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133 results for “backbone”
Protein-protein docking with significant backbone flexibility
<p>The trajectory of a single replica from the protein-protein docking simulation of barnase/barstar system. The movie shows the barnase receptor in surface representation and the barstar ligand in ribbon. The presented replica reached the model with interface RMSD value 1.9 Angstrom from the complex X-ray structure, shown as transparent ribbon. </p>
Data from: Employing hypothesis testing and data from multiple genomic compartments to resolve recalcitrant backbone nodes in Goodenia s.l. (Goodeniaceae)
Goodeniaceae is a primarily Australian flowering plant family with a complex taxonomy and evolutionary history. Previous phylogenetic analyses have successfully resolved the backbone topology of the largest clade in the family, Goodenia s.l., but have failed to clarify relationships within the species-rich and enigmatic Goodenia clade C, a prerequisite for taxonomic revision of the group. We used genome skimming to retrieve sequences for chloroplast, mitochondrial, and nuclear markers for 24 taxa representing Goodenia s.l., with a particular focus on Goodenia clade C. We performed extensive hypothesis tests to explore incongruence in clade C and evaluate statistical support for clades within this group, using datasets from all three genomic compartments. The mitochondrial dataset is comparable to the chloroplast dataset in providing resolution within Goodenia clade C, though backbone support values within this clade remain low. The hypothesis tests provided an additional, complementary means of evaluating support for clades. We propose that the major subclades of Goodenia clade C (C1–C3 + Verreauxia) are the result of a rapid radiation, and each represents a distinct lineage.
Data sets for phylogenomic analyses in: Ant backbone phylogeny resolved by modelling compositional heterogeneity among sites in genomic data
<p>Ants are the most ubiquitous and ecologically dominant arthropods on Earth, and understanding their phylogeny is crucial for deciphering their character evolution, species diversification, and biogeography. Although recent genomic data have shown promise in clarifying intrafamilial relationships across the tree of ants, inconsistencies between molecular datasets have also emerged. Here I re-examine the most comprehensive published Sanger-sequencing and genome-scale datasets of ants using model comparison methods that model among-site compositional heterogeneity to understand the sources of conflict in phylogenetic studies. My results under the best-fitting model, selected on the basis of Bayesian cross-validation and posterior predictive model checking, identify contentious nodes in ant phylogeny whose resolution is <a>modelling-dependent. </a>I show that the Bayesian infinite mixture CAT model outperforms empirical finite mixture models (C20, C40 and C60) and that, under the best-fitting CAT-GTR+G4 model, the enigmatic <a><em>Martialis</em> </a><em>heureka</em> is sister to all ants except Leptanillinae, rejecting the more popular hypothesis supported under worse-fitting models, that place it as sister to Leptanillinae. These analyses resolve a lasting controversy in ant phylogeny and highlight the significance of model comparison and adequate modelling of among-site compositional heterogeneity in reconstructing the deep phylogeny of insects.</p>
Data from: phylogenomics of the Neogastropoda: the backbone hidden in the bush
<p>The molluscan order Neogastropoda encompasses over 15,000 almost exclusively marine species playing important roles in benthic communities and in the economics of coastal countries. Neogastropoda underwent intensive cladogenesis in early stages of diversification, generating a 'bush' at the base of their evolutionary tree, that has been hard to resolve even with high throughput molecular data. In the present study we analyze a comprehensive exon capture dataset of 1,817 loci (79.6% data occupancy), comprising 112 taxa of 48 (out of 60) recent Neogastropoda families with a variety of phylogenetic inference methods to resolve their relationships. Our results show consistent topologies and high support in all analyses at (super)family level, supporting monophyly of Muricoidea, Mitroidea, Conoidea, and, with some reservations, Olivoidea and Buccinoidea. Volutoidea and Turbinelloidea as currently circumscribed are clearly paraphyletic. Despite our analyses consistently resolve most