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2,395 results for “Contrast”
Contrasting parasite-mediated reductions in fitness within vs. between patches of a nematode host
<p>Host and parasites interact across spatial scales, but parasite-mediated fitness effects are often measured at local scales only. Recent work suggests that parasites can reduce host fitness during dispersal between patches, highlighting the potential for both within- and between-patch effects to contribute to the net fitness consequences of parasitism. Building on this work, we measured the contribution of the dispersal phase to parasite-mediated reductions in host fitness. We used the nematode <em>Caenorhabditis elegans</em> and its natural microsporidian parasite <em>Nematocida parisii</em> to quantify the fitness consequences of parasitism at the individual, population, and metapopulation level. <em>N. parisii</em> reduced individual fecundity and population growth but had its greatest fitness impact at the dispersal stage: parasitism reduced the fitness of dispersing larvae by 63 – 100%. These results indicate that the cost of parasitism in this system is greatly underestimated if the metapopulation level is not taken into account. We also found that the effects of <em>N. parisii</em> vary with host genotype, and the relative advantage of the most resistant genotype increases with inclusion of the dispersal stage. Taken together, our findings demonstrate that host-parasite interactions at the dispersal stage magnify selection for parasite resistance.</p>
Genetic basis of growth reaction to drought stress differs in contrasting high-latitude treeline ecotones of a widespread conifer
<p>Raw and filtered SNP data and raw tree ring data of the analysed trees. R scripts for SNP filtering, phenotypic data and genotype-phenotype association analysis. </p>
Data and code for Zheng et al. Contrasting coloured ventral wings are a visual collision avoidance signal in birds
<p>This repository contains codes and data for Zheng et al. Contrasting coloured ventral wings are a visual collision avoidance signal in birds. We have three folders, each containing one of the three datasets of contrast scores of avian ventral wings. These include the mean manual contrast ventral wing scores for 1780 species, a subset of 1745 diurnal species, 648 species with high-resolution museum ventral images, and the mean Root-Mean-Square (RMS) contrast ventral wing scores for the same 648 species. We tested the collision avoidance hypothesis for each dataset by assessing the relationships between the contrast scores and ecological traits. We used the Bayesian Generalized Linear Mixed Models in MCMCglmm with considering the phylogenetic relatedness among species and the uncertainties of 100 phylogenetic trees (downloaded in birdtree.org). We included body mass, flock size, coloniality (colonial vs. non-colonial breeding species), activity time (nocturnal vs. diurnal), the number of sympatric predators, and the interaction between coloniality and body mass as the predictors. In each folder, we included four files, including an R source file, a dataset containing the contrast scores and the ecological traits of the corresponding species, and a tree file containing 100 randomly sampled phylogenetic trees among these species. See the Methods of the paper for detail. </p>
Data collected for: The contrasted impacts of grasshoppers on soil microbial activities in function of ecosystem productivity and herbivore diet
