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NASA NTRSDate not supplied

CyanoSCape: Freshwater Phytoplankton and Floating Aquatic Vegetation Biodiversity

In Southern Africa, the impacts of anthropogenic activities on biodiversity and ecosystem services are exacerbated by the climate crisis. Rapid land use change and the lack of emphasis on environmentally sustainable agricultural practices has hindered hydrological processes and compromised riverine and aquatic ecosystems. This poses obvious risks to natural/indigenous aquatic biodiversity and long-term ecosystem sustainability. Phytoplankton serve as the foundation of the freshwater food web with zooplankton as consumers, which feed fish, invertebrates, and so on up the food chain that comprises the biodiversity of the freshwater system that serves as habitat for biodiversity as well. The diversity of phytoplankton (microscopic organisms) includes photosynthesizing bacteria (cyanobacteria), plant-like diatoms, dinoflagellates, and green algae. Nutrient run-off from agricultural fertilizers and urban overflows, warm temperatures, abundant light availability and compromised hydrological systems provide an ideal environment for cyanobacteria to flourish. Increased prevalence of cyanobacteria, due to both natural and anthropogenic causes, can incur significant effects on the biodiversity of the overall phytoplankton assemblage. These bloom forming algae can significantly outcompete other phytoplankton classes in warmer and eutrophic waters where they are quick to dominate the freshwater system. Eutrophication and toxic cyanobacteria blooms (cyanoHABs) in the inland waters of the Greater Cape Floristic Region (GCFR) incur significant effects on the biodiversity of the overall phytoplankton assemblage and provide a favorable environment forthe overgrowth of floating aquatic vegetation (FAV), which is often invasive and associated with reduced aquatic biodiversity. The algal biodiversity of the GCFR’s freshwater systems is not well characterized. Hyperspectral optical observations are expected to facilitate the improvement of current phytoplankton functional type retrievals significantly, as the sensitivity is sufficient that the distinctive, fine spectral features of different phytoplankton groups can be detected. This will enable testing emerging algorithms and inform the development of new algorithms for use with upcoming hyperspectral satellite missions in this decade. Innovations in optical sensor sensitivity and next generation machine learning capabilities considerably enhance the potential for accurate and rapid detection of phytoplankton, namely the presence, extent, and diversity of cyanobacteria present in cyanoHABs and additionally, invasive FAV. Upcoming hyperspectral satellite missions such as NASA’s Surface Biology and Geology (SBG), Plankton, Aerosol, Cloud, ocean Ecosystem (PACE), and the European Space Agency’s Copernicus Hyperspectral Imaging Mission (CHIME) will provide imagery with unprecedented spectral and spatial resolution which will further enable the discovery of linkages between the seasonality and dynamics of HABs and FAV. The overarching goal of this project is to utilize hyperspectral data, with recently developed and next-generation algorithms, to determine the biodiversity of freshwater systems phytoplankton assemblage with emphasis on genus level distinction, as well as monitor the prevalence and diversity of FAV.

CyanoSCape↗
NASA NTRSDate not supplied

Mineralogical Variation of the C1 Lithologies in the Kaidun Meteorite

The Kaidun meteorite is a polymict microbreccia, characterized by the presence of numerous clasts of various chondritic and achondritic materials. Chondritic clasts are C1, C2, CM1, CM2, CV3, EH3-5, EL3, and ordinary chondrite-related materials [1,2]. There is significant evidence that aqueous alteration in the carbonaceous chondrite clasts in Kaidun occurred primarily in their parent bodies prior to accumulation to the Kaidun body. Thus, the observation of highly aqueously altered materials (C1 clasts) can provide quantitative constraints on the geologic history of the asteroidal parent bodies. Additionally, the lack of petrological features complicates the classification of C1 materials, but recent studies of new meteorite falls and Ryugu samples continue to emphasize the diversity of C1 materials [e.g., 3,4]. Considering the unprecedentedly diverse origin of clasts in Kaidun, it is expected to contain unknown C1 materials, including ungrouped C1, which will further increase the diversity of those materials. Therefore, this study aims to systematically understand C1 materials by comparing Kaidun C1 with known C1s such as CI1, CM1 and CR1. Since textural observations and chemical composition analyses of carbonates, sulfides, and phyllosilicates in C1 materials in Kaidun have not yet been adequately performed, this study focuses mainly on such analyses.

