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All sourcesNASA NTRSDOE OSTI
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↗
DOE OSTI2022

Can we use antipredator behavior theory to predict wildlife responses to high-speed vehicles?

Animals seem to rely on antipredator behavior to avoid vehicle collisions. There is an extensive body of antipredator behavior theory that have been used to predict the distance/time animals should escape from predators. These models have also been used to guide empirical research on escape behavior from vehicles. However, little is known as to whether antipredator behavior models are appropriate to apply to an approaching high-speed vehicle scenario. We addressed this gap by (a) providing an overview of the main hypotheses and predictions of different antipredator behavior models via a literature review, (b) exploring whether these models can generate quantitative predictions on escape distance when parameterized with empirical data from the literature, and (c) evaluating their sensitivity to vehicle approach speed using a simulation approach wherein we assessed model performance based on changes in effect size with variations in the slope of the flight initiation distance (FID) vs. approach speed relationship. The slope of the FID vs. approach speed relationship was then related back to three different behavioral rules animals may rely on to avoid approaching threats: the spatial, temporal, or delayed margin of safety. We used literature on birds for goals (b) and (c). Our review considered the following eight models: the economic escape model, Blumstein’s economic escape model, the optimal escape model, the perceptual limit hypothesis, the visual cue model, the flush early and avoid the rush (FEAR) hypothesis, the looming stimulus hypothesis, and the Bayesian model of escape behavior. We were able to generate quantitative predictions about escape distance with the last five models. However, we were only able to assess sensitivity to vehicle approach speed for the last three models. The FEAR hypothesis is most sensitive to high-speed vehicles when the species follows the spatial (FID remains constant as speed increases) and the temporal margin of safety (FID increases with an increase in speed) rules of escape. The looming stimulus effect hypothesis reached small to intermediate levels of sensitivity to high-speed vehicles when a species follows the delayed margin of safety (FID decreases with an increase in speed). The Bayesian optimal escape model reached intermediate levels of sensitivity to approach speed across all escape rules (spatial, temporal, delayed margins of safety) but only for larger (> 1 kg) species, but was not sensitive to speed for smaller species. Overall, no single antipredator behavior model could characterize all different types of escape responses relative to vehicle approach speed but some models showed some levels of sensitivity for certain rules of escape behavior. We derive some applied applications of our findings by suggesting the estimation of critical vehicle approach speeds for managing populations that are especially susceptible to road mortality. Overall, we recommend that new escape behavior models specifically tailored to high-speeds vehicles should be developed to better predict quantitatively the responses of animals to an increase in the frequency of cars, airplanes, drones, etc. they will face in the next decade.

54 ENVIRONMENTAL SCIENCES↗
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↗
DOE OSTI2025

Deer Vigilance and Movement Behavior Are Affected by Edge Density and Connectivity

ABSTRACT Animal behavior is an important component of individual, population, and community responses to anthropogenic habitat alteration. For example, antipredator behavior (e.g., vigilance) and animal movement behavior may both be important behavioral responses to the increased density of habitat edges and changes in patch connectivity that characterize highly modified habitats. Importantly, edge density and connectivity might interact, and this interaction is likely to mediate animal behavior: linear, edge‐rich landscape features often provide structural connectivity between patches, but the functional connectedness of patches for animal use could depend upon how edge density modifies animal vigilance and movement. Using remote cameras in large‐scale experimental landscapes that manipulate edge density (high‐ vs. low‐density edges) and patch connectivity (isolated or connected patches), we examined the effects of edge density and connectivity on the antipredator behavior and movement behavior of white‐tailed deer ( Odocoileus virginianus ). Deer vigilance was 1.38 times greater near high‐density edges compared to low‐density edges, regardless of whether patches were connected or isolated. Deer were also more likely to move parallel to connected high‐density edges than all other edge types, suggesting that connectivity promotes movement along high‐density edges. These results suggest that increases in edge density that accompany human fragmentation of existing habitats may give rise to large‐scale changes in the antipredator behavior of deer. These results also suggest that conservation strategies that simultaneously manipulate edge density and connectivity (i.e., habitat corridors) may have multiple effects on different aspects of deer behavior: linear habitat corridors were areas of high vigilance, but also areas where deer movement behavior implied increased movement along the habitat edge.

