR) contract beginning in 2002. The PCR instrument's primary function post 9/11 was to enable frontline soldiers and first responders to detect biological threat agents and bioterrorism activities in remote locations to include field environments. With its success for DoD, the instrument has also been employed by other governmental agencies including Department of Homeland Security (DHS). The RAZOR (Trademark) EX underwent stringent testing by the vendor, as well as through the DoD, and was certified in 2005. In addition, the RAZOR (trademark) EX passed DHS security sponsored Stakeholder Panel on Agent Detection Assays (SPADA) rigorous evaluation in 2011. The identification and quantitation of microbial pathogens is necessary both on the ground as well as during spaceflight to maintain the health of astronauts and to prevent biofouling of equipment. Currently, culture-based monitoring technology has been adequate for short-term spaceflight missions but may not be robust enough to meet the requirements for long-duration missions. During a NASA-sponsored workshop in 2011, it was determined that the more traditional culture-based method should be replaced or supplemented with more robust technologies. NASA scientists began investigating innovative molecular technologies for future space exploration and as a result, PCR was recommended. Shortly after, NASA sponsored market research in 2012 to identify and review current, commercial, cutting edge PCR technologies for potential applicability to spaceflight operations. Scientists identified and extensively evaluated three candidate technologies with the potential to function in microgravity. After a thorough voice-of-the-customer trade study and extensive functional and safety evaluations, the RAZOR (trademark) EX PCR instrument(Bio-Fire Defense, Salt Lake City, UT) was selected as the most promising current technology for spaceflight monitoring applications.
Agricultural systems science generates knowledge that allows researchers to consider complex problems or take informed agricultural decisions. The rich history of this science exemplifies the diversity of systems and scales over which they operate and have been studied. Modeling, an essential tool in agricultural systems science, has been accomplished by scientists from a wide range of disciplines, who have contributed concepts and tools over more than six decades. As agricultural scientists now consider the next generation models, data, and knowledge products needed to meet the increasingly complex systems problems faced by society, it is important to take stock of this history and its lessons to ensure that we avoid re-invention and strive to consider all dimensions of associated challenges. To this end, we summarize here the history of agricultural systems modeling and identify lessons learned that can help guide the design and development of next generation of agricultural system tools and methods. A number of past events combined with overall technological progress in other fields have strongly contributed to the evolution of agricultural system modeling, including development of process-based bio-physical models of crops and livestock, statistical models based on historical observations, and economic optimization and simulation models at household and regional to global scales. Characteristics of agricultural systems models have varied widely depending on the systems involved, their scales, and the wide range of purposes that motivated their development and use by researchers in different disciplines. Recent trends in broader collaboration across institutions, across disciplines, and between the public and private sectors suggest that the stage is set for the major advances in agricultural systems science that are needed for the next generation of models, databases, knowledge products and decision support systems. The lessons from history should be considered to help avoid roadblocks and pitfalls as the community develops this next generation of agricultural systems models.
As aerosol amount and type are key factors in the 'atmospheric correction' required for remote-sensing chlorophyll alpha concentration (Chl) retrievals, the Multi-angle Imaging SpectroRadiometer (MISR) can contribute to ocean color analysis despite a lack of spectral channels optimized for this application. Conversely, an improved ocean surface constraint should also improve MISR aerosol-type products, especially spectral single-scattering albedo (SSA) retrievals. We introduce a coupled, self-consistent retrieval of Chl together with aerosol over dark water. There are time-varying MISR radiometric calibration errors that significantly affect key spectral reflectance ratios used in the retrievals. Therefore, we also develop and apply new calibration corrections to the MISR top-of-atmosphere (TOA) reflectance data, based on comparisons with coincident MODIS (Moderate Resolution Imaging Spectroradiometer) observations and trend analysis of the MISR TOA bidirectional reflectance factors (BRFs) over three pseudo-invariant desert sites. We run the MISR research retrieval algorithm (RA) with the corrected MISR reflectances to generate MISR-retrieved Chl and compare the MISR Chl values to a set of 49 coincident SeaBASS (SeaWiFS Bio-optical Archive and Storage System) in situ observations. Where Chl(sub in situ) less than 1.5 mg m(exp -3), the results from our Chl model are expected to be of highest quality, due to algorithmic assumption validity. Comparing MISR RA Chl to the 49 coincident SeaBASS observations, we report a correlation coefficient (r) of 0.86, a root-mean-square error (RMSE) of 0.25, and a median absolute error (MAE) of 0.10. Statistically, a two-sample Kolmogorov- Smirnov test indicates that it is not possible to distinguish between MISR Chl and available SeaBASS in situ Chl values (p greater than 0.1). We also compare MODIS-Terra and MISR RA Chl statistically, over much broader regions. With about 1.5 million MISR-MODIS collocations having MODIS Chl less than 1.5 mg m(exp -3), MISR and MODIS show very good agreement: r = 0.96, MAE = 0.09, and RMSE = 0.15. The new dark water aerosol/Chl RA can retrieve Chl in low-Chl, case I waters, independent of other imagers such as MODIS, via a largely physical algorithm, compared to the commonly applied statistical ones. At a minimum, MISR's multi-angle data should help reduce uncertainties in the MODIS-Terra ocean color retrieval where coincident measurements are made, while also allowing for a more robust retrieval of particle properties such as spectral single-scattering albedo.
