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373 results for “stochastic”
Stochastic pulsing of gene expression enables the generation of spatial patterns in Bacillus subtilis biofilms
<p>Data extracted from confocal microscopy associated with the paper "Stochastic pulsing of gene expression enables the generation of spatial patterns in Bacillus subtilis biofilms"</p> <p>Stochastic pulsing of gene expression can generate phenotypic diversity in a genetically identical population of cells, but it is unclear whether it has a role in the development of multicellular systems. Here, we show how stochastic pulsing of gene expression enables spatial patterns to form in a model multicellular system, Bacillus subtilis bacterial biofilms. We use quantitative microscopy and time-lapse imaging to observe pulses in the activity of the general stress response sigma factor σ<sup>B</sup> in individual cells during biofilm development. Both σ<sup>B</sup> and sporulation activity increase in a gradient, peaking at the top of the biofilm, even though σ<sup>B</sup> represses sporulation. As predicted by a simple mathematical model, increasing σ<sup>B</sup> expression shifts the peak of sporulation to the middle of the biofilm. Our results demonstrate how stochastic pulsing of gene expression can play a key role in pattern formation during biofilm development.</p>
Data supplement for "Adaptive stochastic continuation with a modified lifting procedure applied to complex systems"
<p>This dataset contains the data and source files for the diagrams of the following preprint:</p> <p><em>Clemens Willers, Uwe Thiele, Andrew J. Archer, David J. B. Lloyd, and Oliver Kamps<br> Adaptive stochastic continuation with a modified lifting procedure applied to complex systems<br> arXiv preprint arXiv:2002.01705, 2020 </em></p> <p>Please follow the instructions given in 'Readme.txt'.</p>
Data presented in: Walking Drosophila navigate complex plumes using stochastic decisions biased by the timing of odor encounters
<p>How insects navigate complex odor plumes, where the location and timing of odor packets are uncertain, remains unclear. Here, we imaged complex odor plumes simultaneously with freely-walking flies, quantifying how behavior is shaped by encounters with individual odor packets. We found that navigation was stochastic, and did not rely on the continuous modulation of speed or orientation. Instead, flies turned stochastically with stereotyped saccades, whose direction was biased upwind by the timing of prior odor encounters, while the magnitude and rate of saccades remained constant. Further, flies used the timing of odor encounters to modulate the transition rates between walks and stops. In more regular environments, flies continuously modulate speed and orientation, even though encounters can still occur randomly due to animal motion. We find that in less predictable environments, where encounters are random in both space and time, walking flies instead navigate with random walks biased by encounter timing.</p>
The encoding of stochastic regularities is facilitated by action-effect predictions
<p>The data represent the raw EEG datasets generated and analysed during the current study, along with the Principal Component Analysis (PCA) solutions computed for the active and passive tasks, respectively. For details regarding the EEG preprocessing, statistical analyses, and results, please refer to the main manuscript body.</p>
Stochastic-GENeSYS-MOD Japan: Model, technology, demand, and renewable data
<p>This zenodo-repository contains a stochastic version of GENeSYS-MOD, an application to the Japanese energy system, and the underlying data.</p>
Multiple-batch spawning as a bet-hedging strategy in highly stochastic environments: an exploratory analysis of Atlantic cod
<p><span><span><span><span><span><span><span><span><span><span><span>Stochastic environments shape life-history traits and can promote selection for risk-spreading strategies, such as bet-hedging. Although the strategy has often been hypothesised to exist for various species, empirical tests providing firm evidence have been rare, mainly due to the challenge in tracking fitness across generations. Here, we take a 'proof of principle' approach to explore whether the reproductive strategy of multiple-batch spawning constitutes a bet-hedging. We used Atlantic cod (<i>Gadus morhua</i>) as the study species and parameterised an eco-evolutionary model, using empirical data on size-related reproductive and survival traits. To evaluate the fitness benefits of multiple-batch spawning (within a single breeding period), the mechanistic model separately simulated multiple-batch and single-batch spawning populations under temporally varying environments. We followed the arithmetic and geometric mean fitness associated with both strategies and quantified the mean changes in fitness under several environmental stochasticity levels. We found that, by spreading the environmental risk among batches, multiple-batch spawning increases fitness under fluctuating environmental conditions. The multiple-batch spawning trait is, thus, advantageous and acts as a bet-hedging strategy when the environment is exceptionally unpredictable. Our research identifies an analytically flexible, stochastic, life-history modelling approach to explore the fitness consequences of a risk-spreading strategy and elucidates the importance of evolutionary applications to life-history diversity. </span></span></span></span></span></span></span></span></span></span></span></p>
