scholarly journals Integration between ROS Regulatory Systems and Other Signals in the Regulation of Various Types of Heat Responses in Plants

2018 ◽  
Vol 19 (11) ◽  
pp. 3370 ◽  
Author(s):  
Kazuma Katano ◽  
Kohey Honda ◽  
Nobuhiro Suzuki

Because of their sessile lifestyle, plants cannot escape from heat stress and are forced to alter their cellular state to prevent damage. Plants, therefore, evolved complex mechanisms to adapt to irregular increases in temperature in the natural environment. In addition to the ability to adapt to an abrupt increase in temperature, plants possess strategies to reprogram their cellular state during pre-exposure to sublethal heat stress so that they are able to survive under subsequent severe heat stress. Such an acclimatory response to heat, i.e., acquired thermotolerance, might depend on the maintenance of heat memory and propagation of long-distance signaling. In addition, plants are able to tailor their specific cellular state to adapt to heat stress combined with other abiotic stresses. Many studies revealed significant roles of reactive oxygen species (ROS) regulatory systems in the regulation of these various heat responses in plants. However, the mode of coordination between ROS regulatory systems and other pathways is still largely unknown. In this review, we address how ROS regulatory systems are integrated with other signaling networks to control various types of heat responses in plants. In addition, differences and similarities in heat response signals between different growth stages are also addressed.

HortScience ◽  
1990 ◽  
Vol 25 (9) ◽  
pp. 1143e-1143
Author(s):  
Abbas M. Shirazi ◽  
Leslie H. Fuchigami ◽  
Tony H.H. Chen

Red-osier dogwood sterns, Cornus sericea L., at ten different growth stages were subjected to a series of temperatures ranging from 25C to 60C by immersing them in a water bath for one hour. After heat treatments, the viability of internode tissues were determined by electrical conductivity and ethylene production. Heat tolerance was expressed as LT50, the temperature at which 50% of the tissues were injured. The results suggest that the LT50 of dormant plants remained relatively constant, approximately 53C. During dormancy, heat stress did not stimulate ethylene production from internode tissues. In contrast, tissues from non-dormant plants exposed to heat stress produced increasing levels of ethylene reaching a peak at 40C followed by a steady decrease at higher temperatures. Application of 1-aminocyclopropane-1-carboxylic acid (ACC) to stem segments from dormant plants, following heat treatment, enhanced production of ethylene.


2018 ◽  
Vol 10 (2) ◽  
pp. 240-244 ◽  
Author(s):  
Ezekiel Dare OLOWOLAJU ◽  
Gideon O. OKUNLOLA ◽  
Abiodun M. ADEJUMO ◽  
Adekunle A. ADELUSI

The present study aimed at investigating the impact of abrupt heat stress on growth and phytochemical contents accumulation in Amaranthus hybridus. The treatments were as follows: control without heat treatment, seedlings subjected to heat at 45 oC for two hours and seedlings subjected to heat at 45 oC for four hours. After the stipulated time for each category, plants were removed from the Gallenkamp oven and were transplanted into other sets of thirty six pots (of 21 cm deep and 24 cm in diameter), as well as the control. The seedlings were kept in a screen house to minimise extraneous factors such as pests and rodents. They were watered daily with 200 mL of tap water in the morning and 200 mL of tap water in the evening until they were fully established. The phytochemical contents were determined at vegetative, flowering and fruiting stage using ethanolic extracts from the dried leaves of plant samples. From the results obtained, it was observed that leaf, shoot and root fresh and dry weights of the stressed plants were lower than the control plants. Exposure of the plants at different durations of heat treatment enhanced and inhibits the quantities of phytochemicals at different growth stages. From the present study it can be concluded that heat stress, on the basis of global warming in the future, will likely have overall negative effects on the growth of Amaranthus hybridus that will become more severe as the time of exposure increases and and might cause variation in the level of phytochemical constituents of Amaranthus hybridus at different growth stages.


1997 ◽  
Vol 99 (1) ◽  
pp. 185-189
Author(s):  
Wen-Shaw Chen ◽  
Kuang-Liang Huang ◽  
Hsiao-Ching Yu

2013 ◽  
Vol 39 (5) ◽  
pp. 919 ◽  
Author(s):  
Bo MING ◽  
Jin-Cheng ZHU ◽  
Hong-Bin TAO ◽  
Li-Na XU ◽  
Bu-Qing GUO ◽  
...  

GigaScience ◽  
2021 ◽  
Vol 10 (5) ◽  
Author(s):  
Teng Miao ◽  
Weiliang Wen ◽  
Yinglun Li ◽  
Sheng Wu ◽  
Chao Zhu ◽  
...  

Abstract Background The 3D point cloud is the most direct and effective data form for studying plant structure and morphology. In point cloud studies, the point cloud segmentation of individual plants to organs directly determines the accuracy of organ-level phenotype estimation and the reliability of the 3D plant reconstruction. However, highly accurate, automatic, and robust point cloud segmentation approaches for plants are unavailable. Thus, the high-throughput segmentation of many shoots is challenging. Although deep learning can feasibly solve this issue, software tools for 3D point cloud annotation to construct the training dataset are lacking. Results We propose a top-to-down point cloud segmentation algorithm using optimal transportation distance for maize shoots. We apply our point cloud annotation toolkit for maize shoots, Label3DMaize, to achieve semi-automatic point cloud segmentation and annotation of maize shoots at different growth stages, through a series of operations, including stem segmentation, coarse segmentation, fine segmentation, and sample-based segmentation. The toolkit takes ∼4–10 minutes to segment a maize shoot and consumes 10–20% of the total time if only coarse segmentation is required. Fine segmentation is more detailed than coarse segmentation, especially at the organ connection regions. The accuracy of coarse segmentation can reach 97.2% that of fine segmentation. Conclusion Label3DMaize integrates point cloud segmentation algorithms and manual interactive operations, realizing semi-automatic point cloud segmentation of maize shoots at different growth stages. The toolkit provides a practical data annotation tool for further online segmentation research based on deep learning and is expected to promote automatic point cloud processing of various plants.


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