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1,773 results for “Predictive model”
Predicting soil interpedal macroporosity and hydraulic conductivity dynamics: A model for integrating laser-scanned profile imagery with soil moisture sensor data
Open the record for dataset details and reuse information.
Epigenetic models developed for plains zebras predict age in domestic horses and endangered equids
Open the record for dataset details and reuse information.
MESA Prediction models
<p>PrediXcan prediction models from MESA cohort.</p> <p>Also includes LD compilation for S-PrediXcan</p>
Protein Subcellular localization prediction data used in the article entitled "MSclassifier: Median-Supplement model-based Classification tool for automated knowledge discovery"
<p>This repository contains data used to obtain results from a 5-fold cross-validation testing of how MSclassifier and other packages accurately predict protein subcellular localization in the software article entitled "MSclassifier: median-supplement model-based classification tool for automated knowledge discovery." The data used in the software article is derived from data generated in "G. K. Acquaah-Mensah, S. M. Leach, and C. Guda, Predicting the subcellular localization of human proteins using machine learning and exploratory data analysis, Genomics Proteomics Bioinformatics, 4(2):120-133, 2006, <a href="https://doi.org/10.1016/S1672-0229(06)60023-5">https://doi.org/10.1016/S1672-0229(06)60023-5</a>"</p>
Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding-Model setup and source code
<p>Source code and model setup/inputs for the paper titled "Dynamic Load Balancing for Predictions of Storm Surge and Coastal Flooding" article. Simulations were conducted using a modified version of ADCIRC+DLB (ADCIRC + Dynamic Load Balancing) on unstructured triangular meshes.</p> <p>Contains:</p> <ol> <li>Model input files. <ol> <li>ADCIRC model input files for the ideal channel setup and Hurricane Irene simulation (*.13, *.14, *.15)</li> </ol> </li> <li>Zipped archive of the ADCIRC code (adcirc-cg-DLB.zip) used to produce the simulations for the paper.</li> <li>Step-by-step compilation and usage instructions for ADCIRC+DLB. <ol> <li>Installation.html </li> <li>Usage.html</li> </ol> </li> </ol>
Correlated and geographically predictable Neanderthal and Denisovan legacies are difficult to reconcile with a simple model based on inter-breeding
<p>Although the presence of archaic hominin legacies in humans is taken for granted, little attention has been given as to how the data fit with how humans colonised the world. Here I show that Neanderthal and Denisovan legacies are strongly correlated and that, like heterozygosity, distance from Africa predicts legacy size. Simulations confirm that, once created, legacy size is extremely stable: it may reduce through admixture with lower legacy populations but cannot increase detectably through neutral drift. Consequently, populations carrying the highest legacies must also be those whose ancestors inter-bred most with archaics. However, the populations with the highest legacies are globally scattered and are unified, not by having origins within the known Neanderthal range, but instead by living in locations that lie furthest from Africa. Furthermore, the Simons Genome Diversity Project data reveal two very distinct correlations between Neanderthal and Denisovan legacies, one that starts in North Africa and increases west to east across Eurasia and into some parts of Oceania, and a second, much steeper trend that starts in Africa, peaking with the San and Ju/'hoansi and which, if extrapolated, predicts the large inferred legacies of both archaics found in Oceania / Australia. These trends are difficult to reconcile with classical models of how introgression occurred but may fit a speculative model in which the loss of diversity that occurred when humans moved further from Africa created a gradient in heterozygosity that in turn progressively reduced mutation rate such that populations furthest from Africa have diverged less from our common ancestor and hence from the archaics. The two distinct trends could be interpreted in terms of two 'out of Africa' events, an early one ending in Oceania and Australia and a later one that colonised Eurasia and the Americas.</p>
Experimental evidence of warming-induced disease emergence and its prediction by a trait-based mechanistic model
<p>Predicting the effects of seasonality and climate change on the emergence and spread of infectious disease remains difficult, in part because of poorly understood connections between warming and the mechanisms driving disease. Trait-based mechanistic models combined with thermal performance curves arising from the Metabolic Theory of Ecology (MTE) have been highlighted as a promising approach going forward; however, this framework has not been tested under controlled experimental conditions that isolate the role of gradual temporal warming on disease dynamics and emergence. Here, we provide experimental evidence that a slowly warming host – parasite system can be pushed through a critical transition into an epidemic state. We then show that a trait-based mechanistic model with MTE functional forms can predict the critical temperature for disease emergence, subsequent disease dynamics through time, and final infection prevalence in an experimentally warmed system of <i>Daphnia </i>and a microsporidian parasite. Our results serve as a proof of principle that trait-based mechanistic models using MTE sub-functions can predict warming-induced disease emergence in data-rich systems – a critical step towards generalizing the approach to other systems.</p>
