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124 results for “Signal pattern”
Figure 4. A in Somatic musculature of Tardigrada: phylogenetic signal and metameric patterns
Figure 4. A, musculature of Paramacrobiotus richtersi (ventro-lateral view). B, C, musculature of Bertolanius volubilis (A, dorsal view; B, ventro-lateral view; colour coded by depth). Ph, pharynx. Letters and numbers identify the muscle attachment points (see text). Nodes of ventral muscle groups are marked by hexagons; attachment points and nodes of lateral muscle group are marked by squares; attachment points and nodes of dorsal muscle group are marked by circles. A–C, CLSM, maximum projection. C, colour coded by depth. Scale bars: A–C = 50 μm.
Figure 9. Ventral musculature associated with the A in Somatic musculature of Tardigrada: phylogenetic signal and metameric patterns
Figure 9. Ventral musculature associated with the A node; the A-LIc muscle fibre crosses the corresponding fibre of the opposite leg (arrow). A, Paramacrobiotus richtersi; B, Milnesium cf. tardigradum; C, Echiniscus testudo. A–C, CSLM, maximum projection. Letters and numbers identify the muscle attachment points (see text). Ph, pharynx. Scale bars: 10 μm.
Figure 12 in Somatic musculature of Tardigrada: phylogenetic signal and metameric patterns
Figure 12. Schematic representations of hypothetical ancestral serial homologies of the different muscle groups in tardigrades. A, dorsal muscle group in eutardigrades; B, dorsal muscle group in heterotardigrade echiniscids; C, lateral muscle group in tardigrades; D, ventral muscle group in tardigrades.
Figure 7 in Somatic musculature of Tardigrada: phylogenetic signal and metameric patterns
Figure 7. Schematic representations of musculature. A, Paramacrobiotus richtersi; B, Bertolanius volubilis. Muscles of the ventral group are in red, their nodes are marked by hexagons; muscles of the lateral group are in blue, their attachment points and nodes are marked by squares; muscles of the dorsal group are in green, their attachment points and nodes are marked by circles. Letters and numbers identify the muscle attachment points (see text). Ph, pharynx.
Figure 11 in Somatic musculature of Tardigrada: phylogenetic signal and metameric patterns
Figure 11. Phylogenetic tree obtained with Bayesian analyses based on molecular data (18S rRNA + 28S rRNA). The posterior probability values are indicated next to each node.
Figure 2. A, B in Somatic musculature of Tardigrada: phylogenetic signal and metameric patterns
Figure 2. A, B, the eutardigrade Milnesium cf. tardigradum in active state (A, ventral view; B, lateral view). C, D, the eutardigrade Paramacrobiotus richtersi in active state (C; dorsal view) and in dry anhydrobiotic state (D; tun shape). Asterisk indicates front. Letters and numbers identify the muscle attachment points (see text). A–D, SEM. Scale bars: A–C = 50 μm; D = 20 μm.
Figure 1. A in Somatic musculature of Tardigrada: phylogenetic signal and metameric patterns
Figure 1. A, muscle fibres (white arrows) and ventral ganglion (black arrow) of the nervous system in the eutardigrade Paramacrobiotus richtersi. B, C, dorsal view of the heterotardigrade Echiniscus testudo in active state (B) and anhydrobiotic state (C; tun shape). Asterisk indicates the front. Letters identify muscle attachment points (see text). A–C, SEM. Scale bars: A = 20 μm, B–C = 50 μm.
Figure 5. A, B in Somatic musculature of Tardigrada: phylogenetic signal and metameric patterns
Figure 5. A, B, musculature of Echiniscus testudo (A, dorsal view; B, lateral view). C, musculature of Batillipes bullacaudatus (ventral view). Ph, pharynx. Letters and numbers identify the muscle attachment points (see text). Nodes of ventral muscle groups are marked by hexagons; attachment points and nodes of lateral muscle group are marked by squares; attachment points and nodes of dorsal muscle group are marked by circles. A–C, CLSM, maximum projection. Scale bars: A, B = 50 μm; C = 25 μm.
