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829 results for “Evolvability”
Pinopsin evolved as the ancestral dim-light visual opsin in vertebrates
<p>Pinopsin is the opsin most closely related to vertebrate visual pigments on the phylogenetic tree. This opsin has been discovered among many vertebrates, except mammals and teleosts, and was thought to exclusively function in their brain for extraocular photoreception. Here, we show the possibility that pinopsin also contributes to scotopic vision in some vertebrate species. Pinopsin is distributed in the retina of non-teleost fishes and frogs, especially in their rod photoreceptor cells, in addition to their brain. Moreover, the retinal chromophore of pinopsin exhibits a thermal isomerization rate considerably lower than those of cone visual pigments, but comparable to that of rhodopsin. Therefore, pinopsin can function as a rhodopsin-like visual pigment in the retinas of these lower vertebrates. Since pinopsin diversified before the branching of rhodopsin on the phylogenetic tree, two-step adaptation to scotopic vision would have occurred through the independent acquisition of pinopsin and rhodopsin by the vertebrate lineage.</p>
Data from: Ancient DNA from the extinct South American giant glyptodont Doedicurus sp. (Xenarthra: Glyptodontidae) reveals that glyptodonts evolved from Eocene armadillos
Glyptodonts were giant (some of them up to ~2400 kg), heavily armoured relatives of living armadillos, which became extinct during the Late Pleistocene/early Holocene alongside much of the South American megafauna. Although glyptodonts were an important component of Cenozoic South American faunas, their early evolution and phylogenetic affinities within the order Cingulata (armoured New World placental mammals) remain controversial. In this study, we used hybridization enrichment and high-throughput sequencing to obtain a partial mitochondrial genome from Doedicurus sp., the largest (1.5 m tall, and 4 m long) and one of the last surviving glyptodonts. Our molecular phylogenetic analyses revealed that glyptodonts fall within the diversity of living armadillos. Reanalysis of morphological data using a molecular 'backbone constraint' revealed several morphological characters that supported a close relationship between glyptodonts and the tiny extant fairy armadillos (Chlamyphorinae). This is surprising as these taxa are among the most derived cingulates: glyptodonts were generally large-bodied and heavily armoured, while the fairy armadillos are tiny (~9–17 cm) and adapted for burrowing. Calibration of our phylogeny with the first appearance of glyptodonts in the Eocene resulted in a more precise timeline for xenarthran evolution. The osteological novelties of glyptodonts and their specialization for grazing appear to have evolved rapidly during the Late Eocene to Early Miocene, coincident with global temperature decreases and a shift from wet closed forest towards drier open woodland and grassland across much of South America. This environmental change may have driven the evolution of glyptodonts, culminating in the bizarre giant forms of the Pleistocene.
Input files and movie visualizations for convection models discussed in Becker and Fuchs, "Generation of evolving plate boundaries and toroidal flow from visco-plastic damage-rheology mantle convection and continents", manuscript revised for G-Cubed
<p>These input files are for the CitcomS software as available on github.com/geodynamics/citcoms and used in the version under commit 2bda530. They can be used to recreate the models discussed in Becker and Fuchs (revised manuscript submitted to G-Cubed, 11/2023), with model codes discussed and listed in Table 1 of the preprint as provided here. We also provide selected animations of the time dependence of model output, referenced to the same model names.</p>
Evaluating the Robustness of Deep-learning Algorithm-selection Models by Evolving Adversarial Instances - Code and Data
<p>This repository contains the code and data for reproducibility of the paper 'Evaluating the Robustness of Deep-learning Algorithm-selection Models by Evolving Adversarial Instances'. </p> <p>The following files are included:</p> <ul> <li>Data.zip : contains the original instances in the datasets;</li> <li>Models.zip : trained Deep Neural Networks models used in the paper;</li> <li>New_instances.zip : generated instances using the approach;</li> <li>Parsed_data.zip : results and statistics of the experiments;</li> <li>script_adversarial_v3.py : Python script used to generate the results</li> </ul>
Genomic evidence of evolved symbiotic strategies in fungus-associated bacteria
<p>Here we provide the data used in the comparative genomic analysis of fungus-associated bacteria by Gohar et al. The description of the dataset files is as follows:</p> <p>Dataset 1: Nucleotide FASTA files (assemblies)</p> <p>Dataset 2: Annotations</p> <p>Dataset 3: CAZyme annotations, and</p> <p>Dataset 4: Metadata table</p>
