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464 results for “model structure”
Data from: Fuelwood sustainability revisited: integrating size structure and resprouting into a spatially realistic fuelshed model
Much concern has been expressed about the sustainability of fuelwood harvesting in Africa. Most models predict that demand will outstrip supply within a few decades, resulting in severe deforestation. However, despite substantial impacts of harvesting on woody vegetation structure, the 'fuelwood crisis' predicted since the 1970s has not materialized. We propose that this is at least partially because regeneration through coppicing has been poorly accounted for in most models. We developed a local fuelwood model that is demographically and spatially explicit, and that incorporates coppice dynamics. The model simulates the dynamics of multiple stem size classes (seedling, sapling, pole and adult), the harvesting decisions of villagers based on fuelwood availability and village demand across the landscape. Importantly, we specify size-dependent coppice production of cut stems, and the probability of progression of coppice shoots into larger size classes, after accounting for self-thinning of shoots. In general, our model projections for a rural South African savanna system suggest that current levels of harvesting (barring changes in human population size) are relatively sustainable. Declines in total woody biomass were predicted to be modest (˜20%), and the loss of intact stems of sapling size was predicted to be more than offset by increases in coppiced stems. Synthesis and applications. The results from our local fuelwood model clearly demonstrate that the impact of deforestation and wood removal on tree populations and wood resources is strongly influenced by the resprouting ability of trees. This highlights the importance of considering coppice dynamics when assessing the sustainability of wood harvesting. Our model is not system specific, and can be transferred to other systems, with the relevant parameters and geographic information system layers specified. Because of the transferability of this model, it can help address key international concerns about deforestation and sustainable fuelwood management.
Data from: Landscape resistance and habitat combine to provide an optimal model of genetic structure and connectivity at the range margin of a small mammal
We evaluated the effect of habitat and landscape characteristics on the population genetic structure of the white-footed mouse. We develop a new approach that uses numerical optimization to define a model that combines site differences and landscape resistance to explain the genetic differentiation between mouse populations inhabiting forest patches in southern Québec. We used ecological distance computed from resistance surfaces with Circuitscape to infer the effect of the landscape matrix on gene flow. We calculated site differences using a site index of habitat characteristics. A model that combined site differences and resistance distances explained a high proportion of the variance in genetic differentiation and outperformed models that used geographical distance alone. Urban and agriculture related land uses were, respectively, the most and the least resistant landscape features influencing gene flow. Our method detected the effect of rivers and highways as highly resistant linear barriers. The density of grass and shrubs on the ground best explained the variation in the site index of habitat characteristics. Our model indicates that movement of white-footed mouse in this region is constrained along routes of low resistance. Our approach can generate models that may improve predictions of future northward range expansion of this small mammal.
Data from: Integration of anatomy ontologies and evo-devo using structured Markov models suggests a new framework for modeling discrete phenotypic traits
Modeling discrete phenotypic traits for either ancestral character state reconstruction or morphology-based phylogenetic inference suffers from ambiguities of character coding, homology assessment, dependencies, and selection of adequate models. These drawbacks occur because trait evolution is driven by two key processes – hierarchical and hidden – which are not accommodated simultaneously by the available phylogenetic methods. The hierarchical process refers to the dependencies between anatomical body parts, while the hidden process refers to the evolution of gene regulatory networks underlying trait development. Herein, I demonstrate that these processes can be efficiently modeled using structured Markov models equipped with hidden states, which resolves the majority of the problems associated with discrete traits. Integration of structured Markov models with anatomy ontologies can adequately incorporate the hierarchical dependencies, while the use of the hidden states accommodates hidden evolution of gene regulatory networks and substitution rate heterogeneity. I assess the new models using simulations and theoretical synthesis. The new approach solves the long-standing "tail color problem," in which the trait is scored for species with tails of different colors or no tails. It also presents a previously unknown issue called the "two-scientist paradox," in which the nature of coding the trait and the hidden processes driving the trait's evolution are confounded; failing to account for the hidden process may result in a bias, which can be avoided by using hidden state models. All this provides a clear guideline for coding traits into characters. This paper gives practical examples of using the new framework for phylogenetic inference and comparative analysis.