Figure 10. A in Somatic musculature of Tardigrada: phylogenetic signal and metameric patterns
Figure 10. A, unrooted phylogenetic tree based on the musculature dataset. The posterior probability values for MrBayes analysis (top), bootstrap supports for TNT analysis (middle) and PAUP analysis (bottom) are indicated next to each node. B, unrooted phylogenetic tree based on the total evidence approach (morphological data + 18S rRNA + 28S rRNA). The posterior probability values for MrBayes analysis are indicated next to each node.
Characterization of signal and transit peptides based on motif composition and taxon-specific patterns
<p>These files contain data, sequences and supplementary information for the article entitled '<em>Characterization of signal and transit peptides based on motif composition and taxon-specific patterns</em>'.</p> <p><strong>Contents</strong></p> <ol> <li><strong>Raw_data.zip</strong> - contains raw data and sequences downloaded from UniProt and other databases. These files were used to create datasets used for described analyses.</li> <li><strong>Datasets.zip</strong> - presequence and peptide datasets obtained from raw data and used for all subsequent analyses.</li> <li><strong>Motif_analysis_reports_presequences_AMP.zip</strong> - HTML reports with detailed analyses of motif composition of presequences and AMPs</li> <li><strong>Motif_analysis_reports_presequences_encoded.zip</strong> - HTML reports with detailed analyses of motif composition of presequences using reduced alphabets</li> <li><strong>Motif_analysis_reports_presequences_taxonomy.zip</strong> - HTML reports with detailed analyses of motif composition of presequences for different taxons</li> <li><strong>Motif_analysis_reports_SP_taxonomy.zip</strong> - HTML reports with detailed analyses of motif composition of signal peptides on different taxonomic levels</li> <li><strong>Motif_analysis_reports_presequences_counts.zip </strong> - HTML reports with detailed analyses of motifs occuring more than once in presequences and AMPs</li> </ol> <p> </p> <p><strong>Abstract</strong></p> <p>Targeting peptides or presequences are N-terminal extensions of proteins that encode information about their cellular localization. They include signal peptides (SP), which target proteins to the endoplasmic reticulum (ER), and transit peptides (TP) directing proteins to the organelles of endosymbiotic origin: chloroplasts and mitochondria. TPs were hypothesized to have evolved from antimicrobial peptides (AMPs), which are responsible for the host defence against microorganisms, including bacteria, fungi and viruses. In this study, we performed a comprehensive bioinformatic analyses of amino acid motifs of targeting peptides and AMPs using a curated set of experimentally verified proteins. We identified motifs frequently occurring in each type of presequence showing specific patterns associated with their amino acid composition, and investigated their position within the presequence. We also compared motif patterns among different taxonomic groups and identified taxon-specific features, providing some evolutionary insights. Considering the functional relevance and many practical applications of targeting peptides and AMPs, we believe that our analyses will prove useful for their design, and better understanding of protein import mechanism and presequence evolution.</p>
Data from: Anthropogenic ecosystem fragmentation drives shared and unique patterns of sexual signal divergence among three species of Bahamian mosquitofish
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Balancing selection in Pattern Recognition Receptor signalling pathways is associated with gene function and pleiotropy in a wild rodent
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Data from: Warning signals are seductive: relative contributions of color and pattern to predator avoidance and mate attraction in Heliconius butterflies
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Data from: Distinguishing noise from signal in patterns of genomic divergence in a highly polymorphic avian radiation
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Data from: Conflicting phylogenomic signals reveal a pattern of reticulate evolution in a recent high-Andean diversification (Asteraceae: Astereae: Diplostephium)
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Data from: Patterns of genomic divergence and signals of selection in sympatric and allopatric northeastern Pacific and Sea of Cortez populations of the sargo (Anisotremus davidsonii) and longjaw mudsucker (Gillichthys mirabilis)
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Data from: Congruent signals of population history but radically different patterns of genetic diversity between mitochondrial and nuclear markers in a mountain lizard
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Rest-task modulation of fMRI-derived global signal topography is mediated by transient co-activation patterns