K. Ogiya↗
NASA NTRSDate not supplied

AgMIP Local, National and Global Assessments of Food System Challenges in A Changing Climate

Food systems connect a diverse network of stakeholders around the world, with complex dynamics governing production, processing, transportation, trade and consumption of food products. Extreme conditions can disrupt multiple components of the food system, with systemic connections providing structure that can both buffer and exacerbate risks. Climate change is shifting current practices in agricultural production and the broader food system, encouraging shifts toward more diverse value chains capable of drawing from multiple food producing regions to reduce the chance that an entire value chain be subject to simultaneous impacts. The IPCC Working Group I report assessed that heat waves, fires, floods and severe storms are becoming more pronounced and widespread with each degree of global warming, increasing food system risk. Food system disruptions can also come from outside the climate system, including from viral outbreaks, geopolitical conflict and socioeconomic changes. This presentation will highlight how the Agricultural Model Intercomparison and Improvement Project (AgMIP) develops modeling approaches that connect climate, biophysical and socioeconomic models in order to capture complex responses of the food system and potential interventions by diverse stakeholders in the public and private sectors. Models are applied on a range of scales that match decision contexts. These include households in specific agricultural production regions (e.g., within a country), national-level policymakers considering policies, sustainability and development priorities for a country’s agricultural sector, and global food market models that balance production and consumption around the world with competition for land, water, energy and sustainability priorities. Models are capable of capturing many important responses, but further development is needed to represent key systemic risks and the possibility for additional interventions beyond the farm gate. Improved food system models will provide important insights into our society’s ability to cope with climate change, as well as our ability to identify agricultural adaptation and mitigation opportunities to reduce overall risk.

Food systems↗
NASA NTRS2022

Effects of Amazonian Flying Rivers on Frog Biodiversity and Populations in the Atlantic Rainforest

Given the speed at which humans are changing the climate, species with high degrees of endemism may not have time to avoid extinction through adaptation. We investigated through teleconnection analysis the origin of rainfall that determines the phylogenetic diversity of rainforest frogs and the effects of microclimate differences in shaping the morphological traits of isolated populations (which contribute to greater phylogenetic diversity and speciation). We also investigated through teleconnection analysis how deforestation in Amazonia can affect ecosystem services that are fundamental to maintaining the climate of the Atlantic rainforest biodiversity hotspot. Seasonal winds known as flying rivers carry water vapor from Amazonia to the Atlantic Forest, and the breaking of this ecosystem service could lead Atlantic Forest species to population decline and extinction in the short term. Our results suggest that the selection of morphological traits that shape Atlantic Forest frog diversity and their population dynamics are influenced by the Amazonian flying rivers. Our results also suggest that the increases of temperature anomalies in the Atlantic Ocean due to global warming and in the Amazon forest due to deforestation are already breaking this cycle and threaten the biodiversity of the Atlantic Forest hotspot.