Spaceflight impacts human physiology, including well documented immune system dysregulation. Diet, immune function, and the microbiome are interlinked, but diet is the only one of these factors that we have the ability to easily, and significantly, alter on Earth or during flight. As we understand dietary impacts on physiology more thoroughly, we may then improve the spaceflight diet to improve crew health and potentially reduce spaceflight-associated physiological alterations. It is expected that increasing the consumption of fruits and vegetables and bioactive compounds (e.g., omega-3 fatty acids, lycopene, flavonoids) and therefore enhancing overall nutritional intake from the nominal shelf-stable, fully-processed space food system could serve as a countermeasure to improve human immunological profiles, the taxonomic profile of the gut microbiota, and nutritional status, especially where currently dysregulated during spaceflight. This interdisciplinary study will determine the effect of the current shelf-stable spaceflight diet compared to an "enhanced" shelf-stable spaceflight diet (25% more foods rich in omega-3 fatty acids, lycopene, flavonoids, and more fruits, and vegetables in general). The NASA Human Exploration Research Analog (HERA) 2017 missions, consisting of four 45-day missions with closed chamber confinement and realistic mission simulation in a high-fidelity mock space vehicle, will serve as a platform to replicate mission stressors and the effects on crew biochemistry, immunology, and the gut microbiome. Bio sampling of crewmembers is scheduled for selected intervals pre- and in-mission. Data collection also includes dietary intake recording. Outcome measures will include immune markers (e.g., peripheral leukocyte distribution, inflammatory cytokine profiles, T cell function), the taxonomic and metatranscriptomic profile of the gut microbiome, and nutritional status biomarkers and metabolites. Statistical evaluations will determine physiological and biochemical shifts in relation to nutrient intake and study phase. To date, sample collection has been completed for 2 crewmembers from the first mission, aka Campaign 4 Mission 1. Mission 2 was terminated after 22 days due to effects of Hurricane Harvey, and sample collection was not completed. Sample collection will continue for Campaign 4 Mission 3 and 4 prior to comprehensive sample analysis. Beneficial improvements will provide evidence of the impact of diet on crew health and adaptation to this spaceflight analog, and will aid in the design and development of more-efficient targeted dietary interventions for exploration missions.
Bio-mathematical fatigue models that predict levels of alertness and performance are one potential tool for use within integrated fatigue risk management approaches. A number of models have been developed that provide predictions based on acute and chronic sleep loss, circadian desynchronization, and sleep inertia. Some are publicly available and gaining traction in settings such as commercial aviation as a means of evaluating flight crew schedules for potential fatigue-related risks. Yet, most models have not been rigorously evaluated and independently validated for the operations to which they are being applied and many users are not fully aware of the limitations in which model results should be interpreted and applied.
The classical multi-spectral Atmospheric Correction (AC) algorithm is inadequate for the new generation of spaceborne hyperspectral sensors such as NASA's first hyperspectral Ocean Color Instrument (OCI) onboard the anticipated Plankton, Aerosol, Cloud, ocean Ecosystem (PACE) satellite mission. The AC process must estimate and remove the atmospheric path radiance contribution due to the Rayleigh scattering by air molecules and scattering by aerosols from the measured top-of-atmosphere (TOA) radiance, compensate for the absorption by atmospheric gases, and correct for reflection and refraction of the air-sea interface. In this work, we present and evaluate an improved AC for hyperspectral sensors developed within NASA's Sea-viewing Wide Field-of-view Sensor (SeaWiFS) Data Analysis System software package (SeaDAS). The improvement is based on combining the classical AC approach of multi-spectral capabilities to correct for the atmospheric path radiance, extended to hyperspectral, with a gas correction algorithm to compensate for absorbing gases in the atmosphere, including water vapor. The SeaDAS-hyperspectral version is capable of operationally processing the AC of any hyperspectral airborne or spaceborne sensor. The new algorithm development was evaluated and assessed using the Hyperspectral Imager for Coastal Ocean (HICO) scenes collected at the Marine Optical BuoY (MOBY) site, and other SeaWiFS Bio-optical Archive and Storage System (SeaBASS) and AERosol Robotic NETwork - Ocean Color (AERONET-OC) coastal sites. A hyperspectral vicarious calibration was applied to HICO, showing the validity and consistency of HICO's ocean color products. The hyperspectral AC capability is currently available in SeaDAS to the scientific community at https://oceancolor.gsfc.nasa.gov/.
