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NASA NTRS2011

A New Business Model for Problem Solving-Infusing Open Collaboration and Innovation Health and Human Services

This slide presentation reviews the Space Life Sciences Directorate (SLSD) new business model for problem solving, with emphasis on open collaboration and innovation. The topics that are discussed are: an overview of the work of the Space Life Sciences Directorate and the strategic initiatives that arrived at the new business model. A new business model was required to infuse open collaboration/innovation tools into existing models for research, development and operations (research announcements, procurements, SBIR/STTR etc). This new model involves use of several open innovation partnerships: InnoCentive, Yet2.com, TopCoder and NASA@work. There is also a new organizational structure developed to facilitate the joint collaboration with other NASA centers, international partners, other U.S. Governmental organizations, Academia, Corporate, and Non-Profit organizations: the NASA Human Health and Performance Center (NHHPC).

Davis, Jeffrey R.↗

DOE OSTI2023

The Impact of Crop Rotation and Spatially Varying Crop Parameters in the E3SM Land Model (ELMv2)

Abstract Earth System Models (ESMs) are increasingly representing agriculture due to its impact on biogeochemical cycles, local and regional climate, and fundamental importance for human society. Realistic large scale simulations may require spatially varying crop parameters that capture crop growth at various scales and among different cultivars, as well as common crop management practices, but their importance is uncertain, and they are often not represented in ESMs. In this study, we examine the impact of using constant versus spatially varying crop parameters using a novel, realistic crop rotation scenario in the Energy Exascale Earth System Model (E3SM) Land Model version 2 (ELMv2). We implemented crop rotation by using ELMv2's dynamic land unit capability, and then calibrated and validated the model against observations collected at three AmeriFlux sites in the US Midwest with corn soybean rotation. The calibrated model closely captured the magnitude and observed seasonality of carbon and energy fluxes across crops and sites. We performed regional simulations for the US Midwest using the calibrated model and found that spatially varying only a few crop parameters across the region, as opposed to using constant parameters, had a large impact, with the carbon fluxes and energy fluxes both varying by up to 40%. These results imply that large scale ESM simulations using spatially invariant crop parameters may result in biased energy and carbon fluxes estimation from agricultural land, and underline the importance of improving human‐earth systems interactions in ESMs.

54 ENVIRONMENTAL SCIENCES↗

NASA NTRSDate not supplied

Temporal Changes in Astronauts’ Muscle and Cardiorespiratory Physiology Before, During and After Spaceflight

Background: NASA’s planned space exploration missions will require astronauts to safely perform extravehicular activity (EVA) and to safely egress vehicles in a variety of landing scenarios. Prolonged exposure to spaceflight can diminish tolerance for physical activity, decrease cardiovascular and sensorimotor function, and cause loss of bone mineral density, and reduced muscle mass and strength. Although exercise can mitigate these spaceflight-induced physiological decrements, little is known regarding the time-course of changes in muscle and aerobic performance during spaceflight. Furthermore, these exercise countermeasures are not fully protective. For example, maximal aerobic capacity (VO2pk), lower body muscle cross-sectional area, and strength decrease by about 10% to 15% after short- (~ 14 days) and long-duration (~ 6 months) missions on the International Space Station (ISS). Future space missions that are longer in duration and further from Earth will employ exploration vehicles that will have exercise hardware with less robust and more constrained exercise capabilities than of those available on the ISS. Thus, countermeasures will need to be optimized to protect crew health and performance on exploration-class missions that will last up to 3 years. This will require a more detailed understanding of the dynamic effects of spaceflight on human health and performance, and the ability of exercise to protect against this deconditioning, and the interaction of exercise with interrelated factors like nutrition, sleep, and environmental conditions. Methods: We will use standardized research and medical testing protocols previously validated in 1-G and 0-G to quantify the time course and the inter-individual variability of changes in physical performance, including cardiorespiratory fitness, and muscle strength, and endurance, before, during and after spaceflight missions lasting 2 months, 6 months, and 1 year. Additionally, we will use an extrapolation model to predict changes associated with 2–3-year exploration missions. Additionally, we will monitor in-flight exercise, nutrition, and sleep using in-flight assessment tools. Significance: Our testing protocols will provide valuable information for determining time course of change and the interindividual variability of spaceflight-induced deconditioning of aerobic capacity and muscle strength and endurance over the course of spaceflight missions up to and beyond 1 year. This information will be vital to assess whether humans can be physically ready for deep space exploration, such as on a mission to Mars, using current technology, or if additional mitigation strategies are necessary.

