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At least 1,639 records · Page 91

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NASA NTRSDate not supplied

Paternal Imprinting of Dosage-Effect Defective1 Contributes to Seed Weight Xenia in Maize4

Historically, xenia effects were hypothesized to be unique genetic contributions of pollen to seed phenotype, but most examples represent standard complementation of Mendelian traits. We identified the imprinted dosage-effect defective1 (ded1) locus in maize (Zea mays) as a paternal regulator of seed size and development. Hypomorphic alleles show a 5-10% seed weight reduction when ded1 is transmitted through the male, while homozygous mutants are defective with a 70-90% seed weight reduction. Ded1 encodes an R2R3-MYB transcription factor expressed specifically during early endosperm development with paternal allele bias. DED1 directly activates early endosperm genes and endosperm adjacent to scutellum cell layer genes, while directly repressing late grain-fill genes. These results demonstrate xenia as originally defined: Imprinting of Ded1 causes the paternal allele to set the pace of endosperm development thereby influencing grain set and size.

Genomics↗

NASA NTRS2022

Resolving the fine structure in the energy landscapes of repeat proteins

Ankyrin (ANK) repeat proteins are coded by tandem occurrences of patterns with around 33 amino acids. They often mediate protein–protein interactions in a diversity of biological systems. These proteins have an elongated non-globular shape and often display complex folding mechanisms. This work investigates the energy landscape of representative proteins of this class made up of 3, 4 and 6 ANK repeats using the energy-landscape visualisation method (ELViM). By combining biased and unbiased coarse-grained molecular dynamics AWSEM simulations that sample conformations along the folding trajectories with the ELViM structure-based phase space, one finds a three-dimensional representation of the globally funnelled energy surface. In this representation, it is possible to delineate distinct folding pathways. We show that ELViMs can project, in a natural way, the intricacies of the highly dimensional energy landscapes encoded by the highly symmetric ankyrin repeat proteins into useful low-dimensional representations. These projections can discriminate between multiplicities of specific parallel folding mechanisms that otherwise can be hidden in oversimplified depictions.

Murilo N. Sanches↗

NASA NTRS2018

The Photochemistry of Purine in Ice Analogs Relevant to Dense Interstellar Clouds

The aromatic nitrogen heterocyclic compound purine is the core structural framework of many important biomolecules, particularly nucleobases. Purine and purine derivatives have been observed in carbonaceous chondrites, and it has been hypothesized that the exogenous delivery of these compounds, along with many other biologically relevant compounds, may have played a role in the emergence of life. Numerous experiments in our laboratory have demonstrated that the nucleobases used by life to encode genetic material could have been produced abiotically under astrophysically relevant conditions. Specifically, the UV photoprocessing of pyrimidine and purine in simple ices of astrophysical interest has resulted in the production of all five biological nucleobases, namely, uracil (RNA), cytosine (RNA and DNA), thymine (DNA), adenine (RNA and DNA), and guanine (RNA and DNA). Additionally, follow-up work has examined the photochemistry of pyrimidine in more complex astrophysical ice mixtures to better understand the formation of these compounds under realistic conditions. In this work, we examine the photochemistry of purine in more complex ices of astrophysical interest and compare our results with those from simpler ice mixtures. We also examine the effects of competing parallel synthesis of organic compounds in the ices (unrelated to purine). Finally, we discuss the astrophysical and astrobiological implications of our findings.

Astrobiology↗

NASA NTRS2017

Functional Horizontal Gene Transfer From Bacteria to Eukaryotes

The antiquity of bacteria and archaea in part explains why they, along with viruses, encode most of the genetic and biochemical diversity on Earth. Eukaryotic life evolved into a world teeming with prokaryotes, and so bacteria (especially) have inevitably affected eukaryotic biology as parasitic, commensal, or beneficial symbionts. But along with these important organismal interactions, the ubiquity and diversity of bacteria have also made them frequent sources of horizontally transferred DNA into eukaryotic genomes. Here we survey the role of bacterial genes throughout the eukaryotic lineage. We review what steps horizontal gene transfers (HGTs) take in becoming functional, what bacterial groups these HGTs come from, and what functions these HGTs typically bestow on their eukaryotic recipient. We classify HGTs into two broad types: those that maintain preexisting functions and those that add new functionality to the recipient. We find that genes involved in host nutrition, protection, and adaptation to extreme environments are the most common HGTs from bacterial to eukaryotic genomes.

