Plant Science Research Research Articles

Novel adsorption material for solid phase extraction in sample pretreatment of plant hormones

植物激素在植物生长过程中具有重要作用,调节植物生长、发育及抗逆的各个过程。植物激素超微精准定量分析一直是植物生理学研究的瓶颈问题。植物激素的准确、高效检测目前大多是基于液相色谱-串联质谱联用技术。样品前处理是植物激素色谱-质谱分析中必不可少的一个步骤,直接影响后续检测方法的灵敏度和准确性。在植物激素各种前处理方法中,固相萃取(SPE)技术应用非常广泛。在萃取小柱基础上发展了多种新形式(分散固相萃取、磁性固相萃取、固相微萃取等,称之为SPE相关方法)。在上述SPE相关方法中,吸附材料的选择均是关键因素,决定了样品前处理过程的目标物提取、净化和富集效果。碳基材料(包括碳纳米管、石墨烯、碳氮化合物等)和有机骨架材料(包括金属有机骨架、共价有机材料)拥有结构可设计、比表面积大、稳定性良好等特性,非常适合作为吸附材料。分子印迹聚合物和超分子化合物依靠主-客体特异性分子识别作用,能显著提高样品前处理方法的选择性。本文重点针对植物激素样品前处理中的SPE技术,综述了近5年来上述几类功能化吸附材料的最新应用进展,并对其发展趋势进行展望。

Recent Advances of Ambient Mass Spectrometry Imaging and Its Applications in Lipid and Metabolite Analysis.

Ambient mass spectrometry imaging (AMSI) has attracted much attention in recent years. As a kind of unlabeled molecular imaging technique, AMSI can enable in situ visualization of a large number of compounds in biological tissue sections in ambient conditions. In this review, the developments of various AMSI techniques are discussed according to one-step and two-step ionization strategies. In addition, recent applications of AMSI for lipid and metabolite analysis (from 2016 to 2021) in disease diagnosis, animal model research, plant science, drug metabolism and toxicology research, etc., are summarized. Finally, further perspectives of AMSI in spatial resolution, sensitivity, quantitative ability, convenience and software development are proposed.

Application of Artificial Intelligence (AI) in Plant Sciences Research

The current world needs to use modern technologies in plant science research due to increasing food requirements, and to cope up with global climate changes (1). Consequently, Artificial Intelligence (AI) based technologies are considered as the most recent and advanced research tools in every sector of science. Biological researches are also making progress surprisingly due to the assistance of AI affiliated tools directly or indirectly. Nowadays, plant physiology to genomic manipulation has witnessed, how it is easier and accurate than previous (2). Moreover, continuous monitoring of plants' growth, growing media, and relevant surroundings were time and labor-consuming in the past; even, the obtained results were erroneous somewhat. Data processing and analysis were based on individual performances of the participated researchers; surprisingly, the scenarios have been changed after the emergence of AI (3).

Electrochemistry of plants: basic theoretical research and applications in plant science

Electrochemistry of plants: basic theoretical research and applications in plant science

Biosynthesis and Characterization of ZnO Nanoparticles Using Ochradenus arabicus and Their Effect on Growth and Antioxidant Systems of Maerua oblongifolia.

Zincoxide nanoparticles (ZnO NPs) are among the most produced and used nanomaterials worldwide, and in recent times these nanoparticles have also been incorporate in plant science and agricultural research. The present study was planned to synthesize ZnO NPs biologically using Ochradenus arabicus leaves and examine their effect on the morphology and physiology properties of Maerua oblongifolia cultured in vitro. ZnO NPs were characterized by UV–visible spectroscopy (UV–vis), X-ray diffractometer (XRD), Fourier transform infrared spectroscopy (FT-IR), and transmission electron microscopy, which demonstrated hexagonal shape nanoparticles of size ranging from 10 to 50 nm. Thus, the study uncovered an efficient, eco-friendly and simple technique for biosynthesis of multifunctional ZnO NPs using Ochradenus arabicus following growth of Maerua oblongifolia shoots in different concentrations of ZnO NPs (0, 1.25, 2.5, 5, 10, or 20 mg L−1) in Murashige and Skoog medium. Remarkable increases in plant biomass, photosynthetic pigments, and total protein were recorded up to a concentration of 5 mg L−1; at the same time, the results demonstrated a significant reduction in lipid peroxidation levels with respect to control. Interestingly, the levels of proline and the antioxidant enzyme catalase (CAT), superoxide dismutase (SOD), and glutathione reductase (GR) activities were increased significantly in response to all ZnO NP treatments. These findings indicate that bioengineered ZnO NPs play a major role in accumulation of biomass and stimulating the activities of antioxidant enzymes in plant tissues. Thus, green-synthesized ZnO NPs might be of agricultural and medicinal benefit owing to their impacts on plants in vitro.

