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285 results for “evolution models”
ChannelLeveeModel: Decoupled Channel-levee Evolution Model for MATLAB
<h1>ChannelLeveeModel v1.0.1</h1> <p>This is the archive of a numerical model that decoupled channel bed and levee evolution with associated simulations, dataset, and figures using MATLAB</p> <p><strong>Features</strong></p> <ul> <li>Generate random weekly hydrographs and solve the divided channel method to compute the flooded water surface elevation (Lotter, 1933)</li> <li>Identify two flood styles: Front loading and Back loading</li> <li>Run advection-settling model (Han and Kim, 2022) for Front loading events and Ponded water model (Nicholas and Walling, 1996) for Back loading events</li> <li>Visualizes results with MATLAB plotting functions</li> </ul> <p><strong>File lists</strong></p> <ul> <li><strong>Main file:</strong> <code>DecoupledCLM.m</code></li> <li><strong>Function files:</strong> <code>HydraulicGeometry.m</code>, <code>Hydrograph.m</code>, <code>OverflowLevel.m</code>, <code>LeveeBimodal.m</code>, <code>Backloading_wellmix.m</code></li> <li><strong>Plotting files:</strong> <code>plot_BE.m</code>, <code>plot_ConfinedRelease.m</code>, <code>plot_figures.m</code></li> <li><strong>Output folder:</strong> <code>simulation_code/output/</code> contains example model simulations in <code>.m</code> format. and figures in <code>.pdf</code> format.</li> <li><code>README.md</code> and <code>LICENSE</code></li> </ul>
Sonora Bobcat: cloud-free, substellar atmosphere models, spectra, photometry, evolution, and chemistry
<p><strong>OVERVIEW</strong></p> <p>Presented here are models for non-irradiated, substellar mass objects belonging to the Sonora model series, described in Marley et al. (2021). The files presented here are model temperature-pressure structures ("structure"), emergent spectra from the top of the atmosphere ("spectra"), thermal evolution and photometry ("evolution_and_photometry"), and rainout chemical equilibrium tables used to compute the models ("chemistry"). </p> <p>Atmospheric structure and spectra .tar file names specify metallicity [M/H] and carbon-to-oxygen ratio (C/O) relative to solar. For example, "structures+0.0_co1.5<a href="../api/files/2e9ce76a-67fc-4fd6-ae5c-f88f16c610ea/structures%2B0.0_co1.5.tar.gz">.</a>tar.gz" contains the set of radiative-convective equilibrium atmospheric structures for solar metallicity ("+0.0") with C/O=1.5 times the solar abundance. The _co*.* is omitted for solar C/O, or co_1.0. The individual file naming convention is described below. All stated abundances and ratios are relative to Lodders (2010) abundances, see Marley et al. (2021) for details and use caution when referring to other abundance tabulations.</p> <p>This particular set of model atmosphere structures and associated spectra, photometry, and evolution, which we name <strong>Sonora Bobcat</strong>, are for cloudless objects with 3.25 ≤ log g (cgs) ≤ 5.5 and 200 ≤ Teff ≤ 2400K. Steps in T<sub>eff</sub> vary from 25K to 1000K and steps in log g are 0.25 or 0.5. Some combinations of model grid parameters include additional values of the gravity. Models are provided for [M/H] = -0.5, 0.0, and +0.5 and "rainout" chemical equilibrium. A limited set of models with carbon-to-oxygen ratio of 0.5 and 1.5 times solar abundance are also included. For the convenience of having a rectangular table in (T<sub>eff</sub>, gravity) space, models are calculated in regimes that are not reached by the evolution, such as very high gravity and very low T<sub>eff</sub>. Refer to the companion evolution tables to identify combinations of T<sub>eff</sub> and log g outside the bounds covered by the evolution.</p> <p><strong>ATMOSPHERIC STRUCTURE</strong></p> <p>Atmospheric structure and spectra filenames specify Teff and gravity (in mks units) along with [M/H] and (C/O) relative to solar. "co1.5" in version and spectra header nomenclature refers to 1.5 times the solar C/O ratio. _co*.