Bacteria↗

NASA NTRS2019

Local frustration around enzyme active sites

Conflicting biological goals often meet in the specification of protein sequences for structure and function. Overall, strong energetic conflicts are minimized in folded native states according to the principle of minimal frustration, so that a sequence can spontaneously fold, but local violations of this principle open up the possibility to encode the complex energy landscapes that are required for active biological functions. We survey the local energetic frustration patterns of all protein enzymes with known structures and experimentally annotated catalytic residues. In agreement with previous hypotheses, the catalytic sites themselves are often highly frustrated regardless of the protein oligomeric state, overall topology, and enzymatic class. At the same time a secondary shell of more weakly frustrated interactions surrounds the catalytic site itself. We evaluate the conservation of these energetic signatures in various family members of major enzyme classes, showing that local frustration is evolutionarily more conserved than the primary structure itself.

Maria I. Freiberger↗

NASA NTRSDate not supplied

Image-to-Image Wildfire Detection via Quantum-Compatible Variational Segmentation from Remotely-sensed Data

Over the last decade, the incidence of wildfires has surged, causing widespread destruction globally. To better comprehend and manage these incidents, remote sensing and aerial missions have been implemented in recent efforts. However, this has resulted in an exponential rise in the amount of remote sensing data utilization, leading to a need for intelligent automation of data extraction in wildfire studies. Machine learning provides an accurate automated approach for detecting these natural anomalies and facilitates decision-makers to take prompt actions. To make insightful decisions in wildfire management, it is imperative to move beyond simple detection and explore the potential of probabilistic generative machine learning for creating "what-if" scenarios for various wildfire conditions. Such models offer improved representation of the stochastic nature of wildfire events. However, the optimization of these models can be computationally expensive, especially when using classical computers. Quantum computers have recently emerged as a promising solution to reduce the computational cost of training such models and improve their performance. In this study, we aim to utilize quantum-compatible machine learning techniques to implement our probabilistic generative approach. To that end, we propose a supervised probabilistic variational model consisting of a U-NET-based image-to-image component along with encoder and decoder networks which work as a variational autoencoder (VAE) component. Additionally, we explore the type of latent distribution type in the VAE component and implement different means for modeling the prior distribution. We further investigate the quantum-compatible versions of the model compared to the classical counterpart and benchmark potential benefits of quantum compatibility over the classical model.

quantum machine learning↗

NASA NTRSDate not supplied

Impact Ice Microstructure Segmentation Using Transfer Learned Model

A process of using machine learning to segment impact ice microstructure is presented and analyzed. The segmentation was conducted with the goal of obtaining average grain size estimations. The model was trained on a set of micrographs of impact ice grown at NASA Glenn’s Icing Research Tunnel. The model leveraged a model pre-trained on a large set of micrographs of various materials as a starting point. Post-processing of the segmented images was done to connect broken boundaries. An automatic method of determining grain size following an ASTM standard was implemented. Segmentation results using different training sets as well as different encoder and decoder pairs are presented. Calculated sizes are compared to manual grain size measurement methods. Results show promise in accuracy as well as a possible improvement in repeatability and consistency. Next steps for improving the model are suggested.

Machine learning↗

NASA NTRSDate not supplied

APRES Prototype Mission Planner System Demonstration

Activity Planning with Resources for the Exploration of Space (APRES) is a mixed-initiative mission planning system for ground operations. APRES has been designed to support multi-spacecraft missions. The APRES Interface is browser-based and includes a plan editor, a timeline plan display, a temporal constraint editor, display of the state and numeric chronicles, and a violation resolution manager. Automation support is supplied by the APRES Service, which includes components that provide the following capabilities:(1) plan simulation, which determines the state and numeric chronicles (values of the model variables over time) and determines when "processes" are triggered and terminated based on world states in the execution trace, (2) violation detection of constraints and flight rules encoded in the domain model, and of the temporal constraints created by the user, (3) violation resolution suggestions as to how to fix the plan's violations via rescheduling. The user controls when and how to utilize the automation support. Demo video included with paper, runtime 8:54 in color with sound.

