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Fig. 6 in Taxonomy and distribution of Pectinariidae (Annelida) from Iceland with a comparative analysis of uncinal morphology
Fig. 6. Amphictene auricoma (O.F. Müller, 1776). SEM micrographs from a large specimen (BIOICE sample 2998, IINH-40213). A–C. Unciniger 1, ventral uncinus, frontolateral, frontal and lateral views, respectively. D. Scaphe. E. Scaphal hooks (framed in D).
Fig. 5 in Taxonomy and distribution of Pectinariidae (Annelida) from Iceland with a comparative analysis of uncinal morphology
Fig. 5. Amphictene auricoma (O.F. Müller, 1776). SEM micrographs from two large specimens (BIOICE sample 2998, IINH-40213). A. Anterior end, left lateral view (framed: cirri of opercular rim, detail). B. Ventral rows of ciliated tufts (framed: two tufts, detail). C. Notochaetae; distal end. D. Uncinigers 1 to 4. E. Unciniger 2, median uncini (framed in D). F. Unciniger 4, dorsal uncini (framed in D).
Fig. 3 in Taxonomy and distribution of Pectinariidae (Annelida) from Iceland with a comparative analysis of uncinal morphology
Fig. 3. Biometric features of the Pectinariidae specimens in BIOICE samples. A. Amphictene auricoma (O.F. Müller, 1776). B. Lagis koreni Malmgren, 1866. C. Cistenides granulata (Linnaeus, 1767). D. Cistenides hyperborea Malmgren, 1866. Specimens studied under SEM are indicated with black arrows and sample numbers.
Fig. 4 in Taxonomy and distribution of Pectinariidae (Annelida) from Iceland with a comparative analysis of uncinal morphology
Fig. 4. Stereomicrographs of anterior body ends (dorsal view) and anterior end of the tube. A–B. Amphictene auricoma (O.F. Müller, 1776) (BIOICE sample 2946, IINH-40181). C–D. Cistenides granulata (Linnaeus, 1767) (sample 2126, IINH-40466). E–H. Cistenides hyperborea Malmgren, 1866 (E–F: sample 3252, IINH-40477; G–H: sample 2060, IINH-40471). I–L. Lagis koreni Malmgren, 1866 (I–J: sample 2557; K–L: sample 2827, IINH-40485).
Fig. 11. Cistenides hyperborea Malmgren, 1866 in Taxonomy and distribution of Pectinariidae (Annelida) from Iceland with a comparative analysis of uncinal morphology
Fig. 11. Cistenides hyperborea Malmgren, 1866. SEM micrographs from four large specimens (A–D: BIOICE sample 3252, IINH-40477; E–F: sample 3249, IINH-40476). A. Scaphe, left lateral view. B. Anal lobe and anal papilla (framed in A). C–D. Scaphal hooks (framed in A). E. Posterior end (framed: scaphal hooks, detail). F. Scaphe and anal lobe and papilla (framed in E).
Fig. 16. Lagis koreni Malmgren, 1866 in Taxonomy and distribution of Pectinariidae (Annelida) from Iceland with a comparative analysis of uncinal morphology
Fig. 16. Lagis koreni Malmgren, 1866. SEM micrographs from two specimens: large (A, F) and medium sized (B–E), respectively (BIOICE sample 2827, IINH-40485). A. Anterior end, right lateral view. B. Uncinigers 4 to 6, ventrolateral view. C. Unciniger 3, ventral uncini. D. Unciniger 5, dorsal uncini. E. Unciniger 6, median uncini. F. Scaphe and scaphal hooks (framed: scaphal hook, detail).
Fig. 1 in Taxonomy and distribution of Pectinariidae (Annelida) from Iceland with a comparative analysis of uncinal morphology
Fig. 1. Maps of Iceland showing distribution of the species of Pectinariidae found in BIOICE samples. A. Amphictene auricoma (O.F. Müller, 1776). B. Lagis koreni Malmgren, 1866. C. Cistenides granulata (Linnaeus, 1767). D. Cistenides hyperborea Malmgren, 1866. The three main coastal sectors around Iceland are shown in A. The specimens photographed and studied under SEM are indicated with green and red arrowheads, respectively. Abbreviations: H = Höfn; NWF = North-western fjords; RP = Reykjanes Peninsula.
