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45 results for “hybrid map”
Reference data set used to validate the hybrid cropland map at 500m (Fritz, S. 2024)
<p>This is a reference data set for validation of the hybrid cropland map at 500m resolution for the year 2019 (Fritz, 2024, map <a title="Hybrid cropland map (GLAD/WorldCereal)" href="../doi/10.5281/zenodo.10818823" target="_blank" rel="noopener">available here</a>)</p> <p>Sampling design: random whithin areas of improvement, where the WorldCereal map is performing better (less errors) than the GLAD cropland map 2019. </p> <p>Number of sample sites: 500</p> <p>Method of data collection: visual interpreation of various sources of information, including very high resolution images and photos. </p> <p><br>Tool for data collection: <a href="https://www.geo-wiki.org" target="_blank" rel="noopener">Geo-Wiki</a></p>
Hybrid AI trustworthiness characteristics mind map
<p>Mind map that considers the majority of AI criteria to be assessed for trustworthiness purpose on Hybrid AI robotic systems. We labelled the terms that have been emphasized, as requirements or European values to be respected, by references such as ALTAI and IEEE Ethically Aligned Design.</p>
Data from: Admixture mapping reveals evidence for multiple mitonuclear incompatibilities in swordtail fish hybrids
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Data from: Mapping reduced introgression loci to the X chromosome of the hybridizing field crickets, Gryllus firmus and G. pennsylvanicus
The genomic architecture of barriers to gene exchange during the speciation process is poorly understood. The genomic islands model suggests that loci associated with barriers to gene exchange prevent introgression of nearby genomic regions via linkage disequilibrium. But few analyses of the actual genomic location of non-introgressing loci in closely related species exist. In a previous study Maroja et al. showed that in the hybridizing field crickets, Gryllus firmus and G. pennsylvanicus, 50 non-introgressing loci are localized on two autosomal regions and the X chromosome, but they were not able to map the loci along the X chromosome because they used a male informative cross. Here, we localize the introgressing and non-introgressing loci on the X chromosome and reveal that all X-linked non-introgressing loci are restricted to a 50-cM region with 10 of these loci mapped to a single location. We discuss the implications of this finding to speciation.
Data from: Integrating Bayesian genomic cline analyses and association mapping of morphological and ecological traits to dissect reproductive isolation and introgression in a Louisiana Iris hybrid zone
Hybrid zones provide unique opportunities to examine reproductive isolation and introgression in nature. We utilized 45,384 Single Nucleotide Polymorphism (SNP) loci to perform association mapping of 14 floral, vegetative, and ecological traits that differ between Iris hexagona and Iris fulva, and to investigate, using a Bayesian Genomic Cline (BGC) framework, patterns of genomic introgression in a large and phenotypically diverse hybrid zone in southern Louisiana. Many loci of small effect-size were consistently found to be associated with phenotypic variation across all traits, and several individual loci were revealed to influence phenotypic variation across multiple traits. Patterns of genomic introgression were quite heterogeneous throughout the Louisiana Iris genome, with I. hexagona alleles tending to be favored over those of I. fulva. Loci that were found to have exceptional patterns of introgression were also found to be significantly associated with phenotypic variation in a small number of morphological traits. However, this was the exception rather than the rule, as most loci that were associated with morphological trait variation were not significantly associated with excess ancestry. These findings provide insights into the complexity of the genomic architecture of phenotypic differences and are a first step towards identifying loci that are associated with both trait variation and reproductive isolation in nature.
MLR - Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams
<p> Supplementary artifact for Mapping Collaboration Challenges in Hybrid Working Environments of Information Technology Teams</p>
Data from: Mapping reduced introgression loci to the X chromosome of the hybridizing field crickets, Gryllus firmus and G. pennsylvanicus
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Data from: Integrating Bayesian genomic cline analyses and association mapping of morphological and ecological traits to dissect reproductive isolation and introgression in a Louisiana Iris hybrid zone
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Data from: Sex-determining chromosomes and sexual dimorphism: insights from genetic mapping of sex expression in a natural hybrid Fragaria × ananassa subsp. cuneifolia
We studied the natural hybrid (Fragaria × ananassa subsp. cuneifolia) between two sexually dimorphic octoploid strawberry species (Fragaria virginiana and Fragaria chiloensis) to gain insight into the dynamics of sex chromosomes and the genesis of sexual dimorphism. Male sterility is dominant in both the parental species and thus will be inherited maternally, but the chromosome that houses the sex-determining region differs. Thus, we asked whether (1) the cytotypic composition of hybrid populations represents one or both maternal species, (2) the sex-determining chromosome of the hybrid reflects the location of male sterility within the maternal donor species and (3) crosses from the hybrid species show less sexual dimorphism than the parental species. We found that F. × ananassa subsp. cuneifolia populations consisted of both parental cytotypes but one predominated within each population. Genetic linkage mapping of two crosses showed dominance of male sterility similar to the parental species, however, the map location of male sterility reflected the maternal donor in one cross, but not the other. Moreover, female function mapped to a single region in the first cross, but to two regions in the second cross. Aside from components of female function (fruit set and seed set), other traits that have been found to be significantly sexually dimorphic in the pure species were either not dimorphic or were dimorphic in the opposite direction to the parental species. These results suggest that hybrids experience some disruption of dimorphism in secondary sexual traits, as well as novel location and number of quantitative trait locus (QTL) affecting sex function.
