What is he best known for?
The fields of study he is best known for:
- Gene
- Genetics
- Cell membrane
Takashi Ueda mainly focuses on Cell biology, Arabidopsis, Endosome, Biochemistry and GTPase.
His Cell biology study incorporates themes from Endocytic cycle and Endocytosis.
The study incorporates disciplines such as Arabidopsis thaliana and Cytoplasm in addition to Arabidopsis.
He combines subjects such as Cytoskeleton and Actin with his study of Endosome.
His work on Rab as part of his general GTPase study is frequently connected to Fatty acylation, thereby bridging the divide between different branches of science.
His Vacuole research includes elements of Organelle and Vacuolar transport.
His most cited work include:
- The Arabidopsis GNOM ARF-GEF Mediates Endosomal Recycling, Auxin Transport, and Auxin-Dependent Plant Growth (944 citations)
- Systematic analysis of SNARE molecules in Arabidopsis: Dissection of the post-Golgi network in plant cells (452 citations)
- Insights into Land Plant Evolution Garnered from the Marchantia polymorpha Genome (433 citations)
What are the main themes of his work throughout his whole career to date?
His scientific interests lie mostly in Cell biology, Arabidopsis, Arabidopsis thaliana, GTPase and Rab.
His studies link Mutant with Cell biology.
In his work, Oil body is strongly intertwined with Marchantia polymorpha, which is a subfield of Arabidopsis.
His Arabidopsis thaliana research is multidisciplinary, incorporating elements of Cell and Hypha.
His research in Rab intersects with topics in Endocytic cycle and Yeast.
His Golgi apparatus research includes elements of Transport protein and Cytoplasm, Subcellular localization.
He most often published in these fields:
- Cell biology (113.26%)
- Arabidopsis (41.67%)
- Arabidopsis thaliana (44.70%)
What were the highlights of his more recent work (between 2018-2021)?
- Cell biology (113.26%)
- Arabidopsis (41.67%)
- Marchantia polymorpha (17.05%)
In recent papers he was focusing on the following fields of study:
The scientist’s investigation covers issues in Cell biology, Arabidopsis, Marchantia polymorpha, Arabidopsis thaliana and Mutant.
The concepts of his Cell biology study are interwoven with issues in Lateral root morphogenesis, Mitochondrial RNA processing and Clathrin.
His Arabidopsis study combines topics in areas such as Secretion, V-ATPase, Endoplasmic reticulum, Vacuole and Membrane protein.
His study looks at the relationship between Secretion and topics such as Vacuolar transport, which overlap with Golgi apparatus.
His biological study spans a wide range of topics, including ATG8, Phosphatidylinositol and Plant evolution.
He has researched Mutant in several fields, including Sterol ester, Asymmetric cell division and Phospholipid.
Between 2018 and 2021, his most popular works were:
- A Golgi-Released Subpopulation of the Trans-Golgi Network Mediates Protein Secretion in Arabidopsis. (34 citations)
- A Golgi-Released Subpopulation of the Trans-Golgi Network Mediates Protein Secretion in Arabidopsis. (34 citations)
- A whole-cell electron tomography model of vacuole biogenesis in Arabidopsis root cells. (27 citations)
In his most recent research, the most cited papers focused on:
- Gene
- Genetics
- Cell membrane
Cell biology, Arabidopsis, Arabidopsis thaliana, Endoplasmic reticulum and Mutant are his primary areas of study.
Takashi Ueda merges Cell biology with Extra-invasive hyphal membrane in his study.
The various areas that Takashi Ueda examines in his Arabidopsis study include Golgi apparatus, Secretion, Secretory protein and Membrane protein.
Takashi Ueda interconnects Colletotrichum higginsianum, Colletotrichum, Hypha and Phosphatidylinositol 4,5-bisphosphate, Phosphatidylinositol in the investigation of issues within Arabidopsis thaliana.
His studies in Endoplasmic reticulum integrate themes in fields like Sterol homeostasis, Phospholipid, Organelle and Sterol ester.
Takashi Ueda has included themes like Marchantia, Marchantia polymorpha, Oil body, Botany and Abiotic stress in his Mutant study.
This overview was generated by a machine learning system which analysed the scientist’s
body of work. If you have any feedback, you can
contact us
here.
