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655 results for “constrain”
Naturalis barcode-constrained-phylogeny: pipeline output files used in internship graduate paper
<p>Data used for thesis written by Naomi van Es, bioinformatics student at University of Applied Sciences Leiden. Pipeline was contributed to during internship at Naturalis Biodiversity Centre, supervised by Dr Rutger A. Vos. Include all content of '/data' directory after running the pipeline barcode-constrained-phylogeny, branch <a href="https://github.com/naturalis/barcode-constrained-phylogeny/tree/researchpaper_version">researchpaper_version</a>. Configuration file was set to only use data from taxonomic order Primates and DNA marker COI-5P. Output files include custom database, alignments, constraint trees and subtrees of BOLD barcode data. Example taxonomic family in thesis results is<em> Lemuridae. </em></p>
Data from: hespdiv: an R package for spatially constrained, hierarchical and contiguous regionalization in palaeobiogeography
<p>This is data for the '"hespdiv": an R package for spatially constrained, hierarchical, and contiguous regionalization in palaeobiogeography' paper. It contains datasets used, their metada, dataset processing scripts, a list of references to data contributors, and R files containing some of the results presented in the paper.</p>
The evolution of antimicrobial peptide resistance in Pseudomonas aeruginosa is severely constrained by random peptide mixtures
<p><span>The prevalence of antibiotic-resistant pathogens has become a major threat to public health, requiring swift initiatives for discovering new strategies to control bacterial infections. Hence, antibiotic stewardship and rapid diagnostics, but also the development, and prudent use, of novel effective antimicrobial agents are paramount. Ideally, these agents should be less likely to select for resistance in pathogens than currently available conventional antimicrobials. The usage of antimicrobial Peptides (AMPs), key components of the innate immune response, and combination therapies, have been proposed as strategies to diminish the emergence of resistance.</span></p> <p><span>Herein, we investigated whether newly developed random antimicrobial peptide mixtures (RPMs) can significantly reduce the risk of resistance evolution <em>in vitro</em> to that of single sequence AMPs, using the ESKAPE pathogen <em>Pseudomonas aeruginosa</em> (<em>P. aeruginosa</em>) as a model Gram-negative bacterium. Infections of this pathogen are difficult to treat due the inherent resistance to many drug classes, enhanced by the capacity to</span><span> form biofilms. </span><em><span>P. aeruginosa</span></em><span> was experimentally evolved in the presence of AMPs or RPMs, subsequentially assessing the extent of resistance evolution and cross-resistance/collateral sensitivity between treatments. Furthermore, the fitness costs of resistance on bacterial growth were studied, and whole-genome sequencing used to investigate which mutations could be candidates for causing resistant phenotypes. Lastly, changes in the pharmacodynamics of the evolved bacterial strains were examined.</span></p> <p><span>Our findings suggest that using RPMs bears a much lower risk of resistance evolution compared to AMPs and mostly prevents cross-resistance development to other treatments, while maintaining (or even improving) drug sensitivity. This strengthens the case for using random cocktails of AMPs in favour of single AMPs, against which resistance evolved <em>in vitro</em>, providing an alternative to classic antibiotics worth pursuing.</span></p>
Sparsity-constrained wavefront optimization by leveraging complex media
<div> <div>Wavefront shaping gains increasing importance in complex photonics, which can manipulate light spatially and temporally to counter the scattering effect. Important applications include deep-tissue imaging, microendoscopy, optical communications, nanofabrication, and remote sensing. However, high-speed and high-fidelity wavefront shaping is fundamentally hindered by the dimensionality limitation of hardware devices, evinced by the competition between the frame rate, pixel count, and modulation depth. To overcome the speed-fidelity tradeoff, we leverage complex media (e.g., diffusers or multimode fibers) as analogue random multiplexers for pattern compression to address the demand for high-dimensional spatiotemporal control. Sparsity-constrained wavefront optimization is designed to solve the problem by seeking a low-dimensional, robust representation of wavefronts with a carefully designed sparsity constraint. This optimization framework can achieve high-fidelity wavefront shaping through complex media using high-speed, yet relatively low-precision spatial light modulation devices (e.g., digital micromirror devices) without compromising the frame rate.</div> </div>