Data from: Admixture mapping of male nuptial color and body shape in a recently formed hybrid population of threespine stickleback
Despite recent progress, we still know relatively little about the genetic architecture that underlies adaptation to divergent environments. Determining whether the genetic architecture of phenotypic adaptation follows any predictable patterns requires data from a wide variety of species. However, in many organisms, genetic studies are hindered by the inability to perform genetic crosses in the laboratory or by long generation times. Admixture mapping is an approach that circumvents these issues by taking advantage of hybridization that occurs between populations or species in the wild. Here, we demonstrate the utility of admixture mapping in a naturally occurring hybrid population of threespine sticklebacks (Gasterosteus aculeatus) from Enos Lake, British Columbia. Until recently, this lake contained two species of sticklebacks adapted to divergent habitats within the lake. This benthic-limnetic species pair diverged in a number of phenotypes, including male nuptial coloration and body shape, which were previously shown to contribute to reproductive isolation between them. However, recent ecological disturbance has contributed to extensive hybridization between the species, and there is now a single, admixed population within Enos Lake. We collected over 500 males from Enos Lake and found that most had intermediate nuptial color and body shape. By genotyping males with nuptial color at the two extremes of the phenotypic distribution, we identified seven genomic regions on three chromosomes associated with divergence in male nuptial color. These genomic regions are also associated with variation in body shape, suggesting that tight linkage and/or pleiotropy facilitated adaptation to divergent environments in this benthic-limnetic species pairs.
Data from: Admixture mapping in a hybrid zone reveals loci associated with avian feather coloration
Identifying the genetic bases for color patterns has provided important insights into the control and expression of pigmentation and how these characteristics influence fitness. However, much more is known about the genetic bases for traits based on melanin pigments than for traits based on another major class of pigments, carotenoids. Here we use natural admixture in a hybrid zone between Audubon's and myrtle warblers (Setophaga coronata auduboni / S. c. coronata) to identify genomic regions associated with both types of pigmentation. Warblers are known for rapid speciation and dramatic differences in plumage. For each of five plumage coloration traits, we found highly significant associations with multiple SNPs across the genome and these were clustered in discrete regions. Regions near significantly associated SNPs were enriched for genes associated with keratin filaments, fibrils that makeup feathers. A carotenoid-based trait that differs between the taxa—throat color—had more than a dozen genomic regions of association. One cluster of SNPs for this trait overlaps the Scavenger Receptor Class F Member 2 (SCARF2) gene. Other scavenger receptors are presumed to be expressed at target tissues and involved in the selective movement of carotenoids into the target cells, making SCARF2 a plausible new candidate for carotenoid processing. In addition, two melanin-based plumage traits—colors of the eye line and eye spot—show very strong associations with a single genomic region mapping to chromosome 20 in the zebra finch. These findings indicate that only a subset of the genomic regions differentiated between these two warblers are associated with the plumage differences between them and demonstrate the utility of reduced-representation genomic scans in hybrid zones.
