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150 results for “evolutionary models”
Science ready spectra and their best-fitting models described in the research paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al.
<p>Science ready spectra of nine ultra-diffuse galaxies in the Coma cluster collected with the Binospec multi-object spectrograph and their best-fitting PEGASE.HR templates obtained using the NBursts full spectrum fitting code. These spectra were presented in the paper ``Internal dynamics and stellar content of nine ultra-diffuse galaxies in the Coma cluster prove their evolutionary link with dwarf early-type galaxies'' by Chilingarian et al. accepted for publication in the Astrophysical Journal on Sep/3/2019 (arXiv:1901.05489).</p> <p>Each spectrum is presented as a binary FITS table, which contains a spectrum (wavelength, flux, uncertainties), best-fitting template, best-fitting parameters (radial velocity, age, metallicity), and a pixel mask used in the fitting procedure. For six galaxies there are two files provided: (i) one-dimensional optimally extracted integrated spectrum and (ii) two dimensional spectrum for spatially resolved radial velocity information. For the remaining three galaxies, only spatially resolved spectra are provided.</p>
Data for: The structure of evolutionary model space for proteins across the tree of life
<p>Supporting data for "The structure of evolutionary model space for proteins across the tree of life," submitted by GE Scolaro and EL Braun. The data files correspond to three gzipped tarballs including protein multiple sequence alignments, PAML format models of protein evolution, and model fit data; see included README for details.</p>
Data from: Spatial processes and evolutionary models: a critical review
Evolution is a fundamentally population level process in which variation, drift, and selection produce both temporal and spatial patterns of change. Statistical model fitting is now commonly used to estimate which kind of evolutionary process best explains patterns of change through time, using models like Brownian motion, stabilizing selection (Ornstein-Uhlenbeck), and directional selection on traits measured from stratigraphic sequences or on phylogenetic trees. But these models assume that the traits possessed by a species are homogeneous. Spatial processes such as dispersal, gene flow, and geographic range changes can produce patterns of trait evolution that do not fit the expectations of standard models, even when evolution at the local-population level is governed by drift or a typical OU model of selection. The basic properties of population level processes (variation, drift, selection, and population size) are reviewed and the relationship between their spatial and temporal dynamics is discussed. Typical evolutionary models used in palaeontology incorporate the temporal component of these dynamics, but not the spatial. Range expansions and contractions introduce rate variability into drift processes, range expansion under a drift model can drive directional change in trait evolution, and spatial selection gradients can create spatial variation in traits that can produce long-term directional trends and punctuation events depending on the balance between selection strength, gene flow, extirpation probability, and model of speciation. Using computational modelling that spatial processes can create evolutionary outcomes that depart from basic population-level notions from these standard macroevolutionary models.
Data and code for the paper "Precision Groundwater Modeling: when cokriging meets evolutionary and iterative algorithms"
<ul> <li>exemplary dataset for 2019 yearly water table measurements in Northeaster Italy</li> <li>MATLAB code for the pre-processing GA-driven and the post-processing iterative validation part</li> </ul>
Kiauhoku Stellar Evolutionary Model Grids
<p>Stellar evolutionary model grids for use with Python Kiauhoku package (presented by <a href="https://ui.adsabs.harvard.edu/abs/2020ApJ...888...43C/abstract">Claytor et al. 2020</a>). This dataset contains models from MIST, YREC, GARSTEC, and Dartmouth projects.</p> <p><strong>Model Grids</strong></p> <p><em>fastlaunch</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2020ApJ...888...43C/abstract">Claytor et al. (2020)</a>. Computed using the Yale Rotating stellar Evolution Code (YREC, <a href="https://ui.adsabs.harvard.edu/abs/1989ApJ...338..424P/abstract">Pinsonneault et al. 1989</a>) with rotational evolution computed separately using the magnetic braking law of <a href="https://ui.adsabs.harvard.edu/abs/2013ApJ...776...67V/abstract">van Saders and Pinsonneault (2013)</a> under "fast launch" condition of <em>P</em><sub>init </sub>~ 8 days.