Data from: When are extinctions simply bad luck? rarefaction as a framework for disentangling selective and stochastic extinctions
1. A key challenge in conservation biology is that not all species are equally likely to go extinct when faced with a disturbance. Traditionally, differences in species extinction risk are considered a form of extinction selectivity, a nanrondom process by which species' extinction risks are associated with their traits. While selectivity clearly contributes to varation in extinction among taxa, it is also clear that rare species are more likely to go extinct than are common species. While obvious, this law of extinction suggests that random chance, operating on species abundance, plays an important role in the extinction process. Unless ecologists and conservation biologists can disentangle random and nonrandom extinction processes, then the prediction and prevention of future extinctions will continue to be an elusive challenge. 2. We suggest that a modified version of a common null model procedure, rarefaction, can be used to disentangle the influence of stochastic species loss from selective nonrandom processes. To this end we applied a rarefaction based null model to three published data sets to characterize the influence of species rarity in driving biodiversity loss following three disturbance events: i) disease-associated bat declines; ii) disease-associated amphibian declines; and iii) habitat loss and invasive species-associated gastropod declines. For each case study, we used rarefaction to generate null expectations of stochastic biodiversity loss and species-specific extinction probabilities. 3. In each of our case studies we find evidence for random and nonrandom (selective) extinctions. Our findings highlight the importance of explicitly considering that some species extinctions are the result of stochastic processes, i.e., bad luck. 4. Policy Implications If there is a first law of extinction, it is that rare species are most likely than common species to go extinct. We suggest that taking this law into account in analyses of extinction risk is critical to the identification of selective extinctions. Our results suggest that rarefaction can be used to identify nonrandom decline, extirpation, and extinction events and provide an important baseline comparison point for future extinction analyses.12-Jul-2019
Data from: Interacting effects of unobserved heterogeneity and individual stochasticity in the life-history of the Southern fulmar
1.Individuals are heterogeneous in many ways. Some of these differences are incorporated as individual states (e.g., age, size, breeding status) in population models. However, substantial amounts of heterogeneity may remain unaccounted for, due to unmeasurable genetic, maternal, or environmental factors. 2.Such unobserved heterogeneity (UH) affects the behavior of heterogeneous cohorts via intra-cohort selection and contributes to inter-individual variance in demographic outcomes such as longevity and lifetime reproduction. Variance is also produced by individual stochasticity, due to random events in the life cycle of wild organisms, yet no study thus far has attempted to decompose the variance in demographic outcomes into contributions from unobserved heterogeneity and individual stochasticity for an animal population in the wild. 3.We developed a stage-classified matrix population model for the Southern fulmar breeding on Ile des Pétrels, Antarctica. We applied multi-event, multi-state markrecapture methods to estimate a finite mixture model accounting for UH in all vital rates and Markov chain methods to calculate demographic outcomes. Finally, we partitioned the variance in demographic outcomes into contributions from unobserved heterogeneity and individual stochasticity. 4.We identify three UH groups, differing substantially in longevity, lifetime reproductive output, age at first reproduction, and in the proportion of the life spent in each reproductive state. 14% of individuals at fledging have a delayed but high probability of recruitment and extended reproductive lifespan. 67% of individuals are less likely to reach adulthood, recruit late and skip breeding often but have the highest adult survival rate. 19% of individuals recruit early and attempt to breed often. They are likely to raise their offspring successfully, but experience a relatively short lifespan. Unobserved heterogeneity only explains a small fraction of the variances in longevity (5.9%), age at first reproduction (3.7%) and lifetime reproduction (22%). 5.UH can affect the entire life cycle, including survival, development, and reproductive rates, with consequences over the lifetime of individuals and impacts on cohort dynamics. The respective role of unobserved heterogeneity versus individual stochasticity varies greatly among demographic outcomes. We discuss the implication of our finding for the gradient of life-history strategies observed among species and argue that individual differences should always be accounted for in demographic studies of wild populations.