<p>Herbivory can have contrasted impacts on soil microbes and nutrient cycling, which has stimulated the development of conceptual frameworks exploring the links between below- and aboveground processes. The "productivity model" predicts that herbivores stimulate microbial activities and accelerate nutrient mineralization in productive ecosystems, while they have an opposite effect in less productive ecosystems. In parallel, the "diet model" predicts that herbivores feeding on conservative plants accelerate nutrient cycling while those feeding on exploitative plants decelerate nutrient cycling, due to changes in litter inputs. Since these two frameworks can lead to conflicting predictions in some cases, experimental evidence combining herbivore diet and productivity is required.</p> <p>During two consecutive years, we conducted an experiment controlling the presence of three grasshopper species consuming either grasses, forbs or both in twelve natural and managed alpine grasslands of contrasted productivities. In order to assess the effects of herbivory on soil microbes, we measured their enzymatic activities, their biomass and the soil potential nitrogen mineralization (PNM). Soil and vegetation characteristics were also determined in order to test if they modulated the effects of herbivory on microbes.</p> <p>Contrary to the predictions of the diet model, the effects of herbivory on microbial characteristics did not depend on the herbivores diet but relied on ecosystem productivity. The most productive sites were characterized by exploitative plant species which depleted N resources in the soil, and by microbes producing relatively few extracellular enzymes, leading to a lower PNM. Herbivory increased microbial biomass and decreased the production of extracellular enzymes in those sites, possibly through the stimulation of root exudates produced by exploitative species. The least productive sites were characterized by conservative plants, which led to the sequestration of soil C, and by microbes having a resource acquisition strategy (more extracellular enzymes, higher PNM). Herbivory decreased microbial biomass and increased the production of extracellular enzymes in those sites. This pattern can be explained by the loss of carbon associated with insect respiration, which increases the need for microbes to acquire resources and by a lower production of root exudates by conservative species. Therefore, the effects of two years of herbivory on soil microbes were at odds with the productivity model, which focuses instead on longer term effects corresponding to herbivory-induced changes in plant species composition. This highlights the multidimensional feature of the impacts of herbivory on ecosystem functioning, both in space and time.</p>
Phase Contrast Time-Lapse and F-actin Imaging of Mechanically Compressed or Irradiated Pseudostratified Human Bronchial Epithelial Cells
<p><strong>Overview</strong></p> <p>This dataset includes phase contrast time-lapse imaging of <em>in vitro</em> pseudostratified airway epithelial cells to visualize their collective cellular migration after exposure to mechanical compression (mimicking bronchoconstriction) or irradiation. Additionally, the cells were fixed and stained for F-actin to visualize the apical cell boundaries, basal cell boundaries, and basal cell stress fibers.</p> <p><strong>Cell Culture and Treatment</strong></p> <p>Primary human bronchial epithelial cells (from a single donor) were grown on transwells in air-liquid interface (ALI) culture for 14 days to model a well-differentiated, pseudostratified airway epithelium. Cells were then exposed to either mechanical compression (30 cmH2O for 3 hours) mimicking asthmatic bronchoconstriction or irradiation (1Gy of ionizing radiation using a RS 2000 Biological Research Irradiator (RadSource) on ALI days 7, 10, and 14).</p> <p><strong>Phase Contrast Time-Lapse Imaging</strong></p> <p>At 24 or 72 hours after final treatment, cells were imaged to visualize collective cellular migration. For each independent experimental replicate (2 transwells per treatment per timepoint), six fields of view per well were imaged every 6 minutes over 1.5 hours. The imaging chamber was supplied with 37°C, 5% CO2, humidified air on a Zeiss Axio Observer Z1 to collect phase contrast images. <em>The image resolution is 0.586 µm/pixel.