backbone nodes, three prove problematic. First, uncertain placement of Cancellariidae, as a sister group of either a Ficoidea-Tonnoidea clade, or of the rest of Neogastropoda, leaves monophyly of Neogastropoda unresolved. Second, relationships are contradictory at the base of the major grouping the 'core Neogastropoda'. Third, coalescence-based analyses reject monophyly of the Buccinoidea in relation to Vasidae. We analysed loci phylogenetic signal in relation with potential biases, and propose most probable resolutions in the two latter recalcitrant nodes. The uncertain placement of Cancellariidae may be explained by orthology violations due to the differential paralog loss short after the whole genome duplication, and should be resolved with a curated set of longer loci.</p>
Input data and modelling files for a model of the Finnish energy system with focus on cascade hydropower and the addition of a hydrogen storage system realised in Backbone
<p>The files show the input data and modelling files used for the publication "Cascade hydropower integration in a techno-economic power system model: A study of Finnish hydropower plants" (Kiehle et al., 2025 - submitted). The paper's <a title="Preprint on SSRN" href="https://dx.doi.org/10.2139/ssrn.4971685" target="_blank" rel="noopener">preprint</a> is available. A model of the Finnish energy system in 2022 was built in the techno-economic modelling framework Backbone (available on GitLab: https://gitlab.vtt.fi/backbone/backbone). The focus was on implementing cascading hydropower plants in a power system model, including individual reservoirs, generation and spillage capacities. </p> <p>"ModellingFiles_Debug" are GAMS-based data that can be used to run the scenario in Backbone or display the results. "ModellingResults" are gdx files that purely list the results. Those are also presented in more detail in the scientific paper. The Excel files present the input data used for modelling and can also be used to run the model. </p>
A phylogenomic backbone for gastropod molluscs
<p>Gastropods have survived several mass extinctions during their evolutionary history resulting in extraordinary diversity in morphology, ecology, and developmental modes, which complicate the reconstruction of a robust phylogeny. Currently, gastropods are divided into six subclasses: Caenogastropoda, Heterobranchia, Neomphaliones, Neritimorpha, Patellogastropoda, and Vetigastropoda. Phylogenetic relationships among these taxa historically lack consensus, despite numerous efforts using morphological and molecular information. We generated sequence data for transcriptomes derived from twelve taxa belonging to clades with little or no prior representation in previous studies in order to infer the deeper cladogenetic events within Gastropoda and, for the first time, infer the position of the deep-sea Neomphaliones using a phylogenomic approach. We explored the impact of missing data, homoplasy, and compositional heterogeneity on the inferred phylogenetic hypotheses. We recovered a highly supported backbone for gastropod relationships that is congruent with morphological and mitogenomic evidence, in which Patellogastropoda, true limpets, are the sister lineage to all other gastropods (Orthogastropoda) which are divided into two main clades (i) Vetigastropoda s.l. (including Pleurotomariida + Neomphaliones) and (ii) Neritimorpha + (Caenogastropoda + Heterobranchia). As such, our results support the recognition of five subclasses (or infraclasses) in Gastropoda: Patellogastropoda, Vetigastropoda, Neritimorpha, Caenogastropoda and Heterobranchia.</p>
Data for "Image-based Backbone Reconstruction for Non-Slender Soft Robots"
<p>This dataset provides the data for the forthcoming paper "Image-based Backbone Reconstruction for Non-Slender Soft Robots". The backbone reconstruction method used is based on the method described in Hoffmann et al. [1]. The modifications to this method to support the non-slender soft robot in this dataset are described in the forthcoming paper mentioned above. This dataset holds raw images of pressurized and elongated soft robots and the corresponding reconstructed backbones.</p> <h2>Dataset</h2> <p>The dataset is split into two subsets with similar structure. The first subset is contained in `dataset_01`. The second dataset is contained in `dataset_02`.