* is generally omitted for 1.0, the solar value. For example, the file t1000g316nc_m-0.5.dat contains the structure of a model with T<sub>eff</sub>=1000K, g=316m/s<sup>2</sup> (the exact value of the gravity is given<sup> </sup>in the first line of the file, see below) , [Fe/H]=-0.5, and C/O=1.0 times the solar value. </p> <p>Temperature structure and spectra files have a one line header giving "Teff, grav(MKS), Y, f_sed, kz_min, [Fe/H], C/O, f_hole". Teff and grav are the effective temperature (K) and gravity (MKS), Y is the He mass fraction. f_sed is a cloud parameterization which is not relevant for these cloudless models and is arbitrarily given as 0.0. Likewise kz_min relates to the atmospheric eddy diffusion coefficient, which is also not relevant for these chemical equilibrium models and is arbitrarily set equal to a placeholder value that is not used in these models. [Fe/H] and C/O are the metallicity and C/O ratios as described above. [Fe/H] is identical to [M/H]. f_hole is another cloud parameter for cloudy models, not relevant to these cloudless models.</p> <p>Columns in the atmosphere structure files describe the atmosphere at discrete levels. Columns give: level index, P(bar), T(K), internally used check parameter, adiabatic temperature gradient (d ln T / d ln P), local temperature gradient (d ln T / d ln P), atmospheric density (g / cm<sup>3</sup>).</p> <p><strong>EVOLUTION AND PHOTOMETRY</strong></p> <p>Evolution and Photometry tables are described in detail in a README file included in that tar file. Evolution files connect mass, effective temperature, radius, age, gravity, and moment of inertia for these model sets. Each set of model spectra is complemented with tables of fluxes and of absolute magnitudes in a number of photometric systems commonly used in brown dwarf and exoplanet research (MKO, Keck, 2MASS, SDSS, WISE, Spitzer IRAC, etc). Fluxes and magnitudes for the full set of JWST filters is also included in separate tables. Magnitudes are computed on the Vega system (using the Vega spectrum of Bohlin & Gilliland 2004) or on the AB system (e.g. for SDSS).</p> <p><strong>SPECTRA</strong></p> <p>The model spectra each contain close to 362000 wavelength points. The resolving power varies with wavelength and ranges from R=6000 to 200000 but is otherwise the same for all spectra. The first line gives the model parameters in the same format as the structure files described above. This is followed by the spectrum</p> <p>Column 1: wavelength in µm</p> <p>Column 2: <strong>Radiation flux <em>F<sub>ν</sub></em></strong><sub> </sub>= \(4\pi\) x Eddington flux <em>H</em><sub>ν</sub>, in erg/cm<sup>2</sup>/s/Hz (always exercise caution with factors of \(4\pi\) when comparing to the radiation and Eddington flux, e.g., see Section 3.3 of Hubeny & Mihalas, "Theory of Stellar Atmospheres")</p> <p>The spectral fluxes are given at the top of the atmosphere and are strictly monochromatic. The model spectrum provides no information in the wavelength range between two tabulated points. Unless a spectral line or feature is well resolved,<em> interpolation in wavelength is not advised</em>. For comparison with data, the model spectra need to be convolved and binned to the instrumental resolution and sampling. In our experience, a minimum of 10 wavelength points is necessary to obtain a reasonable average flux over a wavelength interval. This is a rule of thumb and caution is advised, especially when comparing with high resolution data. The flux received at Earth is that given in the table scaled by (R/D)<sup>2</sup> where R is the radius of the object (given in the companion evolution tables) and D its distance. </p> <p>The solar spectra and photometry are the same as those archived at https://zenodo.org/record/1309035#.YOyz4S1h2X0, which did not provide the T(P) profiles available here.</p> <p><strong>CHEMISTRY</strong></p> <p>We also separately include rainout chemical equilibrium tables for these same atmospheric bulk abundances. These chemistry files are described in detail by their own README file. Additional chemistry tables, beyond those used for the models presented here, are also included for completeness. Users interested in the chemical abundances of the structure models must interpolate within the matching chemistry file for the atmospheric species of interest.</p> <p><strong>CREDITS</strong></p> <p>If you use these tables in your research, please cite Marley et al. (2021, Astrophysical Journal, Volume 920, Issue 2, id.85.)</p> <p>16 Feb 2024: Error corrected in Column 2 heading. Column 2 is the Radiation flux, not the Eddington flux as previously stated. Citation updated.</p> <p> </p>
