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Superconducting (SC) tokamak JT-60SA plays an essential role in fusion research and development by supporting and complementing the ITER project, providing directions to the DEMO design activity and fostering next generation scientists and engineers. Since the short circuit incident at the terminal joints of equilibrium field coil #1 during the integrated commissioning (IC) in March 2021, both EU and JA implementing agencies (IAs) have examined how to ensure safe operation of JT-60SA by mitigating the risk of possible discharge occurrence inside the cryostat. Based on the experience of the global Paschen tests, the IAs have established a strategy of risk mitigation measures, which is a combination of (i) reinforcement of insulation, (ii) avoiding unnecessary voltage application to the coil systems and (iii) immediate de-energization of the coils when deteriorated vacuum conditions are detected. Thanks to the considerable efforts of the Integrated Project Team members, the IC restarted in May 2023. After confirmation of the SC state of the coil systems (TF, EF and CS), the coil energization test and the plasma operation phase 1 (OP-1) started. The first plasma was successfully achieved on 23 October 2023 with a limited value of voltage and current applied to the coils. The plasma configuration control was also confirmed with low plasma current and low auxiliary heating power conditions. Based on the IO–F4E–QST collaboration, activities of JT-60SA have been shared with the IO and provided an important lesson for ITER assembly and commissioning, and will provide an outstanding contribution to fusion research at large. After OP-1, maintenance & enhancement phase 1 (M/E-1) starts from January 2024, in which in-vessel components are installed, and heating and diagnostic systems are extensively upgraded to allow a high power heating experiment planned in OP-2. In order to make the best use of JT-60SA, a newly organized JT-60SA experiment team will refine the research plan for the future high heating power operation phase.

High-throughput phenotyping serves as a framework to reduce chronological costs and accelerate breeding cycles. In this study, we developed models to estimate the phenotypes of biomass-related traits in soybean ( Glycine max ) using unmanned aerial vehicle (UAV) remote sensing and deep learning models. In 2018, a field experiment was conducted using 198 soybean germplasm accessions with known whole-genome sequences under 2 irrigation conditions: drought and control. We used a convolutional neural network (CNN) as a model to estimate the phenotypic values of 5 conventional biomass-related traits: dry weight, main stem length, numbers of nodes and branches, and plant height. We utilized manually measured phenotypes of conventional traits along with RGB images and digital surface models from UAV remote sensing to train our CNN models. The accuracy of the developed models was assessed through 10-fold cross-validation, which demonstrated their ability to accurately estimate the phenotypes of all conventional traits simultaneously. Deep learning enabled us to extract features that exhibited strong correlations with the output (i.e., phenotypes of the target traits) and accurately estimate the values of the features from the input data. We considered the extracted low-dimensional features as phenotypes in the latent space and attempted to annotate them based on the phenotypes of conventional traits. Furthermore, we validated whether these low-dimensional latent features were genetically controlled by assessing the accuracy of genomic predictions. The results revealed the potential utility of these low-dimensional latent features in actual breeding scenarios., Competing Interests: Competing interests: The authors declare that they have no competing interests., (Copyright © 2024 Mashiro Okada et al.)

Nutritropism is a positive tropism toward nutrients in plant roots. An NH4+ gradient is a nutritropic stimulus in rice (Oryza sativa L.). When rice roots are exposed to an NH4+ gradient generated around nutrient sources, root tips bend toward and coil around the sources. The molecular mechanisms are largely unknown. Here, we analyzed the transcriptomes of the inside and outside of bending root tips exhibiting nutritropism to reveal nutritropic signal transduction. Tissues facing the nutrient sources (inside) and away (outside) were separately collected by laser microdissection. Principal component analysis revealed distinct transcriptome patterns between the two tissues. Annotations of 153 differentially expressed genes implied that auxin, gibberellin and ethylene signaling were activated differentially between the sides of the root tips under nutritropism. Exogenous application of transport and/or biosynthesis inhibitors of these phytohormones largely inhibited the nutritropism. Thus, signaling and de novo biosynthesis of the three phytohormones are necessary for nutritropism. Expression patterns of IAA genes implied that auxins accumulated more in the inside tissues, meaning that ammonium stimulus is transduced to auxin signaling in nutritropism similar to gravity stimulus in gravitropism. SAUR and expansin genes, which are known to control cell wall modification and to promote cell elongation in shoot gravitropism, were highly expressed in the inside tissues rather than the outside tissues, and our transcriptome data are unexplainable for differential elongation in root nutritropism., (© The Author(s) 2024. Published by Oxford University Press on behalf of Japanese Society of Plant Physiologists. This is an Open Access article distributed under the terms of the Creative Commons Attribution-NonCommercial License (https://creativecommons.org/licenses/by-nc/4.0/), which permits non-commercial re-use, distribution, and reproduction in any medium, provided the original work is properly cited. For commercial re-use, please contact journals.permissions@oup.com.)