anurans↗
NASA NTRS2024

2023 Cleanroom Monitoring for OSIRIS-REx

OSIRIS-REx is an asteroid sample return mission that delivered asteroid regolith from Bennu to Earth on 9/24/2023 [1]. Cleanroom monitoring of OSIRIS-REx Curation facilities has been ongoing since 2021 when construction of the OSIRIS-REx lab at Johnson Space Center (JSC) was completed [2]. The monitoring continues inside of the OSIRIS-REx lab in Houston. Monitoring was also conducted on a temporary cleanroom constructed inside a hangar at the Utah Test and Training Range (UTTR) to initially receive the Sample Return Capsule (SRC) upon its landing there. The following reports on cleanroom monitoring that has taken place in the months leading up to as well as after OSIRIS-REx return. Monitoring techniques used include particle counts, deployment of Si wafer witness plates (Balazs, Inc.) and Al foil witness plates, gas sampling (Balazs, Inc.) and microbiological monitoring. We collected particle counts at both the lab at JSC as well as the temporary cleanroom at UTTR. Particle counts were taken monthly from six locations in the lab at JSC. Particle counts were taken from seven locations at UTTR in July, August, and September. The UTTR cleanroom particle counts were taken daily in the five days leading to OSIRIS-REx return as well as consistently the day of return (every 2 minutes and 15 seconds 7:21 am- 6:27 pm) from one location inside the cleanroom. The particle counts at UTTR consistently stayed well within the ranges of its required ISO 7 equivalent classification (352,000 maximum particles/ m3 ≥ 0.5 μm). Most analysis showed that the particle counts were far below ISO 7 requirements (highest measured count was 61,484 particles/ m3 ≥ 0.5 um). but the elevated instances correlated to exterior hangar doors being open. Particle counts were reduced when keeping exterior hangar doors closed. The particle counts at JSC have stayed consistently within their ISO 5 equivalent classification (3,520 maximum particles/ m3 ≥ 0.5 um). In most instances, the counts are much better except in one instance where the count measured 13,381 particles/ m3 ≥ 0.5 um. The high spike in particle counts in the JSC lab correlated with the air handlers being shut down temporarily the previous day. The particle counts taken in the same location quickly reduced and measured 0 when taken a month later with the air handlers on. We deployed 8-inch semiconductor polished Si wafer witness plates for 24-hour periods inside of the OSIRIS-REx Curation lab at JSC (in July, August, September, and November 2023) and the cleanroom at UTTR (July and September 2023) to test for possible organic and inorganic contaminants. We also deployed these wafers inside of the OSIRIS-REx Touch-and-Go sample Acquisition Mechanism (TAGSAM) glovebox (where Bennu sample processing occurs) and desiccator (Bennu sample storage) at JSC in August 2023. These samples were all sent to Balazs for analysis via Thermal Desorption Gas Chromatography Mass Spectroscopy (TD-GC-MS) to quantify organic compounds and vapor phase decomposition inductively coupled plasma mass spectrometry (VPD-ICP-MS) to quantify inorganic contaminants. We collected air samples using an adsorbent tube connected to a pump for six hours (100 mL/minute) inside of both the JSC lab and the UTTR cleanroom. These samples were taken at JSC in July, August, September, November, and December and at UTTR in July and September. Additionally, a sample was taken just outside of the TAGSAM glovebox airlock door in July. These samples were sent to Balazs for analysis of volatile organics in air. Overall, the OSIRIS-REx lab at JSC and cleanroom at UTTR yielded very low organic and inorganic contaminants (similar results to Genesis lab at JSC which is ISO 4 equivalent) except for boron, which is attributed to the borosilicate glass in the fan filter units. We collected monthly surface and air samples inside the JSC lab to monitor potential microbial contamination. We report the recovery rate for each sampling event, which is defined as the number of samples exhibiting bacterial or fungal growth divided by the total number of samples collected. Since the lab was commissioned in 2021 the median recovery rate is 29%. This recovery rate is consistent with other ISO 5 equivalent labs used to curate astromaterials collections. Increases in recovery rate correspond to construction and/or extra activity in the lab. However, increases in recovery rate do not reliably correlate to increases in particle counts, which highlights the need for a dedicated microbial monitoring program for biologically sensitive collections. We sampled the temporary clean room at UTTR in July, August, and September of 2023 to monitor potential microbial contamination. The recovery rate decreased from 80% in August to 29% in September. However, in all instances, the diversity of bacteria and fungi was higher in the UTTR cleanroom than in the JSC lab. We routinely collected more than twenty different organisms from the UTTR cleanroom. In the JSC lab the median diversity is 2 organisms. Based on these results we hypothesize that microbes from UTTR could be transported back to JSC with the Bennu samples. To mitigate this risk, in September of 2023, we implemented additional cleaning procedures in the JSC lab to reduce the bioburden on surfaces that could come into direct contact with hardware used to process Bennu samples. Since adopting these additional cleaning measures, the median recovery rate has decreased to 14%. We did not observe an increase in fungal or bacterial diversity in the lab in the October sampling. We will continue to monitor this trend closely for the next several months. Supported by NASA under Award NNH09ZDA007O and Contract NNM10AA11C.