countermeasures↗

NASA NTRS2022

In Situ Measurements of Surface Texture with Virtual Environments Support Science-Driven Human Surface Operations on the Moon and Beyond

Visualization tools enabling real-time scientific analysis are important for supporting future astronaut operations on the lunar surface. Such tools can be built into virtual environments to support scientific investigations, as well as situational awareness, real-time decision making, and efficient communication between astronauts and ground and support systems. Understanding how these tools can be optimized for science is essential for upcoming Artemis missions. In this contribution, we discuss how measurements of surface texture at multiple length scales can greatly enhance in situ science on/of the Moon, and eventually Mars, asteroids, and beyond. Roughness measurements at various wavelengths directly support objectives defined in the Artemis Science Plan, including (O1) “understanding planetary processes,” (O2) “understanding volatile cycles,” and (O3) “interpreting the impact history of the Earth-Moon system” . Key scientific analyses enabled by texture measurements at different length scales include: ● Sub-centimeter scales: Texture measurements can help constrain lava flow crystallinity, lava rheology, emplacement flow dynamics, and cooling histories (O1). Measurements of lacunarity (voids in fractal fill space) can shed light on eruptive volatile content, residence time of migrating volatiles, and near-surface volume available for micro-cold trapping of volatiles (O1, O2). ● Centimeter–meter scales: Texture measurements can be used for the differentiation of individual lava flows, the reconstruction of local stratigraphies and emplacement sequences, characterization of post-emplacement surface modification processes (O1, O3). Derived roughness (polarization) metrics can be used in the detection of water ice and characterization of ice properties (e.g., purity, grade, depth, abundance). ● Hectometer–Kilometer scales: Texture measurements can be used to differentiate major geologic surface units and surface structures (O1), constrain the presence of abundant ground ices (O2), and analyze surface modification and estimate surface age (O3). Real-time measurements of surface texture across these multiple length scales will enable efficient sample identification and scientific investigations by future astronauts. To support these investigations and the objective classification of surface texture, virtual environments employed by astronauts should be able to instantaneously convert raw data into processed data (e.g., digital terrain and elevation models) and derived metrics (e.g., RMS, std, Hurst, CPR) and perform statistical analyses (e.g., PCA, outliers, correlation matrices). Such tools are being developed and tested by the Resource Exploration and Science of our Cosmic Environment (RESOURCE) team, a node of NASA’s Solar System Exploration Research Virtual Institute (SSERVI), and are an excellent example of the powerful synergies of human and robotic ground assets critical in the return of humans to the Moon.

Ariel N. Deutsch↗

NASA NTRS2017

New Stagnation Arc Jet Model Design for Testing ADEPT 3-D Carbon Cloth

The ADEPT concept has been considered as an entry, descent and landing system to enable Human Mars class missions. Ground rules for the Mars studies required aerocapture, orbit, and then entry. The design utilizes a 3-D woven carbon cloth fabric as both heatshield and primary structure and design guidelines required 6 layers remaining after all entry events. The peak heating predicted for the ADEPT carbon cloth was 35 Wcm2 and resulting temperatures were predicted to be 1400K. Predictions for carbon mass loss were performed using equilibrium thermochemistry, which is only accurate for T2000K. Carbon oxidation is kinetically controlled at T2000K, and mass loss drops off considerably from equilibrium values. Design of the cloth thickness and mass would be significantly reduced if kinetics were considered. This effort was to design a stagnation test article design that could be used in the AHF with varying levels of oxygen where the results could be used to develop an engineering model to describe the recession rate of the carbon as a function of the partial pressure of monotomic oxygen.

