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196 results for “16S rRNA”
Figure 1 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
Figure 1 - The male habitus of species and subspecies of Dicoronocephalus. A Dicronocephalus adamsi adamsi B Dicronocephalus adamsi drumonti C Dicranocephalus yui yui D Dicronocephalus dabryi E Dicronocephalus uenoi katoi F Dicronocephalus wallichii bowringi G Dicronocephalus wallichii wallichii H Dicronocephalus wallichii bourgoini.
Figure 8 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
Figure 8 - Metasternal process (in the circle) and aedeagi of Dicronocephalus adamsi drumonti and Dicronocephalus adamsi adamsi. A, B, C, D Dicronocephalus adamsi drumonti (Tibet) E, F, G, H Dicronocephalus adamsi drumonti (Sichuan) I, J, K, L Dicronocephalus adamsi adamsi (South Korea) M, N, O, P Dicronocephalus adamsi adamsi (North Korea) Q, R, S, T Dicronocephalus adamsi adamsi (Dandong, China).
Figure 3 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
Figure 3 - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using 16S rRNA sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 2 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
Figure 2 - Phylogenetic relationships among Dicronocephalus species reconstructed with Bayesian inference using COI sequences. Numbers above branches indicate ML bootstrap values and Bayesian posterior probabilities. Numbers below branches are bootstrap, symmetric resampling, and jacknife support from parsimony searches, respectively. Scale bar represents 10% nucleotide mutation rate.
Figure 7 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
Figure 7 - Umbone (in the circle) of shoulder of Dicronocephalus. A Dicronocephalus adamsi adamsi B Dicronocephalus adamsi drumonti C Dicranocephalus yui yui D Dicronocephalus dabryi E Dicronocephalus uenoi katoi F Dicronocephalus wallichii bowringi G Dicronocephalus wallichii wallichii H Dicronocephalus wallichii bourgoini.
Figure 6 from: Lee G-E, Han T, Jeong J, Kim S-H, Park IG, Park H (2015) Molecular phylogeny of the genus Dicronocephalus (Coleoptera, Scarabaeidae, Cetoniinae) based on mtCOI and 16S rRNA genes. ZooKeys 501: 63-87. https://doi.org/10.3897/zookeys.501.8658
Figure 6 - Apicosutural angle of Dicronocephalus. A Dicronocephalus adamsi adamsi B Dicronocephalus adamsi drumonti C Dicranocephalus yui yui D Dicronocephalus dabryi E Dicronocephalus uenoi katoi F Dicronocephalus wallichii bowringi G Dicronocephalus wallichii wallichii H Dicronocephalus wallichii bourgoini.
Bacterial 16s rRNA gene amplicon data (V3-V4) and qPCR data
<p><span>Within a given species, considerable inter-individual, spatial, and temporal variation in the composition of the host microbiome exists. In group-living animals, social interactions homogenize microbiome composition among group members, nevertheless, divergence in microbiome composition among related groups arises. Such variation can result from deterministic and stochastic processes. Stochastic changes, or ecological drift, can occur among symbionts with potential for colonizing a host and within individual hosts, and drive divergence in microbiome composition among hosts or host groups. We tested whether ecological drift associated with dispersal and foundation of new groups cause divergence in microbiome composition between natal and newly formed groups in the social spider <em>Stegodyphus</em> <em>dumicola</em>. We simulated initiation of new groups and compared variation in microbiome composition among and within groups. Theory predicts a decrease in beta diversity with increasing group size, and we found that single founders harboured the highest diversity. Divergence in microbiome composition from the natal nest was mainly driven by a higher number of non-core symbionts. This suggests that stochastic divergence in host microbiomes can arise during the process of group formation by individual founders, which could explain the existence of among-group variation in microbiome composition in the wild. Consistent host-symbiont relationships in the species must then be maintained by other processes. Individual founders harboured higher relative abundances of non-core symbionts some of which are possible pathogens, compared with founders in small groups. These symbionts vary in occurrence with group size, indicating that group dynamics influence various core and non-core symbionts differently.</span></p>
DATA set for 'Molecular characterization of MRSA using 16S rRNA from different sources in Jazan region of Saudi Arabia.
<p>Nucleotide sequence of 16S rRNA gene of S. aureus isolates</p>
Bacterial 16s rRNA gene amplicon data (V3-V4) and qPCR data
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Data from: Bacterial characterization of Beijing drinking water by flow cytometry and MiSeq sequencing of the 16S rRNA gene
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Data from: Highly divergent 16S rRNA sequences in ribosomal operons of Scytonema hyalinum (Cyanobacteria)
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Exploring protocol bias in airway microbiome studies: One versus two PCR steps and 16S rRNA gene region V3 V4 versus V4
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Data from: Assessment of a 16S rRNA amplicon Illumina sequencing procedure for studying the microbiome of a symbiont-rich aphid genus
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Data from: COI is better than 16S rRNA for DNA barcoding Asiatic salamanders (Amphibia: Caudata: Hynobiidae)
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Data from: Phylogenetic relatedness determined between antibiotic resistance and 16S rRNA genes in actinobacteria
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Cecal and colinic 16s rRNA sequencing OTUs
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Macrosystems 16S rRNA Genes for Bacteria and Archaea in 6 Plot Soil Samples
Patterns of biodiversity, such as the increase toward the tropics and the peaked curve during ecological succession, are fundamental phenomena for ecology. Such patterns have multiple, interacting causes, but temperature emerges as a dominant factor across organisms from microbes to trees and mammals, and across terrestrial, marine, and freshwater environments. However, there is little consensus on the underlying mechanisms, even as global temperatures increase and the need to predict their effects becomes more pressing. The purpose of this project is to generate and test theory for how temperature impacts biodiversity through its effect on biochemical processes and metabolic rate. A combination of standardized surveys in the field and controlled experiments in the field and laboratory measure diversity of three taxa -- trees, invertebrates, and microbes -- and key biogeochemical processes of decomposition in seven forests distributed along a geographic gradient of increasing temperature from cold temperate to warm tropical. This data set captures temperature-dependent latitudinal microbial diversity sampled for in forest soils based on taxonomic and phylogenetic diversity observed on 16S rRNA genes for bacteria and archaea in the V3-V4 regions by the University of Oklahoma Institute for Environmental Genomics as part of a macrosystems biodiversity and latitude project supported by the National Science Foundation under Cooperative Agreement DEB#1065836.
PTCD1 is required for 16S rRNA maturation complex stability and mitochondrial ribosome assembly
GEO Series GSE105406. Mus musculus. 12 samples. Type: Expression profiling by high throughput sequencing.
Ileal Crohn’s disease exhibits reduced activity of phospholipase C-β3 (PLC-β3)-dependent Wnt/b-catenin signaling pathway [16S rRNA]
GEO Series GSE244936. feces metagenome. 26 samples. Type: Other.
Alternations in gut microbiota and host transcriptome of patients with coronary artery disease [16S_rRNA]
GEO Series GSE242047. Homo sapiens. 52 samples. Type: Expression profiling by high throughput sequencing.
ScienceDex guides
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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)
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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.