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Seyed Mohammad Mahdi Mortazavian

Researcher at University of Tehran

Publications -  29
Citations -  391

Seyed Mohammad Mahdi Mortazavian is an academic researcher from University of Tehran. The author has contributed to research in topics: Cuminum & Somatic embryogenesis. The author has an hindex of 10, co-authored 23 publications receiving 281 citations.

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Journal Article

GGE Biplot and AMMI Analysis of Yield Performance of Barley Genotypes across Different Environments in Iran

TL;DR: In this article, the effects of GEI on grain yield were found to be significant, accounting for 60.38, 4.52 and 35.09% of treatment combination sum of squares.
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Image Processing and Artificial Neural Network-Based Models to Measure and Predict Physical Properties of Embryogenic Callus and Number of Somatic Embryos in Ajowan ( Trachyspermum ammi (L.) Sprague)

TL;DR: A high-precision image-processing approach was successfully applied to measure physical properties of embryogenic callus in Trachyspermum ammi (L.) Sprague (Ajowan) and the number of somatic embryos produced.
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Genetic stability of regenerated plants via indirect somatic embryogenesis and indirect shoot regeneration of Carum copticum L.

TL;DR: Ajowan ( Carum copticum L.) is an important and endangered industrial medicinal plant that growing in some parts of Iran as discussed by the authors, and two efficient protocols, without somaclonal variation induction, were developed for indirect somatic embryogenesis and indirect shoot regeneration of three Iranian ecotypes of ajowan.
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Tissue Culture-based Agrobacterium-mediated and in planta Transformation Methods

TL;DR: All methods of in planta transformation comparing them with regular Agrobacterium-mediated transformation methods based on tissue culture are highlighted and successful recent transformations using these methods are presented.
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An artificial intelligence approach for modeling volume and fresh weight of callus – A case study of cumin (Cuminum cyminum L.)

TL;DR: The network with conjugate gradient fletcher-reeves algorithm, tangent sigmoid transfer function, 17 hidden nodes and 2000 training epochs was selected as the final ANN model and was able to predict the fresh weight and volume of calli more precisely relative to multiple linear models.