Molecular Analysis of Rice CIPKs Involved in Both Biotic and Abiotic Stress Responses

Plant calcineurin B-like （CBL） proteins have been proposed as important Ca2＋ sensors and specifically interact with CBL-interacting protein kinases （CIPKs） in plant-specific calcium signaling. Here, we identified and isolated 15 CIPK genes in a japonica rice variety Nipponbare based on the predicted sequences of rice CIPK gene family. Gene structure analysis showed that these 15 genes were divided into intron-less and intron-rich groups, and OsCIPK3 and OsCIPK24 exhibited alternative splicing in their mature process. The phylogenetic analyses indicated that rice CIPKs shared an ancestor with Arabidopsis and poplar CIPKs. Analyses of gene expression showed that these OsCIPK genes were differentially induced by biotic stresses such as bacterial blight and abiotic stresses （heavy metal such as Hg2＋, high salinity, cold and ABA）. Interestingly, five OsCIPK genes, OsCIPK1, 2, 10, 11 and 12, were transcriptionally up-regulated after bacterial blight infection whereas four OsCIPK genes, OsCIPK2, 10, 11 and 14, were induced by all treatments, indicating that some of OsCIPK genes are involved in multiple stress response pathways in plants. Our finding suggests that CIPKs play a key role in both biotic and abiotic stress responses.

The cytochrome P450 superfamily: Key players in plant development and defense

The cytochrome P450 （CYP） superfamily is the largest enzymatic protein family in plants, and it also widely exists in mammals, fungi, bacteria, insects and so on. Members of this superfamily are involved in multiple metabolic pathways with distinct and complex functions, playing important roles in a vast array of reactions. As a result, numerous secondary metabolites are synthesized that function as growth and developmental signals or protect plants from various biotic and abiotic stresses. Here, we summarize the characterization of CYPs, as well as their phylogenetic classification. We also focus on recent advances in elucidating the roles of CYPs in mediating plant growth and development as well as biotic and abiotic stresses responses, providing insights into their potential utilization in plant breeding.

Potential Effects of Global Change to Urban Vegetation： Vulnerability and Adaptations

An urban area is a space with high population density which develops new, major and complex structures in comparison to the areas surrounding it. In order to develop these structures and maintain population and its activity, the metabolism of urban areas needs a lot of external sources of energy and nutrients （water, food, materials...）, which produces heat waste, garbage, sewage and pollution which are some of the major problems for urban sites, and the related areas from it. This metabolism promotes major environmental changes in the urban areas, which promote stress on vegetation used in gardening. The main environmental factors that affect vegetation in urban areas are the same that have been defined in literature from long time ago, but now they are acting as the sum of complementary and synergic effects of these classical stresses at the same moment, in the same place, which happen due to the incredibly amount of energy that we place in the systems. This is called global change. Ecophysiological studies can provide objective information to be used as a tool to improve the vegetation management in urban areas from design to process, and consequently avoiding the potential vulnerabilities associated with global change. Present paper tries to show several examples about the plant response, measurement tools and vulnerabilities and adaptations to global change under urban conditions. It can be concluded that the large availability of vegetal material and the great technical development can be highlighted as strong points of gardening and urban landscaping while, as weak points, it could be mentioned the changing taste of consumers, which can force the introduction of new vegetal material with no time for adaptation, Urban gardening and landscaping can be considered to be exposed to global change, but in our opinion it is necessary to carry out more studies to determine the real degree of vulnerability of this activity to this complex kind of stress.

A Comprehensive Review of High Throughput Phenotyping and Machine Learning for Plant Stress Phenotyping

During the last decade,there has been rapid adoption of ground and aerial platforms with multiple sensors for phenotyping various biotic and abiotic stresses throughout the developmental stages of the crop plant.High throughput phenotyping(HTP)involves the application of these tools to phenotype the plants and can vary from ground-based imaging to aerial phenotyping to remote sensing.Adoption of these HTP tools has tried to reduce the phenotyping bottleneck in breeding programs and help to increase the pace of genetic gain.More specifically,several root phenotyping tools are discussed to study the plant’s hidden half and an area long neglected.However,the use of these HTP technologies produces big data sets that impede the inference from those datasets.Machine learning and deep learning provide an alternative opportunity for the extraction of useful information for making conclusions.These are interdisciplinary approaches for data analysis using probability,statistics,classification,regression,decision theory,data visualization,and neural networks to relate information extracted with the phenotypes obtained.These techniques use feature extraction,identification,classification,and prediction criteria to identify pertinent data for use in plant breeding and pathology activities.This review focuses on the recent findings where machine learning and deep learning approaches have been used for plant stress phenotyping with data being collected using various HTP platforms.We have provided a comprehensive overview of different machine learning and deep learning tools available with their potential advantages and pitfalls.Overall,this review provides an avenue for studying various HTP platforms with particular emphasis on using the machine learning and deep learning tools for drawing legitimate conclusions.Finally,we propose the conceptual challenges being faced and provide insights on future perspectives for managing those issues.

