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1,049 results for “Pseudomonas”
Periodically disturbing biofilms reduces expression of quorum sensing regulated virulence factors in Pseudomonas aeruginosa
<p>The opportunistic pathogen <em>Pseudomonas aeruginosa</em> uses quorum sensing to control the expression of multiple virulence factors. In this dataset, we provide raw data demonstrating that periodically disturbing biofilms composed of <em>P. aeruginosa</em> using a physical force reduces the expression of multiple quorum sensing regulated virulence factors from the three major regulons. This dataset also contains cell density measurements of bacteria both the in biofilm and planktonic state under a variety of conditions that use physical force to manipulate the spatial positioning of bacteria. Finally, the data set contains raw outputs from a network logic model that described the core quorum sensing network. This data is affiliated with the manuscript entitled "Periodically disturbing biofilms reduces expression of quorum sensing regulated virulence factors in <em>Pseudomonas aeruginosa</em>."</p>
Data : Effects of Fructooligosaccharides (FOS) on the Immune Response of the Shrimp Penaeus vannamei and on the Reduction in Vibrio spp. and Pseudomonas spp. in Cultures of Post-Larvae.
<p>Data related with Corrales Barrios, Y.; Roncarati, A.; Martín Ríos, L.D.; Rodríguez González, M.; González Salotén, M.; López Zaldívar, Y.; Arenal, A. Effects of Fructooligosaccharides (FOS) on the Immune Response of the Shrimp <em>Penaeus vannamei</em> and on the Reduction in <em>Vibrio</em> spp. and <em>Pseudomonas</em> spp. in Cultures of Post-Larvae. Microbiol. Res. 2023, 14,</p>
Datasets for phylogeny-guided analysis of pseudogenes in Pseudomonas aeruginosa
<p>In the study entitled "A large-scale phylogeny-guided analysis of pseudogenes in <em>Pseudomonas aeruginosa</em> bacterium" we analyzed the genomic data of 4699 strains of the bacterium <em>Pseudomonas aeruginosa P. aeruginosa)</em> as they exhibit high variability in the number of annotated pseudogenes. In particular, we looked for correlations between the number of pseudogenes and other genomic- and meta- features of the strains. We identified orthologous genes and pseudogenes and compared cluster size distributions and length homogeneity within clusters. We mapped and examined orthology relationships between genes and pseudogenes. We generated a phylogenetic tree of the strains and found that phylogenetically related strains are more homogeneous in the number of pseudogenes and share a significant amount of pseudogenes. Finally, we dived into clusters of orthologues genes and pseudogenes and quantified their phylogenetic neighborhood, classifying pseudogenes into evolutionary preserved pseudogenes, misannotated pseudogenes, or pseudogenes formed by failed horizontal transfer events. This in-depth study provides important insights that can be incorporated into pseudogene annotation pipelines in the future.<br> <br> We provide the following files:</p> <p>protein_seq.zip: A fasta file containing 28,948,105 protein sequences of coding genes across all strains.</p> <p>pseudo_seq.zip: A fasta file containing 86,273 DNA sequences of pseudogenes across all strains. </p> <p>protein_clustering_output.zip: Clustering of protein sequences with CD-HIT clustering algorithm and the following parameters: similarity threshold of 70%, word size of 5, and the slow mode version.</p> <p>pseudo_clustering_output.zip: Clustering of pseudogene sequences with CD-HIT-EST clustering algorithm and the following parameters: similarity threshold of 80\%, word size of 5, and the slow mode version.</p> <p> </p>
Figure 16: Antibiotic sensitivity and resistance pattern of Pseudomonas aeruginosa
<p><strong>Figure 16: Antibiotic sensitivity and resistance pattern of Pseudomonas aeruginosa </strong></p>
Figure 17: Theoretical Prediction of Imipenem Resistance in Pseudomonas aeruginosa (2020-2030)
<p><strong>Figure 17: Theoretical Prediction of Imipenem Resistance in Pseudomonas aeruginosa (2020-2030)</strong></p>
Fig. 7 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 7. Exogenous infiltration of dihydrocamalexic acid (DHCA) and bacterial quantification of Pseudomonas syringae in rosette leaves of 7-week old Col-0 and cyp71a12/cyp71a13. DHCA (0.07 μg/mL or 0.25 μg/mL) was applied via pressure infiltration to 7-week-old plants at 24 h postinoculation with P. syringae or mock solution (0.06% DMSO in 10 mM MgCl2). Bacterial levels were quantified at 3 days postinoculation with Pst. Values represent the mean ± standard deviation of three sample replicates (n = 3) consisting of 8 plants each. Different letters indicate statistically significant differences (ANOVA, Tukey's honestly significant difference [HSD], P <0.05).