carbon cloth↗

NASA NTRS1998

A link-segment model of upright human posture for analysis of head-trunk coordination

Sensory-motor control of upright human posture may be organized in a top-down fashion such that certain head-trunk coordination strategies are employed to optimize visual and/or vestibular sensory inputs. Previous quantitative models of the biomechanics of human posture control have examined the simple case of ankle sway strategy, in which an inverted pendulum model is used, and the somewhat more complicated case of hip sway strategy, in which multisegment, articulated models are used. While these models can be used to quantify the gross dynamics of posture control, they are not sufficiently detailed to analyze head-trunk coordination strategies that may be crucial to understanding its underlying mechanisms. In this paper, we present a biomechanical model of upright human posture that extends an existing four mass, sagittal plane, link-segment model to a five mass model including an independent head link. The new model was developed to analyze segmental body movements during dynamic posturography experiments in order to study head-trunk coordination strategies and their influence on sensory inputs to balance control. It was designed specifically to analyze data collected on the EquiTest (NeuroCom International, Clackamas, OR) computerized dynamic posturography system, where the task of maintaining postural equilibrium may be challenged under conditions in which the visual surround, support surface, or both are in motion. The performance of the model was tested by comparing its estimated ground reaction forces to those measured directly by support surface force transducers. We conclude that this model will be a valuable analytical tool in the search for mechanisms of balance control.

NASA Discipline Neuroscience↗

NASA NTRS2006

Comprehensive Environmental Informatics System (CEIS) Integrating Crew and Vehicle Environmental Health

Integrated Vehicle Health Management (IVHM) systems have been pursued as highly integrated systems that include smart sensors, diagnostic and prognostics software for assessments of real-time and life-cycle vehicle health information. Inclusive to such a system is the requirement to monitor the environmental health within the vehicle and the occupants of the vehicle. In this regard an enterprise approach to informatics is used to develop a methodology entitled, Comprehensive Environmental Informatics System (CEIS). The hardware and software technologies integrated into this system will be embedded in the vehicle subsystems, and maintenance operations, to provide both real-time and life-cycle health information of the environment within the vehicle cabin and of its occupants. This comprehensive information database will enable informed decision making and logistics management. One key element of the CEIS is interoperability for data acquisition and archive between environment and human system monitoring. With comprehensive components the data acquired in this system will use model based reasoning systems for subsystem and system level managers, advanced on-board and ground-based mission and maintenance planners to assess system functionality. Knowledge databases of the vehicle health state will be continuously updated and reported for critical failure modes, and routinely updated and reported for life cycle condition trending. Sufficient intelligence, including evidence-based engineering practices which are analogous to evidencebased medicine practices, will be included in the CEIS to result in more rapid recognition of off-nominal operation to enable quicker corrective actions. This will result from better information (rather than just data) for improved crew/operator situational awareness, which will produce significant vehicle and crew safety improvements, as well as increasing the chance for mission success, future mission planning as well as training. Other benefits include improved reliability, increase safety in operations and cost of operations. The cost benefits stem from significantly reduced processing and operations manpower, predictive maintenance for systems and subjects. The improvements in vehicle functionality and cost will result from increased prognostic and diagnostic capability due to the detailed total human exploration system health knowledge from CEIS. A collateral benefit is that there will be closer observations of the vehicle occupants as wrist watch sized devices are worn for continuous health monitoring. Additional database acquisition will stem from activities in countermeasure practices to ensure peak performance capability by occupants of the vehicle. The CEIS will provide data from advanced sensing technologies and informatics modeling which will be useful in problem troubleshooting, and improving NASA s awareness of systems during operation.