Fig. 9 in Taxonomy and distribution of Pectinariidae (Annelida) from Iceland with a comparative analysis of uncinal morphology
Fig. 9. Cistenides granulata (Linnaeus, 1767). SEM micrographs from a small specimen (BIOICE sample 3249, IINH-40467). A. Anterior end, ventral view. B–C. Unciniger 1, dorsal and ventral uncini, respectively. D–E. Unciniger 7, dorsal and median uncini, respectively. F. Unciniger 12, dorsal uncini.
Supplementary material 4 from: Wu Y-A, Gao J-W, Cheng X-F, Xie M, Yuan X-P, Liu D, Song R (2020) Characterization and comparative analysis of the complete mitochondrial genome of Azygia hwangtsiyui Tsin, 1933 (Digenea), the first for a member of the family Azygiidae. ZooKeys 945: 1-16. https://doi.org/10.3897/zookeys.945.49681
Figure S1
Supplementary material 1 from: Wu Y-A, Gao J-W, Cheng X-F, Xie M, Yuan X-P, Liu D, Song R (2020) Characterization and comparative analysis of the complete mitochondrial genome of Azygia hwangtsiyui Tsin, 1933 (Digenea), the first for a member of the family Azygiidae. ZooKeys 945: 1-16. https://doi.org/10.3897/zookeys.945.49681
Table S1
Supplementary material 3 from: Wu Y-A, Gao J-W, Cheng X-F, Xie M, Yuan X-P, Liu D, Song R (2020) Characterization and comparative analysis of the complete mitochondrial genome of Azygia hwangtsiyui Tsin, 1933 (Digenea), the first for a member of the family Azygiidae. ZooKeys 945: 1-16. https://doi.org/10.3897/zookeys.945.49681
Table S3
Supplementary material 2 from: Wu Y-A, Gao J-W, Cheng X-F, Xie M, Yuan X-P, Liu D, Song R (2020) Characterization and comparative analysis of the complete mitochondrial genome of Azygia hwangtsiyui Tsin, 1933 (Digenea), the first for a member of the family Azygiidae. ZooKeys 945: 1-16. https://doi.org/10.3897/zookeys.945.49681
Table S2
Supplementary material 5 from: Wu Y-A, Gao J-W, Cheng X-F, Xie M, Yuan X-P, Liu D, Song R (2020) Characterization and comparative analysis of the complete mitochondrial genome of Azygia hwangtsiyui Tsin, 1933 (Digenea), the first for a member of the family Azygiidae. ZooKeys 945: 1-16. https://doi.org/10.3897/zookeys.945.49681
Figure S2
Figure 2 from: Wu Y-A, Gao J-W, Cheng X-F, Xie M, Yuan X-P, Liu D, Song R (2020) Characterization and comparative analysis of the complete mitochondrial genome of Azygia hwangtsiyui Tsin, 1933 (Digenea), the first for a member of the family Azygiidae. ZooKeys 945: 1-16. https://doi.org/10.3897/zookeys.945.49681
Figure 2 Phylogenetic relationships and gene arrangement of Azygia hwangtsiyui with other selected digeneas based on translated mitochondrial proteins. The concatenated amino-acid sequence datasets of the 12 protein-coding genes were analyzed by Bayesian Inference (BI) and Maximum Likelihood (ML), utilizing Cloacotaenia megalops (NC_032295.1) and Dibothriocephalus latus (NC_008945.1) as the outgroups. Both ML and BI analyses constructed identical tree topologies.