Bartel, Savannah L. [University of Wisconsin‐Madis↗
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↗
DOE OSTI2020

Data-driven occupant-behavior analytics for residential buildings

Many advances have been made in building technology to help save energy, but influencing the behavior of the occupants is still necessary to achieve low-energy use targets. One of the most practical ways to influence and change occupant behaviors is through incentives. Developing incentives for energy-saving and quantifying the impact of occupant behaviors are both active areas of research. Here, we propose a data analytics framework for detecting changes in occupant behaviors, which will help build an analytics feedback loop from behavior impact to incentive design. The framework has two major parts. The first forecasts energy consumption for each occupant, while the second determines a probability distribution for changes in energy consumption. The parts are interchangeable with other existing machine learning and statistical methods. A specific instantiation of the framework, using kernel ridge-regression for forecasting and k-means to find an empirical behavior distribution, is described in detail. An HVAC use-case with 5 different incentivized behaviors is used as an example to show that the framework can detect behavior changes induced by incentives. Furthermore, we show that some simpler behavior-change detection methods do not work, further justifying the use of advanced analytics.

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATI↗
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↗
DOE OSTI2021

National variability in Americans’ COVID-19 protective behaviors: Implications for vaccine roll-out

Protective behaviors such as mask wearing and physical distancing are critical to slow the spread of COVID-19, even in the context of vaccine scale-up. Understanding the variation in self-reported COVID-19 protective behaviors is critical to developing public health messaging. The purpose of the study is to provide nationally representative estimates of five self-reported COVID-19 protective behaviors and correlates of such behaviors. In this cross-sectional survey study of US adults, surveys were administered via internet and telephone. Adults were surveyed from April 30-May 4, 2020, a time of peaking COVID-19 incidence within the US. Participants were recruited from the probability-based AmeriSpeak® national panel. Brief surveys were completed by 994 adults, with 73.0% of respondents reported mask wearing, 82.7% reported physical distancing, 75.1% reported crowd avoidance, 89.8% reported increased hand-washing, and 7.7% reported having prior COVID-19 testing. Multivariate analysis (p critical value .05) indicates that women were more likely to report protective behaviors than men, as were those over age 60. Respondents who self-identified as having low incomes, histories of criminal justice involvement, and Republican Party affiliation, were less likely to report four protective behaviors, though Republicans and individuals with criminal justice histories were more likely to report having received COVID-19 testing. The majority of Americans engaged in COVID-19 protective behaviors, with low-income Americans, those with histories of criminal justice involvement, and self-identified Republicans less likely to engage in these preventive behaviors. Culturally competent public health messaging and interventions might focus on these latter groups to prevent future infections. These findings will remain highly relevant even with vaccines widely available, given the complementarities between vaccines and protective behaviors, as well as the many challenges in delivering vaccines.

60 APPLIED LIFE SCIENCES↗
DOE OSTI2021

Experimental Study of Transport Behavior of Swellable Microgel Particles in Superpermeable Channels for Conformance Control

Summary Gel treatment is an effective way to attack excessive water production during oil development. The transport behavior of gel materials in reservoirs is of crucial importance to the effectiveness of gel treatments. The aim of this paper is investigating the transport behavior of swellable micrometer-sized preformed particle gels (PPGs, or microgels) through superpermeable (super-K) channels. Sandpacks with permeabilities ranging from 27 to 221 darcies were used to mimic the super-K channels. Multiple pressure sensors were applied along the sandpack models to monitor the propagation behavior of the microgels. The tested microgel particles could transport through the super-K channels, and a higher driving pressure gradient was required when the particle/pore size ratio was larger. The pressure gradient distribution along the super-K channels was relatively uniform when the particle/pore ratio was low (less than 1.3). However, the inlet section would show increasingly higher pressure gradients as the particle/pore ratio was increased, indicating increased difficulty in propagation. The propagation of the gel particles was significantly slower compared with the carrying fluid. The delayed propagation behavior was more pronounced when the particle/pore ratio was higher. The injection pressure was much less sensitive to the injection flow rate compared with a Newtonian fluid. The gel dispersion exhibited an apparent shear thinning (pseudoplastic) behavior when transporting through the porous channels. Breakage of the gel particles was observed especially at high superficial velocities. The particle breakage was partially responsible for the apparent shear thinning behavior. The breakage phenomenon was in favor of deep placement of the gel particles. The channel permeabilities were significantly reduced by the microgels, bringing sufficient resistance to subsequent waterflooding (more than 99.5%). At given matching size conditions, softer gels were more likely to establish in-depth placement and uniform water blocking capacity in the channels. The microgel particles exhibited salinity-responsive behavior to the post-brine flush. The gel particles could shrink and reswell according to the salinity of the injected water. Possibilities were discussed to use this salinity-responsive behavior. Also, the microgels exhibited a particular disproportionate permeability reduction (DPR) effect. After gel injection, the channel permeability to water flow was reduced by more than 20 to 92 times of the permeability to oil flow. This work provides important support to understand the transport behavior of gel particles in super-K channels. The achievements are helpful for gel product selection and gel treatment design.