Advances in structure and function of auxin response factor in plants

Auxin is a crucial phytohormone that has various effects on the regulators of plant growth and development.Auxin signal transduction is mainly controlled by two gene families:auxin response factor(ARF)and auxin/indole-3-acetic acid(Aux/IAA).ARFs are plant-specific transcription factors that bind directly to auxin response elements in the promoters of auxinresponsive genes.ARF proteins contain three conserved regions:a conserved N-terminal B3DNA-binding domain,a variable intermediate middle region domain that functions in activation or repression,and a C-terminal domain including the Phox and Bem1p region for dimerization,similar to theⅢandⅣelements of Aux/IAA,which facilitate protein–protein interaction through homodimerization of ARF proteins or heterodimerization of ARF and Aux/IAA proteins.In the two decades following the identification of the first ARF,23 ARF members have been identified and characterized in Arabidopsis.Using whole-genome sequencing,22,25,23,25,and 36 ARF genes have been identified in tomato,rice,wheat,sorghum,and maize,respectively,in addition to which the related biofunctions of some ARFs have been reported.ARFs play crucial roles in regulating the growth and development of roots,leaves,flowers,fruits,seeds,responses to biotic and abiotic stresses,and phytohormone signal crosstalk.In this review,we summarize the research progress on the structures and functions of ARFs in Arabidopsis,tomato,and cereal crops,to provide clues for future basic research on phytohormone signaling and the molecular design breeding of crops.

Deep learning approach for recognition and classification of yield affecting paddy crop stresses using field images

On-time recognition and early control of the stresses in the paddy crops at the booting growth stage is the key to prevent qualitative and quantitative loss of agricultural yield.The conventional paddy crop stress recognition and classification activities invariably rely on human experts identifying visual symptoms as a means of categorization.This process is admittedly subjective and error-prone,which in turn can lead to incorrect actions being taken in stress management decisions.The work presented in this paper aims to design a deep convolutional neural network(DCNN)framework for automatic recognition and classification of various biotic and abiotic paddy crop stresses using the field images.Thework has adopted the pre-trained VGG-16 CNN model for the automatic classification of stressed paddy crop images captured during the booting growth stage.The trained models achieve an average accuracy of 92.89%on the held-out dataset,demonstrating the technical feasibility of using the deep learning approach utilizing 30,000 field images of 5 different paddy crop varieties with 12 different stress categories(including healthy/normal).The proposed work finds applications in developing the decision support systems and mobile applications for automating the field crop and resource management practices.

Stress responses of plants through transcriptome plasticity by mRNA alternative polyadenylation

The sessile nature of plants confines their responsiveness to changing environmental conditions.Gene expression regulation becomes a paramount mechanism for plants to adjust their physiological and morphological behaviors.Alternative polyadenylation(APA)is known for its capacity to augment transcriptome diversity and plasticity,thereby furnishing an additional set of tools for modulating gene expression.APA has also been demonstrated to exhibit intimate associations with plant stress responses.In this study,we review APA dynamic features and consequences in plants subjected to both biotic and abiotic stresses.These stresses include adverse environmental stresses,and pathogenic attacks,such as cadmium toxicity,high salt,hypoxia,oxidative stress,cold,heat shock,along with bacterial,fungal,and viral infections.We analyzed the overarching research framework employed to elucidate plant APA response and the alignment of polyadenylation site transitions with the modulation of gene expression levels within the ambit of each stress condition.We also proposed a general APA model where transacting factors,including poly(A)factors,epigenetic regulators,RNA m6A modification factors,and phase separation proteins,assume pivotal roles in APA related transcriptome plasticity during stress response in plants.

High-Throughput Phenotyping:A Platform to Accelerate Crop Improvement

Development of high-throughput phenotyping technologies has progressed considerably in the last 10 years.These technologies provide precise measurements of desired traits among thousands of field-grown plants under diversified environments;this is a critical step towards selection of better performing lines as to yield,disease resistance,and stress tolerance to accelerate crop improvement programs.High-throughput phenotyping techniques and platforms help unrave-ling the genetic basis of complex traits associated with plant growth and development and targeted traits.This review focuses on the advancements in technologies involved in high-throughput,field-based,aerial,and unmanned platforms.Development of user-friendly data management tools and softwares to better understand phenotyping will increase the use of field-based high-throughput techniques,which have potential to revolutionize breeding strategies and meet the future needs of stakeholders.