Rachel Comstock Funk↗
NASA NTRS2024

Artificial Intelligence Medical Support for Long-Duration Space Missions

We envision an artificial intelligence (AI) based system that will provide support and recommendations to the crew medical officer (CMO) and ground flight surgeon during long-duration space missions. Such a system would be pretrained on the knowledgebase of clinical knowledge on Earth, minimizing the amount of Earth data that needs to be transferred into space. Then during deployment, the system would be constantly refined through active learning from diverse streams of data from sensors in the spacecraft, data collected daily from individual astronauts, and human-in-the-loop feedback from the crew. The model could be interrogated for predictions and recommendations on personalized crew health based on the overall status of the spacecraft, medicinal stores, and status of other crew members. Adaptation techniques would be used to incorporate spaceflight data that have very different distributions from the training data due to the extreme environment. Edge computing and the most advanced neuromorphic processing would enable computation in scenarios with low power and bandwidth, while dimensionality reduction would be employed to ensure that the input data streams from spaceflight are as small as possible. In order to realize this long-term vision, several hardware and software aspects need to be developed and assembled. First, models pretrained on Earth biomedical data would need to be evaluated for predictive accuracy, and the best one selected. That model would need to be adapted to learn from diverse, sparse, and inconsistently measured data streams, as well as human-in-the-loop feedback. A data integration, standardization, and dimensionality reduction methodology would need to be developed to handle all data types and feed them into the model. Once the software and data infrastructure is developed, it would need to be integrated with small footprint compute processors and tested in high-radiation, high-vibration, unregulated temperature situations. As a short-term goal, we recommend to focus on the development of the data and model software structure. Several large language models (LLM) already exist that have been trained on Earth biomedical and clinical knowledgebases, including BioMedLLM, Med-PaLM, SPOKE LLM, and Foresight. These models need to be evaluated for accuracy and the best one chosen for a proof-of-concept structure, while maintaining awareness of the accelerating AI field and incorporating any newly improved model architectures as needed. Then, we recommend to develop a database of synthetic data types to mimic the diverse data streams that are expected in a long-duration space mission. This should include environmental and microbial data from the spacecraft, non-invasive data from wearables and point-of-care devices employed by astronauts, and more invasive molecular and physiological monitoring of clinical and biomarker data from astronauts. The data standardization methodology should be developed, and these data streams used to refine the clinical LLM. Several scenarios should be developed that could plausibly come up in a long-duration space mission, and changes or aberrations introduced to the data at specific times to mimic these scenarios. Then, question and answer tasks should be designed to interrogate the model for predictions and recommendations, with acceptable answers already identified.

Artificial Intelligence↗
NASA NTRS2023

Evolutionary Flexibility and Rigidity in the Bacterial Methylerythritol Phosphate (Mep) Pathway

Terpenoids are a diverse class of compounds with wide-ranging uses including as industrial solvents, pharmaceuticals, and fragrances. Efforts to produce terpenoids sustainably by engineering microbes for fermentation are ongoing, but industrial production still largely relies on nonrenewable sources. The methylerythritol phosphate (MEP) pathway generates terpenoid precursor molecules and includes the enzyme Dxs and two iron–sulfur cluster enzymes: IspG and IspH. IspG and IspH are rate limiting-enzymes of the MEP pathway but are challenging for metabolic engineering because they require iron–sulfur cluster biogenesis and an ongoing supply of reducing equivalents to function. Therefore, identifying novel alternatives to IspG and IspH has been an on-going effort to aid in metabolic engineering of terpenoid biosynthesis. We report here an analysis of the evolutionary diversity of terpenoid biosynthesis strategies as a resource for exploration of alternative terpenoid biosynthesis pathways. Using comparative genomics, we surveyed a database of 4,400 diverse bacterial species and found that some may have evolved alternatives to the first enzyme in the pathway, Dxs making it evolutionarily flexible. In contrast, we found that IspG and IspH are evolutionarily rigid because we could not identify any species that appear to have enzymatic routes that circumvent these enzymes. The ever-growing repository of sequenced bacterial genomes has great potential to provide metabolic engineers with alternative metabolic pathway solutions. With the current state of knowledge, we found that enzymes IspG and IspH are evolutionarily indispensable which informs both metabolic engineering efforts and our understanding of the evolution of terpenoid biosynthesis pathways.

Bailey Marshall↗
NASA NTRSDate not supplied

Wildfire Segmentation From Remotely Sensed Data Using Quantum-Compatible Conditional Vector Quantized-Variational Autoencoders

