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Fig. 7 in Natural variation of diterpenoid phytoalexins in cultivated and wild rice species
Fig. 7. Accumulation of oryzalactone and phytocassane G in the WRC cultivars and wild rice species after UV irradiation. (A) Oryzalactone amounts in WRC cultivars. (B) Phytocassane G amounts in WRC cultivars. (C) Oryzalactone and phytocassane G amounts in wild rice species.Or-I, Or-II, and Or-III indicate clades of O. rufipogon. O. gla: O. glaberrima; O. bar: O. barthii; O. glu: O. glumaepatula; O. mer: O. meridionalis; O. pun: O. punctata; O. bra: O. brachyantha. Values are means ± SD of three biological replicates. n.d.: not detected.
Fig. 6 in Natural variation of diterpenoid phytoalexins in cultivated and wild rice species
Fig. 6. Accumulation of oryzalactone and phytocassane G in response to rice blast infection and the antifungal activities of these compounds against rice blast. (A) Accumulation of oryzalactone in 'Jaguary' leaves in response to rice blast infection. (B) Accumulation of phytocassane G in 'Basilanon' leaves in response to rice blast infection.Amounts of 1 and 2 were determined 72 h after the start of treatment. n.d.: not detected. Values are means ± SD of three biological replicates. Asterisks indicate significant differences (*p <0.05, **p <0.01; Dunnett's test). (C) Effect of compounds on the germination of P. oryzae 12 h after incubation. (D) Effect of compounds on conidial germ tube elongation, which was measured 12 h after treatment.Values and error bars are means ± SD (n = 3). Different letters indicate significant differences (p <0.05; Tukey–Kramer method).
Fig. 3 in Natural variation of diterpenoid phytoalexins in cultivated and wild rice species
Fig. 3. Multiple reaction monitoring (MRM) chromatogram in momilactone A analysis. (A) Analysis of authentic momilactone A; retention time of 7.2 min. (B) Compound 1 was detected in the extract from 'Jaguary' with a retention time of 7.7 min. (C) Compound 2 was detected in the extract from 'Basilanon' with a retention time of 7.4 min.
Fig. 6 in Variation in production of cyanogenic glucosides during early plant development: A comparison of wild and domesticated sorghum
Fig. 6. Proportion of nitrogen allocated to dhurrin and nitrate (NO3) in dried, finely ground tissues of S. bicolor, S. brachypodum and S. macrospermum plants at 35 d post-germination. A) Dhurrin allocation; B) Nitrate allocation; C) C:N ratio. Graphs show mean ± 1 standard error (n = 3). Columns with different letters within each tissue are significantly different (p <0.05).
Fig. 3 in Variation in production of cyanogenic glucosides during early plant development: A comparison of wild and domesticated sorghum
Fig. 3. Leaf characteristics of S. bicolor, S. brachypodum and S. macrospermum plants at six harvest points during the first 35 d post-germination. A) Total leaf number; B) Total leaf area (TLA); C) Specific leaf area (SLA); D) Leaf area ratio (LAR). Graphs show mean ± 1 standard error (n = 5), with statistically significant differences indicated at each time point: *p <0.05.
Fig. 5 in Variation in production of cyanogenic glucosides during early plant development: A comparison of wild and domesticated sorghum
Fig. 5. Tissue-specific hydrogen cyanide potential (HCNp, mg HCN per g dw 1) and morphology of individual A) S. bicolor, B) S. brachypodum and C) S. macrospermum plants at six time points during seedling development. The HCNp of a section of the sheath, roots, and each individual leaf was measured at 3, 7, 14, 21, 28 and 35 days (D) post-germination. Colour scale indicates HCNp, used as a proxy for dhurrin concentration (green = low; red = high). (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 4 in Variation in production of cyanogenic glucosides during early plant development: A comparison of wild and domesticated sorghum
Fig. 4. Hydrogen cyanide potential (HCNp, mg HCN per g dw 1) and concentration of nitrate (NO) in dried, finely ground tissues of S. bicolor, S. brachypodum and 3 S. macrospermum plants at six harvest points during the first 35 d post-germination (at 35 dpg only for NO3). A) Leaf HCNp; B) Sheath HCNp; C) Root HCNp; D) Total NO3. Graphs show mean ± 1 standard error (n = 5), with statistically significant differences indicated at each time point: *p <0.05. Columns with different letters within each tissue are significantly different (p <0.05). Data for leaf HCNp at 3 days post-germination not shown as a true leaf had not emerged at this stage.
