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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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All sourcesNASA NTRSDOE OSTI
NASA NTRS1978

A solution procedure for behavior of thick plates on a nonlinear foundation and postbuckling behavior of long plates

Approximate solutions for three nonlinear orthotropic plate problems are presented: (1) a thick plate attached to a pad having nonlinear material properties which, in turn, is attached to a substructure which is then deformed; (2) a long plate loaded in inplane longitudinal compression beyond its buckling load; and (3) a long plate loaded in inplane shear beyond its buckling load. For all three problems, the two dimensional plate equations are reduced to one dimensional equations in the y-direction by using a one dimensional trigonometric approximation in the x-direction. Each problem uses different trigonometric terms. Solutions are obtained using an existing algorithm for simultaneous, first order, nonlinear, ordinary differential equations subject to two point boundary conditions. Ordinary differential equations are derived to determine the variable coefficients of the trigonometric terms.

Stein, M.↗
NASA NTRS1992

Buoyant thermocapillary flow with nonuniform supra-heating. I - Liquid-phase behavior. II - Two-phase behavior

The present computational study of transient heat transfer and fluid flow in a circular pool of n-decane which is undergoing central radiative heating from above gives attention to the volumetric absorption of the radiation incident on the pool surface. The first part of this study notes that buoyancy influences the number and recirculation rates of the subsurface vortices by stabilizing hot subsurface fluid above the colder core fluid; this affects the liquid surface temperature profile and in turn governs the velocity profile that is due to thermocapillarity. In the second part, the effects of gas-liquid phase coupling, variable density and thermophysical properties, and vaporization are considered.

Schiller, David N.↗
NASA NTRS1993

Selection of behavioral tasks and development of software for evaluation of rhesus monkey behavior during spaceflight

The results of several experiments were disseminated professionally during this semiannual period. These peer-reviewed papers that were accepted for publication represent the growth of our research areas, as follow-up experiments to previously published work in cognition and enrichment have been completed and are being published. The presentations not only reflect the latest interesting results that we have obtained, but also serve as a testament to the intense interest that is being expressed for our test system and findings.

Rumbaugh, Duane M.↗
NASA NTRS2025

Navigating Team Dynamics: Automated Detection of Micro-Behaviors Between Team Members Through Longitudinal Interaction Data

The success in future long term space exploration missions will depend on the cooperation, coordination, and mutual understanding among the crew members. Micro-behaviors are momentary, subtle linguistic and paralinguistic indicators of thinking and feeling toward another member of the team (Cortina et al., 2001; Smith & Griffiths, 2022) that can significantly impact team dynamics and influence the overall team performance (Paromita & Chaspari, 2024). Due to their interactive nature, micro-behaviors have a sender (i.e., the team member expressing the micro-behavior) and a target (the team member impacted by the micro-behavior). Detection of these behaviors can assist in avoiding possible conflict among crew members and promoting the overall team success. Our prior research focused on an initial proof of concept of machine learning (ML) models and natural language processing (NLP) techniques that were used for automatically detect micro-behaviors among crew members of the US National Aeronautics and Space Administration’s (NASA) Human Exploration Research Analog (HERA) Campaigns 4 and 5 missions (Paromita et al., 2023). Results underscored the importance of incorporating contextual information in the ML models in the form of sentiment analysis, type of task, and dyadic interaction among team members. Here, we expand the scope of our prior work in two ways. First, we assess ML/NLP methods on new behavioral annotations coded using an adapted version of Smith & Griffins (2022) theoretical framework in terms of Violation (i.e., presence of valenced behavior, uplifting/positive or discouraging/negative), Intensity (i.e., force of behavior in terms of how uplifting or discouraging is the behavior), and Intent (i.e., motive of the behavior in terms of whether it was deliberate or unintentional). Second, we expand the design of the ML model to preserve information about the role of each team member within the occurrence of the micro-behavior (in contrast to the previous model that only considered the sender and the target without determining the team member role). This allows to consider all team members' contributions in the conversation and model long-term dependencies in the dialogue. Our experiments for this study are conducted on data from 5 teams of the NASA HERA C4 (NASA grant NNX16AQ48G (PI: Bell)). Conversations were extracted from the 1.5 hour Team Interaction Battery (TIB) task that occurred 5 times in-mission per crew. This resulted in a total of 13,058 conversational turns (i.e., 17.8% uplifting, 3.3% discouraging, 75.76% neutral, 3.14% nulls). Our findings with the revised behavioral coding and ML/NLP models indicate a 43.66% macro F1-score (i.e., 38.29% precision (P), 50.8% recall (R)) for a dialog state-tracking model that includes information from the sender only, and a 40.9% F1-score (i.e., 38.7% P, 43.36% R) for the same model that includes information from both the sender and the target of the micro-behavior. These are significantly higher compared to simple random forest models that classify behaviors strictly based on speech content and do not consider iterative team dynamics, achieving a 36.07% F1-score (i.e., 39.04% R, 33.53% P). Our findings demonstrate potential ways to leverage large conversational datasets to better capture complex team dynamics. We will discuss future directions including proposed models that can incorporate additional mission days and tasks beyond the TIB for objectively quantifying team behavior at high temporal resolution in space exploration missions.

