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236 results for “Behavior Modeling”
Data from: Investigating behavioral drivers of seasonal Shiga-Toxigenic Escherichia Coli (STEC) patterns in grazing cattle using an agent-based model
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Data from: Is there still a French eating model? A taxonomy of eating behaviors in adults living in the Paris metropolitan area in 2010
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Data from: Analyzing movement behavior and dynamic space-use strategies among habitats using multi-event capture-recapture modeling
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The effects of exploratory behavior on physical activity in a common animal model of human disease, zebrafish (Danio rerio)
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Data from: Multiple predator species alter prey behavior, population growth and a trophic cascade in a model estuarine food web
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Data from: A dynamic state model of migratory behavior and physiology to assess the consequences of environmental variation and anthropogenic disturbance on marine vertebrates
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Data and code from: Live birth in lizards: A process-based model for the roles of temperature, behavior, and life-history
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Data for: Temperature and hygrometry of amphibian agar models in behavioral simulation and operational temperature of two forested areas
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Data-driven models reveal mutant cell behaviors important for myxobacterial aggregation
<p>Single mutations frequently alter several aspects of cell behavior but rarely reveal whether a particular statistically significant change is biologically significant. To determine which behavioral changes are most important for multicellular self-organization, we devised a new methodology using <em>Myxococcus xanthus</em> as a model system. During development, myxobacteria coordinate their movement to aggregate into spore-filled fruiting bodies. We investigate how aggregation is restored in two mutants, <em>csgA</em> and <em>pilC</em>, that cannot aggregate unless mixed with wild type (WT) cells. To this end, we use cell tracking to follow the movement of fluorescently labeled cells in combination with data-driven agent-based modeling. The results indicate that just like WT cells, both mutants bias their movement toward aggregates and reduce motility inside aggregates. However, several aspects of mutant behavior remain uncorrected by WT demonstrating that perfect recreation of WT behavior is unnecessary. In fact, synergies between errant behaviors can make aggregation robust.</p>
Data from: Correlations of behavioral deficits with brain pathology assessed through longitudinal MRI and histopathology in the HDHQ150/Q150 mouse model of Huntington's disease
A variety of mouse models have been developed that express mutant huntingtin (mHTT) leading to aggregates and inclusions that model the molecular pathology observed in Huntington's disease. Here we show that although homozygous HdhQ150 knock-in mice developed motor impairments (rotarod, locomotor activity, grip strength) by 36 weeks of age, cognitive dysfunction (swimming T maze, fear conditioning, odor discrimination, social interaction) was not evident by 94 weeks. Concomitant to behavioral assessments, T2-weighted MRI volume measurements indicated a slower striatal growth with a significant difference between wild type (WT) and HdhQ150 mice being present even at 15 weeks. Indeed, MRI indicated significant volumetric changes prior to the emergence of the "clinical horizon" of motor impairments at 36 weeks of age. A striatal decrease of 27% was observed over 94 weeks with cortex (12%) and hippocampus (21%) also indicating significant atrophy. A hypothesis-free analysis using tensor-based morphometry highlighted further regions undergoing atrophy by contrasting brain growth and regional neurodegeneration. Histology revealed the widespread presence of mHTT aggregates and cellular inclusions. However, there was little evidence of correlations between these outcome measures, potentially indicating that other factors are important in the causal cascade linking the molecular pathology to the emergence of behavioral impairments. In conclusion, the HdhQ150 mouse model replicates many aspects of the human condition, including an extended pre-manifest period prior to the emergence of motor impairments.
Data from: Space-use behavior of woodland caribou based on a cognitive movement model
1. Movement patterns offer a rich source of information on animal behaviour and the ecological significance of landscape attributes. This is especially useful for species occupying remote landscapes where direct behavioural observations are limited. In this study, we fit a mechanistic model of animal cognition and movement to GPS positional data of woodland caribou (Rangifer tarandus caribou; Gmelin 1788) collected over a wide range of ecological conditions. 2. The model explicitly tracks individual animal informational state over space and time, with resulting parameter estimates that have direct cognitive and ecological meaning. Three biotic landscape attributes were hypothesized to motivate caribou movement: forage abundance (dietary digestible biomass), wolf (Canis lupus; Linnaeus, 1758) density and moose (Alces alces; Linnaeus, 1758) habitat. Wolves are the main predator of caribou in this system and moose are their primary prey. 3. Resulting parameter estimates clearly indicated that forage abundance is an important driver of caribou movement patterns, with predator and moose avoidance often having a strong effect, but not for all individuals. From the cognitive perspective, our results support the notion that caribou rely on limited sensory inputs from their surroundings, as well as on long-term spatial memory, to make informed movement decisions. Our study demonstrates how sensory, memory and motion capacities may interact with ecological fitness covariates to influence movement decisions by free-ranging animals.
