Journal articles on the topic 'High-throughput shoot phenotyping'
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1
Wu, Sheng, Weiliang Wen, Yongjian Wang, Jiangchuan Fan, Chuanyu Wang, Wenbo Gou, and Xinyu Guo. "MVS-Pheno: A Portable and Low-Cost Phenotyping Platform for Maize Shoots Using Multiview Stereo 3D Reconstruction." Plant Phenomics 2020 (March 12, 2020): 1–17. http://dx.doi.org/10.34133/2020/1848437.
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Plant phenotyping technologies play important roles in plant research and agriculture. Detailed phenotypes of individual plants can guide the optimization of shoot architecture for plant breeding and are useful to analyze the morphological differences in response to environments for crop cultivation. Accordingly, high-throughput phenotyping technologies for individual plants grown in field conditions are urgently needed, and MVS-Pheno, a portable and low-cost phenotyping platform for individual plants, was developed. The platform is composed of four major components: a semiautomatic multiview stereo (MVS) image acquisition device, a data acquisition console, data processing and phenotype extraction software for maize shoots, and a data management system. The platform’s device is detachable and adjustable according to the size of the target shoot. Image sequences for each maize shoot can be captured within 60-120 seconds, yielding 3D point clouds of shoots are reconstructed using MVS-based commercial software, and the phenotypic traits at the organ and individual plant levels are then extracted by the software. The correlation coefficient (R2) between the extracted and manually measured plant height, leaf width, and leaf area values are 0.99, 0.87, and 0.93, respectively. A data management system has also been developed to store and manage the acquired raw data, reconstructed point clouds, agronomic information, and resulting phenotypic traits. The platform offers an optional solution for high-throughput phenotyping of field-grown plants, which is especially useful for large populations or experiments across many different ecological regions.
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Joshi, Sameer, Emily Thoday-Kennedy, Hans D. Daetwyler, Matthew Hayden, German Spangenberg, and Surya Kant. "High-throughput phenotyping to dissect genotypic differences in safflower for drought tolerance." PLOS ONE 16, no. 7 (July 23, 2021): e0254908. http://dx.doi.org/10.1371/journal.pone.0254908.
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Drought is one of the most severe and unpredictable abiotic stresses, occurring at any growth stage and affecting crop yields worldwide. Therefore, it is essential to develop drought tolerant varieties to ensure sustainable crop production in an ever-changing climate. High-throughput digital phenotyping technologies in tandem with robust screening methods enable precise and faster selection of genotypes for breeding. To investigate the use of digital imaging to reliably phenotype for drought tolerance, a genetically diverse safflower population was screened under different drought stresses at Agriculture Victoria’s high-throughput, automated phenotyping platform, Plant Phenomics Victoria, Horsham. In the first experiment, four treatments, control (90% field capacity; FC), 40% FC at initial branching, 40% FC at flowering and 50% FC at initial branching and flowering, were applied to assess the performance of four safflower genotypes. Based on these results, drought stress using 50% FC at initial branching and flowering stages was chosen to further screen 200 diverse safflower genotypes. Measured plant traits and dry biomass showed high correlations with derived digital traits including estimated shoot biomass, convex hull area, caliper length and minimum area rectangle, indicating the viability of using digital traits as proxy measures for plant growth. Estimated shoot biomass showed close association having moderately high correlation with drought indices yield index, stress tolerance index, geometric mean productivity, and mean productivity. Diverse genotypes were classified into four clusters of drought tolerance based on their performance (seed yield and digitally estimated shoot biomass) under stress. Overall, results show that rapid and precise image-based, high-throughput phenotyping in controlled environments can be used to effectively differentiate response to drought stress in a large numbers of safflower genotypes.
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Li, Yinglun, Weiliang Wen, Xinyu Guo, Zetao Yu, Shenghao Gu, Haipeng Yan, and Chunjiang Zhao. "High-throughput phenotyping analysis of maize at the seedling stage using end-to-end segmentation network." PLOS ONE 16, no. 1 (January 12, 2021): e0241528. http://dx.doi.org/10.1371/journal.pone.0241528.
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Image processing technologies are available for high-throughput acquisition and analysis of phenotypes for crop populations, which is of great significance for crop growth monitoring, evaluation of seedling condition, and cultivation management. However, existing methods rely on empirical segmentation thresholds, thus can have insufficient accuracy of extracted phenotypes. Taking maize as an example crop, we propose a phenotype extraction approach from top-view images at the seedling stage. An end-to-end segmentation network, named PlantU-net, which uses a small amount of training data, was explored to realize automatic segmentation of top-view images of a maize population at the seedling stage. Morphological and color related phenotypes were automatic extracted, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle. The results show that the approach can segment the shoots at the seedling stage from top-view images, obtained either from the UAV or tractor-based high-throughput phenotyping platform. The average segmentation accuracy, recall rate, and F1 score are 0.96, 0.98, and 0.97, respectively. The extracted phenotypes, including maize shoot coverage, circumscribed radius, aspect ratio, and plant azimuth plane angle, are highly correlated with manual measurements (R2 = 0.96–0.99). This approach requires less training data and thus has better expansibility. It provides practical means for high-throughput phenotyping analysis of early growth stage crop populations.
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Banerjee, Bikram P., Sameer Joshi, Emily Thoday-Kennedy, Raj K. Pasam, Josquin Tibbits, Matthew Hayden, German Spangenberg, and Surya Kant. "High-throughput phenotyping using digital and hyperspectral imaging-derived biomarkers for genotypic nitrogen response." Journal of Experimental Botany 71, no. 15 (March 18, 2020): 4604–15. http://dx.doi.org/10.1093/jxb/eraa143.
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Abstract The development of crop varieties with higher nitrogen use efficiency is crucial for sustainable crop production. Combining high-throughput genotyping and phenotyping will expedite the discovery of novel alleles for breeding crop varieties with higher nitrogen use efficiency. Digital and hyperspectral imaging techniques can efficiently evaluate the growth, biophysical, and biochemical performance of plant populations by quantifying canopy reflectance response. Here, these techniques were used to derive automated phenotyping of indicator biomarkers, biomass and chlorophyll levels, corresponding to different nitrogen levels. A detailed description of digital and hyperspectral imaging and the associated challenges and required considerations are provided, with application to delineate the nitrogen response in wheat. Computational approaches for spectrum calibration and rectification, plant area detection, and derivation of vegetation index analysis are presented. We developed a novel vegetation index with higher precision to estimate chlorophyll levels, underpinned by an image-processing algorithm that effectively removed background spectra. Digital shoot biomass and growth parameters were derived, enabling the efficient phenotyping of wheat plants at the vegetative stage, obviating the need for phenotyping until maturity. Overall, our results suggest value in the integration of high-throughput digital and spectral phenomics for rapid screening of large wheat populations for nitrogen response.
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Aharon, Shlomi, Zvi Peleg, Eli Argaman, Roi Ben-David, and Ran N. Lati. "Image-Based High-Throughput Phenotyping of Cereals Early Vigor and Weed-Competitiveness Traits." Remote Sensing 12, no. 23 (November 26, 2020): 3877. http://dx.doi.org/10.3390/rs12233877.
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Cereals grains are the prime component of the human diet worldwide. To promote food security and sustainability, new approaches to non-chemical weed control are needed. Early vigor cultivars with enhanced weed-competitiveness ability are a potential tool, nonetheless, the introduction of such trait in breeding may be a long and labor-intensive process. Here, two image-driven plant phenotyping methods were evaluated to facilitate effective and accurate selection for early vigor in cereals. For that purpose, two triticale genotypes differentiating in vigor and growth rate early in the season were selected as model plants: X-1010 (high) and Triticale1 (low). Two modeling approaches, 2-D and 3-D, were applied on the plants offering an evaluation of various morphological growth parameters for the triticale canopy development, under controlled and field conditions. The morphological advantage of X-1010 was observed only at the initial growth stages, which was reflected by significantly higher growth parameter values compared to the Triticale1 genotype. Both modeling approaches were sensitive enough to detect phenotypic differences in growth as early as 21 days after sowing. All growth parameters indicated a faster early growth of X-1010. However, the 2-D related parameter [projected shoot area (PSA)] is the most available one that can be extracted via end user-friendly imaging equipment. PSA provided adequate indication for the triticale early growth under weed-competition conditions and for the improved weed-competition ability. The adequate phenotyping ability for early growth and competition was robust under controlled and field conditions. PSA can be extracted from close and remote sensing platforms, thus, facilitate high throughput screening. Overall, the results of this study may improve cereal breeding for early vigor and weed-competitiveness.
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Momen, Mehdi, Malachy T. Campbell, Harkamal Walia, and Gota Morota. "Predicting Longitudinal Traits Derived from High-Throughput Phenomics in Contrasting Environments Using Genomic Legendre Polynomials and B-Splines." G3: Genes|Genomes|Genetics 9, no. 10 (August 19, 2019): 3369–80. http://dx.doi.org/10.1534/g3.119.400346.
