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At least 595 records · Page 33
All sourcesNASA NTRSDOE OSTI
NASA NTRS1971

Techniques for generation of control and guidance signals derived from optical fields, part 2

The development is reported of a high resolution technique for the detection and identification of landmarks from spacecraft optical fields. By making use of nonlinear regression analysis, a method is presented whereby a sequence of synthetic images produced by a digital computer can be automatically adjusted to provide a least squares approximation to a real image. The convergence of the method is demonstrated by means of a computer simulation for both elliptical and rectangular patterns. Statistical simulation studies with elliptical and rectangular patterns show that the computational techniques developed are able to at least match human pattern recognition capabilities, even in the presence of large amounts of noise. Unlike most pattern recognition techniques, this ability is unaffected by arbitrary pattern rotation, translation, and scale change. Further development of the basic approach may eventually allow a spacecraft or robot vehicle to be provided with an ability to very accurately determine its spatial relationship to arbitrary known objects within its optical field of view.

Hemami, H.↗
NASA NTRS1997

Advanced Algorithms and High-Performance Testbed for Large-Scale Site Characterization and Subsurface Target Detecting Using Airborne Ground Penetrating SAR

A team of US Army Corps of Engineers, Omaha District and Engineering and Support Center, Huntsville, let Propulsion Laboratory (JPL), Stanford Research Institute (SRI), and Montgomery Watson is currently in the process of planning and conducting the largest ever survey at the Former Buckley Field (60,000 acres), in Colorado, by using SRI airborne, ground penetrating, Synthetic Aperture Radar (SAR). The purpose of this survey is the detection of surface and subsurface Unexploded Ordnance (UXO) and in a broader sense the site characterization for identification of contaminated as well as clear areas. In preparation for such a large-scale survey, JPL has been developing advanced algorithms and a high-performance restbed for processing of massive amount of expected SAR data from this site. Two key requirements of this project are the accuracy (in terms of UXO detection) and speed of SAR data processing. The first key feature of this testbed is a large degree of automation and a minimum degree of the need for human perception in the processing to achieve an acceptable processing rate of several hundred acres per day. For accurate UXO detection, novel algorithms have been developed and implemented. These algorithms analyze dual polarized (HH and VV) SAR data. They are based on the correlation of HH and VV SAR data and involve a rather large set of parameters for accurate detection of UXO. For each specific site, this set of parameters can be optimized by using ground truth data (i.e., known surface and subsurface UXOs). In this paper, we discuss these algorithms and their successful application for detection of surface and subsurface anti-tank mines by using a data set from Yuma proving Ground, A7, acquired by SRI SAR.

Fijany, Amir↗
NASA NTRS2016

Operational Characteristics Identification and Simulation Model Verification for Incheon International Airport

Incheon International Airport (ICN) is one of the hub airports in East Asia. Airport operations at ICN have been growing more than 5 percent per year in the past five years. According to the current airport expansion plan, a new passenger terminal will be added and the current cargo ramp will be expanded in 2018. This expansion project will bring 77 new stands without adding a new runway to the airport. Due to such continuous growth in airport operations and future expansion of the ramps, it will be highly likely that airport surface traffic will experience more congestion, and therefore, suffer from efficiency degradation. There is a growing awareness in aviation research community of need for strategic and tactical surface scheduling capabilities for efficient airport surface operations. Specific to ICN airport operations, a need for A-CDM (Airport - Collaborative Decision Making) or S-CDM (Surface - Collaborative Decision Making), and controller decision support tools for efficient air traffic management has arisen since several years ago. In the United States, there has been independent research efforts made by academia, industry, and government research organizations to enhance efficiency and predictability of surface operations at busy airports. Among these research activities, the Spot and Runway Departure Advisor (SARDA) developed and tested by National Aeronautics and Space Administration (NASA) is a decision support tool to provide tactical advisories to the controllers for efficient surface operations. The effectiveness of SARDA concept, was successfully verified through the human-in-the-loop (HITL) simulations for both spot release and runway operations advisories for ATC Tower controllers of Dallas-Fort Worth International Airport (DFW) in 2010 and 2012, and gate pushback advisories for the ramp controller of Charlotte-Douglas International Airport (CLT) in 2014. The SARDA concept for tactical surface scheduling is further enhanced and is being integrated into NASA's Airspace Technology Demonstration-2 (ATD-2) project for technology demonstration of Integrated Arrival-Departure-Surface (IADS) operations at CLT. This study is a part of the international research collaboration between KAIA (Korea Agency for Infrastructure Technology Advancement), KARI (Korea Aerospace Research Institute) and NASA, which is being conducted to validate the effectiveness of SARDA concept as a controller decision support tool for departure and surface management of ICN. This paper presents the preliminary results of the collaboration effort. It includes investigation of the operational environment of ICN, data analysis for identification of the operational characteristics of the airport, construction and verification of airport simulation model using Surface Operations Simulator and Scheduler (SOSS), NASA's fast-time simulation tool.

