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13,064 results for “predictions”
Data from: Efficacy of negative feedback in the HPA axis predicts recovery from acute challenges.
The glucocorticoid stress response mediates a suite of physiological and behavioral changes that allow vertebrates to cope with transient stressors. Chronically elevated glucocorticoid levels are known to result in a variety of organismal costs, but relatively little is known about the downstream effects of mounting a series of brief, acute spikes in circulating glucocorticoids. Conceptual models of stress suggest that repeated acute stressors might produce 'wear-and-tear' on the stress response system when encountered in sequence. We used a novel technique to experimentally induce acute corticosterone spikes on either three or six consecutive days in incubating tree swallows. Consistent with the 'wear-and-tear' hypothesis, we found that i) a sequence of corticosterone spikes produced cumulative effects on corticosterone regulation, ii) treatment frequency predicted the severity of consequences, and iii) individual variation in the ability to terminate the stress response through negative feedback predicted the duration of physiological disruption in the group that experienced the most frequent challenges. Our results illustrate the importance of assessing multiple aspects of the hormonal stress response and have implications for understanding both individual and population resilience to repeated transient stressors.
Input files for "Predicting the orientation of adsorbed proteins steered with electric fields using a simple electrostatic model"
<p>In files to run PyGBe (https://github.com/pygbe/pygbe), and reproduce the results from the J. Phys. Chem. B article "Predicting the orientation of adsorbed proteins steered with electric fields using a simple electrostatic model". </p>
Switchgrass flowering time measurements for genomic prediction
<p>The seasonal timing of the transition from vegetative to reproductive growth has a major impact on biomass accumulation in switchgrass. Late-flowering switchgrass cultivars produce greater biomass, a critical trait for sustainable bioenergy production. Genomic prediction (GP) may allow rapid selection of late-flowering individuals with reduced time and expense for field evaluations. To evaluate GP, two flowering time traits (heading date and anthesis date) were collected on 1,532 genotypes from four breeding populations: Midwest, Gulf, Atlantic, and Hybrid. These were sequenced using genotype-by-sequencing (530,792 SNPs). Predictive ability of single-trait and multi-trait models were evaluated by cross-validation, by prediction of a progeny trial (n=122), and through prediction of yield performance in a parallel experiment (n=52). Predictive ability was not improved by sharing information among breeding groups. Overall, multi-trait models provided an advantage during cross-validation, but a smaller advantage during progeny prediction. Within populations, GP resulted in lower per-cycle progress than previously reported field evaluations (3.1 vs 5.0 day<sup>-1</sup> cycle<sup>-1</sup>). However, GP cycles are potentially much faster than field evaluations. When directly predicting biomass yield, the Hybrid training population had a predictive ability of 0.54-0.63. This reinforces the strong linkage between biomass yields in swards and flowering time. These results highlight the value of GP for rapid yield improvement in switchgrass, particularly in a breeding program designed to share information between biomass yield trials and low-cost flowering time evaluations.</p>
Predicting past and future SARS-CoV-2-related sick leave using discrete time Markov modelling
<p><strong>Background: </strong>Prediction of SARS-CoV-2-induced sick leave among healthcare workers (HCWs) is essential for being able to plan the healthcare response to the epidemic.</p> <p><strong>Methods: </strong>During first wave of the SARS-Cov-2 epidemic (April 23<sup>rd </sup>to June 24<sup>th</sup>, 2020), the HCWs in the greater Stockholm region in Sweden were invited to a study of past or present SARS-CoV-2 infection. We develop a discrete time Markov model using a cohort of 9449 healthcare workers (HCWs) who had complete data on SARS-CoV-2 RNA and antibodies as well as sick leave data for the calendar year 2020. The one-week and standardized longer term transition probabilities of sick leave and the ratios of the standardized probabilities for the baseline covariate distribution were compared with the referent period (an independent period when there were no SARS-CoV-2 infections) in relation to PCR results, serology results and gender.</p> <p><strong>Results:</strong> The one-week probabilities of transitioning from healthy to partial sick leave or full sick leave during the outbreak as compared to after the outbreak were highest for healthy HCWs testing positive for large amounts of virus (ratio: 3.69, (95% confidence interval, CI: 2.44-5.59) and 6.67 (95% CI: 1.58-28.13), respectively). The proportion of all sick leaves attributed to COVID-19 during outbreak was at most 55% (95% CI: 50%-59%).</p> <p><strong>Conclusions: </strong>A robust Markov model enabled use of simple SARS-CoV-2 testing data for quantifying past and future COVID-related sick leave among HCWs, which can serve as a basis for planning of healthcare during outbreaks.</p>