Advanced PySPAM: An Infrastructure to Constrain Underlying Interacting Galaxy Parameters Synthetic Results
<p>This database contains the results for the Chapter 3 of DOR's thesis. For a full description of these results and the way they were built, please see <em>Link to be added on publication</em>.</p> <p>The aim of this Chapter was to use MCMC methods with a fast, efficient simulation algorithm (APySPAM) to constrain the underyling parameters of observed interacting galaxy systems. This algorithm used a Chi-Squared distance minimisation between morphology distributions of observed and simulated images to constrain 13 underlying parameters of galaxy interaction. We applied our algorithm to to 50 of the 62 systems described in <a href="https://ui.adsabs.harvard.edu/abs/2016MNRAS.459..720H/abstract">Holincheck et al. (2016).</a></p> <p>We opted to use the Holincheck et al. sample as the underlying parameters of these systems had already been constrained using a Citizen Science project named <a href="https://mergers.galaxyzoo.org/">Galaxy Zoo: Mergers</a>. This gave us a ground truth to which compare our constraints to. We created synthetic observations of each image, and then ran our MCMC over them, achieving constraint across the sample and parameter space. However, when applied to observational data (we opted to use SDSS images of these systems) we are unable to constrain the full parameter space. This is particularily true of the orientations of the interacting system and their relative sizes.</p> <p>Exploring using velocity information in our constraints find that we improve almost all our constrains considerably. Therefore, adding in spectroscopic information to this method could drastically improve it. The main limitation of this approach, however, is computation time with each system taking approximately 20 hours on a well parallelised HPC to converge. Alternatives to improve performance lie in simulation based inference (SBI, an introduction can be found <a href="https://arxiv.org/pdf/2009.08459">here</a>) or including the use of GPUs (such as done by NVIDEA in fluid dynamics <a href="https://developer.nvidia.com/blog/ai-powered-simulation-tools-for-surrogate-modeling-engineering-workflows-with-siml-ai-and-nvidia-modulus/">here</a>)</p> <p>The results are portrayed as corner plots, with contour plots showing the distribution of likelihoods found in each MCMC run and the histograms on the side showing the marginalised posterior distributions.</p>
Constraining the dense matter equation of state with new NICER mass-radius measurements and new chiral effective field theory constraints: prior and posterior samples and scripts for generating plots
<p>Full reproduction package accompanying the paper: <em>Constraining the dense matter equation of state with new NICER mass-radius measurements and </em><em>new chiral effective field theory inputs</em></p> <p> </p> <p><em>*Note, the changes made from version to version are made visible in the CHANGELOG.rst file</em></p>
Supporting Information for "Geochemistry constrains global hydrology on Early Mars"
<p>Copy of the Supporting Information for "Geochemistry constrains global hydrology on Early Mars", by Edwin S. Kite and Mohit Melwani Daswani.</p>
Constraining the Neutron Star Mass-Radius Relation and Dense Matter Equation of State with NICER. I. The Millisecond Pulsar X-Ray Data Set
<p>This deposit includes the cleaned, filtered and phase folded NICER event data set for the millisecond pulsar (MSP) PSR J0030+0451 in the 0.25-3 keV band. The data processing and filtering was performed using HEASoft 6.251 and NICERDAS version 5.0; the specific parameters and filtering criteria used are detailed in the ApJ Letter listed above. This event list was used to produce what is shown for PSR J0030+0451 in Figures 2, 3, and 4 in the accepted ApJ Letter listed above and was also used for the neutron star mass-radius and equation of state inference analyses presented in the companion papers (Miller et al. 2019, Riley et al. 2019, and Raaijmakers et al. 2019).</p> <p>The event file and its MD5 checksum is:<br> J0030+0451_merged_phase_0.25-3keV.fits (463bbac7203bb45bb02ea0deed49f083)<br> </p>
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively. in Factors affecting the diet of the red fox (Vulpes vulpes) in a heterogeneous Mediterranean landscape
Figure. Constrained ordination plot as produced from canonical correspondence analysis (CCA). The variability of environmental variables is summarized on Axis 1 and Axis 2 of the constrained biplot, explaining the variability of the trophic groups included in the red fox's diet. Trophic groups are shown with black line (unfilled) pyramids, whereas environmental variables are shown with black filled pyramids. Proximity and distance of response centroids to predictor centroids indicate positive and negative correlations between them, respectively.