surface operations analysis↗
DOE OSTI2021

Neutralizing Antibodies to SARS-CoV-2 Selected from a Human Antibody Library Constructed Decades Ago

Combinatorial antibody libraries not only effectively reduce antibody discovery to a numbers game, but enable documentation of the history of antibody responses in an individual. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic has prompted a wider application of this technology to meet the public health challenge of pandemic threats in the modern era. Herein, a combinatorial human antibody library constructed 20 years before the coronavirus disease 2019 (COVID-19) pandemic is used to discover three highly potent antibodies that selectively bind SARS-CoV-2 spike protein and neutralize authentic SARS-CoV-2 virus. Compared to neutralizing antibodies from COVID-19 patients with generally low somatic hypermutation (SHM), these three antibodies contain over 13–22 SHMs, many of which are involved in specific interactions in their crystal structures with SARS-CoV-2 spike receptor binding domain. The identification of these somatically mutated antibodies in a pre-pandemic library raises intriguing questions about the origin and evolution of these antibodies with respect to their reactivity with SARS-CoV-2.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2023

Materials characterization: Can artificial intelligence be used to address reproducibility challenges?

Material characterization techniques are widely used to characterize the physical and chemical properties of materials at the nanoscale and, thus, play central roles in material scientific discoveries. However, the large and complex datasets generated by these techniques often require significant human effort to interpret and extract meaningful physicochemical insights. Artificial intelligence (AI) techniques such as machine learning (ML) have the potential to improve the efficiency and accuracy of surface analysis by automating data analysis and interpretation. In this perspective paper, we review the current role of AI in surface analysis and discuss its future potential to accelerate discoveries in surface science, materials science, and interface science. We highlight several applications where AI has already been used to analyze surface analysis data, including the identification of crystal structures from XRD data, analysis of XPS spectra for surface composition, and the interpretation of TEM and SEM images for particle morphology and size. We also discuss the challenges and opportunities associated with the integration of AI into surface analysis workflows. These include the need for large and diverse datasets for training ML models, the importance of feature selection and representation, and the potential for ML to enable new insights and discoveries by identifying patterns and relationships in complex datasets. Most importantly, AI analyzed data must not just find the best mathematical description of the data, but it must find the most physical and chemically meaningful results. In addition, the need for reproducibility in scientific research has become increasingly important in recent years. The advancement of AI, including both conventional and the increasing popular deep learning, is showing promise in addressing those challenges by enabling the execution and verification of scientific progress. By training models on large experimental datasets and providing automated analysis and data interpretation, AI can help to ensure that scientific results are reproducible and reliable. Although integration of knowledge and AI models must be considered for the transparency and interpretability of models, the incorporation of AI into the data collection and processing workflow will significantly enhance the efficiency and accuracy of various surface analysis techniques and deepen our understanding at an accelerated pace.

Materials Science↗
NASA NTRS2013

The ISIS Mission Concept: An Impactor for Surface and Interior Science

The Impactor for Surface and Interior Science (ISIS) mission concept is a kinetic asteroid impactor mission to the target of NASA's OSIRIS-REx (Origins-Spectral Interpretation-Resource Identification-Security-Regolith Explorer) asteroid sample return mission. The ISIS mission concept calls for the ISIS spacecraft, an independent and autonomous smart impactor, to guide itself to a hyper-velocity impact with 1999 RQ36 while the OSIRIS-REx spacecraft observes the collision. Later the OSIRIS-REx spacecraft descends to reconnoiter the impact site and measure the momentum imparted to the asteroid through the impact before departing on its journey back to Earth. In this paper we discuss the planetary science, human exploration and impact mitigation drivers for mission, and we describe the current mission concept and flight system design.

