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133 results for “backbone”
Data from: Phenotypic integration in the carnivoran backbone and the evolution of functional differentiation in metameric structures
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Data from: Role of backbone fault system on earthquake spawning and geohazards in the Seoul metropolitan area
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Data from: Adaptation and constraint in the evolution of the mammalian backbone
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Conflicting signal in transcriptomic markers leads to a poorly resolved backbone phylogeny of Chalcidoid wasps
<p>Chalcidoidea (Hymenoptera) are a megadiverse superfamily of wasps with astounding variation in both morphology and biology. Most species are parasitoids and important natural enemies of insects in terrestrial ecosystems. In this study, we explored a transcriptome-based phylogeny of Chalcidoidea and found that poorly resolved relationships could only be marginally improved by adding more genes (a total of 5,591) and taxa (a total of 65), proof-checking for errors of homology and contamination, and decreasing missing data. Concatenation analyses consistently place Mymaridae and Trichogrammatidae sister to remaining Chalcidoidea. However, our coalescent analyses provide a different hypothesis with a grouping of (Mymaridae (((Trichogrammatidae, Eulophidae), (Encyrtidae, Aphelinidae)), remaining Chalcidoidea)). This hypothesis complicates our hypothesis of egg parasitism as being ancestral in Chalcidoidea. At the deeper nodes, the results uncovered a wide spectrum of gene discordance in the transcriptomic markers and identified a strong signal of functional bias in genes supporting alternative phylogenies. The basal nodes of the phylogeny are thus strongly influenced by biased support from different functional gene complexes. Shallower nodes showed similar gene discordance, but without strong functional bias. Understanding and identifying mechanisms that result in gene tree discordance may be beneficial and even essential for sorting out backbone relationships, especially for groups that have undergone extremely rapid radiation.</p>
Data from: Phylogenomics resolves a spider backbone phylogeny and rejects a prevailing paradigm for orb web evolution
Spiders represent an ancient predatory lineage known for their extraordinary biomaterials, including venoms and silks. These adaptations make spiders key arthropod predators in most terrestrial ecosystems. Despite ecological, biomedical, and biomaterial importance, relationships among major spider lineages remain unresolved or poorly supported. Current working hypotheses for a spider "backbone" phylogeny are largely based on morphological evidence, as most molecular markers currently employed are generally inadequate for resolving deeper-level relationships. We present here a phylogenomic analysis of spiders including taxa representing all major spider lineages. Our robust phylogenetic hypothesis recovers some fundamental and uncontroversial spider clades, but rejects the prevailing paradigm of a monophyletic Orbiculariae, the most diverse lineage, containing orb-weaving spiders. Based on our results, the orb web either evolved much earlier than previously hypothesized and is ancestral for a majority of spiders or else it has multiple independent origins, as hypothesized by precladistic authors. Cribellate deinopoid orb weavers that use mechanically adhesive silk are more closely related to a diverse clade of mostly webless spiders than to the araneoid orb-weaving spiders that use adhesive droplet silks. The fundamental shift in our understanding of spider phylogeny proposed here has broad implications for interpreting the evolution of spiders, their remarkable biomaterials, and a key extended phenotype—the spider web.
Data from: Knowledge-based prediction of protein backbone conformation using a structural alphabet
Libraries of structural prototypes that abstract protein local structures are known as structural alphabets and have proven to be very useful in various aspects of protein structure analyses and predictions. One such library, Protein Blocks, is composed of 16 standard 5-residues long structural prototypes. This form of analyzing proteins involves drafting its structure as a string of Protein Blocks. Predicting the local structure of a protein in terms of protein blocks is the general objective of this work. A new approach, PB-kPRED is proposed towards this aim. It involves (i) organizing the structural knowledge in the form of a database of pentapeptide fragments extracted from all protein structures in the PDB and (ii) applying a knowledge-based algorithm that does not rely on any secondary structure predictions and/or sequence alignment profiles, to scan this database and predict most probable backbone conformations for the protein local structures. Though PB-kPRED uses the structural information from homologues in preference, if available. The predictions were evaluated rigorously on 15,544 query proteins representing a non-redundant subset of the PDB filtered at 30% sequence identity cut-off. We have shown that the kPRED method was able to achieve mean accuracies ranging from 40.8% to 66.3% depending on the availability of homologues. The impact of the different strategies for scanning the database on the prediction was evaluated and is discussed. Our results highlights the usefulness of the method in the context of proteins without any known structural homologues. A scoring function that gives a good estimate of the accuracy of prediction was further developed. This score estimates very well the accuracy of the algorithm (R2 of 0.82). An online version of the tool is provided freely for non-commercial usage at http://www.bo-protscience.fr/kpred/.
