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28 results for “evolutionary information”
Supporting Data: ontophylo: Reconstructing the evolutionary dynamics of phenomes using new ontology-informed phylogenetic methods
<p>This dataset contains all scripts and data for reproducing the analyses of the paper. The README files contain additional information.</p>
Figure 4 in New information on the cranial and postcranial anatomy of the early synapsid Ianthodon schultzei (Sphenacomorpha: Sphenacodontia), and its evolutionary significance
Figure 4. Ianthodon schultzei. (a) Referred left maxilla KUVP 133736; (b) referred right maxilla FO 176.
Figure 1. Ianthodon schultzei holotype KUVP 133735 in New information on the cranial and postcranial anatomy of the early synapsid Ianthodon schultzei (Sphenacomorpha: Sphenacodontia), and its evolutionary significance
Figure 1. Ianthodon schultzei holotype KUVP 133735, combined slab, with outlines of skull bones (outlines by D. Scott, made prior to Kissel and Reisz, 2004, Fig. 2; now exposed as embedded counter slab) and a disarticulated skeleton of the diapsid reptile Petrolacosaurus kansensis (shaded areas); a – angular; ar – articular; c – vertebra centrum; cau – caudal neural arch; cle – cleithrum; co – anterior coracoid; cp – cultriform process of parasphenoid–basisphenoid complex; cr – cervical rib; d – dentary; dr – dorsal rib; h – humerus; n – nasal; na – presacral neural arch; pal – palatine; pc – posterior coronoid; pm – premaxilla; pra – prearticular; ps – parasphenoid–basisphenoid complex; pt – pterygoid; qj – quadratojugal; sa – surangular; sc – scapula; soc – supraoccipital; sp – splenial; v – vomer.
Figure 3. Ianthodon schultzei holotype KUVP 133735 in New information on the cranial and postcranial anatomy of the early synapsid Ianthodon schultzei (Sphenacomorpha: Sphenacodontia), and its evolutionary significance
Figure 3. Ianthodon schultzei holotype KUVP 133735. (a) Redocumented skull; (b) dislocated premaxillae. Fr – frontal; j – jugal; l – lacrimal; m – maxilla; p – parietal; po – postorbital; pof – postfrontal; pp – postparietal; prf – prefrontal; sq – squamosal; st – supratemporal; t – tabular.
Figure 6 in New information on the cranial and postcranial anatomy of the early synapsid Ianthodon schultzei (Sphenacomorpha: Sphenacodontia), and its evolutionary significance
Figure 6. Ianthodon schultzei cranial and skeletal reconstruction. Three-dimensional arrangement and projections based on a wax maquette. Skull in dorsal, ventral and lateral view; mandible in lateral and medial view.
Figure 2. Ianthodon schultzei holotype KUVP 133735 in New information on the cranial and postcranial anatomy of the early synapsid Ianthodon schultzei (Sphenacomorpha: Sphenacodontia), and its evolutionary significance
Figure 2. Ianthodon schultzei holotype KUVP 133735, slab in present condition, combined with photograph of skull area (lower left) before its removal.
Figure 5. Ianthodon schultzei holotype KUVP 133735 in New information on the cranial and postcranial anatomy of the early synapsid Ianthodon schultzei (Sphenacomorpha: Sphenacodontia), and its evolutionary significance
Figure 5. Ianthodon schultzei holotype KUVP 133735. (a) Close-up of central block; (b) detail of right posterior coronoid with eroded denticles; (c) detail of right pterygoid transverse flange dentition in dorsolateral aspect. Ic – intercentrum; pt-a – pterygoid anterior ramus; pt-q – quadrate ramus of pterygoid.
Figure 7 in New information on the cranial and postcranial anatomy of the early synapsid Ianthodon schultzei (Sphenacomorpha: Sphenacodontia), and its evolutionary significance
Figure 7. Majority rule and strict consensus cladograms of the 10 most parsimonious trees, with a key for bootstrap values above 50 %, the frequency of node occurrence and Bremer decay values. For nodes that collapse at one extra step, the Bremer decay values are not shown.