Interpretable Deep Learning for Probabilistic MJO Prediction: CNN Forecasts
<p>This repository contains data produced for the paper "Interpretable Deep Learning for Probabilistic MJO Prediction" by A. Delaunay and H. M. Christensen (2021).</p> <p>>> mu_ens_XX.pt<br> contains the mean forecasts from each ensemble member at a lead time of XX days</p> <p>>> cov_alea_XX.pt<br> contains the aleatoric predictions of each ensemble member at a lead time of XX days</p>
Prediction of the broadcasting model and various baselines
<p>The ground truth values, and the predictions from the broadcasting model and the baselines (SC and CMAQ) in the submission <em>Development of an LSTM-Broadcasting deep-learning framework for regional air pollution forecast improvement</em>. The namelists of the WRF and CMAQ models are also included.</p>
Predicting peak daily maximum 8-hour ozone, and linkages to emissions and meteorology, in Southern California using machine learning methods
<p>crestlinetop30ozone19902019.csv includes the data used to build the modes for the annual 30 highest MDA8 concentrations at the Crestline site.</p>
Data for: Telomere length predicts timing and intensity of migratory behavior in a nomadic songbird
<p>Our understanding of state-dependent behavior is reliant on identifying physiological indicators of condition. Telomeres are of growing interest for understanding behavior as they capture differences in biological state and residual lifespan. To understand the significance of variable telomere lengths for behavior and test two hypotheses describing the relationship between telomeres and behavior (i.e., the causation and the selective adoption hypotheses), we assessed if telomere lengths are longitudinally repeatable traits related to spring migratory behavior in captive pine siskins (<em>Spinus pinus</em>). Pine siskins are nomadic songbirds that exhibit highly flexible, facultative migrations, including a period of spring nomadism. Captive individuals exhibit extensive variation in spring migratory restlessness and are an excellent system for mechanistic studies of migratory behavior. Telomere lengths were found to be significantly repeatable (R = 0.51) over 4 months, and shorter pre-migratory telomeres were associated with earlier and more intense expression of spring nocturnal migratory restlessness. Telomere dynamics did not vary with migratory behavior. Our results describe the relationship between telomere length and migratory behavior and provide support for the selective adoption hypothesis. More broadly, we provide a novel perspective on the significance of variable telomere lengths for animal behavior and the timing of annual cycle events.</p>
A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2
<p>Data produced and analyzed in the manuscript "A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2" by Pasquadibisceglie et al.</p> <p><br> If you include these data in your manuscript, please cite: Pasquadibisceglie A, Leccese A and Polticelli F (2022) A computational study of the structure and function of human Zrt and Irt-like proteins metal transporters: An elevator-type transport mechanism predicted by AlphaFold2. <em>Front. Chem.</em> 10:1004815. doi: 10.3389/fchem.2022.1004815</p>
Data from: Learning to predict spatio-temporal movement dynamics from weather radar networks
<p>This dataset contains the following:</p> <ul> <li><strong>data</strong>: <ul> <li><em>preprocessed</em>: hourly European weather radar data (here: <em>radar</em>) and aggregated simulation outputs (here: <em>abm</em>), combined with ERA5 reanalysis data and Voronoi tessellation details</li> <li><em>shapes: </em>geographical shapes used for plotting</li> </ul> </li> <li><strong>results</strong><em>:</em> trained models and corresponding results for both simulated data (here: <em>abm</em>) and weather radar data (here: <em>radar</em>).</li> <li><strong>figures</strong><em>: </em>final figures presented in our paper "Learning to predict spatio-temporal movement dynamics from weather radar networks" to summarize the results</li> </ul> <p>The corresponding code used to train and evaluate models is archived here: <a href="https://doi.org/10.5281/zenodo.6921595">10.5281/zenodo.6921595</a>.</p>