Models and Predictions for "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"
<p><strong>Models and Predictions for the paper "Rainfall-Runoff Prediction at Multiple Timescales with a Single Long Short-Term Memory Network"</strong></p> <p>GitHub: <a href="https://github.com/gauchm/mts-lstm">https://github.com/gauchm/mts-lstm</a></p> <p><strong>Results</strong></p> <p>The file `results.tar.gz` contains:</p> <ul> <li>ensembled predictions for all models (generated from the models in `models/` using the <a href="https://neuralhydrology.readthedocs.io/en/latest/api/neuralhydrology.utils.nh_results_ensemble.html">`nh-results-ensemble` command</a>). These predictions were used in the `results-analysis.ipynb` and `odelstm-analysis.ipynb` notebooks on the GitHub repository for the paper.</li> <li>the NWM predictions <ul> <li>`nwm_chrt_v2_1h.p` contains hourly NWM predictions for the CAMELS basins between 1993 and 2007. The file is derived from the reanalysis on <a href="https://docs.opendata.aws/nwm-archive/readme.html">aws</a>.</li> <li>`nwm_results.p` is derived from `nwm_chrt_v2_1h.p` and contains hourly and day-aggregated results and performance metrics for the test period of our paper.</li> </ul> </li> <li>a file `signatures.p` with hydrologic signatures that were calculated from the models' predictions. These signatures were used in the `results-analysis.ipynb` notebook on the GitHub repository for the paper.</li> </ul> <p><strong>Models</strong></p> <p>The tar.gz files prefixed with `models-` contain the trained MTS-LSTM, sMTS-LSTM, and ODE-LSTM models from our experiments. For each experiment, there exist 10 model setups (one for each random seed).<br> Besides the trained models, each model's tar.gz also contains the predictions on the test or validation perod and the configuration file used to train the model.</p> <p><em>MTS-LSTM</em></p> <ul> <li>`mtslstm_seed*` -- the MTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`mtslstm_multiforcing_seed*` -- the MTS-LSTM from the section on per-timescale input data, experiment "multi-forcing B" (using just NLDAS as hourly inputs)</li> <li>`mtslstm_multiforcing_dailyhourly_seed*` -- the MTS-LTSM from the section on per-timescale input data, experiment "multi-forcing A" (ingesting daily forcings into the hourly model)</li> <li>`mtsltsm_136H1D_seed*` -- the MTS-LTSM from the section on prediction at other timescales (1-, 3-, 6-hourly and daily predictions)</li> </ul> <p><em>sMTS-LSTM</em></p> <ul> <li>`smtslstm_seed*` -- the sMTS-LSTM from the benchmarking section of the paper (using one forcings product, trained on daily and hourly data)</li> <li>`smtslstm_noregularization_seed*` -- the sMTS-LSTM from the section on cross-timescale consistency (trained without regularization)</li> </ul> <p><em>Time-Continuous Experiments</em></p> <p>The file `models-timecontinuous.tar.gz` contains one sub-folder per basin on which we conducted our initial experiments.<br> Each basin directory contains:</p> <ul> <li>Experiment A (trained on daily and 12-hourly, evaluated on hourly): <ul> <li>`odelstm_a_seed*` -- the ODE-LSTM from experiment A</li> <li>`mtslstm_a_seed*` -- the MTS-LSTM from experiment A</li> </ul> </li> <li>Experiment B (trained on hourly and 3-hourly, evaluated on daily) <ul> <li>`odelstm_b_seed*` -- the ODE-LSTM from experiment B</li> <li>`mtslstm_b_seed*` -- the MTS-LSTM from experiment B</li> </ul> </li> </ul> <p><em>Related Datasets: </em><a href="https://doi.org/10.5281/zenodo.4072700">https://doi.org/10.5281/zenodo.4072700</a> contains the hourly NLDAS forcings and USGS streamflow required to use the models from this dataset.</p>
Integrating QSAR models predicting acute contact toxicity and mode of action profiling in honey bees (A. mellifera): Data curation using open source databases, performance testing and validation
<p>This excel file (DOI: <a href="https://doi.org/10.5281/zenodo.3755675">https://doi.org/10.5281/zenodo.3755675</a>) provides the collection of raw data used for developing the first integrative Quantitative Structure-Activity Relationship (QSAR) model using EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase i) to predict acute contact toxicity (LD<sub>50</sub>) and ii) to profile the Mode of Action (MoA) of pesticides active substances in honey bees (<em>Apis mellifera</em>)<em>. </em>Chemical identifiers (e.g. SMILES, CAS n., InChI) and acute contact toxicity data (LD<sub>50</sub>) on honey bees were used to develop and validate i) a two-category QSAR model (toxic/non-toxic; n=411) (sensitivity =0.93), specificity =0.85), balanced accuracy =0.90), Matthews correlation coefficient MCC=0.78), and ii) a regression-based model (n=113) (R2=0.74; MAE=0.52). Similarly, current study proposes the first MoA profiling for 113 pesticides active substances and the first harmonised MoA classification scheme for acute contact toxicity in honey bees, including LD<sub>50s</sub> data points from three different databases such as EFSA's OpenFoodTox, US-EPA ECOTOX and Pesticide Properties DataBase. Such classification allows to further define MoAs and the target site of Plant Protection Products (PPPs) active substances, thus enabling regulators and scientists to refine chemical grouping and toxicity extrapolations for single chemicals and component-based mixture risk assessment of multiple chemicals.