Early 3D Evolution of the SARS-CoV-2 proteome -- Supplementary Tables and Models
<p><strong>Evolution of the SARS-CoV-2 proteome in three dimensions (3D) during the first six months of the COVID-19 pandemic</strong></p> <p><a href="https://iqb.rutgers.edu/covid-19_proteome_evolution">https://iqb.rutgers.edu/covid-19_proteome_evolution</a></p> <p> </p> <p><strong>Legends for Supplementary Figures for 29 </strong><strong>SARS-CoV-2 Study Proteins</strong></p> <p><strong>Separate analysis of protein changes was performed for each study protein and complex. Description below applies to all figures.</strong></p> <p><strong>A</strong>: Observed frequencies for all USV substitutions of Native Residue (i.e., amino acid type in the reference protein sequence) changing to Substituted Residue for a given protein/complex. Red boxes enclose conservative substitutions for hydrophobic, uncharged polar, positively charged, and negatively charged amino acids, respectively in order from upper left to lower right. Cysteine, Glycine and Proline are excluded from these groupings.</p> <p><strong>B-D</strong>: Normalized Frequency histograms for ΔΔG<sup>App</sup> calculated for all USVs for a given protein/complex. These were calculated using three methods, which we refer to as hard-hard (B), soft-hard (C), and soft-soft (D), based on the scoring functions used for sidechain rotamer optimization and gradient-based energy minimization respectively (see methods). All energy values described in the text were obtained using the soft-hard method. Overlay of energy histogram with fitted bi-Gaussian curve (solid red line) and fitted single Gaussian curves for subsets of USVs with surface (green), boundary layer (yellow), or core (blue) substitutions. USVs with multiple substitutions were included in single Gaussian fitting when all substitutions mapped to the same region of the study protein. The data used for fitting includes the energies of all unique protein models produced by a given method, excluding extreme outliers with energy values greater than 3 standard deviations away from the central mean.</p> <p><strong>E-G</strong>: USV Count histograms indicate the number of USVs among the full set for a given protein in which each site included a substitution. Sites are separated by burial layer. Substitutions at sites that are absent from the available crystal structures are excluded from the histograms. In most cases, only a single protein is analyzed, and only panel E is included. In the case of complexes, a separate histogram is provided for each protein in the complex: for methyltransferase nsp10-nsp16, E is nsp10 and F is nsp16; for RDRP nsp12-nsp7-nsp8, E is nsp7, F is nsp8, and G is nsp12.</p> <p> </p> <p><strong>Legends for Supplementary Tables for 29 </strong><strong>SARS-CoV-2 Study Proteins</strong></p> <p><strong>Table: USVs</strong>: All identified USVs for a protein/complex. Columns are:</p> <ul> <li>date: Date of first collection of a strain with the USV reported to GISAID</li> <li>gisaid_count: The number of sequences in the GISAID database that include the USV</li> <li>id: The GISAID strain identification for the first collected instance of the USV</li> <li>location: The country in which the first strain including the USV was collected</li> <li>substitutions: All substitutions in the USV, in the form [chain]_[sequence][site][substitution], with multiple substitutions separated by semicolons</li> <li>is_in_PDB: whether a substitution is present in the PDB model used to generate the USV structure, with multiple substitutions separated by semicolons</li> <li>multiple: whether more than one amino acid substitution is present in the