Detection of genome edits in plants\u2014from editing to seed

Genome editing (also known as gene editing) employs a range of tools such as Meganucleases, Zinc Finger Nucleases, TALENs, and more recently CRISPR to make defined changes in genes, regulatory sequences, untranslated regions, or intergenic regions. It is increasingly being applied in plant science research and to improve plant varieties. The benefits of having effective detection tools begin with optimization of the genome editing process itself and continue with selection and characterization of tissue cultures and/or regenerated plants. Detection tools are also used throughout the breeding process, and for preparation of regulatory dossiers when required, as well as for seed production, and may be necessary for monitoring products in the marketplace. Detection and identification of genome edits employs a wide range of analytical approaches including PCR, digital PCR, and sequencing methods. This article examines the applicability of each category of detection or identification approach, from the optimization of genome editing processes, through creation of edits, selection and characterization, and breeding. The challenges surrounding the detection of genome edits present at low levels in large seed, plant, or grain populations and of differentiating directed genome edits from conventional mutations are also explained.

Metabolomics-Guided Elucidation of Plant Abiotic Stress Responses in the 4IR Era: An Overview.

Plants are constantly challenged by changing environmental conditions that include abiotic stresses. These are limiting their development and productivity and are subsequently threatening our food security, especially when considering the pressure of the increasing global population. Thus, there is an urgent need for the next generation of crops with high productivity and resilience to climate change. The dawn of a new era characterized by the emergence of fourth industrial revolution (4IR) technologies has redefined the ideological boundaries of research and applications in plant sciences. Recent technological advances and machine learning (ML)-based computational tools and omics data analysis approaches are allowing scientists to derive comprehensive metabolic descriptions and models for the target plant species under specific conditions. Such accurate metabolic descriptions are imperatively essential for devising a roadmap for the next generation of crops that are resilient to environmental deterioration. By synthesizing the recent literature and collating data on metabolomics studies on plant responses to abiotic stresses, in the context of the 4IR era, we point out the opportunities and challenges offered by omics science, analytical intelligence, computational tools and big data analytics. Specifically, we highlight technological advancements in (plant) metabolomics workflows and the use of machine learning and computational tools to decipher the dynamics in the chemical space that define plant responses to abiotic stress conditions.

Multispectral Imaging and Its Applications in Plant Science Research

Multispectral Imaging and Its Applications in Plant Science Research

Sir Leslie Fowden. 13 October 1925—16 December 2008

Professor Sir Leslie Fowden was an organic chemist and one of the most influential plant scientists of his generation. He pioneered the new field of phytochemistry and was the world authority on plant amino acids. He was distinguished for the identification, characterization and biological function of a new category of nitrogen containing molecules in plants, the non-protein amino acids. During his research career, he isolated at least 50 of these compounds from a wide range of different plant genera and established their structural diversity, metabolic and toxic functions, and their scientific significance. In achieving this, he developed novel techniques for the rapid, quantitative separation of compounds by chromatography and electrophoresis, which were widely adopted in other disciplines. As Director of Rothamsted Experimental Station, he opened up new areas of research in plant biochemistry and molecular biology, and became an effective voice for the importance of agricultural research in the UK. He provided strategic leadership in the translational application of plant biochemistry to the agrichemical industry and to global crop resistance and production. His discoveries established the complexity and central role of plant nitrogen metabolism in plant growth and laid the foundations for current research in plant sciences aimed at improving both food security and the nutritional value of plants.