</p> <p>Each subset consists of five folders and one schedule file. The schedule file `schedule.csv` contains the index of the schedule entry, the angle <span>α</span> in degree, the pressure of each chamber p_1 to p_3 in bar and if the pressurization is active. Furthermore, the five folders of the subset can be described as follows</p> <p>- `raw`: Contains the raw cropped images. The filenames are formatted as `CROPPED_C{CAMERA_INDEX}_E{SCHEDULE_ENTRY}.png` with the camera index `CAMERA_INDEX` and the schedule entry `SCHEDULE_ENTRY`.</p> <p>-`constant_curvature_slender`, `constant_curvature_volumetric`, `cubic_curvature_slender` and `cubic_curvature_volumetric`. These folders contain the actual reconstructed backbones based on the raw data from the `raw` folder. A different reconstruction approach was used in each of these folders<br> - `constant_curvature_slender` - A constant curvature backbone kinematic based on the slender model,<br> - `constant_curvature_volumetric` - A constant curvature backbone kinematic based on the volumetric model,<br> - `cubic_curvature_slender` - A cubic curvature backbone kinematic based on the slender model,<br> - `cubic_curvature_volumetric` - A cubic curvature backbone kinematic based on the volumetric model.<br>Each of these folders contain a `data` and `figures` folder. The data folder consists of `PARAMETER_E{SCHEDULE_ENTRY}.json` files listing the optimization parameters for each schedule entry `SCHEDULE_ENTRY` in the JSON format. The `figures` folder contains annotated images of the reconstructed backbone on the cropped raw images. The filenames are structured `ANNOTATED_E{SCHEDULE_ENTRY}_C{CAMERA_INDEX}_EPOCH{EPOCH}.png` with the schedule entry `SCHEDULE_ENTRY`, the camera index `CAMERA_INDEX` and the epoch `EPOCH` of the optimization algorithm.</p> <p>The optimization parameters include the base position `base_position` of the reconstructed backbone in world coordinates, the coefficients for the curvature polynomials `ux` and `uy`, and the constant coefficient for the elongation polynomial `la`.</p> <h2>Calibration Data</h2> <p>The calibration data is located in the `calibration` folder and consists of multiple `.npy` files in the numpy format. The corresponding camera index for the calibrated camera is abbreviated with `CAMERA_INDEX` in the following:</p> <ul> <li>`C{CAMERA_INDEX}.npy` - Stores the reprojection error, camera matrix, distortion coefficients, rotation, and translation vectors as returned by the `cv2.calibrateCamera` [2] method. </li> <li>`C{CAMERA_INDEX}_camera_matrix.npy` - Stores the camera_matrix as returned by the `cv2.calibrateCamera` [2] method. </li> <li>`C{CAMERA_INDEX}_distortion_coefficients.npy` - Stores the distortion coefficients as returned by the `cv2.calibrateCamera` [2] method. </li> <li> `C{CAMERA_INDEX}_projection_matrix.npy` - Stores the projection matrix from world space to pixel space based on the stereo camera calibration.</li> <li> `STEREO.npy` - Stores the reprojection error, R, T, E, F as returned by the `cv2.stereoCalibrate` [2] method as an object datatype.</li> </ul> <h2>Acknowledgement</h2> <p>Funded by the Deutsche Forschungsgemeinschaft (DFG, German Research Foundation) – 501861263 – SPP2353</p> <h2>References</h2> <p>[1] M. K. Hoffmann, J. Mühlenhoff, Z. Ding, T. Sattel and K. Flaßkamp. An iterative closest point algorithm for marker-free 3D shape registration of continuum robots. arXiv.<br>https://arxiv.org/abs/2405.15336</p> <p>[2] OpenCV. Camera Calibration and 3D Reconstruction. OpenCV Documentation. https://docs.opencv.org/4.x/d9/d0c/group__calib3d.html, accessed May 27, 2024.</p>
African wood density database with matches to the taxonomic backbone data sets of World Flora Online (version 2023.12) and the World Checklist of Vascular Plants (version 11)
<p>The <strong><span>African Wood Density Database </span></strong><span>provides air-dry wood density data for over 750 tree species grown in Africa.</span></p> <p>This archive provides taxonomic matches with recent versions of <strong>World Flora Online</strong> (WFO; <a href="../records/10425161">version 2023.12 downloaded from Zenodo</a>; Borch et al. <a href="https://onlinelibrary.wiley.com/doi/10.1002/tax.12373">2020</a>) and the <strong>World Checklist of Vascular Plants</strong> (WCVP; <a href="https://sftp.kew.org/pub/data-repositories/WCVP/Archive/">version 11 downloaded from the Kew data depository</a>; Govaerts et al. <a href="https://doi.org/10.1038/s41597-021-00997-6">2021</a>). Matching was done via the <strong>WorldFlora</strong> package (<a href="https://cran.r-project.org/package=WorldFlora">version 1.14-3</a>; Kindt <a href="https://bsapubs.onlinelibrary.wiley.com/doi/full/10.1002/aps3.11388">2020</a>), using similar scripts as documented in this Rpub: <a href="https://rpubs.com/Roeland-KINDT/1134151">https://rpubs.com/Roeland-KINDT/1134151</a>.