著者

Takuya Morimoto Takashi Akagi Ryutaro Tao

出版者

The Japanese Society for Horticultural Science

雑誌

The Horticulture Journal (ISSN:21890102)

巻号頁・発行日

pp.MI-060, (Released:2015-04-04)

被引用文献数

14 24

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Flowering plants have developed a genetically determined self-incompatibility (SI) system to maintain genetic diversity within a species. The Solanaceae, the Rosaceae, and the Plantaginaceae have the S-RNase-based gametophytic SI (GSI) system, which uses S-RNase and F-box proteins as the pistil S and pollen S determinants, respectively. SI is associated with culture and breeding difficulties in rosaceous fruit trees, such as apple, pear, and stone fruit species; therefore, researchers in the pomology field have long studied the mechanism and genetics of SI in order to obtain clues to overcome these difficulties. Here, we investigated the evolutionary paths of the S-RNase genes by tracking their duplication patterns. Phylogenetic analysis and estimation of proxy ages for the establishment of S-RNase and its homologs in several rosaceous species showed that the divergence of S-RNase in the subtribe Malinae and the genus Prunus predated the gene in most recent common ancestors of Rosaceae species. Furthermore, the duplicated S-RNase-like genes were accompanied by duplicated pollen S-like F-box genes, suggesting segmental duplications of the S locus. Analysis of the expression patterns and evolutionary speeds of duplicated S-RNase-like genes in Prunus suggested that these genes have lost the SI recognition function, resulting in a single S locus. Furthermore, the S loci in the current Rosaceae species might have evolved independently from the duplicated S loci, which could explain the presence of genus-specific SI recognition mechanisms in the Rosaceae. The results of the present study should be valuable for the future development of artificial SI control and for self-compatible breeding in rosaceous horticultural plant species.

著者

Kanae Masuda Takashi Akagi

出版者

Japanese Society of Breeding

雑誌

Breeding Science (ISSN:13447610)

巻号頁・発行日

vol.73, no.2, pp.95-107, 2023 (Released:2023-06-06)

参考文献数

136

被引用文献数

2

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Sexuality is the main strategy for maintaining genetic diversity within a species. In flowering plants (angiosperms), sexuality is derived from ancestral hermaphroditism and multiple sexualities can be expressed in an individual. The mechanisms conferring chromosomal sex determination in plants (or dioecy) have been studied for over a century by both biologists and agricultural scientists, given the importance of this field for crop cultivation and breeding. Despite extensive research, the sex determining gene(s) in plants had not been identified until recently. In this review, we dissect plant sex evolution and determining systems, with a focus on crop species. We introduced classic studies with theoretical, genetic, and cytogenic approaches, as well as more recent research using advanced molecular and genomic techniques. Plants have undergone very frequent transitions into, and out of, dioecy. Although only a few sex determinants have been identified in plants, an integrative viewpoint on their evolutionary trends suggests that recurrent neofunctionalization events are potentially common, in a “scrap and (re)build” cycle. We also discuss the potential association between crop domestication and transitions in sexual systems. We focus on the contribution of duplication events, which are particularly frequent in plant taxa, as a trigger for the creation of new sexual systems.

著者

Abdul H. Kazimi Oscar W. Mitalo Azimullah Azimi Kanae Masuda Chikara Yano Takashi Akagi Koichiro Ushijima Yasutaka Kubo

出版者

The Japanese Society for Horticultural Science

雑誌

The Horticulture Journal (ISSN:21890102)

巻号頁・発行日

pp.QH-012, (Released:2022-11-25)