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Recent advancements in phenomics coupled with increased output from sequencing technologies can create the platform needed to rapidly increase abiotic stress tolerance of crops, which increasingly face productivity challenges due to climate change. In particular, high-throughput phenotyping (HTP) enables researchers to generate large-scale data with temporal resolution. Recently, a random regression model (RRM) was used to model a longitudinal rice projected shoot area (PSA) dataset in an optimal growth environment. However, the utility of RRM is still unknown for phenotypic trajectories obtained from stress environments. Here, we sought to apply RRM to forecast the rice PSA in control and water-limited conditions under various longitudinal cross-validation scenarios. To this end, genomic Legendre polynomials and B-spline basis functions were used to capture PSA trajectories. Prediction accuracy declined slightly for the water-limited plants compared to control plants. Overall, RRM delivered reasonable prediction performance and yielded better prediction than the baseline multi-trait model. The difference between the results obtained using Legendre polynomials and that using B-splines was small; however, the former yielded a higher prediction accuracy. Prediction accuracy for forecasting the last five time points was highest when the entire trajectory from earlier growth stages was used to train the basis functions. Our results suggested that it was possible to decrease phenotyping frequency by only phenotyping every other day in order to reduce costs while minimizing the loss of prediction accuracy. This is the first study showing that RRM could be used to model changes in growth over time under abiotic stress conditions.
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Watts-Williams, S. J., N. Jewell, C. Brien, B. Berger, T. Garnett, and T. R. Cavagnaro. "Using High-Throughput Phenotyping to Explore Growth Responses to Mycorrhizal Fungi and Zinc in Three Plant Species." Plant Phenomics 2019 (March 25, 2019): 1–12. http://dx.doi.org/10.34133/2019/5893953.
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There are many reported benefits to plants of arbuscular mycorrhizal fungi (AMF), including positive plant biomass responses; however, AMF can also induce biomass depressions in plants, and this response receives little attention in the literature. High-throughput phenotyping (HTP) technology permits repeated measures of an individual plant’s aboveground biomass. We examined the effect on AMF inoculation on the shoot biomass of three contrasting plant species: a vegetable crop (tomato), a cereal crop (barley), and a pasture legume (Medicago). We also considered the interaction of mycorrhizal growth responses with plant-available soil zinc (Zn) and phosphorus (P) concentrations. The appearance of a depression in shoot biomass due to inoculation with AMF occurred at different times for each plant species; depressions appeared earliest in tomato, then Medicago, and then barley. The usually positive-responding Medicago plants were not responsive at the high level of soil available P used. Mycorrhizal growth responsiveness in all three species was also highly interactive with soil Zn supply; tomato growth responded negatively to AMF inoculation in all soil Zn treatments except the toxic soil Zn treatment, where it responded positively. Our results illustrate how context-dependent mycorrhizal growth responses are and the value of HTP approaches to exploring the complexity of mycorrhizal responses.
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Watts-Williams, S. J., N. Jewell, C. Brien, B. Berger, T. Garnett, and T. R. Cavagnaro. "Using High-Throughput Phenotyping to Explore Growth Responses to Mycorrhizal Fungi and Zinc in Three Plant Species." Plant Phenomics 2019 (March 25, 2019): 1–12. http://dx.doi.org/10.1155/2019/5893953.
Full textAbstract:
There are many reported benefits to plants of arbuscular mycorrhizal fungi (AMF), including positive plant biomass responses; however, AMF can also induce biomass depressions in plants, and this response receives little attention in the literature. High-throughput phenotyping (HTP) technology permits repeated measures of an individual plant’s aboveground biomass. We examined the effect on AMF inoculation on the shoot biomass of three contrasting plant species: a vegetable crop (tomato), a cereal crop (barley), and a pasture legume (Medicago). We also considered the interaction of mycorrhizal growth responses with plant-available soil zinc (Zn) and phosphorus (P) concentrations. The appearance of a depression in shoot biomass due to inoculation with AMF occurred at different times for each plant species; depressions appeared earliest in tomato, then Medicago, and then barley. The usually positive-responding Medicago plants were not responsive at the high level of soil available P used. Mycorrhizal growth responsiveness in all three species was also highly interactive with soil Zn supply; tomato growth responded negatively to AMF inoculation in all soil Zn treatments except the toxic soil Zn treatment, where it responded positively. Our results illustrate how context-dependent mycorrhizal growth responses are and the value of HTP approaches to exploring the complexity of mycorrhizal responses.
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Dambreville, Anaëlle, Mélanie Griolet, Gaëlle Rolland, Myriam Dauzat, Alexis Bédiée, Crispulo Balsera, Bertrand Muller, Denis Vile, and Christine Granier. "Phenotyping oilseed rape growth-related traits and their responses to water deficit: the disturbing pot size effect." Functional Plant Biology 44, no. 1 (2017): 35. http://dx.doi.org/10.1071/fp16036.
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Following the recent development of high-throughput phenotyping platforms for plant research, the number of individual plants grown together in a same experiment has raised, sometimes at the expense of pot size. However, root restriction in excessively small pots affects plant growth and carbon partitioning, and may interact with other stresses targeted in these experiments. In work reported here, we investigated the interactive effects of pot size and soil water deficit on multiple growth-related traits from the cellular to the whole-plant scale in oilseed rape (Brassica napus L.). The effects of pot size on responses to water deficit and allometric relationships revealed strong, multilevel interactions between pot size and watering regime. Notably, water deficit increased the root : shoot ratio in large pots, but not in small pots. At the cellular scale, water deficit decreased epidermal leaf cell area in large pots, but not in small pots. These results were consistent with changes in the level of endoreduplication factor in leaf cells. Our study illustrates the disturbing interaction of pot size with water deficit and raises the need to carefully consider this factor in the frame of the current development of high-throughput phenotyping experiments.
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Yasrab, Robail, Jincheng Zhang, Polina Smyth, and Michael P. Pound. "Predicting Plant Growth from Time-Series Data Using Deep Learning." Remote Sensing 13, no. 3 (January 20, 2021): 331. http://dx.doi.org/10.3390/rs13030331.
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Phenotyping involves the quantitative assessment of the anatomical, biochemical, and physiological plant traits. Natural plant growth cycles can be extremely slow, hindering the experimental processes of phenotyping. Deep learning offers a great deal of support for automating and addressing key plant phenotyping research issues. Machine learning-based high-throughput phenotyping is a potential solution to the phenotyping bottleneck, promising to accelerate the experimental cycles within phenomic research. This research presents a study of deep networks’ potential to predict plants’ expected growth, by generating segmentation masks of root and shoot systems into the future. We adapt an existing generative adversarial predictive network into this new domain. The results show an efficient plant leaf and root segmentation network that provides predictive segmentation of what a leaf and root system will look like at a future time, based on time-series data of plant growth. We present benchmark results on two public datasets of Arabidopsis (A. thaliana) and Brassica rapa (Komatsuna) plants. The experimental results show strong performance, and the capability of proposed methods to match expert annotation. The proposed method is highly adaptable, trainable (transfer learning/domain adaptation) on different plant species and mutations.
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Campbell, Malachy T., Alexandre Grondin, Harkamal Walia, and Gota Morota. "Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice." Journal of Experimental Botany 71, no. 18 (June 12, 2020): 5669–79. http://dx.doi.org/10.1093/jxb/eraa280.
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Abstract Elucidating genotype-by-environment interactions and partitioning its contribution to phenotypic variation remains a challenge for plant scientists. We propose a framework that utilizes genome-wide markers to model genotype-specific shoot growth trajectories as a function of time and soil water availability. A rice diversity panel was phenotyped daily for 21 d using an automated, high-throughput image-based, phenotyping platform that enabled estimation of daily shoot biomass and soil water content. Using these data, we modeled shoot growth as a function of time and soil water content, and were able to determine the time point where an inflection in the growth trajectory occurred. We found that larger, more vigorous plants exhibited an earlier repression in growth compared with smaller, slow-growing plants, indicating a trade-off between early vigor and tolerance to prolonged water deficits. Genomic inference for model parameters and time of inflection (TOI) identified several candidate genes. This study is the first to utilize a genome-enabled growth model to study drought responses in rice, and presents a new approach to jointly model dynamic morpho-physiological responses and environmental covariates.
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Jiménez, Juan de la Cruz, Luisa Leiva, Juan A. Cardoso, Andrew N. French, and Kelly R. Thorp. "Proximal sensing of Urochloa grasses increases selection accuracy." Crop and Pasture Science 71, no. 4 (2020): 401. http://dx.doi.org/10.1071/cp19324.