SAMPLER representations of TCGA-BRCA DX WSIs using an InceptionV3 backbone pretrained on imagenet
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SAMPLER representations of CPTAC-LSCC WSIs using an InceptionV3 backbone pretrained on imagenet
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SAMPLER representations of frozen and FFPE TCGA-KICH WSIs using an InceptionV3 backbone pretrained on imagenet
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SAMPLER representations of TCGA-BRCA frozen WSIs using an InceptionV3 backbone pretrained on imagenet
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SAMPLER representations of CPTAC-LUAD WSIs using an InceptionV3 backbone pretrained on imagenet
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SAMPLER representations of frozen and FFPE TCGA-KIRC WSIs using an InceptionV3 backbone pretrained on imagenet
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SAMPLER representations of CPTAC-BRCA WSIs using an InceptionV3 backbone pretrained on imagenet
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SAMPLER representations of frozen and FFPE TCGA-LUSC WSIs using an InceptionV3 backbone pretrained on imagenet
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A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)
<div> <div>Publication date:</div> <div>2024-03-12T14:26:33-03:00</div> <br><br> <div>A Repackaged Taxonomic Backbone of Global Biodiversity Information Facility (GBIF)</div> <div>---</div> <br> <div>Global Biodiversity Information Facility (GBIF) facilitates access to billions of biodiversity data records. These records include detailed accounts of life on earth.</div> <br> <div>To help records of specific life forms, GBIF provides a taxonomic backbone [1,2]. This backbone contains a long list of names used to describe species and associated hierarchies and taxonomic publications. These lists are sourced from datasets around the world.</div> <br> <div>At time of writing (18 Aug 2021), GBIF publishes a simplified version of their taxonomic backbone at [https://hosted-datasets.gbif.org/datasets/backbone/](https://hosted-datasets.gbif.org/datasets/backbone/) [1].</div> <br> <div>This repository provides script to pre-process https://hosted-datasets.gbif.org/datasets/backbone/backbone-current-simple.txt.gz to help facilitate access and improve performance of the creation of search indexes.</div> <br> <div>Pre-process steps currently include:</div> <div>1. reducing amount of columns</div> <div>2. reverse sort by id</div> <div>3. reverse sort by name</div> <br><br> <div>Contents</div> <div>---</div> <br> <div>README:</div> <div>this file</div> <br> <div>repackage-gbif-backbone.sh:</div> <div>script used to repackage GBIF Simple Backbone.</div> <br> <div>backbone-current-simple.txt.gz:</div> <div>original GBIF backbone archive</div> <br> <div>gbif-backbone-by-name.tsv.gz:</div> <div>two columns, gzipped, tab-separated text file with columns name, and id</div> <div>reverse sorted by name</div> <br> <div>gbif-backbone-by-name.tsv.sha256:</div> <div>sha256 hash of the uncompressed gbif-backbone-by-name.tsv.gz</div> <br> <div>gbif-backbone-by-id.tsv.gz:</div> <div>20 columns, gzipped, tab-separated text file with first 20 columns of repackaged GBIF backbone file</div> <div>reverse sorted by id</div> <br> <div>gbif-backbone-by-id.tsv.sha256:</div> <div>sha256 hash of the uncompressed gbif-backbone-by-id.tsv.gz</div> <br> <div>References</div> <div>---</div> <br> <div>[1] Simplied GBIF Backbone Taxonomy. Accessed at https://hosted-datasets.gbif.org/datasets/backbone/ on 2023-08-28.