cratering experiment↗
NASA NTRSDate not supplied

EXCAP-LR: Exploration Capabilities Logistics Reduction

All human space missions require substantial logistical mass and volume. As our exploration missions increase in distance and duration, reduction of these logistics requirements becomes more important. The movement and tracking of logistics between vehicle elements are increasingly recognized as critical to crew efficiency and on-orbit mission assurance. This project targets the best opportunities to demonstrate logistics reduction, repurposing, and efficient tracking and management of logistics. These technologies and innovations will make future exploration missions more affordable. - REALM-3 (RFID (Radio Frequency Identification) Enabled Autonomous Logistics Management) – Hydra (HYper-Distributed RFID Antenna) Smart Stow: receives RFID signal from readers, use very small % of signal to self-power on-board microcontroller and RF switch, multiplex RFID signal to other nodes in a chain or to a multitude of antennas. ISS HYDRA Smart Stow is over-instrumented to allow evaluation of a branched network HYDRA chain. - UWMS (Universal Waste Management System) – provides a compact, lower mass/volume toilet for exploration missions - ACS (Advanced Clothing Systems) – investigates ways to reduce clothing mass and volume directly or through efficiently cleaning them for reuse.

Patrick Fink↗
DOE OSTI2026

Agnostic capture of pathogens for the detection and diagnostics of emerging threats

The continued emergence of pathogens, whether novel, re-emerging, or engineered, poses a persistent global biosecurity and public health challenge. Recent outbreaks, including COVID-19, Lassa fever, Marburg virus, mpox, and avian influenza, underscore the urgent need for robust systems that enable rapid surveillance, early diagnosis, and timely countermeasures before widespread human transmission occurs. In this article, we focus on early detection technologies and systematically evaluate current diagnostic and sensing modalities. We highlight sequencing and spectroscopy as two complementary approaches capable of providing broad, agnostic detection and rich biological insight. Our analysis emphasizes that scientific innovation alone is insufficient: effective preparedness also requires improved data curation, integration, and sharing to build AI-ready resources that accelerate future responses. We argue for coordinated advances in both technological capabilities and supporting infrastructure to enable the rapid identification and characterization of emerging pathogens and to fully leverage modern science against evolving infectious threats.

Environmental health↗
NASA NTRS2006

NASA Langley Research Center Systems Analysis & Concepts Directorate Participation in the Exploration Systems Architecture Study

The NASA Langley Research Center (LaRC) Systems Analysis & Concepts Directorate (SACD) began studying human exploration missions beyond low Earth orbit (LEO) in the year 1999. This included participation in NASA s Decadal Planning Team (DPT), the NASA Exploration Team (NExT), Space Architect studies and Revolutionary Aerospace Systems Concepts (RASC) architecture studies that were used in formulating the new Vision for Space Exploration. In May of 2005, NASA initiated the Exploration Systems Architecture Study (ESAS). The primary outputs of the ESAS activity were concepts and functional requirements for the Crewed Exploration Vehicle (CEV), its supporting launch vehicle infrastructure and identification of supporting technology requirements and investments. An exploration systems analysis capability has evolved to support these functions in the past and continues to evolve to support anticipated future needs. SACD had significant roles in supporting the ESAS study team. SACD personnel performed the liaison function between the ESAS team and the Shuttle/Station Configuration Options Team (S/SCOT), an agency-wide team charged with using the Space Shuttle to complete the International Space Station (ISS) by the end of Fiscal Year (FY) 2010. The most significant of the identified issues involved the ability of the Space Shuttle system to achieve the desired number of flights in the proposed time frame. SACD with support from the Kennedy Space Center performed analysis showing that, without significant investments in improving the shuttle processing flow, that there was almost no possibility of completing the 28-flight sequence by the end of 2010. SACD performed numerous Lunar Surface Access Module (LSAM) trades to define top level element requirements and establish architecture propellant needs. Configuration trades were conducted to determine the impact of varying degrees of segmentation of the living capabilities of the combined descent stage, ascent stage, and other elements. The technology assessment process was developed and implemented by SACD as the ESAS architecture was refined. SACD implemented a rigorous and objective process which included (a) establishing architectural functional needs, (b) collection, synthesis and mapping of technology data, and (c) performing an objective decision analysis resulting in technology development investment recommendations. The investment recommendation provided budget, schedule, and center/program allocations to develop required technologies for the exploration architecture, as well as the identification of other investment opportunities to maximize performance and flexibility while minimizing cost and risk. A summary of the trades performed and methods utilized by SACD for the Exploration Systems Mission Directorate (ESAS) activity is presented along with how SACD is currently supporting the implementation of the Vision for Space Exploration.