Development of a Raspberry Pi-Based Sensor System for Automated In-Field Monitoring to Support Crop Breeding Programs

Sensor applications for plant phenotyping can advance and strengthen crop breeding programs. One of the powerful sensing options is the automated sensor system, which can be customized and applied for plant science research. The system can provide high spatial and temporal resolution data to delineate crop interaction with weather changes in a diverse environment. Such a system can be integrated with the internet to enable the internet of things (IoT)-based sensor system development for real-time crop monitoring and management. In this study, the Raspberry Pi-based sensor (imaging) system was fabricated and integrated with a microclimate sensor to evaluate crop growth in a spring wheat breeding trial for automated phenotyping applications. Such an in-field sensor system will increase the reproducibility of measurements and improve the selection efficiency by investigating dynamic crop responses as well as identifying key growth stages (e.g., heading), assisting in the development of high-performing crop varieties. In the low-cost system developed here-in, a Raspberry Pi computer and multiple cameras (RGB and multispectral) were the main components. The system was programmed to automatically capture and manage the crop image data at user-defined time points throughout the season. The acquired images were suitable for extracting quantifiable plant traits, and the images were automatically processed through a Python script (an open-source programming language) to extract vegetation indices, representing crop growth and overall health. Ongoing efforts are conducted towards integrating the sensor system for real-time data monitoring via the internet that will allow plant breeders to monitor multiple trials for timely crop management and decision making.

Imaging Mass Cytometry: A promising multiplex detection tool for plant science research

In recent years there has been an increased tendency to shift from single-marker immunohistochemistry toward multiplexed marker in situ localization. The latter approach allows detection of the spatial distribution of multiple markers in a single tissue section and enables investigation of marker interaction, hence greatly improving our understanding of tissue physiology. This is critical when tissues are composed of multiple cell types or cell states, but equally important in instances where samples are precious and/or limited in size.

Distribution of endemic angiosperm species in Brazil on a municipality level.

BackgroundHerbarium collections and the data they hold are the main sources of plant biodiversity information. These collections contain taxonomical and spatial data on living and extinct species; consequently, they are the fundamental basis for temporal and spatial biogeographical studies of plants. Mega projects focused on providing digital and free access to accurate biodiversity data have transformed plant science research, mainly in the past two decades. In this sense, researchers today are overwhelmed by the many different datasets in online repositories. There are also several challenges involved in using these data for biogeographical analyses. Analyses performed on the data available in the repositories show that 70-75% of the total amount of data have spatial deficiencies and a high number of records lack coordinates. This shortage of reliable primary biogeographical information creates serious impediments for biogeographical analyses and conservation assessments and taxonomic revisions consequently produces obstacles for evaluations of threats to biodiversity at global, regional and local levels. With the aim of contributing to botanical and biogeographical research, this paper provides georeferenced spatial data for angiosperm species endemic to Brazil. The information from two reliable online databases, i.e. the Flora do Brasil 2020 floristic database (BFG) and Plantas do Brasil: Resgate Histórico e Herbário Virtual para o Conhecimento e Conservação da Flora Brasileira (REFLORA), which are both based on records collected over the course of the last two centuries, is used to create this spatial dataset.New informationWe provide three taxonomically-edited and georeferenced datasets for basal angiosperms, monocots and eudicots, covering a total of 14,992 endemic species from Brazil. Producing this consolidated dataset involved several months of detailed revision of coordinates and nomenclaturally updating of the names in these datasets. The information provided in this geo-referenced dataset, covering two centuries of specimen collections, will contribute to several botanical and mainly biogeographical studies.

Novel 3D Imaging Systems for High-Throughput Phenotyping of Plants

The use of 3D plant models for high-throughput phenotyping is increasingly becoming a preferred method for many plant science researchers. Numerous camera-based imaging systems and reconstruction algorithms have been developed for the 3D reconstruction of plants. However, it is still challenging to build an imaging system with high-quality results at a low cost. Useful comparative information for existing imaging systems and their improvements is also limited, making it challenging for researchers to make data-based selections. The objective of this study is to explore the possible solutions to address these issues. We introduce two novel systems for plants of various sizes, as well as a pipeline to generate high-quality 3D point clouds and meshes. The higher accuracy and efficiency of the proposed systems make it a potentially valuable tool for enhancing high-throughput phenotyping by integrating 3D traits for increased resolution and measuring traits that are not amenable to 2D imaging approaches. The study shows that the phenotype traits derived from the 3D models are highly correlated with manually measured phenotypic traits (R2 > 0.91). Moreover, we present a systematic analysis of different settings of the imaging systems and a comparison with the traditional system, which provide recommendations for plant scientists to improve the accuracy of 3D construction. In summary, our proposed imaging systems are suggested for 3D reconstruction of plants. Moreover, the analysis results of the different settings in this paper can be used for designing new customized imaging systems and improving their accuracy.