</div> <div>[2] GBIF Secretariat (2021). GBIF Backbone Taxonomy. Checklist dataset https://doi.org/10.15468/39omei accessed via GBIF.org on 2023-08-28.</div> <br><br> <div>Hash URIs</div> <div>---</div> <div>This publication includes the following content uris:</div> <br> <div>hash://sha256/82d5f2153b4533322692d95eeb18b0f103e1b2297e38bd9ea935b07ba86cd7d5</div> <div>hash://sha256/fde017e1315b4ae6fc1e1bae79f9cfd234b8ba40f6f4fb5ac031084a3b1763f0</div> <div>hash://sha256/1804594be92a0e9a7b60c245925a1a488d4d98a4a38028cb0c8a420faefa36c2 (uncompressed)</div> <div>hash://sha256/480926c8a1f218f8d5d76db7b4687c09ea11ab1c7ee9b0788238f2ff1eab7298</div> <div>hash://sha256/8184f1e96d306ba5355e3e229d8e93eacd3fee4ab19107ae99b71bc4b9d523b6 (uncompressed)</div> <div>hash://sha256/6241ffc32d0e1dbd826b36e45dfa469046ce69e671224bbb43f89996ca577955</div> <div>hash://sha256/6df36a48615d9a3d7541995c6fc01da7f3ee34679d560ec619212a2c5f037679 (uncompressed)</div> </div>
Figure 8 from: Yang J, Liu JK, Hyde KD, Jones EBG, Liu ZY (2018) New species in Dictyosporium, new combinations in Dictyocheirospora and an updated backbone tree for Dictyosporiaceae. MycoKeys 36: 83-105. https://doi.org/10.3897/mycokeys.36.27051
Figure 8 Dictyosporium nigroapice (MFLU18-1043). a Colonies on submerged wood b, c Conidia and conidiophores d–j Conidia k Germinated conidium l, m Culture, l from above, m from reverse. Scale bars: a = 100 μm, b, c, j = 20 μm, d–i = 10 μm, k = 30 μm.
Figure 6 from: Yang J, Liu JK, Hyde KD, Jones EBG, Liu ZY (2018) New species in Dictyosporium, new combinations in Dictyocheirospora and an updated backbone tree for Dictyosporiaceae. MycoKeys 36: 83-105. https://doi.org/10.3897/mycokeys.36.27051
Figure 6 Dictyosporium tratense (MFLU 18-1042, holotype). a Colonies on submerged wood b Squash mount of a sporodochium c Germinated conidium d–i Conidia j, k Culture j from above k from reverse. Scale bars: a = 200 μm, b = 50 μm, c = 30 μm, d–i = 20 μm.
Figure 4 from: Yang J, Liu JK, Hyde KD, Jones EBG, Liu ZY (2018) New species in Dictyosporium, new combinations in Dictyocheirospora and an updated backbone tree for Dictyosporiaceae. MycoKeys 36: 83-105. https://doi.org/10.3897/mycokeys.36.27051
Figure 4 Dictyocheirospora rotunda (MFLU 18-1041). a Colonies on submerged wood b, c Germinated conidia d Conidia e, f Culture, e from above, f from reverse. Scale bars: a = 200 μm, b, c = 20 μm, d = 50 μm.
Figure 3 from: Yang J, Liu JK, Hyde KD, Jones EBG, Liu ZY (2018) New species in Dictyosporium, new combinations in Dictyocheirospora and an updated backbone tree for Dictyosporiaceae. MycoKeys 36: 83-105. https://doi.org/10.3897/mycokeys.36.27051
Figure 3 Dictyocheirospora indica (MFLU 15-1169, reference specimen). a Substrate b, c Colonies on woody substrate d, e Conidial formation f–i Conidia with partial conidiophores j–o Conidia p Germinated conidium q–r Culture, q from above, r from reverse. Scale bars: b = 200 μm, c = 100 μm, d–i, l–o = 20 μm, j = 10 μm, k = 15 μm, p = 30 μm.
Figure 2 from: Yang J, Liu JK, Hyde KD, Jones EBG, Liu ZY (2018) New species in Dictyosporium, new combinations in Dictyocheirospora and an updated backbone tree for Dictyosporiaceae. MycoKeys 36: 83-105. https://doi.org/10.3897/mycokeys.36.27051
Figure 2 Dictyocheirospora bannica (MFLU 18-1040) a Colonies on submerged wood b Conidia and conidiophores c–f Conidia g Germinated conidium h, i Culture, h from above, i from reverse. Scale bars: a = 200 μm, b, g = 50 μm, c–f = 30 μm.
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.