Examining the utility of hyperspectral remote sensing and partial least squares to predict plant stress responses to sulphur dioxide pollution: a case study of Trichilia dregeana Sond.

2016 ◽  
Vol 100 (1) ◽  
pp. 22-40 ◽  
Author(s):  
Minoli Appalasamy ◽  
Boby Varghese ◽  
Sershen ◽  
Riyad Ismail
Keyword(s):  
Remote Sensing ◽  
Least Squares ◽  
Partial Least Squares ◽  
Stress Responses ◽  
Sulphur Dioxide ◽  
Plant Stress ◽  
Hyperspectral Remote Sensing ◽  
Plant Stress Responses

scholarly journals Versatile Physiological Functions of Plant GSK3-Like Kinases

Genes ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 697
Author(s):  
Juan Mao ◽  
Wenxin Li ◽  
Jing Liu ◽  
Jianming Li
Keyword(s):  
Stress Responses ◽  
Glycogen Synthase ◽  
Plant Stress ◽  
Normal Growth ◽  
Growth Conditions ◽  
Genetic Studies ◽  
Physiological Functions ◽  
Environmental Signals ◽  
Plant Stress Responses ◽  
Diverse Plant

The plant glycogen synthase kinase 3 (GSK3)-like kinases are highly conserved protein serine/threonine kinases that are grouped into four subfamilies. Similar to their mammalian homologs, these kinases are constitutively active under normal growth conditions but become inactivated in response to diverse developmental and environmental signals. Since their initial discoveries in the early 1990s, many biochemical and genetic studies were performed to investigate their physiological functions in various plant species. These studies have demonstrated that the plant GSK3-like kinases are multifunctional kinases involved not only in a wide variety of plant growth and developmental processes but also in diverse plant stress responses. Here we summarize our current understanding of the versatile physiological functions of the plant GSK3-like kinases along with their confirmed and potential substrates.

scholarly journals OsNAC45 is Involved in ABA Response and Salt Tolerance in Rice

Rice ◽  
2020 ◽  
Vol 13 (1) ◽  
Author(s):  
Xiang Zhang ◽  
Yan Long ◽  
Jingjing Huang ◽  
Jixing Xia
Keyword(s):  
Transcription Factors ◽  
Salt Stress ◽  
Salt Tolerance ◽  
Stress Responses ◽  
Crop Yields ◽  
Plant Stress ◽  
Root Cells ◽  
Nac Transcription Factors ◽  
Rice Plants ◽  
Plant Stress Responses

Abstract Background Salt stress threatens crop yields all over the world. Many NAC transcription factors have been reported to be involved in different abiotic stress responses, but it remains unclear how loss of these transcription factors alters the transcriptomes of plants. Previous reports have demonstrated that overexpression of OsNAC45 enhances salt and drought tolerance in rice, and that OsNAC45 may regulate the expression of two specific genes, OsPM1 and OsLEA3–1. Results Here, we found that ABA repressed, and NaCl promoted, the expression of OsNAC45 in roots. Immunostaining showed that OsNAC45 was localized in all root cells and was mainly expressed in the stele. Loss of OsNAC45 decreased the sensitivity of rice plants to ABA and over-expressing this gene had the opposite effect, which demonstrated that OsNAC45 played an important role during ABA signal responses. Knockout of OsNAC45 also resulted in more ROS accumulation in roots and increased sensitivity of rice to salt stress. Transcriptome sequencing assay found that thousands of genes were differently expressed in OsNAC45-knockout plants. Most of the down-regulated genes participated in plant stress responses. Quantitative real time RT-PCR suggested that seven genes may be regulated by OsNAC45 including OsCYP89G1, OsDREB1F, OsEREBP2, OsERF104, OsPM1, OsSAMDC2, and OsSIK1. Conclusions These results indicate that OsNAC45 plays vital roles in ABA signal responses and salt tolerance in rice. Further characterization of this gene may help us understand ABA signal pathway and breed rice plants that are more tolerant to salt stress.

Electrochemical Monitoring of Plant Stress Responses

2001 ◽  
Vol 13 (6) ◽  
pp. 451-456 ◽  
Author(s):  
Takeshi Kinpara ◽  
Yuji Murakami ◽  
Kenji Yokoyama ◽  
Eiichi Tamiya
Keyword(s):  
Stress Responses ◽  
Plant Stress ◽  
Plant Stress Responses ◽  
Electrochemical Monitoring

scholarly journals Polymethyl Methacrylate Quality Modeling with Missing Data Using Subspace Based Model Identification

Processes ◽  
2021 ◽  
Vol 9 (10) ◽  
pp. 1691
Author(s):  
Nikesh Patel ◽  
Kavitha Sivanathan ◽  
Prashant Mhaskar
Keyword(s):  
Missing Data ◽  
Least Squares ◽  
Partial Least Squares ◽  
Polymethyl Methacrylate ◽  
Model Identification ◽  
Principal Component ◽  
Quality Modeling ◽  
The Impact ◽  
Quality Measurements

This paper addresses the problem of quality modeling in polymethyl methacrylate (PMMA) production. The key challenge is handling the large amounts of missing quality measurements in each batch due to the time and cost sensitive nature of the measurements. To this end, a missing data subspace algorithm that adapts nonlinear iterative partial least squares (NIPALS) algorithms from both partial least squares (PLS) and principal component analysis (PCA) is utilized to build a data driven dynamic model. The use of NIPALS algorithms allows for the correlation structure of the input–output data to minimize the impact of the large amounts of missing quality measurements. These techniques are utilized in a simulated case study to successfully model the PMMA process in particular, and demonstrate the efficacy of the algorithm to handle the quality prediction problem in general.

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