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124 results for “behavior prediction”
Data from: Behavioral tactic predicts preoptic-hypothalamic gene expression more strongly than developmental morph in fish with alternative reproductive tactics
Reproductive success relies on the coordination of social behaviors, such as territory defense, courtship, and mating. Species with extreme variation in reproductive tactics are useful models for identifying the neural mechanisms underlying social behavior plasticity. The plainfin midshipman (Porichthys notatus) is a teleost fish with two male reproductive morphs that follow widely divergent developmental trajectories and display alternative reproductive tactics (ARTs). Type I males defend territories, court females, and provide paternal care, but will resort to cuckoldry if they cannot maintain a territory. Type II males reproduce only through cuckoldry. We sought to disentangle gene expression patterns underlying behavioral tactic, in this case ARTs, from those solely reflective of developmental morph. Using RNA-sequencing, we investigated differential transcript expression in the preoptic area-anterior hypothalamus (POA-AH) of courting type I males, cuckolding type I males, and cuckolding type II males. Unexpectedly, POA-AH differential expression was more strongly coupled to behavioral tactic than morph. This included a suite of transcripts implicated in hormonal regulation of vertebrate social behavior. Our results reveal that divergent expression patterns in a conserved neuroendocrine center known to regulate social-reproductive behaviors across vertebrate lineages may be uncoupled from developmental history to enable plasticity in the performance of reproductive tactics.
Data from: Corticosterone predicts foraging behavior and parental care in macaroni penguins
Corticosterone has received considerable attention as the principal hormonal mediator of allostasis or physiological stress in wild animals. More recently, it has also been implicated in the regulation of parental care in breeding birds, particularly with respect to individual variation in foraging behaviour and provisioning effort. There is also evidence that prolactin can work either inversely or additively with corticosterone to achieve this. Here we test the hypothesis that endogenous corticosterone plays a key physiological role in the control of foraging behaviour and parental care using a combination of exogenous corticosterone treatment, time-depth telemetry, and physiological sampling of female macaroni penguins (Eudyptes chrysolophus) during the brood-guard period of chick rearing, while simultaneously monitoring patterns of prolactin secretion. Plasma corticosterone levels were significantly higher in females given exogenous implants relative to those receiving sham implants. Increased corticosterone levels were associated with significantly higher levels of foraging and diving activity, and greater mass gain in implanted females. Elevated plasma corticosterone was also associated with an apparent fitness benefit in the form of increased chick mass. Plasma prolactin levels did not correlate with corticosterone levels at any time, nor was prolactin correlated with any measure of foraging behaviour or parental care. Our results provide support for the corticosterone-adaptation hypothesis, which predicts that higher corticosterone levels support increased foraging activity and parental effort.
The supplementary materials for "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools".
<p>This is the supplementary materials for a paper named "Roughness prediction of end milling surface for behavior mapping of digital twined machine tools" published on the Digital Twin journal.</p>
Intracranial and behavioral data from "Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex"
<p>Preprocessed intracranial EEG and behavioral data from Hoy, Quiroga-Martinez, et al. manuscript titled "Asymmetric coding of reward prediction errors in human insula and dorsomedial prefrontal cortex" published in Nature Communications (2023). Source data files for figures are included as well.</p>
Creep behavior of short glass fiber reinforced poly(butylene terephthalate) and its prediction based on the matrix creep using an elementary volume approach