Nall, Mark E.↗

DOE OSTI2026

Operating advanced scientific instruments with AI agents that learn on the job

Advanced scientific user facilities, such as next generation X-ray light sources and self-driving laboratories, are revolutionizing scientific discovery by automating routine tasks and enabling rapid experimentation and characterizations. However, these facilities must continuously evolve to support new experimental workflows, adapt to diverse user projects, and meet growing demands for more intricate instruments and experiments. This continuous development introduces significant operational complexity, necessitating a focus on usability, reproducibility, and intuitive human-instrument interaction. In this work, we explore the integration of agentic AI, powered by Large Language Models (LLMs), as a transformative tool to achieve this goal. We present our approach to developing a human-in-the-loop pipeline for operating advanced instruments including an X-ray nanoprobe beamline and an autonomous robotic station dedicated to the design and characterization of materials. Specifically, we evaluate the potential of various LLMs as trainable scientific assistants for orchestrating complex, multi-task workflows, which also include multimodal data, optimizing their performance through optional human input and iterative learning. We demonstrate the ability of AI agents to bridge the gap between advanced automation and user-friendly operation, paving the way for more adaptable and intelligent scientific facilities.

Large Language Models↗

NASA NTRSDate not supplied

Problem Complexity and LLM: H-M Team Reliability in Challenging Environments

In traditional human-machine operations, the functional decomposition of actions and responsibilities among various agents is assigned a priori. For instance, in current air traffic operations, although assisted by software, human pilots have the ultimate control of aircraft. Multiagent human-machine and machine-machine systems will face problems of varying and potentially unpredicted complexity in future challenging scenarios of planetary, en route, and orbital activities. Hence, it is important to enable dynamic transfer of decision-making to appropriate team members, human or machine, depending on which agent is best equipped to solve that specific problem on a time budget. In this paper, we consider aspects of problem-solving and its modeling that affect the outcomes of decision-making as a function of solution quality and the likelihood of solving the problem on a required time budget. We focus on Large Language Models (LLM) as potential machine teammates and conclude that practical, predictive modeling of their performance, at the current stage of their development, is infeasible. Simple examples help us illustrate that current LLM will require fundamental advancements to provide reliable support in team decision-making, especially in safety-critical and time-critical domains. The study is not meant to diminish the value of the remarkable capabilities of LLM, but rather to gain a better understanding of the technology’s appropriate use and the needed additions.

function allocation↗

NASA NTRSDate not supplied

Exploration Medical Capability - Advancing Medical System Design and Risk-Informed Decision Making for Deep Space Exploration

BACKGROUND: Within NASA’s Human Research Program, the Exploration Medical Capability (ExMC) Element has three primary focus areas: clinical and scientific research, systems engineering and trade space analysis, and technology development and demonstrations. These focus areas feed into the overarching goal of enabling progressively Earth-Independent Medical Operations (EIMO), a new paradigm that will be necessary for future Artemis and Mars medical and vehicle systems. This EIMO end state aligns with NASA’s Moon to Mars Objectives, which clearly outline the need for NASA deep space exploration missions to reduce their reliance upon Earth and become increasingly autonomous, in preparation for the first human Mars mission. OVERVIEW: To advance exploration medical systems and ultimately, integrated crew health and performance systems, ExMC’s portfolio includes: funding ground development & testing of novel medical capabilities; creation of new approaches for the development of medical protocols and procedures; deployment of innovative technologies into analog environments; technology demonstrations in spaceflight; and eventual transition to operations of new capabilities for deep space exploration missions. The portfolio also includes: pharmaceutical research targeting stability, pharmacokinetics, and pharmacodynamics; integrated data architectures and clinical decision support tools; and systems engineering and trade space analysis tools to assist NASA in the development of future medical system models as well as the medical system requirements that can serve as a foundation for deep space exploration missions. All of these investments are done in a collaborative and coordinated fashion with other NASA stakeholders, such as the Environmental Control and Life Support Systems – Crew Health and Performance System Capability Leadership Team and the Health and Medical Technical Authority. DISCUSSION: In this presentation, ExMC will provide an overview of our work from across our portfolio, all of which will inform future EIMO efforts at NASA. ExMC’s research and development investments are targeted to reduce the human system risks associated with deep space exploration to the Moon and Mars.