Fig. 1 in Variation in production of cyanogenic glucosides during early plant development: A comparison of wild and domesticated sorghum
Fig. 1. Known geographic distribution of the two wild Sorghum species S. brachypodum and S. macrospermum and the site of collection of the accessions examined in the current study. Seeds were obtained from the Australian Grains Genebank (AGG), Horsham, Victoria. Occurrence records of S. brachypodum and S. macrospermum were obtained from the Atlas of Living Australia (ALA), htt p://www.ala.org.au. Each blue circle represents an occurrence record of S. brachypodum and each orange circle represents S. macrospermum (circled). Collection localities of individual accessions examined here are marked by darker coloured circles. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 7 in Early transcriptional response of terpenoid metabolism to Colletotrichum gloeosporioides in a resistant wild strawberry Fragaria nilgerrensis
Fig. 7. Phylogenetic relationship of the five FnTPS candidates with other known terpene synthases and a sequence alignment showing the conserved protein motifs. (A) A maximum-likelihood tree of the TPS proteins depicting the TPS-a, TPS-b, TPS-d, TPS-e/f, and TPS-g clades, with bootstrap values greater than 50% shown for the branching. The scale bar corresponds to 6% amino acid substitution. The five F. nilgerrensis proteins are in red. Selected proteins with available three-dimensional structural data are shown in bold using their PDB code followed by the abbreviated species name. The known enzymatic products are in light blue followed with the UniProt Accession numbers of the proteins. (B) Alignment of the five candidates FnTPSs with α-farnesene synthase from apple (Malus domestica) and α-bergamotene synthase from Lavender (Lavandula Angustifolia). The DxDD motif of typical class II terpene synthases in FnTPS6 is boxed and the highly conserved class I DDxxD as well as the lesser conserved RRx8W and NSE/DTE motifs are indicated. The color regime of amino acids is set in Bioedit version 7.2.6. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 3 in Early transcriptional response of terpenoid metabolism to Colletotrichum gloeosporioides in a resistant wild strawberry Fragaria nilgerrensis
Fig. 3. Most enriched metabolic pathways of the DEGs during the first 18 hpi with C. gloeosporioides. Only significant pathways with a Q-value below 1 are shown. Terpene metabolism clusters are highlighted.
Fig. 1 in Early transcriptional response of terpenoid metabolism to Colletotrichum gloeosporioides in a resistant wild strawberry Fragaria nilgerrensis
Fig. 1. Determination of terpenoids in leaves challenged with C. gloeosporioides. (A) Total contents of monoterpenes and sesquiterpenes, (B–C) The contents of individual monoterpene and sesquiterpene identified. Data are average of two independent experiments.