</p> <p>The full data collection and analysis of QSAR models, toxicity data (LD<sub>50</sub>) and Mode of Action (Moa) data are described in Carnesecchi et al., 2020 (DOI: doi.org/10.1016/j.scitotenv.2020.139243).</p> <p>This work was supported by the European Food Safety Authority (EFSA) [contract number: OC/EFSA/SCER/2018/01 and NP/EFSA/AFSCO/2016/02 (Edoardo Carnesecchi)].</p>
A novel clinical model for predicting malignancy of solitary pulmonary nodules: A multicenter study in Chinese population
<p><strong>Supplementary Data</strong></p> <p> </p> <p>Supplement Figure 1: The calibration curves for the novel model in training cohort (A), internal validation cohort (B) and external validation cohort (C), respectively.</p> <p> </p> <p>Supplement Table 1. Demographics and clinical characteristics of patients from Sun Yat-sen University Cancer Center.</p> <p> </p> <p>Supplement Table 2. Demographics and clinical characteristics of patients from Henan Tumor Hospital.</p> <p> </p> <p>Supplement Table 3. Comparison of the sensitivity, specificity, positive likelihood ratio, negative likelihood ratio of the three models analyzed in this study</p> <p> </p> <p>Supplement Table 4. The correlation between our model, PKUPH model and Mayo model</p>
Supplementary material 1 from: Bustamante RO, Alves L, Goncalves E, Duarte M, Herrera I (2020) A classification system for predicting invasiveness using climatic niche traits and global distribution models: application to alien plant species in Chile. NeoBiota 63: 127-146. https://doi.org/10.3897/neobiota.63.50049
Table S1. Exotic species located in Quadrant 1 (see Figure 3) and impacts on biodiversity, agriculture and cattle raisng
Calibration of probability predictions from machine-learning and statistical models
<p>This data set describes the occurrence (yes/no) of a bird, the Southern Whiteface (<i>Aphelocephala leucopsis)</i> in Australia. A suite of environmental variables is provided, which are used in the paper to illustrate a statistical problem. The data are meant to allow reproduction of the analysis in this paper. They are not intended for actual ecological analysis. The data come as .Rdata-file, i.e. as an R-dataset (described technically here: https://www.loc.gov/preservation/digital/formats/fdd/fdd000470.shtml).</p> <p>Here is the paper's abstract:</p> <p><span>Aim: Predictions from statistical models may be uncalibrated, meaning that the predicted values do not have the nominal coverage probability. This is easiest seen with probability predictions in machine-learning classification, including the common species occurrence probabilities. Here, a predicted probability of, say, 0.7 should indicate that out of 100 cases with these environmental conditions, and hence the same predicted probability, the species should be present in 70 and absent in 30.</span><br> <span>Innovation: A simple calibration plot shows that this is not necessarily the case, particularly not for over-fitted models or algorithms that use non-likelihood target functions. As a consequence, "raw" predictions from such model could easily be off by 0.2, are unsuitable for averaging across model types, and resulting maps hence be substantially distorted. The solution, a flexible calibration regression, is simple and can be applied whenever deviations are observed.</span><br> <span>Conclusion: "Raw", uncalibrated probability predictions should be calibrated before interpreting or averaging them in a probabilistic way.</span></p>
Data from: Use of simulation-based statistical models to complement bioclimatic models in predicting continental scale invasion risks
Invasive species represent one of the greatest risks to global biodiversity and economic productivity of agroecosystems. The development of certain novel crops—e.g., herbaceous perennial biomass crops—may create a risk of novel invasions by these crops. Therefore, potential benefits and risks need to be weighed in making decisions about their introduction and subsequent management. Ideally, such a weighing will be based on good estimates of invasion risks in realistic scenarios pertaining to actual landscapes of concern regarding invasion. Most previous large-scale analyses of invasion risk have used species distribution models and their established methods. Unfortunately, these approaches are unable to incorporate local scale biotic and spatial factors that influence invasion risk. Here we present a case study for how such factors can be efficiently incorporated in large-scale analyses of invasion risk, by extending simulation models with statistical modeling tools. By these means, we predict invasion risk at the scale of the entire United States for a major biomass crop, Miscanthus × giganteus. We then combine invasion risk predictions for this method with those from bioclimatic methods, producing a map of aggregated invasion risk that can offer more nuanced predictions of invasion risk than either approach alone. Lastly, we evaluate potential risks for invasive crops that differ in invasiveness traits, to examine how geographic patterns of invasion risk vary among invaders as a result of their particular constellation of traits.