A comparative analysis of the photoprotection hypothesis for the evolution of autumn colors
<p><span><span><span><span><span><span><span><span><span><span><span>The adaptive <span><span>value </span></span>of autumn colors – the seasonal production of red anthocyanins observed in <span><span>many </span></span>species of trees <span><span>and shrubs </span></span>– is still debated. According to the photoprotection hypothesis, anthocyanins protect leaves from photoinhibition and photooxidation at low temperatures, enabling the tree to reabsorb nutrients more efficiently before leaf fall. Hence, the hypothesis predicts that autumn colors are more likely to evolve in species growing in colder environments. We tested this prediction by comparing the climatic parameters of 237 North American tree species. We found that, while species with yellow autumn leaves grow under lower minimum temperatures than species with green leaves, there is no significant difference in <span><span>temperature</span></span>between species with red autumn leaves and species with green or yellow autumn leaves. We conclude that, while reabsorbing chlorophyll in autumn, and the consequent unmasking of yellow carotenoids, may be an adaptation to cold temperatures, the production of red anthocyanins is not. Hence, our inter-specific comparative analysis does not support the photoprotection hypothesis <span><span>as an explanation </span></span>for the evolution of autumn colors.</span></span></span></span></span></span></span></span></span></span></span></p>
Data from: Comparative analysis of microbiota along the length of the gastrointestinal tract of two tree squirrel species (Sciurus aberti and S. niger) living in sympatry
<p>Microbiota inhabiting the gastrointestinal (GI) tract of animals have important impacts on many host physiological processes. Although host diet is a major factor influencing the composition of the gut microorganismal community, few comparative studies have considered how differences in diet influence community composition across the length the GI tract. We used 16S sequencing to compare the microbiota along the length of the GI tract in Abert's (<i>Sciurus aberti</i>) and fox squirrels (<i>S. niger</i>) living in the same habitat. While fox squirrels are generalist omnivores, the diet of Abert's squirrels is unusually high in plant fiber, particularly in winter when they extensively consume fiber-rich inner bark of ponderosa pine (<i>Pinus ponderosa</i>). Consistent with previous studies, microbiota of the upper GI tract of both species consisted primarily of facultative anaerobes and was less diverse than that of the lower GI tract, which included mainly obligate anaerobes. While we found relatively little differentiation between the species in the microbiota of the upper GI tract, the community composition of the lower GI tract was clearly delineated. Notably, the Abert's squirrel lower GI community was more stable in composition and enriched for microbes that play a role in the degradation of plant fiber. In contrast, overall microbial diversity was higher in fox squirrels. We hypothesize that these disparities reflect differences in diet quality and diet breadth between the species.</p>
Comparative transcriptome analysis reveals key genes potentially related to organic acid and sugar accumulation in loquat
<p class="MDPI17abstract">Organic acids and sugars are the primary components that determine the quality and flavor of loquat fruits. In the present study, major organic acids, sugar content, enzyme activities, and the expression of related genes were analyzed during fruit development in two loquat cultivars, 'JieFangZhong' (JFZ) and 'BaiLi' (BL). Our results showed that the sugar content increased during fruit development in the two cultivars; however, the organic acid content dramatically decreased in the later stages of fruit development. The differences in organic acid and sugar content between the two cultivars primarily occured in the late stage of fruit development and the related enzymes showed dynamic changes in activies during development. Phosphoenolpyruvate carboxylase (PEPC) and mNAD malic dehydrogenase (mNAD-MDH) showed higher activities in JFZ at 95 days after flowering (DAF) than in BL. However, NADP-dependent malic enzyme (NADP-ME) activity was the lowest at 95 DAF in both JFZ and BL with BL showing higher activity compared with JFZ. At 125 DAF, the activity of fructokinase (FRK) was significantly higher in JFZ than in BL. The activity of sucrose synthase (SUSY) in the sucrose cleavage direction (SS-C) was low at early stages of fruit development and increased at 125 DAF. SS-C activity was higher in JFZ than in BL. vAI and sucrose phosphate synthase (SPS) activities were similar in the two both cultivars and increased with fruit development. RNA-sequencing was performed to determine the candidate genes for organic acid and sugar metabolism. Our results showed that the differentially expressed genes (DEGs) with the greated fold changes in the later stages of fruit development between the two cultivars were phosphoenolpyruvate carboxylase 2 (<i>PEPC2</i>), mNAD-malate dehydrogenase (<i>mNAD-MDH</i>), cytosolic NADP-ME (<i>cyNADP-ME2</i>), aluminum-activated malate transporter (<i>ALMT9</i>), subunit A of vacuolar H<sup>+</sup>-ATPase (<i>VHA-A</i>), vacuolar H<sup>+</sup>-PPase (<i>VHP1</i>), NAD-sorbitol dehydrogenase (<i>NAD-SDH</i>), fructokinase (<i>FK</i>), sucrose synthase in sucrose cleavage (<i>SS-C</i>), sucrose-phosphate synthase 1 (<i>SPS1)</i>, neutral invertase (NI), and vacuolar acid invertase (<i>vAI)</i>. The expression of 12 key DEGs was validated by quantitative reverese transcription PCR (RT-qPCR). Our findings will help understand the molecular mechanism of organic acid and sugar formation in loquat, which will aid in breeding high-quality loquat cultivars.</p>
Data from: Social call divergence in bats: a comparative analysis
Acoustic signals mediate important life history events in a variety of species, providing new vistas for understanding speciation. It has been proposed that animal acoustic signals undergo complex interactions among morphology, ecology, social pressure, and phylogenetic history. Yet, the relative importance of these factors in shaping acoustic divergence is rarely assessed within a comparative framework. Herein, we aim to investigate the key determinants of social call divergence across 31 bat species from 5 families. We compiled a wide data set on bat aggressive calls, body size, foraging habitats, foraging modes, climatic conditions, colony size, and phylogenetic components. We identified remarkable interspecific divergence versus within-species variation in aggressive vocalizations. Despite weak effects of ecological factors, colony size, body size, and phylogenetic components accounted for the majority of variation in call parameters among species. The colony size and body size played a major role in influencing spectral parameters, whereas phylogenetic relationships determined call duration and minimum frequency. Together, our findings constitute convincing evidence that sociality, morphological constraint, and phylogenetic constraint mold social call divergence in bats. This study expands our limited knowledge of the evolution of bat social calls, and highlights the importance of sociality in driving acoustic phenotype diversity.