Finding love: fruit fly males evolving under higher sexual selection are inherently better at finding receptive females
<p><span>Courtship is an important component of male reproductive behaviour that enables males to learn about the suitability of their mating partners. Experimental evidence suggests that <i>Drosophila melanogaster </i>males can learn to modify their courtship behaviour based on their prior experience with unreceptive females. This courtship learning is expected to provide a fitness advantage to males and could therefore evolve given suitable heritable variation. We investigated the role of sexual selection in the evolution of courtship learning ability of males, using populations of <i>D. melanogaster</i> evolving under high and low levels of sexual selection for over 170 generations. We exposed males from both types of population to unreceptive females and then tested their ability to discriminate between receptive and unreceptive females in a complex mating environment. <span>After being exposed to unreceptive females, males from both types of population (1) courted females less (both receptive and unreceptive), (2) took longer to initiate courtship, but took less time to start mating after initiating courtship and (3) increased the proportion of courtship directed towards receptive females, indicating the ability of both types of male to learn from experience</span><span>.</span> We did not find any difference in the courtship learning ability of males from the two types of population. However, males from populations with higher levels of sexual selection were better able to recognize and court receptive females, even when they were not previously exposed to unreceptive females. Taken together, these results show that sexual selection may not result in improved learning abilities but can lead to the evolution of an improved innate ability of males to assess the receptivity of females. </span></p>
Phenotype evaluation rawdata of Acinetobacter baumannii harboring chromosomal parallel mutations or an evolved plasmid
<p>OXA-23 is the predominant carbapenemase in carbapenem-resistant <em>Acinetobacter baumannnii</em>. The co-evolutionary dynamics of <em>A. baumannii</em> and OXA-23-encoding plasmids are poorly understood. Here, we transformed <em>A. baumannnii</em> ATCC 17978 with pAZJ221, a <em>bla</em><sub>OXA-23</sub>-containing plasmid from a clinical <em>A. baumannnii</em> isolate A221, and subjected the transformant to experimental evolution in the presence of a sub-inhibitory concentration of imipenem for nearly 400 generations. We used population sequencing to track genetic changes at six time-points and evaluated phenotypic changes. Increased fitness of evolving populations, temporary duplication of <em>bla</em><sub>OXA-23</sub> in pAZJ221, interfering allele dynamics, and chromosomal locus-level parallelism were observed. To characterize genotype-to-phenotype associations, we focused on six mutations in parallel targets predicted to affect small RNAs and a cyclic dimeric (3'→5') GMP-metabolizing protein. Six isogenic mutants with or without pAZJ221 were engineered to test for the causal effects of these mutations on fitness costs and plasmid kinetics, the evolved plasmid containing two copies of <em>bla</em><sub>OXA-23</sub> was transferred to ancestral ATCC 17978. Five of the six mutations contributed to improved fitness in the presence of pAZJ221 under imipenem pressure, and all but one of them impaired plasmid conjugation ability. The duplication of <em>bla</em><sub>OXA-23</sub> contributed to host fitness under carbapenem pressure but imposed a burden on the host in antibiotic-free media relative to the unevolved pAZJ221. Overall, our study provides a framework for the co-evolution of <em>A. baumannii</em> and a clinical blaOXA-23-containing plasmid, involving early <em>bla</em><sub>OXA-23</sub> duplication followed by chromosomal adaptations.</p>
Kinematic analysis of social interactions deconstructs the evolved loss of schooling behavior in cavefish
<p>Video tracking software (<a href="https://zenodo.org/api/files/cfb2f8f7-ecb0-4ef8-a16c-3f50e620549c/trilab-tracker-0.2.0.zip?versionId=ec71e34e-8049-450e-b00e-102d67cc3a43">trilab-tracker-0.2.0.zip</a>) and dataset (<a href="https://zenodo.org/api/files/cfb2f8f7-ecb0-4ef8-a16c-3f50e620549c/dataset.zip?versionId=025b9b87-2b06-4012-adb3-cd1b508d5bd1">dataset.zip</a>) for the research article "Kinematic analysis of social interactions deconstructs the evolved loss of schooling behavior in cavefish". The latest version of Trilab-Tracker can be found at <a href="https://github.com/yffily/trilab-tracker">https://github.com/yffily/trilab-tracker</a>.</p>
Male mating success evolves in response to increased levels of male-male competition