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In the American tropics, livestock production is highly restricted by forage availability. In addition, the breeding and development of new forage varieties with outstanding yield and high nutritional quality is often limited by a lack of resources and poor technology. Non-destructive, high-throughput phenotyping offers a rapid and economical means of evaluating large numbers of genotypes. In this study, visual assessments, digital colour images, and spectral reflectance data were collected from 200 Urochloa hybrids in a field setting. Partial least-squares regression (PLSR) was applied to relate visual assessments, digital image analysis and spectral data to shoot dry weight, crude protein and chlorophyll concentrations. Visual evaluations of biomass and greenness were collected in 68 min, digital colour imaging data in 40 min, and hyperspectral canopy data in 80 min. Root-mean-squared errors of prediction for PLSR estimations of shoot dry weight, crude protein and chlorophyll were lowest for digital image analysis followed by hyperspectral analysis and visual assessments. This study showed that digital colour image and spectral analysis techniques have the potential to improve precision and reduce time for tropical forage grass phenotyping.
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Shi, Rongli, Astrid Junker, Christiane Seiler, and Thomas Altmann. "Phenotyping roots in darkness: disturbance-free root imaging with near infrared illumination." Functional Plant Biology 45, no. 4 (2018): 400. http://dx.doi.org/10.1071/fp17262.
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Root systems architecture (RSA) and size properties are essential determinants of plant performance and need to be assessed in high-throughput plant phenotyping platforms. Thus, we tested a concept that involves near-infrared (NIR) imaging of roots growing along surfaces of transparent culture vessels using special long pass filters to block their exposure to visible light. Two setups were used to monitor growth of Arabidopsis, rapeseed, barley and maize roots upon exposure to white light, filter-transmitted radiation or darkness: root growth direction was analysed (1) through short-term cultivation on agar plates, and (2) using soil-filled transparent pots to monitor long-term responses. White light-triggered phototropic responses were detected for Arabidopsis in setup 1, and for rapeseed, barley and maize roots in setups 1 and 2, whereas light effects could be avoided by use of the NIR filter thus confirming its suitability to mimic darkness. NIR image-derived ‘root volume’ values correlated well with root dry weight. The root system fractions visible at the different pot sides and in different zones revealed species- and genotype-dependent variation of spatial root distribution and other RSA traits. Following this validated concept, root imaging setups may be integrated into shoot phenotyping facilities in order to enable root system analysis in the context of whole-plant performance investigations.
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Nagel, Kerstin A., Alexander Putz, Frank Gilmer, Kathrin Heinz, Andreas Fischbach, Johannes Pfeifer, Marc Faget, et al. "GROWSCREEN-Rhizo is a novel phenotyping robot enabling simultaneous measurements of root and shoot growth for plants grown in soil-filled rhizotrons." Functional Plant Biology 39, no. 11 (2012): 891. http://dx.doi.org/10.1071/fp12023.
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Root systems play an essential role in ensuring plant productivity. Experiments conducted in controlled environments and simulation models suggest that root geometry and responses of root architecture to environmental factors should be studied as a priority. However, compared with aboveground plant organs, roots are not easily accessible by non-invasive analyses and field research is still based almost completely on manual, destructive methods. Contributing to reducing the gap between laboratory and field experiments, we present a novel phenotyping system (GROWSCREEN-Rhizo), which is capable of automatically imaging roots and shoots of plants grown in soil-filled rhizotrons (up to a volume of ~18 L) with a throughput of 60 rhizotrons per hour. Analysis of plants grown in this setup is restricted to a certain plant size (up to a shoot height of 80 cm and root-system depth of 90 cm). We performed validation experiments using six different species and for barley and maize, we studied the effect of moderate soil compaction, which is a relevant factor in the field. First, we found that the portion of root systems that is visible through the rhizotrons’ transparent plate is representative of the total root system. The percentage of visible roots decreases with increasing average root diameter of the plant species studied and depends, to some extent, on environmental conditions. Second, we could measure relatively minor changes in root-system architecture induced by a moderate increase in soil compaction. Taken together, these findings demonstrate the good potential of this methodology to characterise root geometry and temporal growth responses with relatively high spatial accuracy and resolution for both monocotyledonous and dicotyledonous species. Our prototype will allow the design of high-throughput screening methodologies simulating environmental scenarios that are relevant in the field and will support breeding efforts towards improved resource use efficiency and stability of crop yields.
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Nehe, A. S., M. J. Foulkes, I. Ozturk, A. Rasheed, L. York, S. C. Kefauver, F. Ozdemir, and A. Morgounov. "Root and canopy traits and adaptability genes explain drought tolerance responses in winter wheat." PLOS ONE 16, no. 4 (April 5, 2021): e0242472. http://dx.doi.org/10.1371/journal.pone.0242472.
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Bread wheat (Triticum aestivum L) is one of the three main staple crops worldwide contributing 20% calories in the human diet. Drought stress is the main factor limiting yields and threatening food security, with climate change resulting in more frequent and intense drought. Developing drought-tolerant wheat cultivars is a promising way forward. The use of holistic approaches that include high-throughput phenotyping and genetic markers in selection could help in accelerating genetic gains. Fifty advanced breeding lines were selected from the CIMMYT Turkey winter wheat breeding program and studied under irrigated and semiarid conditions in two years. High-throughput phenotyping was done for wheat crown root traits and canopy senescence dynamics using vegetation indices (green area using RGB images and Normalized Difference Vegetation Index using spectral reflectance). In addition, genotyping by KASP markers for adaptability genes was done. Overall, under semiarid conditions yield reduced by 3.09 t ha-1 (-46.8%) compared to irrigated conditions. Genotypes responded differently under drought stress and genotypes 39 (VORONA/HD24-12//GUN/7/VEE#8//…/8/ALTAY), 18 (BiII98) and 29 (NIKIFOR//KROSHKA) were the most drought tolerant. Root traits including shallow nodal root angle under irrigated conditions and root number per shoot under semiarid conditions were correlated with increased grain yield. RGB based vegetation index measuring canopy green area at anthesis was better correlated with GY than NDVI was with GY under drought. The markers for five established functional genes (PRR73.A1 –flowering time, TEF-7A –grain size and weight, TaCwi.4A - yield under drought, Dreb1- drought tolerance, and ISBW11.GY.QTL.CANDIDATE- grain yield) were associated with different drought-tolerance traits in this experiment. We conclude that–genotypes 39, 18 and 29 could be used for drought tolerance breeding. The trait combinations of canopy green area at anthesis, and root number per shoot along with key drought adaptability makers (TaCwi.4A and Dreb1) could be used in screening drought tolerance wheat breeding lines.
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Gano, Boubacar, Joseph Sékou B. Dembele, Adama Ndour, Delphine Luquet, Gregory Beurier, Diaga Diouf, and Alain Audebert. "Using UAV Borne, Multi-Spectral Imaging for the Field Phenotyping of Shoot Biomass, Leaf Area Index and Height of West African Sorghum Varieties under Two Contrasted Water Conditions." Agronomy 11, no. 5 (April 27, 2021): 850. http://dx.doi.org/10.3390/agronomy11050850.
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Meeting food demand for the growing population will require an increase to crop production despite climate changes and, more particularly, severe drought episodes. Sorghum is one of the cereals most adapted to drought that feed millions of people around the world. Valorizing its genetic diversity for crop improvement can benefit from extensive phenotyping. The current methods to evaluate plant biomass, leaves area and plants height involve destructive sampling and are not practical in breeding. Phenotyping relying on drone based imagery is a powerful approach in this context. The objective of this study was to develop and validate a high throughput field phenotyping method of sorghum growth traits under contrasted water conditions relying on drone based imagery. Experiments were conducted in Bambey (Senegal) in 2018 and 2019, to test the ability of multi-spectral sensing technologies on-board a UAV platform to calculate various vegetation indices to estimate plants characteristics. In total, ten (10) contrasted varieties of West African sorghum collection were selected and arranged in a randomized complete block design with three (3) replicates and two (2) water treatments (well-watered and drought stress). This study focused on plant biomass, leaf area index (LAI) and the plant height that were measured weekly from emergence to maturity. Drone flights were performed just before each destructive sampling and images were taken by multi-spectral and visible cameras. UAV-derived vegetation indices exhibited their capacity of estimating LAI and biomass in the 2018 calibration data set, in particular: normalized difference vegetative index (NDVI), corrected transformed vegetation index (CTVI), seconded modified soil-adjusted vegetation index (MSAVI2), green normalize difference vegetation index (GNDVI), and simple ratio (SR) (r2 of 0.8 and 0.6 for LAI and biomass, respectively). Developed models were validated with 2019 data, showing a good performance (r2 of 0.92 and 0.91 for LAI and biomass accordingly). Results were also promising regarding plant height estimation (RMSE = 9.88 cm). Regression plots between the image-based estimation and the measured plant height showed a r2 of 0.83. The validation results were similar between water treatments. This study is the first successful application of drone based imagery for phenotyping sorghum growth and development in a West African context characterized by severe drought occurrence. The developed approach could be used as a decision support tool for breeding programs and as a tool to increase the throughput of sorghum genetic diversity characterization for adaptive traits.