Evolutionary Information Encoded in pLMs
<p>This dataset was created to test the effect of combining evolutionary information with protein language model embeddings by evaluating the effect on secondary structure prediction. Our method for predicting secondary structure used <em>PDB</em> (Berman et al., 2000) structures as ground truths. Sequences were cross-checked with <em>PDBredo DB</em> (Joosten et al., 2014) and <em>CATH</em> (Sillitoe et al., 2021). This resulted in 296,596 protein chain sequences from 117,623 different proteins. HSSP-values (HVAL) (<span>Rost, 1999; Sander & Schneider, 1991</span>) were computed for all protein chain pairs, and the sequences split into training test and validation set as follows:</p> <p><strong>TEST100:</strong> 100 randomly selected sequences meeting the following criteria: </p> <ol> <li>Deposited after April 2018 to allow a fair comparison to other recent methods</li> <li>Resolution: ≤2Å</li> <li>Any sequence pair (a,b) with a,b ∈ TEST100 must have an HVAL≤0</li> </ol> <p><strong>VAL100:</strong> 100 additional randomly selected sequences constrained to:</p> <ol> <li>Deposited before April 2018</li> <li>Resolution: ≤2Å</li> <li>Any sequence pair (a,b) with either a ∈ TEST100 or a ∈ VAL100 and b ∈ VAL100 had a maximal HVAL≤0</li> </ol> <p><strong>TRAIN6727:</strong> we used the remaining sequences for training if and only if the following criteria were fulfilled:</p> <ol> <li>Deposition before April 2018</li> <li>CATH annotations on the topology level (T) had to be different from any contained in TEST100 or VAL100</li> <li>HVAL≤0 for any pair (a,b) with a ∈ TEST100 or a ∈ VAL100 and b ∈ TRAIN6727</li> <li>PIDE≤70 for any pair (a,b) with a,b ∈ TRAIN6727, if a≠b</li> </ol> <p>This yielded 6,727 protein chains for training.</p> <p> </p> <p>This resource provided sequences, secondary structure annotations in 3-states, annotation of disordered regions, MSAs generated by <em>MMseqs2</em> (<span>Steinegger & Söding, 2017)</span>, PSSMs generated by MMseqs2 and meta files containing possible alternative PDB sequence IDs and CATH annotations.</p> <p>The original 8 DSSP (<span>Kabsch & Sander, 1983)</span> classes for secondary structure annotations were reduced to 3 following this protocol:</p> <ul> <li>DSSP-H, DSSP-G, and DSSP-I to helix (H)</li> <li>DSSP-E and DSSP-B to strand (E)</li> <li>all remaining classes to other (-)</li> </ul> <p>Disorder annotations were used to mask out residues in our evaluation that could not be resolved experimentally. All unresolved (disordered) residues are marked with X, while a dash (-) indicates a resolved position.</p> <p>Multiple Sequence alignments are provided in Stockholm format and the PSSMs are generated based on the provided MSAs. PSSMs were enumerated during creation. The mapping between the original PDB identifiers and the enumerated PSSMs is provided in the xxx.lookup files. </p>
Data from: Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases.
<p>This data accompanies the paper entitled <strong><em>Biochemical, structural and dynamical characterizations of the lactate dehydrogenase from Selenomonas ruminantium provide information about an intermediate evolutionary step prior to complete allosteric regulation acquisition in the super family of lactate and malate dehydrogenases.</em></strong></p> <p>The zip archive contains the results of molecular dynamics simulations of the 2 systems investigated in the paper: <em>S. rum</em> and <em>T. mar</em> LDHs. The systems have been simulated at 315 K for <em>S. rum </em>and 340 K for <em>T. mar</em>. Final configurations of the proteins after productions are provided for all the systems in GRO Gromos87 format. Trajectories with the positions of the proteins every 100 ps are provided for all the systems in XTC gromacs format.</p>
Information organizing: an evolutionary and development framework
<p>Information behaviour has emerged as an important aspect of human life; however, our<br> knowledge and understanding of it is incomplete and underdeveloped scientifically. Our<br> understanding of information behaviour and the sub-process information organizing<br> behaviour is largely contemporary in focus. In this presentation Professor Spink discusses<br> an evolutionary and developmental framework for information behaviour and information<br> organizing from her recent book Information Behaviour: An Evolutionary Instinct (2010,<br> Springer) by incorporating related findings, theories and models from evolutionary<br> psychology, cognitive archaeology, cognitive neuroscience and developmental psychology.<br> In her presentation, she argues that information behavior and information organizing are<br> important instinctive socio-cognitive abilities for all humans that can only be fully under-<br> stood with an evolutionary and developmental perspective.<br> Professor Spink’s presentation addresses four important research questions. Firstly, what is the evolutionary, biological and developmental basis for information behaviour and information organizing behaviour? Secondly, what is the role of instinct versus environment in shaping information behaviour and information organizing behaviour? Thirdly, how have information behaviour and organizing capabilities evolved and developed over human species? Fourthly, when and how do information behaviour and in particular information organizing abilities emerge in young children? An evolutionary and developmental approach lays the foundation for a more holistic perspective on information behaviour and information organizing behaviour, and opens many new research directions.</p>