</em></p> <p><strong>Immunofluorescence Imaging</strong></p> <p>Cells were fixed (4% PFA for 30 minutes) at 24 or 72 hours after final treatment (and after phase contrast time-lapse imaging). Fixed transwells were stained for F-actin (Alexa fluor 488-Phalloidin, ThermoFisher Scientific, diluted 1:40, 30 minutes). Transwell membranes were cut from the plastic support and mounted on glass slides. Slides were imaged using a Zeiss Axio Observer Z1 with an apotome module controlled using Zen Blue 2.0 software. Five random fields of view were imaged from each transwell membrane in a z-stack from substrate to apical cell surface. To visualize various planes through the pseudostratified epithelial layer (apical cell boundaries, basal cell boundaries, and basal cell stress fibers), maximum intensity projections were generated from regions of interest through the z-stack. <em>The image resolution is 0.293 µm/pixel.</em></p> <p><strong>Dataset</strong></p> <p>Phase contrast time-lapse movies are provided as *.avi files. Immunofluorescence images are provided as *.tif files. For an individual transwell, the imaging dataset includes:</p> <ul> <li>6 phase contrast time-lapse movies</li> <li>5 immunofluorescence images of apical cell boundaries</li> <li>5 immunofluorescence images of basal cell boundaries</li> <li>5 immunofluorescence images of basal cell stress fibers</li> </ul> <p>Phase contrast time-lapse filenames contain</p> <ul> <li>Donor: U13</li> <li>Timepoint: 24 or 72 hours</li> <li>Treatment & Well: control (C), mechanical compression (P), or irradiation (R); well 1 or 2</li> <li>Field of View: (1) – (6)</li> </ul> <p>Immunofluorescence image filenames contain:</p> <ul> <li>Donor: <strong>U13</strong></li> <li>Timepoint: <strong>24</strong> or <strong>72</strong> hours</li> <li>Treatment & Well: control (<strong>C</strong>), mechanical compression (<strong>P</strong>), or irradiation (<strong>R</strong>); well <strong>1</strong> or <strong>2</strong></li> <li>Field of View: <strong>1-5</strong></li> <li>Region of Interest: apical cell boundaries (<strong>ACB</strong>), basal cell boundaries (<strong>BCB</strong>), or basal stress fibers (<strong>SF</strong>)</li> </ul> <p>Phase contrast time-lapse and immunofluorescence from the same transwell will all start with the same “Donor_Timepoint_Treatment/Well...” (i.e. U13_24_C1…). <strong>Note that the images from phase contrast and immunofluorescence are not necessarily from matched locations within the transwell and are at different spatial scales.</strong></p> <p>Immunofluorescence images from the same z-stack field of view will start with the same “Donor_Timepoint_Treatment/Well_FieldofView…” (i.e. U13_24_C1_1…).</p>
Supporting data: phase-contrast virtual chest radiography
<p>This dataset contains supporting data for the publication below:</p> <ul> <li>Ilian Häggmark, Kian Shaker, Sven Nyrén, Bariq Al-Amiry, Ehsan Abadi, William P. Segars, Ehsan Samei, and Hans M. Hertz, "Phase-contrast virtual chest radiography<em>"</em>, <em>Proceedings of the National Academy of Sciences </em><strong>120</strong><em> </em>(1), e2210214120 (2023). <a href="https://doi.org/10.1073/pnas.2210214120">https://doi.org/10.1073/pnas.2210214120</a></li> </ul> <p>If you use this dataset for your work, <strong>please cite this publication.</strong></p> <p>-------------------------------------</p> <p><strong>1. Virtual patient (2D) </strong></p> <p>An upsampled and projected virtual patient derived from the XCAT model (see paper for more details). The projected thickness (unit: [m]) of 28 separate materials are stored in the mat-file <em>'virtual_patient.mat'</em>. The accompanying text file <em>'virtual_patient_materials.txt' </em>lists all 28 materials (3rd dimension in the virtual patient .mat file) .</p> <p>Data information:</p> <ul> <li>File type: .mat</li> <li>Size: 39200x52200x28 (3D matrix, single, 32-bit)</li> <li>Pixel size: 7.69x7.69 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>2. Full-chest virtual radiographs</strong></p> <p>Three full-size virtual chest radiographs of the virtual patient (above) simulated with different settings:</p> <ul> <li>Conventional (<em>z</em> = 0 m, 120 kVp tungsten spectrum)</li> <li>Control (<em>z</em> = 0 m, 60 keV monochromatic)</li> <li>Phase contrast (<em>z</em> = 12 m, 60 keV monochromatic)</li> </ul> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 8000x6000 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>3. Zoom in on chest radiographs at different propagation distances</strong></p> <p>This is the underlying data for <strong>Figure 2</strong> in the paper.