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153 results for “Behavioral analysis”
Data from: Toward a precision behavioral medicine approach to addressing high-risk sun exposure: a qualitative analysis
Objectives: Precision behavioral medicine techniques integrating wearable ultraviolet radiation (UVR) sensors may help individuals avoid sun exposure that places them at-risk for skin cancer. As a preliminary step in our patient-centered process of developing a just-in-time adaptive intervention, this study evaluated reactions and preferences to UVR sensors among melanoma survivors. Materials and Methods: Early stage adult melanoma survivors were recruited for a focus group (n=11) or 10-day observational study, which included daily wearing a UVR sensor and sun exposure surveys (n=39). Both the focus group moderator guide and observational study exit interviews included questions on UVR sensing as a potential intervention strategy. These responses were transcribed and coded using an inductive strategy. Results: Most observational study participants (84.6%) said they would find information provided by UVR sensors to be useful to help them learn about how specific conditions (e.g., clouds, location) impact sun exposure and provide in-the-moment alerts. Focus group participants expressed enthusiasm for UVR information and identified preferred qualities of a UVR sensor, such as small size and integration with other devices. Participants in both studies indicated concern that UVR feedback may be difficult to interpret and some expressed that a UVR sensor may not be convenient or desirable to wear in daily life. Discussion: Melanoma survivors believe that personalized UVR exposure information could improve their sun protection and want this information delivered in a method that is meaningful and actionable. Conclusion: UVR sensing is a promising component of a precision behavioral medicine strategy to reduce skin cancer risk.
Data from: Research on the mechanical behavior of shale based on multi-scale analysis
In view of the difficulty in obtaining the mechanical properties of shale, the multi-scale analysis of shale was performed on a shale outcrop from the Silurian Longmaxi Formation in the Changning area, Sichuan Basin, China. The nano/micro indentation test is an effective method for multi-scale mechanical analysis. In this paper, effective criteria for the shale indentation test were evaluated. The elastic modulus was evaluated at a multi-scale and the engineering validation of drilling cuttings was performed. The porosity tests showed that the pore distribution of shale from the nano-scale to macro-pore could be better displayed by the nuclear magnetic resonance test. The micro-scale elastic modulus and hardness increased nonlinearly with the increase in the clay packing density. It was observed that the size effect of the micro-hardness was based on porosity and composition. The partial spalling of shale at the micro-scale could lead to irregular bulges or steps in a load–displacement curve. The elastic modulus of pure clay minerals was 24.2 GPa on the parallel bedding plane, and 15.8 GPa on the vertical bedding plane. The contact hardness (pure clay minerals) was 0.51 GPa. The indentation results showed that the micro elastic modulus of shale obeyed the normal distribution, and the statistical average could predict the macro mechanical properties effectively. The present work provides a novel idea regarding the cognitive mechanics state of shale in the formation.
Toothbrushing behavior over time: a correlational analysis of repeatedly assessed brushing performance
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BERT-assisted behavioral profiling of polysemy: contrastive analysis of HONG in Chinese and RED in English
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Data from: Territoriality modifies the effects of habitat complexity on animal behavior: a meta-analysis
<p>Augmenting habitat complexity by adding structure has been used to increase the population density of some territorial species in the wild and to reduce aggression among captive animals. However, it is unknown if all territorial species are affected similarly by habitat complexity, and whether these effects extend to non-territorial species. We conducted a meta-analysis to compare the behavior of a wide range of territorial and non-territorial taxa in complex and open habitats to determine the effects of habitat complexity on<b> </b>1) territory size, 2) population density, 3) rate and time spent on aggression, 4) rate and time devoted to foraging, 5) rate and time spent being active, 6) shyness/boldness, 7) survival rate, and 8) exploratory behavior. Overall, all measures were significantly affected by habitat complexity, but the responses of territorial and non-territorial species differed. As predicted, territorial species were less aggressive, had smaller territories and higher densities in complex habitats, whereas non-territorial species were more aggressive and did not differ in population density. Territorial species were bolder but not more active in complex habitats, whereas non-territorial species were more active but not bolder. While the survival of non-territorial species increased in complex habitats, no such increase was observed for territorial species. The increased safety from predators provided by complex habitats may have been balanced by the higher population densities and bolder behavior in territorial species. Our analysis suggests that territorial and non-territorial animals respond differently to habitat complexity, perhaps due to the strong reliance on visual cues by territorial animals.</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>
Comprehensive analysis of locomotion dynamics in the protochordate Ciona intestinalis reveals how neuromodulators flexibly shape its behavioral repertoire.