Wildfires represent a critical environmental hazard with multifaceted implications for ecosystems, communities, and public health [1]. The escalating frequency and intensity of wildfires globally have intensified the urgency for robust segmentation methodologies to facilitate effective mitigation, response, and recovery strategies [2]. Accurate wildfire segmentation is pivotal for delineating fire boundaries, assessing progression patterns, and prioritizing resource allocation during emergency scenarios. Furthermore, precise segmentation enables stakeholders, including policymakers, environmental scientists, and emergency responders, to formulate evidence-based strategies, thereby minimizing socio-economic disruptions and ecological degradation. Consequently, advancing wildfire segmentation techniques through innovative technological interventions remains a paramount research imperative. Although foundational in wildfire segmentation, traditional deterministic models exhibit inherent limitations that compromise their efficacy in dynamic and uncertain environments. These models often operate on rigid algorithms prioritizing deterministic classifications, thereby overlooking the inherent complexities and uncertainties associated with wildfire behavior and satellite data variability. Such deterministic frameworks tend to produce oversimplified representations that fail to capture the intricate nuances of evolving fire dynamics, spatial heterogeneity, and environmental interactions [1]. Consequently, the deterministic approach’s propensity for uncertainty collapsing [1, 3] hampers the accuracy, reliability, and applicability of segmentation outcomes in real-world scenarios. Contrastingly, stochastic models offer a more nuanced and adaptable framework for wildfire segmentation. By integrating probabilistic elements into the modeling paradigm, stochastic approaches, particularly probabilistic approaches such as variational auto encoders (VAEs) [4], facilitate comprehensive uncertainty assessment, enabling researchers to quantify and incorporate uncertainties into segmentation outcomes effectively. This probabilistic nature empowers stochastic models to encapsulate variability, account for data inconsistencies, and adapt to evolving environmental conditions, enhancing segmentation accuracy, reliability, and robustness. Embracing stochastic methodologies thus catalyzes advancements in wildfire science by fostering a more holistic, adaptive, and resilient segmentation framework. Despite VAEs demonstrating significant promise in various applications, they come with inherent limitations that have garnered attention within the machine learning community. One of the primary drawbacks lies in their reliance on static priors, which essentially assume a fixed distribution for latent variables, thereby limiting the model’s flexibility to capture complex data structures effectively [5]. This static nature leads to suboptimal representations, especially when dealing with complex and high-dimensional data. Additionally, VAEs often struggle with generating sharp and realistic samples, a phenomenon commonly referred to as mode collapse [5, 7, 6]. Furthermore, the optimization process in VAEs, which involves balancing the reconstruction loss and the regularization term, can sometimes be challenging to fine-tune [7]. In recent efforts to address these shortcomings, alternative approaches like Vector Quantized Variational Auto encoders(VQ-VAEs) [7], address the challenges by incorporating discrete latent variables and leveraging techniques that enhance the quality and diversity of generated samples while maintaining efficient training dynamics. VQ-VAEs propose a dynamic prior distribution generation mechanism that diverges from the static priors commonly associated with traditional VAEs. This dynamic approach allows for more adaptive and context-aware latent variable representations, thereby potentially capturing complex data structures more effectively. Unlike autoregressive prior models such as PixelCNN, which, despite their ability to model dependencies across data dimensions, suffer from significant computational inefficiencies and lack flexibility in handling diverse datasets. In our work, we propose to use a generative quantum-compatible approach to help alleviate the shortcomings of autoregressive prior model in VQ-VAEs. Restricted Boltzmann Machines (RBMs) are a viable alternative prior model that can learn prior distributions in a faster and more flexible manner. In this research endeavor, we meticulously curate a state-of-the-art dataset leveraging satellite MODIS data in conjunction with VIIRS fire masks, derived from Fire Radiative Power (FRP), thereby encapsulating diverse wildfire scenarios and environmental contexts. We developed a conditional VQ-VAE architecture with the RBM prior model that is trained in a supervised manner for segmenting wildfire masks. This innovative approach synergistically harnesses deep learning capabilities, enabling the generation of segmentation maps characterized by heightened precision, granularity, and contextual relevance. Furthermore, replacing the autoregressive prior learning method proposed by the original VQ-VAE with a prior density approximation via quantum-compatible RBM facilitates expedited inference processes, augments flexibility in prior sampling, optimizes computational efficiency and establishes a groundbreaking benchmark in wildfire segmentation methodologies.

