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Figure 6 in Phylogenetic analysis of Zygaenoidea small-subunit rRNA structural variation implies initial oligophagy on cyanogenic host plants in larvae of the moth genus Zygaena (Insecta: Lepidoptera)
Figure 6. Secondary structure models of the highly variable proximal part of helix 43 (S6, Fig. 1) in Zygaeninae. All structures are based on thermodynamic folding by minimizing the free energy and have been calculated considering the entire nucleotide sequence of helix 43. The change in the Gibb's free energy (dG) refers to the S6 structure only. Applied ambiguity code: G/A = R, C/U = Y.
Figure 3 in Phylogenetic analysis of Zygaenoidea small-subunit rRNA structural variation implies initial oligophagy on cyanogenic host plants in larvae of the moth genus Zygaena (Insecta: Lepidoptera)
Figure 3. Consensus structure and base pair probability matrix of helix E23-5 (S3, Fig. 1) in Lepidoptera. Nucleotides in circles indicate consistent and/or compensatory substitutions. The size of squares in the grid is proportional to the probability of a base pairing. Note that the species Z. centaureae, Z. laeta and Z. huguenini have been omitted from this analysis because of their deviating secondary structure (compare with Fig. 4).
Figure 2 in Phylogenetic analysis of Zygaenoidea small-subunit rRNA structural variation implies initial oligophagy on cyanogenic host plants in larvae of the moth genus Zygaena (Insecta: Lepidoptera)
Figure 2. Distribution of pairwise tree edit distances between highly variable SSU rRNA secondary structure areas (S1–S6, Fig. 1) of Zygaenoidea excluding taxa of the subgenus Mesembrynus (top) and of Zygaena species belonging to the subgenus Mesembrynus only (bottom). The extreme values in the Zygaenoidea tree edit distance distribution on the right all involve Z. excelsa, a species showing a highly derived secondary structure in the area S6 (compare with Fig. 6).
Figure 1 in Phylogenetic analysis of Zygaenoidea small-subunit rRNA structural variation implies initial oligophagy on cyanogenic host plants in larvae of the moth genus Zygaena (Insecta: Lepidoptera)
Figure 1. Secondary structure model of the SSU (18S) rRNA gene sequence of Zygaena (Mesembrynus) sarpedon lusitanica Reiss, 1936 (Lepidoptera: Zygaenidae; accession no. AJ830858) and structure variation in the helices E10-1 and E23-12 among species of the subfamily Zygaeninae. Nucleotides in the model are continuously numbered beginning at the 5′-end of the molecule; tick marks identify every tenth base. Light shading indicate helices numbered according to Wuyts et al. (2002). S1–S6 (dark shades) denote areas with variable secondary structure in the subfamily Zygaeninae. Roman numerals specify the domains I, II, III and IV. The following ambiguity code has been applied: A/C = M, C/U = Y, G/A = R.
Figure 7 in Phylogenetic analysis of Zygaenoidea small-subunit rRNA structural variation implies initial oligophagy on cyanogenic host plants in larvae of the moth genus Zygaena (Insecta: Lepidoptera)
Figure 7. Neighbour-joining tree based on structural differences in the variable areas S1–S6 (compare with Fig. 1) of the small-subunit (18S) rRNA in taxa of the genus Zygaena. The topology is rooted with Reissita simonyi and Epizygaenella caschmirensis as outgroup. Taxa of the subgenus Mesembrynus are indicated by shading. Numbers in parentheses specify the number of species in a particular group.
Figure 5 in Phylogenetic analysis of Zygaenoidea small-subunit rRNA structural variation implies initial oligophagy on cyanogenic host plants in larvae of the moth genus Zygaena (Insecta: Lepidoptera)
Figure 5. Consensus structure and base pair probability matrix of the proximal part of helix 43 (S6, Fig. 1) in Lepidoptera. Nucleotides in circles indicate consistent and/or compensatory substitutions. The size of squares in the grid is proportional to the probability of a base pairing. Note that the unpaired nucleotides C and G in the helix will most likely bind in individual structures having this specific nucleotide combination, but non-Watson–Crick pairings are too frequent in the alignment for assuming a generally nucleotide interaction at this position in the consensus structure.
