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4,480 results for “hybrid”
Hybridization and the coexistence of species: HZAM-Sym code and data files
<p><span><span><span><span><span><span><span><span><span><span><span>It is thought that two species can coexist if they use different resources present in the environment, yet this assumes that species are completely reproductively isolated. We model coexistence outcomes for two sympatric species that are ecologically differentiated but have incomplete reproductive isolation. The consequences of interbreeding depend crucially on hybrid fitness. When hybrid fitness is high, just a small rate of hybridization can lead to collapse of two species into one. Low hybrid fitness can cause population declines, making extinction of one or both species likely. High intrinsic growth rates result in higher reproductive rates when populations are below carrying capacity, reducing the probability of extinction and increasing the probability of stable coexistence at moderate levels of assortative mating and hybrid fitness. Very strong but incomplete assortative mating can induce low hybrid fitness via a mating disadvantage to rare genotypes, and this can stabilize coexistence of two species at high but incomplete levels of assortative mating. Given these results and evidence that it may take many millions of years of divergence before related species become sympatric, we postulate that coexistence of closely-related species is more often limited by insufficient assortative mating than by insufficient ecological differentiation.</span></span></span></span></span></span></span></span></span></span></span></p>
Figure 1 in Evolutionary systematics of the Indian mouse Mus famulus Bonhote, 1898: molecular (DNA/DNA hybridization and 12S rRNA sequences) and morphological evidence
Figure 1. Phylogenetic trees derived from the DNA/DNA hybridization analysis. A and B: Consensus trees resulting from the bootstrap analysis of delta-Tm (A) and delta-mode (B) 12*12 matrices. BP values are indicated when different from 100%. The lengths of the branches correspond to one tree arbitrarily selected among those of the consensus. C and D: Average consensus trees resulting from the weighted jacknife procedure for delta-Tm (C) and delta-mode (D) 13*13 matrices. The thin lines represent nodes that were not present in maximum and minimum consensus trees or that are not supported for all combinations of single deletion analysis. uUnlabelled taxa. The names in bold indicate the differences that can be observed between the two distance estimators (Tm, Mode).
Figure 4 in Evolutionary systematics of the Indian mouse Mus famulus Bonhote, 1898: molecular (DNA/DNA hybridization and 12S rRNA sequences) and morphological evidence
Figure 4. Fifty per cent majority rule consensus of 52 trees derived from the morphological analysis. Each mostparsimonious tree is 54 steps long, and has a Consistency Index of 0.52, a Retention Index of 0.72, and a Rescaled Consistency Index of 0.37. Values given below the branches represent the percentage of trees containing the specified clades.
Figure 3. Synthetic tree derived from the 12S in Evolutionary systematics of the Indian mouse Mus famulus Bonhote, 1898: molecular (DNA/DNA hybridization and 12S rRNA sequences) and morphological evidence
Figure 3. Synthetic tree derived from the 12S rRNA datasets with the inclusion of all substitutions (TV + TI). The thin lines indicate nodes that are not robustly supported by all kinds of analysis. The robustness of the different nodes are indicated as follows: [BP(BPweighted analysis)/BSI (Parsimony)]/[BP(NJ)/Reliability Percentage (ML)].
SQLite3 databases of candidate marker loci from ddRADseq in Quercus suber, Quercus ilex and their hybrids
<p>The dataset contatins four SQLite3 databases of candidate marker loci from ddRADseq in <em>Quercus suber</em>, <em>Quercus ilex </em>and their hybrids, corresponding to the data collected for the four filtering/imputation scenarios considered in the manuscript "ddRAD sequencing-based identification of species genomic boundaries and permeability in <em>Quercus ilex</em> and <em>Q. suber</em> hybrids" submitted to Frontiers in Plant Sciences. This work was funded by the project AGL2015-67495-C2-2-R (Spanish Ministry of Economy and Competitiveness).</p>
Avena sativa L. fatuoid x fatua L. F1 hybrid (BR0000011471783)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Avena sativa L. x sterilis L. F1 hybrid (BR0000011471424)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Avena sativa L. fatuoid x fatua L. F1 hybrid (BR0000011471455)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Avena fatua L. x sativa L. F1 hybrid (BR0000011471127)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Avena fatua L. x sativa L. F1 hybrid (BR0000011470809)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Avena fatua L. x sativa L. F1 hybrid (BR0000011472889)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Avena fatua L. x sativa L. F1 hybrid (BR0000011472643)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Avena fatua L. x sativa L. F1 hybrid (BR0000011472971)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Avena fatua L. x sativa L. F1 hybrid (BR0000011472551)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Avena fatua L. x sativa L. F1 hybrid (BR0000011473213)
Belgium Herbarium image of <a href="https://www.plantentuinmeise.be">Meise Botanic Garden</a>.
