Open Access
Issue |
OCL
Volume 28, 2021
Sunflower / Tournesol
|
|
---|---|---|
Article Number | 42 | |
Number of page(s) | 6 | |
DOI | https://doi.org/10.1051/ocl/2021029 | |
Published online | 16 August 2021 |
- Balliau T, Duruflé H, Blanchet N, et al. 2021. Proteomic data from leaves of twenty-four sunflower genotypes underwater deficit. OCL 28: 12. https://doi.org/10.1051/ocl/2020074. [EDP Sciences] [Google Scholar]
- Bantan-Polak T, Kassai M, Grant KB. 2001. A comparison of fluorescamine and naphthalene-2, 3-dicarboxaldehyde fluorogenic reagents for microplate-based detection of amino acids. Anal Biochem 297(2): 128–136. https://doi.org/10.1006/abio.2001.5338. [Google Scholar]
- Biais B, Bénard C, Beauvoit B, et al. 2014. Remarkable reproducibility of enzyme activity profiles in tomato fruits grown under contrasting environments provides a roadmap for studies of fruit metabolism. Plant Physiol 164(3): 1204–1221. https://doi.org/10.1104/pp.113.231241. [Google Scholar]
- Blanchet N, Casadebaig P, Debaeke P, et al. 2018. Data describing the eco-physiological responses of twenty-four sunflower genotypes to water deficit. Data Brief 21: 1296–1301. https://doi.org/10.1016/j.dib.2018.10.045. [Google Scholar]
- Debaeke P, Casadebaig P, Flénet F, et al. 2017. Sunflower crop and climate change: vulnerability, adaptation, and mitigation potential from case-studies in Europe. OCL 24(1): 15. https://doi.org/10.1051/ocl/2016052. [Google Scholar]
- Fernandez O, Urrutia M, Berton T, et al. 2019. Metabolomic characterization of sunflower leaf allows discriminating genotype groups or stress levels with a minimal set of metabolic markers. Metabolomics 15(4): 56. https://doi.org/10.1007/s11306-019-1515-4. [Google Scholar]
- Gody L, Duruflé H, Blanchet N, et al. 2020. Transcriptomic data of leaves from eight sunflower lines and their sixteen hybrids under water deficit. OCL 27: 48. https://doi.org/10.1051/ocl/2020044. [EDP Sciences] [Google Scholar]
- Gosseau F, Blanchet N, Varès D, et al. 2019. Heliaphen, an outdoor high-throughput phenotyping platform for genetic studies and crop modeling. Front Plant Sci 9: 1908. https://doi.org/10.3389/fpls.2018.01908. [CrossRef] [PubMed] [Google Scholar]
- Hendriks JHM, Kolbe A, Gibon Y, et al. 2003. ADP-glucose pyrophosphorylase is activated by posttranslational redox-modification in response to light and to sugars in leaves of Arabidopsis and other plant species. Plant Physiol 133(2): 838–849. https://doi.org/10.1104/pp.103.024513. [Google Scholar]
- Jacob D, Deborde C, Moing A. 2020. BioStatFlow-Statistical Analysis Workflow for “Omics” Data. ArXiv preprint: 2007.04599. [Google Scholar]
- Moschen S, Di Rienzo JA, Higgins J, et al. 2017. Integration of transcriptomic and metabolic data reveals hub transcription factors involved in drought stress response in sunflower (Helianthus annuus L.). Plant Mol Biol 94(4): 549–564. https://doi.org/10.1007/s11103-017-0625-5. [Google Scholar]
- Porter JR, Challinor AJ, Henriksen CB, et al. 2019. Invited review: Intergovernmental Panel on Climate Change, agriculture, and food − A case of shifting cultivation and history. Glob Chang Biol 25(8): 2518–2529. https://doi.org/10.1111/gcb.14700. [Google Scholar]
- R Core Team. 2018. A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/. [Google Scholar]
- Shen X, Gong X, Cai Y, et al. 2016. Normalization and integration of large-scale metabolomics data using support vector regression. Metabolomic 12(5): 89. https://doi.org/10.1007/s11306-016-1026-5. [Google Scholar]
- Smith CA, Want EJ, O’Maille G, et al. 2006. XCMS: processing mass spectrometry data for metabolite profiling using nonlinear peak alignment, matching, and identification. Anal Chem 78(3): 779–787. https://doi.org/10.1021/ac051437y. [Google Scholar]
- Stelzner J, Roemhild R, Garibay-Hernández A, et al. 2019. Hydroxycinnamic acids in sunflower leaves serve as UV-A screening pigments. Photochem Photobiol Sci 18(7): 1649–1659. https://doi.org/10.1039/C8PP00440D. [Google Scholar]
- Stitt M, Lilley RM, Gerhardt R, et al. 1989. Metabolite levels in specific cells and subcellular compartments of plant leaves. In: Fleischer S, Fleischer B, eds. Biomembranes part U: cellular and subcellular transport: eukaryotic (nonepithelial) cells. Methods Enzymol 174: 518–552. [Google Scholar]
- Sumner LW, Amberg A, Barrett D, et al. 2007. Proposed minimum reporting standards for chemical analysis. Metabolomics 3(3): 211–221. https://doi.org/10.1007/s11306-007-0082-2. [Google Scholar]
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.