Data from: Phylogenetic structural equation modelling reveals no need for an 'origin' of the leaf economics spectrum
The leaf economics spectrum (LES) is a prominent ecophysiological paradigm that describes global variation in leaf physiology across plant ecological strategies using a handful of key traits. Nearly a decade ago, Shipley et al. (2006) used structural equation modelling to explore the causal functional relationships among LES traits that give rise to their strong global covariation. They concluded that an unmeasured trait drives LES covariation, sparking efforts to identify the latent physiological trait underlying the 'origin' of the LES. Here, we use newly developed phylogenetic structural equation modelling approaches to reassess these conclusions using both global LES data as well as data collected across scales in the genus Helianthus. For global LES data, accounting for phylogenetic non-independence indicates that no additional unmeasured traits are required to explain LES covariation. Across datasets in Helianthus, trait relationships are highly variable, indicating that global-scale models may poorly describe LES covariation at non-global scales.
Data from: Landscape models for nuclear genetic diversity and genetic structure in white-footed mice (Peromyscus leucopus)
Dramatic changes in the North American landscape over the last 12 000 years have shaped the genomes of the small mammals, such as the white-footed mouse (Peromyscus leucopus), which currently inhabit the region. However, very recent interactions of populations with each other and the environment are expected to leave the most pronounced signature on rapidly evolving nuclear microsatellite loci. We analyzed landscape characteristics and microsatellite markers of P. leucopus populations along a transect from southern Ohio to northern Michigan, in order to evaluate hypotheses about the spatial distribution of genetic heterogeneity. Genetic diversity increased to the north and was best approximated by a single-variable model based on habitat availability within a 0.5-km radius of trapping sites. Interpopulation differentiation measured by clustering analysis was highly variable and not significantly related to latitude or habitat availability. Interpopulation differentiation measured as FST values and chord distance was correlated with the proportion of habitat intervening, but was best explained by agricultural distance and by latitude. The observed gradients in diversity and interpopulation differentiation were consistent with recent habitat availability being the major constraint on effective population size in this system, and contradicted the predictions of both the postglacial expansion and core-periphery hypotheses.
Data from: Intra-population genomics in a model mutualist: population structure and candidate symbiosis genes under selection in Medicago truncatula
Bottom-up evolutionary approaches, including geographically-explicit population genomic analyses, have the power to reveal the mechanistic basis of adaptation. Here we conduct a population genomic analysis in the model legume, Medicago truncatula, in order to characterize population genetic structure and identify symbiosis-related genes showing evidence of spatially-variable selection. Using RAD-seq, we generated over 26,000 SNPs from 191 accessions from within three regions of the native range in Europe. Results from STRUCTURE analysis identify 5 distinct genetic clusters with divisions that separate east and west regions in the Mediterranean basin. Much of the genetic variation is maintained within sampling sites, and there is evidence for isolation by distance. Extensive linkage disequilibrium was identified, particularly within populations. We conducted genetic outlier analysis with FST-based genome scans and a bayesian modeling approach (PCAdapt). There were 70 core outlier loci shared between these distinct methods with one clear candidate symbiosis related gene, DMI1. This work sets that stage for functional experiments to determine the important phenotypes that selection has acted upon and complementary efforts in rhizobium populations.
Data from: Identifying drivers of breeding success in a long-distance migrant using structural equation modelling
In migrant animals, conditions encountered at various times and places throughout their annual cycle may affect breeding success. Yet, most studies so far have only investigated the effect of specific parts of the annual cycle, despite the importance to understand how different stages can interact and how these stages compare to intrinsic quality to properly modulate breeding success. Using a structural equation modelling approach, we investigated drivers of breeding success (migration cycle, individual quality, breeding conditions) in hoopoes (Upupa epops), a long-distant migrant. Our causal framework explained 75% of the variation in breeding success. The effect of the migration schedule was negligible, whereas the previous breeding attempt strongly influenced current breeding success. We suggest that the interplay of individual quality and environmental conditions during both previous and current breeding season may be more important drivers of breeding success than migration schedules, even in a long-distance migrant. We conclude that structural equation modeling is a promising tool to investigate causal relationships. Applied to hoopoes, we demonstrated that current breeding success is strongly linked to previous breeding success. Complementary analysis integrating weather and climate conditions during migration and the breeding season may provide a deeper and wider overview of the annual cycle of hoopoes and additional insights into the existence of carry-over effects in breeding success.