<p>Recent resting-state fMRI studies have revealed that the global signal (GS) exhibits a non-uniform spatial distribution across the gray matter. Whether this topography is informative remains largely unknown. We therefore tested rest-task modulation of global signal topography by analyzing static global signal correlation and dynamic co-activation patterns in a large sample of fMRI dataset (n=837) from the Human Connectome Project. The GS topography in the resting-state and in seven different tasks was first measured by correlating the global signal with the local timeseries (GSCORR). In the resting state, high GSCORR was observed mainly in the primary sensory and motor regions, while low GSCORR was seen in the association brain areas. This pattern changed during the seven tasks, with mainly decreased GSCORR in sensorimotor cortex. Importantly, this rest-task modulation of GSCORR could be traced to transient co-activation patterns at the peak period of global signal (GS-peak). By comparing the topography of GSCORR and respiration effects, we observed that the topography of respiration mimicked the topography of global signal in the resting-state whereas both differed during the task states; due to such partial dissociation, we assume that GSCORR could not be equated with a respiration effect. Finally, rest-task modulation of GS topography could not be exclusively explained by other sources of physiological noise. Together, we here demonstrate the informative nature of global signal topography by showing its rest-task modulation, the underlying dynamic co-activation patterns, and its partial dissociation from respiration effects during task states.</p>
Data from: Evolution of patterned plumage as a sexual signal in estrildid finches
Colour patterns, such as bars or dots, that cover the body surface of animals are generally thought to play roles in signalling and camouflage. In birds, however, the macroscopic aspects of plumage colouration are less well understood, as past studies typically described plumage colourations by using spectrophotometric analyses. To provide insight into the evolution of plumage patterns as sexual signals, we characterised interspecific and intersexual variations in the plumage patterns of estrildid finches and tested their associations with other courtship signals and life-history traits using a comparative phylogenetic approach. Our results support the idea that plumage patterns in estrildids are favoured by sexual selection because large-sized conspicuous plumage patterns are possessed by species with an elaborate courtship dance. These plumage patterns may also play roles in social signalling because patterns are more conspicuous in species with intraspecific brood parasitism. We predict that pattern traits can be favoured by mate choice or intra-sexual competition when they can serve as honest indicators of individual condition. As our results are consistent between the sexes, we suggest that the same selective force is acting on the evolution of plumage patterns in males and females in parallel. Finally, we also found a trade-off between large size and vivid colour patterns, suggesting that too conspicuous patterns are costly, presumably because of the risk of catching the eyes of potential predators. Therefore, plumage patterns are also shaped by natural selection.
Data from: Distance-dependent pattern blending can camouflage salient aposematic signals
The effect of viewing distance on the perception of visual texture is well known: spatial frequencies higher than the resolution limit of an observer's visual system will be summed and perceived as a single combined colour. In animal defensive colour patterns, distance-dependent pattern blending may allow aposematic patterns, salient at close range, to match the background to distant observers. Indeed, recent research has indicated that reducing the distance from which a salient signal can be detected can increase survival over camouflage or conspicuous aposematism alone. We investigated whether the spatial frequency of conspicuous and cryptically coloured stripes affects the rate of avian predation. Our results are consistent with pattern blending acting to camouflage salient aposematic signals effectively at a distance. Experiments into the relative rate of avian predation on edible model caterpillars found that increasing spatial frequency (thinner stripes) increased survival. Similarly, visual modelling of avian predators showed that pattern blending increased the similarity between caterpillar and background. These results show how a colour pattern can be tuned to reveal or conceal different information at different distances, and produce tangible survival benefits.
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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.