John L. Bresina↗

NASA NTRS2023

BERT-Based Topic Modeling and Information Retrieval to Support Fishbone Diagramming for Safe Integration of Unmanned Aircraft Systems in Wildfire Response

Recent concepts for emerging wildfire response operations have included unmanned aircraft systems (UAS) due to their increasing accessibility and capabilities. To integrate UAS into wildfire response safely, researchers have studied the use of large repositories of historic incident reports to improve the scope of root cause analysis. Recent work has emphasized applying state-of-the-art natural language processing techniques to extract useful information from these repositories. However, it has not yet been studied how these results can be interpreted and integrated into the systems engineering process. In this work, we propose a process in which Bidirectional Encoder Representations from Transformers (BERT)-based topic modeling and information retrieval are applied to a relevant set of documents in order to support the development of a fishbone diagram in a semiautomated process. High-level themes in the document set are identified using topic modeling, which are then refined and interpreted by a human analyst. Then, the themes are used to guide a finer search using information retrieval, which returns specific incident reports of relevance. This provides traceability to specific incidents as well as broader categorizations that comprise the fishbone branches. We apply the proposed process to relevant documents from NASA’s Aviation Safety Reporting System (ASRS). The proposed process is widely applicable when relevant documents are available, and the results from this study will be useful to identifying potential causes of wildfire response UAS incidents.

Hazard analysis↗

NASA NTRS2022

MLtool Python Code

Machine Learning (ML) is a subfield of Artificial Intelligence that gives computers the ability to learn from past data without being explicitly programmed. The predictive capabilities of ML models have already been used to facilitate several scientific breakthroughs. However, the practical application of ML is often limited due to the gaps in technical knowledge of its users. The common issue faced by many scientific researchers is the inability to choose the appropriate ML pipelines that are needed to treat real-world data, which is often sparse and noisy. To solve this problem, we have developed an automated Machine Learning tool (MLtool) that includes a set of ML algorithms and approaches to aid scientific researchers. The current version of MLtool is implemented as an object-oriented Python code that is easily extensible. It includes 44 different regression algorithms used to model data. MLtool helps users select the best model for their data, based on the scoring metrics used. Besides regression algorithms, MLtool also includes a suite of pre- and post-processing techniques such as missing value imputation, categorical variable encoding, input feature normalization, uncertainty quantification, exploratory data analysis (EDA), etc. MLtool was tested on several publicly available multi-dimensional data sets and was found capable of making accurate predictions.

Machine Learning↗

NASA NTRSDate not supplied

SafeAeroBERT: Towards a Safety-Informed Aerospace-Specific Language Model

As aviation systems continue to operate with high traffic, large amounts of documents containing safety-relevant data continue to be generated via reporting systems such as the Aviation Safety Reporting System (ASRS). Advanced natural language processing techniques, specifically pre-trained language models, have shown great success in domain-specific applications; however, the text in aviation safety reports is inundated with jargon and thus not fully utilized by general pre-trained models. In this research, we work towards developing a safety-informed aerospace-specific language model by pre-training a Bidirectional Encoder Representations from Transformer (BERT) model on reports from the Aviation Safety Reporting System and the National Transportation Safety Board. The resulting model, called SafeAeroBERT, is fine-tuned for the specific task of document classification, and can be further tuned for named-entity recognition, relation detection, information retrieval, and summarization. Results from the classification task are compared between SafeAeroBERT, the base BERT, and SciBERT models and show SafeAeroBERT outperforms the general BERT and SciBERT on classifying reports about weather and procedure. SafeAeroBERT can be used on custom tasks, not limited to document classification, and is intended to aid an intelligent knowledge manager for safety report repositories.

Aviation↗

NASA NTRS2021

Polarimetric calibration of the Multi-Angle Imager for Aerosols (MAIA)