Fig. 4 in Early transcriptional response of terpenoid metabolism to Colletotrichum gloeosporioides in a resistant wild strawberry Fragaria nilgerrensis
Fig. 4. Schematic illustration of general terpenoid biosynthesis pathways showing identified unigenes from F. nilgerrensis leaf transcriptome data. The interconvertible precursors IPP and DMAPP, two phosphorylated C5 unites, are produced by the MVA and MEP pathways which are exchangeable from both compartmentations. The chloroplast is a major site for synthesis of hemiterpene (C5), monoterpenoids (C10), diterpenoids (C20) carotenoids (C40) and chlorophyll, while the cytosol and other organelle are responsible for synthesis of monoterpenoids (C10), sesquiterpene (C15) and triterpene (C30). But that is not strictly conclusive. Arrow with lines indicate reactions catalyzed by enzymes and the encoding genes, with unigenes identified in this experiment boxed. The color highlights are for better visualization. Abbreviations: AACT, acetoacetyl-CoA thiolase; CMK, 4-diphosphocytidyl-2-C-methyl-D-erythritol kinase; DXR, 1-deoxy-D-xylulose 5-phosphate reductase; DXS, 1-deoxy-D-xylulose 5-phosphate synthase; HDR, (E)-4-hydroxy-3-methyl-but-2-enyl diphosphate reductase; HDS, (E)-4-hydroxy-3- methyl-but-2-enyl diphosphate synthase; HMGR, 3-hydroxy-3-methylglutaryl-CoA reductase; HMGS, 3-hydroxy-3-methylglutaryl-CoA synthase; IDI, isopentenyl diphosphate isomerase; MCT, MEP cytidyltransferase; MDC, mevalonate-5-diphosphate decarboxylase; MDS, 2-C-methyl-D-erythritol 2,4-cyclodiphosphate synthase; MVK, mevalonate kinase. (For interpretation of the references to color in this figure legend, the reader is referred to the Web version of this article.)
Fig. 2 in Early transcriptional response of terpenoid metabolism to Colletotrichum gloeosporioides in a resistant wild strawberry Fragaria nilgerrensis
Fig. 2. Global differentially-expressed-genes (DEG) in leaves of F. nilgerrensis after challenged by C. gloeosporioides. (A) DEG heatmap depicts clustering of the early upregulated genes (up to 18-h post challenge) and the late respondents (>24 h) in the upper and lower parts, respectively, with top GO terms indicated. (B) Major GO term enrichment in an early responsive gene cluster 7 showing that terpenoid biosynthesis genes are co-expressed with antipathogen immune-responsive pathway genes. Each time-point contains 2 biological replications and the expression data was zero-normalized before hierarchical clustering is performed (Pearson uncentered metric, average linkage ordering) using Cluster 3.0 version 1.58. Heatmap was examined and generated using Java Tree- View version 1.1.6r4. Gene Ontology (GO) enrichment analysis of the DEGs is implemented by the topGO R package-based Kolmogorov–Smirnov test.
Fig. 5 in Early transcriptional response of terpenoid metabolism to Colletotrichum gloeosporioides in a resistant wild strawberry Fragaria nilgerrensis
Fig. 5. Heat maps depicting the transcript levels of terpenoid biosynthesis genes from F. nilgerrensis leaf transcriptome in response to C. gloeosporioides. (A) Fifty-six MVA and MEP pathway-associated genes. Annotation of the unigenes by homologous are listed in Table S4. (B) Fifty-nine terpene synthase-like or related genes. Annotation of the unigenes by homologous are listed in Table S5. Average FPKM (n = 2) were log2 transformed. The heatmap was generated by Prism GraphPad version 7.04.
Fig. 3 in Characterization of phytochemicals in the roots of wild herbaceous peonies from China and screening for medicinal resources
Fig. 3. The range and distribution of the five metabolite categories in 20 populations of sect. Paeonia distributed in China. Median values are shown as horizontal lines within each box while 50% of the data is presented within the box. Data outside each box are indicated by black dots.
Fig. 2 in Characterization of phytochemicals in the roots of wild herbaceous peonies from China and screening for medicinal resources
Fig. 2. Chemical structures of major compounds identified in roots of sect. Paeonia native to China.
Fig. 4 in Characterization of phytochemicals in the roots of wild herbaceous peonies from China and screening for medicinal resources
Fig. 4. Hierarchical cluster dendritic diagram of 20 populations of sect. Paeonia distributed in China.
Fig. 1 in Characterization of phytochemicals in the roots of wild herbaceous peonies from China and screening for medicinal resources
Fig. 1. Representative HPLC chromatographic profiles of roots at 254 nm in samples from sect. Paeonia native to China. A: P2 (Paeonia lactiflora), B: P7 (P. anomala subsp. veitchii), C: P9 (P. anomala subsp. anomala), D: P10 (P. sterniana), E: P11 (P. emodi), F: P12 (P. obovata subsp. willottiae), G: P16 (P. obovata subsp. obovata), H: P18 (P. mairei), I: P20 (P. intermedia).