We report the design, development, and testing of the Sample Processor for Life on Icy Worlds (SPLIce) system, a microfluidic sample processor to enable autonomous detection of signatures of life and measurements of habitability parameters in Ocean Worlds. This monolithic fluid processing-and-handling system (Figure 1; mass 0.5 kg) retrieves a 50-L-volume sample and prepares it to supply a suite of detection instruments, each with unique preparation needs. SPLIce has potential applications in orbiter missions that sample ocean plumes, such as found in Saturns icy moon Enceladus, or landed missions on the surface of icy satellites, such as Jupiters moon Europa. Answering the question Are we alone in the universe? is captivating and exceptionally challenging. Even general criteria that define life very broadly include a significant role for water [1,2]. Searches for extinct or extant life therefore prioritize locations of abundant water whether in ancient (Mars), or present (Europa and Enceladus) times. Only two previous planetary missions had onboard fluid processing: the Viking Biology Experiments [3] and Phoenixs Wet Chemistry Laboratory (WCL) [4]. SPLIce differs crucially from those systems, including its capability to process and distribute L-volume samples and the integration autonomous control of a wide range of fluidic functions, including: 1) retrieval of fluid samples from an evacuated sample chamber; 2) onboard multi-year storage of dehydrated reagents; 3) integrated pressure, pH, and conductivity measurement; 4) filtration and retention of insoluble particles for microscopy; 5) dilution or vacuum-driven concentration of samples to accommodate instrument working ranges; 6) removal of gas bubbles from sample aliquots; 7) unidirectional flow (check valves); 8) active flow-path selection (solenoid-actuated valves); 9) metered pumping in 100 nL volume increments. The SPLIce manifold, made of three thermally fused layers of precision-machined cyclo-olefin polymer, supports all fluidic components (Figure 1) and integrated microchannels (125 x 250 m). Fluid is pumped by a stepper-motor-driven pump (Lee Co.). The functionality of the integrated MEMS pressure sensor (Honeywell) and passive check valves (Figure 2) were tested in conjunction with our newly designed integral bubble traps (Figure 3) and hydrophobic membrane-based concentrator (Figure 4). The concentrator (initially tested as a standalone component) demonstrated 5-fold vacuum-evaporative concentration. Polyethylene fused bead beds (PEFBBs; 50 porosity) store drylyophilized buffers, calibrants, and fluorescent dyes, and also promote mixing of sample with calibrant, dye, or H2O. Software-controlled automated tests demonstrated successful 1) fluid delivery to each component 2) valve and pump synchronization 3) sample aliquot delivery to instrument interface ports, and 4) rehydration of vacuum-dried fluorescent dye. In Figure 5, fluorescein on PEFBBs was rehydrated for 15 min using a pump-delivered water aliquot; it is displaced as H2O enters the bottom of the channel and pushes the dye into a check valve. Ultimately, SPLIce will fluorescently label amino acids in the sample for microchip-based electrophoretic (MCE) chiral separation and detection to seek and quantify key organic bio-signatures [5]; it will also deliver sample to a microfluidic version of WCL (mWCL) to measure soluble ions and redox-active species.
Tensegrity structures are becoming increasingly popular as mechanical structures for robots. Their inherent compliance makes them extremely robust to environmental disturbances, and their design allows them to have a high strength-to-weight ratio whilst being lightweight compared to traditional robots. For these reasons they would be of interest to the aerospace industry, particularly for planetary exploration. However, being such compliant structures thanks to their network of elastic elements also means that their control is not an easy task. Relying solely on traditional control strategies to generate efficient locomotion would surely be near impossible due to the complex oscillatory motions and nonlinear interactions of its members. The goal of this project was to use bio-inspired control techniques to generate locomotion for a tensegrity icosahedron, namely the SUPERball project of the Intelligent Robotics Group of NASA Ames Research Center.
In the development of intelligent systems that interact with humans, there is often confusion between how the system functions with respect to the humans it interacts with and how it interfaces with those humans. The former is a much deeper challenge than the latter it requires a system-level understanding of evolving human roles as well as an understanding of what humans need to know (and when) in order to perform their tasks. This talk will focus on some of the challenges in getting this right as well as on the type of research and development that results in successful human-autonomy teaming. Brief Bio: Dr. Alonso Vera is Chief of the Human Systems Integration Division at NASA Ames Research Center. His expertise is in human-computer interaction, information systems, artificial intelligence, and computational human performance modeling. He has led the design, development and deployment of mission software systems across NASA robotic and human space flight missions, including Mars Exploration Rovers, Phoenix Mars Lander, ISS, Constellation, and Exploration Systems. Dr. Vera received a Bachelor of Science with First Class Honors from McGill University in 1985 and a Ph.D. from Cornell University in 1991. He went on to a Post-Doctoral Fellowship in the School of Computer Science at Carnegie Mellon University from 1990-93.