Data from: Integrated modeling predicts shifts in waterbird population dynamics under climate change
Climate change has been identified as one of the most important drivers of wildlife population dynamics. The in-depth knowledge of the complex relationships between climate and population sizes through density dependent demographic processes is important for understanding and predicting population shifts under climate change, which requires integrated population models (IPMs) that unify the analyses of demography and abundance data. In this study we developed an IPM based on Gaussian approximation to dynamic N-mixture models for large scale population data. We then analyzed four decades (1972-2013) of Mallard (Anas platyrhynchos) breeding population survey, band-recovery, and climate data covering a large spatial extent from North American prairies through boreal habitat to Alaska. We aimed to test the hypothesis that climate change will cause shifts in population dynamics if climatic effects on demographic parameters that have substantial contribution to population growth vary spatially. More specifically, we examined the spatial variation of climatic effects on density dependent population demography, identified the key demographic parameters that are influential to population growth, and forecasted population responses to climate change. Our results revealed that recruitment, which explained more variance of population growth than survival, was sensitive to the temporal variation of precipitation in the southern portion of the study area but not in the north. Survival, by contrast, was insensitive to climatic variation. We then forecasted a decrease in Mallard breeding population density in the south and an increase in the northwestern portion of the study area, indicating potential shifts in population dynamics under future climate change. Our results implied that different strategies need to be considered across regions to conserve waterfowl populations in the face of climate change. Our modelling approach can be adapted for other species and thus has wide application to understanding and predicting population dynamics in the presence of global change.
Data from: Predicting spatial patterns of plant species richness: a comparison of direct macroecological and species stacking modelling approaches
PLEASE NOTE, THESE DATA ARE ALSO REFERRED TO IN TWO OTHER PUBLICATIONS. PLEASE SEE http://dx.doi.org/10.1111/j.1365-2486.2008.01766.x AND http://dx.doi.org/10.1111/2041-210X.12222 FOR MORE INFORMATION. Aim: This study compares the direct, macroecological approach (MEM) for modelling species richness (SR) with the more recent approach of stacking predictions from individual species distributions (S-SDM). We implemented both approaches on the same dataset and discuss their respective theoretical assumptions, strengths and drawbacks. We also tested how both approaches performed in reproducing observed patterns of SR along an elevational gradient. Location: Two study areas in the Alps of Switzerland. Methods: We implemented MEM by relating the species counts to environmental predictors with statistical models, assuming a Poisson distribution. S-SDM was implemented by modelling each species distribution individually and then stacking the obtained prediction maps in three different ways – summing binary predictions, summing random draws of binomial trials and summing predicted probabilities – to obtain a final species count. Results: The direct MEM approach yields nearly unbiased predictions centred around the observed mean values, but with a lower correlation between predictions and observations, than that achieved by the S-SDM approaches. This method also cannot provide any information on species identity and, thus, community composition. It does, however, accurately reproduce the hump-shaped pattern of SR observed along the elevational gradient. The S-SDM approach summing binary maps can predict individual species and thus communities, but tends to overpredict SR. The two other S-SDM approaches – the summed binomial trials based on predicted probabilities and summed predicted probabilities – do not overpredict richness, but they predict many competing end points of assembly or they lose the individual species predictions, respectively. Furthermore, all S-SDM approaches fail to appropriately reproduce the observed hump-shaped patterns of SR along the elevational gradient. Main conclusions: Macroecological approach and S-SDM have complementary strengths. We suggest that both could be used in combination to obtain better SR predictions by following the suggestion of constraining S-SDM by MEM predictions.