While it is commonly understood that air temperature can greatly affect the process of photosynthesis and the growth of higher plants, the impact of root zone temperature (RZT) on plant growth, metabolism, essential elements, as well as key metabolites like chlorophyll and carotenoids, remains an area that necessitates extensive research. Therefore, this study aimed to investigate the impact of raising the RZT on the growth, metabolites, elements, and proteins of red leaf lettuce. Lettuce was hydroponically grown in a plant factory with artificial light at four different air temperatures (17, 22, 27, and 30°C) and two treatments with different RZTs. The RZT was raised 3°C above the air temperature in one group, while it was not in the other group. Increasing the RZT 3°C above the air temperature improved plant growth and metabolites, including carotenoids, ascorbic acids, and chlorophyll, in all four air temperature treatments. Moreover, raising the RZT increased Mg, K, Fe, Cu, Se, Rb, amino acids, and total soluble proteins in the leaf tissue at all four air temperatures. These results showed that raising the RZT by 3°C improved plant productivity and the metabolites of the hydroponic lettuce by enhancing nutrient uptake and activating the metabolism in the roots at all four air temperatures. Overall, this research demonstrates that plant growth and metabolites can be improved simultaneously with an increased RZT relative to air temperature. This study serves as a foundation for future research on optimizing RZT in relation to air temperature. Further recommended studies include investigating the differential effects of multiple RZT variations relative to air temperature for increased optimization, examining the effects of RZT during nighttime versus daytime, and exploring the impact of stem heating. This research has the potential to make a valuable contribution to the ongoing growth and progress of the plant factory industry and fundamental advancements in root zone physiology. Overall, this research demonstrates that plant growth and metabolites can be improved simultaneously with an increased RZT relative to air temperature. This study serves as a foundation for future research on optimizing RZT in relation to air temperature. Further recommended studies include investigating the differential effects of multiple RZT variations relative to air temperature for increased optimization, examining the effects of RZT during nighttime versus daytime, and exploring the impact of stem heating. This research has the potential to make a valuable contribution to the ongoing growth and progress of the plant factory industry and fundamental advancements in root zone physiology., Competing Interests: Authors MU and AY were employed by the company Plants Laboratory Inc. The remaining authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2024 Hayashi, Levine, Yu, Usui, Yukawa, Ohmori, Kusano, Kobayashi, Nishizawa, Kurimoto, Kawabata and Yamori.)

Increasing nutrient uptake and use efficiency in plants can contribute to improved crop yields and reduce the demand for fertilizers in crop production. In this study, we characterized a rice mutant, 88n which showed long roots under low nitrogen (N) or phosphorus (P) conditions. Low expression levels of N transporter genes were observed in 88n root, and total N concentration in 88n shoots were decreased, however, C concentrations and shoot dry weight in 88n were comparable to that in WT. Therefore, 88n showed high nitrogen utilization efficiency (NUtE). mRNA accumulation of Pi transporter genes was higher in 88n roots, and Pi concentration and uptake activity were higher in 88n than in WT. Therefore, 88n also showed high phosphorus uptake efficiency (PUpE). Molecular genetic analysis revealed that the causal gene of 88n phenotypes was OsbZIP1, a monocot-specific ortholog of the A. thaliana bZIP transcription factor HY5. Similar to the hy5 mutant, chlorophyll content in roots was decreased and root angle was shallower in 88n than in WT. Finally, we tested the yield of 88n in paddy fields over 3 years because 88n mutant plants showed higher PUpE and NUtE activity and different root architecture at the seedling stage. 88n showed large panicles and increased panicle weight/plant. Taken together, a mutation in OsbZIP1 could contribute to improved crop yields., (© 2024 The Authors. The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.)

Key Message: We proposed models to predict the effects of genomic and environmental factors on daily soybean growth and applied them to soybean growth data obtained with unmanned aerial vehicles. Advances in high-throughput phenotyping technology have made it possible to obtain time-series plant growth data in field trials, enabling genotype-by-environment interaction (G × E) modeling of plant growth. Although the reaction norm is an effective method for quantitatively evaluating G × E and has been implemented in genomic prediction models, no reaction norm models have been applied to plant growth data. Here, we propose a novel reaction norm model for plant growth using spline and random forest models, in which daily growth is explained by environmental factors one day prior. The proposed model was applied to soybean canopy area and height to evaluate the influence of drought stress levels. Changes in the canopy area and height of 198 cultivars were measured by remote sensing using unmanned aerial vehicles. Multiple drought stress levels were set as treatments, and their time-series soil moisture was measured. The models were evaluated using three cross-validation schemes. Although accuracy of the proposed models did not surpass that of single-trait genomic prediction, the results suggest that our model can capture G × E, especially the latter growth period for the random forest model. Also, significant variations in the G × E of the canopy height during the early growth period were visualized using the spline model. This result indicates the effectiveness of the proposed models on plant growth data and the possibility of revealing G × E in various growth stages in plant breeding by applying statistical or machine learning models to time-series phenotype data., (© 2024. The Author(s).)