NASA’s Multi-Angle Imager for Aerosols (MAIA) mission, under development at the Jet Propulsion Laboratory, is designed to study the adverse health effects of different types of particulate air pollution. Planned for launch in 2023 for a 3-year mission, the MAIA satellite instrument will focus on a selected set of metropolitan target areas, where air quality monitors and health data are available. Aerosol concentration and speciation are inferred from multi-angle measurements of backscattered sunlight in 14 spectral bands from 350-2200 nm, with bands near 442, 645 and 1040 nm measuring the degree (DoLP) and angle of linear polarization (AoLP) in addition to radiance. The pushbroom camera has a ~240-km cross-track field of view with a nadir resolution of ~200 m, and is mounted onto a biaxial gimbal to provide along-track view angles within ±60°, to extend the field of regard to ±48°, and to view the instrument’s onboard calibrator (OBC) and dark target. The OBC consists of a sunlit transmissive diffuser, followed by 12 polarizers at different orientations. MAIA’s polarimetry is implemented using miniature wire grid polarizers on the focal plane array, and dual photoelastic modulators (PEMs) and achromatic quarter-wave plates to rapidly rotate the polarization. The resulting ~26-Hz intensity modulation encodes the linearly polarized and total radiance in each pixel, leaving the DoLP and AoLP insensitive to gain calibration. We report on the polarimetric calibration of the MAIA camera using a vacuum-compatible polarization state generator, consisting of a 1600W Xenon lamp, 12-inch integrating sphere, and rotating high-extinction polarizer. Mueller-matrix-based calibration coefficients for each detector pixel are derived from measurements at multiple polarizer angles, and are used to correct the measurements for instrumental polarization aberrations. Prior to flight, the calibrated MAIA camera is panned across the OBC to characterize its output, using uniform illumination with an irradiance similar to the Sun.

Werne, Thomas↗

NASA NTRS2022

Enabling a Larger Deep Space Mission Suite: A Deep Space Network Queuing Antenna for Demand Access

The advent of deep space small spacecraft, as exemplified by the Mars Cubesat One (MarCO), Lunar Trailblazer, Janus, the Escape and Plasma Acceleration and Dynamics Explorers (EscaPADE), and the thirteen Artemis 1 missions, opens the possibility that a much larger number of deep space spacecraft may be launched over the next 10 years and beyond. While scientifically exciting, the prospect of a (much) larger mission suite raises significant challenges for the current approach to ground stations and mission operations. We have been investigating an integrated approach for ground stations and missions operations to enable new modes of operation while maintaining the capabilities of the current operational techniques. This integrated approach is built around three core capabilities: (1) A queuing antenna that enables monitoring the status of a much larger number of spacecraft, and allows spacecraft to transmit requests for telemetry with NASA’s Deep Space Network (DSN); (2) a flexible scheduling system that expands the current DSN scheduling services to enable allocating time on DSN antennas in near real-time; and (3) a cloud-based ground data system that can be spun up and down according to how tracks are assigned by the flexible scheduling system. We shall show that an 18 meter DSN queuing antenna equipped with cyrogenic receivers would enable use of the DSN Demand Access Service for small spacecraft throughout the inner Solar System, thus providing service to a large mission suite. We first discuss the architecture of the queuing antenna and its supporting systems, including, for instance, the service required to generate the schedule for the queueing antenna (which dictates how it slews to monitor multiple spacecraft in a day of operations). Next, we describe the signaling scheme used to encode a request, which is inherited from the already operational DSN Beacon Tone Service, and describe two alternative ways to detect the incoming tone at the ground station, one based on maximum likelihood estimation (MLE), and another one based on Fast-Fourier Transfer (FFT) processing. We then use these results to estimate the maximum range at which a request can be reliably detected as a function of the spacecraft and ground station communication capabilities. Finally, the last part of this part of this paper briefly describes the prototyping effort undertaken at Morehead State University (MSU) and JPL to demonstrate the viability of this new DSN demand access. In particular, we describe the suite of tests conducted using MSU’s 21 meter ground station to validate its use a queuing antenna.

Mattle, Emily↗

NASA NTRS2022

New Rover Conops with High-Performance Onboard Computing: Give Up Raw Data to Reduce Ops Cost and Do More Science