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Henke, Michael, and Evgeny Gladilin. "Virtual Laser Scanning Approach to Assessing Impact of Geometric Inaccuracy on 3D Plant Traits." Remote Sensing 14, no. 19 (September 21, 2022): 4727. http://dx.doi.org/10.3390/rs14194727.
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In recent years, 3D imaging became an increasingly popular screening modality for high-throughput plant phenotyping. The 3D scans provide a rich source of information about architectural plant organization which cannot always be derived from multi-view projection 2D images. On the other hand, 3D scanning is associated with a principle inaccuracy by assessment of geometrically complex plant structures, for example, due the loss of geometrical information on reflective, shadowed, inclined and/or curved leaf surfaces. Here, we aim to quantitatively assess the impact of geometrical inaccuracies in 3D plant data on phenotypic descriptors of four different shoot architectures, including tomato, maize, cucumber, and arabidopsis. For this purpose, virtual laser scanning of synthetic models of these four plant species was used. This approach was applied to simulate different scenarios of 3D model perturbation, as well as the principle loss of geometrical information in shadowed plant regions. Our experimental results show that different plant traits exhibit different and, in general, plant type specific dependency on the level of geometrical perturbations. However, some phenotypic traits are tendentially more or less correlated with the degree of geometrical inaccuracies in assessing 3D plant architecture. In particular, integrative traits, such as plant area, volume, and physiologically important light absorption show stronger correlation with the effectively visible plant area than linear shoot traits, such as total plant height and width crossover different scenarios of geometrical perturbation. Our study addresses an important question of reliability and accuracy of 3D plant measurements and provides solution suggestions for consistent quantitative analysis and interpretation of imperfect data by combining measurement results with computational simulation of synthetic plant models.
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van Oosterom, Erik J., Zongjian Yang, Fenglu Zhang, Kurt S. Deifel, Mark Cooper, Carlos D. Messina, and Graeme L. Hammer. "Hybrid variation for root system efficiency in maize: potential links to drought adaptation." Functional Plant Biology 43, no. 6 (2016): 502. http://dx.doi.org/10.1071/fp15308.
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Water availability can limit maize (Zea mays L.) yields, and root traits may enhance drought adaptation if they can moderate temporal patterns of soil water extraction to favour grain filling. Root system efficiency (RSE), defined as transpiration per unit leaf area per unit of root mass, represents the functional mass allocation to roots to support water capture relative to the allocation to aerial mass that determines water demand. The aims of this study were to identify the presence of hybrid variation for RSE in maize, determine plant attributes that drive these differences and illustrate possible links of RSE to drought adaptation via associations with water extraction patterns. Individual plants for a range of maize hybrids were grown in large containers in shadehouses in Queensland, Australia. Leaf area, shoot and root mass, transpiration, root distribution and soil water were measured in all or selected experiments. Significant hybrid differences in RSE existed. High RSE was associated with reduced dry mass allocation to roots and more efficient water capture per unit of root mass. It was also weakly negatively associated with total plant dry mass, reducing preanthesis water use. This could increase grain yield under drought. RSE provides a conceptual physiological framework to identify traits for high-throughput phenotyping in breeding programs.
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Schmidt, Laura, Kerstin A. Nagel, Anna Galinski, Wiebke Sannemann, Klaus Pillen, and Andreas Maurer. "Unraveling Genomic Regions Controlling Root Traits as a Function of Nitrogen Availability in the MAGIC Wheat Population WM-800." Plants 11, no. 24 (December 14, 2022): 3520. http://dx.doi.org/10.3390/plants11243520.
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An ever-growing world population demands to be fed in the future and environmental protection and climate change need to be taken into account. An important factor here is nitrogen uptake efficiency (NUpE), which is influenced by the root system (the interface between plant and soil). To understand the natural variation of root system architecture (RSA) as a function of nitrogen (N) availability, a subset of the multiparent advanced generation intercross (MAGIC) winter wheat population WM-800 was phenotyped under two contrasting N treatments in a high-throughput phenotyping system at the seedling stage. Fourteen root and shoot traits were measured. Subsequently, these traits were genetically analyzed using 13,060 polymorphic haplotypes and SNPs in a genome-wide association study (GWAS). In total, 64 quantitative trait loci (QTL) were detected; 60 of them were N treatment specific. Candidate genes for the detected QTL included NRT1.1 and genes involved in stress signaling under N−, whereas candidate genes under N+ were more associated with general growth, such as mei2 and TaWOX11b. This finding may indicate (i) a disparity of the genetic control of root development under low and high N supply and, furthermore, (ii) the need for an N specific selection of genes and genotypes in breeding new wheat cultivars with improved NUpE.
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Zhou, Jing, Huawei Mou, Jianfeng Zhou, Md Liakat Ali, Heng Ye, Pengyin Chen, and Henry T. Nguyen. "Qualification of Soybean Responses to Flooding Stress Using UAV-Based Imagery and Deep Learning." Plant Phenomics 2021 (June 28, 2021): 1–13. http://dx.doi.org/10.34133/2021/9892570.
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Soybean is sensitive to flooding stress that may result in poor seed quality and significant yield reduction. Soybean production under flooding could be sustained by developing flood-tolerant cultivars through breeding programs. Conventionally, soybean tolerance to flooding in field conditions is evaluated by visually rating the shoot injury/damage due to flooding stress, which is labor-intensive and subjective to human error. Recent developments of field high-throughput phenotyping technology have shown great potential in measuring crop traits and detecting crop responses to abiotic and biotic stresses. The goal of this study was to investigate the potential in estimating flood-induced soybean injuries using UAV-based image features collected at different flight heights. The flooding injury score (FIS) of 724 soybean breeding plots was taken visually by breeders when soybean showed obvious injury symptoms. Aerial images were taken on the same day using a five-band multispectral and an infrared (IR) thermal camera at 20, 50, and 80 m above ground. Five image features, i.e., canopy temperature, normalized difference vegetation index, canopy area, width, and length, were extracted from the images at three flight heights. A deep learning model was used to classify the soybean breeding plots to five FIS ratings based on the extracted image features. Results show that the image features were significantly different at three flight heights. The best classification performance was obtained by the model developed using image features at 20 m with 0.9 for the five-level FIS. The results indicate that the proposed method is very promising in estimating FIS for soybean breeding.
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Asif, Muhammad A., Melissa Garcia, Joanne Tilbrook, Chris Brien, Kate Dowling, Bettina Berger, Rhiannon K. Schilling, et al. "<i>Corrigendum to</i>: Identification of salt tolerance QTL in a wheat RIL mapping population using destructive and non-destructive phenotyping." Functional Plant Biology 49, no. 7 (June 8, 2022): 672. http://dx.doi.org/10.1071/fp20167_co.
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Bread wheat (<italic>Triticum aestivum</italic> L.) is one of the most important food crops, however it is only moderately tolerant to salinity stress. To improve wheat yield under saline conditions, breeding for improved salinity tolerance of wheat is needed. We have identified nine quantitative trail loci (QTL) for different salt tolerance sub-traits in a recombinant inbred line (RIL) population, derived from the bi-parental cross of Excalibur × Kukri. This population was screened for salinity tolerance subtraits using a combination of both destructive and non-destructive phenotyping. Genotyping by sequencing (GBS) was used to construct a high-density genetic linkage map, consisting of 3236 markers, and utilised for mapping QTL. Of the nine mapped QTL, six were detected under salt stress, including QTL for maintenance of shoot growth under salinity (<italic>QG</italic><sub>(</sub><italic><sub>1-5</sub></italic><sub>)</sub><italic>.asl</italic>-<italic>5A</italic>, <italic>QG</italic><sub>(</sub><italic><sub>1-5</sub></italic><sub>)</sub><italic>.asl</italic>-<italic>7B</italic>) sodium accumulation (<italic>QNa.asl</italic>-<italic>2A</italic>), chloride accumulation (<italic>QCl.asl</italic>-<italic>2A</italic>, <italic>QCl.asl</italic>-<italic>3A</italic>) and potassium : sodium ratio (<italic>QK</italic>:<italic>Na.asl</italic>-<italic>2DS2</italic>). Potential candidate genes within these QTL intervals were shortlisted using bioinformatics tools. These findings are expected to facilitate the breeding of new salt tolerant wheat cultivars. </abstract> <abstract abstract-type="toc"> Soil salinity causes major yield losses in bread wheat, which is moderately tolerant to salinity stress. Using high throughput genotyping and phenotyping techniques, we identified quantitative trail loci (QTL) for different salt tolerance sub-traits in bread wheat and shortlisted potential candidate genes. These QTL and candidate genes may prove useful in breeding for salt tolerant wheat cultivars.