<p><span>The creep compliances of short glass fiber reinforced poly(butylene terephthalate) composites (SFRC PBT) plates processed by conventional injection molding and push pull processing were determined parallel and perpendicular to the flow direction using a tensile creep stand. The fiber weight contents of the plates were 0, 20 and 30 %. Tensile test bars were taken out of the plates parallel and perpendicular to the flow direction to determine short-term mechanical properties, fiber length distribution and fiber orientation distribution necessary for subsequent modelling. An elementary volume approach was used to calculate the longitudinal and transverse creep compliances in the main axes system showing that their time dependencies are governed by the creep of the PBT matrix only. As the plates hardly exhibit any fiber orientation in the thickness direction, the components of the 2D compliance tensor were transformed according to RM Jones’ <em>Mechanics of composite materials</em> to take into account the fiber misalignment in SFRC. This transformation introduces the unknown shear modulus G<sub>12</sub> to the components of the transformed compliances tensor. This problem could be overcome as G<sub>12</sub> can be expressed in terms of the transverse compliance J<sub>22</sub> and a shear correction factor within the EV approach. Comparing the predicted creep compliances to measured ones resulted in an underestimation of 15 to 30 % parallel and 5 to 15 % perpendicular to the flow direction. This underestimation can be attributed to a large extent to a non-perfect fiber matrix adhesion. Furthermore, SEM pictures of fracture surfaces show different failure behavior parallel to the fiber axis and perpendicular to it. Thus, the fiber matrix adhesion seems to depend on the direction of stress with respect to the fiber axes. The time range of well correspondence of matrix creep and composites creep can be expressed by the creep time limit which decreases exponentially with increasing creep stresses.</span></p>
Quasi-distributed fiber optic monitoring of thermo-hydro behavior of frozen loess for frost heave prediction
<p>We developed a quasi-distributed fiber optic monitoring technology to investigate the thermo-hydro-mechanical (THM) behaviors of frozen loess. A combined method was proposed to measure in-situ ice content, providing critical parameters for understanding THM coupled effects. To characterize the soil freezing-thawing process, we further carried out subsurface automated multiphysics monitoring at a site located on the Loess Plateau, China, during 2020/2021 winter by employing quasi-distributed fiber optic sensing arrays. The inner mechanism of frost heave was revealed via field monitoring data and correlation analysis, which directly promoted the development of a semiempirical frost heave prediction method. For this study area, the proposed method requiring only temperature data as inputs was validated using our field monitoring results.</p> <p>The data presented here are the monitoring data of in-situ temperature, moisture content, and soil displacement. The results of frost heave prediction based on the field monitoring data are shown in the files.</p>
Data from: Size, species, and fire behavior predict tree and liana mortality from experimental burns in the Brazilian Amazon
<p>Anthropogenic understory fires have affected large areas of tropical forest in recent decades, particularly during severe droughts. Yet, the mechanisms that control fire-induced mortality of tropical trees and lianas remain ambiguous due to the challenges associated with documenting mortality given variation in fire behavior and forest heterogeneity. In a seasonally dry Amazon forest, we conducted a burn experiment to quantify how increasing understory fires alter patterns of stem mortality. From 2004 to 2007, tree and liana mortality was measured in adjacent 50-ha plots that were intact (B0 – control), burned once (B1), and burned annually for 3 years (B3). After 3 years, cumulative tree and liana mortality (≥1 cm dbh) in the B1 (5.8% yr<sup>−1</sup>) and B3 (7.0% yr<sup>−1</sup>) plots significantly exceeded mortality in the control (3.2% yr<sup>−1</sup>). However, these fire-induced mortality rates are substantially lower than those reported from more humid Amazonian forests. Small stems were highly vulnerable to fire-induced death, contrasting with drought-induced mortality (measured in other studies) that increases with tree size. For example, one low-intensity burn killed >50% of stems <10 cm within a year. Independent of stem size, species-specific mortality rates varied substantially from 0% to 17% yr<sup>−1</sup> in the control, 0% to 26% yr<sup>−1</sup> in B1, and 1% to 23% yr<sup>−1</sup> in B3, with several species displaying high variation in their vulnerability to fire-induced mortality. <em>Protium guianense</em> (Burseraceae) exhibited the highest fire-induced mortality rates in B1 and B3, which were 10- and 9-fold greater than the baseline rate. In contrast, <em>Aspidosperma excelsum</em> (Apocynaceae), appeared relatively unaffected by fire (0.3% to 1.0% mortality