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491 results for “population modelling”
Figure 2 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 2 The time step processes. The three parts of the time step (BeginStep, Step, EndStep) process can run in multithreaded mode for each object 1 to n, extant at that time and are separated by customisable methods for reporting or list management by the Population_Manager class.
Figure 1 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 1 The current class hierarchy for beetle population managers, starting with the parent class Population_Manager_Base.
Figure 3 from: Topping CJ, Duan X (2024) Managing large and complex population operations with agent-based models: The ALMaSS Population_Manager. Food and Ecological Systems Modelling Journal 5: e117593. https://doi.org/10.3897/fmj.5.117593
Figure 3 Change in the maximum and minimum sizes and population numbers for two scenarios using the Theoretical1 species, N = do nothing, Rand = randomise the execution order.
Predicting atrial fibrillation recurrence by combining population data and virtual cohorts of patient-specific left atrial models
<p><strong>Abstract</strong></p> <p><strong>Background: </strong>Current ablation therapy for atrial fibrillation is sub-optimal and long-term response is challenging to predict. Clinical trials identify bedside properties that provide only modest prediction of long-term response in populations, while patient-specific models in small cohorts primarily explain acute response to ablation. We aimed to predict long-term atrial fibrillation recurrence after ablation in large cohorts, by using machine learning to complement biophysical simulations by encoding more inter-individual variability.</p> <p><strong>Methods: </strong>Patient-specific models were constructed for 100 atrial fibrillation patients (43 paroxysmal, 41 persistent, 16 long-standing persistent), undergoing first ablation. Patients were followed for 1-year using ambulatory ECG monitoring. Each patient-specific biophysical model combined differing fibrosis patterns, fibre orientation maps, electrical properties and ablation patterns to capture uncertainty in atrial properties and to test the ability of the tissue to sustain fibrillation. These simulation stress tests of different model variants were post-processed to calculate atrial fibrillation simulation metrics. Machine learning classifiers were trained to predict atrial fibrillation recurrence using features from the patient history, imaging and atrial fibrillation simulation metrics.</p> <p><strong>Results: </strong>We performed 1100 atrial fibrillation ablation simulations across 100 patient-specific models. Models based on simulation stress tests alone showed a maximum accuracy of 0.63 for predicting long-term fibrillation recurrence. Classifiers trained to history, imaging and simulation stress tests (average ten-fold cross-validation area under the curve 0.85 ± 0.09, recall 0.80 ± 0.13, precision 0.74 ± 0.13) outperformed those trained to history and imaging (area under the curve 0.66 ± 0.17), or history alone (area under the curve 0.61 ± 0.14). </p> <p><strong>Conclusion: </strong>A novel computational pipeline accurately predicted long-term atrial fibrillation recurrence in individual patients by combining outcome data with patient-specific acute simulation response. This technique could help to personalise selection for atrial fibrillation ablation.</p> <p><strong>Dataset Description: </strong>We include surface meshes in vtk format, consisting of the nodes, triangular elements, the atrial coordinate fields defined on the nodes, and the endocardial and epicardial fibre fields defined on the elements. </p> <p>We also include universal atrial coordinate fields alpha and beta, which are a lateral-septal coordinate and posterior-anterior coordinate for the LA. More details on the coordinate construction are given in our manuscript and <a href="https://www.ncbi.nlm.nih.gov/pubmed/31026761">https://www.ncbi.nlm.nih.gov/pubmed/31026761</a>. These coordinates can be used for registering datasets. </p> <p><strong>Publication</strong>: https://pubmed.ncbi.nlm.nih.gov/35089057/</p>
Data from: A time series model for estimating temporal variation in phenotypic selection on laying dates in a Dutch great tit population
[No abstract entered]
Data for: Modeling climate-driven range shifts in populations of two bird species limited by habitat independent of climate
