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196 results for “16S rRNA”
FIGURE 1 in A mitochondrial 12S and 16S rRNA phylogeny of critical genera of Phoridae (Diptera) and related families of Aschiza
FIGURE 1. Maximum likelihood tree derived from analysis of concatenated dipteran 12S and 16S mitochondrial DNA sequences (ln likelihood = 5910.51681, proportion of invariable sites 0.291, gamma shape parameter = 0.480). Taxa as in Table 1. The Hilara maura sequence was assigned as outgroup. The names of suborders, series, families, subfamilies and tribes are also indicated where they are relevant to the discussion in the text. Numbers refer to Bayesian posterior probabilities as percent (top) or percent support in a nonparametric bootstrap analysis by neighborjoining of maximum likelihood distances (bottom). Where only one number occurs it refers to a Bayesian posterior probability: that branch received less than 50% support in the nonparametric bootstrap analysis. Support values for the branch leading to the Phoridae are in bold.
FIGURE 1 in Phylogenetic relationships among the genera of the Penaeidae (Crustacea: Decapoda) revealed by mitochondrial 16S rRNA gene sequences
FIGURE 1. Morphological phylogeny of the penaeid genera proposed by (a) Kubo 1949, reconstructed from text (genera in brackets were not fully analyzed and '?' refers to uncertain relationship) and (b) Burkenroad 1983, reconstructed from key (mentioned by the author as "...a natural key down to the level of genus"), with Penaeini as Peneini, Parapenaeini as Parapeneini, Trachypenaeini as Trachypeneini, and Metapenaeus as Mangalura. *Considered to be the most primitive genus in the family.
FIGURE 2 in Phylogenetic relationships among the genera of the Penaeidae (Crustacea: Decapoda) revealed by mitochondrial 16S rRNA gene sequences
FIGURE 2. BIO-neighbor-joining (BIO-NJ) tree of Penaeidae based on partial mitochondrial 16S rRNA gene sequences. Numbers on branches indicate bootstrap values from BIO-NJ (normal text), maximum parsimony (in italics), maximum likelihood (in bold) analyses and posterior probability values from Bayesian (in italics bold) analyses. Bootstrap values below 50% are not shown. A, B, C refer to the three main clades in the tree. Parapenaeini, Trachypenaeini and Penaeini are the three groups as defined by Burkenroad (1983).
FIGURE 3 in Phylogenetic relationships within the genus Staurois (Anura, Ranidae) based on 16S rRNA sequences
FIGURE 3. Relationships among the genus Staurois as estimated using maximum parsimony. Tree was a consensus of two equally parsimonious trees.
FIGURE 1 in Phylogenetic relationships within the genus Staurois (Anura, Ranidae) based on 16S rRNA sequences
FIGURE 1. Picture of Staurois from Borneo, Brunei and the Philippines: a) Staurois nubilus from Palawan (photo: Rafe Brown); b) S. natator from Mindanao (photo: Rafe Brown); c) S. guttatus from Kalimantan (photo: Umilaela Arifin); d) S. tuberilinguis from Sabah (photo: Biofagri Rahmayuningtyas); e) S. parvus from Brunei (photo: Maxmilian Dehling); and f) S. latopalmatus from Kalimantan (photo: Umilaela Arifin)
FIGURE 21. Maximum parsimony 16S rRNA phylogram for the Boophis albipunctatus group. From 485 total characters, 391 were constant and 72 in Integrative taxonomy of Malagasy treefrogs: combination of molecular genetics, bioacoustics and comparative morphology reveals twelve additional species of Boophis 2383
FIGURE 21. Maximum parsimony 16S rRNA phylogram for the Boophis albipunctatus group. From 485 total characters, 391 were constant and 72 parsimony informative. MP searches retained 26 trees of which a strict consensus is shown. Consensus support values higher than 50, from 2000 bootstrap replicates, are shown; an asterisk indicates Bayesian posterior probabilities equal or higher than 95%. Species newly described herein are in bold.
FIGURE 2 in Phylogeny and affiliation of European Anthomyzidae (Diptera) based on mitochondrial 12S and 16S rRNA
FIGURE 2. Resulting phylogram conducted by Bayesian analyses of the combined 12S and 16S rRNA gene sequences. The posterior probabilities (over 0.50) are shown above the branches.
FIGURE 1 in Phylogeny and affiliation of European Anthomyzidae (Diptera) based on mitochondrial 12S and 16S rRNA
FIGURE 1. Anthomyza gracilis Fallén, 1823, female (Russia: Moscow region), body length 2.8 mm. Photo by D. Gavryushin.
16s rRNA sequences, R code used for amplicon analysis and example code for NMGS analysis
<p>This submission contains the following data presented in: "Selection processes of Arctic seasonal glacier snowpack bacterial communities" by Keuschnig et al.</p> <p>the R code used to analyze the 16S rRNA amplicon data</p> <p>the script used for NMGS analysis</p> <p>the sequences obtained from snow samples</p>
The 16S rRNA genes of five strains of the genus Vibrio
<p>The 16S rRNA genes of five strains of the genus Vibrio. These strains isolated from marine sediments.</p>
Alpha Defensin and 16S rRNA Gene in Diagnosis of PJI
ClinicalTrials.gov study NCT03714165. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Using 16S rRNA Gene Sequencing Analysis Intestinal Microbiota in Constipation Patients
ClinicalTrials.gov study NCT02984969. IPD Sharing: UNDECIDED. Countries: 1. Publications: 1.
16S rRNA data for an in vitro model of the human dental plaque bacterial community (3 hosts)
Open the record for dataset details and reuse information.
16S rRNA gene data for aerobic BTEX-degrading enrichments exposed to sulfonamide polyfluorinated substances in fire-fighting foams and transformation products
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Lower St. Lawrence Estuary bacterial 16S rRNA gene diversity
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Data from: Biogeography in a continental island: population structure of the relict endemic centipede Craterostigmus tasmanianus (Chilopoda, Craterostigmomorpha) in Tasmania using 16S rRNA and COI
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Data from: 16S rRNA amplicon sequencing for epidemiological surveys of bacteria in wildlife
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Caulerpa-associated bacterial 16S rRNA in response to environmental stress
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Macrosystems 16S rRNA Genes for Bacteria and Archaea at HJA, HFR, BCI, CWT, LUQ, and NWT - UPARSE Resample 20K
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 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.
Macrosystems Gigante Soil Sample Data on N, P, K and Micronutrient Treated Plots 16S rRNA Resampled
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 abundance of OTUs (Operational Taxonomic Units) sampled for in forest soils at the Gigante Peninsula plots in Panama. Prior to macrosystems collection, these plots had been fertilized with N, P, K, and micronutrients for 14 years. This data represents abundance of 16S rRNA genes in soil samples at Gigante processed 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.
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