Data, Code and Computational Environment for: Wild olive tree genetic diversity in Greece: a diverse resource in danger of erosion
<p><strong>Data</strong></p> <p>The csv file "2023-06-27_data.csv" contains genetic profiles for all olive trees analyzed in the publication: Wild olive tree genetic diversity in Greece: a diverse resource in danger of erosion". The genotypes are provided in the GenAlEx format.</p> <p> </p> <p><strong>Code</strong></p> <p>Code is provided in the compressed folder "Tourvas_et_al_Olive". It is structured as a R project and can easily be opened, after decompressing, from the Rstudio interface. If you prefer to review and/or reuse code you can access it from the "analysis" folder inside the "Tourvas_et_al_Olive" folder.</p> <p> </p> <p><strong>Computational Environment</strong></p> <p>A tarball for the Docker image "tourvas_et_al_olive" is also provided. This is the recommended way to reproduce the results of the publication: "Wild olive tree genetic diversity in Greece: a diverse resource in danger of erosion".</p> <p>It is assumed that you already have Docker installed on your system. If not, please visit <a href="https://docs.docker.com/get-started/">https://docs.docker.com/get-started/</a></p> <p>To use it:</p> <ul> <li>download the image file tourvas_et_al_olive.tar</li> <li>load it with docker with the command:</li> </ul> <pre><code class="language-bash">docker load --input tourvas_et_al_olive.tar</code></pre> <ul> <li>then launch the Docker container with the command:</li> </ul> <pre><code class="language-bash">docker run --name popgen --rm -dp 8787:8787 -e ROOT=TRUE -e DISABLE_AUTH=true -v "`pwd`":/home/rstudio/working nikostourvas/tourvas_et_al_olive</code></pre> <ul> <li>start your favorite web browser and go to: http://localhost:8787/</li> <li>from the bottomright pane of the Rstudio server click on the directory "Tourvas_et_al_Olive" and open the project by clicking on the "Tourvas_et_al_Olive.Rproj" file</li> <li>launch the scripts inside the "analysis" directory and run them to reproduce results</li> </ul>
Data from: probing variation in reaction norms in wild populations: the importance of reliable environmental proxies
<p><span>Many traits are phenotypically plastic, i.e., the same genotype expresses different phenotypes depending on the environment. Genotypes and individuals can vary in their response to the environment and this genetic (G×E) and individual (I×E) variation in reaction-norm slopes can have important ecological or evolutionary consequences. Studies on I×E/G×E often fail to show slope variation, potentially due to the choice of the environmental covariate. Identifying the genuine environmental driver of phenotypic plasticity (the cue) is practically impossible and hence only proxies can be used. If the proxy is too weakly correlated with the cue, this may lead researchers to conclude there is little or no (variation in) plasticity, and hence lead to downwardly biased estimates of the potential for plastic responses (or evolutionary change in the slope) in response to environmental change. Alternatively, the Environment-Specific Mean phenotype (ESM) across individuals—which captures all environmental effects on the phenotype—as covariate should be less prone to such bias. We showed by simulation—after verifying the concept analytically—that using weakly correlated proxies indeed biased estimates of slope variation vis-à-vis the true cue downward but that ESM as a covariate held up well, even when multiple sources of I×E or an interaction between environments (I×E×E) existed in the data. Analysis of two real datasets revealed that estimated I×E and G×E, respectively, were more sizeable and precise when using ESM as opposed to reasonably informative environmental proxies. We argue that the ESM approach should be adopted by biologists as a yardstick in the study of (variation in) plasticity in the wild and that it may serve as a useful starting point for the search of better environmental proxies and unravelling complex I×E or G×E patterns.</span></p>
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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.