Data and analysis and plotting scripts for Swaminathan et al., "Regional Impacts Poorly Constrained by Climate Sensitivity"
<p>The datasets included here are of the plotted data from the figures of the paper entitled "Regional Impacts Poorly Constrained by Climate Sensitivity", by Ranjini Swaminathan, Jacob Schewe, Jeremy Walton, Klaus Zimmermann, Colin Jones, Richard A. Betts, Chantelle Burton, Chris D. Jones, Matthias Mengel, Christopher Reyer, Andrew G. Turner & Katja Weigel, submitted for publication in Earth's Futures. Scripts used for plotting and analysis are also included.</p>
Input data and analyzed data of "Topology of synaptic connectivity constrains neuronal stimulus representation (...)"
<p>This dataset contains the input data, as well as the analyzed data that our <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">preprint</a></p> <p><em><strong>Topology of synaptic connectivity constrains neuronal stimulus representation, predicting two complementary coding strategies</strong></em></p> <p>to be found on <a href="https://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">bioRxiv</a> is based on. The input data (<em>input_data.zip</em>) contains everything that is needed to run the full <a href="https://github.com/BlueBrain/topological_sampling/">analysis pipeline</a> from start to the generation of the figures found in the manuscript. However, some of the analysis steps can be computationally heavy, so we also provide the output of these expensive steps, that can be simply used in conjunction with jupyter notebooks (<em>notebooks.zip)</em> to generate the figures.</p> <p><strong>Overview</strong></p> <p>An overview image can be found <a href="https://raw.githubusercontent.com/BlueBrain/topological_sampling/master/toposampling_pipeline_overview.png"><strong>here</strong></a></p> <p>Blue squares denote input / output files (that are part of this dataset). Grey circles denote steps of the analysis pipeline (that are implemented in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>). Red rectangles denote configuration files (that are part of this dataset and also in the <a href="https://github.com/BlueBrain/topological_sampling/">github repository</a>).</p> <p>This Dataset can also be browsed, downloaded and accessed as linked open data from the <a href="https://bbp.epfl.ch/nexus/web/studios/public/topological-sampling/studios/data:a7cc7e9f-53c5-4940-929c-95f4c4f57728?workspaceId=data:165e54c5-e8f6-4d85-ac94-53bc3dfe5cd4">BBP knowledge Graph based Data studios</a>.</p> <p><strong>Contained file types and their structure</strong></p> <p>Here, we provide four types of files. Configuration files specify analysis parameters and define the expected locations of the data files. Input files are the inputs into the analysis pipeline. Analyzed files are the outputs of said pipeline. Finally, we provide a number of jupyter notebooks that use the analyzed files to generate the manuscript figures. If you want to re-run the entire analysis pipeline, you need the code and configuration files from the <a href="https://github.com/BlueBrain/topological_sampling/">repository</a>, the input files and notebooks; the analyzed files will be generated as you run the pipeline. For information how to run this, refer to the <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a>. If you only want to generate the figures, you still need the code and configuration files from the repository, as it contains a package related to reading the result files; further, you need the analyzed files in addition to the input files. Of course, you can also run parts of the analysis pipeline and download the outputs for the rest.</p> <p>To run everything smoothly, the files have to be placed into the expected file structure. You can look up and configure the file structure in the configuration files. Below, we describe the default layout, which is very simple (<em>root</em> is where you placed the code from our <a href="https://github.com/BlueBrain/topological_sampling/">repository</a> and can be any location on your file system):</p> <ul> <li>Configuration files<em>: </em>Part of the <a href="https://github.com/BlueBrain/topological_sampling/">repository.