yr<sup>−1</sup> across plots), which may be explained by fenestration that protects the inner concave trunk portions from fire. For stems ≥10 cm, both char height (approximating fire intensity) and number of successive burns were significant predictors of fire-induced mortality, whereas only the number of consecutive annual burns was a strong predictor for stems <10 cm. Three years after the initial burn, 62 ± 26 Mg ha<sup>−1</sup> (s.e.) of live biomass, predominantly stems <30 cm, was transferred to the dead biomass pool, compared with 8 ± 3 Mg ha<sup>−1</sup> in the control. This biomass loss from fire represents ∼30% of this forest's aboveground live biomass (192 (±3) Mg ha<sup>−1</sup>; >1 cm DBH). Although forest transition to savanna has been predicted based on future climate scenarios, our results indicate that wildfires from agricultural expansion pose a more immediate threat to the current carbon stocks in Amazonian forests.</p>
Raw Data from - Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures
<p><em><strong>Folder and Data Description for dataset of:</strong></em></p> <p><strong>Speech Auditory Brainstem Responses in Adult Hearing Aid Users: Effects of Aiding and Background Noise, and Prediction of Behavioral Measures</strong></p> <p>Ghada BinKhamis, Antonio Elia Forte, Tobias Reichenbach, Martin O’Driscoll, and Karolina Kluk</p> <p><strong>Please site the paper when using this dataset</strong> (DOI: 10.1177/2331216519848297)</p> <p> </p> <p><strong>Shared dataset is as follows:</strong></p> <ul> <li><strong>Behavioral data is in the excel spread sheet entitled:</strong> “BinKhamis_et_al_behavioral_data .xlsx”<br> </li> <li><strong>Speech-ABRs (raw EEG (speech-ABR) data) are contained within the five 'zip' folders.</strong></li> </ul> <p><strong>Description of the “Speech-ABRs” folders, subfolders, and raw EEG files:</strong></p> <p><strong>“Speech-ABRs” Folder Information:</strong></p> <ul> <li><strong>Each Speech_ABR folder</strong> contains subfolders from a subset of participants (e.g. Speech_ABR_1_20.zip contains data from participant number 1 to participant number 20)</li> <li><strong>Subfolders:</strong> <ul> <li>Each subfolder starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, …, HA98</li> <li>Next is the background condition: noise or quiet</li> <li>Next is whether recordings were: aided or unaided</li> </ul> </li> <li><strong>Example subfolder names:</strong> <ul> <li><strong><em>HA1 noise aided:</em></strong> participant number 1, aided speech-ABRs in background noise</li> <li><strong><em>HA4 noise unaided:</em></strong> participant number 4, unaided speech-ABRs in background noise</li> <li><strong><em>HA55 quiet aided:</em></strong> participant number 55, aided speech-ABRs in quiet</li> <li><strong><em>HA97 quiet unaided</em></strong>: participant number 97, unaided speech-ABRs in quiet</li> </ul> </li> <li>Each participant has 4 subfolders for the four recording conditions (aided quiet, aided noise, unaided quiet, unaided noise) <ul> <li><strong>Each subfolder contains four ‘.mat’ files, ‘.mat’ file names:</strong> <ul> <li>Each ‘.mat’ file starts with the participant code: e.g. HA1, HA2, HA3, HA4, HA5, …, HA98</li> <li>Next is the stimulus: 40 da</li> <li>Next is ‘unaided’ only if recordings were without HA</li> <li>Next is ‘noise’ only if the background condition was noise</li> <li>Next is the stimulus polarity: <ul> <li>‘Pos’ for positive/standard</li> <li>‘Neg’ for negative (reversed polarity stimulus)</li> </ul> </li> <li>And finally the test ear and recording number for that polarity <ul> <li>R1 is the first recording from the right ear, R2 is the second recording from the right ear</li> <li>L1 is the first recording from the left ear, L2 is the second recording from the left ear</li> </ul> </li> <li><strong>Example ‘.mat’ file name:</strong> <ul> <li><strong><em>HA1 40 da Neg Noise R1.mat: </em></strong>participant number 1, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, in background noise, right ear recording number 1.</li> <li><strong><em>HA4 40 da unaided Pos Noise L2.mat:</em></strong> participant number 1, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, left ear recording number 2.</li> <li><strong><em>HA7 40 da Neg R2.mat:</em></strong> participant number 7, aided speech-ABR in response to the 40 ms [da], reversed stimulus polarity, right ear recording number 2.</li> <li><strong><em>HA10 40 da unaided Pos Noise L1.mat:</em></strong> participant number 10, unaided speech-ABR in response to the 40 ms [da], standard stimulus polarity, in background noise, left ear recording number 1.