A major portion of time during the tactical operation of Mars rovers is spent for selecting, prioritizing, and coordinating sciences and engineering activities such that they fit within resource constraints, including the downlink data volume, energy, and time. In particular, the downlink data volume constraint is getting particularly tighter in recent missions because modern instruments produce increasingly high data volume while the communication bandwidth is essentially bounded by the law of physics. Tactical operation would be substantially simplified, hence the operation cost could be reduced, if the data volume constraint is relaxed or even removed. In this abstract, we propose a new operation paradigm for achieving this goal. The key observation is that, both in science and engineering applications, the bit size of raw data is typically much greater than the volume of processed information that is needed for scientific or engineering analysis. For example, a full-resolution image from Mastcam-Z, the main science camera on Perseverance, is about 700 kB in volume and we downlinked 29,685 images up to Sol 243, totaling ~20 GB of data. But of course, scientists do not use every pixel of these images; what they really look for in the images are geological features, typically represented by specific geometric configurations or textures. An end product after processing hundreds of Mascam-Z images could be a single geological map summarizing the spatial distribution of the features. For another example, a 100-meter drive of Perseverance produces 7-12 MB of drive telemetry, which records every detail of the rover's motion at 8 Hz, including position, attitude, steering angles, encoder readings, motor currents and many other information. But what the ground engineers eventually pay attention to is the signs of anomaly, such as excessive motor currents or high slip; if a drive is nominal, the vast majority of this data is unused. What if, then, we process the raw data onboard and only downlink the processed data that is relevant to scientific or engineering analyses, such as a list of detected science features (with cropped images) or a list of potential signs of anomaly while driving? A major roadblock for such onboard, high-level information processing has been the onboard computational resource. RAD750, the main onboard computer of Perseverance, is obviously not sufficient for performing complex image or signal processing such as object detection, semantic segmentation, or anomaly detection. Interestingly, RAD750 is not the best processor that Perseverance has; Qualcomm's Snapdragon 801, a modern mobile processor, is on her Heli Base Station, a device for communicating with Mars Helicopter Ingenuity; also, Intel's Atom E3845 processors are on engineering cameras. In the reminder of this paper, we will introduce two particular uses cases of these high-performance co-processors (meaning auxiliary CPU, GPU, or other types of processors that are separate from the main processor that runs the main flight software) for lowering operation cost and accommodating more science activities for a given communication constraint.

Didier, A.↗

NASA NTRSDate not supplied

A Dominant Arabidopsis Thaliana ACTIN7 Mutant for Studies of Cytoskeletal-Mediated Root Gravity and Spaceflight Stress Responses

A forward genetic screen for Arabidopsis thaliana mutants that exhibited differential sensitivity to the actin-disrupting compound, latrunculin B (LatB), was conducted to uncover new players involved in actin-mediated root gravity responses. This to the isolation of a mutant that exhibited robust primary root growth at 100 nM LatB, which is a concentration that severely inhibits wild-type root elongation. Phenotypic analysis revealed that hypocotyl elongation in the dark and root hair tip growth in the mutant could tolerate LatB concentrations that impaired these processes in wild type. A cross between the mutant and wild type resulted in progeny resistance to LatB, which indicated that the mutant is dominant (hereafter referred to as LBR1 for LatB Resistant1). Filamentous-actin (F-actin) organization in LBR1 primary roots remained intact at 100 nM LatB, while that of wild type exhibited fragmented F-actin. Next generation sequencing revealed that LBR1 had a single nucleotide polymorphism (SNP) in the AT5G09810 gene that changed cytosine to a thiamine at the first exon. AT5G09810 encodes ACTIN7 (ACT7), which is one of three vegetative actin isoforms in A. thaliana. The SNP in the ACT7 gene led to a change in a single amino acid from proline at position 32 to a serine. Transgenic complementation of LBR1 plants with wild-type ACT7 under the control of the ACT7 promoter (pACT7:ACT7) and wild-type plants with LBR1, which contained the proline to serine mutation, also under the ACT7 promoter (pACT7:LBR1) confirmed that LBR1 is ACT7. The pACT7:LBR1 construct was also able to confer LatB resistance to the act7-5 and act2-3 vegetative ACT mutants. Moreover, LBR1 exhibited partial tolerance to salt and low phosphate, and enhanced root skewing on a clinostat, suggesting that site-directed engineering of vegetative ACT presents a strategy for generating stress-tolerant plants for spaceflight applications and studies of actin-mediated gravity responses.