Data for replication of the publication: Probabilistic leak localization in water distribution networks using a hybrid data-driven and model-based approach
<p>20 to 30% of drinking water produced is lost due to leaks in water distribution pipes. In times of water scarcity, losing so much treated water comes at a significant cost, both environmentally and economically. In this paper, we propose a hybrid leak localization approach combining both model-based and data-driven modeling. Pressure heads of leak scenarios are simulated using a hydraulic model, and then used to train a machine-learning based leak localization model. A key element of our approach is that discrepancies between simulated and measured pressures are accounted for using a dynamically calculated bias correction, based on historical pressure measurements. Data of in-field leak experiments in operational water distribution networks were produced to evaluate our approach on realistic test data. Two problematic settings for leak localization were examined. In the first setting, an uncalibrated hydraulic model was used. In the second setting, an extended version of the water distribution network was considered, where large parts of the network were insensitive to leaks. Our results show that the leak localization model is able to reduce the leak search region in parts of the network where leaks induce detectable drops in pressure. When this is not the case, the model still localizes the leak but is able to indicate a higher level of uncertainty with respect to its leak predictions.</p>
Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)
<p>This video is the sixth talk from our two day Future Blood Testing: Challenges & Opportunities Event that took place on the 13/09/2022.</p> <p>Improving triaging from primary care into secondary care using heterogeneous data-driven hybrid machine learning: A real-world case study of decision support system using blood test & GP referral letters - Bing Wang and Prof Weizi (Vicky) Li (University of Reading)</p> <p>Bio: Dr Weizi (Vicky) Li is the PI of the Future Blood Testing Network, an Associate Professor of Informatics and Digital Health, Deputy Director in Informatics Research Centre, Henley Business School, University of Reading. She is an interdisciplinary researcher focusing on using informatics, data science, machine learning, and digital information systems to solve real-world healthcare challenges. She is the academic lead of a large collaborative project of Improving the Quality of Healthcare through an Integrated Clinical Pathway Management Approach and Cloud based Digital Data Integration Platform, which was awarded ESRC O2RB Excellence in Impact Award in 2018 for her research impact on healthcare quality improvement. She is the academic lead of machine learning based decision support system for outpatient management which has successfully been implemented in Royal Berkshire NHS Foundation Trust and has received Research Engagement and Impact award in 2020. She has been PI on projects funded by ESRC, EPSRC, The Health Foundation, NHS and companies, working on data-driven decision support systems that use real-world data (under privacy preserving framework) from multiple sources including Electronic Patient Record in acute, community hospital and primary care settings, remote health monitoring and patient reported outcomes to develop novel technologies (including AI based methods) to support clinical and operational decision makings in patient pathway. Bing Wang is currently a PhD candidate in informatics and system science at the Informatics Research Center, Henley Business School, University of Reading. Bing’s research interests are Natural Language Processing, Machine Learning and Graph Machine Learning. Bing been working as a data scientist at Royal Berkshire NHS Foundation Trust since December 2019 during his PhD.</p> <p>Further details on this event can be found at: https://futurebloodtesting.org/event/13-14-09-2022/</p> <p>This video is an output from the Future Blood Testing Network which is funded by EPSRC under Grant Number EP/W000652/1</p> <p>YouTube Link: https://youtu.be/W6EH5l80NmU</p>
Pollinator and host sharing lead to hybridization and introgression in Panamanian free-standing figs, but not in their pollinator wasps
<p>Obligate pollination mutualisms, in which plant and pollinator lineages depend on each other for reproduction, often exhibit high levels of species-specificity. However, cases in which two or more pollinator species share a single host species (host sharing), or two or more host species share a single pollinator species (pollinator sharing), are known to occur in the current ecological time. Further, evidence for host switching in evolutionary time is increasingly being recognized in these systems. The degree to which departures from strict specificity differentially affect the potential for hybridization and introgression in the associated host or pollinator is unclear. We addressed this question using genome-wide sequence data from five sympatric Panamanian free-standing fig species (<em>Ficus</em> subgenus <em>Pharmacosycea</em>, section <em>Pharmacosycea</em>) and their six associated fig pollinator wasp species (<em>Tetrapus</em>). Two of the five fig species, <em>F. glabrata</em> and <em>F. maxima</em>, were found to regularly share pollinators. In these species, ongoing hybridization was demonstrated by the detection of several first-generation (F1) hybrid individuals, and historical introgression was indicated by phylogenetic network analysis. In contrast, although two of the pollinator species regularly share hosts, all six species were genetically distinct and deeply divergent, with no evidence for either hybridization or introgression. This pattern is consistent with results from other obligate pollination mutualisms, suggesting that, in contrast to their host plants, pollinators appear to be reproductively isolated, even when different species of pollinators mate in shared hosts.</p>
Raw data files for the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse"
<p>Datasets for the data presented in the manuscript entitled "Hybridization of Synthetic Humins with a Metal–Organic Framework for Precious Metal Recovery and Reuse" published in ACS Applied Materials and Interfaces.</p>
A complete picture of cation dynamics in hybrid perovskite materials from solid-state NMR spectroscopy
<p> Raw, collated NMR and XRD data for the article "A Complete Picture of Cation Dynamics in Hybrid Perovskite Materials from Solid-State NMR Spectroscopy". For further details see the readme.txt file.</p>
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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.