Projna Paromita↗
NASA NTRS2016

Risk of Adverse Cognitive or Behavioral Conditions and Psychiatric Disorders: Evidence Report

In April 2010, President Obama declared a space pioneering goal for the United States in general and NASA in particular. "Fifty years after the creation of NASA, our goal is no longer just a destination to reach. Our goal is the capacity for people to work and learn and operate and live safely beyond the Earth for extended periods of time, ultimately in ways that are more sustainable and even indefinite." Thus NASA's Strategic Objective 1.1 emerged as "expand human presence into the solar system and to the surface of Mars to advance exploration, science, innovation, benefits to humanity, and international collaboration" (NASA 2015b). Any space flight, be it of long or short duration, occurs in an extreme environment that has unique stressors. Even with excellent selection methods, the potential for behavioral problems among space flight crews remain a threat to mission success. Assessment of factors that are related to behavioral health can help minimize the chances of distress and, thus, reduce the likelihood of adverse cognitive or behavioral conditions and psychiatric disorders arising within a crew. Similarly, countermeasures that focus on prevention and treatment can mitigate the cognitive or behavioral conditions that, should they arise, would impact mission success. Given the general consensus that longer duration, isolation, and confined missions have a greater risk for behavioral health ensuring crew behavioral health over the long term is essential. Risk, which within the context of this report is assessed with respect to behavioral health and performance, is addressed to deter development of cognitive and behavioral degradations or psychiatric conditions in space flight and analog populations, and to monitor, detect, and treat early risk factors, predictors and other contributing factors. Based on space flight and analog evidence, the average incidence rate of an adverse behavioral health event occurring during a space mission is relatively low for the current conditions. While mood and anxiety disturbances have occurred, no behavioral emergencies have been reported to date in space flight. Anecdotal and empirical evidence indicate that the likelihood of an adverse cognitive or behavioral condition or psychiatric disorder occurring greatly increases with the length of a mission. Further, while cognitive, behavioral, or psychiatric conditions might not immediately and directly threaten mission success, such conditions can, and do, adversely impact individual and crew health, welfare, and performance.

Slack, Kelley J.↗
NASA NTRS2016

Rodent Habitat On ISS: Spaceflight Effects On Mouse Behavior

The NASA Decadal Survey (2011), Recapturing a Future for Space Exploration: Life and Physical Sciences Research for a New Era, emphasized the importance of expanding NASA life sciences research to long duration, rodent experiments on the International Space Station (ISS). To accomplish this objective, flight hardware, operations, and science capabilities supporting mouse studies in space were developed at NASA Ames Research Center. The first flight experiment carrying mice, Rodent Research Hardware and Operations Validation (Rodent Research-1), was launched on Sept 21, 2014 in an unmanned Dragon Capsule, SpaceX4, exposing the mice to a total of 37 days in space. Ground control groups were maintained in environmental chambers at Kennedy Space Center. Mouse health and behavior were monitored for the duration of the experiment via video streaming. Here we present behavioral analysis of two groups of five C57BL/6 female adult mice viewed via fixed camera views compared with identically housed Ground Controls. Flight (Flt) and Ground Control (GC) mice exhibited the same range of behaviors, including eating, drinking, exploratory behavior, self- and allo-grooming, and social interactions at similar or greater levels of occurrence. Mice propelled themselves freely and actively throughout the Habitat using their forelimbs to push off or by floating from one cage area to another, and they quickly learned to anchor themselves using tails and/or paws. Overall activity was greater in Flt as compared to GC mice, with spontaneous ambulatory behavior including the development of organized ‘circling’ or ‘race-tracking’ behavior that emerged within the first few days of flight and encompassed the primary dark cycle activity for the remainder of the experiment. We quantified the bout frequency, duration and rate of circling with respect to characteristic behaviors observed in the varying stages of the progressive development of circling: flipping utilizing two sides of the habitat, circling, multi-lap circling and group-circling. Once begun, mice did not regress to flipping behavior or other previous behavioral milestones for the remainder of flight. An overall upward trend in circling frequency, rate, duration, participation, and organization was observed over the course of the 37-day spaceflight experiment. In this presentation, we will summarize qualitative observations and quantitative comparisons of mice in microgravity and 1g conditions. Behavioral analyses provide important insights into the overall health and adaptation of mice to the space environment, and identify unique behaviors and social interactions to guide future habitat development and research on rodents in space.

