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310 results for “State Model”
Arctic Ocean state estimates for 2014 using the GECCO model
<p>The dataset contains the 2014 data of a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
Arctic Ocean state estimates for 2007-2016 using the GECCO model
<p>The dataset contains a 10-year ocean synthesis (2007-2016) obtained by assimilating available observations of sea ice and ocean parameters into the GECCO model. Data from, among others, several satellite programs such as AMSRE, SSMI, AMSR2, Envisat, Jason, Cryosat., AVHRR, and SMOS, and available moorings in the Davis Strait, the Bering Strait, the Fram Strait, the Barents Sea Opening, and by the Nansen and Amundsen Basins Observational System (NABOS), the North Pole Environmental Observatory (NPEO), and the Beaufort Gyre Exploration Project (BGEP) project. A detailed description can be found in Lyu et al., 2020.</p> <p>Guokun Lyu, Nuna Serra, Armin Koehl and Detlef Stammer, 2020. INTAROS Deliverable 6.4 Ice-ocean statistics and state estimation V1. https://intaros.nersc.no/sites/intaros.nersc.no/files/D6.4_INTAROS_Data_assimilation_v1.3.pdf </p>
A multi-state occupancy modeling framework for robust estimation of disease prevalence in multi-tissue disease systems
<p>1. Given the public health, economic, and conservation implications of zoonotic diseases, their effective surveillance is of paramount importance. The traditional approach to estimating pathogen prevalence as the proportion of infected individuals in the population is biased because it fails to account for imperfect detection. A statistically robust way to reduce bias in prevalence estimates is to obtain repeated samples (or sample many tissues in multi-tissue disease systems) and to apply statistical methods that account for imperfect detection and permit the interdependence of the infection process across multiple tissues.</p> <p>2. We developed a multi-state occupancy modeling framework which considers two scenarios about the infection process, one where no assumptions about the dependencies among the tissues are made (general), and another where dependence among tissues is not permitted (constrained).</p> <p>3. We applied this model to pseudorabies virus (PrV) DNA detection data obtained from whole blood; and oral, nasal, and genital mucosa of 510 feral swine (Sus scrofa) during the years 2014-2016 in Florida, USA.</p> <p>4. The constrained model was better supported by data. Estimated PrV prevalence varied among tissues, ranging from to 0.06 (CI: 0.02-0.14) in genital to 0.54 (CI: 0.14-0.82) in nasal tissue. Probability of PrV detection ranged from 0.11 (CI: 0.06-0.18) in nasal to 0.51 (CI: 0.21-0.81) in genital tissue. Estimates of PrV prevalence after accounting for imperfect detection were higher than the naïve estimates for all four tissues.</p> <p>5. PrV prevalence was not affected by the age or sex of the animal or the year of sampling, but prevalence increased as drought severity increased.</p> <p>6. The conditional probability of detecting PrV given infection in at least one tissue type within an individual was highest for nasal tissue, suggesting that nasal is the best tissue to sample for PrV surveillance if only one tissue can be sampled, at least for systems with tissue-specific prevalence and detection probabilities similar to ours.</p> <p>7. We found that pathogen prevalence in multi-tissue disease systems can vary across tissues. Our results emphasize the importance of sampling multiple tissues, and the application of robust statistical models to account for imperfect detection in the surveillance of systemic diseases. The multi-state modeling framework is broadly applicable to the surveillance of pathogens that infect multiple tissues and where the infection status or detection of the pathogen in one tissue may depend on the infection status of the pathogen in other tissues). 29-Jul-2020</p>
Data from: Alternative tree-cover states of the boreal ecosystem: a conceptual model
Aim: Previous analyses of remotely-sensed data detected the multimodality of the tree-cover distribution of the boreal forest, and identified areas with potentially alternative tree-cover states. This paper aims at investigating the causes of multimodality and multistability of the boreal forest, their influence on the asymmetric tree-species distribution between Eurasia and North America, and whether multistability could be associated with recent greening trends in LAI/NDVI. Location: Eurasian and North American boreal forests. Time period: 2000--2010. Major taxa studied: Boreal forest plant functional types. Methods: We employ a conceptual model based on tree-species competition to simulate the sensitivity of tree cover to stochastic disturbances and to changes in environmental factors. We include different plant functional types based on survival adaptations, and force the model with remotely-sensed environmental data. We analyse the model as a dynamical system. We use metrics from statistics and information theory to compare the detection of alternative tree-cover states and greening trends in LAI/NDVI. Results: We find that multimodality and multistability can emerge through competition between different plant functional types. Additionally, our model is able to reproduce the asymmetry in tree-species distribution between Eurasia and North America. Moreover, changes in permafrost distribution can be associated with phenomenological bifurcation points of the model. Finally, we find that the detection of multistable areas is not affected by recent vegetation trends, whereas shifts between alternative states could have affected the greening trends. Main conclusions: Tree-cover multistability in the boreal region can emerge through competition between species subject to periodic disturbances. Changes in permafrost thaw and distribution could be responsible for the asymmetry in tree-species distribution between North America and Eurasia. Climate change and permafrost degradation could cause shifts in tree-cover states and dominant species. Recent vegetation greening trends in multistable areas could have been affected by shifts between alternative states.