</p> <p><em>slowlaunch</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2020ApJ...888...43C/abstract">Claytor et al. (2020)</a>. Computed using the Yale Rotating stellar Evolution Code (YREC, <a href="https://ui.adsabs.harvard.edu/abs/1989ApJ...338..424P/abstract">Pinsonneault et al. 1989</a>) with rotational evolution computed separately using the magnetic braking law of <a href="https://ui.adsabs.harvard.edu/abs/2013ApJ...776...67V/abstract">van Saders and Pinsonneault (2013)</a> under "slow launch" condition of <em>P</em><sub>init </sub>~ 13 days.</p> <p><em>rocrit</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2020ApJ...888...43C/abstract">Claytor et al. (2020)</a>. Computed using the Yale Rotating stellar Evolution Code (YREC, <a href="https://ui.adsabs.harvard.edu/abs/1989ApJ...338..424P/abstract">Pinsonneault et al. 1989</a>) with rotational evolution computed separately using the stalled magnetic braking law of <a href="https://ui.adsabs.harvard.edu/abs/2016Natur.529..181V/abstract">van Saders et al. (2016)</a> under "fast launch" condition of <em>P</em><sub>init </sub>~ 8 days.</p> <p><em>mist</em></p> <p>Evolutionary tracks from the MESA Isochrones and Stellar Tracks (MIST, <a href="https://ui.adsabs.harvard.edu/abs/2016ApJ...823..102C/abstract">Choi et al. 2016</a>). Computed using Modules for Experiments in Stellar Astrophysics (MESA, <a href="https://ui.adsabs.harvard.edu/abs/2010ascl.soft10083P/abstract">Paxton et al. 2010</a>).</p> <p><em>yrec</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2020arXiv201207957T/abstract">Tayar et al. (2022)</a>. Computed using the Yale Rotating stellar Evolution Code (YREC, <a href="https://ui.adsabs.harvard.edu/abs/1989ApJ...338..424P/abstract">Pinsonneault et al. 1989</a>).</p> <p><em>dartmouth</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2008ApJS..178...89D/abstract">Dotter et al. (2008)</a>. Models from the Dartmouth Stellar Evolution Program (DSEP).</p> <p><em>garstec</em></p> <p>Originally presented by <a href="https://ui.adsabs.harvard.edu/abs/2013MNRAS.429.3645S/abstract">Serenelli et al. (2013)</a>. Computed using the Garching Stellar Evolution Code (GARSTEC, <a href="https://ui.adsabs.harvard.edu/abs/2008Ap%26SS.316...99W/abstract">Weiss & Schlattl 2008</a>).</p>
Structure and stability constrained substitution models outperform traditional substitution models used for evolutionary inference
<p>The current knowledge about how protein structures influence sequence evolution is rarely incorporated into substitution models adopted for phylogenetic inference, which are commonly based on independent with the same substitution process and ignore the known variation of the evolutionary rates across sites with different structural properties. In previous works, we presented site-specific substitution models of protein evolution based on selection on the folding stability of the native state (Stab-CPE), which predict more realistically the evolutionary variability across protein sites. However, those Stab-CPE present qualitative differences from observed data, probably because they ignore changes in the native structure, despite empirical studies suggesting that conservation of the native structure is a strong selective force. Here we present novel structurally constrained substitution models (Str-CPE) based on Julián Echave's model of the structural change due to a mutation as the linear response of the protein to a perturbation and on the explicit model of the perturbation generated by a specific amino-acid mutation. Compared to our previous Stab-CPE models, the novel Str-CPE models are more stringent (they predict lower sequence entropy and substitution rate), provide higher likelihood to multiple sequence alignments (MSA) of the wild-type protein, and better predict the observed substitution rates. Next, we combine Str-CPE and Stab-CPE models to obtain structure and stability constrained substitution models (SSCPE) that fit the empirical MSAs even better. Importantly, these SSCPE models present a relevant improvement of the phylogenetic likelihood for all ten protein families that we analyzed with the program RAxML-NG. We implemented the SSCPE models in the program Prot evol, freely available at <a href="https://github.com/ugobas/Prot_evol">https://github.com/ugobas/Prot_evol</a>.</p>
Figure 1 in Climatic preferences and distribution of 6 evolutionary lineages of Typhlops vermicularis Merrem, 1820 in Turkey using ecological niche modeling
Figure 1. Important mountain chains of Anatolia and ecological niche modeling of T. vermicularis in Turkey under current climatic conditions.
Figure 3 in Climatic preferences and distribution of 6 evolutionary lineages of Typhlops vermicularis Merrem, 1820 in Turkey using ecological niche modeling
Figure 3. Predicted models of lineages G, H, and I according to Last Interglacial (LIG) and Last Glacial Maximum (LGM; CCSM and MIROC) (4, 4A, 4B, 4C for lineage G; 5, 5A, 5B, 5C for lineage H; 6, 6A, 6B, 6C for lineage I).