A Brief Early History of Plant Science in St. Louis and the Partnership between Washington University and the Missouri Botanical Garden

Since the founding of the Missouri Botanical Garden (MBG) in 1859, the emphasis on research and the distribution of research findings in botany has been, and will remain, one of the central components of the garden’s mission. Likewise, Washington University in St. Louis (WUSTL), the MBG’s partner in graduate programs since 1885, has had a continuous and similarly strong emphasis on research and the dissemination of research findings in plant science through publications. Since the beginning of this partnership, the ongoing extension of common research themes has been critical, through the early focus on traditional botanical studies (1885–1930) at the MBG, the move toward a focus on physiology and the emerging field of ecology (1930–1960), and eventually the shift to the study of biochemistry, molecular biology, and genomic studies in plant science (1960–present), primarily at WUSTL. For more than 135 years (1885–2020), this St. Louis–based collaboration has had a prominent place in the region’s rich history in plant science. In recent years, collaboration with and contributions from other St. Louis–area degree-granting institutions in the field (such as Saint Louis University [SLU] and the University of Missouri–St. Louis [UMSL]) have steadily increased. Couple this with the addition of the Donald Danforth Plant Science Center (Danforth Center) in 2000, which, like the MBG, has undertaken research and training in plant science, and you now have impressive depth and diversity within St. Louis’s plant science offerings. As a result, both organizations train students and carry out peer-reviewed research funded by the same agencies (i.e., National Institutes of Health, National Science Foundation, U.S. Department of Agriculture) as the region’s degree-granting institutions. Every year, a significant number of master’s degree and Ph.D. graduates in this consortium comprise an impressive pool of talent available for postdoctoral training, research, and teaching positions, as well as employment in government entities and private and public life science corporations. To this end, St. Louis has one of the largest concentrations of plant science Ph.D.’s in the world (with more than 1,000 such individuals residing in the region [BioSTL, 2018]), as well as a broad diversity of disciplines represented. In addition, the faculties at both the Danforth Center and MBG frequently serve as adjunct members of university departments and as advisors to graduate students, and greatly increase the breadth of topics offered in the St. Louis plant science community, particularly in areas not directly supported by the universities. Both organizations contribute to an increasingly important part of this ecosystem. Below is a short history of the relationship between the MBG and WUSTL, and how this collaboration, primarily through graduate research education, has been foundational for the St. Louis area’s impressive plant science ecosystem. This is not a detailed review of the science generated by these organizations, but rather an account of the initial events and leaders that led to the region becoming the present-day hub for plant science.

Fuzzy Tuned PID Controller for Envisioned Agricultural Manipulator

The implementation of image-based phenotyping systems has become an important aspect of crop and plant science research which has shown tremendous growth over the years. Accurate determination of features using images requires stable imaging and very precise processing. By installing a camera on a mechanical arm driven by motor, the maintenance of accuracy and stability becomes non-trivial. As per the state-of-the-art, the issue of external camera shake incurred due to vibration is a great concern in capturing accurate images, which may be induced by the driving motor of the manipulator. So, there is a requirement for a stable active controller for sufficient vibration attenuation of the manipulator. However, there are very few reports in agricultural practices which use control algorithms. Although, many control strategies have been utilized to control the vibration in manipulators associated to various applications, no control strategy with validated stability has been provided to control the vibration in such envisioned agricultural manipulator with simple low-cost hardware devices with the compensation of non-linearities. So, in this work, the combination of proportional-integral-differential (PID) control with type-2 fuzzy logic (T2-F-PID) is implemented for vibration control. The validation of the controller stability using Lyapunov analysis is established. A torsional actuator (TA) is applied for mitigating torsional vibration, which is a new contribution in the area of agricultural manipulators. Also, to prove the effectiveness of the controller, the vibration attenuation results with T2-F-PID is compared with conventional PD/PID controllers, and a type-1 fuzzy PID (T1-F-PID) controller.

