Transcriptomic and hormonal analysis of the roots of maize seedlings grown hydroponically at low temperature

Friero, Iván; Larriba, Eduardo; Martínez-Melgarejo, Purificación A.; Justamante, María Salud; Alarcón, M. Victoria; Albacete, Alfonso; Salguero, Julio; Pérez-Pérez, José Manuel

doi:10.1016/j.plantsci.2022.111525

Cited by 4 publications

(1 citation statement)

References 64 publications

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“…Maize is more sensitive to low temperature in its early developmental stage (seed germination, seedling emergence, and vegetative stages) than in the latter developmental stages. Low-temperature stress affects the content of hormones, such as abscisic acid, cytokinin, and gibberellins, in seedlings and inhibits antioxidant enzyme activities and protein synthesis [17][18][19]. In addition, it reduces seed germination rate, germination potential, seed viability, and seedling emergence, which are detrimental to seedling morphology and photosynthetic performance, leading to a decline in seedling quality, a shortening of the grouting period, an increase in the rate of empty culm and bald tips, and insufficient panicles and grains, ultimately limiting grain yield [20][21][22].…”

Section: Introductionmentioning

confidence: 99%

A Phenotypic Extraction and Deep Learning-Based Method for Grading the Seedling Quality of Maize in a Cold Region

Zhang,

Lu,

Guan

et al. 2024

Agronomy

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Background: Low-temperature stress significantly restricts maize germination, seedling growth and development, and yield formation. However, traditional methods of evaluating maize seedling quality are inefficient. This study established a method of grading maize seedling quality based on phenotypic extraction and deep learning. Methods: A pot experiment was conducted using different low-temperature combinations and treatment durations at six different stages between the sowing and seedling phases. Changes in 27 seedling quality indices, including plant morphology and photosynthetic performance, were investigated 35 d after sowing and seedling quality grades were classified based on maize yield at maturity. The 27 quality indices were extracted, and a total of 3623 sample datasets were obtained and grouped into training and test sets in a 3:1 ratio. A convolutional neural network-based grading method was constructed using a deep learning model. Results: The model achieved an average precision of 98.575%, with a recall and F1-Score of 98.7% and 98.625%, respectively. Compared with the traditional partial least squares and back propagation neural network, the model improved recognition accuracy by 8.1% and 4.19%, respectively. Conclusions: This study provided an accurate grading of maize seedling quality as a reference basis for the standardized production management of maize in cold regions.

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Section: Introductionmentioning

confidence: 99%

A Phenotypic Extraction and Deep Learning-Based Method for Grading the Seedling Quality of Maize in a Cold Region

Zhang,

Lu,

Guan

et al. 2024

Agronomy

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Design and experimental analysis of a closed-loop autonomous rotary hydroponics system for revolutionizing fenugreek yield and enhancing food security

Kadam,

Mohite

2024

Discov Sustain

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The rotary hydroponics system offers a space-saving and potentially high-yielding solution for soilless cultivation, promoting efficient resource use and automation for sustainable food production. This research investigates a closed-loop rotary hydroponics system designed to revolutionize fenugreek yield and enhance food security through resource-efficient and automated cultivation. Building upon the established benefits of aeroponics, particularly its efficient use of space and resources, rotary hydroponics introduces a groundbreaking, cylindrical design. This innovative approach not only maximizes space efficiency but also paves the way for further automation within the cultivation process. The proposed study delves into the design, development, and experimental analysis of a closed-loop, self-sustaining, and low-maintenance rotary hydroponics system specifically designed for fenugreek cultivation. The system prioritizes minimal human intervention through the integration of software-controlled monitoring and parameter adjustments. The research investigates the effectiveness of the system in promoting plant growth and analyzes the growth stages of fenugreek seedlings transplanted into the system. The experiment yielded promising results, with fenugreek plants reaching full maturity within 30 days and achieving an average height of 15–20 cm. These findings highlight the potential of the rotary hydroponics system to revolutionize fenugreek yield and bolster food security through its resource-efficient and sustainable cultivation approach.

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TARGET OF MONOPTEROS: key transcription factors orchestrating plant development and environmental response

Chen,

Dai,

Liao

et al. 2024

Journal of Experimental Botany

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Plants have an incredible ability to sustain root and vascular growth after initiation of the embryonic root and the specification of vascular tissue in early embryos. Microarray assays have revealed that a group of transcription factors (TARGET OF MONOPTEROS) are important for embryonic root initiation in Arabidopsis. Despite their discovery of auxin responsiveness early on, their function and mode of action remained unknown for many years. The advent of genome editing has accelerated the study of TMO transcription factors, revealing novel functions for biological processes such as vascular development, root system architecture, and response to environmental cues. This review covers recent achievements in understanding the developmental function and the genetic mode of action of TMO transcription factors in Arabidopsis and other plant species. We highlight the transcriptional and post-transcriptional regulation of TMO transcription factors in relation to their function, mainly in Arabidopsis. Finally, we provide suggestions for further research and possible applications in plant genetic engineering.

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Transcriptomic and hormonal analysis of the roots of maize seedlings grown hydroponically at low temperature

Cited by 4 publications

References 64 publications

A Phenotypic Extraction and Deep Learning-Based Method for Grading the Seedling Quality of Maize in a Cold Region

A Phenotypic Extraction and Deep Learning-Based Method for Grading the Seedling Quality of Maize in a Cold Region

Design and experimental analysis of a closed-loop autonomous rotary hydroponics system for revolutionizing fenugreek yield and enhancing food security

TARGET OF MONOPTEROS: key transcription factors orchestrating plant development and environmental response

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