Kris Lehnhardt↗

DOE OSTI2021

Activation of the CaR-CSE/H2S pathway confers cardioprotection against ischemia-reperfusion injury

Ischemia-reperfusion (I/R) injury is a multifactorial process triggered when an organ is subjected to transiently reduced blood supply. The result is a cascade of pathological complications and organ damage due to the production of reactive oxygen species following reperfusion. The present study aims to evaluate the role of activated calcium-sensing receptor (CaR)-cystathionine γ-lyase (CSE)/hydrogen sulfide (H2S) pathway in I/R injury. Firstly, an I/R rat model with CSE knockout was constructed. Transthoracic echocardiography, TTC and HE staining were performed to determine the cardiac function of rats following I/R Injury, followed by TUNEL staining observation on apoptosis. Besides, with the attempt to better elucidate how CaR-CSE/H2S affects I/R, in-vitro culture of human coronary artery endothelial cells (HCAECs) was conducted with gadolinium chloride (GdCl3, a CaR agonist), H{sub 2}O{sub 2}, siRNA against CSE (siCSE), or W7 (a CaM inhibitor). The interaction between CSE and CaM was subsequently detected. Plasma oxidative stress indexes, H2S and CSE, and apoptosis-related proteins were all analyzed following cell apoptosis. We found that H2S elevation led to the improvement whereas CSE knockdown decreased cardiac function in rats with I/R injury. Moreover, oxidative stress injury in I/R rats with CSE knockout was aggravated, while the increased expression of H2S and CSE in the aortic tissues resulted in alleviated the oxidative stress injury. Moreover, increased H2S and CSE levels were found to inhibit cell apoptotic ability in the aortic tissues after I/R injury, thus attenuating oxidative stress injury, accompanied by inhibited expression of apoptosis-related proteins. In HCAECs following oxidative stress treatment, siCSE and CaM inhibitor were observed to reverse the protection of CaR agonist. Coimmunoprecipitation assay revealed the interaction between CSE and CaM. Taken together, all above-mentioned data provides evidence that activation of the CaR-CSE/H2S pathway may confer a potent protective effect in cardiac I/R injury.

60 APPLIED LIFE SCIENCES↗

DOE OSTI2019

A Kriging-Based Approach to Autonomous Experimentation with Applications to X-Ray Scattering

Abstract Modern scientific instruments are acquiring data at ever-increasing rates, leading to an exponential increase in the size of data sets. Taking full advantage of these acquisition rates will require corresponding advancements in the speed and efficiency of data analytics and experimental control. A significant step forward would come from automatic decision-making methods that enable scientific instruments to autonomously explore scientific problems—that is, to intelligently explore parameter spaces without human intervention, selecting high-value measurements to perform based on the continually growing experimental data set. Here, we develop such an autonomous decision-making algorithm that is physics-agnostic, generalizable, and operates in an abstract multi-dimensional parameter space. Our approach relies on constructing a surrogate model that fits and interpolates the available experimental data, and is continuously refined as more data is gathered. The distribution and correlation of the data is used to generate a corresponding uncertainty across the surrogate model. By suggesting follow-up measurements in regions of greatest uncertainty, the algorithm maximally increases knowledge with each added measurement. This procedure is applied repeatedly, with the algorithm iteratively reducing model error and thus efficiently sampling the parameter space with each new measurement that it requests. We validate the method using synthetic data, demonstrating that it converges to faithful replica of test functions more rapidly than competing methods, and demonstrate the viability of the approach in an experimental context by using it to direct autonomous small-angle (SAXS) and grazing-incidence small-angle (GISAXS) x-ray scattering experiments.