The objective of this project is to develop a compact, high sensitivity Raman sensor for detection of life signatures in a flow cell configuration to enable bio-exploration and life detection during future mission to our Solar Systems Icy Worlds. The specific project objectives are the following: 1) Develop a Raman spectroscopy liquid analysis sensor for biosignatures; 2) Demonstrate applicability towards a future Enceladus or other Icy Worlds missions; 3) Establish key parameters for integration with the ARC Sample Processor for Life on Icy Worlds (SPLIce); 4) Position ARC for a successful response to upcoming Enceladus or other Icy World mission instrument opportunities.
Bacteria are the most important microorganisms in the world oceans, accounting for up to 75% of the total biomass. They are responsible for fundamental biogeochemical processes and therefore often used as ecological indicators. In this study, bacteria were quantified by flow cytometry and their diversity assessed by High Throughput Sequencing (HTS) in the southern Adriatic Sea. The most abundant bacterial groups were also quantified by qPCR. The samples were collected from the surface to the seabed over a total of 16 different depths at four stations during the late winter BIOTA (BIO-Tracing Adriatic water masses) cruise conducted in March 2016. The investigated area showed unusual water mass properties and was characterized by a shallow mixed layer, which differed from the usual winter convection conditions, typical of middle-altitude ecosystems and important for the seasonal picoplankton dynamics of this area. Heterotrophic bacteria were separated into HNA (relative High Nucleic Acid content) and LNA (Low Nucleic Acid content) subpopulations with abundances up to 1.8×10(exp 5) and 8.8×10(exp 5) cells/mL, respectively. HNA dominated at offshore stations reaching their maximum at depths below the euphotic zone. The bacterial community was dominated by Alphaproteobacteria, accounting for greater than 40% of the total sequence reads and were mainly represented by the SAR11 clade (90.84%), followed by Marinimicrobia (18% of the total sequence reads), mainly represented by clade SAR406 (8.44%). Distinctive bacterial groups were found in the euphotic layer (Bacteroidetes and Actinobacteria) and aphotic layer samples (Deltaproteobacteria, Marinimicrobia, Chloroflexi, Acidobacteria and Planctomycetes). Results of the qPCR analyses further confirmed HTS results with highest abundances obtained for Alphaproteobacteria, followed by Gammaproteobacteria and Bacteroidetes. The adopted multiple approach, combining different molecular tools, critically supported by optics and flow cytometry, reveal changes in the bacterial assemblages during the unusual thermohaline conditions observed in 2016 in the southern Adriatic Sea.
Clean room standards like ISO 14644 used for facilities that construct spacecraft and store returned samples do not explicitly account for microbial contamination. While there are associated ISO standards for monitoring and controlling bio-contamination in clean rooms it is not always standard practice to do so. The NASA Astromaterials Acquisition and Curation Office maintains seven separate clean labs for storing extraterrestrial samples from the Moon, meteorites, cosmic dust, asteroids, comets, solar wind particles, and microparticle impact samples. These labs are routinely monitored for particulate and trace metal contamination. However, the sample collections are either non-sterile at the time of collection (e.g., meteorites) or are no longer being used to address scientific questions that could be affected by non-sterile conditions (e.g., Lunar samples). Outside of isolated studies there has not been a systematic, longitudinal characterization of the microbial ecology of NASA curation clean rooms. In accordance with the advanced curation initiative, and to prepare for future sample return missions, we have initiated a routine microbiological monitoring program in the Antarctic Meteorite Lab. This monitoring program will be used to determine what microbes are capable of surviving in these oligotrophic environments and whether or not they are capable of altering the sample collections in any significant manner. Repeat sampling will allow us to understand how routine use of these labs affects the microbial ecology over time.
In order to gain insight into how cells sense gravity, NASA has developed a platform to investigate the transient (short-term) response of biological cells to a sudden exposure to microgravity. Utilizing NASA's Zero Gravity Research Facility offers unique capabilities and opportunity to investigate this phenomena. A review of current literature indicates that it remains unclear how biological cells sense and respond to gravitational forces. It is believed that a combination of bio-structural and fluid transport mechanisms are at play. This Technical Memorandum outlines a pathway to address this fundamental biological challenge. A series of low-cost systematic drop tower experiments were conducted to observe the biomechanical behavior of cell membranes (such as vestibular hair, endothelial, and bone cells) that are known to exhibit sensitivity to gravity. These experiments demonstrated a useful, low cost experiment concept that can potentially be employed by researchers interested in observing, identifying and characterizing changes in biological cell morphology.