USV</li> <li>conservative: whether a substitution is conservative, with multiple substitutions separated by semicolons</li> <li>layer: Identification of the burial layer (surface, boundary, or core) of a substitution in the reference structure, with multiple substitutions separated by semicolons and substitutions absent from the PDB excluded</li> <li>sh_rmsd: The RMSD of the USV to the reference structure when modeled using the soft-hard method</li> <li>sh_ddG: The ΔΔG<sup>App</sup> of the USV when modeled using the soft-hard method</li> <li>hh_rmsd: The RMSD of the USV to the reference structure when modeled using the hard-hard method</li> <li>hh_ddG: The ΔΔG<sup>App</sup> of the USV when modeled using the hard-hard method</li> <li>ss_rmsd: The RMSD of the USV to the reference structure when modeled using the soft-soft method</li> <li>ss_ddG: The ΔΔG<sup>App</sup> of the USV when modeled using the soft-soft method</li> </ul> <p> </p> <p><strong>Table: Substitutions</strong>: All substitutions identified for a protein/complex</p> <ul> <li>chain: The chain identifier of the protein in the PDB file in which the substitution is present</li> <li>site: The residue number at which the substitution is present</li> <li>reference: The one-letter amino acid name of the residue in the reference sequence</li> <li>mutant: The one-letter amino acid name of the residue in a USV</li> <li>conservative: Indication of whether a substitution is conservative</li> <li>in_pdb: whether the substitution site is present in the PDB model used to generate the USV structure</li> <li>layer: Identification of the burial layer (surface, boundary, or core) of a substitution in the reference structure</li> <li>date: date: Date of first collection of a strain with the substitution reported to GISAID</li> <li>location: The country in which the first strain including the substitution was collected</li> <li>gisaid_count: The number of sequences in the GISAID database including the substitution</li> <li>usv_count: The number of identified USVs including the substitution</li> <li>ddG: The soft-hard ΔΔG<sup>App</sup> of the USV that includes only the substitution, left empty if no single-substitution USV was identified with the substitution</li> <li>single: Indication of whether the substitution was present in a single-substitution USV</li> <li>multiple: Indication of whether the substitution was present in a USV with multiple substitutions</li> <li>associates: List of all other substitutions that were identified in a USV that included the substitution</li> <li>strains: List of all USV-representative GISAID strains that included the substitution, with the single-substitution USV strain listed first if one was available</li> </ul> <p> </p> <p><strong>Table: Gaussian Fit Statistics</strong>: Fitted models for the energies of all USVs either together (ALL) or by study protein.</p> <ul> <li>fit: The number of Gaussian curves in the fitted energy model </li> <li>protein: The protein/complex name</li> <li>method: The modeling method used to calculate energy values</li> <li>layer: The subset burial layer (surface, boundary, or core) of USVs for which the energy model was fitted, excluding all USVs with substitutions not in that layer</li> <li>μ<sub>1</sub>: Mean of the first Gaussian in the fitted model</li> <li>σ<sub>1</sub>: Variance of the first Gaussian in the fitted model</li> <li>wt<sub>1</sub>: Weight of the first Gaussian in the fitted model</li> <li>μ<sub>2</sub>: Mean of the second Gaussian in the fitted model</li> <li>σ<sub>2</sub>: Variance of the second Gaussian in the fitted model</li> <li>wt<sub>2</sub>: Weight of the second Gaussian in the fitted model</li> <li>R<sup>2</sup>: R-squared value indicating the goodness of fit</li> </ul> <p> </p> <p><strong>Description of Computed Structural Models </strong><strong>for Unique Sequence Variants for 29 </strong><strong>SARS-CoV-2 Study Proteins.