quantum machine learning↗
NASA NTRSDate not supplied

The BioSensor Instrument Beyond LEIA: a Versatile Platform for Lunar Biology

Introduction: The BioSensor is a deep-space-compatible automated microfluidic culturing instrument. While originally designed to measure the effects of deep space radiation on yeast growth for the BioSentinel mission, it has the potential to host a diverse range of life science experiments with single- and/or multi-celled organisms and can be adapted to interface with a diverse range of platforms in both crewed and uncrewed settings. It is therefore a leading candidate for hosting life sciences experimentation associated with a lunar surface habitat. BioSensor design: The function of the BioSensor is to monitor the growth and metabolic activity of samples in batch fluidic culture, without the need for crew involvement. The current configuration houses organisms in 16 wells within 16 microfluidic cards, accommodating a total of 256 samples, replicates, and controls. Each well has an optical system including three LEDs and a photodiode detector to measure absorbance at three wavelengths, enabling measurement of optical density, color change in dyes such as alamarBlue, and bioproduction of pigmented compounds. Organisms are loaded into fluidic wells and air-dried for storage during integration, launch, and transit, then activated by the introduction of culture medium from storage bags via manifolds that fill one card at a time. Temperature is controlled by individual card heaters, and timing of all activities (fluidics fills, optical measurements, temperatures) is directed by an experiment script. The self-contained BioSensor payload is roughly 4U in volume; with electrical/mechanical/thermal interface, e.g. for operation on ISS or a lunar lander, as well as a linear energy-transfer (LET) charged-particle radiation spectrometer, the entire system is closer to 6U. BioSentinel and LEIA: Flown on the ISS and in a deep-space free flyer for BioSentinel, the BioSensor has been modified for use in the LEIA mission, including improvements to reduce the sensitivity to lengthy launch delays. LEIA will monitor yeast growth in the radiation and reduced-gravity environment of the lunar surface no earlier than 2026, on a CLPS lander [4]. Changes include accommodating additional culture media and an additional LED color for a new biological assay (bioproduction of carotenoids-- dietary antioxidants), as well as modifications to the housing to allow late-load biology changeout and improved isolation between fluidics and electronics. Future prospects: Future work with the BioSensor, beyond LEIA, will include expanding the range and diversity of organisms and assays accommodated. Preliminary work has demonstrated the growth of Arabidopsis seedlings in BioSensor fluidic cards, including optical measurements of growth rate over time. Minor modifications could allow measurement of phenotypes related to photosynthetic capacity in both plants and cyanobacteria. The experimental capabilities of the BioSensor could be dramatically increased by introducing the capability for fluorescence measurements, and/or the design of novel biological assays using luminescence. The BioSensor can also be adapted for new platforms and experiment settings; in addition to free-flyer, ISS, and CLPS lander, a preliminary design concept has been developed for crewed deployment directly to the lunar surface. The instrument could be accommodated inside a lunar habitat, where its automated operation would make it an excellent candidate for experiments from fundamental investigations into the response of organisms to lunar surface conditions to applied-science purposes such as screening engineered strains of various organisms for bioproduction capability.

J A Lee↗
NASA NTRS2024

Effects of Water Limitation and Competition on Tree Carbon Allocation in an Earth System Modeling Framework

Earth system models (ESMs) have a limited capacity to represent plant functional diversity and shifts in trait distributions. Approaches to improving the representation of this complexity in ESMs include (i) optimality-based approaches that predict trait–environment responses and (ii) explicitly modelling coexistence and community assembly. These approaches are expected to converge only when optimality-based approaches identify competitively dominant strategies, which often differ from strategies that maximize ecosystem functioning or fitness components in monoculture. We used two models, LM3-PPA (a vegetation demographic model designed as an ESM component) and BiomeE (a computationally efficient analog for LM3-PPA), to explore how water limitation affects carbon allocation strategies of canopy trees. We compared competitive allocation strategies and those that maximize biomass or productivity in monoculture. We did not explicitly model coexistence or community assembly. Rather, we used model experiments to identify competitive and maximizing strategies in a two-dimensional trait space under different precipitation and mortality scenarios. At 10 eastern US locations, we simulated historical, wet and dry climate scenarios, novel drought and three different mortality scenarios (low, medium or high sensitivity to water deficit). For each site and scenario, we identified the competitive strategy and three maximizing strategies (maximum biomass, productivity or drought-tolerance). Root: leaf ratios tended to increase and leaf area tended to decrease with increasing water stress (increasing water limitation and its effects on mortality). However, relative to maximizing strategies, competitive strategies shifted towards greater allocation to roots and leaves with increasing water stress. Competitive overinvestments (greater allocation to roots and leaves by competitive strategies compared with maximizing strategies) were robust across different modelling contexts, including vegetation parameter sets (Acer vs. Populus), models (LM3-PPA vs. BiomeE) and uncalibrated vs. calibrated BiomeE versions. Synthesis: The theoretical prediction that competitive and maximizing allocation strategies differ under water limitation is confirmed for a demographic model designed as an ESM component. Optimality-based trait predictions can simplify representing trait diversity in ESMs but do not always correspond to competitive outcomes. Explicitly modelling coexistence and community assembly in ESMs is challenging but is likely the most general approach to representing trait diversity.

vegetation demographic model↗
NASA NTRS2026

Molecular Inventory and Comparative Organic Profiling of A Homogenized Aggregate Sample From Asteroid (101955) Bennu