High-resolution climate simulations using the Model for Prediction Across Scales - Atmosphere (MPAS-A; version 5.1)
<p>We present multi-seasonal simulations representative of present-day and future environments using the global Model for Prediction Across Scales – Atmosphere (MPAS-A) version 5.1 with high resolution (15 km) throughout the Northern Hemisphere. We select 10 simulation years with varying phases of El Niño–Southern Oscillation (ENSO) and integrate each for 14.5 months. We use analyzed sea surface temperature (SST) patterns for present-day simulations. For the future climate simulations, we alter present-day SSTs by applying monthly-averaged temperature changes derived from a 20-member ensemble of Coupled Model Intercomparison Project phase 5 (CMIP5) general circulation models (GCMs) following the Representative Concentration Pathway (RCP) 8.5 emissions scenario. Daily sea ice fields, obtained from the monthly-averaged CMIP5 ensemble mean sea ice, are used for present-day and future simulations.</p> <p>Due to storage limitations, the full dataset is much too large to be published (~50TB). Instead, a subset consisting of 6-hourly warm season (May-September) 2-meter temperature, precipitation, and 500hPa height is presented. If you wish to access the full dataset (as presented in Michaelis et al. 2019), please contact one of the authors.</p>
Skillful decadal UK flood prediction - DO NOT USE
<p>The scripts in this repository perform the analysis shown in the manuscript "Skillful decadal UK flood prediction" by Moulds et al., which is currently under consideration in Geophysical Research Letters.</p>
Multimodal dataset: Protein Function Prediction using STRING data & COVID19 Mortality Model by EI
<p>The PFP.zip file contains 1. 5 well-formated GO terms dataset for EI, 2. STRING data 3. GO term annotation. The last two could be merged by the 'generate_data.py' script in https://github.com/GauravPandeyLab/ensemble_integration</p> <p>The covid19_model_built.zip contained the EI model built based on the COVID-19 Mortality dataset, the detail of usage are here:.</p>
Non-structural carbohydrates predict survival in saplings of temperate trees under carbon stress
<p>1. Non-structural carbohydrates (NSCs) mediate plant survival when the plant's carbon (C) balance is negative, suggesting that NSCs could predict plant survival under C stress. To examine this possibility, we exposed saplings of six temperate tree species to diverse levels of C stress created by the combination of two light conditions (full light availability and deep shade) and two defoliation levels (severe defoliation and non-defoliation). We then measured survival, biomass, and total NSCs and soluble sugar (SSs) concentrations in different organs of both dead and live saplings.</p> <p>2. We estimated mean NSCs and SSs contents and concentrations per sapling and fitted logistic generalized mixed-effects models to determine if NSCs and SSs predict survival. Using inverse prediction modelling, we also determined whether there is a common NSCs and SS threshold across species at the time of sapling's death.</p> <p>3. Defoliation and shade reduced the mean sapling's NSCs and SSs contents, indicating C stress. Mean sapling NSCs and SSs contents and concentrations predicted survival and the robustness of the models improved with the inclusion of species. At death, saplings of the exotic deciduous tree species Acer pseudoplatanus exhibited significantly lower mean NSCs and SSs contents than saplings of the evergreen conifer species Podocarpus nubigenus and lower stem NSCs and SSs concentrations than the broadleaf evergreen species Drimys winteri.</p> <p>4. The energetic role that NSCs and SSs play in plants under C stress was evidenced by the capacity of these compounds to predict sapling survival under C stress. No common threshold of NSCs and SSs contents or concentrations for sapling survival amongst species was found, indicating that the level of these compounds may not be good proxies for interspecific comparisons of tolerance to C stress. Presumably, there are species-specific limits for the mobilization and use of NSCs and SSs in metabolism.</p> <p>5. Our results anticipate that the inclusion of NSCs and SSs in modelling will improve predictions regarding tree responses to ongoing climate change. Nonetheless, a better understanding of the many roles that carbohydrates play in plant survival under C stress is required to scale predictions up to the community level.</p>
Data for: Mating strategy predicts gene presence/absence patterns in a genus of simultaneously hermaphroditic flatworms
<p>This repository contains a record of analysis scripts and similarity score data used for the analyses presented in the manuscript.</p> <p>Some of the R scripts depend on supplementary tables associated with the manuscript.</p> <p>A preprint of the manuscript is available at: <a href="https://www.biorxiv.org/content/10.1101/2022.04.25.489193v2">https://www.biorxiv.org/content/10.1101/2022.04.25.489193v2</a></p>