</li> </ul> </li> </ul> </li> </ul> </li> </ul> <p><strong>File Information:</strong></p> <p><strong>Description of ‘.mat’ files that can be accessed and processed using MATLAB (MathWorks):</strong></p> <p>Each ‘.mat’ file is a structure that contains the following fields:</p> <ul> <li>The first nine fields are informational, for example:</li> <li><strong><em>xunits</em></strong>: ‘s’ indicates that the recording time window is in seconds, conversion to milliseconds would be required to plot the data in milliseconds</li> <li><strong><em>start: </em></strong>‘0’ indicates that both stimulus and recording start at 0 seconds</li> <li><strong><em>points:</em></strong> <strong>2200</strong> is the number of sample points</li> <li><strong><em>chans:</em></strong> 2 is the number of channels <ul> <li><em>Right ear:</em> channel 2, <em>Left ear:</em> channel 1</li> </ul> </li> <li><strong><em>frames:</em></strong> 2500 is the number of epochs</li> <li>The last filed <strong>‘values’</strong> is what contains the raw EEG data (2200x2x2500) <ul> <li><strong>2200 </strong>is the number of samples</li> <li><strong>2 </strong>is the number of channels (channel one is recorded from the left ear lobe (A1) and channel two is from the right ear lobe (A2))</li> <li><strong>2500 </strong>is the number of epochs <ul> <li>Stimulus starts at 0 seconds per epoch, pre-stimulus baseline may be extracted from the end of each epoch (i.e. before the next stimulus).</li> <li>Data are in Volts; conversion to <strong>μVolts </strong>(multiply by 1000) is required.</li> </ul> </li> </ul> </li> </ul> <p><strong>Date of data collection: </strong>October 2017 to July 2018</p>
Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality [Dataset]
<p>The calcium imaging data of the telencephalon of head-tethered adult zebrafish during GO/NOGO tasks in the virtual reality environment and the behavior data were deposited.</p> <p>The codes to process the neural activity data by calcium imaging to perform Non-negative Matrix Factorization </p> <p>For details, see "Zebrafish capable of generating future state prediction error show improved active avoidance behavior in virtual reality" Torigoe et al., Nature Communications in press.</p>
Predicting readers' prototypical eye-movement behavior using MASC, a model of Attention in the Superior Colliculus: Stimulus materials, model code, data, and statistical analyses.
<p>The goal of the present research was to determine the role of rudimentary visuo-motor pathways, from the retina and the primary visual cortex to the superior colliculus (SC), in the guidance of human eye movement during reading. To this end, we used MASC, our model of Attention in the Superior Colliculus (Adeli et al., Journal of Neuroscience 2017), a model that relies on well-established saccade-programming principles in the SC. MASC predicts sequences of fixations over an input image by spatially integrating incoming signals in the space of the SC.</p> <p>Here, MASC computed the distribution of luminance contrast over sentences' images (visual-saliency map), after blurring it proportional to retinal eccentricity (retina transformation). It then projected the visual-saliency map into SC space, using a logarithmic afferent-mapping function (magnification factor). Input signals were averaged over retinotopically organized populations of neurons (point images) of constant size, first in the visual map and then in a spatially-registered motor map. The most active population was identified through a winner-take-all process. After jitter applied to the winning population, the next fixation location was determined using inverse efferent mapping. This sequence of events was then repeated to predict following fixation locations, but inserting after each saccade an inhibitory spatial tag (Inhibition of Saccade Return; ISR -referred to as IOR in the uploaded files). All MASC's parameters, but one, were biologically determined, using electrophysiological data in macaque; the ISR window was the one fit parameter.</p> <p>MASC was tested by comparing its predicted sequences of fixations over sentences from the French-Sentence Corpus (FSC) to the eye-movement behavior of 40 French-native speakers reading the same sentences for comprehension (Albrengues et al., Plos One 2019). Then, MASC was dissected to determine the crucial processing steps enabling prediction of human behavior (10 comparison models -see the general README file). Finally, to address crucial issues in the reading literature, i.e., the role of inter-word spacing and character print size in eye-movement guidance, MASC was additionally tested in four additional display conditions: the same sentences from the FSC, but with blank spaces between words being either filled or removed, or with the screen width angle being multiplied by 2 or 4, such that characters were larger in angular size (0.5° and 1°) than in the original experiment (0.25°). MASC's predicted effects of inter-word spacing and print size were compared to previously published data.