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Gioia, Tania, Anna Galinski, Henning Lenz, Carmen Müller, Jonas Lentz, Kathrin Heinz, Christoph Briese, et al. "GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system based on germination paper to quantify crop phenotypic diversity and plasticity of root traits under varying nutrient supply." Functional Plant Biology 44, no. 1 (2017): 76. http://dx.doi.org/10.1071/fp16128.
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New techniques and approaches have been developed for root phenotyping recently; however, rapid and repeatable non-invasive root phenotyping remains challenging. Here, we present GrowScreen-PaGe, a non-invasive, high-throughput phenotyping system (4 plants min–1) based on flat germination paper. GrowScreen-PaGe allows the acquisition of time series of the developing root systems of 500 plants, thereby enabling to quantify short-term variations in root system. The choice of germination paper was found to be crucial and paper ☓ root interaction should be considered when comparing data from different studies on germination paper. The system is suitable for phenotyping dicot and monocot plant species. The potential of the system for high-throughput phenotyping was shown by investigating phenotypic diversity of root traits in a collection of 180 rapeseed accessions and of 52 barley genotypes grown under control and nutrient-starved conditions. Most traits showed a large variation linked to both genotype and treatment. In general, root length traits contributed more than shape and branching related traits in separating the genotypes. Overall, results showed that GrowScreen-PaGe will be a powerful resource to investigate root systems and root plasticity of large sets of plants and to explore the molecular and genetic root traits of various species including for crop improvement programs.
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Bagley, Stuart A., Jonathan A. Atkinson, Henry Hunt, Michael H. Wilson, Tony P. Pridmore, and Darren M. Wells. "Low-Cost Automated Vectors and Modular Environmental Sensors for Plant Phenotyping." Sensors 20, no. 11 (June 11, 2020): 3319. http://dx.doi.org/10.3390/s20113319.
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High-throughput plant phenotyping in controlled environments (growth chambers and glasshouses) is often delivered via large, expensive installations, leading to limited access and the increased relevance of “affordable phenotyping” solutions. We present two robot vectors for automated plant phenotyping under controlled conditions. Using 3D-printed components and readily-available hardware and electronic components, these designs are inexpensive, flexible and easily modified to multiple tasks. We present a design for a thermal imaging robot for high-precision time-lapse imaging of canopies and a Plate Imager for high-throughput phenotyping of roots and shoots of plants grown on media plates. Phenotyping in controlled conditions requires multi-position spatial and temporal monitoring of environmental conditions. We also present a low-cost sensor platform for environmental monitoring based on inexpensive sensors, microcontrollers and internet-of-things (IoT) protocols.
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BAEK, JeongHo, Eungyeong Lee, Nyunhee Kim, Song Lim Kim, Inchan Choi, Hyeonso Ji, Yong Suk Chung, Man-Soo Choi, Jung-Kyung Moon, and Kyung-Hwan Kim. "High Throughput Phenotyping for Various Traits on Soybean Seeds Using Image Analysis." Sensors 20, no. 1 (January 1, 2020): 248. http://dx.doi.org/10.3390/s20010248.
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Data phenotyping traits on soybean seeds such as shape and color has been obscure because it is difficult to define them clearly. Further, it takes too much time and effort to have sufficient number of samplings especially length and width. These difficulties prevented seed morphology to be incorporated into efficient breeding program. Here, we propose methods for an image acquisition, a data processing, and analysis for the morphology and color of soybean seeds by high-throughput method using images analysis. As results, quantitative values for colors and various types of morphological traits could be screened to create a standard for subsequent evaluation of the genotype. Phenotyping method in the current study could define the morphology and color of soybean seeds in highly accurate and reliable manner. Further, this method enables the measurement and analysis of large amounts of plant seed phenotype data in a short time, which was not possible before. Fast and precise phenotype data obtained here may facilitate Genome Wide Association Study for the gene function analysis as well as for development of the elite varieties having desirable seed traits.
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Rascher, Uwe, Stephan Blossfeld, Fabio Fiorani, Siegfried Jahnke, Marcus Jansen, Arnd J. Kuhn, Shizue Matsubara, et al. "Non-invasive approaches for phenotyping of enhanced performance traits in bean." Functional Plant Biology 38, no. 12 (2011): 968. http://dx.doi.org/10.1071/fp11164.
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Plant phenotyping is an emerging discipline in plant biology. Quantitative measurements of functional and structural traits help to better understand gene–environment interactions and support breeding for improved resource use efficiency of important crops such as bean (Phaseolus vulgaris L.). Here we provide an overview of state-of-the-art phenotyping approaches addressing three aspects of resource use efficiency in plants: belowground roots, aboveground shoots and transport/allocation processes. We demonstrate the capacity of high-precision methods to measure plant function or structural traits non-invasively, stating examples wherever possible. Ideally, high-precision methods are complemented by fast and high-throughput technologies. High-throughput phenotyping can be applied in the laboratory using automated data acquisition, as well as in the field, where imaging spectroscopy opens a new path to understand plant function non-invasively. For example, we demonstrate how magnetic resonance imaging (MRI) can resolve root structure and separate root systems under resource competition, how automated fluorescence imaging (PAM fluorometry) in combination with automated shape detection allows for high-throughput screening of photosynthetic traits and how imaging spectrometers can be used to quantify pigment concentration, sun-induced fluorescence and potentially photosynthetic quantum yield. We propose that these phenotyping techniques, combined with mechanistic knowledge on plant structure–function relationships, will open new research directions in whole-plant ecophysiology and may assist breeding for varieties with enhanced resource use efficiency varieties.
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de Langre, E., O. Penalver, P. Hémon, J. M. Frachisse, M. B. Bogeat-Triboulot, B. Niez, E. Badel, and B. Moulia. "Nondestructive and Fast Vibration Phenotyping of Plants." Plant Phenomics 2019 (June 25, 2019): 1–10. http://dx.doi.org/10.34133/2019/6379693.
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The frequencies of free oscillations of plants, or plant parts, depend on their geometries, stiffnesses, and masses. Besides direct biomechanical interest, free frequencies also provide insights into plant properties that can usually only be measured destructively or with low-throughput techniques (e.g., change in mass, tissue density, or stiffness over development or with stresses). We propose here a new high-throughput method based on the noncontact measurements of the free frequencies of the standing plant. The plant is excited by short air pulses (typically 100 ms). The resulting motion is recorded by a high speed video camera (100 fps) and processed using fast space and time correlation algorithms. In less than a minute the mechanical behavior of the plant is tested over several directions. The performance and versatility of this method has been tested in three contrasted species: tobacco (Nicotiana benthamian), wheat (Triticum aestivum L.), and poplar (Populus sp.), for a total of more than 4000 data points. In tobacco we show that water stress decreased the free frequency by 15%. In wheat we could detect variations of less than 1 g in the mass of spikes. In poplar we could measure frequencies of both the whole stem and leaves. The work provides insight into new potential directions for development of phenotyping.
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Sharma, Santosh, and Marcelo J. Carena. "BRACE: A Method for High Throughput Maize Phenotyping of Root Traits for Short-Season Drought Tolerance." Crop Science 56, no. 6 (October 6, 2016): 2996–3004. http://dx.doi.org/10.2135/cropsci2016.02.0116.
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Lyra, Danilo H., Nicolas Virlet, Pouria Sadeghi-Tehran, Kirsty L. Hassall, Luzie U. Wingen, Simon Orford, Simon Griffiths, Malcolm J. Hawkesford, and Gancho T. Slavov. "Functional QTL mapping and genomic prediction of canopy height in wheat measured using a robotic field phenotyping platform." Journal of Experimental Botany 71, no. 6 (February 25, 2020): 1885–98. http://dx.doi.org/10.1093/jxb/erz545.
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Abstract Genetic studies increasingly rely on high-throughput phenotyping, but the resulting longitudinal data pose analytical challenges. We used canopy height data from an automated field phenotyping platform to compare several approaches to scanning for quantitative trait loci (QTLs) and performing genomic prediction in a wheat recombinant inbred line mapping population based on up to 26 sampled time points (TPs). We detected four persistent QTLs (i.e. expressed for most of the growing season), with both empirical and simulation analyses demonstrating superior statistical power of detecting such QTLs through functional mapping approaches compared with conventional individual TP analyses. In contrast, even very simple individual TP approaches (e.g. interval mapping) had superior detection power for transient QTLs (i.e. expressed during very short periods). Using spline-smoothed phenotypic data resulted in improved genomic predictive abilities (5–8% higher than individual TP prediction), while the effect of including significant QTLs in prediction models was relatively minor (<1–4% improvement). Finally, although QTL detection power and predictive ability generally increased with the number of TPs analysed, gains beyond five or 10 TPs chosen based on phenological information had little practical significance. These results will inform the development of an integrated, semi-automated analytical pipeline, which will be more broadly applicable to similar data sets in wheat and other crops.