Data from: Analysis and visualization of H7 influenza using genomic, evolutionary and geographic information in a modular web service
We have reported previously on use of a web-based application, Supramap (http://supramap.org) for the study of biogeographic, genotypic, and phenotypic evolution. Using Supramap we have developed maps of the spread of drug-resistant influenza and host shifts in H1N1 and H5N1 influenza and coronaviruses such as SARS. Here we report on another zoonotic pathogen, H7 influenza, and provide an update on the implementation of Supramap as a web service. We find that the emergence of pathogenic strains of H7 is labile with many transitions from high to low pathogenicity, and from low to high pathogenicity. We use Supramap to put these events in a temporal and geospatial context. We identify several lineages of H7 influenza with biomarkers of high pathogenicity in regions that have not been reported in the scientific literature. The original implementation of Supramap was built with tightly coupled client and server software. Now we have decoupled the components to provide a modular web service for POY (http://poyws.org) that can be consumed by a data provider to create a novel application. To demonstrate the web service, we have produced an application, Geogenes (http://geogenes.org). Unlike in Supramap, in which the user is required to create and upload data files, in Geogenes the user works from a graphical interface to query an underlying dataset. Geogenes demonstrates how the web service can provide underlying processing for any sequence and metadata database.
Supplementary information for Jones et al., 2021: "Size and shape variation in the calcareous nannoplankton genus Braarudosphaera following the Cretaceous-Paleogene (K/Pg) mass extinction: clues as to its evolutionary success"
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Data from: Analysis and visualization of H7 influenza using genomic, evolutionary and geographic information in a modular web service
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GECCO Industrial Challenge 2015 Dataset: A heating system dataset for the 'Recovering missing information in heating system operating data' competition at the Genetic and Evolutionary Computation Conference 2015, Madrid, Spain
<p>Dataset of the 'Industrial Challenge: Recovering missing information in heating system operating data' competition hosted at The Genetic and Evolutionary Computation Conference (GECCO) July 11th-15th 2015, Madrid, Spain</p> <p> </p> <p>The task of the competition was to recover (impute) missing information in heating system operation time series'.</p> <p> </p> <p>Included in zenodo: </p> <p>- dataset of heating system operational time series with missing values</p> <p>- additional material and descriptions provided for the competition</p> <p> </p> <p>The competition was organized by:</p> <p>M. Friese, A. Fischbach, C. Schlitt, T. Bartz-Beielstein (TH Köln)</p> <p> </p> <p>The dataset was provided by:</p> <p>Major German heating systems supplier (S. Moritz)</p> <p> </p> <p> </p> <p>Industrial Challenge: Recovering missing information in heating system operating data</p> <p> </p> <p>The Industrial Challenge will be held in the competition session at the Genetic and Evolutionary Computation Conference. It poses difficult real-world problems provided by industry partners from various fields. Highlights of the Industrial Challenge include interesting problem domains, real-world data and realistic quality measurement</p> <p>Overview</p> <p>In times of accelerating climate change and rising energy costs, increasing energy efficiency and reducing expenses becomes a high priority goal for businesses and private households alike. Modern heating systems record detailed operating data and report this data to a central system. Here, the operating data can be correlated and analyzed to detect potential optimization opportunities or anomalies like unusually high energy consumption. Due to various difficulties this data might be incomplete which makes accurate forecasting even harder.</p> <p>Goal of the GECCO 2015 Industrial Challenge is to develop capable procedures to recover missing information in heating system operating data. Adequate recovery of the missing data enables more accurate forecastings which allow for intelligent control of the heating systems, and therefore contributes to a positive energy balance and reduced expenses.</p> <p> </p> <p><strong>Submission deadline:</strong><br> June 22, 2015</p> <p><strong>Official Webpage:</strong><br> <a href="http://www.spotseven.de/gecco-challenge/gecco-challenge-2015/">www.spotseven.de/gecco-challenge/gecco-challenge-2015/</a></p> <p> </p>
Data from: Organellar phylogenomics inform systematics in the green algal family Hydrodictyaceae (Chlorophyceae) and provide clues to the complex evolutionary history of plastid genomes in the green algal Tree of Life.