</p> <p> </p> <ul> <li><span>Carsan, S. Orwa, C. Harwood, C. Kindt, R. Stroebel, A. Neufeldt, H. and Jamnadass, R. 2012. African Wood Density Database. World Agroforestry Centre, Nairobi. <a href="https://apps.worldagroforestry.org/treesnmarkets/wood/">https://apps.worldagroforestry.org/treesnmarkets/wood/#</a> </span></li> <li><span>Borsch, T., Berendsohn, W., Dalcin, E., Delmas, M., Demissew, S., Elliott, A., Fritsch, P., Fuchs, A., Geltman, D., Güner, A., Haevermans, T., Knapp, S., le Roux, M.M., Loizeau, P.-A., Miller, C., Miller, J., Miller, J.T., Palese, R., Paton, A., Parnell, J., Pendry, C., Qin, H.-N., Sosa, V., Sosef, M., von Raab-Straube, E., Ranwashe, F., Raz, L., Salimov, R., Smets, E., Thiers, B., Thomas, W., Tulig, M., Ulate, W., Ung, V., Watson, M., Jackson, P.W. and Zamora, N. (2020), World Flora Online: Placing taxonomists at the heart of a definitive and comprehensive global resource on the world's plants. TAXON, 69: 1311-1341. <a href="https://doi.org/10.1002/tax.12373">https://doi.org/10.1002/tax.12373</a></span></li> <li><span>Govaerts, R., Nic Lughadha, E., Black, N. <em>et al.</em> The World Checklist of Vascular Plants, a continuously updated resource for exploring global plant diversity. <em>Sci Data</em> <strong>8</strong>, 215 (2021). <a href="https://doi.org/10.1038/s41597-021-00997-6">https://doi.org/10.1038/s41597-021-00997-6</a></span></li> <li><span>Kindt, R. 2020. WorldFlora: An R package for exact and fuzzy matching of plant names against the World Flora Online taxonomic backbone data. <em>Applications in Plant Sciences</em> 8(9): e11388. <a href="https://doi.org/10.1002/aps3.11388">https://doi.org/10.1002/aps3.11388</a></span></li> </ul> <p> </p> <p>Original funding for the database was provided <span>by the Carbon Benefits Project (CBP) supported by The Global Environment Facility (GEF). Development of the 2024 version </span>was supported by the <strong>Darwin Initiative</strong> to project DAREX001 of <em>Developing a Global Biodiversity Standard certification for tree-planting and restoration</em>, by <strong>Norway’s International Climate and Forest Initiative through the Royal Norwegian Embassy in Ethiopia</strong> to the <em>Provision of Adequate Tree Seed Portfolio</em> project in Ethiopia, by the <strong>Green Climate Fund</strong> through the IUCN-led <em>Transforming the Eastern Province of Rwanda through Adaptation</em> project and through the <em>Readiness proposal on Climate Appropriate Portfolios of Tree Diversity for Burkina Faso</em>, by the <strong>Bezos Earth Fund</strong> to the <em>Bezos Quality Tree Seed for Africa in Kenya and Rwanda</em> project and by the <strong>German International Climate Initiative (IKI)</strong> to the regional tree seed programme on <em>The Right Tree for the Right Place for the Right Purpose in Africa</em>. When using <strong>African Wood Density database</strong> in your work, cite the 2012 version (Carsan et al. <a href="https://apps.worldagroforestry.org/treesnmarkets/wood/">2012</a>) as well as this repository using the DOI.</p>
MASA Backbone preliminary test
<p>During the MASA backbone analysis, we tested three municipality points of connection to the infrastructure network.<br>The source zip contains all the scripts used to run the tests and the related data acquired during the test campaign.</p> <p>All the tests were performed using the FLENT tool, which is available on Linux.<br>Info about the tool can be found at: <a href="https://flent.org/">https://flent.org/</a><br>The Flent GUI can be used to surf the data and visualize the output in a friendly way.</p> <p>All the tests report the performance of upload and download throughput in a congested and non-congested network, using different setups like TCP congestion control and queueing disciplines.</p>
StarDist model for Arabidopsis using Resnet backbone
<p>Stardist model for Arabidopsis using Resnet backbone to be used in conjugation with U-Net model in vollseg setting</p>
Phylogenomics of novel ploeotid taxa contribute to the backbone of the euglenid tree