Data from: Cell death and survival due to cytotoxic exposure modeled as a two-state Ising system
<p>Cancer chemotherapy agents are assessed for their therapeutic utility primarily by their ability to cause apoptosis of cancer cells and their potency is given by an IC50 value. Chemotherapy uses both target-specific and systemic-action drugs and drug combinations to treat cancer. It is important to judiciously choose a drug type, its dosage, and schedule for optimized drug selection and administration. Consequently, the precise mathematical formulation of cancer cells response to chemotherapy may assist in the selection process. In this paper, we propose a mathematical description of the cancer cell response to chemotherapeutic agent exposure based on a time-tested physical model of two-state multiple-component systems near criticality. We describe the Ising model methodology and apply it to a diverse panel of cytotoxic drugs administered against numerous cancer cell lines in a dose-response manner. The analyzed dataset was generated by the Netherlands Translational Research Center B.V.(Oncolines). This approach allows for an accurate and consistent analysis of cytotoxic agents' effects on cancer cell lines and reveals the presence or absence of the bystander effect through the interaction constant. By calculating the susceptibility function, we see the value of IC50 coinciding with the peak of this measure of the system's sensitivity to external perturbations.</p>
Data from: A two-state model of tree evolution and its applications to alu retrotransposition
Models of tree evolution have mostly focused on capturing the cladogenesis processes behind speciation. Processes that derive the evolution of genomic elements, such as repeats, are not necessarily captured by these existing models. In this paper, we design a model of tree evolution that we call the dual-birth model, and we show how it can be useful in studying the evolution of short Alu repeats found in the human genome in abundance. The dual-birth model extends the traditional birth-only model to have two rates of propagation, one for active nodes that propagate often, and another for inactive nodes, that with a lower rate, activate and start propagating. Adjusting the ratio of the rates controls the expected tree balance. We present several theoretical results under the dual-birth model, introduce parameter estimation techniques, and study the properties of the model in simulations. We then use the dual-birth model to estimate the number of active Alu elements and their rates of propagation and activation in the human genome based on a large phylogenetic tree that we build from close to one million Alu sequences.
Data from: State-space modelling of the flight behaviour of a soaring bird provides new insights to migratory strategies
1. Characterizing the spatiotemporal variation of animal behaviour can elucidate the way individuals interact with their environment and allocate energy. Increasing sophistication of tracking technologies paired with novel analytical approaches allows the characterisation of movement dynamics even when an individual is not directly observable. 2. In this study, high-resolution movement data collected via global positioning system (GPS) tracking in three dimensions were paired with topographical information and used in a Bayesian state-space model to describe the flight modes of migrating golden eagles (Aquila chrysaetos) in eastern North America. 3. Our model identified five functional behavioural states, two of which were previously undescribed variations on thermal soaring. The other states comprised gliding, perching and orographic soaring. States were discriminated by movement features in the horizontal (step length and turning angle) and vertical (change in altitude) planes, and by the association with ridgelines promoting wind deflection. Tracked eagles spent 2%, 31%, 38%, 9% and 20% of their day time in directed thermal soaring, gliding, convoluted thermal soaring, perching and orographic soaring, respectively. The analysis of the relative occurrence of these flight modes highlighted yearly, seasonal, age, individual and sex differences in flight strategy and performance. Particularly, less energy-efficient orographic soaring was more frequent in autumn, when thermals were less available. Adult birds were also better at optimising energy efficiency than sub-adults. 4. Our approach represents the first example of a state-space model for bird flight mode using altitude data in conjunction with horizontal locations, and is applicable to other flying organisms where similar data are available. The ability to describe animal movements in a three-dimensional habitat is critical to advance our understanding of the functional processes driving animals' decisions.