Fig. 6 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 6. Effect of dihydrocamalexic acid (DHCA) and salicylic acid (SA) on biofilm formation of Pseudomonas syringae (Pst) in vitro. Dose-dependent effect of (A) SA and (B) DHCA on Pst biofilm formation in Hrp-inducing minimal medium as measured by crystal violet staining of surface-adherent cells and de-staining with acetic acid (OD570) after stationary incubation for 24, 32, 48, or 60 h. Each data point is the mean ± SD of five wells per concentration from a 96-well non-tissueculture-treated plate. Different letters indicate statistically significant differences (one-way ANOVA, Tukey's honestly significant difference [HSD], P <0.05). Ns indicates not significant. Bars (from left to right) within each timepoint are: 18 μg/mL, 4.5 μg/mL, 1.2 μg/mL, 0.3 μg/mL, and 0 μg/mL. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 5 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 5. Growth of Pseudomonas syringae (Pst) in the presence of dihydrocamalexic acid (DHCA), camalexin, or DHCA analogs in vitro. Dose-dependent effect of (S)-dihydrocamalexic acid (A), camalexin (B), (1) (R)-2-(phenyl)-4,5-dihydrothiazole-4-carboxylic acid (C), (2) (S)-2-(phenyl)-4,5-dihydrothiazole-4-carboxylic acid (D), (3) (R)-2-(4-hydroxyphenyl)-4,5-dihydrothiazole-4-carboxylic acid (E), and (4) (S)-2-(4-hydroxyphenyl)-4,5-dihydrothiazole-4-carboxylic acid (F) on the growth of Pst in Hrp-inducing minimal medium as measured by turbidity (OD) after incubation for 68 h at room temperature (approximately 25 ◦ C). Each data 600 point is the mean ± SD of three wells per concentration from a 96-well non-tissue-culture-treated plate.
Fig. 4 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 4. Quantification of dihydrocamalexic acid (DHCA) (m/z 247.0541, [C12H10N2O2S þ H]) in intercellular washing fluids (IWFs). DHCA levels measured in IWFs from Col-0, cyp71a12/cyp71a13, and cyp71b15 (all 7-weeks post-germination) 24 h after inoculation with P. syringae (Pst) or 10 mM MgCl2 (mock-inoculation) measured by UPLC-MS electrospray ionization in positive mode (ESI+). Values represent the mean ± standard deviation of three sample replicates (n = 3). Different letters indicate statistically significant differences (one-way ANOVA, Tukey's honestly significant difference [HSD], P <0.05). Standard curves were prepared using synthetic DHCA (2 pg–6 μg on column).
Fig. 3 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 3. Identification of dihydrocamalexic acid (DHCA) in intercellular washing fluids. Extracted ion chromatograms and mass spectra for DHCA (m/z 247.0541, [C12H10N2O2S + H] in intercellular washing fluids from Pseudomonas syringae-inoculated leaves compared to a synthetic standard. (A) Extracted ion chromatograms and (B) MSMS (25 eV). n. d. Indicates compound not detected. Samples were run in positive electrospray ionization mode.
Fig. 1 in Metabolite profiling reveals a role for intercellular dihydrocamalexic acid in the response of mature Arabidopsis thaliana to Pseudomonas syringae
Fig. 1. Biosynthesis pathway of tryptophan-derived specialised metabolism in Arabidopsis thaliana (simplified). Dashed arrows indicate potential nonenzymatic reactions. Multiple arrows indicate multiple reaction steps simplified for presentation. IAOx: indole-3-acetaldoxime, I3M: indole-3- methylglucosinolate, IAN: indole-3-acetonitrile, ICHO: indole-3-carbaldehyde, ICOOH: indole-3-carboxylic acid, ICN: indole-3-carbonyl nitrile, 4-OH-ICN: 4- hydroxyindole-3-carbonyl nitrile, NSP: nitrile-specifier protein, FOX1: flavin-dependent oxidoreductase, AAO1: Arabidopsis aldehyde oxidase I, GGP: gammaglutamyl peptidase, GGT: gamma-glutamyl transpeptidase DHCA: dihydrocamalexic acid. Modified from Rajniak et al. (2015); Müller et al. (2019).
Efficacy & Tolerability of Tobramycin Podhaler in Bronchiectasis Patients With Chronic Pseudomonas Aeruginosa Infection
ClinicalTrials.gov study NCT02102152. IPD Sharing: Not stated. Countries: 1. Publications: 3.
The SENSOR Study: A Mixed-methods Study of SElf-management Checks to Predict exacerbatioNs of Pseudomonas Aeruginosa in Patients With Long-term reSpiratORy Conditions
ClinicalTrials.gov study NCT02458807. IPD Sharing: Not stated. Countries: 1. Publications: 3.
A Study to Evaluate the Efficacy, Safety, and PK of AZD0292 Administered IV in Participants 12 Years of Age and Older With Bronchiectasis and Chronic Pseudomonas Aeruginosa Colonization
ClinicalTrials.gov study NCT07088926. IPD Sharing: YES. Countries: 25. Publications: 35.
Nasal Inhalation of Tobramycin in Patients With Cystic Fibrosis and Pseudomonas Aeruginosa Colonization
ClinicalTrials.gov study NCT00774072. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Anti-pseudomonas IgY to Prevent Infections in Cystic Fibrosis
ClinicalTrials.gov study NCT00633191. IPD Sharing: NO. Countries: 1. Publications: 8.
Role of Toxins in Lung Infections Caused by Pseudomonas Aeruginosa
ClinicalTrials.gov study NCT00027183. IPD Sharing: Not stated. Countries: 1. Publications: 3.
Advanced Understanding of Staphylococcus Aureus and Pseudomonas Aeruginosa Infections in EuRopE - ICU
ClinicalTrials.gov study NCT02413242. IPD Sharing: UNDECIDED. Countries: 1. Publications: 2.
Role of Pseudomonas Aeruginosa Biofilms in Exacerbations in Patients With Bronchiectasis With and Without Chronic Obstructive Pulmonary Disease
ClinicalTrials.gov study NCT04803695. IPD Sharing: UNDECIDED. Countries: 1. Publications: 33.
Capecitabine and Pseudomonas Aeruginosa Combination in Metastatic Breast Cancer (MBC)
ClinicalTrials.gov study NCT01380808. IPD Sharing: Not stated. Countries: 1. Publications: 1.
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)
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.