被引用文献数

2

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A major challenge in terms of commercializing 1-methylclopropene (1-MCP) to extend the storage life and control physiological disorders in European pears is that it irreversibly inhibits fruit ripening in some cultivars, particularly flesh softening that is necessary for optimal consumption quality. In this study, we examined the effect of 1-MCP pretreatments on fruit ripening and associated transcriptomic changes in ‘La France’ (Pyrus communis L.) pears during storage at 20°C and 5°C. Compared to non-treated controls, 1-MCP pretreatment suppressed fruit respiration and ethylene production rates, and markedly delayed flesh softening. Normal ripening (ethylene production and flesh softening to eating quality firmness) was observed in 1-MCP treated fruit after 42 d at 20°C, and 112 d at 5°C. Subsequent RNA-Seq analysis revealed that 6,427 genes, including those associated with ethylene biosynthesis (ACS1, ACS1b, ACO1, and ACO2), cell wall degrading enzymes (PG3, β-GAL, EG, and EXP1), and transcription factors (AGL18 and NAC29) were up- or down-regulated in non-treated fruit both at 20°C and 5°C. The expression patterns of these genes were disrupted by 1-MCP pretreatment, but up- or down-regulation was also observed when ethylene was detected in 1-MCP-treated fruit. Together, these findings demonstrate the potential for practical use of 1-MCP to extend storage life in ‘La France’ pears given that (i) a single application markedly extended storage life to 56 d at 20°C and 112 d at 5°C, and (ii) treated fruit could regain their softening capacity, thus eliminating previous irreversible ripening blockage concerns.

著者

Nobuyuki Kanematsu Takashi Akagi Yasuyuki Futami Akio Higashi Tatsuaki Kanai Naruhiro Matsufuji Hiromi Tomura Haruo Yamashita

出版者

Japan Society of Medical Physics

雑誌

放射線医学物理 (ISSN:09188010)

巻号頁・発行日

vol.18, no.1, pp.88-103, 1998 (Released:2012-09-24)

参考文献数

21

被引用文献数

1

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This technical report describes a 3D dose calculation code which we have developed for proton therapy treatment planning. In order to achieve fast and accurate calculation in inhomogeneous matter like a human body, the code employs the pencil beam algorithm with the infinite-slab-layer approximation. The code handles the given configuration of proton beam, beam modifying devices, and voxel-based structure of a patient and calculates the 3D dose distribution in the patient. The code showed good performance for treatment planning in terms of speed and accuracy in comparison with detailed Monte Carlo calculations.

著者

Maria Suzuki Kanae Masuda Hideaki Asakuma Kouki Takeshita Kohei Baba Yasutaka Kubo Koichiro Ushijima Seiichi Uchida Takashi Akagi

出版者

The Japanese Society for Horticultural Science

雑誌

The Horticulture Journal (ISSN:21890102)

巻号頁・発行日

pp.UTD-323, (Released:2022-05-25)

被引用文献数

6

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In contrast to the progress in the research on physiological disorders relating to shelf life in fruit crops, it has been difficult to non-destructively predict their occurrence. Recent high-tech instruments have gradually enabled non-destructive predictions for various disorders in some crops, while there are still issues in terms of efficiency and costs. Here, we propose application of a deep neural network (or simply deep learning) to simple RGB images to predict a severe fruit disorder in persimmon, rapid over-softening. With 1,080 RGB images of ‘Soshu’ persimmon fruits, three convolutional neural networks (CNN) were examined to predict rapid over-softened fruits with a binary classification and the date to fruit softening. All of the examined CNN models worked successfully for binary classification of the rapid over-softened fruits and the controls with > 80% accuracy using multiple criteria. Furthermore, the prediction values (or confidence) in the binary classification were correlated to the date to fruit softening. Although the features for classification by deep learning have been thought to be in a black box by conventional standards, recent feature visualization methods (or “explainable” deep learning) has allowed identification of the relevant regions in the original images. We applied Grad-CAM, Guided backpropagation, and layer-wise relevance propagation (LRP), to find early symptoms for CNNs classification of rapid over-softened fruits. The focus on the relevant regions tended to be on color unevenness on the surface of the fruit, especially in the peripheral regions. These results suggest that deep learning frameworks could potentially provide new insights into early physiological symptoms of which researchers are unaware.