<p>Male-biased operational sex ratios can increase male-male competition and can potentially select for both increased pre-and post-copulatory male success. In the present study, using populations of Drosophila melanogaster evolved under male-biased (M) or female-biased (F) sex ratios, we asked whether (a) male mating success can evolve (b) males are better at mating females that they have co-evolved with (c) males mating success is affected by female mating status and (d) male mating success is correlated with their courtship effort. We directly competed M and F males for mating with (a) virgin ancestral (common) females, (b) virgin females from the M and F populations, and (c) singly mated females from the M and F populations. We also assessed the courtship frequency of the males when paired with mated M or F females. Our results show that M males, evolving under an increased level of male-male competition, have higher mating success than F males irrespective of the female evolutionary history. However, the difference in mating success is more pronounced if the females had mated before. M males also have a higher courtship frequency than F males, but we did not find any correlation between mating success and courtship frequency. </p>
Earth and life evolve together from something ancestral — reply to Britz et al
<p class="MsoNormal"><span>Ricefishes of the family Adrianichthyidae are considered to have dispersed eastward "out-of-India" after the collision of the Indian subcontinent with Eurasia and subsequently diversified in Southeast<span class="s1"><span>-</span></span>East Asia. In this study, </span><span>we reconstructed ancestral areas of Adrianichthyidae with BioGeoBEARS, expanding the scope to include Cyprinodontiformes, the outgroup of Beloniformes to which Adrianichthyidae belongs. The results again supported the "out-of-India" dispersal scenario. The dataset contained all files necessary for the BioGeoBEARS analysis.</span></p>
Partial-envelope stripping and nuclear-timescale mass transfer from evolved supergiants at low metallicity
<p>Model data and input files (inlists) used to compute MESA binary models to paper<br> "Partial-envelope stripping and nuclear-timescale mass transfer from evolved supergiants at low metallicity"<br> (<a href="https://ui.adsabs.harvard.edu/abs/2021arXiv211110271K/abstract">ADS link</a>)<br> MESA version r11554.<br> <br> The data consists of stellar tracks of the primary (donor) star. Provided are MESA history.data files and the final MESA model of the primary (terminated at core-He depletion).<br> <br> The data for solar metallicity models is also available on request.<br> </p>
Replication Package - How Do Requirements Evolve During Elicitation? An Empirical Study Combining Interviews and App Store Analysis
<p>This is the replication package for the paper titled "How Do Requirements Evolve During</p> <p>Elicitation? An Empirical Study Combining Interviews and App Store Analysis", by Alessio Ferrari, Paola Spoletini and Sourav Debnath.</p> <p> </p> <p>The package contains the following folders and files. </p> <p> </p> <p>**<strong>/Experiment Material</strong>**</p> <p>This folder contains the material used for the experiment, and provided to the participants.</p> <p>In particular, it includes the following files:</p> <p> </p> <p>- Happy CampingTM_briefdescription.pdf/docx: brief description of the product for which requirements need to be elicited</p> <p>- Hw_description.pdf/docx: desciption of the tasks to be performed by the participants</p> <p>- Modeling_Intro_Slides.pdf: introductory slides to modelling for requirements engineering</p> <p>- Self-assessment Questionnaire.pdf: first questionnaire to self-assess the mistakes, from the SaPeer method (https://doi.org/10.1007/s00766-020-00334-0) </p> <p>- Self-assessment Questionnaire (Second Interview).pdf: second questionnare to self-assess the mistakes, from the Sapeer method</p> <p> </p> <p>**<strong>/R-analysis</strong>**</p> <p> </p> <p>This is a folder containing all the R implementations of the the statistical tests included in the paper, together with the source .csv file used to produce the results. Each R file has the same title as the associated .csv file. The titles of the files reflect the RQs as they appear in the paper. The association between R files and Tables in the paper is as follows:</p> <p> </p> <p>- RQ1-1-analyse-story-rates.R: Tabe 1, user story rates </p> <p>- RQ1-1-analyse-role-rates.R: Table 1, role rates</p> <p>- RQ1-2-analyse-story-category-phase-1.R: Table 3, user story category rates in phase 1 compared to original rates</p> <p>- RQ1-2-analyse-role-category-phase-1.R: Table 5, role category rates in phase 1 compared to original rates</p> <p>- RQ2.1-analysis-app-store-rates-phase-2.R: Table 8, user story and role rates in phase 2</p> <p>- RQ2.2-analysis-percent-three-CAT-groups-ph1-ph2.R: Table 9, comparison of the categories of user stories in phase 1 and 2</p> <p>- RQ2.2-analysis-percent-two-CAT-roles-ph1-ph2.R: Table 10, comparison of the categories of roles in phase 1 and 2. </p> <p> </p> <p>The .csv files used for statistical tests are also used to produce boxplots. The association betwee boxplot figures and files is as follows. </p> <p> </p> <p>- RQ1-1-story-rates.csv: Figure 4 </p> <p>- RQ1-1-role-rates.csv: Figure 5</p> <p>- RQ1-2-categories-phase-1.csv: Figure 8</p> <p>- RQ1-2-role-category-phase-1.csv: Figure 9</p> <p>- RQ2-1-user-story-and-roles-phase-2.csv: Figure 13</p> <p>- RQ2.2-percent-three-CAT-groups-ph1-ph2.csv: Figure 14</p> <p>- RQ2.2-percent-two-CAT-roles-ph1-ph2.csv: Figure 17</p> <p>- IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv: Figure 15</p> <p>- IMG-only-RQ2.2-frequent-roles.csv: Figure 18</p> <p> </p> <p>NOTE: The last two .csv files do not have an associated statistical tests, but are used solely to produce boxplots.