Data from: Hyaluronan and N-ERC/mesothelin as key biomarkers in a specific two-step model to predict pleural malignant mesothelioma
Purpose: Diagnosis of malignant mesothelioma is challenging. The first available diagnostic material is often an effusion and biochemical analysis of soluble markers may provide additional diagnostic information. This study aimed to establish a predictive model using biomarkers from pleural effusions, to allow early and accurate diagnosis. Patients and Methods: Effusions were collected prospectively from 190 consecutive patients at a regional referral centre. Hyaluronan, N-ERC/mesothelin, C-ERC/mesothelin, osteopontin, syndecan-1, syndecan-2, and thioredoxin were measured using ELISA and HPLC. A predictive model was generated and validated using a second prospective set of 375 effusions collected consecutively at a different referral centre. Results: Biochemical markers significantly associated with mesothelioma were hyaluronan (odds ratio, 95% CI: 8.82, 4.82–20.39), N-ERC/mesothelin (4.81, 3.19–7.93), CERC/mesothelin (3.58, 2.43–5.59) and syndecan-1 (1.34, 1.03–1.77). A two-step model using hyaluronan and N-ERC/mesothelin, and combining a threshold decision rule with logistic regression, yielded good discrimination with an area under the ROC curve of 0.99 (95% CI: 0.97–1.00) in the model generation dataset and 0.83 (0.74–0.91) in the validation dataset, respectively. Conclusions: A two-step model using hyaluronan and N-ERC/mesothelin predicts mesothelioma with high specificity. This method can be performed on the first available effusion and could be a useful adjunct to the morphological diagnosis of mesothelioma.
Data from: Bioclimatic envelope models predict a decrease in tropical forest carbon stocks with climate change in Madagascar
1. Recent studies have underlined the importance of climatic variables in determining tree height and biomass in tropical forests. Nonetheless, the effects of climate on tropical forest carbon stocks remain uncertain. In particular, the application of process-based dynamic global vegetation models have led to contrasting conclusions regarding the potential impact of climate change on tropical forest carbon storage. 2. Using a correlative approach based on a bioclimatic envelope model and data from 1771 forest plots inventoried during the period 1996-2013 in Madagascar over a large climatic gradient, we show that temperature seasonality, annual precipitation and mean annual temperature are key variables in determining forest aboveground carbon density. 3. Taking into account the explicative climate variables, we obtained an accurate (R2 = 70% and RMSE = 40 Mg.ha-1) forest carbon map for Madagascar at 250 m resolution for the year 2010. This national map was more accurate than previously published global carbon maps (R 2 ≤ 26% and RMSE ≥ 63 Mg.ha −1 ). 4. Combining our model with the climatic projections for Madagascar from seven IPCC CMIP5 global climate models following the RCP 8.5, we forecast an average forest carbon stock loss of 17% (range: 7-24%) by the year 2080. For comparison, a spatially homogeneous deforestation of 0.5% per year on the same period would lead to a loss of 30% of the forest carbon stock. 5. Synthesis: Our study shows that climate change is likely to induce a decrease in tropical forest carbon stocks. This loss could be due to a decrease in the average tree size and to shifts in tree species distribution, with the selection of small-statured species. In Madagascar, climate-induced carbon emissions might be, at least, of the same order of magnitude as emissions associated to anthropogenic deforestation.