Evaluating the distribution and diversity of solvent-soluble organic molecules in extraterrestrial materials provide information about the physicochemical environments where prebiotic chemistry occurred and is essential for assessing the inventory of prebiotic compounds available to the early Earth. In this work, we conducted a comprehensive molecular characterization of solvent-soluble organic compounds in a homogenized aggregate sample (ID: OREX-800107-128) of unsorted regolith from asteroid (101955) Bennu returned by NASA’s OSIRIS-REx mission. Using two-dimensional gas chromatography coupled with high-resolution time-of-flight mass spectrometry (GC×GC-HRMS), we identified 87 organic compounds across several molecular classes, including alkanes, polycyclic aromatic hydrocarbons, and nitrogen-, oxygen-, and sulfur-bearing species. Comparison with the organic inventory of CI (Orgueil), C2-ungrouped (Tagish Lake and Tarda), and CM2 (Murchison) carbonaceous chondrites revealed that Bennu sample OREX-800107-128 is most similar to Tagish Lake and Tarda yet compositionally distinct from all of these meteorites. This Bennu sample exhibits a relatively high abundance of low-molecular-weight aromatics (e.g., toluene), pyridine derivatives, and sulfur species such as dimethyl sulfite, consistent with episodic low-temperature aqueous alteration. The presence of these organic compounds suggests that this material experienced chemically diverse processes in the parent body conducive to complex organic synthesis, including aldol condensation reactions responsible for N- and O-bearing compounds. We also compared the molecular distributions of this Bennu sample and previously analyzed samples from (162173) Ryugu to assess potential common origins between these two carbonaceous asteroids. Bennu shows more chemically diverse and nitrogen-rich organic materials than Ryugu. While both asteroids share broadly similar primitive compositions, the observed chemical differences indicate distinct evolutionary histories within a likely common formation environment though whether Bennu and Ryugu derive from a single compositionally heterogeneous parent body or from multiple closely related progenitors remains unresolved. Further detailed comparative analyses of material from Bennu, Ryugu, and specific carbonaceous meteorites (e.g., Tagish Lake and Tarda) will provide new insights into how the N-rich molecular profile observed in Bennu samples was formed and evolved in the outer Solar System.

sample return↗
NASA NTRS2004

Target Selection and Imaging Requirements for JWST Fine Phasing

To achieve and maintain the fine alignment of its segmented primary mirror the James Webb Space Telescope (JWST) plans to use focus-diverse wavefront sensing (WFS) techniques with science camera imagery. The optical requirements for JWST are such that the error contribution from the WFS itself must be limited 10nm rms over all the controllable degrees of freedom of the telescope. In this paper, we will explore the requirements on the target selection and imaging requirements necessary to achieve the desired level of WFS accuracy. Using Monte Carlo simulations we explore the WFS error as a function of wavefront aberrations level, defocus-diversity level, optical bandwidth and imaging signal-to-noise ratio to establish the key imaging requirements. By taking into account practical integration time limits along with the distribution of the defocused point-spread functions, we establish the bright and faint star magnitude limits suitable for WFS target selection.

wavefront sensing↗
NASA NTRS2012

Genetic Inventory Task Final Report

Contaminant terrestrial microbiota could profoundly impact the scientific integrity of extraterrestrial life-detection experiments. It is therefore important to know what organisms persist on spacecraft surfaces so that their presence can be eliminated or discriminated from authentic extraterrestrial biosignatures. Although there is a growing understanding of the biodiversity associated with spacecraft and cleanroom surfaces, it remains challenging to assess the risk of these microbes confounding life-detection or sample-return experiments. A key challenge is to provide a comprehensive inventory of microbes present on spacecraft surfaces. To assess the phylogenetic breadth of microorganisms on spacecraft and associated surfaces, the Genetic Inventory team used three technologies: conventional cloning techniques, PhyloChip DNA microarrays, and 454 tag-encoded pyrosequencing, together with a methodology to systematically collect, process, and archive nucleic acids. These three analysis methods yielded considerably different results: Traditional approaches provided the least comprehensive assessment of microbial diversity, while PhyloChip and pyrosequencing illuminated more diverse microbial populations. The overall results stress the importance of selecting sample collection and processing approaches based on the desired target and required level of detection. The DNA archive generated in this study can be made available to future researchers as genetic-inventory-oriented technologies further mature.

microbial diversity↗
NASA NTRS2021

Characterization of Spacesuit Associated Microbial Communities and their Implications for NASA Missions

