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1. Additional file 5 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Abstract

Additional file 5: Figure S5. Validation of the screening method by interfering with auxins. Per treatment, 1 µM of NAA, 2,4-D, IAA, and NPA was added. The shoot parameters are the lengths (in mm) of the total shoot, internode, coleoptile, and leaves 1 and 2. The root parameters are the lengths (in mm) of the seminal and crown roots and the number of emerged crown roots. Different letters indicate statistically significant differences between treatments (see “Methods”). The root data for the 2,4-D treatment are not available, because the root system was unmeasurable. The picture shows the harvested rice seedling treated with 1 µM 2,4-D with the “stumpy” root phenotype. Scale bar, 1 cm.

Published
2020
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4. Additional file 10 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Abstract

Additional file 10: Figure S10. Validation of the screening method by means of a concentration range of NaCl in Oryza sativa (L.) cv. Chilbo. Also 1 µM GA3 was added in the test tubes. Mock_DMSO corresponds to GA3 as this is also dissolved in DMSO, while NaCl is dissolved in sterile dH2O. The shoot parameters (in mm) are lengths of total shoot, internode, coleoptile, and leaves 1 and 2. The root parameters are lengths (in mm) of the seminal and crown roots and the number of emerged crown roots. Different letters indicate statistically significant differences between the treatments (see “Methods”).

Published
2020
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6. Additional file 13 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Abstract

Additional file 13: Figure S11. Validation of the screening method by means of a concentration range of DCMU in Oryza sativa (L.) cv. (New) Dongjin. The shoot parameters (in mm) are lengths of total shoot, internode, coleoptile, and leaves 1 and 2. The root parameters are lengths (in mm) of the seminal and crown roots and the number of emerged crown roots. Different letters indicate statistically significant differences between the treatments (see “Methods”).

Published
2020
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7. Additional file 4 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Abstract

Additional file 4: Figure S4. Graphical User Interface of Plength and its interference functionalities. Top panel: GUI with an uploaded image. Information on the file directory and image size are displayed. Bottom panel: Interference functionalities. (A) The four settings that can be changed, i.e. plant type, coleoptile or seedling, scale bar position and length, and minimum detection area. (B) Preprocessing. Before the analysis, the image can be cropped and the detected areas can be checked. (top) cropping tool; (bottom) color thresholding tool. (C) Postprocessing. The display after analysis completion. Detected regions are framed and numbered and the leaves are traced. For each detected area, the shoot, internode and leaf lengths are given.

Published
2020
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8. Additional file 6 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Abstract

Additional file 6: Figure S6. Validation of the screening method by interfering with cytokinins. Per treatment, 1 µM of kinetin, 2iP, and trans-zeatin was added. The shoot parameters are the lengths (in mm) of the total shoot, internode, coleoptile, and leaves 1 and 2. The root parameters are the lengths (in mm) of the seminal and crown roots and the number of emerged crown roots. Different letters indicate statistically significant differences between treatments (see “Methods”).

Published
2020
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9. Additional file 11 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Abstract

Additional file 11. Least squares means for each parameter in the NaCl experiment for the different cultivars tested (Oryza sativa (L.) cv. (New) Dongjin, cv. Chucheongbyeo and cv. Chilbo, a semi-dwarf variety).

Published
2020
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10. Additional file 3 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Abstract

Additional file 3: Figure S3. Theoretical example of the analysis of artificial plants with three or four leaves. Detected regions are numbered and framed. Leaves are traced in yellow.

Published
2020
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11. Additional file 8 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Abstract

Additional file 8: Figure S8. Validation of the screening method by interference with the ethylene pathway. Per treatment, 1 µM of ACC and AgNO3 was added. The shoot parameters are the lengths (in mm) of the total shoot, internode, coleoptile, and leaves 1 and 2. The root parameters are the lengths (in mm) of the seminal and crown roots and the number of emerged crown roots. Different letters indicate statistically significant differences between treatments (see “Methods”).

Published
2020
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12. Additional file 1 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Abstract

Additional file 1: Figure S1. Workflow overview of the RIVA method. Seeds were sterilized and pregerminated by complete submergence in 2.0 mM CaSO4 in the dark. After 3 days of pregermination, selected seeds were transferred to test tubes filled with 1 mL 2.0 mM CaSO4 and a compound of interest. After 7 days, rice seedlings were harvested. Shoots and coleoptiles were transferred to plates and scanned. Yield-related parameters were automatically generated from these images via an in-house developed software, designated Plength.