</strong></p> <p><strong>USV Computed Structural Models</strong>. Computed structural models for all amino acid substituted USVs. We are providing the structural models of all study proteins modeled using the soft-hard modeling method (see Methods). Structural models are named according to the GISAID strain identification of the first strain in which the USV was identified, followed by an underscore-separated list of substitutions in the form [chain]_[sequence][site][substitution]. Atomic coordinates for each computed structural model are provided in the legacy Protein Data Bank format used by most molecular graphics software tools (see <a href="https://www.wwpdb.org/documentation/file-format-content/format33/v3.3.html">https://www.wwpdb.org/documentation/file-format-content/format33/v3.3.html</a> for detailed description).</p>
Fast mvSLOUCH: Model comparison for multivariate Ornstein--Uhlenbeck-based models of trait evolution on large phylogenies
<p>These are the Supplementary Material, R scripts and numerical results accompanying Bartoszek, Fuentes Gonzalez, Mitov, Pienaar, Piwczyński, Puchałka, Spalik and Voje "Model Selection Performance in Phylogenetic Comparative Methods under multivariate Ornstein–Uhlenbeck Models of Trait Evolution".</p> <p>The four data files concern two datasets. Ungulates: measurements of muzzle width, unworn lower third molar crown height, unworn lower third molar crown width and feeding style and their phylogeny; Ferula: measurements of ratio of canals, periderm thickness, wing area, wing thickness, and fruit mass, and their phylogeny.</p>
PALM Model System v 6.0 input and configuration files for coupled large eddy simulations of land surface heterogeneity effects and diurnal evolution of late summer and early autumn atmospheric boundary layers during the CHEESEHEAD19 field campaign
<p>Namelist, configuration and forcing files for the PALM Model System 6.0 revision number 21.10-rc.2 used for the numerical simulations Coupled Large Eddy Simulations of land surface heterogeneity induced atmospheric boundary layer response during the CHEESEHEAD19 field campaign.</p>
Statistical and Dynamic Model of Surface Morphology Evolution during Polishing in Additive Manufacturing
<p>This repository maintains data and code associated with our accepted paper in IISE Transactions titled "Statistical and Dynamical model of Surface Morphology Evolution during Polishing in Additive Manufacturing". To briefly summarize,</p> <p><strong>1. Polishing_stagewise_data.zip</strong> - Contains height values measured at 32 different locations on the 3D printed sample using an optical profilometer prior to polishing (Stage 0) and post every stage of polishing (Stages 1 to 6). Please refer to the following paper for experimentation details and process parameters: "<em>Jin, S., A. Iquebal, S. Bukkapatnam, A. Gaynor, and Y. Ding (2019, 10). A gaussian process model-guided surface polishing process in additive manufacturing. Journal of Manufacturing Science and Engineering 142, 1–17.</em>"</p> <p><strong>2. Initial_surface_generation.m</strong> - Script containing the Initial surface generation algorithm using the random circle packing algorithm. This file generates the surface asperity distribution and their graph connectivity of a 3D printed sample prior to polishing (Figure 4(b) in paper). One such realization is stored and compared with experimental data (Refer #3).</p> <p><strong>3. Stage0_fitted_data.mat</strong> - .mat file containing data pertaining to height measures of the 3D printed sample prior to polishing and generated initial surface (simulation) which is statistically similar to the actual data.</p> <p><strong>4. Parameter_fitting_Polishing.m</strong> - Script containing the model capturing polishing dynamics with network formation, evaluated at each stage of polishing. This file generates the Bearing Area Curves of the initial surface simulated after each stage of polishing and compares with experimental data (Figures 3, 5, 6, 7 and 8 in paper). (The script makes use of other functions defined in #5).</p> <p><strong>5. surface_roughness.m, graph_evolution.m, solve_for_d.m, KLDiv.m</strong> and <strong>Gen_hurst.m</strong> - Matlab scripts containing functions that are called within the main script (Parameter_fitting_Polishing.m)</p> <p><strong>6. Simulated_Annealing.zip</strong> - Zip file containing files related to Simulated Annealing Algorithm. Please read the <strong>README_Simulated_Annealing.txt</strong> for instructions to reproduce the optimized parameter solutions.</p> <p><strong>7. pub_fig.m</strong> - Script containing the formatting options for plots and figures.</p>