</p> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 600x600 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul> <p>-------------------------------------</p> <p><strong>4. Observing airway wall thickening</strong></p> <p>This is the underlying data for<strong> Figure 5</strong> in the paper.</p> <p>Data information:</p> <ul> <li>File type: .tif (16-bit)</li> <li>Data size: 750x750 pixels</li> <li>Pixel size: 50x50 µm<sup>2</sup></li> </ul>
Data for Contrasting life-history responses to climate variability in eastern and western North Pacific sardine populations
<p><span>Massive populations of sardines inhabit both the western and eastern boundaries of the world's subtropical ocean basins, supporting both commercial fisheries and populations of marine predators. Sardine populations in western and eastern boundary current systems have responded oppositely to decadal scale anomalies in ocean temperature, but the mechanism for differing variability has remained unclear. Here, based on otolith microstructure and high-resolution stable isotope analyses, we show that habitat temperature, early life growth rates, energy expenditure, metabolically optimal temperature and, most importantly, the relationship between growth rate and temperature were remarkably different between the two subpopulations in the western and eastern North Pacific. Varying metabolic response to environmental changes partly explain the contrasting growth responses. Consistent differences in the life-history traits are observed between subpopulations in the western and eastern boundary current systems around South Africa. These growth and survival characteristics can facilitate the contrasting responses of sardine populations to climate change.</span></p>
Spatial scaling of pollen-plant diversity relationship in landscapes with contrasting diversity patterns
<p>Data for paper "Spatial scaling of pollen-plant diversity relationship in landscapes with contrasting diversity patterns" in Scientific Reports.</p> <p>Code for analysis and plots <a href="https://github.com/vojtechabraham/SpatialScalingPollenDiversity">https://github.com/vojtechabraham/SpatialScalingPollenDiversity</a>. Download original pollen and resample them to the same pollen sum by function spectra_to_target_sum in <a href="https://github.com/vojtechabraham/pollen">https://github.com/vojtechabraham/pollen</a> or work with resampled datasets below.</p> <p>Original pollen data stored in <a href="https://www.neotomadb.org/">https://www.neotomadb.org/</a>:</p> <table> <tbody> <tr> <td><strong>species-poor region Bohemian-Moravian Highland (Vrchovina)</strong></td> </tr> <tr> <td><strong>forested</strong></td> <td> </td> <td> </td> <td><strong>open</strong></td> </tr> <tr> <td><strong>SiteName</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>SiteName</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> </tr> <tr> <td>Račín</td> <td>V06</td> <td><a href="https://data.neotomadb.org/54872">54872</a></td> <td>Plíčky</td> <td>V03</td> <td><a href="https://data.neotomadb.org/54870">54870</a></td> </tr> <tr> <td>Vepřová-Žlábek</td> <td>V07</td> <td><a href="https://data.neotomadb.org/54873">54873</a></td> <td>Louky u Černého lesa</td> <td>V04</td> <td><a href="https://data.neotomadb.org/54871">54871</a></td> </tr> <tr> <td>Stropnická cesta</td> <td>V18</td> <td><a href="https://data.neotomadb.org/54882">54882</a></td> <td>Suché Kopce</td> <td>V10</td> <td><a href="https://data.neotomadb.org/54874">54874</a></td> </tr> <tr> <td>Žižkov</td> <td>V19</td> <td><a href="https://data.neotomadb.org/54883">54883</a></td> <td>Pihoviny</td> <td>V11</td> <td><a href="https://data.neotomadb.org/54875">54875</a></td> </tr> <tr> <td>Chlum</td> <td>V21</td> <td><a href="https://data.neotomadb.org/54885">54885</a></td> <td>Kocanda</td> <td>V12</td> <td><a