<p>This record contains (as of 2022-06-27) datasets corresponding to the study of behavior in swimming <em>Ciona intestinalis</em> larvae (controls and drug treated animals) that were recorded in our behavioural setups. In particular, it contains:</p> <ol> <li>multi-point tracking data of the larvae obtained using the Tierpsy Tracker (developed by Andre Brown's lab, MRC LMS).</li> <li>features like curvature, speed etc calculated from the tracking data</li> <li>results of time-series analyses (matrix-profiling, hidden markov modelling, spatio-temporal clustering) performed on the feature dataset</li> <li>Hidden Markov Models trained for inferences</li> </ol>
Dataset of Association of depression and anxiety symptoms, and personal factors related to health behavior with tobacco abuse: A secondary data analysis.
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Dataset for "A Clustering Analysis of Lebanese Adaptive Driving Behaviors in Response to Road Complexity" By Kobeissy et al. Submitted to The Open Transportation Journal
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Analysis Customer Behavior of Mobile Food Online Delivery (MFOD) Applications for SMEs
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Figure 5 in A behavioral analysis of achromatic cue perception by the ant Cataglyphis aenescens (Hymenoptera; Formicidae)
Figure 5. Choice frequencies of foragers in intensity discrimination experiments. Test wavelengths and intensities are given under the abscissa. The gray bars represent the choice frequencies for the rewarded intensities and the black bars represent the choice frequencies for the nonrewarded intensities. I = 1.1 × 1011 photons for 370 and 540 nm and I = 1.1 × 1012 photons for 440 and 640 nm. a) Test wavelength 370 nm, N = 44, the rewarded intensity was brighter by a factor of 40, binomial test, P> 0.05, n.s. b) Test wavelength 540 nm, N = 50, the rewarded intensity was brighter by a factor of 40, binomial test, P> 0.05, n.s. c) Test wavelength 440 nm; N = 30, the rewarded intensity was brighter by a factor of 10, binomial test, P <0.05. d) Test wavelength 640 nm; N = 30, the rewarded intensity was brighter by a factor of 10, binomial test, P <0.0001.
Figure 4 in A behavioral analysis of achromatic cue perception by the ant Cataglyphis aenescens (Hymenoptera; Formicidae)
Figure 4. Angular distributions and tracks of foragers on the orientation platform during intensity threshold experiments. Only the results of the control tests and the last critical tests with which the ants' homeward orientations were lost were given for each stimulus. a) 540 nm control test, I = 1.1 × 1011 photons, P <0.0005; b) last critical test, I = 2.75 × 108 photons, P> 0.05, n.s.; c) 640 nm control test, I = 1.1 × 1011 photons, P <0.0005; d) last critical test, I = 1.1 × 1011 photons, P> 0.05, n.s. The triangle above each circle indicates the home angle. The dots around the circumference show the actual distribution of angles of foragers. Sample size = 30; h.a. = home angle; a = mean vector angle; r = mean vector length; u = critical values of the V test; d = deviation values around the 95% confidence interval. The dashed lines denote the 95% confidence interval around each sample mean.
Dataset for the manuscript: "Sedentary behavior patterns and adiposity in children: a study based on compositional data analysis"
<p>This dataset includes all variables required to replicate the published study.</p> <p>Gába, A., Pedišić, Ž., Štefelová, N., Dygrýn, J., Hron, K., Dumuid, D., & Tremblay, M. (2020). Sedentary behavior patterns and adiposity in children: a study based on compositional data analysis. <em>BMC Pediatrics, 20</em>(1), 147. doi:10.1186/s12887-020-02036-6</p> <p>Dataset contains demographic characteristics, movement behavior data measured by an ActiGraph GT3X accelerometer (eg., time spent in physical activity and sedentary behavior), and obesity indicators assessed using the InBody 720 device (eg., fat mass percentage).</p>
The Open Field test as a tool for behavior analysis in pigs - is a standardization of setup necessary? A systematic review.
<p>Systematic review.</p>
Behavioral Economic Analysis of Demand for Marijuana
ClinicalTrials.gov study NCT03518567. IPD Sharing: Not stated. Countries: 1. Publications: 0.
Analysis of Parameters Indicating the Intensity of Suicidal Behavior in Psychiatric Patients
ClinicalTrials.gov study NCT05803447. IPD Sharing: NO. Countries: 0. Publications: 13.
Data from: QTL analysis of behavior in nine-spined sticklebacks (Pungitius pungitius)
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Data from: Toward a precision behavioral medicine approach to addressing high-risk sun exposure: a qualitative analysis
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Data from: Development of a 44K SNP Assay focussing on the analysis of a varroa specific defense behavior in honey bees (Apis mellifera carnica)
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Data from: Automated analysis of long-term grooming behavior in Drosophila using a k-nearest neighbors classifier
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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.