Hcropland30: A hybrid 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model
<p><strong>Hcropland30</strong><strong>:</strong><strong>A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model</strong></p> <p><strong>***Please note this dataset is undergoing peer review***</strong></p> <p><strong>Version</strong>: <strong>1.0</strong></p> <p><strong>Authors</strong>: Qiong Hu <sup>a, 1</sup>, Zhiwen Cai<sup> b, 1</sup>, Liangzhi You<sup> c, d</sup>, Steffen Fritz<sup> e</sup>, Xinyu Zhang<sup> c</sup>, He Yin<sup> f</sup>, Haodong Wei<sup>c</sup>, Jingya Yang<sup> g</sup>, Zexuan Li<sup> a</sup>, Qiangyi Yu<sup> g</sup>, Hao Wu<sup> a</sup>, Baodong Xu<sup> b *</sup>, Wenbin Wu<sup> g, *</sup></p> <p><em><sup>a</sup></em><em> Key Laboratory for Geographical Process Analysis & Simulation of Hubei Province/College of Urban and Environmental Sciences, Central China Normal University, Wuhan 430079, China</em></p> <p><em><sup>b</sup></em><em> College of Resources and Environment, Huazhong Agricultural University, Wuhan 430070, China</em></p> <p><em><sup>c</sup></em><em> Macro Agriculture Research Institute, College of Plant Science and Technology, Huazhong Agricultural University, Wuhan 430070, China</em></p> <p><em><sup>d </sup></em><em>International Food Policy Research Institute, 1201 I Street, NW, Washington, DC 20005, USA</em></p> <p><em><sup>e </sup></em><em>Novel Data Ecosystems for sustainability Research Group, International Institute for Applied Systems Analysis (IIASA), Schlossplatz 1, Laxenburg A-2361, Austria</em></p> <p><em><sup>f </sup></em><em>Department of Geography, Kent State University, 325 S. Lincoln Street, Kent, OH 44242, USA</em></p> <p><a name="_Hlk166672711"></a><em><sup>g </sup></em><em>State Key Laboratory of Efficient Utilization of Arid and Semi-arid Arable Land in Northern China, the Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences, Beijing 100081, China</em></p> <p><strong> </strong></p> <p><strong>Introduction</strong></p> <p>We are pleased to introduce a comprehensive global cropland mapping dataset (named Hcropland30) in 2020, meticulously curated to support a wide range of research and analysis applications related to agricultural land and environmental assessment. This dataset encompasses the entire globe, divided into 16,284 grids, each measuring an area of 1°×1°. Hcropland30 was produced by leveraging global land cover products and Landsat data based on a deep learning model. Initially, we established a hierarchal sampling strategy that used the simulated annealing method to identify the representative 1°×1° grids globally and the sparse point-level samples within these selected 1°×1°grids. Subsequently, we employed an ensemble learning technique to expand these sparse point-level samples into the densely pixel-wise labels, creating the area-level 1°×1° cropland labels. These area-level labels were then used to train a U-Net model for predicting global cropland distribution, followed by a comprehensive evaluation of the mapping accuracy.</p> <p> </p> <p><strong>Dataset</strong></p> <p><strong><em><u>1. Hcropland30</u></em></strong><strong>:</strong> A hybrid 30-m global cropland map in 2020</p> <p>****<strong>Data format</strong>: GeoTiff</p> <p>****<strong>Spatial resolution</strong>: 30 m</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Values</strong>: 1 denotes cropland and 0 denotes non-cropland</p> <p>The dataset has been uploaded in 16,284 tiles. The extent of each tile can be found in the file of “Grids.shp”. Each file is named according to the grid’s Id number. For example, “000015.tif” corresponds to the cropland mapping result for the 15-th 1°×1° grid. This systematic naming convention ensures easy identification and retrieval of the specific grid data.</p> <p><strong><em><u>2. </u></em></strong><strong><em><u>1°×1° </u></em></strong><strong><em><u>Grids</u></em></strong><strong>:</strong> This file contains all 16,284 1°×1° grids used in the dataset. The vector file includes 18 attribute fields, providing comprehensive metadata for each grid. These attributes are essential for users who need detailed information about each grid’s characteristics.</p> <p>****<strong>Data format</strong>: ESRI shapefile</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Attribute Fields</strong>:</p> <p><strong>Id:</strong> The grid’s ID number.</p> <p><strong>area:</strong> The area of the grid.</p> <p><strong>mode:</strong> Indicates the representative sample grid.</p> <p><strong>climate:</strong> The climate type the grid belongs to.</p> <p><strong>dem: </strong>Average DEM value of the grid.</p> <p><strong>ndvi_s1 to ndvi_s4:</strong> Average NDVI values for four seasons within the grid.</p> <p><strong>esa, esri, fcs30, fromglc, glad, globeland30:</strong> Proportion of cropland pixels of different publicly available cropland products.</p> <p><strong>inconsistent:</strong> Proportion of inconsistent pixels within the grid according to different public cropland products.</p> <p><strong>hcropland30:</strong> Proportion of cropland pixels of our Hcropland30 dataset.</p> <p><strong><em><u>3. Samples</u></em></strong>: The selected representative pixel-level samples, including 32,343 cropland and 67657 non-cropland samples. The category information of each sample was determined based on visual interpretation on Google Earth image and three-year NDVI time series curves from 2019-2021.