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13 results for “Abiotic resistance”
Figure 3 in Metagenomic study of the communities of bacterial endophytes in the desert plant Senna Italica and their role in abiotic stress resistance in the plant
Figure 3. Alfa rarefaction curve observed based on observed species (OTUs) value. The curve has shown flatter to the right, which indicates the comparatively high species richness of the senna italica samples. Roots samples: Roots.1, Roots.2, and Roots.3. Leaves samples: Leaves.1, Leaves.2, and Leaves.3 are associated with Senna italica.
Figure 1 in Metagenomic study of the communities of bacterial endophytes in the desert plant Senna Italica and their role in abiotic stress resistance in the plant
Figure 1. (A) Results of clustering: Assembling a group of organisms (The organisms in the same group are similar). (B) The number of OTUs generated for each sample. The Root.1 sample had the most OTUs of 24, while the Leave.1 sample had the fewest of 13. Roots samples: Roots.1, Roots.2, and Roots.3. Leaves samples: Leaves.1, Leaves.2, and Leaves.3 are associated with Senna italica.
Figure 6. A in Metagenomic study of the communities of bacterial endophytes in the desert plant Senna Italica and their role in abiotic stress resistance in the plant
Figure 6. A. The phylum level in Bacteria (bar chart), the bacterial composition of the different samples was similar, while the distribution of each phylum varied in all samples. Based on the V3-V4 region of the 16S rRNA region. Bacterial communities at the phylum classification among the samples (pie chart), as a percentage of the total bacteria isolated from roots and leaves endophyte region. Based on the full-length 16S rRNA sequences. (B) The number of Actinobacteria among the samples. (C) The number of Proteobacteria among the samples. (D) The number of unclassified phyla among the samples. (E) The number of Firmicutes phyla among the samples. (F) The number of Cyanobacteria/Chloroplast among the samples. Roots samples: Roots.1, Roots.2, and Roots.3. Leaves samples: Leaves.1, Leaves.2, and Leaves.3 are associated with Senna italica.
Figure 5 in Metagenomic study of the communities of bacterial endophytes in the desert plant Senna Italica and their role in abiotic stress resistance in the plant
Figure 5. Phylogenetic tree based on 16S rRNA gene sequences representing the diversity of endophytic bacterial communities associated with the leaves and roots from the desert medicinal plant Senna italica "at the Phylum level". The tree was constructed using the "one-click" mode in Phylogeny.fr.(Dereeper et al., 2008).
Rapid evolutionary tradeoffs between resistance to herbivory and tolerance to abiotic stress in an invasive plant
<p>The datatset was collected from field survey, common garden experiments and lab experiments. All data analyses were performed in R 4.1.3 (R Development Core Team 2021) and SPSS 20.0 (IBM SPSS, Somers, NY, USA). The effects of re-association history (infested vs. uninfested; 0-, 9-, 13-, 17-year-reassociation) on insect bioassays (i.e., development time, weight, pepsin and trypsin enzyme activity of <em>O. communa</em>), plant assays including biomass ratio, MDA change rate and antioxidant capacity (DPPH and ABTS), and leaf chemical assays (concentrations of tannin, lignin, CGA, two individual flavonol derivates, nitrogen and carbon) were estimated using general linear mixed models (LMMs) in R, with reassociation history as a fixed factor and population (nested within reassociation history) as a random factor. For all these data, we also separately assessed correlations with the duration of re-association with the specialist natural enemy (the number of re-association years, i.e., 0, 9, 13, 17) at the individual level using Spearman correlation assays in SPSS 20.0. To analyze the correlation between insect traits (i.e., development time, weight, pepsin and trypsin enzyme activity of <em>O. communa</em>) and leaf defensive chemicals (i.e., tannin and lignin), we calculated the mean value of each index at the population level for each of the four re-association durations and used Pearson correlation assays in SPSS 20.0. The same procedure was followed for analyzing the correlation between drought stress tolerance and concentrations of leaf antioxidant chemicals. In addition, for the widely targeted metabolic data, principal component analysis (PCA) was performed using R to visualize the sample distributions.</p>
Data from: Microbial community composition interacts with local abiotic conditions to drive colonization resistance in human gut microbiome samples
Open the record for dataset details and reuse information.
Figure 7. A in Metagenomic study of the communities of bacterial endophytes in the desert plant Senna Italica and their role in abiotic stress resistance in the plant
Figure 7. A. The Genus level in Bacteria (bar chart), the 12genera of the five bacteria were detected at the level of the phylum. Based on the V3-V4 region of the 16S rRNA region. The relative most abundance in the taxonomic composition distribution in samples of Genus -level (pie chart) as a percentage of the total bacteria isolated from roots and leaves endophyte region. Based on the full-length 16S rRNA sequences. (B) and (C) The most abundant genera found in the phylum of Actinobacteria. (D) The most abundant genus found in the phylum of Cyanobacteria. Roots samples: Roots.1, Roots.2, and Roots.3. Leaves samples: Leaves.1, Leaves.2, and Leaves.3 are associated with Senna italica.
Figure 2 in Metagenomic study of the communities of bacterial endophytes in the desert plant Senna Italica and their role in abiotic stress resistance in the plant
Figure 2. Different curve based on observed Shannon value and Inversed Simpson value. Roots samples: Roots.1, Roots.2, and Roots.3. Leaves samples: Leaves.1, Leaves.2, and Leaves.3 are associated with Senna italica.
Figure 4 in Metagenomic study of the communities of bacterial endophytes in the desert plant Senna Italica and their role in abiotic stress resistance in the plant
Figure 4. Beta diversity analysis. Unweighted PCoA of UniFrac distances, Principal coordinate analysis illustrates differences between bacterial communities in senna italica roots and leaves. Two first components (PC1 and PC2) were plotted and represented 94.33% of whole inertia. Roots samples: Roots.1, Roots.2, and Roots.3. Leaves samples: Leaves.1, Leaves.2, and Leaves.3 are associated with Senna italica. The red triangle indicates Leaves.1. The green triangle indicates Root.1. The purple triangle indicates Root.2. The yellow square indicates Root.3. The blue square indicates Leaves.2. The orange circle indicates Leaves.3.
Robust Host Engineering: Enhancing Escherichia coli´s Abiotic Stress Resistance through Ornithine Lipid Membrane Modification
GEO Series GSE233657. Escherichia coli. 8 samples. Type: Expression profiling by high throughput sequencing.
Rice plants exhibiting bacterial blight resistance at high temperature suppress abiotic response
GEO Series GSE95668. Oryza sativa Indica Group. 28 samples. Type: Expression profiling by high throughput sequencing.
CsIND regulates vascular formation and resistance to biotic and abiotic stresses in cucumber (Cucumis sativus L.)
GEO Series GSE86496. Cucumis sativus. 8 samples. Type: Expression profiling by high throughput sequencing.
Genomic profile of maize response to A. flavus infection identifies genes associated with biotic and abiotic resistance
GEO Series GSE9546. Zea mays. 8 samples. Type: Expression profiling by array.
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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.
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DANDI Archive for NWB datasets
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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.