Mouse↗
NASA NTRS2012

A Model-Based Approach to Engineering Behavior of Complex Aerospace Systems

One of the most challenging yet poorly defined aspects of engineering a complex aerospace system is behavior engineering, including definition, specification, design, implementation, and verification and validation of the system's behaviors. This is especially true for behaviors of highly autonomous and intelligent systems. Behavior engineering is more of an art than a science. As a process it is generally ad-hoc, poorly specified, and inconsistently applied from one project to the next. It uses largely informal representations, and results in system behavior being documented in a wide variety of disparate documents. To address this problem, JPL has undertaken a pilot project to apply its institutional capabilities in Model-Based Systems Engineering to the challenge of specifying complex spacecraft system behavior. This paper describes the results of the work in progress on this project. In particular, we discuss our approach to modeling spacecraft behavior including 1) requirements and design flowdown from system-level to subsystem-level, 2) patterns for behavior decomposition, 3) allocation of behaviors to physical elements in the system, and 4) patterns for capturing V&V activities associated with behavioral requirements. We provide examples of interesting behavior specification patterns, and discuss findings from the pilot project.

SysML↗
NASA NTRS2023

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into micro-aggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of micro-behaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney Begerowski↗
NASA NTRS1990

Toward applied behavior analysis of life aloft

This article deals with systems at multiple levels, at least from cell to organization. It also deals with learning, decision making, and other behavior at multiple levels. Technological development of a human behavioral ecosystem appropriate to space environments requires an analytic and synthetic orientation, explicitly experimental in nature, dictated by scientific and pragmatic considerations, and closely approximating procedures of established effectiveness in other areas of natural science. The conceptual basis of such an approach has its roots in environmentalism which has two main features: (1) knowledge comes from experience rather than from innate ideas, divine revelation, or other obscure sources; and (2) action is governed by consequences rather than by instinct, reason, will, beliefs, attitudes or even the currently fashionable cognitions. Without an experimentally derived data base founded upon such a functional analysis of human behavior, the overgenerality of "ecological systems" approaches render them incapable of ensuring the successful establishment of enduring space habitats. Without an experimentally derived function account of individual behavioral variability, a natural science of behavior cannot exist. And without a natural science of behavior, the social sciences will necessarily remain in their current status as disciplines of less than optimal precision or utility. Such a functional analysis of human performance should provide an operational account of behavior change in a manner similar to the way in which Darwin's approach to natural selection accounted for the evolution of phylogenetic lines (i.e., in descriptive, nonteleological terms). Similarly, as Darwin's account has subsequently been shown to be consonant with information obtained at the cellular level, so too should behavior principles ultimately prove to be in accord with an account of ontogenetic adaptation at a biochemical level. It would thus seem obvious that the most productive conceptual and methodological approaches to long-term research investments focused upon human behavior in space environments will require multidisciplinary inputs from such wide-ranging fields as molecular biology, environmental physiology, behavioral biology, architecture, sociology, and political science, among others.