</a> Placed into <em>root/working_dir/configs</em></li> <li>Input data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>input_data.zip</em> -- Input data. Contains details on the model used in the manuscript and the output (spike times) of the simulation described in the manuscript. Within the file: <ul> <li>For details, see <a href="https://github.com/BlueBrain/topological_sampling/blob/master/README.md">readme</a></li> </ul> </li> </ul> </li> <li>Analyzed data: Place into <em>root/working_dir/data</em>, then unzip in place <ul> <li><em>classifier_features_results.zip </em>-- Output of the "classifier" step. Results of stimulus classification on the data in <em>features.zip</em></li> <li><em>classifier_manifold_result</em>s.zip -- Output of the "classifier" step. Results of stimulus classification on the data in <em>extracted_components.zip</em></li> <li><em>community_database.zip</em> -- Output of "gen_topo_db". Various topological parameters related to the close neighborhood of neurons in the model</li> <li><em>extracted_components.zip </em>-- Output of "manifold_analysis". Results of factor analysis on the spike times in the <em>input_data</em></li> <li><em>features.zip</em> -- Output of "topological_featurization". A new dimensionality reduction method we introduce in the <a href="http://www.biorxiv.org/content/10.1101/2020.11.02.363929v1">manuscript</a></li> <li><em>split_spike_trains.zip -- </em>Output of "split_time_windows". The spike trains, split into time windows that are the responses to individual stimuli injected in the simulation</li> <li><em>structural_parameters.zip</em><em> -- </em>Output of "Structural tribe analysis". Values for the topological parameters in <em>community_database.zip</em> associated with the neuron samples specified in <em>tribes.zip</em></li> <li><em>structural_parameters_vol.zip</em> -- Output of "Structural tribe analysis". Same as above, but for volumetric neuron samples.</li> <li><em>triads.zip</em> -- Output of "Triad-counts". Over- and under-expression of triad motifs in the samples in <em>tribes.zip</em>.</li> <li><em>tribes.zip</em><em> -- </em>Output of "sample_tribes". Specific neuron samples that are then analyzed further.</li> </ul> </li> <li>Notebooks: Place into <em>root/notebooks</em> and unzip in place <ul> <li><em>notebooks.zip</em><em> -- </em>Jupyter notebooks. Run them to generate the figures in the manuscript.</li> </ul> </li> </ul> <p> </p> <p><strong>Updates:</strong></p> <p>v1.1.0 (2020/12/11): Added some additional control cases to the results for figure 7. These results will probably not be updated on bioRxiv, but go into the submission to a journal.</p> <p>v1.2.0 (2021/10/05): Updated the notebooks.zip with changes we made in response to reviewers' feedback.</p>
Datasets for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants"
<p>All data used for "Needle in a Bayes Stack: a Hierarchical Bayesian Method for Constraining the Neutron Star Equation of State with an Ensemble of Binary Neutron Star Post-merger Remnants", Criswell, A.W., et al. (2022). The code used to create the paper results from this data can be found at <a href="https://github.com/criswellalexander/hbpm_paper">https://github.com/criswellalexander/hbpm_paper</a> and the underlying software package can be found at <a href="https://github.com/criswellalexander/bayestack">https://github.com/criswellalexander/bayestack</a>.</p>
Emission, AOD, AAOD and DRE constrained by PARASOL/GRASP
<p><strong>Dataset - Emission, AOD, AAOD and DRE constrained by PARASOL/GRASP</strong></p> <p>1. Emission of absorbing aerosol species (black carbon - BC, organic carbon - OC and desert dust - DD) constrained by PARASOL/GRASP spectral AOD and AAOD (https://www.grasp-open.com/products/polder-data-release/) (Chen et al., 2018, 2019, 2020).</p> <p>2. Spectral AOD, AAOD and DRE simulated using GEOS-Chem v11-01 coupled with a radiative transfer mode (RRTMG) (Heald et al., 2014).</p> <p> </p> <p><em>References:</em></p> <p>Chen, C., Dubovik, O., Schuster, G.L. <em>et al.</em> Multi-angular polarimetric remote sensing to pinpoint global aerosol absorption and direct radiative forcing. <em>Nat Commun</em> <strong>13</strong>, 7459 (2022). https://doi.org/10.1038/s41467-022-35147-y</p> <p>Chen, C., Dubovik, O., Henze, D. K., Lapyonak, T., Chin, M., Ducos, F., Litvinov, P., Huang, X. and Li, L.: Retrieval of desert dust and carbonaceous aerosol emissions over Africa from POLDER/PARASOL products generated by the GRASP algorithm, Atmos. Chem. Phys., 18(16), 12551–12580, doi:10.5194/acp-18-12551-2018, 2018.</p> <p>Chen, C., Dubovik, O., Henze, D. K., Chin, M., Lapyonok, T., Schuster, G. L., Ducos, F., Fuertes, D., Litvinov, P., Li, L., Lopatin, A., Hu, Q. and Torres, B.: Constraining global aerosol emissions using POLDER/PARASOL satellite remote sensing observations, Atmos. Chem. Phys., 19(23), 14585–14606, doi:10.5194/acp-19-14585-2019, 2019.</p> <p>Chen, C., Dubovik, O., Fuertes, D., Litvinov, P., Lapyonok, T., Lopatin, A., Ducos, F., Derimian, Y., Herman, M., Tanré, D., Remer, L., Lyapustin, A., Sayer, A., Levy, R., Hsu, N. C., Descloitres, J., Li, L., Torres, B., Karol, Y., Herrera, M., Herreras, M., Aspetsberger, M., Wanzenboeck, M., Bindreiter, L., Marth, D., Hangler, A. and Federspiel, C.: Validation of GRASP algorithm product from POLDER/PARASOL data and assessment of multi-angular polarimetry potential for aerosol monitoring, Earth Syst. Sci. Data, 12(4), 3573–3620, doi:10.5194/essd-12-3573-2020, 2020.</p> <p>Heald, C. L., Ridley, D. A., Kroll, J. H., Barrett, S. R. H., Cady-Pereira, K. E., Alvarado, M. J. and Holmes, C. D.: Contrasting the direct radiative effect and direct radiative forcing of aerosols, Atmos. Chem. Phys, 14, 5513–5527, doi:10.5194/acp-14-5513-2014, 2014.</p>
