S
Stefan de Folter
Researcher at Instituto Politécnico Nacional
Publications - 106
Citations - 5213
Stefan de Folter is an academic researcher from Instituto Politécnico Nacional. The author has contributed to research in topics: Arabidopsis & Gynoecium. The author has an hindex of 32, co-authored 94 publications receiving 4374 citations. Previous affiliations of Stefan de Folter include Wageningen University and Research Centre & CINVESTAV.
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Journal ArticleDOI
Control of stem cell activity in the carpel margin meristem (CMM) in Arabidopsis.
TL;DR: A review of the current understanding of the molecular mechanisms that regulate meristem activity in the CMM compared to the SAM and discusses similarities and differences found in the SAM.
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Effect of Constitutive miR164 Expression on Plant Morphology and Fruit Development in Arabidopsis and Tomato
Flor de Fátima Rosas Cárdenas,Yolanda Ruiz Suárez,Rosa María Cano Rangel,Valentín Luna Garcia,Karla Lorena González Aguilera,Nayelli Marsch Martínez,Stefan de Folter +6 more
TL;DR: The results suggest that miR164 plays general and specific roles during development in Arabidopsis and tomato, including fruit development, which could be exploited for the improvement of traits of agronomic interest in important species.
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In vivo monitoring of nicotine biosynthesis in tobacco leaves by low-temperature plasma mass spectrometry.
TL;DR: The auxin-regulated nicotine biosynthesis in tobacco (Nicotiana tabacum) is monitored and the proof-of-concept for measuring the biosynthetic activity of plant surfaces in vivo is provided.
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Gynoecium and fruit development in Arabidopsis.
TL;DR: In this paper , the authors highlight recent discoveries, including the players, interactions and mechanisms that govern gynoecium and fruit development in Arabidopsis, and present the currently known gene regulatory networks from gynocium initiation until fruit maturation.
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Metabolic fingerprinting of Arabidopsis thaliana accessions.
Mariana Sotelo-Silveira,Mariana Sotelo-Silveira,Anne-Laure Chauvin,Nayelli Marsch-Martínez,Robert Winkler,Stefan de Folter +5 more
TL;DR: A predictive Random Forest Model was developed, which was able to reliably classify tissue type and accession of samples based on LC-MS profile and demonstrated that the morphological differences among A. thaliana accessions are reflected also as distinct metabolic phenotypes within leaves and inflorescences.