href="https://data.neotomadb.org/54876">54876</a></td> </tr> <tr> <td>Míšek</td> <td>V22</td> <td><a href="https://data.neotomadb.org/54886">54886</a></td> <td>Porostliny</td> <td>V13</td> <td><a href="https://data.neotomadb.org/54877">54877</a></td> </tr> <tr> <td>Knížecí studánka</td> <td>V23</td> <td><a href="http://data.neotomadb.org/54887">54887</a></td> <td>Bahna</td> <td>V14</td> <td><a href="https://data.neotomadb.org/54878">54878</a></td> </tr> <tr> <td>Pod Šindelným vrchem</td> <td>V24</td> <td><a href="https://data.neotomadb.org/54888">54888</a></td> <td>Ratajské rybníky</td> <td>V15</td> <td><a href="https://data.neotomadb.org/54879">54879</a></td> </tr> <tr> <td>Rampoltův mlýn</td> <td>V25</td> <td><a href="https://data.neotomadb.org/54889">54889</a></td> <td>Zubří</td> <td>V16</td> <td><a href="https://data.neotomadb.org/54880">54880</a></td> </tr> <tr> <td>Brožova skála</td> <td>V26</td> <td><a href="https://data.neotomadb.org/54890">54890</a></td> <td>Nový Rybník</td> <td>V17</td> <td><a href="https://data.neotomadb.org/54881">54881</a></td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>Samotín</td> <td>V20</td> <td><a href="https://data.neotomadb.org/54884">54884</a></td> </tr> </tbody> </table> <p> </p> <table> <tbody> <tr> <td><strong>species-rich region White-Carpathians Mountains (Bílé Karpaty)</strong></td> </tr> <tr> <td><strong>forested</strong></td> <td> </td> <td><strong>open</strong></td> </tr> <tr> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> <td><strong>Handle</strong></td> <td><strong>Dataset ID</strong></td> </tr> <tr> <td>BK1</td> <td><a href="https://data.neotomadb.org/54770">54770</a></td> <td>BK2</td> <td><a href="https://data.neotomadb.org/54771">54771</a></td> </tr> <tr> <td>BK3</td> <td><a href="https://data.neotomadb.org/54772">54772</a></td> <td>BK4</td> <td><a href="https://data.neotomadb.org/54773">54773</a></td> </tr> <tr> <td>BK5</td> <td><a href="https://data.neotomadb.org/54774">54774</a></td> <td>BK6</td> <td><a href="https://data.neotomadb.org/54775">54775</a></td> </tr> <tr> <td>BK9</td> <td><a href="https://data.neotomadb.org/54777">54777</a></td> <td>BK8</td> <td><a href="https://data.neotomadb.org/54776">54776</a></td> </tr> <tr> <td>BK11</td> <td><a href="https://data.neotomadb.org/54779">54779</a></td> <td>BK10</td> <td><a href="https://data.neotomadb.org/54778">54778</a></td> </tr> <tr> <td>BK13</td> <td><a href="https://data.neotomadb.org/54781">54781</a></td> <td>BK12</td> <td><a href="https://data.neotomadb.org/54780">54780</a></td> </tr> <tr> <td>BK15</td> <td><a href="https://data.neotomadb.org/54783">54783</a></td> <td>BK14</td> <td><a href="https://data.neotomadb.org/54782">54782</a></td> </tr> <tr> <td>BK16</td> <td><a href="https://data.neotomadb.org/54784">54784</a></td> <td>BK20</td> <td><a href="https://data.neotomadb.org/54788">54788</a></td> </tr> <tr> <td>BK17</td> <td><a href="https://data.neotomadb.org/54785">54785</a></td> <td>BK23</td> <td><a href="https://data.neotomadb.org/54791">54791</a></td> </tr> <tr> <td>BK18</td> <td><a href="https://data.neotomadb.org/54786">54786</a></td> <td>BK25</td> <td><a href="https://data.neotomadb.org/54793">54793</a></td> </tr> <tr> <td>BK19</td> <td><a href="https://data.neotomadb.org/54787">54787</a></td> <td>BK27</td> <td><a href="https://data.neotomadb.org/54795">54795</a></td> </tr> <tr> <td>BK21</td> <td><a href="https://data.neotomadb.org/54789">54789</a></td> <td>BK29</td> <td><a href="https://data.neotomadb.org/54797">54797</a></td> </tr> <tr> <td>BK22</td> <td><a href="https://data.neotomadb.org/54790">54790</a></td> <td>BK31</td> <td><a href="https://data.neotomadb.org/54799">54799</a></td> </tr> <tr> <td>BK24</td> <td><a href="https://data.neotomadb.org/54792">54792</a></td> <td>BK33</td> <td><a href="https://data.neotomadb.org/54801">54801</a></td> </tr> <tr> <td>BK26</td> <td><a href="https://data.neotomadb.org/54794">54794</a></td> <td>BK35</td> <td><a href="https://data.neotomadb.org/54803">54803</a></td> </tr> <tr> <td>BK28</td> <td><a href="https://data.neotomadb.org/54796">54796</a></td> <td>BK36</td> <td><a