</p> <p> </p> <p>**<strong>/Data-Analysis</strong>**</p> <p> </p> <p>This folder contains all the data used to answer the research questions. </p> <p> </p> <p>**<strong>RQ1.xlsx</strong>**: includes all the data associated to RQ1 subquestions, two tabs for each subquestion (one for user stories and one for roles). The names of the tabs are self-explanatory of their content.</p> <p> </p> <p>**<strong>RQ2.1.xlsx</strong>**: includes all the data for the RQ1.1 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: for each category of user story, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that category for phase 1, and the second one reports the</p> <p>number of user stories in that category for phase 2, considering the specific analyst. </p> <p> </p> <p>* Data Source-role: for each category of role, and for each analyst, there are two lines. </p> <p>The first one reports the number of user stories in that role for phase 1, and the second one reports the</p> <p>number of user stories in that role for phase 2, considering the specific analyst. </p> <p> </p> <p>* RQ2.1 rates: reports the final rates for RQ2.1. </p> <p>NOTE: The other tabs are used to support the computation of the final rates.</p> <p> </p> <p>**<strong>RQ2.2.xlsx</strong>**: includes all the data for the RQ2.2 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.2-category-group: comparison between groups of categories in the different phases, used to produce Figure 14</p> <p> </p> <p>* RQ2.2-role-group: comparison between role groups in the different phases, used to produce Figure 17</p> <p> </p> <p>* RQ2.2-specific-roles-diff: difference between specific roles, used to produce Figure 18</p> <p> </p> <p>**<strong>NOTE:</strong>** the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.2-single-US-category.xlsx</strong>**: includes the data for the RQ2.2 subquestion associated to single categories of user stories.</p> <p>A separate tab is used given the complexity of the computations. </p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Totals: total number of user stories for each analyst in phase 1 and phase 2</p> <p> </p> <p>* Results-Rate-Comparison: difference between rates of user stories in phase 1 and phase 2, used to produce the file</p> <p>"img/IMG-only-RQ2.2-us-category-comparison-ph1-ph2.csv", which is in turn used to produce Figure 15</p> <p> </p> <p>* Results-Analysts: number of analysts using each novel category produced in phase 2, used to produce Figure 16.</p> <p>NOTE: the other tabs are used to support the computation of the values reported in the tabs above. </p> <p> </p> <p>**<strong>RQ2.3.xlsx</strong>**: includes the data for the RQ2.3 subquestion. Specifically, it includes the following tabs:</p> <p> </p> <p>* Data Source-US-category: same as RQ2.1.xlsx</p> <p> </p> <p>* Data Source-role: same as RQ2.1.xlsx</p> <p> </p> <p>* RQ2.3-categories: novel categories produced in phase 2, used to produce Figure 19</p> <p> </p> <p>* RQ2-3-most-frequent-categories: most frequent novel categories</p> <p> </p> <p>**<strong>/Raw-Data-Phase-I</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx, plus the file of the original user stories with annotations (original-us.xlsx). Each file contains two tabs:</p> <p> </p> <p>- Evaluation: includes the annotation of the user stories as existing user story in the original categories (annotated with "E"), novel user story in a certain category (refinement, annotated with "N"), and novel user story in novel category (Name of the category in column "New Feature"). **<strong>NOTE 1:</strong>** It should be noticed that in the paper the case "refinement" is said to be annotated with "R" (instead of "N", as in the files) to make the paper clearer and easy to read. </p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Raw-Data-Phaes-II</strong>**</p> <p>The folder contains one Excel file for each analyst, s1.xlsx...s30.xlsx. Each file contains two tabs:</p> <p> </p> <p>- Analysis: includes the annotation of the user stories as belonging to existing original </p> <p>category (X), or to categories introduced after interviews, or to categories introduced </p> <p>after app store inspired elicitation (name of category in "Cat. Created in PH1"), or to </p> <p>entirely novel categories (name of category in "New Category").</p> <p> </p> <p>- Roles: roles used in the user stories, and count of the user stories belonging to a certain role.</p> <p> </p> <p>**<strong>/Figures</strong>**</p> <p> </p> <p>This folder includes the figures reported in the paper. The boxplots are generated from the </p> <p>data using the tool http://shiny.chemgrid.org/boxplotr/. The histograms and other plots are </p> <p>produced with Excel, and are also reported in the excel files listed above. </p>