Linking socioeconomic inequalities and type 2 diabetes through obesity and lifestyle factors among Mexican adults: a structural equations modeling approach
<p><strong>Objective. </strong>To assess the association between type 2 diabetes (DM2) and socioeconomic inequalities, mediated by the contribution of body mass index (BMI), physical activity (PA), and diet (diet-DII). <strong>Materials and methods</strong>. We conducted a cross-sectional analysis using data of adults participating in the Diabetes Mellitus Survey of Mexico City. Socioeconomic and demographic characteristics as well as height and weight, dietary intake, leisure time activity and the presence of DM2 were measured. We fitted a structural equation model (SEM) with DM2 as the main outcome, and BMI, diet-DII and PA served as mediator variables between socioeconomic inequalities index (SII) and DM2. <strong>Results. </strong>The prevalence of DM2 was 13.6%. From the fitted SEM, each standard deviation increases in the SII was associated with increased scores of DM2 (β=0.174, <em>P</em><0.001). <strong>Conclusion. </strong>The results in the present study show how high scores in the index of SII may influence the presence of DM2.</p>
Data from: Ocean circulation model predicts high genetic structure in a long-lived pelagic developer
Understanding the movement of genes and individuals across marine seascapes is a long-standing challenge in marine ecology, and can inform our understanding of local adaptation, the persistence and movement of populations, and the spatial scale of effective management. Patterns of gene flow in the ocean are often inferred based on population genetic analyses coupled with knowledge of species' dispersive life histories. However, genetic structure is the result of time-integrated processes, and may not capture present-day connectivity between populations. Here we use a high-resolution oceanographic circulation model to predict larval dispersal along the complex coastline of western Canada that includes the transition between two well-studied zoogeographic provinces. We simulate dispersal in a benthic sea star with a 6-10 week pelagic larval phase, and test predictions of this model against previously observed genetic structure including a strong phylogeographic break within the zoogeographical transition zone. We also test predictions with new genetic sampling in a site within the phylogeographic break. We find that the coupled genetic and circulation model predicts the high degree of genetic structure observed in this species, despite its long pelagic duration. High genetic structure on this complex coastline can thus be explained through ocean circulation patterns which tend to retain passive larvae within 20 - 50 km of their parents, suggesting a necessity for close-knit design of Marine Protected Area networks.
Data from: Host resistance, population structure and the long-term persistence of bubonic plague: contributions of a modelling approach in the Malagasy focus
Although bubonic plague is an endemic zoonosis in many countries around the world, the factors responsible for the persistence of this highly virulent disease remain poorly known. Classically, the endemic persistence of plague is suspected to be due to the coexistence of plague resistant and plague susceptible rodents in natural foci, and/or to a metapopulation structure of reservoirs. Here, we test separately the effect of each of these factors on the long-term persistence of plague. We analyse the dynamics and equilibria of a model of plague propagation, consistent with plague ecology in Madagascar, a major focus where this disease is endemic since the 1920s in central highlands. By combining deterministic and stochastic analyses of this model, and including sensitivity analyses, we show that (i) endemicity is favoured by intermediate host population sizes, (ii) in large host populations, the presence of resistant rats is sufficient to explain long-term persistence of plague, and (iii) the metapopulation structure of susceptible host populations alone can also account for plague endemicity, thanks to both subdivision and the subsequent reduction in the size of subpopulations, and extinction-recolonization dynamics of the disease. In the light of these results, we suggest scenarios to explain the localized presence of plague in Madagascar.
Virus Capsomer Model [wedge-shaped structure 1]
PICORNAVIRIDAE – Designing Model and Visualization Concepts in Virology Educational T=3 [and T=pseudo3] Virus Structure Model [assembly unit 02/08] Common schematic representation for visualizing "wedge-shaped" asymmetric units. These are formed by virus protein subunits, five asymmetric units [structural units] combined form a capsomere [advancement: visual subdivision by gaps]. Source: Objaverse 1.0 / Sketchfab
Virus Capsomer Model [surface structure 3]
PICORNAVIRIDAE – Designing Model and Visualization Concepts in Virology Educational T=3 [and T=pseudo3] Virus Structure Model [assembly unit 07/08] Protein structure [modified] of a hepatitis A virus, projected on a pentagonal pyramid [RCSB PDB: 4QPG] for a more precise surface visualization of a selected picornavirus. Source: Objaverse 1.0 / Sketchfab
Data for model analysis in "Beyond the growth rate of cosmic structure: Testing modified gravity models with an extra degree of freedom", arXiv:1502.03710
<p>SQLite databases containing theoretical predictions for the model comparison in arXiv:1502:03710.</p>
Supplementary information for: "A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling".