Data from: Comparing the effectiveness of metagenomics and metabarcoding for diet analysis of a leaf-feeding monkey (Pygathrix nemaeus)
Fecal samples are of great value as a non-invasive means to gather information on the genetics, distribution, demography, diet, and parasite infestation of endangered species. Direct shotgun sequencing of fecal DNA could give information on these simultaneously, but this approach is largely untested. Here we use two fecal samples to characterize the diet of two Red-Shanked Doucs Langurs (Pygathrix nemaeus) that were fed a known combination of foliage, fruits, vegetables and cereals. Illumina HiSeq sequencing produced ~70 million paired reads per sample, of which ~10000 (0.014%) and ~44000 (0.066%) respectively corresponded to chloroplast genomes. Sequences were matched against a database of available chloroplast 'barcodes' for angiosperms. The results were compared with 'metabarcoding' using PCR amplification of the P6 loop of trnL. Shotgun sequencing identified 7 and 9 of the likely 16 diet plants, against 6 and 5 plant species identified by metabarcoding. Metabarcoding produced thousands of reads that were consistent with the known diet, but the barcodes were too short to identify several diet plants to genus. Metagenomics could utilize multiple, longer barcodes that combined had greater power of identification, but rare diet items were not recovered. Read numbers for diet species in metagenomic and metabarcoding data were correlated, indicating that both approaches are useful for determining relative sequence abundance. Metagenomic reads were uniformly distributed across the chloroplast genomes; thus if chloroplast genomes were to be used as reference, the precision of identifications and species recovery would improve further. Metagenomics also recovered the host mitochondrial genome and numerous intestinal parasite sequences in addition to generating data useful for characterizing the microbiome.
Data from: Comparing G: multivariate analysis of genetic variation in multiple populations
The additive genetic variance–covariance matrix (G) summarizes the multivariate genetic relationships among a set of traits. The geometry of G describes the distribution of multivariate genetic variance, and generates genetic constraints that bias the direction of evolution. Determining if and how the multivariate genetic variance evolves has been limited by a number of analytical challenges in comparing G-matrices. Current methods for the comparison of G typically share several drawbacks: metrics that lack a direct relationship to evolutionary theory, the inability to be applied in conjunction with complex experimental designs, difficulties with determining statistical confidence in inferred differences and an inherently pair-wise focus. Here, we present a cohesive and general analytical framework for the comparative analysis of G that addresses these issues, and that incorporates and extends current methods with a strong geometrical basis. We describe the application of random skewers, common subspace analysis, the 4th-order genetic covariance tensor and the decomposition of the multivariate breeders equation, all within a Bayesian framework. We illustrate these methods using data from an artificial selection experiment on eight traits in Drosophila serrata, where a multi-generational pedigree was available to estimate G in each of six populations. One method, the tensor, elegantly captures all of the variation in genetic variance among populations, and allows the identification of the trait combinations that differ most in genetic variance. The tensor approach is likely to be the most generally applicable method to the comparison of G-matrices from any sampling or experimental design.
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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)
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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.