Review↗
NASA NTRS2016

Analysis of Adult Female Mouse (Mus musculus) Group Behavior on the International Space Station (ISS)

As interest in long duration effects of space habitation increases, understanding the behavior of model organisms living within the habitats engineered to fly them is vital for designing, validating, and interpreting future spaceflight studies. A handful of papers have previously reported behavior of mice and rats in the weightless environment of space. The Rodent Research Hardware and Operations Validation (Rodent Research-1; RR1) utilized the Rodent Habitat (RH) developed at NASA Ames Research Center to fly mice on the ISS (International Space Station). Ten adult (16-week-old) female C57BL/6 mice were launched on September 21st, 2014 in an unmanned Dragon Capsule, and spent 37 days in microgravity. Here we report group behavioral phenotypes of the RR1 Flight (FLT) and environment-matched Ground Control (GC) mice in the Rodent Habitat (RH) during this long-duration flight. Video was recorded for 33 days on the ISS, permitting daily assessments of overall health and well-being of the mice, and providing a valuable repository for detailed behavioral analysis. We previously reported that, as compared to GC mice, RR1 FLT mice exhibited the same range of behaviors, including eating, drinking, exploration, self- and allo-grooming, and social interactions at similar or greater levels of occurrence. Overall activity was greater in FLT as compared to GC mice, with spontaneous ambulatory behavior, including organized 'circling' or 'race-tracking' behavior that emerged within the first few days of flight following a common developmental sequence, and comprised the primary dark cycle activity persisting throughout the remainder of the experiment. Participation by individual mice increased dramatically over the course of the flight. Here we present a detailed analysis of 'race-tracking' behavior in which we quantified: (1) Complete lap rotations by individual mice; (2) Numbers of collisions between circling mice; (3) Lap directionality; and (4) Recruitment of mice into a group phenotype. This analysis contributes to the first NASA long-duration study of rodent behavior, providing evidence for the emergence of a distinctive, organized group behavior unique to the weightless space environment.

Behavior in Space↗
NASA NTRS2009

Towards Behavioral Reflexion Models

Software architecture has become essential in the struggle to manage today s increasingly large and complex systems. Software architecture views are created to capture important system characteristics on an abstract and, thus, comprehensible level. As the system is implemented and later maintained, it often deviates from the original design specification. Such deviations can have implication for the quality of the system, such as reliability, security, and maintainability. Software architecture compliance checking approaches, such as the reflexion model technique, have been proposed to address this issue by comparing the implementation to a model of the systems architecture design. However, architecture compliance checking approaches focus solely on structural characteristics and ignore behavioral conformance. This is especially an issue in Systems-of- Systems. Systems-of-Systems (SoS) are decompositions of large systems, into smaller systems for the sake of flexibility. Deviations of the implementation to its behavioral design often reduce the reliability of the entire SoS. An approach is needed that supports the reasoning about behavioral conformance on architecture level. In order to address this issue, we have developed an approach for comparing the implementation of a SoS to an architecture model of its behavioral design. The approach follows the idea of reflexion models and adopts it to support the compliance checking of behaviors. In this paper, we focus on sequencing properties as they play an important role in many SoS. Sequencing deviations potentially have a severe impact on the SoS correctness and qualities. The desired behavioral specification is defined in UML sequence diagram notation and behaviors are extracted from the SoS implementation. The behaviors are then mapped to the model of the desired behavior and the two are compared. Finally, a reflexion model is constructed that shows the deviations between behavioral design and implementation. This paper discusses the approach and shows how it can be applied to investigate reliability issues in SoS.