Data from: Rethinking 'normal': the role of stochasticity in the phenology of a synchronously breeding seabird
1. Phenological changes have been observed in a variety of systems over the past century. There is concern that, as a consequence, ecological interactions are becoming increasingly mismatched in time, with negative consequences for ecological function. 2. Significant spatial heterogeneity (inter-site) and temporal variability (inter-annual) can make it difficult to separate intrinsic, extrinsic, and stochastic drivers of phenological variability. The goal of this study was to understand the timing and variability of breeding phenology of Adélie penguins under fixed environmental conditions, and to use those data to identify a 'null model' appropriate for disentangling the sources of variation in wild populations. 3. Data on clutch initiation were collected from both wild and captive populations of Adélie penguins. Clutch initiation in the captive population was modeled as a function of year, individual, and age to better understand phenological patterns observed in the wild population. 4. Captive populations displayed as much inter-annual variability in breeding phenology as wild populations, suggesting that variability in breeding phenology is the norm and thus may be an unreliable indicator of environmental forcing. The distribution of clutch initiation dates was found to be moderately asymmetric (right skewed) both in the wild and in captivity, consistent with the pattern expected under social facilitation. 5. The role of stochasticity in phenological processes has heretofore been largely ignored. However, these results suggest that inter-annual variability in breeding phenology can arise independent of any environmental or demographic drivers and that synchronous breeding can enhance inherent stochasticity. This complicates efforts to relate phenological variation to environmental variability in the wild. Accordingly, we must be careful to consider random forcing in phenological processes, lest we fit models to data dominated by random noise. This is particularly true for colonial species where breeding synchrony may outweigh each individual's effort to time breeding with optimal environmental conditions. Our study highlights the importance of identifying appropriate null models for studying phenology.
Learning stochastic process-based models of dynamical systems from knowledge and data - Libraries, incomplete models and data
<p>The archive contains all libraries of domain knowledge, the incomplete models and the data used in the experiments described in the manuscript titled "Learning stochastic process-based models of dynamical systems from knowledge and data" pubilshed in BMC Systems Biology</p>
SBML Test Suite Stochastic Test Cases 3.2.0
<p>The SBML Test Suite is a conformance testing system. It allows developers and users to test the degree and correctness of the SBML support provided in a software package. A core part of the SBML Test Suite is the collection of test cases. There are 3 sets of tests: <strong>semantic</strong> (for deterministic simulation behavior), <strong>stochastic</strong>(for stochastic simulation behavior), and <strong>syntactic</strong> (for basic parsing).</p> <p>This is the version 3.2.0 release of the <strong>stochastic</strong> test cases archive.</p> <p>For more information about SBML and the SBML Test Suite, please visit http://sbml.org.</p>
Measured scattering parameters for the coupling of stochastic electromagnetic fields to transmission line networks of single-wire lines above a ground plane in a reverberation chamber
<p>This data set contains the measuremed scattering parameters between two antennas and a transmission line network under test in a reverberation chamber. The purpose of this measurement was an experimental validation of a numerical simulation model for the stochastic field coupling to a transmission line network. For the experiment, an exemplary network consisting of three single-wire lines above a ground plane was created. Different configurations of the network were tested and the average squared magnitude of the coupled voltage at the terminals of the network was analyzed and discussed.</p>
CALLISTO-SPK: A Stochastic Point Kinetics Code for Performing Low Source Nuclear Power Plant Start-up and Power Ascension Calculations Data Repository
<p>This dataset provides data to accompany the submission named "CALLISTO-SPK: A Stochastic Point Kinetics Code for Performing Low Source Nuclear Power Plant Start-up and Power Ascension Calculations" which has been submitted to Annals of Nuclear Energy. Details of the file included may be found in the readme file.</p>
Reward-based option competition in human dorsal stream and transition from stochastic exploration to exploitation in continuous space
<p>Primates exploring and exploiting a continuous sensorimotor space rely on dynamic maps in the dorsal stream. Two complementary perspectives exist on how these maps encode rewards. Reinforcement learning models integrate rewards incrementally over time, efficiently resolving the exploration/exploitation dilemma. Working memory buffer models explain rapid plasticity of parietal maps but lack a plausible exploration/exploitation policy. The reinforcement learning model presented here unifies both accounts, enabling rapid, information-compressing map updates and efficient transition from exploration to exploitation. As predicted by our model, activity in human fronto-parietal dorsal stream regions, but not in <em>MT+</em>, tracks the number of competing options, as preferred options are selectively maintained on the map while spatiotemporally distant alternatives are compressed out. When valuable new options are uncovered, posterior beta<sub>1</sub>/alpha oscillations desynchronize within 0.4-0.7 s, consistent with option encoding by competing beta<sub>1</sub>-stabilized subpopulations. Altogether, outcomes matching locally cached reward representations rapidly update parietal maps, biasing choices toward often-sampled, rewarded options.</p>