36 MATERIALS SCIENCE↗

NASA NTRS1994

Development of life sciences equipment for microgravity and hypergravity simulation

The mission of the Life Science Division at the NASA Ames Research Center is to investigate the effects of gravity on living systems in the spectrum from cells to humans. The range of these investigations is from microgravity, as experienced in space, to Earth's gravity, and hypergravity. Exposure to microgravity causes many physiological changes in humans and other mammals including a headward shift of body fluids, atrophy of muscles - especially the large muscles of the legs - and changes in bone and mineral metabolism. The high cost and limited opportunity for research experiments in space create a need to perform ground based simulation experiments on Earth. Models that simulate microgravity are used to help identify and quantify these changes, to investigate the mechanisms causing these changes and, in some cases, to develop countermeasures.

Mulenburg, G. M.↗

NASA NTRS2016

Taxi Time Prediction at Charlotte Airport Using Fast-Time Simulation and Machine Learning Techniques

Accurate taxi time prediction is required for enabling efficient runway scheduling that can increase runway throughput and reduce taxi times and fuel consumptions on the airport surface. Currently NASA and American Airlines are jointly developing a decision-support tool called Spot and Runway Departure Advisor (SARDA) that assists airport ramp controllers to make gate pushback decisions and improve the overall efficiency of airport surface traffic. In this presentation, we propose to use Linear Optimized Sequencing (LINOS), a discrete-event fast-time simulation tool, to predict taxi times and provide the estimates to the runway scheduler in real-time airport operations. To assess its prediction accuracy, we also introduce a data-driven analytical method using machine learning techniques. These two taxi time prediction methods are evaluated with actual taxi time data obtained from the SARDA human-in-the-loop (HITL) simulation for Charlotte Douglas International Airport (CLT) using various performance measurement metrics. Based on the taxi time prediction results, we also discuss how the prediction accuracy can be affected by the operational complexity at this airport and how we can improve the fast time simulation model before implementing it with an airport scheduling algorithm in a real-time environment.

airport surface traffic↗

NASA NTRS2017

New Development in NASA's Rodent Research Hardware for Conducting Long Duration Biomedical and Basic Research in Space

Animal models, particularly rodents, are the foundation of pre-clinical research to understand human diseases and evaluate new therapeutics, and play a key role in advancing biomedical discoveries both on Earth and in space. The National Research Councils Decadal survey emphasized the importance of expanding NASAs life sciences research to perform long duration, rodent experiments on the International Space Station (ISS). To accomplish this objective, flight hardware, operations, and science capabilities were developed at NASA Ames Research Center (ARC) to enhance science return for both commercial (CASIS) and government-sponsored rodent research. The Rodent Research program at NASA ARC has pioneered a new research capability on the International Space Station and has progressed toward translating research to the ISS utilizing commercial rockets, collaborating with academia and science industry, while training crewmembers to assist in performing research on orbit. Throughout phases of these missions, our practices, hardware and operations have evolved from tested to developed standards, and we are able to modify and customize our procedure and operations for mission specific requirements. The Rodent Research Habitat is capable of providing a living environment for animals on ISS according to standard animal welfare requirements. Using the cameras in the Habitat, the Rodent Research team has the ability to perform daily health checks on animals, and further analyze the collected videos for behavioral studies. A recent development of the Rodent Research hardware is inclusion of enrichment, to provide the animals the ability to rest and huddle. The Enrichment Hut is designed carefully for adult mice (up to 35 week old) within animal welfare, engineering, and operations constraints. The Hut is made out of the same stainless steel mesh as the cage interior, it has an ingress and an egress to allow animals move freely, and a hinge door to allow crewmembers remove the animals easily. The Rodent Research team has also developed Live Animal Return (LAR) capability, which will be implemented during Rodent Research-5 mission for the first time. The animals will be transported from the Habitat to a Transporter, which will return on the Dragon capsule and splashes down in the Pacific Ocean. Once SpaceX retrieves the Dragon, all powered payloads will be transferred to a SeaVan and transferred to the Long Beach pier. The NASA team then receives the transporter and delivers to a PI-designated laboratory within 120 mile radius of Long Beach. This is a significant improvement allowing researchers to examine animals within 72 hrs. of reentry or to conduct recovery experiments. Together, the hardware improvements and experience that the Rodent Research team has gained working with principal investigators and ISS crew to conduct complex experiments on orbit are expanding capabilities for long duration rodent research on the ISS to achieve both basic science and biomedical objectives.