Keyes, Jennifer↗
DOE OSTI2020

Artificial Intelligence Application to D and D - 20492

As aging facilities across the DOE complex await decommissioning, there is an ongoing need to understand any changes in the structural conditions. Many of these facilities were built over 50 years ago and, in some cases, these facilities have gone beyond the expected operational lifetime. Many facilities have been placed in a state of 'cold and dark,' sitting unused and awaiting decommissioning. Especially challenging are the aging facilities that provide unique operational/production capabilities to support critical DOE missions and cannot be shut down. In any of these scenarios, the structural integrity of these facilities may become compromised as time passes. It is critical that adequate inspections be performed on a continual basis and that the data collected undergoes sufficient analysis to support timely identification of any new or worsening structural issues as well as prompt needed maintenance and repairs to maintain the facilities in a safe condition. In recent days, Artificial Intelligence (AI) [1] and its application to various domains are growing at fast speed. FIU is performing research in this area and exploring the associated technologies to solve nuclear decommissioning problems. Artificial intelligence refers to the capability of a program to autonomously act, react and adapt to the working environment. AI enables the machine to behave like humans and perform the cognitive functions such as 'learning' and 'problem solving'. AI systems gradually moving from traditional approaches (algorithms and expert systems) towards more efficient and advanced technologies (machine learning [1] and deep learning [2] [3]). AI is the study of algorithms and statistical models that is being used by computers to perform specific tasks without using explicit instructions. FIU is working to develop a pilot-scale infrastructure to implement structural health monitoring using AI technologies with focus on machine learning, deep learning. This research is focused on Computer Vision/Image Classification area of AI applications. This can also be expanded to other areas of AI related to Object Recognition and Character Recognition in images. In addition to utilizing existing data sets, FIU will collect and investigate image and video data using FIU test-bed mockups to monitor structural health of the facility. Resulting data will be processed and analyzed using machine learning/deep learning technologies. The proposed pilot system is intended to serve as a starting point to engage the DOE field sites on related data sets and their decision making needs. It is anticipated that proposed machine learning/deep learning technologies can be effectively employed using anomaly detection to solve EM challenges in surveillance and maintenance of the D and D facilities. FIU will work with research stakeholders to identify applications at various sites and other DOE facilities. (authors)

12 MANAGEMENT OF RADIOACTIVE AND NON-RADIOACTIVE W↗
DOE OSTI2023

A bacterial sensor taxonomy across earth ecosystems for machine learning applications

Microbial communities have evolved to colonize all ecosystems of the planet, from the deep sea to the human gut. Microbes survive by sensing, responding, and adapting to immediate environmental cues. This process is driven by signal transduction proteins such as histidine kinases, which use their sensing domains to bind or otherwise detect environmental cues and “transduce” signals to adjust internal processes. We hypothesized that an ecosystem’s unique stimuli leave a sensor “fingerprint,” able to identify and shed insight on ecosystem conditions. To test this, we collected 20,712 publicly available metagenomes from Host-associated, Environmental, and Engineered ecosystems across the globe. We extracted and clustered the collection’s nearly 18M unique sensory domains into 113,712 similar groupings with MMseqs2. We built gradient-boosted decision tree machine learning models and found we could classify the ecosystem type (accuracy: 87%) and predict the levels of different physical parameters (R2 score: 83%) using the sensor cluster abundance as features. Feature importance enables identification of the most predictive sensors to differentiate between ecosystems which can lead to mechanistic interpretations if the sensor domains are well annotated. To demonstrate this, a machine learning model was trained to predict patient’s disease state and used to identify domains related to oxygen sensing present in a healthy gut but missing in patients with abnormal conditions. Moreover, since 98.7% of identified sensor domains are uncharacterized, importance ranking can be used to prioritize sensors to determine what ecosystem function they may be sensing. Furthermore, these new predictive sensors can function as targets for novel sensor engineering with applications in biotechnology, ecosystem maintenance, and medicine.

