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Hiroyoshi Iwata

Showing results (71-80 of 185) with videos related to

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Plos One|January 12, 2022
Incidence of common infectious diseases in Japan during the COVID-19 pandemicKenji Hibiya, Hiroyoshi Iwata, Takeshi Kinjo, et al.
TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik|October 25, 2014
Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.)Akio Onogi, Osamu Ideta, Yuto Inoshita, et al.
TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik|September 10, 2017
Genetic dissection of grain traits in Yamadanishiki, an excellent sake-brewing rice cultivarSatoshi Okada, Miki Suehiro, Kaworu Ebana, et al.
Breeding Science|September 1, 2022
Development of a high-throughput field phenotyping rover optimized for size-limited breeding fields as open-source hardwareKen Kuroki, Kai Yan, Hiroyoshi Iwata, et al.
BMC Genetics|April 29, 2014
Effect of advanced intercrossing on genome structure and on the power to detect linked quantitative trait loci in a multi-parent population: a simulation study in riceEiji Yamamoto, Hiroyoshi Iwata, Takanari Tanabata, et al.
The Plant Genome|October 5, 2020
Choosing the optimal population for a genome-wide association study: A simulation of whole-genome sequences from riceKosuke Hamazaki, Hiromi Kajiya-Kanegae, Masanori Yamasaki, et al.
Scientific Reports|May 1, 2021
Genetic dissection of QTLs associated with spikelet-related traits and grain size in sorghumHideki Takanashi, Mitsutoshi Shichijo, Lisa Sakamoto, et al.
Data in Brief|November 2, 2019
Whole genome sequencing data of <i>Oryza sativa</i> L. cv. TakanariHiromi Kajiya-Kanegae, Shiori Yabe, Hiroe Yoshida, et al.
Breeding Science|June 8, 2018
Identification of QTLs for rice grain size using a novel set of chromosomal segment substitution lines derived from Yamadanishiki in the genetic background of KoshihikariSatoshi Okada, Akio Onogi, Ken Iijima, et al.
The Plant Genome|June 26, 2024
Machine learning for genomic and pedigree prediction in sugarcaneMinoru Inamori, Tatsuro Kimura, Masaaki Mori, et al.
Pageof 19

Showing results (71-80 of 185) with videos related to

Sort By:
Pageof 19
Plos One|January 12, 2022
Incidence of common infectious diseases in Japan during the COVID-19 pandemicKenji Hibiya, Hiroyoshi Iwata, Takeshi Kinjo, et al.
TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik|October 25, 2014
Exploring the areas of applicability of whole-genome prediction methods for Asian rice (Oryza sativa L.)Akio Onogi, Osamu Ideta, Yuto Inoshita, et al.
TAG. Theoretical and Applied Genetics. Theoretische Und Angewandte Genetik|September 10, 2017
Genetic dissection of grain traits in Yamadanishiki, an excellent sake-brewing rice cultivarSatoshi Okada, Miki Suehiro, Kaworu Ebana, et al.
Breeding Science|September 1, 2022
Development of a high-throughput field phenotyping rover optimized for size-limited breeding fields as open-source hardwareKen Kuroki, Kai Yan, Hiroyoshi Iwata, et al.
BMC Genetics|April 29, 2014
Effect of advanced intercrossing on genome structure and on the power to detect linked quantitative trait loci in a multi-parent population: a simulation study in riceEiji Yamamoto, Hiroyoshi Iwata, Takanari Tanabata, et al.
The Plant Genome|October 5, 2020
Choosing the optimal population for a genome-wide association study: A simulation of whole-genome sequences from riceKosuke Hamazaki, Hiromi Kajiya-Kanegae, Masanori Yamasaki, et al.
Scientific Reports|May 1, 2021
Genetic dissection of QTLs associated with spikelet-related traits and grain size in sorghumHideki Takanashi, Mitsutoshi Shichijo, Lisa Sakamoto, et al.
Data in Brief|November 2, 2019
Whole genome sequencing data of <i>Oryza sativa</i> L. cv. TakanariHiromi Kajiya-Kanegae, Shiori Yabe, Hiroe Yoshida, et al.
Breeding Science|June 8, 2018
Identification of QTLs for rice grain size using a novel set of chromosomal segment substitution lines derived from Yamadanishiki in the genetic background of KoshihikariSatoshi Okada, Akio Onogi, Ken Iijima, et al.
The Plant Genome|June 26, 2024
Machine learning for genomic and pedigree prediction in sugarcaneMinoru Inamori, Tatsuro Kimura, Masaaki Mori, et al.
Pageof 19