Engineering↗
DOE OSTI2024

Effects of photon irradiation in the presence and absence of hindlimb unloading on the behavioral performance and metabolic pathways in the plasma of Fischer rats

The space environment astronauts experience during space missions consists of multiple environmental challenges, including microgravity. In this study, we assessed the behavioral and cognitive performances of male Fisher rats 2 months after sham irradiation or total body irradiation with photons in the absence or presence of simulated microgravity. We analyzed the plasma collected 9 months after sham irradiation or total body irradiation for distinct alterations in metabolic pathways and to determine whether changes to metabolic measures were associated with specific behavioral and cognitive measures. A total of 344 male Fischer rats were irradiated with photons (6 MeV; 3, 8, or 10 Gy) in the absence or presence of simulated weightlessness achieved using hindlimb unloading (HU). To identify potential plasma biomarkers of photon radiation exposure or the HU condition for behavioral or cognitive performance, we performed regression analyses. The behavioral effects of HU on activity levels in an open field, measures of anxiety in an elevated plus maze, and anhedonia in the M&M consumption test were more pronounced than those of photon irradiation. Phenylalanine, tyrosine, and tryptophan metabolism, and phenylalanine metabolism and biosynthesis showed very strong pathway changes, following photon irradiation and HU in animals irradiated with 3 Gy. Here, 29 out of 101 plasma metabolites were associated with 1 out of 13 behavioral measures. In the absence of HU, 22 metabolites were related to behavioral and cognitive measures. In HU animals that were sham-irradiated or irradiated with 8 Gy, one metabolite was related to behavioral and cognitive measures. In HU animals irradiated with 3 Gy, six metabolites were related to behavioral and cognitive measures. These data suggest that it will be possible to develop stable plasma biomarkers of behavioral and cognitive performance, following environmental challenges like HU and radiation exposure.

59 BASIC BIOLOGICAL SCIENCES↗
DOE OSTI2023

A Simulation Framework for Analyzing the Impact of Stochastic Occupant Behaviors on Demand Flexibility in Typical Commercial Buildings

As one of the primary users of the electric grid, buildings and building equipment, including heating, ventilation, and air conditioning (HVAC) systems, can be leveraged to provide the flexible demand needed to balance the grid. Typical strategies to achieve demand flexibility are to reduce electricity use during peak or critical periods by shutting down equipment or relaxing system setpoints, which will inevitably impact the occupants’ comfort. When occupants feel uncomfortable, they may take actions to regain their comfort, and some of those actions (such as turning on a personal fan) may have a negative impact on meeting the demand response goal. Therefore, it is important to incorporate occupant behaviors into the assessment ofthe building demand flexibility potential. In this study, a simulation framework that includes simulation of zone thermal loads, an HVAC system, and occupant behaviors, was developed to investigate the impact of occupant behaviors on demand flexibility. A case study was conducted using a small office model from the U.S. Department of Energy (DOE) Commercial Prototype Building Models to simulate the building envelope and zone loads. An agent-based occupant thermal behavior model was adapted to forecast occupants’ thermal comfort and their resulting thermal behaviors. An artificial neural network (ANN) based airflow model trained from a computational fluid dynamics (CFD) model of the zone was adopted to better predict the ambient environment of each occupant. An air-source heat pump simulation model that was calibrated from a real two-stage air-source heat pump system was used as the HVAC system. A typical load shedding event during peak hours was studied. Repeated simulations were conducted to capture the stochastic effects of occupant behaviors. The interplay between the demand flexibility, occupant comfort and behavior were analyzed by evaluating key performance indicators, including the energy use, occupant discomfort duration, and occupant behavior duration during the peak period. The results suggest that this framework can be used to analyze typical commercial buildings and their HVAC systems in terms ofdemand flexibility potential under the impact of occupant behaviors.

Chen, Zhelun↗