Figure 2 in Climatic preferences and distribution of 6 evolutionary lineages of Typhlops vermicularis Merrem, 1820 in Turkey using ecological niche modeling
Figure 2. Predicted models of lineages B, C, and E according to Last Interglacial (LIG) and Last Glacial Maximum (LGM; CCSM and MIROC) (1, 1A, 1B, 1C for lineage B; 2, 2A, 2B, 2C for lineage C; 3, 3A, 3B, 3C for lineage E).
Gaia BH1 and BH2 - Evolutionary Models with Overshooting of the Black Hole Progenitors within the Present-Day Binary Separation
<p>Input files and simulation results for stellar evolution tracks computed for the letter "Gaia BH1 and BH2 - Evolutionary Models with Overshooting of the Black Hole Progenitors within the Present-Day Binary Separation". Version 15140 of MESA was used for the simulations. More details in the README.txt file and in the letter.</p>
Data from: Building on 150 years of knowledge: the freshwater isopod Asellus aquaticus as an integrative eco-evolutionary model system
<p><strong>Introduction</strong></p> <p>This is a literature database with reference information of all papers that use the freshwater isopod <em>Asellus aquaticus</em>; published between the years 1867 and 2020. This database is intended as a starting point for scientists interested in conducting research on and with this organism. The database is currently only available as a single CSV file; future versions may be made available through a more frequently updated SQL database. The database includes specific information about the subject area and content of each paper, as well as bibliographic information. This repository is associated with the paper "Building on 150 years of knowledge: the freshwater isopod<em> Asellus aquaticus</em> as an integrative eco-evolutionary model system", published in Frontiers in Ecology and Evolution.</p> <p><strong>Details on Methods from the electronic supplement:</strong></p> <p>We used the we online search tools of Web of Science (WOS; Clarivate analytics) by searching for the term "asellus aquaticus" in six relevant databases (BIOSIS, CABI, FSTA, Medline, WOS Core Collection and Zoological Records). The database was accessed with a University License (Lund University). We manually downloaded the results and combined them to a single CSV file in Excel (Microsoft). All further processing was done in the statistical programming language R, version 4.0.2 (R Core Team 2020).</p> <p>From the 1238 obtained records we discarded three papers that were published after the year 2020 to work with completed years only. We used the subject areas assigned by WOS to provide an overview of the fields of science in which A. aquaticus has been most studied. Each paper had between one and ten subject areas assigned by WOS (2845 assignments to 1235 papers, meaning 2.3 assignments per paper, on average). To represent these multiple assignments in relation to the actual number of papers per year, we calculated "fractional assignments" by adding up all assignments to a field per year, divided by the total number of assignments in that year, and then multiplied by the number of papers. For example, if there were 12 assignments to "toxicology" in 1993, and 133 assignments in 1993, but only 21 papers published, "toxicology" would get a score of 1.9 papers in 1993 (as calculated by = (12/133)*21). In Figure 1, we represent these "fractional assignments" in the top panel, and the total number of assignments in the lower panel.</p> <p><strong>Caption for figure (1) in publication:</strong></p> <p>FIGURE 1 | Over 150 years of research on and with Asellus aquaticus. The figure summarizes published scientific literature on A. aquaticus. We conducted a quantitative literature survey with the search tools of Web of Science (WOS; Clarivate analytics) by searching for the term "asellus aquaticus" in six databases (i.e., BIOSIS, CABI, FSTA, Medline, WOS Core Collection, and Zoological Records). We found 1235 records, published between 1867 and 2020. (A) The graph shows the number of publications per year within a given subject area, as designated by WOS. (B) The graph shows the total number of publications assigned to a specific subject area. The top 10 fields account for 72.58% of all publications, and are indicated by color coding in A and B (multiple assignments are possible, summing up to 2845 assignments). The inset in B shows a wordcloud with the 100 most used keywords from all A. aquaticus’ publications. Furthermore, we compiled all records with relevant information (e.g., title, keywords, research areas, and abstract) to a single file which is available online. More details can be found in the Supplementary Material.</p>
Novel integrative modeling of molecules and morphology across evolutionary timescales
<p>Evolutionary models account for either population or species-level processes, but usually not both. We introduce a new model, the FBD-MSC, which makes it possible for the first time to integrate both the genealogical and fossilization phenomena, by means of the multispecies coalescent (MSC) and the fossilized birth-death (FBD) processes. Using this model, we reconstruct the phylogeny representing all extant and many fossil Caninae, recovering both the relative and absolute time of speciation events. We quantify known inaccuracy issues with divergence time estimates using the popular strategy of concatenating molecular alignments, and show that the FBD-MSC solves them. Our new integrative method and empirical results advance the paradigm and practice of probabilistic total evidence analyses in evolutionary biology.</p>
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