Data from: Applying ecological site concepts and state-and-transition models to a grazed riparian rangeland
Ecological sites and state-and-transition models are useful tools for generating and testing hypotheses about drivers of vegetation composition in rangeland systems. These models have been widely implemented in upland rangelands, but comparatively little attention has been given to developing ecological site concepts for rangeland riparian areas, and additional environmental criteria may be necessary to classify riparian ecological sites. Between 2013 and 2016, fifteen study reaches on five creeks were studied at Tejon Ranch in southern California. Data were collected to describe the relationship between riparian vegetation composition, environmental variables, and livestock management; and to explore the utility of ecological sites and state-and-transition models for describing riparian vegetation communities and for creating hypotheses about drivers of vegetation change. Hierarchical cluster analysis was used to classify the environmental and vegetation data (15 stream reaches 4 years) into two ecological sites and eight community phases that comprised three vegetation states. Classification and regression tree (CART) analysis was used to determine the influence of abiotic site variables, annual precipitation, and cattle activity on vegetation clusters. Channel slope explained the greatest amount of variation in vegetation clusters; however, soil texture, geology, watershed size, and elevation were also selected as important predictors of vegetation composition. The classification tree built with this limited set of abiotic predictor variables explained 90% of the observed vegetation clusters. Cattle grazing and annual precipitation were not linked to qualitative differences in vegetation. Abiotic variables explained almost all of the observed riparian vegetation dynamics—and the divisions in the CART analysis corresponded roughly to the ecological sites—suggesting that ecological sites are well-suited for understanding and predicting change in this highly variable system. These findings support continued development of riparian ecological site concepts and state-and-transition models to aid decision making for conservation and management of rangeland riparian areas.
Data from: Rate heterogeneity across Squamata, misleading ancestral state reconstruction and the importance of proper null model specification
The binary-state speciation and extinction (BiSSE) model has been used in many instances to identify state-dependent diversification and reconstruct ancestral states. However, recent studies have shown that the standard procedure of comparing the fit of the BiSSE model to constant-rate birth–death models often inappropriately favours the BiSSE model when diversification rates vary in a state-independent fashion. The newly developed HiSSE model enables researchers to identify state-dependent diversification rates while accounting for state-independent diversification at the same time. The HiSSE model also allows researchers to test state-dependent models against appropriate state-independent null models that have the same number of parameters as the state-dependent models being tested. We reanalyse two data sets that originally used BiSSE to reconstruct ancestral states within squamate reptiles and reached surprising conclusions regarding the evolution of toepads within Gekkota and viviparity across Squamata. We used this new method to demonstrate that there are many shifts in diversification rates across squamates. We then fit various HiSSE submodels and null models to the state and phylogenetic data and reconstructed states under these models. We found that there is no single, consistent signal for state-dependent diversification associated with toepads in gekkotans or viviparity across all squamates. Our reconstructions show limited support for the recently proposed hypotheses that toepads evolved multiple times independently in Gekkota and that transitions from viviparity to oviparity are common in Squamata. Our results highlight the importance of considering an adequate pool of models and null models when estimating diversification rate parameters and reconstructing ancestral states.
Data from: Population dynamics of an Arctiid caterpillar-tachinid parasitoid system using state-space models
1. Population dynamics of insect host–parasitoid systems are important in many natural and managed ecosystems and have inspired much ecological theory. However, ecologists have a limited knowledge about the relative strengths of species interactions, abiotic effects and density dependence in natural host–parasitoid dynamics. Statistical time-series analyses would be more informative by incorporating multiple factors, measurement error and noisy dynamics. 2. We use a novel maximum likelihood and model-selection analysis of a state-space model for host–parasitoid dynamics to examine 21 years of annual census data for woolly bear caterpillars (Platyprepia virginalis) and their locally host-specific tachinid parasitoids (Thelaira americana). 3. Caterpillar densities varied by three orders of magnitude and were driven by density dependence and precipitation from the previous March but not detectably by parasitoids, despite variable and sometimes high (>50%) parasitism. 4. Fly fluctuations, as estimated from per cent parasitism, were affected by density dependence and precipitation from the previous July. There was marginal evidence that host abundance drives fly fluctuations as a generic linear effect but no evidence for classical Nicholson–Bailey coupling. 5. The state-space model analysis includes new methods for likelihood calculation and allows a balanced consideration of effect magnitude and statistical significance in a nonlinear model with multiple alternative explanatory variables.
Data from: A dynamic state model of migratory behavior and physiology to assess the consequences of environmental variation and anthropogenic disturbance on marine vertebrates
Integrating behavior and physiology is critical to formulating new hypotheses on the evolution of animal life-history strategies. Migratory capital breeders acquire most of the energy they need to sustain migration, gestation and lactation before parturition. Therefore, when predicting the impact of environmental variation on such species, a mechanistic understanding of the physiology of their migratory behavior is required. Using baleen whales as a model system, we developed a dynamic state variable model that captures the interplay among behavioral decisions, energy, reproductive needs and the environment. We applied the framework to blue whales (Balaenoptera musculus) in the Eastern North Pacific Ocean, and explored the effects of environmental and anthropogenic perturbations on female reproductive success. We demonstrate the emergence of migration to track prey resources, enabling us to quantify the trade-offs among capital breeding, body condition, and metabolic expenses. We predict that periodic climatic oscillations affect reproductive success less than unprecedented environmental changes do. The effect of localized, acute anthropogenic impacts depended on whales' behavioral response to the disturbance; chronic, but weaker, disturbances had little effect on reproductive success. Because we link behavior and vital rates by modeling individuals' energetic budgets, we provide a general framework to investigate the ecology of migration and assess the population consequences of disturbance, while identifying critical knowledge gaps.