Ackermann, Christopher↗
NASA NTRS2023

What’s That Supposed to Mean? Capturing Micro-Behaviors in Teams

Future long-duration space exploration (LDSE) crews will require extensive coordination, cooperation, and team functioning as they face a myriad of challenges rooted in both taskwork and teamwork (Bell et al., 2015; Landon et al., 2018). While exposed to extreme conditions, crew members must navigate living and working together in prolonged confinement. Moreover, astronaut teams are becoming increasingly diverse, introducing significant variability in team composition. This increasing diversity, alongside traditional constraints of LDSE, introduces additional challenges into effective team functioning. To date, most methods for capturing team functioning rely on self-report measures. Such measures are prone to several limitations, including but not limited to social desirability bias, halo effect, and leniency effects (Trull & Ebner-Priemer, 2013), which skew data and limit nuanced understandings of phenomena at play. Self-report measures broadly capture team functioning, lending the nature of such methods to identifying underlying “macro”-behaviors (i.e., behaviors that are long-standing and last over time). However, team functioning is far more complex than a series of macro-behaviors, rendering reliance on self-report data deficient for accurate measurement. Recent research demonstrates the potential of alternative methods for capturing team functioning, such as speech and physiological data (Chaffin et al., 2017; Murray & Oertel, 2018). Consequently, these methods are more suitable for capturing micro-behaviors: brief, often unconscious expressions that affect the extent to which an individual feels included by others around them (Paletz et al., 2013). Micro-behaviors can be further classified into microaggressions (i.e., subtle, negative exchanges; Keller & Galgay, 2010) or micro-affirmations (i.e., subtle, positive exchanges; Kyte et al. 2020), both of which influence team functioning. Due to the subtle nature of micro-behaviors, contextual factors have a significant impact when determining if it is aggressive or affirmative. Additionally, several iterations of microbehaviors can have lingering effects on team interactions. For example, the use of “mm-hmm” by a crew member can function as both a micro-affirmation and micro-aggression. Specifically, it can be indication of active listening (i.e., micro-affirmation) or as an expression of annoyance (i.e., aggression) depending on the context in which it occurs. Auditory features (e.g., tone, frequency) can help delineate between the two forms; however, the contextual factors (e.g., previous interactions between team members, crew demographics) add a layer of complexity that render auditory features alone as insufficient to capture micro-behaviors. Consequently, this paper seeks to provide a novel approach in which multi-modal data (i.e., auditory features and contextual features) are used in a random-forest model to better identify distinguishing characteristics between micro-affirmations and micro-aggressions. In turn, detected micro-behaviors are used to predict team performance, thereby demonstrating the value of capturing micro-behaviors as supplemental data to macro-behaviors.

Sydney R. Begerowski↗
NASA NTRS2025

A Preliminary Study on the Feasibility of Large Language Models for Detecting Micro-Behaviors Among Team Members in Space Missions

Large-language models (LLMs) have been recently used for spoken language understanding (SLU) to infer meaning and semantics from speech in tasks such as speaker intent and sentiment classification. Due to being trained on large amounts of data, and their ability to understand context and relationships between words, LLMs are competent, enabling them to generalize across tasks without requiring many task-specific training samples. This research examines the feasibility of few-shot learning in LLMs for detecting subtle, brief, and possibly unconscious interactions between team members, called ``micro-behaviors," and provides insights into the appropriate design of LLMs for this task. Our data came from 5 teams participating in a 45-day mission at the US National Aeronautics and Space Administration’s (NASA) Human Exploration Research Analog (HERA). More specifically we used data collected from team interaction battery (TIB) tasks teams performed five times in-mission which comprise an average 1.5 hours of conversation data per day. Micro-behaviors were coded according to an adapted version of Smith & Griffins (2022) theoretical framework in terms of Violation (i.e., presence of valenced behavior, uplifting/positive or discouraging/negative), Intensity (i.e., force of behavior in terms of how uplifting or discouraging is the behavior), and Intent (i.e., motive of the behavior in terms of whether it was deliberate or unintentional). We explore the ability of LLMs to detect the presence and intensity of micro-behaviors. We examine employing and fine-tuning readily available LLMs (i.e., RoBERTa, DistilBERT), as well as prompting state-of-the-art sequence classification models (i.e., Llama-2, Llama-3). In a total of 13,058 conversational turns (17.8% uplifting, 3.3% discouraging, 75.76% neutral, 3.14% nulls), we compute the macro F1-score of the 3-way micro-behavior classification task (i.e., classifying among uplifting, discouraging, and neutral; 33% chance). Results indicate that the RoBERTa model achieves a F1-score of 36.2% (uplift: 43.3% precision (P), 15.1% recall (R); discourage: 20% P, 0.5% R). These results significantly improve when we augment the data via paraphrasing in the RoBERTa model, reaching a 41.2% macro F1-score (uplift: 37.7% P, 86.3% R; discourage: 3.5% P, 1.8% R). Finally, the Llama-2 model with 3-shot prompting yields 38% macro F1-score (uplift: 28.7% P, 20% R; discourage: 7.2% P, 18% R), which is slightly better compared to the RoBERTa model without data augmentation, highlighting the effectiveness of sequence classification models in detecting minority classes with a small sample size. Findings indicate that LLMs hold potential to detect subtle behaviors in conversations, which could be valuable in assessing team behavior in space exploration missions. Future studies will evaluate the performance of different LLM prompting strategies or fine-tuning methods.