Benchmarking parametric and machine learning models for genomic prediction of complex traits
<p>The usefulness of genomic prediction in crop and livestock breeding programs has prompted efforts to develop new and improved genomic prediction algorithms, such as artificial neural networks and gradient tree boosting. However, the performance of these algorithms has not been compared in a systematic manner using a wide range of datasets and models. Using data of 18 traits across six plant species with different marker densities and training population sizes, we compared the performance of six linear and six non-linear algorithms. First, we found that hyperparameter selection was necessary for all non-linear algorithms and that feature selection prior to model training was critical for artificial neural networks when the markers greatly outnumbered the number of training lines. Across all species and trait combinations, no one algorithm performed best, however predictions based on a combination of results from multiple algorithms (i.e. ensemble predictions) performed consistently well. While linear and non-linear algorithms performed best for a similar number of traits, the performance of non-linear algorithms vary more between traits. Although artificial neural networks did not perform best for any trait, we identified strategies (i.e. feature selection, seeded starting weights) that boosted their performance to near the level of other algorithms. Our results highlight the importance of algorithm selection for the prediction of trait values.</p>
Data from: Predictive modelling of habitat selection by marine predators with respect to the abundance and depth distribution of pelagic prey
1. Understanding the ecological processes that underpin species distribution patterns is a fundamental goal in spatial ecology. However, developing predictive models of habitat use is challenging for species that forage in marine environments, as both predators and prey are often highly mobile and difficult to monitor. Consequently, few studies have developed resource selection functions for marine predators based directly on the abundance and distribution of their prey. 2. We analysed contemporaneous data on the diving locations of two seabird species, the shallow-diving Peruvian Booby (Sula variegata) and deeper diving Guanay Cormorant (Phalacrocorax bougainvilliorum), and the abundance and depth distribution of their main prey, Peruvian anchoveta (Engraulis ringens). Based on this unique data set, we developed resource selection functions to test the hypothesis that the probability of seabird diving behaviour at a given location is a function of the relative abundance of prey in the upper water column. 3. For both species, we show that the probability of diving behaviour is mostly explained by the distribution of prey at shallow depths. While the probability of diving behaviour increases sharply with prey abundance at relatively low levels of abundance, support for including abundance in addition to the depth distribution of prey is weak, suggesting that prey abundance was not a major factor determining the location of diving behaviour during the study period. 4. The study thus highlights the importance of the depth distribution of prey for two species of seabird with different diving capabilities. The results complement previous research that points towards the importance of oceanographic processes that enhance the accessibility of prey to seabirds. The implications are that locations where prey is predictably found at accessible depths may be more important for surface foragers, such as seabirds, than locations where prey is predictably abundant. 5. Analysis of the relative importance of abundance and accessibility is essential for the design and evaluation of effective management responses to reduced prey availability for seabirds and other top predators in marine systems.
Development and validation of a postoperative delirium prediction model for patients admitted to an intensive care unit in China: a prospective study
<p>Objectives: We aimed to develop <span class="il">and</span> validate <span class="il">a</span> <span class="il">postoperative</span> <span class="il">delirium</span> (POD) <span class="il">prediction</span> model for patients admitted to the intensive care unit (ICU).</p> <p>Design: <span class="il">A</span> prospective study was conducted.</p> <p>Setting: The study was conducted in the surgical, cardiovascular surgical, <span class="il">and</span> trauma surgical ICUs <span class="il">of</span> an affiliated hospital <span class="il">of</span> <span class="il">a</span> medical university in Heilongjiang Province, China.</p> <p>Participants: This study included 400 patients (≥18 years old) admitted to the ICU after surgery.</p> <p>Primary <span class="il">and</span> secondary outcome measures: The primary outcome measure was <span class="il">postoperative</span> <span class="il">delirium</span> assessment during ICU stay.</p> <p>Results: The model was developed using 300 consecutive ICU patients <span class="il">and</span> was validated using 100 patients from the same ICUs. The model was based on five risk factors: Physiological <span class="il">and</span> Operative Severity Score for the Enumeration <span class="il">of</span> Mortality <span class="il">and</span> Morbidity; acid-base disturbance; <span class="il">and</span> history <span class="il">of</span> coma, diabetes, or hypertension. The model had an area under the receiver operating characteristics curve <span class="il">of</span> 0.852 (95% confidence interval: 0.802–0.902), Youden index <span class="il">of</span> 0.5789, sensitivity <span class="il">of</span> 70.73%, <span class="il">and</span> specificity <span class="il">of</span> 87.16%. The Hosmer-Lemeshow goodness <span class="il">of</span> fit was 5.203 (P = 0.736). At <span class="il">a</span> cut-off <span class="il">of</span> 24.5%, the sensitivity <span class="il">and</span> specificity were 71% <span class="il">and</span> 69%, respectively.</p> <p>Conclusions: The model, which used readily available data, exhibited high predictive value regarding risk <span class="il">of</span> intensive care unit <span class="il">postoperative</span> <span class="il">delirium</span> (ICU-POD) at admission. Use <span class="il">of</span> this model may facilitate better implementation <span class="il">of</span> preventive treatments <span class="il">and</span> nursing measures.</p>
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.