Dataset presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions"
<p>Dataset (observed tippers, solar quiet global-to-local transfer functions, and global Q responses) presented in the recently submitted AGU manuscript "Constraining the crustal and mantle conductivity structures beneath islands by a joint inversion of multi-source magnetic transfer functions".</p>
Experimental Results for the study "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems"
<p>This repository contains the experiment results (raw data in NPZ and CSV format and Latex tables) for the study "The Hypervolume Newton Method for Constrained Multi-Objective Optimization Problems", which is accepted in <em>Mathematical and Computational Applications</em> journal.</p> <p>The preprint version of the related paper is already online: </p> <p> </p> <p>Wang, H.; Emmerich, M.; Deutz, A.; Hernández, V.A.S.; Schütze, O. The Hypervolume Newton Method for Constrained Multi-objective Optimization Problems. <em>Preprints</em> <strong>2022</strong>, 2022110103 (doi: <a href="http://10.20944/preprints202211.0103.v1">10.20944/preprints202211.0103.v1</a>).</p> <p><strong>Data description:</strong> we benchmarked<strong> </strong>three algorithms: (1) the standalone <strong>Hypervolume Netwon Method</strong> (HVN), (2) NSGA-III, and (3) the <strong>hybridization</strong> of the standalone HVN and NSGA-III on several artificial problems.</p> <ul> <li>For the standalone HVN algorithm, we tested it on three simple artificial test problems - P1, P2, and P3 (proposed in the above paper): <ul> <li>2D-example-50*.tex: problem P1</li> <li>3D-example1*.tex: problem P2</li> <li>3D-example2*.tex: problem P3</li> </ul> </li> <li>For NSGA-III and the hybridization, we tested them on the equality-constrained DTLZ and Inverted DTLZ (IDTLZ) problems: <ul> <li>Eq1DTLZ.*npz: DTLZ problems</li> <li>Eq1IDTLZ*.npz: IDTLZ problems</li> </ul> </li> </ul>
Data for "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover"
<p>Dataset for the "Global Riverine Export of Dissolved Lignin Constrained by Hydrology, Geomorphology and Land-Cover". Dataset 01 includes site locations, basin area, dissolved organic carbon (DOC), dissolved lignin concentration and relevant references. Dataset 03 includes mean/discharge-weighted DOC, mean/discharge-weighted dissolved lignin concentrations. Dataset 03 includes geomorphological, climatic, hydrological and land-cover data for the 25 rivers. Dataset 04 includes the reconstructed yield of dissolved lignin and basin area of the 79 rivers.</p>
Compositionally constrained sites drive long branch attraction
<p>Accurate phylogenies are fundamental to our understanding of the pattern and process of evolution. Yet, phylogenies at deep evolutionary timescales, with correspondingly long branches, have been fraught with controversy resulting from conflicting estimates from models with varying complexity and goodness of fit. Analyses of historical as well as current empirical datasets, such as alignments including Microsporidia, Nematoda or Platyhelminthes, have demonstrated that inadequate modeling of across-site compositional heterogeneity, which is the result of biochemical constraints that lead to varying patterns of accepted amino acids along sequences, can lead to erroneous topologies that are strongly supported. Unfortunately, models that adequately account for across-site compositional heterogeneity remain computationally challenging or intractable for an increasing fraction of contemporary datasets. Here, we introduce "compositional constraint analysis", a method to investigate the effect of site-specific amino acid diversity on phylogenetic inference, and show that more constrained sites with lower diversity and less constrained sites with higher diversity exhibit ostensibly conflicting signal under models ignoring across-site compositional heterogeneity and thus contribute to topological bias and long branch attraction artifacts. We demonstrate that more complex models accounting for across-site compositional heterogeneity can ameliorate this bias. We present CAT-PMSF, a pipeline for diagnosing and resolving phylogenetic bias resulting from inadequate modeling of across-site compositional heterogeneity based on the CAT model. CAT-PMSF is robust against long branch attraction in all alignments we have examined. We suggest using CAT-PMSF when convergence of the CAT model cannot be assured. We find evidence that compositionally constrained sites are driving long branch attraction in two metazoan datasets and recover evidence for Porifera as the sister group to all other animals.</p>