97 MATHEMATICS AND COMPUTING↗
DOE OSTI2023

Identification of over ten thousand candidate structured RNAs in viruses and phages

Structured RNAs play crucial roles in viruses, exerting influence over both viral and host gene expression. However, the extensive diversity of structured RNAs and their ability to act in cis or trans positions pose challenges for predicting and assigning their functions. While comparative genomics approaches have successfully predicted candidate structured RNAs in microbes on a large scale, similar efforts for viruses have been lacking. In this study, we screened over 5 million DNA and RNA viral sequences, resulting in the prediction of 10,006 novel candidate structured RNAs. These predictions are widely distributed across taxonomy and ecosystem. We found transcriptional evidence for 206 of these candidate structured RNAs in the human fecal microbiome. These candidate RNAs exhibited evidence of nucleotide covariation, indicative of selective pressure maintaining the predicted secondary structures. Our analysis revealed a diverse repertoire of candidate structured RNAs, encompassing a substantial number of putative tRNAs or tRNA-like structures, Rho-independent transcription terminators, and potentially cis-regulatory structures consistently positioned upstream of genes. In summary, our findings shed light on the extensive diversity of structured RNAs in viruses, offering a valuable resource for further investigations into their functional roles and implications in viral gene expression and pave the way for a deeper understanding of the intricate interplay between viruses and their hosts at the molecular level.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2021

Facile One-Pot Nanoproteomics for Label-Free Proteome Profiling of 50–1000 Mammalian Cells

Recent advances in sample preparation enable label-free MS-based proteome profiling of small numbers of mammalian cells. However, specific devices are often required to downscale sample processing volume from the standard 50-200 µL to sub-µL for effective nanoproteomics, which greatly impedes the implementation of current nanoproteomics methods by broad proteomics research community. Here we report a facile one-pot nanoproteomics method termed SOPs-MS (Surfactant-assisted One-Pot sample processing at the standard volume coupled with MS) for convenient proteome profiling of 50-1000 mammalian cells. Building upon our recent development of SOP-MS for label-free single-cell proteomics at low µL volume (Commun Bio 2021, 4, 265), we have systematically evaluated its processing volume at 10-200 µL using 100 human cells for robust reproducible nanoproteomic analysis. The processing volume of 50 µL which is in the range of volume for standard proteomics sample preparation, has been selected for easy sample handling with benchtop micropipette. Using the commonly accessible LC-MS platform, SOPs-MS allows for reliable label-free quantification of ~1200-2700 protein groups from 50-1000 MCF10A cells. When applied to small subpopulations of mouse colon crypt cells, SOPs-MS can reveal distinct protein signatures between any two subpopulation cells with identification of ~1500-2500 protein groups for each subpopulation. SOPs-MS may pave the way for routine deep proteome profiling of small numbers of cells as well as low-input samples.

59 BASIC BIOLOGICAL SCIENCES↗
NASA NTRS2015

Flight Testing ALHAT Precision Landing Technologies Integrated Onboard the Morpheus Rocket Vehicle

A suite of prototype sensors, software, and avionics developed within the NASA Autonomous precision Landing and Hazard Avoidance Technology (ALHAT) project were terrestrially demonstrated onboard the NASA Morpheus rocket-propelled Vertical Testbed (VTB) in 2014. The sensors included a LIDAR-based Hazard Detection System (HDS), a Navigation Doppler LIDAR (NDL) velocimeter, and a long-range Laser Altimeter (LAlt) that enable autonomous and safe precision landing of robotic or human vehicles on solid solar system bodies under varying terrain lighting conditions. The flight test campaign with the Morpheus vehicle involved a detailed integration and functional verification process, followed by tether testing and six successful free flights, including one night flight. The ALHAT sensor measurements were integrated into a common navigation solution through a specialized ALHAT Navigation filter that was employed in closed-loop flight testing within the Morpheus Guidance, Navigation and Control (GN&C) subsystem. Flight testing on Morpheus utilized ALHAT for safe landing site identification and ranking, followed by precise surface-relative navigation to the selected landing site. The successful autonomous, closed-loop flight demonstrations of the prototype ALHAT system have laid the foundation for the infusion of safe, precision landing capabilities into future planetary exploration missions.