href="https://data.neotomadb.org/54804">54804</a></td> </tr> <tr> <td>BK30</td> <td><a href="https://data.neotomadb.org/54798">54798</a></td> <td>BK38</td> <td><a href="https://data.neotomadb.org/54805">54805</a></td> </tr> <tr> <td>BK32</td> <td><a href="https://data.neotomadb.org/54800">54800</a></td> <td>BK39</td> <td><a href="https://data.neotomadb.org/54806">54806</a></td> </tr> <tr> <td>BK34</td> <td><a href="https://data.neotomadb.org/54802">54802</a></td> <td>BK40</td> <td><a href="https://data.neotomadb.org/54807">54807</a></td> </tr> <tr> <td> </td> <td> </td> <td>BK41</td> <td><a href="https://data.neotomadb.org/54808">54808</a></td> </tr> </tbody> </table> <p> </p>
РИС. 11. ИЗображениЯ крючков глохидиев при раЗной настройке Яркости и контрастности (Anemina arcaeformis, р. Сита, Хабаровский кр.). А. Оптимальные Яркость и контрастность. В. ИЗбыточнаЯ контрастность. С. ИЗбыточнаЯ Яркость. МасШтаб 20 мкм. Микроскоп Zeiss EVO 40, напыление углеродом. FIG. 11. Imagines of glochidia hooks with different brightness and contrast value (Anemina arcaeformis, Sita River, Khabarovsk Krai). A. Optimal brightness and contrast. B. Excessive contrast. C. Excessive brightness. Scale bars 20 μm. Zeiss EVO 40 microscope, sputter coating with carbon. in Методика подготовки раковин глохидиев (Bivalvia, Unionidae) длЯ работы на сканируюЩем Электронном микроскопе
РИС. 11. ИЗображениЯ крючков глохидиев при раЗной настройке Яркости и контрастности (Anemina arcaeformis, р. Сита, Хабаровский кр.). А. Оптимальные Яркость и контрастность. В. ИЗбыточнаЯ контрастность. С. ИЗбыточнаЯ Яркость. МасШтаб 20 мкм. Микроскоп Zeiss EVO 40, напыление углеродом. FIG. 11. Imagines of glochidia hooks with different brightness and contrast value (Anemina arcaeformis, Sita River, Khabarovsk Krai). A. Optimal brightness and contrast. B. Excessive contrast. C. Excessive brightness. Scale bars 20 μm. Zeiss EVO 40 microscope, sputter coating with carbon.
Data from: Winter-moth populations are isolated on co-occurring tree species with contrasting budburst-phenology
<p>Differences between neighbouring tree species in phenology could isolate populations of host-plant generalists that depend on matching the phenology of their host. We studied the relationship between the budburst phenology of two co-occurring tree species with early (hornbeams) and late (oaks) budburst, and the egg-hatching date of associated winter moths (<em>Operophtera brumata</em>) during two seasons (autumns starting in 2020 and 2021)<em>.</em> A previous study in spring 2019 had found no winter moth larvae on the focal oaks, while we found them mainly on oaks with hornbeam neighbours in 2022. Congruently, adult winter moths were rarely encountered during the autumns of 2018 and 2019 and sparsely in 2020 and 2021, and then mainly near hornbeams.The vast majority of winter moths had early-hatching eggs when both hornbeams and oaks were present, matching the hornbeams rather than the oaks. Where hornbeam was absent in the neighbourhood, the few winter moths had late-hatching eggs.These results suggest that winter moth populations on hornbeam and oak are to some degree isolated from each other despite spatial proximity, so that recolonization of oaks by populations synchronized with budburst of hornbeam is slow. Therefore, trees would benefit from differing from their neighbours in budburst phenology with respect to herbivore damage.</p>
Baseline embeddings from the BBBC022 dataset used in "Semisupervised contrastive learning for bioactivity prediction using Cell Painting image data"
<p>3 Baseline embeddings aclculated from the BBBC022 dataset. A self-supervised contrastive learning-based model, DINO and CellProfiler were used to calculate the embeddings.</p>
Raw data for: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011
<p>This collection of data contains all raw data needed to reproduce the results in the paper:</p> <p>Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011</p> <p>It can also be used as a demonstration dataset for our Python package fours.</p> <p>More details can be found in the online documentation of our python package:<br><a href="https://fours.readthedocs.io/en/latest/">https://fours.readthedocs.io/en/latest/</a></p>