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Kassim, Yussif Baba, Richard Oteng-Frimpong, Doris Kanvenaa Puozaa, Emmanuel Kofi Sie, Masawudu Abdul Rasheed, Issah Abdul Rashid, Agyemang Danquah, et al. "High-Throughput Plant Phenotyping (HTPP) in Resource-Constrained Research Programs: A Working Example in Ghana." Agronomy 12, no. 11 (November 4, 2022): 2733. http://dx.doi.org/10.3390/agronomy12112733.
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In this paper, we present a procedure for implementing field-based high-throughput plant phenotyping (HTPP) that can be used in resource-constrained research programs. The procedure relies on opensource tools with the only expensive item being one-off purchase of a drone. It includes acquiring images of the field of interest, stitching the images to get the entire field in one image, calculating and extracting the vegetation indices of the individual plots, and analyzing the extracted indices according to the experimental design. Two populations of groundnut genotypes with different maturities were evaluated for their reaction to early and late leaf spot (ELS, LLS) diseases under field conditions in 2020 and 2021. Each population was made up of 12 genotypes in 2020 and 18 genotypes in 2021. Evaluation of the genotypes was done in four locations in each year. We observed a strong correlation between the vegetation indices and the area under the disease progress curve (AUDPC) for ELS and LLS. However, the strength and direction of the correlation depended upon the time of disease onset, level of tolerance among the genotypes and the physiological traits the vegetation indices were associated with. In 2020, when the disease was observed to have set in late in medium duration population, at the beginning of the seed stage (R5), normalized green-red difference index (NGRDI) and variable atmospheric resistance index (VARI) derived at the beginning pod stage (R3) had a positive relationship with the AUDPC for ELS, and LLS. On the other hand, NGRDI and VARI derived from images taken at R5, and physiological maturity (R7) had negative relationships with AUDPC for ELS, and LLS. In 2021, when the disease was observed to have set in early (at R3) also in medium duration population, a negative relationship was observed between NGRDI and VARI and AUDPC for ELS and LLS, respectively. We found consistently negative relationships of NGRDI and VARI with AUDPC for ELS and LLS, respectively, within the short duration population in both years. Canopy cover (CaC), green area (GA), and greener area (GGA) only showed negative relationships with AUDPC for ELS and LLS when the disease caused yellowing and defoliation. The rankings of some genotypes changed for NGRDI, VARI, CaC, GA, GGA, and crop senescence index (CSI) when lesions caused by the infections of ELS and LLS became severe, although that did not affect groupings of genotypes when analyzed with principal component analysis. Notwithstanding, genotypes that consistently performed well across various reproductive stages with respect to the vegetation indices constituted the top performers when ELS, LLS, haulm, and pod yields were jointly considered.
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Tu, Keling, Ying Cheng, Cuiling Ning, Chengmin Yang, Xuehui Dong, Hailu Cao, and Qun Sun. "Non-Destructive Viability Discrimination for Individual Scutellaria baicalensis Seeds Based on High-Throughput Phenotyping and Machine Learning." Agriculture 12, no. 10 (October 5, 2022): 1616. http://dx.doi.org/10.3390/agriculture12101616.
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It is crucial to identify and select high-quality seeds for improving Scutellaria baicalensis yield. In this study, we present a non-destructive and accurate method for predicting Scutellaria baicalensis seed viability that used seed phenotypic data with machine-learning algorithms to distinguish between vital and dead seeds. Meanwhile, the SMOTE was used to balance the dataset and make the established viability discrimination model more efficient by avoiding problems of overfitting or under-fitting. The results showed that hyperspectral imaging (HSI) combined with detrend (DT) preprocessing and a support vector machine (SVM) model could predict Scutellaria baicalensis seed viability with a 93.3% accuracy, and increased the germination percentage of the seed lot to 99.1%, while machine vision imaging provided the highest 87.9% accuracy and 87.0% germination percentage. This strategy is suitable for large-scale Scutellaria baicalensis seed viability discrimination operations for ensuring seed quality, expanding the cultivation and production scales of Scutellaria baicalensis, and accelerating the present solving of the problem of short supply. It can help to accelerate the breeding of quality Scutellaria baicalensis varieties.
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Minervini, Massimo, Hanno Scharr, and Sotirios A. Tsaftaris. "The significance of image compression in plant phenotyping applications." Functional Plant Biology 42, no. 10 (2015): 971. http://dx.doi.org/10.1071/fp15033.
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We are currently witnessing an increasingly higher throughput in image-based plant phenotyping experiments. The majority of imaging data are collected using complex automated procedures and are then post-processed to extract phenotyping-related information. In this article, we show that the image compression used in such procedures may compromise phenotyping results and this needs to be taken into account. We use three illuminating proof-of-concept experiments that demonstrate that compression (especially in the most common lossy JPEG form) affects measurements of plant traits and the errors introduced can be high. We also systematically explore how compression affects measurement fidelity, quantified as effects on image quality, as well as errors in extracted plant visual traits. To do so, we evaluate a variety of image-based phenotyping scenarios, including size and colour of shoots, leaf and root growth. To show that even visual impressions can be used to assess compression effects, we use root system images as examples. Overall, we find that compression has a considerable effect on several types of analyses (albeit visual or quantitative) and that proper care is necessary to ensure that this choice does not affect biological findings. In order to avoid or at least minimise introduced measurement errors, for each scenario, we derive recommendations and provide guidelines on how to identify suitable compression options in practice. We also find that certain compression choices can offer beneficial returns in terms of reducing the amount of data storage without compromising phenotyping results. This may enable even higher throughput experiments in the future.
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Gruber, Franz S., Zoe C. Johnston, Neil R. Norcross, Irene Georgiou, Caroline Wilson, Kevin D. Read, Ian H. Gilbert, Jason R. Swedlow, Sarah Martins da Silva, and Christopher L. R. Barratt. "Compounds enhancing human sperm motility identified using a high-throughput phenotypic screening platform." Human Reproduction 37, no. 3 (January 20, 2022): 466–75. http://dx.doi.org/10.1093/humrep/deac007.
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Abstract STUDY QUESTION Can a high-throughput screening (HTS) platform facilitate male fertility drug discovery? SUMMARY ANSWER An HTS platform identified a large number of compounds that enhanced sperm motility. WHAT IS KNOWN ALREADY Several efforts to find small molecules modulating sperm function have been performed but none have used high-throughput technology. STUDY DESIGN, SIZE, DURATION Healthy donor semen samples were used and samples were pooled (3–5 donors per pool). Primary screening was performed singly; dose–response screening was performed in duplicate (using independent donor pools). PARTICIPANTS/MATERIALS, SETTING, METHODS Spermatozoa isolated from healthy donors were prepared by density gradient centrifugation and incubated in 384-well plates with compounds (6.25 μM) to identify those compounds with enhancing effects on motility. Approximately 17 000 compounds from the libraries, ReFRAME, Prestwick, Tocris, LOPAC, CLOUD and MMV Pathogen Box, were screened. Dose–response experiments of screening hits were performed to confirm the enhancing effect on sperm motility. Experiments were performed in a university setting. MAIN RESULTS AND THE ROLE OF CHANCE From our primary single concentration screening, 105 compounds elicited an enhancing effect on sperm motility compared to dimethylsulphoxide-treated wells. Confirmed enhancing compounds were grouped based on their annotated targets/target classes. A major target class, phosphodiesterase inhibitors, were identified, in particular PDE10A inhibitors as well as number of compounds not previously known to enhance human sperm motility, such as those related to GABA signalling. LARGE SCALE DATA N/A. LIMITATIONS, REASONS FOR CAUTION Although this approach provides data about the activity of the compound, it is only a starting point. For example, further substantive experiments are necessary to provide a more comprehensive picture of each compound’s activity, the effect on the kinetics of the cell populations and subpopulations, and their potential mechanisms of action. Compounds have been tested with prepared donor spermatozoa, incubated under non-capacitating conditions, and only incubated with compounds for a relatively short period of time. Therefore, the effect of compounds under different conditions, for example in whole semen, for longer incubation times, or using samples from patient groups, may be different and require further study. All experiments were performed in vitro. WIDER IMPLICATIONS OF THE FINDINGS This phenotypic screening assay identified a large number of compounds that increased sperm motility. In addition to furthering our understanding of human sperm function, for example identifying new avenues for discovery, we highlight potential compounds as promising start-point for a medicinal chemistry programme for potential enhancement of male fertility. Moreover, with disclosure of the results of screening, we present a substantial resource to inform further work in the field. STUDY FUNDING/COMPETING INTEREST(S) This study was supported by the Bill and Melinda Gates Foundation, Scottish Funding Council and Scottish Universities Life Science Alliance. C.L.R.B. is Editor for RBMO. C.L.R.B. receives funding from Chief Scientists Office (Scotland), ESHRE and Genus PLC, consulting fees from Exscientia and lecture fees from Cooper Surgical and Ferring. S.M.d.S. is an Associate Editor of Human Reproduction, and an Associate Editor of Reproduction and Fertility. S.M.d.S. receives funding from Cooper Surgical and British Dietetic Society. No other authors declared a COI.