Carson, John M. III↗
DOE OSTI2021

Capsular polysaccharide correlates with immune response to the human gut microbe Ruminococcus gnavus

Active inflammatory bowel disease (IBD) often coincides with increases of Ruminococcus gnavus, a gut microbe found in nearly everyone. It was not known how, or if, this correlation contributed to disease. We investigated clinical isolates of R. gnavus to identify molecular mechanisms that would link R. gnavus to inflammation. Here, we show that only some isolates of R. gnavus produce a capsular polysaccharide that promotes a tolerogenic immune response, whereas isolates lacking functional capsule biosynthetic genes elicit robust proinflammatory responses in vitro. Germ-free mice colonized with an isolate of R. gnavus lacking a capsule show increased measures of gut inflammation compared to those colonized with an encapsulated isolate in vivo. These observations in the context of our earlier identification of an inflammatory cell-wall polysaccharide reveal how some strains of R. gnavus could drive the inflammatory responses that characterize IBD.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
NASA NTRS2014

2014 SRP Integration Transcript

HRP's mission is to reduce the risks to human health and performance during long-duration spaceflight. The HRP Integrated Research Plan (IRP) contains the research plans for the 32 risks that require research to characterize and mitigate. From its inception the "integrate" aspect of the IRP has denoted the integrated nature of risks to human health and performance. Even though each risk in the IRP has its own research plan and is tracked separately, the interrelated nature of health and performance requires that they be addressed in an integrative or holistic fashion so that the connectedness of physiological systems within the human body and the integrated response to spaceflight can be addressed. Common characteristics of the spaceflight environment include altered gravity, atmospheres, and light/dark cycles; space radiation; isolation; noise; and periods of high or low workload. Long-term exposure to this unique environment produces a suite of physiological effects such as stress; vision, neurocognitive, and anthropometric changes; circadian misalignment; fluid shifts; cardiovascular deconditioning; immune dysregulation; and altered nutritional requirements. Expanding cross-disciplinary integrative approaches that synthesize concepts or data from two or more disciplines would improve the identification and characterization risk factors, and enable the development of countermeasures relevant to multiple risks. Cross-disciplinary approaches might also help to illuminate problem areas that may arise when a countermeasure adversely impacts risks other than those which it was developed to mitigate, or to identify groupings of physiological changes that are likely to occur that may impact the overall risk posture. In 2014 HRP embarked on a pilot study that combined four SRPs (and 12 HRP risks) - Behavioral Health, Sensorimotor, Cardiovascular, and Bone/Muscle - specifically to discuss cross-disciplinary integration. The points outlined below were suggested to seed the discussion, within the bounding constraint that research plans must be feasible and relevant to the HRP mission. While these were suggested starting points, the overall guiding principle was to allow free discussion from panel members on any aspect of integrated research that they felt was important, Existing cross-disciplinary integration as documented in the IRP (HRR), Existing or needed integration already identified by HRP, but not yet well defined within the IRP, Areas of integration that are missing.