<p>Ranges of species around the world are expected to contract in response to climate change. Species distribution models (SDMs) are a powerful tool for predicting changes in habitat availability, but the variables selected to create SDMs influence their performance. In addition to climate, habitat characteristics and species traits can play a role in predicted species distribution. In this paper, we consider how variable selection influences the accuracy of SDMs when applied to isolated subpopulations of two widely distributed bird species: the great gray owl (<em>Strix</em> <em>nebulosa</em>) and the willow flycatcher (<em>Empidonax</em> <em>traillii</em>). In the Sierra Nevada of California, these species are restricted largely to discrete patches of meadow habitat within a forest matrix, providing the potential to identify specific locations to target conservation efforts. We contrast predictions made by SDMs that consider climatic variables alone with those that incorporate both climate and geophysical variables. Adding geophysical variables resulted in differing model predictions. For willow flycatchers, adding geophysical variables improved predictive performance. In the case of great gray owls, models with and without geophysical variables had nearly identical performance under historical conditions but differed starkly in their predictions. The full model (climatic and geophysical variables) predicted habitat availability to decrease moderately, whereas the climate-only model predicted nearly complete loss of favorable habitat by 2099. The climate-only model is consistent with expectations based on previous SDMs of birds across North America, but previous studies also assume homogeneity in species traits and range-wide habitat requirements. The full model appears more consistent with recent trends in great gray owl numbers in the Sierra Nevada specifically, where the population has remained relatively stable over recent decades. Given contradictions in our model predictions, care should be taken when trying to apply similar SDM models to other systems.</p>
Supplementary material 3 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235
Weighted mean SDMs for individual algorithms and evaluation statistics (biomod2) :
Supplementary material 2 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235
Full readout for the MaxEnt northern quoll SDM :
Supplementary material 1 from: Molloy SW, Davis RA, Dunlop JA, van Etten EJB (2017) Applying surrogate species presences to correct sample bias in species distribution models: a case study using the Pilbara population of the Northern Quoll. Nature Conservation 18: 27-46. https://doi.org/10.3897/natureconservation.18.12235
GIS data sets used in variable assessments and map of Pilbara vegetation systems :
An integrated population model of a high-density coyote population in South Carolina
Open the record for dataset details and reuse information.
A three-weight surface modeling approach for optimizing small-scale population disaggregation
<p><span>In recent decades, gridded population data at fine scales has become a popular data source for assessing and monitoring the Sustainable Development Goals (SDGs). However, current population disaggregation methods are facing challenges in generating high-precision population grids for small areas with limited data. To fill this gap, we proposed a lightweight population gridding method that combines basic dasymetric mapping and point-based surface modeling, named three-weight surface modeling. In this method, there are three weights designed to describe the population spatial heterogeneity from different perspectives. The first weight is building-volume weight, which is equivalent to the preliminary results of population assignment based on building volume information. The second weight, POI-center weight, incorporates POI categories and aggregation patterns to express the centers with high population density, which is calculated based on the neighborhood accumulation rule of Spearman's correlation coefficients between POIs and population size. The third weight called POI-distance weight, indicates different rates of population decay with distance from high-density centers. The three-weight surface model allows us to dynamically adjust the parameters so as to correct the building-volume weight according to the remaining two POI-related weights for a more accurate population surface. After analyzing the census population and the disaggregation results of 544 villages in three counties (Huishui, Luodian and Pingtang) in southern Guizhou Province, China, we found that the customized three-weight model constructed using the local parameter groups demonstrated better accuracy performance compared to separate dasymetric mapping or point-based surface modeling. Meanwhile, the 10-m population grid generated by the local parameter model (LPTW-POP) exhibited higher resolution and lower errors (RMSE, MAE and MRE) than widely used gridded population datasets like LandScan, WorldPop and GHS-POP.</span></p>
Supplementary material 7 from: Lommen STE, Jongejans E, Leitsch-Vitalos M, Tokarska-Guzik B, Zalai M, Müller-Schärer H, Karrer G (2018) Time to cut: population models reveal how to mow invasive common ragweed cost-effectively. NeoBiota 39: 53-78. https://doi.org/10.3897/neobiota.39.23398
Deterministic population models per reference data set (graphic results, population dynamics) :
Supplementary material 3 from: Lommen STE, Jongejans E, Leitsch-Vitalos M, Tokarska-Guzik B, Zalai M, Müller-Schärer H, Karrer G (2018) Time to cut: population models reveal how to mow invasive common ragweed cost-effectively. NeoBiota 39: 53-78. https://doi.org/10.3897/neobiota.39.23398