Data from: Sex-related online behaviors, perceived peer norms and adolescents' experience with sexual behavior: testing an integrative model
Research on the role of sex-related Internet use in adolescents' sexual development has often isolated the Internet and online behaviors from other, offline influencing factors in adolescents' lives, such as processes in the peer domain. The aim of this study was to test an integrative model explaining how receptive (i.e., use of sexually explicit Internet material [SEIM]) and interactive (i.e., use of social networking sites [SNS]) sex-related online behaviors interrelate with perceived peer norms in predicting adolescents' experience with sexual behavior. Structural equation modeling on longitudinal data from 1,132 Dutch adolescents (Mage T1 = 13.95; range 11-17; 52.7% boys) demonstrated concurrent, direct, and indirect effects between sex-related online behaviors, perceived peer norms, and experience with sexual behavior. SEIM use (among boys) and SNS use (among boys and girls) predicted increases in adolescents' perceptions of peer approval of sexual behavior and/or in their estimates of the numbers of sexually active peers. These perceptions, in turn, predicted increases in adolescents' level of experience with sexual behavior at the end of the study. Boys' SNS use also directly predicted increased levels of experience with sexual behavior. These findings highlight the need for multisystemic research and intervention development to promote adolescents' sexual health.
Characterizing and Understanding the Behavior of Quantized Models for Reliable Deployment
<p>Benchmark for quantization study</p>
Application of health belief model for the assessment of COVID-19 preventive behavior and its determinants among students: A structural equation modeling analysis
<p><strong>Background:</strong> COVID-19 is a new pandemic that poses a threat to people globally. In Ethiopia, where classrooms are limited, students are at higher risk for COVID-19 unless they take consistent preventative actions. However, there is a lack of evidence in the study area regarding student compliance with COVID-19 preventive behavior (CPB) and its predictors.</p> <p><strong>Objective:</strong> This study aimed to assess CPB and its predictors among students based on the perspective of the Health Belief Model (HBM).</p> <p><strong>Method and materials:</strong> A school-based cross-sectional survey was conducted from November to December 2020 to evaluate the determinants of CPB among high school students using a self-administered structured questionnaire. 370 participants were selected using stratified simple random sampling. Descriptive statistics were used to summarize data, and partial least squares structural equation modeling (PLS-SEM) analyses to evaluate the measurement and structural models proposed by the HBM and to identify associations between HBM variables. A T-value of > 1.96 with 95% CI and a P-value of < 0.05 were used to declare the statistical significance of path coefficients.</p> <p><strong> Result:</strong> A total of 370 students participated with a response rate of 92%. The median (interquartile range) age of the participants (51.9% females) was 18 (2) years. Only 97 (26.2%), 121 (32.7%), and 108 (29.2%) of the students had good practice in keeping physical distance, frequent hand washing, and facemask use respectively. The HBM explained 43% of the variance in CPB. Perceived barrier (β= - 0.15, p < 0.001) and self-efficacy (β= 0.51, p <0.001) were significant predictors of student compliance to CPB. Moreover, the measurement model demonstrated that the instrument had acceptable reliability and validity.</p> <p><strong>Conclusion and recommendations:</strong> COVID-19 prevention practice is quite low among students. HBM demonstrated adequate predictive utility in predicting CPBs among students, where perceived barriers and self-efficacy emerged as significant predictors of CPBs. According to the findings of this study, theory-based behavioral change interventions are urgently required for students to improve their prevention practice. Furthermore, these interventions will be effective if they are designed to remove barriers to CPBs and improve students' self-efficacy in taking preventive measures.</p>
Phenotypes of painful TMD in discordant monozygotic twins according to a cognitive-behavioral-emotional model: a case-control study
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Predicting gut microbial behavior in human diseases via community metabolic modeling and machine learning
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Data from: A simple behavioral model predicts the emergence of complex animal hierarchies
Social dominance hierarchies are widespread, but little is known about the mechanisms that produce non-linear structures. In addition to despotic hierarchies, where a single individual dominates, shared hierarchies exist where multiple individuals occupy a single rank. In vertebrates, these complex dominance relationships are thought to develop from interactions that require higher cognition, but similar cases of shared dominance have been found in social insects. Combining empirical observations with a modeling approach, we show that all three hierarchy structures-linear, despotic, and shared-can emerge from different combinations of simple interactions present in social insects. Our model shows that a linear hierarchy emerges when a typical winner-loser interaction (dominance biting) is present. A despotic hierarchy emerges when a policing interaction is added that results in the complete loss of dominance status for an attacked individual (physical policing). Finally, a shared hierarchy emerges with the addition of a "winner-winner" interaction that results in a positive outcome for both interactors (antennal dueling). Antennal dueling is an enigmatic ant behavior that has previously lacked a functional explanation. These results show how complex social traits can emerge from simple behaviors without requiring advanced cognition.
Fig. 5 in Modeling the peroxyl radical scavenging behavior of Carnosic acid: Mechanism, kinetics, and effects of physiological environments
Fig. 5. ΔG values in kcal/mol of CA + HOO• reaction in the gas phase, pentyl ethanoate, and water.
Fig. 1 in Modeling the peroxyl radical scavenging behavior of Carnosic acid: Mechanism, kinetics, and effects of physiological environments
Fig. 1. Molecular structure of Carnosic acid (CA).
Behavioral Health Collaborative Care Model in Post-ICU Clinic Family Pilot
ClinicalTrials.gov study NCT07390786. IPD Sharing: NO. Countries: 1. Publications: 0.
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.