Steinberg, Susan↗
NASA NTRSDate not supplied

Supervisory Control with Dual Tasking in Post G-Transition Vehicle Landings

BACKGROUND Landing during exploration spaceflight may consist of both planned automated supervisory control and unplanned crew override. Supervisory control, particularly when performed under cognitive load with additional monitoring tasks, is essential for ensuring overall mission success during landing contingencies. Evaluating performance in a relevant Human Landing System (HLS) supervisory landing task after long-duration microgravity exposure can help identify potential risks from human error and sensorimotor alterations. Adaptive changes in the sensorimotor system can manifest during g-transitions as spatial disorientation. Although training and landing aids facilitate successful landings despite disorientation, these adaptive changes may heighten cognitive demand, which must be considered in the landing strategy. It is important to characterize these effects as soon as possible following the G-transition while the sensorimotor system remains in a state of adaptive flux to inform appropriate countermeasures. METHODS A Multi-attribute Lunar Table Battery (MALTB) task was developed for an iOS tablet device to provide flexible crew testing and training capabilities in-flight and on the ground. Elements of the tablet task were derived from the Multi-Attribute Task Battery (MATB, Cegarra et al. 2020). The task requires crew members to study a map of a planned landing site and memorizing the terrain and surface landmarks to inform potential divert maneuvers during landing. The user will oversee a series of approaches through touchdown simulations on the tablet with an external camera view of the Lunar surface. The primary responsibility of the crew member will be to execute a divert if the guidance recommended site is erroneous (e.g., the guidance projected landing target is not within 10m of the planned landing site center), or the projected landing site is no longer suitable due to surface obstacles. Considering vehicle maneuverability and fuel reserves, the divert capabilities will diminish as the task progresses. In cases where a divert is initiated, a new landing target will need to be designated by the user and will be evaluated for the proximity to the original pre-planned site. A secondary operational monitoring task will challenge the user's cognitive reserve by requiring the user to maintain several gauges within acceptable limits and respond to a visual indicator while completing the landing approach. Outcome measures include distance from the planned landing site to the user-initiate divert landing location, ground slope at the new landing site, time to divert, the ability to accomplish the secondary monitoring tasks, and perceived workload. The tablet task is being evaluated in a ground-based study to determine the learning effect of first-time users. The tablet task will be utilized in a flight study to test performance multiple times postflight. Future potential testing in-flight have been identified for capsules with iOS tablet devices. Multi-attribute Lunar Table Battery Task MALTB comprises video footage of thirty distinct landing conditions. The landing scenarios feature various landing sites (n = 3), hazardous object sizes (n = 6), sun azimuth degrees (n = 4), camera modes (i.e., fixed or gimbaled), and navigation bias (i.e., true or false). Each seventy second trial uses a sixty-degree constant glideslope trajectory. The application architecture and layout include user identification setup and data storing, a guided walkthrough of the task and interface components, practice mode for task familiarization, and a modified Bedford workload scale administered following task completion. The time-based dependent measures are saved locally to the iOS Files application and post-processing scripts have been developed to evaluate the remaining measures of performance. RELEVANCE This project will deliver an operational demonstration of crew monitoring capability following spaceflight and identify potential deficits that may require remediation. Comparison of individual vestibular and cognitive changes with crew performance will help better characterize the landing risks associated with sensorimotor alterations. ACKNOWLEDGEMENTS: This project is funded by NASA’s Human Research Program Human Health Countermeasures Element. REFERENCES Cegarra J, Valery B, Avril E, Calmettes C, Navarro J (2020) OpenMATB: A Multi-Attribute Task Battery promoting task customization, software extensibility and experiment replicability. Behav Res Methods 52:1980-1990 doi: 10.3758/s13428-020-01364-w

Matthew McDonnell↗
DOE OSTI2023

Identification of ancestral gnathostome Gli3 enhancers with activity in mammals

Abnormal expression of the transcriptional regulator and hedgehog (Hh) signaling pathway effector Gli3 is known to trigger congenital disease, most frequently affecting the central nervous system (CNS) and the limbs. Accurate delineation of the genomic cis-regulatory landscape controlling Gli3 transcription during embryonic development is critical for the interpretation of noncoding variants associated with congenital defects. Here, we employed a comparative genomic analysis on fish species with a slow rate of molecular evolution to identify seven previously unknown conserved noncoding elements (CNEs) in Gli3 intronic intervals (CNE15–21). Transgenic assays in zebrafish revealed that most of these elements drive activities in Gli3 expressing tissues, predominantly the fins, CNS, and the heart. Intersection of these CNEs with human disease associated SNPs identified CNE15 as a putative mammalian craniofacial enhancer, with conserved activity in vertebrates and potentially affected by mutation associated with human craniofacial morphology. Finally, comparative functional dissection of an appendage-specific CNE conserved in slowly evolving fish (elephant shark), but not in teleost (CNE14/hs1586) indicates co-option of limb specificity from other tissues prior to the divergence of amniotes and lobe-finned fish. In conclusion, these results uncover a novel subset of intronic Gli3 enhancers that arose in the common ancestor of gnathostomes and whose sequence components were likely gradually modified in other species during the process of evolutionary diversification.

59 BASIC BIOLOGICAL SCIENCES↗