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Humplík, Jan F., Dušan Lazár, Tomáš Fürst, Alexandra Husičková, Miroslav Hýbl, and Lukáš Spíchal. "Automated integrative high-throughput phenotyping of plant shoots: a case study of the cold-tolerance of pea (Pisum sativum L.)." Plant Methods 11, no. 1 (2015): 20. http://dx.doi.org/10.1186/s13007-015-0063-9.
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Xu, Xiaoyan, Xiaoyin Xu, Xin Huang, Weiming Xia, and Shunren Xia. "A High-Throughput Analysis Method to Detect Regions of Interest and Quantify Zebrafish Embryo Images." Journal of Biomolecular Screening 15, no. 9 (October 2010): 1152–59. http://dx.doi.org/10.1177/1087057110379155.
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Zebrafish is widely used to understand neural development and model various neurodegenerative diseases. Zebrafish embryos are optically transparent, have a short development period, and can be kept alive in microplates for days, making them amenable to high-throughput microscopic imaging. As a result of high-throughput experiments, a large number of images can be generated in a single experiment, posing a challenge to researchers to analyze them efficiently and quantitatively. In this work, we develop an image processing focused on detecting and quantifying pigments in zebrafish embryos. The algorithm automatically detects a region of interest (ROI) enclosing an area around the pigments and then segment the pigments for quantification. In this process, the algorithm identifies the head and torso at first, and then finds the boundaries corresponding to the back and abdomen by taking advantage of a priori information about the anatomy of zebrafish embryos. The method is robust in terms that it can detect and quantify pigments even when the embryos have different orientations and curvatures. We used real data to demonstrate the performance of the method to extract phenotypic information from zebrafish embryo images and compared its results with manual analysis for verification.
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Park, Eunsoo, Yun-Soo Kim, Mohammad Kamran Omari, Hyun-Kwon Suh, Mohammad Akbar Faqeerzada, Moon S. Kim, Insuck Baek, and Byoung-Kwan Cho. "High-Throughput Phenotyping Approach for the Evaluation of Heat Stress in Korean Ginseng (Panax ginseng Meyer) Using a Hyperspectral Reflectance Image." Sensors 21, no. 16 (August 21, 2021): 5634. http://dx.doi.org/10.3390/s21165634.
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Panax ginseng has been used as a traditional medicine to strengthen human health for centuries. Over the last decade, significant agronomical progress has been made in the development of elite ginseng cultivars, increasing their production and quality. However, as one of the significant environmental factors, heat stress remains a challenge and poses a significant threat to ginseng plants’ growth and sustainable production. This study was conducted to investigate the phenotype of ginseng leaves under heat stress using hyperspectral imaging (HSI). A visible/near-infrared (Vis/NIR) and short-wave infrared (SWIR) HSI system were used to acquire hyperspectral images for normal and heat stress-exposed plants, showing their susceptibility (Chunpoong) and resistibility (Sunmyoung and Sunil). The acquired hyperspectral images were analyzed using the partial least squares-discriminant analysis (PLS-DA) technique, combining the variable importance in projection and successive projection algorithm methods. The correlation of each group was verified using linear discriminant analysis. The developed models showed 12 bands over 79.2% accuracy in Vis/NIR and 18 bands with over 98.9% accuracy at SWIR in validation data. The constructed beta-coefficient allowed the observation of the key wavebands and peaks linked to the chlorophyll, nitrogen, fatty acid, sugar and protein content regions, which differentiated normal and stressed plants. This result shows that the HSI with the PLS-DA technique significantly differentiated between the heat-stressed susceptibility and resistibility of ginseng plants with high accuracy.
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de Carvalho, Ravena Rocha Bessa, Diego Fernando Marmolejo Cortes, Massaine Bandeira e Sousa, Luciana Alves de Oliveira, and Eder Jorge de Oliveira. "Image-based phenotyping of cassava roots for diversity studies and carotenoids prediction." PLOS ONE 17, no. 1 (January 31, 2022): e0263326. http://dx.doi.org/10.1371/journal.pone.0263326.
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Phenotyping to quantify the total carotenoids content (TCC) is sensitive, time-consuming, tedious, and costly. The development of high-throughput phenotyping tools is essential for screening hundreds of cassava genotypes in a short period of time in the biofortification program. This study aimed to (i) use digital images to extract information on the pulp color of cassava roots and estimate correlations with TCC, and (ii) select predictive models for TCC using colorimetric indices. Red, green and blue images were captured in root samples from 228 biofortified genotypes and the difference in color was analyzed using L*, a*, b*, hue and chroma indices from the International Commission on Illumination (CIELAB) color system and lightness. Colorimetric data were used for principal component analysis (PCA), correlation and for developing prediction models for TCC based on regression and machine learning. A high positive correlation between TCC and the variables b* (r = 0.90) and chroma (r = 0.89) was identified, while the other correlations were median and negative, and the L* parameter did not present a significant correlation with TCC. In general, the accuracy of most prediction models (with all variables and only the most important ones) was high (R2 ranging from 0.81 to 0.94). However, the artificial neural network prediction model presented the best predictive ability (R2 = 0.94), associated with the smallest error in the TCC estimates (root-mean-square error of 0.24). The structure of the studied population revealed five groups and high genetic variability based on PCA regarding colorimetric indices and TCC. Our results demonstrated that the use of data obtained from digital image analysis is an economical, fast, and effective alternative for the development of TCC phenotyping tools in cassava roots with high predictive ability.
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Xu, Yaping, Vivek Shrestha, Cristiano Piasecki, Benjamin Wolfe, Lance Hamilton, Reginald J. Millwood, Mitra Mazarei, and Charles Neal Stewart. "Sustainability Trait Modeling of Field-Grown Switchgrass (Panicum virgatum) Using UAV-Based Imagery." Plants 10, no. 12 (December 11, 2021): 2726. http://dx.doi.org/10.3390/plants10122726.
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Unmanned aerial vehicles (UAVs) provide an intermediate scale of spatial and spectral data collection that yields increased accuracy and consistency in data collection for morphological and physiological traits than satellites and expanded flexibility and high-throughput compared to ground-based data collection. In this study, we used UAV-based remote sensing for automated phenotyping of field-grown switchgrass (Panicum virgatum), a leading bioenergy feedstock. Using vegetation indices calculated from a UAV-based multispectral camera, statistical models were developed for rust disease caused by Puccinia novopanici, leaf chlorophyll, nitrogen, and lignin contents. For the first time, UAV remote sensing technology was used to explore the potentials for multiple traits associated with sustainable production of switchgrass, and one statistical model was developed for each individual trait based on the statistical correlation between vegetation indices and the corresponding trait. Also, for the first time, lignin content was estimated in switchgrass shoots via UAV-based multispectral image analysis and statistical analysis. The UAV-based models were verified by ground-truthing via correlation analysis between the traits measured manually on the ground-based with UAV-based data. The normalized difference red edge (NDRE) vegetation index outperformed the normalized difference vegetation index (NDVI) for rust disease and nitrogen content, while NDVI performed better than NDRE for chlorophyll and lignin content. Overall, linear models were sufficient for rust disease and chlorophyll analysis, but for nitrogen and lignin contents, nonlinear models achieved better results. As the first comprehensive study to model switchgrass sustainability traits from UAV-based remote sensing, these results suggest that this methodology can be utilized for switchgrass high-throughput phenotyping in the field.
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Carrington, Blake, Kevin Bishop, and Raman Sood. "A Comprehensive Review of Indel Detection Methods for Identification of Zebrafish Knockout Mutants Generated by Genome-Editing Nucleases." Genes 13, no. 5 (May 11, 2022): 857. http://dx.doi.org/10.3390/genes13050857.
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The use of zebrafish in functional genomics and disease modeling has become popular due to the ease of targeted mutagenesis with genome editing nucleases, i.e., zinc finger nucleases (ZFNs), transcription activator-like effector nucleases (TALENs), and clustered regularly interspaced short palindromic repeats/Cas9 (CRISPR/Cas9). These nucleases, specifically CRISPR/Cas9, are routinely used to generate gene knockout mutants by causing a double stranded break at the desired site in the target gene and selecting for frameshift insertions or deletions (indels) caused by the errors during the repair process. Thus, a variety of methods have been developed to identify fish with indels during the process of mutant generation and phenotypic analysis. These methods range from PCR and gel-based low-throughput methods to high-throughput methods requiring specific reagents and/or equipment. Here, we provide a comprehensive review of currently used indel detection methods in zebrafish. By discussing the molecular basis for each method as well as their pros and cons, we hope that this review will serve as a comprehensive resource for zebrafish researchers, allowing them to choose the most appropriate method depending upon their budget, access to required equipment and the throughput needs of the projects.