</p> <p>****<strong>Data format</strong>: ESRI shapefile</p> <p>****<strong>Projection</strong>: EPSG: 4326 (WGS84)</p> <p>****<strong>Attribute Fields</strong>:</p> <p><strong>type:</strong> 1 denotes cropland sample and 0 denotes non-cropland sample.</p> <p><strong>Citation</strong></p> <p>If you use this dataset, please cite the following paper:</p> <p>Hu, Q., Cai, Z., You, L., Fritz, S., Zhang, X., Yin, H., Wei, H., Yang, J., Li, Z., Yu, Q., Wu, H., Xu, B., Wu, W. (2024). Hcropland30: A 30-m global cropland map by leveraging global land cover products and Landsat data based on a deep learning model, Remote Sensing of Environment, submitted.</p> <p><strong>License</strong></p> <p>The data is licensed under Creative Commons Attribution 4.0 International (CC BY 4.0).</p> <p><strong>Disclaimer</strong></p> <p>This dataset is provided as-is, without any warranty, express or implied. The dataset author is not</p> <p>responsible for any errors or omissions in the data, or for any consequences arising from the use</p> <p>of the data.</p> <p><strong>Contact</strong></p> <p>If you have any questions or feedback regarding the dataset, please contact the dataset author</p> <p>Qiong Hu (huqiong@ccnu.edu.cn)</p>
Data from: Admixture mapping of quantitative traits in Populus hybrid zones: power and limitations
Uncovering the genetic architecture of species differences is of central importance for understanding the origin and maintenance of biological diversity. Admixture mapping can be used to identify the number and effect sizes of genes that contribute to the divergence of ecologically important traits, even in taxa that are not amenable to laboratory crosses due to their long generation time or other limitations. Here, we apply admixture mapping to naturally occurring hybrids between two ecologically divergent Populus species. We map quantitative trait loci (QTL) for eight leaf morphological traits using 77 mapped microsatellite markers from all 19 chromosomes of Populus. We apply multivariate linear regression analysis allowing the modeling of additive and non-additive gene action and identify several candidate genomic regions associated with leaf morphology using an information-theoretic approach. We perform simulation studies to assess the power and limitations of admixture mapping of quantitative traits in natural hybrid populations for a variety of genetic architectures and modes of gene action. Our results indicate that (1) admixture mapping has considerable power to identify the genetic architecture of species differences if sample sizes and marker densities are sufficiently high, (2) modeling of non-additive gene action can help to elucidate the discrepancy between genotype and phenotype sometimes seen in interspecific hybrids, and (3) the genetic architecture of leaf morphological traits in the studied Populus species involves complementary and overdominant gene action, providing the basis for rapid adaptation of these ecologically important forest trees.
Data from: Genome-wide mapping in a house mouse hybrid zone reveals hybrid sterility loci and Dobzhansky-Muller interactions
Mapping hybrid defects in contact zones between incipient species can identify genomic regions contributing to reproductive isolation and reveal genetic mechanisms of speciation. The house mouse features a rare combination of sophisticated genetic tools and natural hybrid zones between subspecies. Male hybrids often show reduced fertility, a common reproductive barrier between incipient species. Laboratory crosses have identified sterility loci, but each encompasses hundreds of genes. We map genetic determinants of testis weight and testis gene expression using offspring of mice captured in a hybrid zone between M. musculus musculus and M. m. domesticus. Many generations of admixture enables high-resolution mapping of loci contributing to these sterility-related phenotypes. We identify complex interactions among sterility loci, suggesting multiple, non-independent genetic incompatibilities contribute to barriers to gene flow in the hybrid zone.
Data from: Admixture mapping of quantitative traits in Populus hybrid zones: power and limitations
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Data from: Admixture mapping in a hybrid zone reveals loci associated with avian feather coloration
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Data from: Genome-wide mapping in a house mouse hybrid zone reveals hybrid sterility loci and Dobzhansky-Muller interactions
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Data from: Admixture mapping of male nuptial color and body shape in a recently formed hybrid population of threespine stickleback
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Data from: Interspecific crossing and genetic mapping reveal intrinsic genomic incompatibility between two Senecio species that form a hybrid zone on Mount Etna, Sicily
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Data from: Use of a natural hybrid zone for genome-wide association mapping of craniofacial traits in the house mouse
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Allen Brain Atlas
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International Brain Laboratory public data
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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.