Data from: Timing of departure from natal areas by Golden Eagles is not constrained by acquisition of flight skills
<p>The post-fledging dependence period (PFDP), which extends from a fledgling’s first flight out of the nest to its departure from the parents’ territory, is crucial in the lifecycle of birds. During this period, juveniles develop their flight and foraging skills to become fully independent. Despite the importance of this life stage in basic bird ecology and conservation, it remains largely overlooked – notably its link with the acquisition of flight skills. In this study, we modeled the variation in seven proxies describing flight skills of 84 GPS-tracked Golden Eagle juveniles in France between 2016 and 2020. Juveniles had a long but highly variable PFDP, averaging 177.9 (±62.2) days after departure from the nest. This period is divided into two phases: a first phase of rapid increase in flight skills over the first 60 days after departure from the nest, followed by a plateau in which flight skills no longer develop until independence. These results suggest that the full development of flight skills is not a constraining factor during the PFDP and that it is advantageous for juveniles to choose to remain in their natal territory. We posit that parents’ tolerance of fledged juveniles is a type of parental care that may maximize their own fitness by improving the survival of their descendants. In future studies, it may be of interest to investigate the factors that may explain the high variability in the duration of this stage between individuals within the same population.</p>
Preprocessed datasets for experiments in the paper "Constrained Monotonic Neural Networks"
<p><strong>Preprocessed datasets used in the experiments of the paper:</strong></p> <ul> <li>Davor Runje, Sharath M. Shankaranarayana. <em>Constrained Monotonic Neural Networks</em>. International Conference on Machine Learning, 2023. [<a href="https://arxiv.org/pdf/2205.11775.pdf">pdf</a>]</li> </ul> <p>The code that runs the experiments can be found at:</p> <p><a href="https://github.com/airtai/monotonic-dense-layer">https://github.com/airtai/monotonic-dense-layer</a></p> <p> </p>
There and back to the present: a model-based framework to estimate phylogenetically constrained alpha diversity gradients
<p>The imprint left by niche evolution on the variation of biological diversity across spatial and environmental gradients is still debated among ecologists. Furthermore, understanding to what extent dispersal limitation may reinforce or blur such an imprint is still a gap in our ecological knowledge. In this article we introduce a simulation approach coupled to Approximate Bayesian Computation (ABC) that parameterizes both the adaptation rate of species' niche positions over the evolution of a monophyletic lineage and the intensity of dispersal limitation associated with the variation of species alpha diversity among assemblages distributed across spatial and environmental gradients. The analytical tool was implemented in the R package <em>mcfly</em>. We evaluated the statistical performance of the analytical framework using simulated datasets, which confirmed the suitability of the analysis to estimate the adaptation rate parameter but showed to be less precise in relation to the dispersal limitation parameter. Also, we found that increased dispersal limitation levels improved the parameterization of the adaptation rate of species' niche positions in simulated datasets. Further, we evaluated the role played by niche evolution and dispersal limitation on species alpha diversity variation of Phyllostomidae bats across the Neotropics. The framework proposed here sheds light on the links between niche evolution, dispersal limitation and gradients of biological diversity, and thereby improved our understanding of evolutionary imprints on current biological diversity patterns.</p>
ScienceDex guides
Understand access before you commit
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.