</p> <p>All material relevant to the project is reported here, including the FSC materials (bitmap and information text files), the Matlab code for our MASC model, raw simulation data for MASC and all our comparison models, as well as MASC's simulations in the different display conditions, the scripts we developed in R to transform raw simulation data into data matrices for statistical analyses of (word-based) eye-movement behavior, the resulting data matrices for all models as well as the data matrix for FSC readers, the R-scripts for statistical comparison of oculomotor behavior between data sets and conditions, literature-review tables of previously published data (for comparison with MASC's predictions), and the R-scripts generating the figures summarizing our results.</p> <p>Further information can be found in the general README file as well as in the README files attached to each folder. The authors' respective contributions to the project, the licence attached to the included materials and their condition of use are listed in the general README file.</p> <p>A manuscript reporting and discussing these modeling data is in preparation (Vitu, F., Adeli, H. & Zelinsky, G. J.); A reference will be provided here when the manuscript appears in a journal.</p> <p>Other references to be cited:</p> <p>- For the model code: Adeli, H., Vitu, F., & Zelinsky, G. J. (2017). A model of the superior colliculus predicts fixation locations during scene viewing and visual search. Journal of Neuroscience, 37(6), 1453-1467. http://www.jneurosci.org/content/37/6/1453</p> <p>- For FSC materials and data: Albrengues, C., Lavigne, F., Aguilar, C., Castet, E., & Vitu, F. (2019). Linguistic processes do not beat visuo-motor constraints, but they modulate where the eyes move regardless of word boundaries: Evidence against top-down word-based eye-movement control during reading. PLoS ONE 14(7): e0219666. https://doi.org/10.1371/journal.pone.0219666<br> </p>
Data associated with Cell Reports publication: Dura-Bernal et al. 2023, "Multiscale model of primary motor cortex circuits predicts in vivo cell type-specific, behavioral state-dependent dynamics"
<p>This dataset includes experimental data used to constrain and validate the model, and model simulation output data. The source code for the associated M1 model and data analysis can be found here: https://github.com/suny-downstate-medical-center/M1_NetPyNE_CellReports_2023</p> <p>Please download the data_v2.zip file, which contains the most updated and complete version of the data.</p> <p>For more information please contact: salvador.dura-bernal@downstate.edu </p>
Dataset for Dynamic expectations: Behavioral and electrophysiological evidence of sub-second updates in reward predictions
<p>This is the dataset for the paper entitled "Dynamic expectations: Behavioral and electrophysiological evidence of sub-second updates in reward predictions" by Marciano et al.</p>
Prediction of Extubation Readiness in Extreme Preterm Infants by the Automated Analysis of CardioRespiratory Behavior
ClinicalTrials.gov study NCT01909947. IPD Sharing: Not stated. Countries: 2. Publications: 2.
Does Personality Predict Patient Adherence, Health Behaviors, and Weight Loss Outcomes During the Latino Crossover Semaglutide Study (LCSS)? (Story-LCSS Project)
ClinicalTrials.gov study NCT05622045. IPD Sharing: UNDECIDED. Countries: 1. Publications: 7.
Approach-Avoidance, Computational Framework for Predicting Behavioral Therapy Outcome (AAC-BeT)
ClinicalTrials.gov study NCT04426461. IPD Sharing: YES. Countries: 1. Publications: 2.
Using Affective Differences to Predict Response to Behavioral Treatment for Major Depressive Disorder
ClinicalTrials.gov study NCT00909220. IPD Sharing: YES. Countries: 0. Publications: 3.
Predict + Protect Study: Exploring the Effectiveness of a Predictive Health Education Intervention on the Adoption of Protective Behaviors Related to ILI
ClinicalTrials.gov study NCT06229444. IPD Sharing: Not stated. Countries: 1. Publications: 16.
Behavioral AI to Predict and Increase Peritoneal Dialysis Uptake
ClinicalTrials.gov study NCT06533254. IPD Sharing: NO. Countries: 1. Publications: 2.
Effectiveness of Visual-Behavioral Approach and Predictive Factors in Dental Exams for Children With Autism
ClinicalTrials.gov study NCT06470724. IPD Sharing: NO. Countries: 1. Publications: 1.
Behavioral Study to Predict the Efficacy of a Self-Help Tool
ClinicalTrials.gov study NCT06631183. IPD Sharing: YES. 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.