ISS↗

NASA NTRSDate not supplied

Modeling Deformable Linear Objects for Autonomous Robotic Outfitting of Lunar Surface Systems

This paper presents structural models of deformable linear objects (DLOs). DLOs are a subclass of deformable objects that encompasses common outfitting elements such as cables and ropes. Models are validated through hardware experiments, and integration in a robotic autonomy architecture for space environments is discussed. A persistent human presence on the lunar surface is one of the next major milestones in space exploration. This requires the development of robust extraplanetary construction technologies including structures and materials modeling and robotic systems. Previous robotic construction technology development has primarily focused on structural assembly, with significantly less focus on robotically performed outfitting tasks to instantiate subsystems providing power, data, life support, etc. These tasks involve manipulation of highly flexible elements, which are difficult to model, such as cable harnesses, ropes, and hoses. Robotic manipulation of DLOs, especially cable harnesses, is an active area of research as cable harnesses are essential for providing power and data to space assets. DLO models that can be used for robot manipulator trajectory generation are necessary for autonomous operation of lunar infrastructure. There are many proposed methods for modeling DLOs, and they primarily fall into three types: 1) discrete model-based, 2) continuum model-based, and 3) Neural Network-based. These types each have pros and cons, and the tradeoff between model accuracy and computational speed informs which type should be used. An understanding of this trade-off is imperative for real-time control of autonomous systems. High computational requirements reduce the speed of the model, making real-time control difficult, while accuracy is critical to preventing collisions. Discrete models, such as a mass-spring multibody representation, require relatively few calculations, and accuracy is directly tied to the step size of the discretization. Continuum models, such as a B-spline representation or a Cosserat rod model (a mix of continuous and discrete), are more informed of the structural properties of the cable and are much more accurate than a rigid body mass-spring model, but at significant computational cost. A Neural Network approach can provide an online solution with very few computational steps, but properly generating training data can be difficult and validation for an in-space application is not trivial. This paper explores the trade-off between different modeling approaches and compares accuracy and computational speed/complexity of the three types mentioned above. Model accuracy is evaluated using a cable in a static configuration. True cable shape is obtained using a depth camera for RGB images and point-cloud segmentation. The purpose of this experiment is to evaluate the trade-offs of different approaches to the DLO modeling problem. Understanding the tradeoffs between different cable modeling techniques paves the way for developing robotic control and planning architectures necessary for real-time manipulation of DLOs for lunar infrastructure outfitting. Real-time control is required for robotic systems to be able to actively manipulate a cable in a harsh environment where model and sensor errors compound, and environmental conditions can cause significant disturbances. Cable routing must be performed in areas with high density of objects/obstacles: through truss structures, near solar panels or mirror arrays, next to bundles of electrical equipment. Understanding the best way to plan and manipulate a cable without disrupting the environment or damaging the cable is imperative to robotic outfitting operations on the lunar surface.

Amy M Quartaro↗

DOE OSTI2021

Targeted metabolomic analysis identifies increased serum levels of GABA and branched chain amino acids in canine diabetes