Data from: Fast mvSLOUCH: Multivariate Ornstein-Uhlenbeck-based models of trait evolution on large phylogenies
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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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Data for: Highly contiguous genome assembly of Drosophila prolongata – a model for evolution of sexual dimorphism and male-specific innovations
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Data from: In vivo functional phenotypes from a computational epistatic model of evolution
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The evolution, complexity and diversity of models of long-term forest dynamics
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Complex models of sequence evolution improve fit, but not gene tree discordance, for tetrapod mitogenomes
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Fast mvSLOUCH: Model comparison for multivariate Ornstein--Uhlenbeck-based models of trait evolution on large phylogenies
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Chronogram or phylogram for ancestral state estimation? Model-fit statistics indicate the branch lengths underlying a binary character’s evolution: R scripts and simulated trees
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Supporting model data for Paleogeographic controls on the evolution of Late Cretaceous ocean circulation by Ladant, J.-B., et al. in Climate of the Past, doi:10.5194/cp-2019-157.
<p>The dataset is comprised of CCSM4 model variables required to reproduce the figures shown in the following manuscript:</p> <p>Ladant, J.-B., C. J. Poulsen, F. Fluteau, C. R. Tabor, K. G. MacLeod, E. E. Martin, S. J. Haynes and M. A. Rostami, Paleogeographic controls on the evolution of Late Cretaceous ocean circulation, Climate of the Past, doi:10.5194/cp-2019-157.</p>
On the effect of asymmetrical trait inheritance on models of trait evolution
Current phylogenetic comparative methods modelling quantitative trait evolution generally assume that, during speciation, phenotypes are inherited identically between the two daughter species. This, however, neglects the fact that species consist of a set of individuals, each bearing its own trait value. Indeed, because descendent populations after speciation are samples of a parent population, we can expect their mean phenotypes to randomly differ from one another potentially generating a ``jump'' of mean phenotypes due to asymmetrical trait inheritance at cladogenesis. Here, we aim to clarify the effect of asymmetrical trait inheritance at speciation on macroevolutionary analyses, focusing on model testing and parameter estimation using some of the most common models of quantitative trait evolution. We developed an individual-based simulation framework in which the evolution of species phenotypes is determined by trait changes at the individual level accumulating across generations and cladogenesis occurs then by separation of subsets of the individuals into new lineages. Through simulations, we assess the magnitude of phenotypic jumps at cladogenesis under different modes of trait inheritance at speciation. We show that even small jumps can strongly alter both the results of model selection and parameter estimations, potentially affecting the biological interpretation of the estimated mode of evolution of a trait. Our results call for caution when interpreting analyses of trait evolution, while highlighting the importance of testing a wide range of alternative models. In the light of our findings, we propose that future methodological advances in comparative methods should more explicitly model the intra-specific variability around species mean phenotypes and how it is inherited at speciation.