Plant Space Biology↗

NASA NTRSDate not supplied

Some Aeronautical Communications Experiments

Classically there has existed an asymmetry between the computing and communicating sides of aerospace systems. Over the past few decades, this asymmetry has shifted to favoring communication link technologies, meaning that advancements in available central processing units (CPUs), storage devices, and internal data buses have stagnated. Indeed, the increased emphasis placed on refining subsystem performance such as with antenna bandwidth in phased arrays, amplifier power efficiency, software defined radio (SDR) flexibility and encoding for data compression and error correction has given rise to successful debuts of multi-gigabit-per-second data return from long space-link distances. These accomplishments are easily quantifiable through link budgets and illustrate what is possible, but also reveal the deficiencies in overall communications capabilities. In particular, the ever-accelerating presence of aerospace vehicles gives rise to newer and larger classes of challenges to address the needs of 21st century systems. Furthermore remote sensing and imaging capabilities have far outpaced our ability to transmit their products to the ground, so we are increasingly dependent on pre-processing and downselection to contend with the communications bottleneck. No longer may we depend upon the constrained logistics in delivering end-to-end data delivery through manual reconfigurations, static event scheduling and execution on a per-vehicle basis, for these methods do not scale and therefore must give way to dynamic, networked approaches with an overall systems view in mind. Emerging mission requirements exhibit a trend toward multiple smaller-scale vehicles working together to perform dissimilar observations. Such operations necessitate sensor fusion across a constellation, and where data processing may be distributed throughout a fairly disconnected network whose topology changes over time in non-deterministic manners. Individual communications link performance is still very relevant to deploying an effective communications system, but now must be embedded within a greater architecture of capability to optimally utilize the bandwidth available from each link to generate an ultimate end-to-end quality of service. The deleterious effects of timing uncertainty across the arrangement presents a challenge to measurement synchronization and delivery, so a successful deployed system needs to be tolerant to the delays inherent in time-of-light between elements and digital processing latencies existing at each node. In this presentation we share the flight test results from a high performance Gbps laser communications terminal evaluated with a suite of store and forward capabilities called High-rate Delay Tolerant Networking (HDTN). The communications payload is operated over Lake Erie across a range of configurations including several convergence layers, and is evaluated to determine recovery time after link disruptions, information loss, efficiency and speed. The effectiveness of utilizing a flying laboratory to increase the Technology Readiness Level (TRL) of an integrated system in relevant environments is discussed, as well as the value of conducting aeronautics experiments to retire risk for technology infusion into space missions. Upcoming flight campaigns will be presented, including opportunities to demonstrate secure command and control, data intensive hyperspectral imaging, quantum link characterization, 4k High Definition (HD) video streaming and internetworked space-ground-aero relay operations. These experiments will pave the way for future missions which will depend upon interoperability across disparate government and privately owned networks, involve contention with uncertain and dynamic timing, and require agility to autonomously configure optimal parameters across networks of ever-increasing size and complexity to ensure data delivery. https://www1.grc.nasa.gov/space/scan/acs/tech-studies/dtn/

Daniel Raible↗

NASA NTRSDate not supplied

BERT-based Topic Modeling and Information Retrieval to Support Fishbone Diagramming for Safe Integration of Unmanned Aircraft Systems in Wildfire Response

hazard analysis↗

NASA NTRS2023

High-Order Coronagraphic Wavefront Control With Algorithmic Differentiation: First Experimental Demonstration

Future space-based coronagraphs will rely critically on focal-plane wavefront sensing and control with deformable mirrors to reach deep contrast by mitigating optical aberrations in the primary beam path. Until now, most focal-plane wavefront control algorithms have been formulated in terms of Jacobian matrices, which encode the predicted effect of each deformable mirror actuator on the focal-plane electric field. A disadvantage of these methods is that Jacobian matrices can be cumbersome to compute and manipulate, particularly when the number of deformable mirror actuators is large. Recently, we proposed a new class of focal-plane wavefront control algorithms that utilize gradient-based optimization with algorithmic differentiation to compute wavefront control solutions while avoiding the explicit computation and manipulation of Jacobian matrices entirely. In simulations using a coronagraph design for the proposed Large UV/Optical/Infrared Surveyor (LUVOIR), we showed that our approach reduces overall CPU time and memory consumption compared to a Jacobian-based algorithm. Here, we expand on these results by implementing the proposed algorithm on the High Contrast Imager for Complex Aperture Telescopes (HiCAT) testbed at the Space Telescope Science Institute (STScI) and present initial experimental results, demonstrating contrast suppression capabilities equivalent to Jacobian-based methods.

wavefront control↗