<p>This is supplementary data for F1000Research article: A voltage-dependent fluorescent indicator for optogenetic applications, archaerhodopsin-3: Structure and optical properties from in silico modeling.</p> <p>Here are files for modeling archaerhodopsin-3 with I-TASSER, Medeller and RosettaCM algorithms, structure postprocessing and spectra calculations.</p> <p>Please, refer to the readme.txt for the description.</p>
MD data for the article "Structure comparison of beta amyloid peptide Aβ 1-42 isoforms. Molecular dynamics modeling" by Anna P. Tolstova, Alexander A. Makarov, Alexei A. Adzhubei.
<p>There are CMD and REMD trajectories for Aβ isoforms discussed in the paper together with final coordinate files for these trajectories. The resulting dataset of modeled structures includes wild type Aβ42, isoD7, pS8, D7H and H6R-Aβ42, and wild type Aβ16, isoD7, pS8, D7H and H6R-Aβ16.</p>
Data and structures for "How accurately can we predict binding poses with AlphaFold models?
<p>Contains structures generated by AlphaFold, models from GPCRdb, and structures of proteins from PDB. </p> <p>Additionally computed rmsds for pockets, backbone, and poses, and scripts to create figures.</p> <p> </p> <p> </p>
DynamicBind: Predicting ligand-specific protein-ligand complex structure with a deep equivariant generative model.
<p>test and training data.</p>
Modelling Cell Shape in 3D Structured Environments: A Quantitative Comparison with Experiments
<p>This repository contains experimental data and computer scripts for the following publication: Link R, Jaggy M, Bastmeyer M, Schwarz US (2024) Modelling cell shape in 3D structured environments: A quantitative comparison with experiments. PLoS Comput Biol 20(4): e1011412. https://doi.org/10.1371/journal.pcbi.1011412</p> <p>There are two directories, “data” and “scripts”.</p> <p> <strong>1) </strong><strong>Directory data</strong></p> <p> WRL-files for experimental data generated with Imaris from Zeiss image files.</p> <p>The WRL-files can be converted to STL-files with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>).</p> <p>The STL-files can be converted to FE-files for the SurfaceEvolver with our script CreateFeFile.py.</p> <p> The WRL-files are named according to the scaffolds:</p> <p>L*.wrl cells in L-shaped scaffolds (n=6).</p> <p>V*.wrl cells in V-shaped scaffolds (n=7).</p> <p>TRight*.wrl cells in right-triangle scaffolds (n=3).</p> <p>TEqui*.wrl cells in equilateral-triangle scaffolds (n=4).</p> <p><strong>2) </strong><strong>Directory scripts</strong></p> <p>ClusterSurfaceLinearPlugin: New CompuCell3D plugin needed to calculate linear surface energy functional for cells with nucleus (using the cluster concept).</p> <p>CompuCell3DScript: Hamiltonian_Comparison.cc3d is the main script for our simulations, uses the directory “Simulation”.</p> <p>CreateFeFile.py: generates surface evolver FE-file from STL-file. A STL-file can be generated from a WRL-file e.g. with MeshLab (<a href="https://www.meshlab.net/">https://www.meshlab.net</a>). </p> <p>SphericalHarmonicsAnalysis.ipynb: Python notebook that calculates the Fourier spectrum and Delta_30, needs WRL-file as input.</p> <p> </p>
Antibody-Antigen Models for McCoy 2024 Paper: "A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases"
<p>Up to the top 20 models generated for each method tested in the 2024 Paper: "A Comparison of Antibody-Antigen Complex Sequence-to-Structure Prediction Methods and their Systematic Biases"</p>
Source data for manuscript(De novo protein design with a denoising diffusion network independent of pre-trained structure prediction models)
<p>This respository contains the source data for figure and supplementary figure in manuscript(SCUBA-D).</p>
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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.