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11 results for “Stochastic mapping”
Stochastic Occupancy Grid Map Prediction in Dynamic Scenes: Dataset
<p>Three occupancy grid map (OGM) datasets for the paper titled "Stochastic Occupancy Grid Map Prediction in Dynamic Scenes" by Zhanteng Xie and Philip Dames</p> <p>1. OGM-Turtlebot2: collected by a simulated Turtlebot2 with a maximum speed of 0.8 m/s navigates around a lobby Gazebo environment with 34 moving pedestrians using random start points and goal points</p> <p>2. OGM-Jackal: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Jackal robot with a maximum speed of 2.0 m/s at the outdoor environment of the UT Austin</p> <p>3. OGM-Spot: extracted from two sub-datasets of the socially compliant navigation dataset (SCAND), which was collected by the Spot robot with a maximum speed of 1.6 m/s at the Union Building of the UT Austin</p> <p>The relevant code is available at: <br> OGM prediction: https://github.com/TempleRAIL/SOGMP<br> OGM mapping with GPU: https://github.com/TempleRAIL/occupancy_grid_mapping_torch</p>
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
<p>Cyanobacteria are the only prokaryotes to have evolved oxygenic photosynthesis paving the way for complex life. Studying the evolution and ecological niche of cyanobacteria and their ancestors is crucial for understanding the intricate dynamics of biosphere evolution. These organisms frequently deal with environmental stressors such as salinity and drought, and they employ compatible solutes as a mechanism to cope with these challenges. Compatible solutes are small molecules that help maintain cellular osmotic balance in high-salinity environments, such as marine waters. Their production plays a crucial role in salt tolerance, which, in turn, influences habitat preference. Among the five known compatible solutes produced by cyanobacteria (sucrose, trehalose, glucosylglycerol, glucosylglycerate, and glycine betaine), their synthesis varies between individual strains. In this study, we work in a Bayesian stochastic mapping framework, integrating multiple sources of information about compatible solute biosynthesis in order to predict the ancestral habitat preference of Cyanobacteria. Through extensive model selection analyses and statistical tests for correlation, we identify glucosylglycerol and glucosylglycerate as the most significantly correlated with habitat preference, while trehalose exhibits the weakest correlation. Additionally, glucosylglycerol, glucosylglycerate, and glycine betaine show high loss/gain rate ratios, indicating their potential role in adaptability, while sucrose and trehalose are less likely to be lost due to their additional cellular functions. Contrary to previous findings, our analyses predict that the last common ancestor of Cyanobacteria (living at around 3180 Ma) had a 97% probability of a high salinity habitat preference and was likely able to synthesize glucosylglycerol and glucosylglycerate. Nevertheless, cyanobacteria likely colonized low-salinity environments shortly after their origin, with an 89% probability of the first cyanobacterium with low-salinity habitat preference arising prior to the Great Oxygenation Event (2460 Ma). Stochastic mapping analyses provide evidence of cyanobacteria inhabiting early marine habitats, aiding in the interpretation of the geological record. Our age estimate of ~2590 Ma for the divergence of two major cyanobacterial clades (Macro- and Microcyanobacteria) suggests that these were likely significant contributors to primary productivity in marine habitats in the lead-up to the Great Oxygenation Event, and thus played a pivotal role in triggering the sudden increase in atmospheric oxygen.</p>
Map-based stochastic simulation data of a transient Ekman boundary layer