Data for "A stochastic model of geomorphic risk due to episodic river aggradation and degradation"
<p>The code and the dataset can be read/run by using Matlab. The description as follows:<br>1. Dataset of riverbed measurement (long profile and water level gauge data), carbon dating data, and rainfall record in the Laonong River (Taiwan). The dataset are used for the model calibration and the model application. <br>2. The developed riverbed stochastic processing model and the maximum likelihood calibration model. </p> <p>Note: this new version includes the corrected Monte Carlo simulation code and a required Matlab function (fminsearchbnd.m) that was missing in the first version.</p>
Data and code for: The role of indirect interspecific effects in the stochastic dynamics of a simple trophic system
<p>Understanding indirect interspecific effects (IIEs) on population dynamics is key for predicting community dynamics. Yet, empirically teasing apart IIEs from other interactions and population drivers is data-demanding. We used stochastic population models parameterized with long-term vital rate time series to simulate population trajectories and examine IIEs in a high-arctic vertebrate trophic chain: Svalbard reindeer, its scavenger (Arctic fox), and a migratory fox prey (barnacle goose). Reindeer carcass supply shaped fox abundance fluctuations, subsequently affecting goose fluctuations. Yet reindeer and goose population growth rates were only weakly correlated, probably due to stochasticity, density dependence, and life history traits. However, by isolating the effects of individual processes within our simulation model, we demonstrate the presence of strong IIEs on goose population fluctuations and extinction probability. Thus, we highlight the long-term impact of species interactions, including IIEs, on species coexistence and communities, beyond immediate effects and short-term fluctuations.</p>
Dynamical ising dataset for the paper Machine learning stochastic differential equations for the evolution of order parameters of classical many-body systems in and out of equilibrium
<p>This dataset provide the evolution in time for the magnetizaion in the 2D Ising model evolved with Gluber dynamics for a lattice of size 64 x 64.</p>
Dataset of bank soil parameters and stochastic modelling of bank erosion processes in the Middle Yangtze River
<p><span>A probabilistic process-based model of bank erosion has proposed, </span><span>embedding the probability distributions of</span><span> different bank soil parameters. <span><span> The dataset includes the spatial distribution characteristics of critical shear stress, friction angle, and cohesion. The prediction results analyzed the effects of soil erosion resistance capacity and the variability of shear strength parameters in the simulation of bank erosion processes, obtaining the probability of mass failure and the distributions of bank erosion width. Additionally, the study investigated the effect of varying water content on bank erosion modeling and further analyzed how considering more influencing factors in the model affects prediction uncertainty and accuracy. Moreover, the relationship between the variability of these factors and river morphology was discussed. The dataset provides the aforementioned prediction results, and relevant plots were generated using MATLAB or Python, with the associated plotting code also uploaded.</span></span></span></p>
Data from: The stochastic dynamics of early epidemics: probability of establishment, initial growth rate, and infection cluster size at first detection
<p>Emerging epidemics and local infection clusters are initially prone to stochastic effects that can substantially impact the epidemic trajectory. While numerous studies are devoted to the deterministic regime of an established epidemic, mathematical descriptions of the initial phase of epidemic growth are comparatively rarer. Here, we review existing mathematical results on the epidemic size over time, and derive new results to elucidate the early dynamics of an infection cluster started by a single infected individual. We show that the initial growth of epidemics that eventually take off is accelerated by stochasticity. These results are critical to improve early cluster detection and control. As an application, we compute the distribution of the first detection time of an infected individual in an infection cluster depending on the testing effort, and estimate that the SARS-CoV-2 variant of concern Alpha detected in September 2020 first appeared in the United Kingdom early August 2020. We also compute a minimal testing frequency to detect clusters before they exceed a given threshold size. These results improve our theoretical understanding of early epidemics and will be useful for the study and control of local infectious disease clusters.</p>
Data used in the publication: Sensitivity of modeled microphysics to stochastically perturbed parameters
<p>These data support the results presented in the manuscript titled "Sensitivity of modeled microphysics to stochastically perturbed parameters". They consist of results from an idealized single vertical column atmospheric model run for a number of experiments that explore methods of representing model uncertainty. </p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.