<p><span>Machine learning can be as good as maximum likelihood when reconstructing phylogenetic topologies and determining the best evolutionary model on four taxon alignments.</span></p> <p><span>Phylogenetic tree reconstruction with molecular data is important in many fields of life science research. The gold standard in this discipline is the Maximum Likelihood tree reconstruction method. Here we show that for quartet trees, Machine Learning using neural networks can be as good as the Maximum Likelihood method to infer the best tree topology and the best model of sequence evolution for nucleotide as well as amino acid sequences. For this purpose we simulated data sets for a wide range of branch lengths, evolutionary models and model parameters and compared the topologies and inferred models obtained with Machine learning with those obtained with the Maximum Likelihood and the Neighbour Joining method. Our results show that neural networks are a promising avenue for determining relatedness between taxa, which is likely to accelerate the construction of phylogenetic trees in the future, while maintaining a high accuracy.</span></p>
Inferring the evolutionary model of community-structuring traits with convolutional kitchen sinks: Code and data
<p>When communities are assembled through processes such as filtering or limiting similarity acting on phylogenetically conserved traits, the evolutionary signature of those traits may be reflected in patterns of community membership. We show how the model of trait evolution underlying community-structuring traits can be inferred from community membership data using both a variation of a traditional eco-phylogenetic metric--the mean pairwise distance (MPD) between taxa--and a recent machine learning tool, Convolutional Kitchen Sinks (CKS). Both methods perform well across a range of phylogenetically informative evolutionary models, but CKS outperforms MPD as tree size increases. We demonstrate CKS by inferring the evolutionary history of freeze tolerance in angiosperms. Our analysis is consistent with a late burst model of freeze tolerance, suggesting it evolved recently. We suggest that data ordered on phylogenies such as trait values, species interactions, or community presence/absence are good candidates for CKS modeling because the generative models produce structured differences between neighboring points that CKS is well-suited for. We introduce the R package <em>kitchen</em> to perform CKS for generic application of the technique.</p>
Jupiter Atmospheric Models and Outer Boundary Conditions for Giant Planet Evolutionary Calculations
<p>This data set consists of 1D radiative-convective equilibrium boundary conditions for Jupiter-like giant planets, computed using coolTLUSTY and a recently updated set of molecular absorption cross sections. Models span internal temperatures of 80 - 450 K, and surface gravities of log10(g / [cm/s^2]) = 1.8 - 3.6. The planet is irradiated by a black body star at a distance of 5.2AU with effective temperature of 5777K with the zenith angle factor (accounting for an average incident angle) being FACFLX=0.5 or 0.67. The models assume a composition of 3.16x solar abundance, and allow the formation of ammonia clouds at low temperatures with characteristic sizes of 1 or 3 micron. More numerical details on the treatments of irradiation and clouds can be found in "Jupiter Atmospheric Models and Outer Boundary Conditions for Giant Planet Evolutionary Calculations", arXiv number TBA.</p> <p>See README.txt for a description of data formats.</p>
Data from: Spatial processes and evolutionary models: a critical review
Open the record for dataset details and reuse information.
Machine learning can be as good as maximum likelihood when reconstructing phylogenetic trees and determining the best evolutionary model on four taxon alignments
Open the record for dataset details and reuse information.
Novel integrative modeling of molecules and morphology across evolutionary timescales
Open the record for dataset details and reuse information.
Inferring the evolutionary model of community-structuring traits with convolutional kitchen sinks: Code and data
Open the record for dataset details and reuse information.
Spatiophylogenetic modelling of extinction risk reveals evolutionary distinctiveness and brief flowering period as threats in a hotspot plant genus
Comparative models used to predict species threat status can help to identify diagnostic features of species at risk. Such models often combine variables measured at the species level with spatial variables, causing multiple statistical challenges, including phylogenetic and spatial non-independence. We present a novel Bayesian approach for modelling threat status that simultaneously deals with both forms of non-independence and estimates their relative contribution, and we apply the approach to modelling threat status in the Australian plant genus Hakea. We find that after phylogenetic and spatial effects are accounted for, species with greater evolutionary distinctiveness and a shorter annual flowering period are more likely to be threatened. The model allows us to combine information on evolutionary history, species biology, and spatial data, to calculate latent extinction risk (potential for non-threatened species to become threatened), estimate the most important drivers of risk for individual species, and map spatial patterns in the effects of different predictors on extinction risk. This could be of value for proactive conservation decision-making based on the early identification of species and regions of potential conservation concern.
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.