<p><strong>Overview</strong><br> <br> A journal paper in Advances in Science and Research [1] details the numerical modeling approach used to create the data. Here, the model input files, the raw data, processed data, and plot scripts are provided that support the research.</p> <p>The code used here [2,3] is an extended version of the one-dimensional turbulence (ODT) model [4,5]. The current model implementation utilizes an adaptive grid that further increases numerical efficiency [6.7]. A truncated version of the adaptive ODT code of this work is described in [8] and publicly available free of charge in [9].</p> <p>The theoretical foundation and numerical as well as experimental evidence for this work is given in [10,11,12,13], and the main motivation in [14].</p> <p>The bash script makePlot.sh is the top-level driver and contains all additional information about the cases. Some other Details are provided by low-level README files. Python-3.8 is required to run the scripts.</p> <p><strong>References</strong></p> <p>[1] M. Klein, and H. Schmidt. Capturing features of turbulent Ekman–Stokes boundary layers<br> with a stochastic modeling approach. <em>Adv. Sci. Res,</em> <strong>20</strong>, 55–64, https://doi.org/10.5194/asr-20-55-2023, 2023.</p> <p>[2] M. Klein, and H. Schmidt. Exploring stratification effects in stable Ekman boundary layers using a stochastic one-dimensional turbulence model, <em>Adv. Sci. Res.</em>, <strong>19</strong>, 117–136, https://doi.org/10.5194/asr-19-117-2022, 2022.</p> <p>[3] M. Klein, and H. Schmidt. A stochastic modeling strategy for intermittently unstable Ekman—Stokes boundary layers, <em>Proc. Appl. Math. Mech.</em>, <strong>20</strong>, e202000127, https://doi.org/10.1002/pamm.202000127, 2020.</p> <p>[4] A. R. Kerstein. One-dimensional turbulence: Model formulation and application to homogeneous turbulence, shear flows, and buoyant stratified flows, <em>J. Fluid Mech.</em>, <strong>392</strong>, 277–334, https://doi.org/10.1017/S0022112099005376, 1999.</p> <p>[5] A. R. Kerstein, and S. Wunsch. _Simulation of a stably stratified atmospheric boundary layer using one-dimensional turbulence, <em>Boundary-Layer Meteorol.</em>, <strong>118</strong>, 325–356, https://doi.org/10.1007/s10546-005-9004-x, 2006.</p> <p>[6] D. O. Lignell, A. R. Kerstein, G. Sun, and E. T. Monson. Mesh adaption for efficient multiscale implementation of one-dimensional turbulence, <em>Theor. Comput. Fluid Dyn.</em>, <strong>27</strong>, 273–295, https://doi.org/10.1007/s00162-012-0267-9, 2013.</p> <p>[7] D. O. Lignell V. B. Lansinger, J. Medina, M. Klein A. R. Kerstein, H. Schmmidt, M. Fistler, and M. Oevermann. One-dimensional turbulence modeling for cylindrical and spherical flows: model formulation and application, <em>Theor. Comput. Fluid Dyn.</em>, <strong>32</strong>, 495–520, https://doi.org/10.1007/s00162-018-0465-1, 2018.</p> <p>[8] V. B. Stephens, and D. O. Lignell. One-dimensional turbulence (ODT): Computationally efficient modeling and simulation of turbulent flows, <em>Software X</em>, <strong>13</strong>, 100641, https://doi.org/10.1016/j.softx.2020.100641, 2021.</p> <p>[9] BYU Ignite. Adaptive ODT source code, https://github.com/BYUignite/ODT.</p> <p>[10] S. Salon, and V. Armenio. A numerical investigation of the turbulent Stokes–Ekman bottom boundary layer, <em>J. Fluid Mech.</em>, <strong>684</strong>, 316–352, https://doi.org/10.1017/jfm.2011.303, 2011.</p> <p>[11] M. Klein, T. Seelig, M. V. Kurgansky, A. Ghasemi V., I. D. Borcia, A. Will, E. Schaller, C. Egbers, and U. Harlander. Inertial wave excitation and focusing in a liquid bounded by a frustum and a cylinder, <em>J. Fluid Mech.</em>, <strong>751</strong>, 255–297, https://doi.org/10.1017/jfm.2014.304, 2014.</p> <p>[12] A. Ghasemi, M. Klein, A. Will, and U. Harlander. Mean flow generation by an intermittently unstable boundary layer over a sloping wall, <em>J. Fluid Mech.</em>, <strong>853</strong>, 111–149, https://doi.org/10.1017/jfm.2018.552, 2018.</p> <p>[13] M. Vincze, N. Fenyvesi, M. Klein, J. Sommeria, S. Viboud, and Y. Ashkenazy. Evidence for wind-induced Ekman layer resonance based on rotating tank experiments, <em>EPL</em>, <strong>125</strong>, 44001, https://doi.org/10.1209/0295-5075/125/44001, 2019.</p> <p>[14] L. S. Freire. Large-eddy simulation of the atmospheric boundary layer with near-wall resolved turbulence, <em>Boundary-Layer Meteorol.</em>, <strong>184</strong>, 25–43, https://doi.org/https://doi.org/10.1007/s10546-022-00702-z, 2022.</p> <p> </p>
Data from: Stochastic character mapping, Bayesian model selection, and biosynthetic pathways shed new light on the evolution of habitat preference in cyanobacteria
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Fig. 1 in PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping
Fig. 1. Amalgamation of stochastic maps. Vertical bars are tree branches, their segments are mapped character states. The amalgamation of the stochastic map S1{0,1} and S2{0,1} yields the map S1,2{00,01,11,10}.