<p>Euglenids are a diverse group of flagellates that inhabit most environments and exhibit many different nutritional modes. The most prominent euglenids are phototrophs, but phagotrophs constitute the majority of phylogenetic diversity of euglenids. They are pivotal to our understanding of euglenid evolution, yet we are only starting to understand relationships amongst phagotrophs, with the backbone of the tree being the most elusive. Ploeotids make up most of this backbone diversity—yet despite their morphological similarities, SSU rDNA analyses and multigene analyses show they are non-monophyletic. As more ploeotid diversity is sampled, known taxa have coalesced into some subgroups (e.g. Alistosa), but the relationships between these are not always supported and some taxa remain unsampled for multigene phylogenetics. Here, we used light microscopy and single-cell transcriptomics to characterize five ploeotid euglenids and place them into a multigene phylogenetic framework. Our analyses place <em>Decastava</em> in Alistosa; while <em>Hemiolia</em> branches with <em>Liburna</em>, establishing the novel clade Karavia. We describe <em>Hemiolia limna</em>, a freshwater-dwelling species in an otherwise marine clade. Intriguingly, two undescribed ploeotids are found to occupy pivotal positions in the tree: <em>Chelandium granulatum</em> nov. gen. nov. sp. branches as sister to <em>Olkasia</em>, and <em>Gaulosia striata</em> nov. gen. nov. sp. remains an orphan taxon.</p>
Scaling of biological rates with body size as a backbone in the assembly of metacommunity biodiversity
<p><span>The dispersal-body mass association has been highlighted as a main determinant of biodiversity patterns in metacommunities. However, less attention has been devoted to other well recognized determinants of metacommunity diversity: the scaling in density and regional richness with body size. Among active dispersers, the increase in movement with body size may enhance local richness and decrease beta diversity. Nevertheless, the reduction of population size and regional richness with body mass may determine a negative diversity-body size association. Consequently, metacommunity assembly probably emerges from a balance between the effect of these scalings. We formalize this hypothesis relating the exponents of size-scaling rules with simulated trends in alpha, beta, and gamma diversity with body size. Our results highlight that the diversity-body size relationship in metacommunities may be driven by the combined effect of different scaling rules. Given their ubiquity in most terrestrial and aquatic biotas, these scaling rules may represent the basic determinants—backbone—of biodiversity, over which other mechanisms operate determining metacommunity assembly. Further studies are needed, aimed at explaining biodiversity patterns from functional relationships between biological rates and body size, as well as their association with environmental conditions and species interactions.</span></p>
Forward-in-time simulation of chromosomal rearrangements: The invisible backbone that sustains long-term adaptation : simulated data
<p>Data simulated with Aevol (<a href="http://www.aevol.fr">www.aevol.fr</a>), a software available on gitlab ( <a href="https://gitlab.inria.fr/aevol/aevol">https://gitlab.inria.fr/aevol/aevol</a> ), for our paper submitted to Molecular Ecology entitled "Forward-in-time simulation of chromosomal rearrangements: The invisible backbone that sustains long-term adaptation".</p>
Serial disparity in the carnivoran backbone unveil a complex adaptive role in metameric evolution
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Data from: phylogenomics of the Neogastropoda: the backbone hidden in the bush
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Data from: Deciphering an extreme morphology: bone microarchitecture of the Hero Shrew backbone (Soricidae: Scutisorex)
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Data from: Charge state-dependent ion condensation near conjugated polymer backbones
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Data from: Advancing Pyrus phylogeny: Deep genome skimming-based inference coupled with paralogy analysis yields a robust phylogenetic backbone and an updated infrageneric classification of the pear genus (Maleae, Rosaceae)
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Supplementary data from: Lacewing-specific universal single-copy orthologs designed towards resolution of backbone phylogeny of Neuropterida
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Data from: Employing hypothesis testing and data from multiple genomic compartments to resolve recalcitrant backbone nodes in Goodenia s.l. (Goodeniaceae)
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