Published
2020
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13. Additional file 2 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Subjects
ComputingMethodologies_IMAGEPROCESSINGANDCOMPUTERVISION
Abstract

Additional file 2: Figure S2. Extra information about the image processing pipeline in Plength. Top panel: Smoothing improving thresholding by homogenizing the colors, removal of the noise, and connection of edges. Middle panel: Segmentation, i.e. thresholding and partitioning of the image into individual plants. Bottom panel: Prior to feature extraction, skeletonization of the contour, conversion to a graph, and pruning. The red and green dots indicate nodes and connecting edges, respectively. Note that the initial graph consists of nine nodes, but the pruned graph of only of seven.

Published
2020
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14. Additional file 12 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Abstract

Additional file 12. Pairwise comparisons for each parameter in the NaCl experiment for the different cultivars tested (Oryza sativa (L.) cv. (New) Dongjin, cv. Chucheongbyeo and cv. Chilbo, a semi-dwarf variety).

Published
2020
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15. Additional file 9 of Development of a novel and rapid phenotype-based screening method to assess rice seedling growth

Author

Vlaminck, Lena, Chananchida Sang-Aram, Botterman, Deborah, Uy, Christine Jewel C., Harper, Mary Kay, Inzé, Dirk, Gheysen, Godelieve, and Depuydt, Stephen

Abstract

Additional file 9: Figure S9. Validation of the screening method by means of a concentration range of NaCl in Oryza sativa (L.) cv. Chucheongbyeo. Also 1 µM GA3 was added in the test tubes. Mock_DMSO corresponds to GA3 as this is also dissolved in DMSO, while NaCl is dissolved in sterile dH2O. The shoot parameters (in mm) are lengths of total shoot, internode, coleoptile, and leaves 1 and 2. The root parameters are lengths (in mm) of the seminal and crown roots and the number of emerged crown roots. Different letters indicate statistically significant differences between the treatments (see “Methods”).

Published
2020
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17. The value of universally available raw NMR data for transparency, reproducibility, and integrity in natural product research