Fig. 3 in PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping
Fig. 3. Amalgamation of stochastic maps corresponding to the characters of legs from Hymenoptera phylogeny (S7, S8, S9) into one 'leg character' (SL); see also Fig. 2.
Data from: A comment on the use of stochastic character maps to estimate evolutionary rate variation in a continuously valued trait
Phylogenetic comparative biology has progressed considerably in recent years. One of the most important developments has been the application of likelihood-based methods to fit alternative models for trait evolution in a phylogenetic tree with branch lengths proportional to time. An important example of this type of method is O'Meara et al.'s (2006) "noncensored" test for variation in the evolutionary rate for a continuously valued character trait through time or across the branches of a phylogenetic tree. According to this method, we first hypothesize evolutionary rate regimes on the tree (called "painting" in Butler and King, 2004); and then we fit an evolutionary model, specifically the popular Brownian model, in which the instantaneous variance of the Brownian random diffusion process has different values in different parts of the phylogeny. The authors suggest that to test a hypothesis that the state of a discrete character influenced the rate of a continuous character, one could use the approach of Neilsen (2002) to first stochastically map the discretely valued trait, and then "test to see whether the portions of the tree with one state for the discrete character have a different rate of evolution for the continuous character than portions of the tree to which the other discrete state has been mapped" (O'Meara et al., 2006, p. 931). Indeed, this has become common practice for this and other closely related methods. Here, I examine this practice. In particular, I show that evolutionary rates estimated this way (i.e., by using maximum likelihood [ML] to fit a multirate model on each stochastically mapped tree; and then averaging across trees) are systematically biased to be more similar to each other than are the underlying generating parameters. My analysis also reveals that this effect is dependent on the rate of evolution for the discrete trait. Specifically, if the rate of evolution for the discrete character is low then the difference between the true history and any stochastically mapped 1 history is generally small. This results in evolutionary rates for the continuous trait that are estimated with little bias. Conversely, if the rate of evolution for the discrete character is very high, then the true and hypothesized character histories are often extremely dissimilar, evolutionary rate estimates are biased to be more similar to each other than their underlying generating values, and we lose power to distinguish evolutionary rates on the tree.
Data from: Stochastic character mapping of state-dependent diversification reveals the tempo of evolutionary decline in self-compatible Onagraceae lineages
A major goal of evolutionary biology is to identify key evolutionary transitions that correspond with shifts in speciation and extinction rates. Stochastic character mapping has become the primary method used to infer the timing, nature, and number of character state transitions along the branches of a phylogeny. The method is widely employed for standard substitution models of character evolution. However, current approaches cannot be used for models that specifically test the association of character state transitions with shifts in diversification rates such as state-dependent speciation and extinction (SSE) models. Here we introduce a new stochastic character mapping algorithm that overcomes these limitations, and apply it to study mating system evolution over a time-calibrated phylogeny of the plant family Onagraceae. Utilizing a hidden state SSE model we tested the association of the loss of self-incompatibility with shifts in diversification rates. Confirming long standing theory, we found that self-compatible lineages have higher extinction rates and lower net diversification rates compared to self-incompatible lineages. Furthermore, these results provide empirical evidence for the "senescing" diversification rates predicted in highly selfing lineages: our mapped character histories show that the loss of self-incompatibility is followed by a short-term spike in speciation rates, which declines after a time lag of several million years resulting in negative net diversification. Lineages that have long been self-compatible such as Fuchsia and Clarkia are in a previously unrecognized and ongoing evolutionary decline. Our results demonstrate that stochastic character mapping of SSE models is a powerful tool for examining the timing and nature of both character state transitions and shifts in diversification rates over the phylogeny.
Data from: A comment on the use of stochastic character maps to estimate evolutionary rate variation in a continuously valued trait
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Data from: Stochastic character mapping of state-dependent diversification reveals the tempo of evolutionary decline in self-compatible Onagraceae lineages
Open the record for dataset details and reuse information.
Fig. 2. PARAMO pipeline.The panels A–E in PARAMO: A Pipeline for Reconstructing Ancestral Anatomies Using Ontologies and Stochastic Mapping
Fig. 2. PARAMO pipeline.The panels A–E represent the five steps of the pipeline (see the text). (E) The size of the stochastic maps S4–S9 is reduced for the illustrative purpose. (F) Three levels of anatomical hierarchy. Abbreviations: C: character, S: stochastic map, ind.: independent character, dep. and syn.: hierarchically and synchronously dependent characters, respectively.
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
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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.