ASE_2022_How do code contexts evolve for software development tasks
<p>The dataset of paper---ASE_2022_How do code contexts evolve for software development tasks.</p> <p>It includes (1) working periods: the 1,375 interaction histories of development tasks and 4,219 working periods, and (2) results: results of our research and study.</p> <p>See README.md for more information.</p>
Promoting extinction or minimizing growth? The impact of treatment on trait trajectories in evolving populations - Data
<p>This is the dataset used in the manuscript "Promoting extinction or minimizing growth? The impact of treatment on trait trajectories in evolving populations" by Raatz &Traulsen. The code used to generate the data can be found at https://doi.org/10.5281/zenodo.6656842.</p>
Ecological effects of stress drive bacterial evolvability under sub-inhibitory antibiotic treatments
<p>Code and data for our publication "Ecological effects of stress drive bacterial evolvability under sub-inhibitory antibiotic treatments"</p>
Spruce giga-genomes: structurally similar yet distinctive with differentially expanding gene families and rapidly evolving genes - orthogroups dataset
<p>Orthogroups clustering and analysis of pines and spruces, as reported in Gagalova et al., 2022</p>
Growth rates of populations evolved and assayed at two temperatures for 6500 generations
<p>Evolutionary biologists have long sought to understand what factors affect the repeatability of adaptive outcomes. To better understand the role of temperature in determining the repeatability of adaptive trajectories, we evolved populations of different genotypes of the ciliate <i>Tetrahymena thermophila</i> at low and high temperatures and followed changes in growth rate over 6,500 generations. As expected, growth rate increased with a decelerating rate for all populations; however, there were differences in the patterns of evolution at the two temperatures. The growth rates of the different genotypes tended to converge as evolution proceeded at both temperatures, but this convergence was quicker and more pronounced at the higher temperature. Additionally, over the first 4,000 generations we found greater repeatability of evolution, in terms of change in growth rate, among replicates of the same genotype at the higher temperature. Finally, we found limited evidence of trade-offs in fitness between temperatures, and an asymmetry in the correlated responses, whereby evolution in a high temperature increases growth rate at the lower temperature significantly more than the reverse. These results demonstrate the importance of temperature in determining the repeatability of evolutionary trajectories for the eukaryotic microbe <i>Tetrahymena thermophila </i>and may provide clues to how temperature affects evolution more generally.</p>
FIGURE 3 in Life-stage association of black flies, using a fast-evolving nuclear gene sequence, and description of the larva of Simulium lampangense Takaoka & Choochote (Diptera: Simuliidae) from Thailand
FIGURE 3. Larva of Simulium lampangense. A. Cephalic apotome, dorsal view. B. Mandible, apex. C. Hypostoma. D. Head capsule showing postgenal cleft, ventral view. Scale bars = 0.1 mm for A and D and 0.05 mm for B and C.
FIGURE 2. Bayesian tree for nuclear elongation complex protein 1 in Life-stage association of black flies, using a fast-evolving nuclear gene sequence, and description of the larva of Simulium lampangense Takaoka & Choochote (Diptera: Simuliidae) from Thailand
FIGURE 2. Bayesian tree for nuclear elongation complex protein 1 (ECP1) sequences of five nominal species and unknown (Unk) larvae in the Simulium multistriatum species group in Thailand. Bootstrap values for neighbor-joining and maximum likelihood and posterior probability of Bayesian analysis are shown above or near the branches. -- denotes bootstrap support less than 50%. Scale bar represents 0.03 substitutions per nucleotide position.
FIGURE 1 in Life-stage association of black flies, using a fast-evolving nuclear gene sequence, and description of the larva of Simulium lampangense Takaoka & Choochote (Diptera: Simuliidae) from Thailand
FIGURE 1. Bayesian tree based on cytochrome c oxidase subunit I (COI) sequences of six nominal species and unknown (Unk) larvae in the Simulium multistriatum species group in Thailand. Bootstrap values for neighbor-joining and maximum likelihood (ML) and posterior probability of Bayesian analysis are shown above or near the branches. -- denotes bootstrap support less than 50%. Scale bar represents 0.03 substitutions per nucleotide position.
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.