Scripts of data selection and analysis: role of community size in driving spatial variation in riverine fish metacommunities around the world
<p>Here we describe how we obtained and analyzed data for the manuscript: High compositional dissimilarity among small communities is decoupled from environmental variation, accepted for publication in Oikos. (10.1111/oik.09802). A preprint is also available: https://doi.org/10.32942/osf.io/vngse</p> <p>We investigated the role of community size in mediate the strength of ecological drift and environmental selection in driving community spatial variation in metacommunities. </p>
Otterly delicious: Spatiotemporal variation in the diet of a recovering population of Eurasian otters (Lutra lutra) revealed through DNA metabarcoding and morphological analysis of prey remains
<p>Eurasian otters are apex predators of freshwater ecosystems and a recovering species across much of their European range; investigating the dietary variation of this predator over time and space therefore provides opportunities to identify changes in freshwater trophic interactions and factors influencing the conservation of otter populations. Here we sampled faeces from 300 dead otters across England and Wales between 2007 and 2016, conducting both morphological analysis of prey remains and dietary DNA metabarcoding. Comparison of these methods showed that greater taxonomic resolution and breadth could be achieved using DNA metabarcoding but combining data from both methodologies gave the most comprehensive dietary description. All otter demographics exploited a broad range of taxa and variation likely reflected changes in prey distributions and availability across the landscape. This study provides novel insights into the trophic generalism and adaptability of otters across Britain, which is likely to have aided their recent population recovery, and may increase their resilience to future environmental changes.</p>
Combined analysis of transposable elements and structural variation in maize genomes reveals genome contraction outpaces expansion
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Comparative genomic analysis identifies potential adaptive variation in Mycoplasma ovipneumoniae
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Analysis of copy number variation in dogs implicates genomic structural variation in the development of anterior cruciate ligament rupture
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Data from: Provenance variation in functional traits of European forest trees: Meta-analysis reveals effects of taxa and age despite critical research gaps
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Otterly delicious: Spatiotemporal variation in the diet of a recovering population of Eurasian otters (Lutra lutra) revealed through DNA metabarcoding and morphological analysis of prey remains
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Detection and analysis of complex structural variation in human genomes across populations and in brains of donors with psychiatric disorders
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Explaining global variation in the latitudinal diversity gradient: Meta-analysis confirms known patterns and uncovers new one
This dataset is also available on the Dryad Digital Repository (link: https://doi.org/10.5061/dryad.rg5rd). The code is also available on GitHub (link: https://github.com/nlkinlock/LDGmeta-analysis). This dataset was created to explore patterns in biodiversity across latitude. The pattern of increasing biological diversity from high latitudes to the equator [latitudinal diversity gradient (LDG)] has been recognized for greater than 200 years. Empirical studies have documented this pattern across many different organisms and locations. In order to quantify the evidence for the global LDG and the associated spatial, taxonomic and environmental factors, a systematic review, followed by a meta-analysis of the resulting dataset, were carried out. This dataset contains a large number of individual LDGs that have been published in the 14 years since Hillebrand's ground‐breaking meta‐analysis of the LDG.
Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants
<p>Data for "Characteristic and spatiotemporal variation of air pollution in Northern China based on correlation analysis and clustering analysis of five air pollutants"</p>
Figure 1 from "Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"
<p> </p> <p>Published as part of <a href="https://doi.org/10.1038/s41467-019-13405-w"><strong>Macrì, S <em>et al</em>., 2019, Nature Communications: 10(1):5560</strong></a></p> <p><strong>"Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"</strong> DOI: https://doi.org/10.1038/s41467-019-13405-w</p> <p> </p> <p><strong>Fig. 1</strong> <strong>Phylogeny and cerebellar diversity of squamates.</strong> <strong>a</strong> Phylogenetic tree of all snake and lizard species used in morphological and volumetric analyses, adapted from the most inclusive phylogenetic study available for extant squamates. The seven major locomotor modes for snakes (coloured squares) and/or lizards (coloured circles), as defined based on anatomical features, habitat use, and movement type, are indicated by the same colour code throughout the entire manuscript (see bottom left corner): limbless or limb-reduced burrower (red squares and circles); limbless or limb-reduced facultative burrower (purple squares and circles); limbless or limb-reduced multi-habitat using lateral undulation (orange squares and circles); limbless or limb-reduced multi-habitat using other movements (yellow squares); quadrupedal arboreal (dark blue circles); quadrupedal terrestrial (light blue circles); quadrupedal facultative bipedal/aerial (green circles). <strong>b</strong>–<strong>o</strong> 3D-volume rendering and high-resolution whole-brain segmentation of iodine-stained adult heads (<strong>b</strong>–<strong>h</strong>) highlighting the cerebellum structure (<strong>b</strong>–<strong>o</strong>, red colour) of selected representative squamates at indicated position in the phylogenetic tree: <em>Pantherophis guttatus</em> (<strong>b</strong>, <strong>i</strong>), <em>Epicrates cenchria</em> (<strong>c</strong>, <strong>j</strong>), <em>Pogona vitticeps</em> (<strong>d</strong>, <strong>k</strong>), <em>Draco volans</em> (<strong>e</strong>,<strong> l</strong>), <em>Bradypodion pumilum</em> (<strong>f</strong>, <strong>m</strong>), <em>Anguis fragilis</em> (<strong>g</strong>, <strong>n</strong>), <em>Melanoseps loveridgei</em> (<strong>h</strong>, <strong>o</strong>). High magnifications of 3D-rendered cerebella (<strong>i</strong>–<strong>o</strong>) are shown in pial surface (left panels) and lateral (right panels) views for each selected species. Scale bars: 1mm (<strong>b</strong>–<strong>h</strong>), 500 μm (<strong>i</strong>–<strong>o</strong>).</p>