Introduction Dogs with naturally occurring diabetes mellitus represent a potential model for human type 1 diabetes, yet signifcant knowledge voids exist in terms of the pathogenic mechanisms underlying the canine disorder. Untargeted metabolomic studies from a limited number of diabetic dogs identifed similarities to humans with the disease. Objective To expand and validate earlier metabolomic studies, identify metabolites that difer consistently between diabetic and healthy dogs, and address whether certain metabolites might serve as disease biomarkers. Methods Untargeted metabolomic analysis via liquid chromatography-mass spectrometry was performed on serum from diabetic (n=15) and control (n=15) dogs. Results were combined with those of our previously published studies using identical methods (12 diabetic and 12 control dogs) to identify metabolites consistently diferent between the groups in all 54 dogs. Thirty-two candidate biomarkers were quantifed using targeted metabolomics. Biomarker concentrations were compared between the groups using multiple linear regression (corrected P<0.0051 considered signifcant). Results Untargeted metabolomics identifed multiple persistent diferences in serum metabolites in diabetic dogs compared with previous studies. Therefore, targeted metabolomics showed increases in gamma amino butyric acid, valine, leucine, isoleucine, citramalate, and 2-hydroxyisobutyric acid in diabetic versus control dogs while indoxyl sulfate, N-acetyl-L-aspartic acid, kynurenine, anthranilic acid, tyrosine, glutamine, and tauroursodeoxycholic acid were decreased. Conclusion Several of these fndings parallel metabolomic studies in both human diabetes and other animal models of this disease. Given recent studies on the role of GABA and branched chain amino acids in human diabetes, the increase in serum concentrations in canine diabetes warrants further study of these metabolites as potential biomarkers, and to identify similarity in mechanisms underlying this disease in humans and dogs.

59 BASIC BIOLOGICAL SCIENCES↗

DOE OSTI2024

CD46-Targeted Theranostics for PET and 225 Ac-Radiopharmaceutical Therapy of Multiple Myeloma

Multiple myeloma is a plasma cell malignancy with an unmet clinical need for improved imaging methods and therapeutics. Recently, we identified CD46 as an overexpressed therapeutic target in multiple myeloma and developed the antibody YS5, which targets a cancer-specific epitope on this protein. We further developed the CD46-targeting PET probe [ 89 Zr]Zr-DFO-YS5 for imaging and [ 225 Ac]Ac-DOTA-YS5 for radiopharmaceutical therapy of prostate cancer. These prior studies suggested the feasibility of the CD46 antigen as a theranostic target in multiple myeloma. Herein, we validate [ 89 Zr]Zr-DFO-YS5 for immunoPET imaging and [ 225 Ac]Ac-DOTA-YS5 for radiopharmaceutical therapy of multiple myeloma in murine models. In vitro saturation binding was performed using the CD46 expressing MM.1S multiple myeloma cell line. ImmunoPET imaging using [ 89 Zr]Zr-DFO-YS5 was performed in immunodeficient (NSG) mice bearing subcutaneous and systemic multiple myeloma xenografts. For radioligand therapy, [ 225 Ac]Ac-DOTA-YS5 was prepared, and both dose escalation and fractionated dose treatment studies were performed in mice bearing MM1.S-Luc systemic xenografts. Tumor burden was analyzed using BLI, and body weight and overall survival were recorded to assess antitumor effect and toxicity. [ 89 Zr]Zr-DFO-YS5 demonstrated high affinity for CD46 expressing MM.1S multiple myeloma cells (K d = 16.3 nmol/L). In vitro assays in multiple myeloma cell lines demonstrated high binding, and bioinformatics analysis of human multiple myeloma samples revealed high CD46 expression. [ 89 Zr]Zr-DFO-YS5 PET/CT specifically detected multiple myeloma lesions in a variety of models, with low uptake in controls, including CD46 knockout (KO) mice or multiple myeloma mice using a nontargeted antibody. In the MM.1S systemic model, localization of uptake on PET imaging correlated well with the luciferase expression from tumor cells. A treatment study using [ 225 Ac]Ac-DOTA-YS5 in the MM.1S systemic model demonstrated a clear tumor volume and survival benefit in the treated groups. Our study showed that the CD46-targeted probe [ 89 Zr]Zr-DFO-YS5 can successfully image CD46-expressing multiple myeloma xenografts in murine models, and [ 225 Ac]Ac-DOTA-YS5 can effectively inhibit the growth of multiple myeloma. These results demonstrate that CD46 is a promising theranostic target for multiple myeloma, with the potential for clinical translation.

59 BASIC BIOLOGICAL SCIENCES↗