Carl Ng.

Carl Ng.

RaspberryPi‐Arduino (RPA) powered smart mirrored and reconfigurable IoT facility for plant science research

Continuous monitoring of crops is critical for the sustainability of agriculture. The effects of changes in temperature, light intensity, humidity, pH, soil moisture, gas intensities, etc. have an overall impact on the plant growth. Growth chambers are environmental controlled facilities which needs to be monitored round‐the‐clock. To improve both the reproducibility, and maintenance of such facilities, remote monitoring plays a very pivotal role. An automated re‐configurable & persistent mirrored storage‐based remote monitoring system is developed with low‐cost open source hardwares and softwares. The system automates sensors deployment, storage (database, logs), and provides an elegant dashboard to visualize the real‐time continuous data stream. We propose a new smart AGRO IoT system with robust data acquisition mechanism, and also propose two software component nodes, (i.e., Mirroring and Reconfiguration) running as an instance of the whole IoT facility. The former one is aimed to minimize/avoid the downtime, while the latter one is aimed to leverage the available cores, and better utilization of the computational resources. Our system can be easily deployed in growth chambers, greenhouses, CNC farming test‐bed setup, cultivation plots, and further can be also extended to support large‐farms with either using multiple individual standalone setup as heterogeneous instances of this facility, or by extending it as master‐slave cluster configuration for communication as a single homogeneous instance. Our RaspberryPi‐Arduino (RPA) powered solution is scalable, and provides stability for monitoring any environment continuously at ease.

Plant Science and Intellectual Property Protections in Taiwan*

AbstractBeginning in 1980, and due to a number of significant court decisions, legislative initiatives and international economic agreements, there has been an increase in the importance and use of intellectual property mechanisms, such as patents, in protecting biological materials, including seeds and engineered crop traits. The trend originated in the U.S. but has since diffused to OECD and non‐OECD nations. A parallel but related set of policies designed to promote the patenting of university and federally funded research discoveries has also become evident in the U.S. and globally. In this paper, we discuss the trend toward more restrictive IP protections in agriculture through national‐level policies and international economic agreements especially for new plant varieties. We then present the case of plant science research in Taiwan, including interviews with 15 plant scientists, to illustrate how transnational trends can impact and interact with national level policies and practices. And, how research scientists can play critical roles in furthering or retarding the progression of the proprietary research model in plant science.

Jagadis Gupta Kapuganti

Jagadis Gupta Kapuganti

Emerging approaches to measure photosynthesis from the leaf to the ecosystem.

Measuring photosynthesis is critical for quantifying and modeling leaf to regional scale productivity of managed and natural ecosystems. This review explores existing and novel advances in photosynthesis measurements that are certain to provide innovative directions in plant science research. First, we address gas exchange approaches from leaf to ecosystem scales. Leaf level gas exchange is a mature method but recent improvements to the user interface and environmental controls of commercial systems have resulted in faster and higher quality data collection. Canopy chamber and micrometeorological methods have also become more standardized tools and have an advanced understanding of ecosystem functioning under a changing environment and through long time series data coupled with community data sharing. Second, we review proximal and remote sensing approaches to measure photosynthesis, including hyperspectral reflectance- and fluorescence-based techniques. These techniques have long been used with aircraft and orbiting satellites, but lower-cost sensors and improved statistical analyses are allowing these techniques to become applicable at smaller scales to quantify changes in the underlying biochemistry of photosynthesis. Within the past decade measurements of chlorophyll fluorescence from earth-orbiting satellites have measured Solar Induced Fluorescence (SIF) enabling estimates of global ecosystem productivity. Finally, we highlight that stronger interactions of scientists across disciplines will benefit our capacity to accurately estimate productivity at regional and global scales. Applying the multiple techniques outlined in this review at scales from the leaf to the globe are likely to advance understanding of plant functioning from the organelle to the ecosystem.