Environmental gradients predict the ratio of environmentally acquired carotenoids to self-synthesised pteridine pigments
<p>Carotenoids are important pigments producing integument coloration; however, their dietary availability may be limited in some environments. Many species produce red to yellow hues using a combination of carotenoids and self-synthesised pteridine pigments. A compelling but untested hypothesis is that pteridines replace carotenoids in environments where carotenoid availability is limited. Based on a phylogenetic comparative analysis of pigment concentrations in agamid lizards, we show that environmental gradients predict the ratio of carotenoids to pteridines; carotenoid concentrations are lower and pteridine concentrations higher in arid environments with low vegetation productivity. Both carotenoid and pteridine pigments were present in all species, but only pteridine concentrations explained colour variation among species and there were no correlations between carotenoid and pteridine pigments with similar hue. These results suggest that pteridine pigments replace carotenoids in carotenoid-limited environments, irrespective of skin hue, presumably because it is metabolically cheaper to synthesise pteridines than to acquire and sequester carotenoids when carotenoids are rare.</p>
Predicting spatio-temporal population patterns of Borrelia burgdorferi, the Lyme disease pathogen
<p>The causative bacterium of Lyme disease, <em>Borrelia burgdorferi</em>, expanded from an undetected human pathogen into the etiologic agent of the most common vector-borne disease in the United States over the last several decades. Systematic field collections of the tick vector reveal increases in the geographic range and population size of <em>B. burgdorferi</em> that coincided with increases in human Lyme disease incidence across New York State. Here we investigate the impact of environmental features on the population dynamics of <em>B. burgdorferi</em>. Analytical models developed using field collections of nearly 19,000 nymphal <em>Ixodes scapularis </em>and spatially- and temporally-explicit environmental features accurately explained the variation of <em>B. burgdorferi </em>population sizes across space and time. Importantly, the model identified environmental features that can be used to predict the biogeographical patterns of <em>B. burgdorferi-</em>infected ticks into future years and in previously unsampled areas. Forecasting the distribution and abundance of a pathogen at fine geographic scales offers a powerful strategy to mitigate a serious public health threat.</p>
Raw EEG data for the experiment reported in "Understanding the effects of constraint and predictability in ERP"
<p>This repository contains the raw EEG data for the above-named paper. Preprocessing scripts are stored at: <a href="http://osf.io/fndk5/">https://osf.io/fndk5/</a></p> <p>The raw EEG data are the files *.eeg, *.vmrk and *.vhdr (BrainVision format EEG data). The numeric prefix indicates the participant ID. All three files must be stored in the same directory to work with the preprocessing script. Individual participant log files from the experimental presentation paradigm are stored in the zipped subdirectory opensesame_logs.zip. To work with the preprocessing script, these must be unzipped into a folder called opensesame_logs, stored in the folder containing the raw EEG files.</p> <p>A repository of intermediate preprocessing files based on the raw data is at <a href="http://zenodo.org/record/7002697">https://zenodo.org/record/7002697</a>.</p> <p>Note that the following raw files are included in the dataset for transparency but were not preprocessed for the final analysis: subject 15 due to a recording software crash mid-experiment, and subjects 40:43 as their EEG data were corrupted.</p>
[SUPERCEDED] Preprocessed EEG data for the experiment reported in "Understanding the effects of constraint and predictability in ERP"
<p>This repository contains <strong><em>an outdated version of</em></strong> intermediate preprocessing files for the above-named paper. Please see the current version here: <a href="https://zenodo.org/record/7334782">https://zenodo.org/record/7334782</a></p>
Ensembles of knowledge graph embedding models improve predictions for drug discovery
<p>This contains data described in detail in our paper, "Ensembles of knowledge graph embedding models improve predictions for drug discovery". The metadata involves the different trained models that were used for prediction analysis as well as all the predictions from the trained models.</p>
ScienceDex guides
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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.