Data from: The multilocus multispecies coalescent: a flexible new model of gene family evolution
<p>Incomplete lineage sorting (ILS), the interaction between coalescence and speciation, can generate incongruence between gene trees and species trees, as can gene duplication (D), transfer (T) and loss (L). These processes are usually modelled independently, but in reality, ILS can affect gene copy number polymorphism, i.e., interfere with DTL. This has been previously recognised, but not treated in a satisfactory way, mainly because DTL events are naturally modelled forward-in-time, while ILS is naturally modelled backwards-in-time with the coalescent. Here we consider the joint action of ILS and DTL on the gene tree/species tree problem in all its complexity. In particular, we show that the interaction between ILS and duplications/transfers (without losses) can result in patterns usually interpreted as resulting from gene loss, and that the realised rate of D, T and L becomes non-homogeneous in time when ILS is taken into account. We introduce algorithmic solutions to these problems. Our new model, the <em>multilocus multispecies coalescent</em> (MLMSC), which also accounts for any level of linkage between loci, generalises the multispecies coalescent model and offers a versatile, powerful framework for proper simulation and inference of gene family evolution.</p>
Eco‐evolutionary dynamics driven by fishing: from single species models to dynamic evolution within complex food webs
<p>Evidence of contemporary evolution across ecological time scales stimulated research on the eco-evolutionary dynamics of natural populations. Aquatic systems provide a good setting to study eco-evolutionary dynamics owing to a wealth of long-term monitoring data and the detected trends in fish life-history traits across intensively harvested marine and freshwater systems. In the present study, we focus on modelling approaches to simulate eco-evolutionary dynamics of fishes and their ecosystems. Firstly, we review the development of modelling from single-species to multispecies approaches. Secondly, we advance the current state-of-the-art methodology by implementing evolution of life-history traits of a top predator into the context of complex food web dynamics as described by the allometric trophic network (ATN) framework. The functioning of our newly developed eco-evolutionary ATNE framework is illustrated using a well-studied lake food web. Our simulations show how both natural selection arising from feeding interactions and size-selective fishing cause evolutionary changes in the top predator and how those feed back to its prey species and further cascade down to lower trophic levels. Finally, we discuss future directions, particularly the need to integrate genomic discoveries into eco-evolutionary projections.</p>
VFTS meeting: animation of main-sequence model evolution
<p>This animation shows the evolution of our binary and single stellar models from 2Myr to 100Myr. We populate 3763 binaries, whose primary mass is in the range of 3Msun to 100Msun, following a Salpeter IMF with an exponent of -2.37. The mass ratio is uniformly distributed from 0.1 to 1. The orbital period logP is uniformly distributed from the minimum value at which the two stars would contact initially to 3.5. Both components rotate at half of their critical velocities initially. </p> <p>Considering a binary fraction of 70%, we populate 1612 single stars with vi=0.5. In addition, we also populate 537 slowly-rotating single stars with vi=0.2 to reproduce the observed blue MS in young star clusters. The number is chosen such that the ratio between the slow and fast rotators is 1/3, which is the same as the ratio of observed blue and red MS stars. </p> <p>Gravity darkening and observational errors are included. Filled circles correspond to single stellar models, while open symbols correspond to binaries with different companions. Single stellar tracks with vi=0.5 are plotted with solid grey lines. The black dotted line represents the ZAMS line of vi=0.2 single stellar models. In the legend, the number in each group outside the parenthesis corresponds to the number of stars whose color and magnitude are in the figure range. While the number in the parenthesis corresponds to the number of stars above the orange dashed line, which is 1.75 mag below the turn-off magnitude. </p>
A codon model for associating phenotypic traits with altered selective patterns of sequence evolution
<p>Detecting the signature of selection in coding sequences and associating it with shifts in phenotypic states can unveil genes underlying complex traits. Of the various signatures of selection exhibited at the molecular level, changes in the pattern of selection at protein coding genes have been of main interest. To this end, phylogenetic branch-site codon models are routinely applied to detect changes in selective patterns along specific branches of the phylogeny. Many of these methods rely on a pre-specified partition of the phylogeny to branch categories, thus treating the course of trait evolution as fully resolved and assuming that phenotypic transitions have occurred only at speciation events. Here we present TraitRELAX, a new phylogenetic model that alleviates these strong assumptions by explicitly accounting for the uncertainty in the evolution of both trait and coding sequences. This joint statistical framework enables the detection of changes in selection intensity upon repeated trait transitions. We evaluated the performance of TraitRELAX using simulations and then applied it to two case studies. Using TraitRELAX, we found an intensification of selection in the primate SEMG2 gene in polygynandrous species compared to species of other mating forms, as well as changes in the intensity of purifying selection operating on sixteen bacterial genes upon transitioning from a free-living to an endosymbiotic lifestyle.</p>
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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.