Figure 8 from "Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"
<p> </p> <p>Published as part of <a href="https://doi.org/10.1038/s41467-019-13405-w"><strong>Macrì, S <em>et al</em>., 2019, Nature Communications: 10(1):5560</strong></a></p> <p><strong>"Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"</strong> DOI: https://doi.org/10.1038/s41467-019-13405-w</p> <p> </p> <p><strong>Fig. 8 Comparative transcriptomics of the squamate cerebellum.</strong> <strong>a</strong> Two-way hierarchical clustering heat map showing three clusters of genes (rows) that behave similarly (clusters 1 and 2) or differently (cluster 3) across ten selected squamate species (columns). Z-score colour intensities reflect scaled gene expression values, ranging from low (blue) to high (yellow), for 630 one-to-one orthologous genes identified in all species. <strong>b</strong> Pie charts showing the distribution of orthologous genes (in %) among all significantly enriched gene ontology terms for biological processes (hypergeometric test with a false discovery rate multiple-hypothesis correction, p-value < 0.01) in clusters identified in <strong>a</strong>. c Hierarchical clustering of pairwise Pearson’s correlation coefficients for 630 orthologous genes identified across all squamate species. Colour intensities of individual tiles in the heat map depict pairwise correlation coefficient values, ranging from low (blue) to high (yellow), between selected species with indicated locomotor mode (see colour code and symbols on the right). Numbers at nodes in the cluster dendrogram represent approximately unbiased p-values (in percentage) obtained by multiscale bootstrap resampling.</p> <p> </p> <p> </p> <p> </p>
Figure 7 from "Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"
<p> </p> <p>Published as part of <a href="https://doi.org/10.1038/s41467-019-13405-w"><strong>Macrì, S <em>et al</em>., 2019, Nature Communications: 10(1):5560</strong></a></p> <p><strong>"Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"</strong> DOI: https://doi.org/10.1038/s41467-019-13405-w</p> <p> </p> <p><strong>Fig. 7</strong> <strong>Variability in the arrangement of Purkinje cells (PCs) in the squamate cerebellum.</strong> <strong>a</strong>, <strong>b</strong> Representative light-sheet microscopy imaging of cleared whole-cerebella showing the 3D distribution and arrangement of calbindin 1 (CALB1)-immunolabelled PCs in two representative species with different locomotor modes (see colour code and symbols in top left corner): <em>Pogona vitticeps </em>(<strong>a</strong>) and <em>Boaedon fuliginosus</em> (<strong>b</strong>). The boxed areas in the coronal 3Drendered cerebellar views (top panels) are shown at higher magnifications in coronal (left) and sagittal (right) views in the lower panels. <strong>c</strong>, Violin plot showing the quantitative distribution of CALB1-immunolabelled PCs in the cerebellar cortex of selected squamate species with similar or different locomotor modes (colour code and symbols as above). Due to intra- and interspecies heterogeneity in molecular layer (ML) thickness, the position of individual cells (n = 250–750 per species) was calculated as the distance (in %) from the granule cell layer (GCL) to the outer border (pial surface) of the ML, and error bars represent the standard deviation. Four major positioning patterns containing three or four squamate species and reflecting the increased scattering of PCs (from I to IV) were identified based on Kruskal-Wallis statistics. Immunohistochemistry with CALB1 marker (red staining) on sagittal sections of the cerebellar cortex in selected representative species are shown at low and high magnifications (insets) for each pattern: <em>Pogona vitticeps</em> (I), <em>Eryx colubrinus</em> (II), <em>Pseudopus apodus</em> (III), <em>Dasypeltis gansi</em> (IV). Scale bars: 30 μm (<strong>a</strong>, <strong>b</strong>), 100 μm (<strong>c</strong>).</p> <p> </p> <p> </p>
Figure 6 from "Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"
<p> </p> <p>Published as part of <a href="https://doi.org/10.1038/s41467-019-13405-w"><strong>Macrì, S <em>et al</em>., 2019, Nature Communications: 10(1):5560</strong></a></p> <p><strong>"Comparative analysis of squamate brains unveils multi-level variation in cerebellar architecture associated with locomotor specialization"</strong> DOI: https://doi.org/10.1038/s41467-019-13405-w</p> <p> </p> <p><strong>Fig. 6 Cerebellar size variation in squamates with different locomotor behaviours.</strong> <strong>a</strong> Scatter plot showing the correlation of cerebellum relative to wholebrain volume (in mm3) for selected representative squamate species (see colour code and symbols in bottom right corner). The coloured lines and shadings represent the phylogenetic generalized least squares (PGLS) regression lines and 95% confidence intervals for each locomotor mode, respectively. <strong>b</strong> Ridgeline plot showing the distribution of the cerebellum-to-whole-brain volume ratios (in percentage) for each indicated limbless or limbreduced (top panels) or quadrupedal (bottom) locomotor mode. Colour gradient, ranging from yellow to blue, reflects the tail distribution probability.</p>
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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.