Burial experiments (location table, methods, graphic results) :
Supplementary material 5 from: Lommen STE, Jongejans E, Leitsch-Vitalos M, Tokarska-Guzik B, Zalai M, Müller-Schärer H, Karrer G (2018) Time to cut: population models reveal how to mow invasive common ragweed cost-effectively. NeoBiota 39: 53-78. https://doi.org/10.3897/neobiota.39.23398
Parametrisation of population models of experimental mowing treatments (model parameterisation) :
Supplementary material 4 from: Lommen STE, Jongejans E, Leitsch-Vitalos M, Tokarska-Guzik B, Zalai M, Müller-Schärer H, Karrer G (2018) Time to cut: population models reveal how to mow invasive common ragweed cost-effectively. NeoBiota 39: 53-78. https://doi.org/10.3897/neobiota.39.23398
Parametrisation of population models of unmanaged references (model parameterisation) :
Supplementary material 2 from: Lommen STE, Jongejans E, Leitsch-Vitalos M, Tokarska-Guzik B, Zalai M, Müller-Schärer H, Karrer G (2018) Time to cut: population models reveal how to mow invasive common ragweed cost-effectively. NeoBiota 39: 53-78. https://doi.org/10.3897/neobiota.39.23398
Demographic survey of reference populations (location table, methods) :
Supplementary material 1 from: Lommen STE, Jongejans E, Leitsch-Vitalos M, Tokarska-Guzik B, Zalai M, Müller-Schärer H, Karrer G (2018) Time to cut: population models reveal how to mow invasive common ragweed cost-effectively. NeoBiota 39: 53-78. https://doi.org/10.3897/neobiota.39.23398
Analysis of mowing experiment data (statistical analysis of empirical data) :
Mercury and Arsenic muscle concentration data as used in "Mixed model approaches can leverage database information to improve the estimation of size-adjusted contaminant concentrations in fish populations"
<p>These mercury and arsenic concentration data, as recieved from Gretchen Lescord, and downloaded from the MOE fish contaminant database, were used to create the publication Mixed model approaches can leverage database information to improve the estimation of size-adjusted contaminant concentrations in fish populations. The markdown and code used for the analysis of this data can be found on Github at https://github.com/GLFC-WET/HGAS_master.</p>
Linked collectors and determiners for: Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data.
Natural history specimen data linked to collectors and determiners held within, "Taxonomic revision of the southern hemisphere pygmy forget-me-not group (Myosotis; Boraginaceae) based on morphological, population genetic and climate-edaphic niche modelling data". Claims or attributions were made on Bionomia by volunteer Scribes, <a href="https://bionomia.net/dataset/32977d5c-8f02-4d26-8f75-ce56bf36f1fa">https://bionomia.net/dataset/32977d5c-8f02-4d26-8f75-ce56bf36f1fa</a> using specimen data from the dataset aggregated by the Global Biodiversity Information Facility, <a href="https://gbif.org/dataset/32977d5c-8f02-4d26-8f75-ce56bf36f1fa">https://gbif.org/dataset/32977d5c-8f02-4d26-8f75-ce56bf36f1fa</a>. Formatted as a Frictionless Data package.
Data from: A game-theoretical model of kleptoparasitic behaviour in an urban gull (Laridae) population.
Kleptoparasitism (food stealing) is a significant behaviour for animals that forage in social groups as it permits some individuals to obtain resources whilst avoiding the costs of searching for their own food. Evolutionary game theory has been used to model kleptoparasitism, with a series of differential equation based compartmental models providing significant theoretical insights into behaviour in kleptoparasitic populations. In this paper we apply this compartmental modelling approach to kleptoparasitic behaviour in a real foraging population of urban gulls (Laridae). Field data was collected on kleptoparasitism and a model developed that incorporated the same kleptoparasitic and defensive strategies available to the study population. Two analyses were conducted: 1. An assessment of whether the density of each behaviour in the population was at an equilibrium. 2. An investigation of whether individual foragers were using Evolutionarily Stable Strategies (ESS) in the correct environmental conditions. The results showed the density of different behaviours in the population could be at an equilibrium at plausible values for handling time and fight duration. Individual foragers used aggressive kleptoparasitic strategies effectively in the correct environmental conditions but some individuals in those same conditions failed to defend food items. This was attributed to the population being composed of three species that differed in competitive ability. These competitive differences influenced the strategies that individuals were able to use. Rather than gulls making poor behavioural decisions these results suggest a more complex three-species model is required to describe the behaviour of this population.
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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.