Ankush Raut↗
NASA NTRS1997

Effect of environmental enrichment devices on behaviors of single- and group-housed squirrel monkeys (Saimiri sciureus)

Squirrel monkeys display an interest in novel places, habituate to new situations, and spend most of their daily activity in the wild in large groups engaging in feeding behaviors over a broad area. Captivity limits these behaviors and consequently may disrupt normal social organizations. In captivity, squirrel monkeys may exhibit stereotypical behaviors that are believed to indicate decreased psychologic well-being. When a monkey's behavior can be made to approach that seen in the wild, and stereotypical behaviors are minimal, it is assumed that psychologic well-being is adequate. Environmental enrichment devices have been used to address the Animal Welfare Act requirement that psychologic well-being of captive nonhuman primates be considered. The purpose of the study reported here was to examine whether various environmental enrichment devices improve the psychologic well-being of captive squirrel monkeys. In the study, we used behavioral observation to quantify the effectiveness of several environmental enrichment devices for reducing stereotypical behaviors in squirrel monkeys housed alone or in groups. Analysis of our results revealed that the environmental enrichment devices did not affect the expression of normal or stereotypical behaviors, but that the type of housing did.

NASA Discipline Neuroscience↗
NASA NTRSDate not supplied

The Effects of Placental-expanded (PLX-PAD) Stromal Cell Treatment, Hindlimb Unloading, and Isolation on the Behavior of Female Mice

Spaceflight can lead to altered immune responses and inflammation (Crucian et al. 2014) and elevated levels of inflammation are connected to anxiety and depression. A recent study on the International Space Station showed that mice exhibited a novel circling behavior during spaceflight (Ronca et al. 2019). However, there is still a gap in knowledge on how microgravity impacts behavior. In this current study, we performed 30 days of hindlimb unloading (HU) on four-month old female mice and analyzed select behavior from video image capture. We also determined the effects of PLacental-eXpanded stromal cells derived from the maternal placenta (PLX-PAD), alone and in combination with HU and isolation, on behavior. We have previously shown that PLX-PAD mitigates select inflammatory responses and changes in cytokine expression caused by HU. In-cage behaviors were analyzed in HU or control female mice treated with 2 injections of PlasmaLyte (Sham) or PLX-PAD (n=7/group). We found that PLX-PAD decreased exploratory behaviors compared to Sham-treated mice at night. Normally loaded (NL) PLX mice slept less than NL Sham mice during the day, and HU animals had significantly higher involuntary movement during sleep compared to NL animals, suggesting sleep disruption. Overall, we show that both HU and PLX affect important behavioral factors. This experiment is the first to study the effects of PLX-PAD on behavior. Additional studies are needed to define the behavioral changes in spaceflight and test possible countermeasures.

PLacental-EXpanded stromal cells, behavior, microg↗
NASA NTRS2021

The Effects of Simulated Microgravity and PLacental-EXpanded (PLX-PAD) Stromal Cell Treatment on The Behavior and Correlation with Cytokine Profiles in Female Mice

Spacelight can lead to altered immune responses and inflammation (Crucian et al. 2014) and elevated levels of inflammation are connected to anxiety and depression. A recent study on the International Space Station showed that mice exhibited a novel circling behavior during spaceflight (Ronca 2019). However, there is still a gap in knowledge on how microgravity impacts behavior. In this current study, we performed 30 days of hindlimb unloading (HU) on four-month old female mice and analyzed select behavior from video image capture. We also determined the effects of PLacental-eXpanded stromal cells dervided from the maternal placenta (PLX-PAD), alone and in combination with HU and isolation, on behavior. We have previoulst shown that PLX-PAD mitigated select inflammatory responses and changes in cytokine expression causes by HU. In-cage behaviors were analyzed in HU or control female mice treated with 2 injections of PlasmaLyte (Sham) or PLX-PAD (n=7/group). We found that PLX-PAD decreased exploratory behaviors compared to Sham treated mice at night. Normally loaded (NL) PLX mice slept less than NL Sham mice during the day, and HU animals had signficantly higher involuntary movement during sleep compared to NL animals, suggesting sleep distruption. Overall, we show that both HU and PLX affect important behavioral factors., This experiment is the first to study the effects of PLX-PAD on behavior. Additional studies are needed to define the behavioral changes in spaceflight and test possible countermeasures. Funding for this project was provided through the 2019 Ames Research Innovation Award (ARIA) provided by NASA Ames Research Center.

Stromal cell treatment↗