Intermediate results for: Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011
<p>This collection contains all intermediate results needed to reproduce the results in the paper:</p> <p>Use the 4S (Signal-Safe Speckle Subtraction): Explainable Machine Learning reveals the Giant Exoplanet AF Lep b in High-Contrast Imaging Data from 2011</p> <p>You can use these intermediate results to create all plots in our paper without the need to run all experiments on a large cluster.</p> <p>More details can be found in the online documentation of our python package:<br><a href="https://fours.readthedocs.io/en/latest/">https://fours.readthedocs.io/en/latest/</a></p>
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable. in External and internal microbiomes of Antarctic nematodes are distinct, but more similar to each other than the surrounding environment
SI Figure 1: Dispersion values (a boxplot using distance to centroids based on Bray Curtis distance matrix) of external and internal bacterial microbiome composition for different hosts. In a mixed linear model, microinvertebrates did not significantly impact dispersion (P=0.44), but microbiome type did (P=0.03). Pairwise contrasts show that while external microbiomes of P. murrayi and Tardigrada are more variable than their internal microbiomes, E. antarcticus external and internal microbiomes are equally variable.
Figure 5 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing
Figure 5. Gene ontology enrichment analysis of genes from genome regions with strong selective signals.
Figure 2 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing
Figure 2. Box plots of diversity, Tajima's D of S. japonicum and their FST. (A) Nucleotide diversity estimated in 100-kb windows sliding in 10-kb steps throughout the genome. (B) Tajima's D estimated within a nonoverlapping 100-kb window throughout the genome. (C) pairwise FST computed in 100-kb windows sliding in 10-kb steps throughout the genome.
Figure 4 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing
Figure 4. Distribution of p ratios (pST/pHX) and FST values calculated in 100-kb windows sliding in 10-kb steps throughout the genome. Data points colored red and blue were identified as selected regions in ST (red dots) and in HX (blue dots), respectively. These points correspond to the 5% left and right tails of the empirical p ratio distribution, where the p ratios are 0.092 and 1.643, respectively (vertical dashed lines), and the 5% right tail of the empirical FST distribution, where FST is 0.965 (horizontal dashed line).
Figure 3 in Genetic difference between two Schistosoma japonicum isolates with contrasting cercarial shedding patterns revealed by whole genome sequencing
Figure 3. Box plots of diversity and relationship of two sample groups. (A) Nucleotide diversity estimated in 100-kb windows sliding in 10-kb steps throughout the genome. (B) Tajima's D estimated within a nonoverlapping 100-kb window throughout the genome. (C) FST between computed in 100-kb windows sliding in 10-kb steps throughout the genome.
Fig. 3 in Molecular and Morphological Snapshot Characterisation of the Protist Communities in Contrasting Alpine Glacier Forefields
Fig. 3. Rarefaction analysis derived from the clone libraries of the vegetated transects of Tiefen forefield and Wildstrubel forefield. Dashed lines correspond to 95% confidence intervals.
Fig. 2 in Molecular and Morphological Snapshot Characterisation of the Protist Communities in Contrasting Alpine Glacier Forefields
Fig. 2. Relative abundances (in percentage) of ciliate-related sequences detected in the 18S rRNA gene clone libraries from the vegetated transects of the (a) Tiefen forefield and (b) Wildstrubel forefield. Species names are based on BLAST comparison of the sequences with the NCBI database (first similarity with a known taxonomic group).
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.