Premise of the study: Phylogenomic analyses across the green algae are resolving relationships at the class, order and family levels, and highlighting dynamic patterns of evolution in organellar genomes. Here we present a within-family phylogenomic study to resolve genera and species relationships in the family Hydrodictyaceae (Chlorophyceae), for which poor resolution in previous phylogenetic studies, along with divergent morphological traits, have precluded taxonomic revisions. Methods: Complete plastome sequences and mitochondrial protein-coding gene sequences were acquired from representatives of the Hydrodictyaceae using Next-Generation sequencing methods. Plastomes were characterized and gene order and content were compared with plastomes spanning the Sphaeropleales. Single-gene and concatenated-gene phylogenetic analyses of plastid and mitochondrial genes were performed. Key results: The Hydrodictyaceae contain the largest sphaeroplealean plastomes thus far fully sequenced. Conservation of plastome gene order within Hydrodictyaceae is striking compared with more dynamic patterns revealed across Sphaeropleales. Phylogenetic analyses resolve Hydrodictyon sister to a monophyletic Pediastrum, though the morphologically distinct P. angulosum and P. duplex continue to be polyphyletic. Analyses of plastid data supported the neochloridacean genus Chlorotetraëdron as sister to Hydrodictyaceae, while conflicting signal was found in the mitochondrial data. Conclusions: A phylogenomic approach resolved within-family relationships not obtainable with previous phylogenetic analyses. Denser taxon sampling across Sphaeropleales is necessary to capture patterns in plastome evolution, and further taxa and studies are needed to fully resolve sister lineage to Hydrodictyaceae and polyphyly of Pediastrum angulosum and P. duplex.
Data from: Information content is more important than sensory system or physical distance in guiding the long-term evolutionary relationships between signaling modalities in Sceloporus lizards
Long-term signal evolution is shaped by a variety of selective pressures including the need to convey additional information or to improve message transfer to specific receivers or through multiple environments. Here, we test the relative importance of information and sensory modality in shaping the long-term evolution of multimodal signals in Sceloporus lizards. To broadcast identity at territorial boundaries, male Sceloporus use both visual motion (headbob) and chemical signals, whereas they use color (blue belly patches) to signal aggression. Using modern phylogenetic comparative methods, we found a negative correlation between evolutionary changes in visual motion (headbobs) and chemical (femoral pore) signals, but only indirect ties between the evolution of color and motion signals (both of which are perceived visually) through viviparity, and no evidence of an evolutionary link between color and chemical signals. We also find a negative correlation between arboreality and chemical signals. Thus, information content (in this case, broadcasting individual identity versus signaling aggression) appears to play a more important role than sensory modality or physical distance in guiding long-term signal evolution. Additional insights into the underlying evolutionary processes are described, illustrating the utility of a phylogenetic approach.
Data from: Robust and accurate prediction of residue-residue interactions across protein interfaces using evolutionary information
Do the amino acid sequence identities of residues that make contact across protein interfaces covary during evolution? If so, such covariance could be used to predict contacts across interfaces and assemble models of biological complexes. We find that residue pairs identified using a pseudo-likelihood based method to covary across protein-protein interfaces in the 50S ribosomal unit and 28 additional bacterial protein complexes with known structure are almost always in contact in the complex provided that the number of aligned sequences is greater than the average of the lengths of the two proteins. We use this method to make subunit contact predictions for an additional 36 protein complexes with unknown structures, and present models based on these predictions for the tripartite ATP-independent periplasmic (TRAP) transporter, the tripartite efflux system, the pyruvate formate lyase-activating enzyme complex, and the methionine ABC transporter.
Data from: Organellar phylogenomics inform systematics in the green algal family Hydrodictyaceae (Chlorophyceae) and provide clues to the complex evolutionary history of plastid genomes in the green algal Tree of Life.
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Data from: Information content is more important than sensory system or physical distance in guiding the long-term evolutionary relationships between signaling modalities in Sceloporus lizards
Open the record for dataset details and reuse information.
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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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.