Author

Jie Li, Ben Shen, Orazio Taglialatela-Scafati, Matthias Niemitz, Sonja Sturm, Jian Min Yue, Andrei G. Kutateladze, Craig M. Williams, Nicholas H. Oberlies, Phillip Crews, Jean-Hugues Renault, Hermann Stuppner, John B. MacMillan, David Coppage, Harald Gross, Bin Gui Wang, Hiroyuki Koshino, Shao-Nong Chen, Bradley S. Moore, Carla M. Saunders, Jonathan Bisson, Andersson Barison, Miaomiao Liu, David G. I. Kingston, Young Hae Choi, Dean J. Tantillo, Mary J. Garson, Christoph Seger, Mehdi A. Beniddir, Hsiau Wei Lee, Wolfgang Robien, Zhengren Xu, Maique W. Biavatti, Tadeusz F. Molinski, David C. Lankin, Stefan Bluml, Damien Jeannerat, Philip G. Williams, José Rivera-Chávez, Julien Wist, Grégory Genta-Jouve, Ram P. Neupane, James B. McAlpine, Chen Zhang, Ronald J. Quinn, Joo-Won Nam, William H. Gerwick, Giovanni Appendino, Fernanda Maria Marins Ocampos, Jean-Marc Nuzillard, Joseph M. Egan, Luke Hunter, Marie Csete, Guohui Pan, Robert Verpoorte, Roger G. Linington, Tyler A. Johnson, Precilia Hermanto, Kerry L. McPhail, Mark S. Butler, Guy Lewin, Charlotte Simmler, Guido F. Pauli, Michael T. Crimmins, Mary Kay Harper, Thomas J. Schmidt, Christoph Steinbeck, James M. Hook, Nai Yun Ji, D. Sai Reddy, Asmaa Boufridi, Robert J. Capon, Pradeep Dewapriya, Jeannerat, Damien, Mcalpine, James B, Chen, Shao-Nong, Kutateladze, Andrei, Macmillan, John B, Appendino, Giovanni, Barison, Andersson, Beniddir, Mehdi A, Biavatti, Maique W, Bluml, Stefan, Boufridi, Asmaa, Butler, Mark S, Capon, Robert J, Choi, Young H, Coppage, David, Crews, Phillip, Crimmins, Michael T, Csete, Marie, Dewapriya, Pradeep, Egan, Joseph M, Garson, Mary J, Genta-Jouve, Grégory, Gerwick, William H, Gross, Harald, Harper, Mary Kay, Hermanto, Precilia, Hook, James M, Hunter, Luke, Nai-Yun, Ji, Johnson, Tyler A, Kingston, David G I, Koshino, Hiroyuki, Lee, Hsiau-Wei, Lewin, Guy, Jie, Li, Linington, Roger G, Liu, Miaomiao, Mcphail, Kerry L, Molinski, Tadeusz F, Moore, Bradley S, Nam, Joo-Won, Neupane, Ram P, Niemitz, Matthia, Nuzillard, Jean-Marc, Oberlies, Nicholas H, Ocampos, Fernanda M M, Pan, Guohui, Quinn, Ronald J, Reddy, D Sai, Renault, Jean-Hugue, Rivera-Chávez, José, Robien, Wolfgang, Saunders, Carla M, Schmidt, Thomas J, Seger, Christoph, Shen, Ben, Steinbeck, Christoph, Stuppner, Hermann, Sturm, Sonja, Taglialatela-Scafati, Orazio, Tantillo, Dean J, Verpoorte, Robert, Wang, Bin-Gui, Williams, Craig M, Williams, Philip G, Wist, Julien, Yue, Jian-Min, Zhang, Chen, Zhengren, Xu, Simmler, Charlotte, Lankin, David C, Bisson, Jonathan, Pauli, Guido F, Thallion Pharmaceuticals Inc., Brunel University London [Uxbridge], Dipartimento di Scienze Chimiche, DIPARTIMENTO DI SCIENZE CHIMICHE, Departamento de Química, Universidade Federal do Paraná (UFPR), Molécules bioactives, conception, isolement et synthèse (MBCIS), Université Paris-Sud - Paris 11 (UP11)-Centre National de la Recherche Scientifique (CNRS), Equipe C-TAC (UMR 8638), Chimie Organique, Médicinale et Extractive et Toxicologie Expérimentale (COMETE - UMR 8638), Université Paris Descartes - Paris 5 (UPD5)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Descartes - Paris 5 (UPD5)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Université Sorbonne Paris Cité (USPC), University of Tübingen, University of New South Wales [Sydney] (UNSW), Department of organic Chemistry - University of Geneva, University of Geneva [Switzerland], Department of Chemistry and Biochemistry, University of California, University of California [Santa Cruz] (UCSC), University of California-University of California, Center for Marine Biotechnology and Biomedicine, University of California, Institut de Chimie Moléculaire de Reims - UMR 7312 (ICMR), SFR Condorcet, Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Centre National de la Recherche Scientifique (CNRS)-SFR CAP Santé (Champagne-Ardenne Picardie Santé), Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Institut de Chimie du CNRS (INC)-Centre National de la Recherche Scientifique (CNRS), Chinese Academy of Sciences [Beijing] (CAS), University of Münster, European Bioinformatics Institute [Hinxton] (EMBL-EBI), EMBL Heidelberg, University of Innsbruck, Section Metabolomics (DIVISION OF PHARMACOGNOSY), Universiteit Leiden [Leiden]-Institute of Biology, Institut National de l'Environnement Industriel et des Risques (INERIS), Université Paris Descartes - Paris 5 (UPD5)-Centre National de la Recherche Scientifique (CNRS)-Université Paris Descartes - Paris 5 (UPD5)-Centre National de la Recherche Scientifique (CNRS), and Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Université de Picardie Jules Verne (UPJV)-Université de Reims Champagne-Ardenne (URCA)-Centre National de la Recherche Scientifique (CNRS)

Subjects
NMR, Natural Products, Raw Data, Reproducibility, Value (ethics), Magnetic Resonance Spectroscopy, Molecular Conformation, [CHIM.INOR]Chemical Sciences/Inorganic chemistry, 010402 general chemistry, Medical and Health Sciences, 01 natural sciences, Biochemistry, Article, [CHIM.ANAL]Chemical Sciences/Analytical chemistry, Component (UML), Drug Discovery, Dissemination, Biological Products, Biological studies, 010405 organic chemistry, Organic Chemistry, Reproducibility of Results, Biological Sciences, Transparency (behavior), Data science, Nmr data, 0104 chemical sciences, Chemical Sciences, ddc:540, Generic health relevance, Natural Product Research, Raw data, [CHIM.CHEM]Chemical Sciences/Cheminformatics
Abstract

International audience; With contributions from the global natural product (NP) research community, and continuing the Raw Data Initiative, this review collects a comprehensive demonstration of the immense scientific value of disseminating raw nuclear magnetic resonance (NMR) data, independently of, and in parallel with, classical publishing outlets. A comprehensive compilation of historic to present-day cases as well as contemporary and future applications show that addressing the urgent need for a repository of publicly accessible raw NMR data has the potential to transform natural products (NPs) and associated fields of chemical and biomedical research. The call for advancing open sharing mechanisms for raw data is intended to enhance the transparency of experimental protocols, augment the reproducibility of reported outcomes, including biological studies, become a regular component of responsible research, and thereby enrich the integrity of NP research and related fields.

Published
2019
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