Data from: Fine-scale population dynamics in a marine fish species inferred from dynamic state-space models
Identifying the spatial scale of population structuring is critical for the conservation of natural populations and for drawing accurate ecological inferences. However, population studies often use spatially aggregated data to draw inferences about population trends and drivers, potentially masking ecologically relevant population sub-structure and dynamics. The goals of this study were to investigate how population dynamics models with and without spatial structure affect inferences on population trends and the identification of intrinsic drivers of population dynamics (e.g. density dependence). Specifically, we developed dynamic, age-structured, state-space models to test different hypotheses regarding the spatial structure of a population complex of coastal Atlantic cod (Gadus morhua). Data were from a 93-year survey of juvenile (age 0 and 1) cod sampled along >200 km of the Norwegian Skagerrak coast. We compared two models: one which assumes all sampled cod belong to one larger population, and a second which assumes that each fjord contains a unique population with locally determined dynamics. Using the best supported model, we then reconstructed the historical spatial and temporal dynamics of Skagerrak coastal cod. Cross-validation showed that the spatially structured model with local dynamics had better predictive ability. Furthermore, posterior predictive checks showed that a model which assumes one homogeneous population failed to capture the spatial correlation pattern present in the survey data. The spatially structured model indicated that population trends differed markedly among fjords, as did estimates of population parameters including density-dependent survival. Recent biomass was estimated to be at a near-record low all along the coast, but the finer scale model indicated that the decline occurred at different times in different regions. Warm temperatures were associated with poor recruitment, but local changes in habitat and fishing pressure may have played a role in driving local dynamics. More generally, we demonstrated how state-space models can be used to test evidence for population spatial structure based on survey time-series data. Our study shows the importance of considering spatially structured dynamics, as the inferences from such an approach can lead to a different ecological understanding of the drivers of population declines, and fundamentally different management actions to restore populations.
Towards Compliance Checking between Business Process Models and Lawful States of Objects
<p>Dataset from publication http://ceur-ws.org/Vol-1230/paper-03.pdf </p>
FIGURE 19 in Gerromorpha (Hemiptera: Heteroptera) of eastern Mato Grosso State, Brazil: checklist, new records, and species distribution modeling
FIGURE 19. Predicted distribution of Rhagovelia zela in Brazil based on literature and collections on eastern Mato Grosso.
FIGURE 18 in Gerromorpha (Hemiptera: Heteroptera) of eastern Mato Grosso State, Brazil: checklist, new records, and species distribution modeling
FIGURE 18. Predicted distribution of Rhagovelia tenuipes in Brazil based on literature and collections on eastern Mato Grosso.
FIGURE 15 in Gerromorpha (Hemiptera: Heteroptera) of eastern Mato Grosso State, Brazil: checklist, new records, and species distribution modeling
FIGURE 15. Predicted distribution of Rhagovelia hambletoni in Brazil based on literature and collections on eastern Mato Grosso.
FIGURE 14 in Gerromorpha (Hemiptera: Heteroptera) of eastern Mato Grosso State, Brazil: checklist, new records, and species distribution modeling
FIGURE 14. Predicted distribution of Platyvelia brachialis in Brazil based on literature and collections on eastern Mato Grosso.
FIGURE 12 in Gerromorpha (Hemiptera: Heteroptera) of eastern Mato Grosso State, Brazil: checklist, new records, and species distribution modeling
FIGURE 12. Predicted distribution of Microvelia ubatuba in Brazil based on literature and collections on eastern Mato Grosso.
FIGURE 11 in Gerromorpha (Hemiptera: Heteroptera) of eastern Mato Grosso State, Brazil: checklist, new records, and species distribution modeling
FIGURE 11. Predicted distribution of Microvelia quieta in Brazil based on literature and collections on eastern Mato Grosso.
FIGURE 9 in Gerromorpha (Hemiptera: Heteroptera) of eastern Mato Grosso State, Brazil: checklist, new records, and species distribution modeling
FIGURE 9. Predicted distribution of Microvelia argentata in Brazil based on literature and collections on eastern Mato Grosso.
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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.