Crewed National Aeronautics and Space Administration (NASA) missions to other solar system bodies are currently being planned. One high-profile scientific focus during such expeditions would be life detection, specifically the discovery of past or present microbial life, if they exist. However, both humans and associated objects typically carry a high microbial burden. Thus, it is essential to distinguish between microbes brought with the expedition and those present on the exploring planets. Modern spacesuits are unique, customized spacecraft which provide protection, mobility and life support to crew during spacewalks, yet they vent continuously, and the mobility of microbes through spacesuits has not been studied. To evaluate the microbial colonization of spacesuits, NASA used an Extravehicular Activity swab kit to examine viable microbial populations of 48 samples from spacesuits using both traditional microbiological methods and molecular sequencing methods. The cultivable microbial population ranged from below the detection limit to 9 x 102 colony forming units per 25 cm2 of sample and also significantly varied by the location. The cultivable microbial diversity was dominated by members of Bacillus, Arthrobacter, and Ascomycota. However, 16S rRNA-based viable bacterial burden ranged from 105 to 106 copies per 25 cm2 of sample. Shotgun metagenome sequencing revealed the presence of a diverse microbial population on the spacesuit surfaces, including Curtobacterium and Methylobacterium from across all sets of spacesuits in high abundance. Among bacterial species identified, higher abundance of Cutibacterium acnes, Methylobacterium oryzae, and M. phyllosphaerae reads were documented. The results of this study provide evidence that identical microbial strains may live on the wrist joint, inner gauntlet, and outer gauntlet of spacesuits. This raises the possibility, but does not confirm, that viable microbes are able to migrate between the interior and exterior of spacesuits. Overall, these data provide the first estimate of microbial dispersion from internal to external spacesuit surfaces, which will help future mission planners develop effective planetary protection strategies.

spacesuit↗
NASA NTRS1972

A brief review of ionospheric scintillation fading effects as observed in NASA satellite tracking and data acquisition networks.

Discussion of some results of the effects of ionospheric irregularities on NASA satellite tracking and data acquisition operations. Ionospheric scintillation fading produced by irregularities has been observed at 136 MHz (vhf), 400 MHz (uhf), 1550 MHz (L-band) and 1700 to 2200 MHz (S-band). Details of these observations are presented. Vhf scintillation effects are evident in both auroral and equatorial regions. Fading effects decrease with increasing radio frequency in the auroral region. The same frequency dependence for fading is not observed in the equatorial region. Although there is a seasonal and diurnal character to scintillation in the equatorial region, fading effects are usually more severe than in the auroral region for a given radio frequency. Space diversity measurements indicate that reasonable solutions for vhf telemetry problems are available for either region. Space diversity should provide a solution for microwave frequencies as well. Ionospheric fading amplitude for 1700 MHz is relatively small in the auroral region. In the equatorial region amplitude fading levels for 1550-MHz signals from ATS-5 are often much larger than expected. Observations of the Apollo Lunar Surface Experiment Package (ALSEP) operating at 2300 MHz observed near the geomagnetic equator show fading peaks in excess of 15 dB.

Golden, T. S.↗
NASA NTRS1973

Water quality parameters of harbors of Charlotte Amalie, St. Thomas, Virgin Islands: Acquisition of in situ water data, intercorrelation of selected water parameters, and initial correlation of these in situ biological, chemical and physical data with ERTS-1 bulk CCT MSS band 5 data

The author has identified the following significant results. Remote sensing by the ERTS-1 satellite was compared with selected water quality parameters including pH, salinity, conductivity, dissolved oxygen, water depth, water temperature, turbidity, plankton concentration, current variables, chlorophylla, total carotenoids, and species diversity of the benthic community. Strong correlation between turbidity and MSS-sensed radiance was recorded and less strong correlations between the two plankton pigments and radiance. Turbidity and benthic species diversity were highly correlated furnishing an inferential tie between an easily sensed water quality variable and a sensitive indicator of average water quality conditions.

Coulbourn, W. C.↗
NASA NTRS1974

High efficiency multifrequency feed

Antenna systems and particularly compact and simple antenna feeds which can transmit and receive simultaneously in at least three frequency bands, each with high efficiency and polarization diversity are described. The feed system is applicable for frequency bands having nominal frequency bands with the ratio 1:4:6. By way of example, satellite communications telemetry bands operate in frequency bands 0.8 - 1.0 GHz, 3.7 - 4.2 GHz and 5.9 - 6.4 GHz. In addition, the antenna system of the invention has monopulse capability for reception with circular or diverse polarization at frequency band 1.

Ajioka, J. S.↗
NASA NTRS1974

A study of lagoonal and estuarine processes in the area of Merritt Island encompassing the space center

A study was conducted to determine the marine biology and dynamic oceanographic properties of the Indian River in Florida. One of the major areas of concentration involved the compilation of a taxonomic list of marine animals in the river. An important conclusion of the study is that diversity of the benthic community is substantially higher than expected. The effect of major climatic factors on the diversity and structure of the benthic community is analyzed.

Source record↗