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Buckingham, Steven D., Frederick A. Partridge, Beth C. Poulton, Benjamin S. Miller, Rachel A. McKendry, Gareth J. Lycett, and David B. Sattelle. "Automated phenotyping of mosquito larvae enables high-throughput screening for novel larvicides and offers potential for smartphone-based detection of larval insecticide resistance." PLOS Neglected Tropical Diseases 15, no. 6 (June 3, 2021): e0008639. http://dx.doi.org/10.1371/journal.pntd.0008639.
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Pyrethroid-impregnated nets have contributed significantly to halving the burden of malaria but resistance threatens their future efficacy and the pipeline of new insecticides is short. Here we report that an invertebrate automated phenotyping platform (INVAPP), combined with the algorithm Paragon, provides a robust system for measuring larval motility in Anopheles gambiae (and An. coluzzi) as well as Aedes aegypti with the capacity for high-throughput screening for new larvicides. By this means, we reliably quantified both time- and concentration-dependent actions of chemical insecticides faster than using the WHO standard larval assay. We illustrate the effectiveness of the system using an established larvicide (temephos) and demonstrate its capacity for library-scale chemical screening using the Medicines for Malaria Venture (MMV) Pathogen Box library. As a proof-of-principle, this library screen identified a compound, subsequently confirmed to be tolfenpyrad, as an effective larvicide. We have also used the INVAPP / Paragon system to compare responses in larvae derived from WHO classified deltamethrin resistant and sensitive mosquitoes. We show how this approach to monitoring larval response to insecticides can be adapted for use with a smartphone camera application and therefore has potential for further development as a simple portable field-assay with associated real-time, geo-located information to identify hotspots.
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Nghi, Do Huu, and Le Mai Huong. "APPLICATION OF IMAGE-BASED HIGH CONTENT ANALYSIS FOR THE SCREENING OF BIOACTIVE NATURAL PRODUCTS." Vietnam Journal of Science and Technology 56, no. 4A (October 19, 2018): 1. http://dx.doi.org/10.15625/2525-2518/56/4a/13065.
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Each bioactive compound induces phenotypic changes in target cells that can be made visible by labelling selected molecules of the cells with fluorescent dyes and/or directly observed under the high-throughput microscope. A comparison of the cellular phenotype induced by a compound of interest with known cellular targets allows predicting its mode of action. Over the past 15 years, high-throughput microscopy has been one of the fastest growing fields in cell biology. When combined with automated multiparametric image and data analysis, it is referred to as high-content screening (HCS). Whilst HCS has been successfully applied to the bioactivity characterization of natural products, recent studies used automated microscopy and software to increase speed and to reduce subjective interpretation. In 2017, Institute of Natural Products Chemistry (INPC-VAST) has been equipped with a HCS platform (Olympus Scan^R) that designed for fully automated image acquisition and analysis of biological samples to visually inspect the cellular morphology induced by hit compounds as well as to discriminate from false positives. Accordingly, this short review covers the concepts of HCS and its application in screening of biologically active natural products whose molecular targets could be identified through such approaches.
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Pieters, Olivier, Tom De Swaef, Peter Lootens, Michiel Stock, Isabel Roldán-Ruiz, and Francis wyffels. "Gloxinia—An Open-Source Sensing Platform to Monitor the Dynamic Responses of Plants." Sensors 20, no. 11 (May 28, 2020): 3055. http://dx.doi.org/10.3390/s20113055.
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The study of the dynamic responses of plants to short-term environmental changes is becoming increasingly important in basic plant science, phenotyping, breeding, crop management, and modelling. These short-term variations are crucial in plant adaptation to new environments and, consequently, in plant fitness and productivity. Scalable, versatile, accurate, and low-cost data-logging solutions are necessary to advance these fields and complement existing sensing platforms such as high-throughput phenotyping. However, current data logging and sensing platforms do not meet the requirements to monitor these responses. Therefore, a new modular data logging platform was designed, named Gloxinia. Different sensor boards are interconnected depending upon the needs, with the potential to scale to hundreds of sensors in a distributed sensor system. To demonstrate the architecture, two sensor boards were designed—one for single-ended measurements and one for lock-in amplifier based measurements, named Sylvatica and Planalta, respectively. To evaluate the performance of the system in small setups, a small-scale trial was conducted in a growth chamber. Expected plant dynamics were successfully captured, indicating proper operation of the system. Though a large scale trial was not performed, we expect the system to scale very well to larger setups. Additionally, the platform is open-source, enabling other users to easily build upon our work and perform application-specific optimisations.
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Neilson, E. H., A. M. Edwards, C. K. Blomstedt, B. Berger, B. Lindberg Møller, and R. M. Gleadow. "Utilization of a high-throughput shoot imaging system to examine the dynamic phenotypic responses of a C4 cereal crop plant to nitrogen and water deficiency over time." Journal of Experimental Botany 66, no. 7 (February 19, 2015): 1817–32. http://dx.doi.org/10.1093/jxb/eru526.
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Tomkowiak, Agnieszka, Jan Bocianowski, Julia Spychała, Joanna Grynia, Aleksandra Sobiech, and Przemysław Łukasz Kowalczewski. "DArTseq-Based High-Throughput SilicoDArT and SNP Markers Applied for Association Mapping of Genes Related to Maize Morphology." International Journal of Molecular Sciences 22, no. 11 (May 29, 2021): 5840. http://dx.doi.org/10.3390/ijms22115840.
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Today, agricultural productivity is essential to meet the needs of a growing population, and is also a key tool in coping with climate change. Innovative plant breeding technologies such as molecular markers, phenotyping, genotyping, the CRISPR/Cas method and next-generation sequencing can help agriculture meet the challenges of the 21st century more effectively. Therefore, the aim of the research was to identify single-nucleotide polymorphisms (SNPs) and SilicoDArT markers related to select morphological features determining the yield in maize. The plant material consisted of ninety-four inbred lines of maize of various origins. These lines were phenotyped under field conditions. A total of 14 morphological features was analyzed. The DArTseq method was chosen for genotyping because this technique reduces the complexity of the genome by restriction enzyme digestion. Subsequently, short fragment sequencing was used. The choice of a combination of restrictases allowed the isolation of highly informative low copy fragments of the genome. Thanks to this method, 90% of the obtained DArTseq markers are complementary to the unique sequences of the genome. All the observed features were normally distributed. Analysis of variance indicated that the main effect of lines was statistically significant (p < 0.001) for all 14 traits of study. Thanks to the DArTseq analysis with the use of next-generation sequencing (NGS) in the studied plant material, it was possible to identify 49,911 polymorphisms, of which 33,452 are SilicoDArT markers and the remaining 16,459 are SNP markers. Among those mentioned, two markers associated with four analyzed traits deserved special attention: SNP (4578734) and SilicoDArT (4778900). SNP marker 4578734 was associated with the following features: anthocyanin coloration of cob glumes, number of days from sowing to anthesis, number of days from sowing to silk emergence and anthocyanin coloration of internodes. SilicoDArT marker 4778900 was associated with the following features: number of days from sowing to anthesis, number of days from sowing to silk emergence, tassel: angle between the axis and lateral branches and plant height. Sequences with a length of 71 bp were used for physical mapping. The BLAST and EnsemblPlants databases were searched against the maize genome to identify the positions of both markers. Marker 4578734 was localized on chromosome 7, the closest gene was Zm00001d022467, approximately 55 Kb apart, encoding anthocyanidin 3-O-glucosyltransferase. Marker 4778900 was located on chromosome 7, at a distance of 45 Kb from the gene Zm00001d045261 encoding starch synthase I. The latter observation indicated that these flanking SilicoDArT and SNP markers were not in a state of linkage disequilibrium.
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Wang, Chunying, Weiting Pan, Xubin Song, Haixia Yu, Junke Zhu, Ping Liu, and Xiang Li. "Predicting Plant Growth and Development Using Time-Series Images." Agronomy 12, no. 9 (September 16, 2022): 2213. http://dx.doi.org/10.3390/agronomy12092213.
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Early prediction of the growth and development of plants is important for the intelligent breeding process, yet accurate prediction and simulation of plant phenotypes is difficult. In this work, a prediction model of plant growth and development based on spatiotemporal long short-term memory (ST-LSTM) and memory in memory network (MIM) was proposed to predict the image sequences of future growth and development including plant organs such as ears. A novel dataset of wheat growth and development was also compiled. The performance of the prediction model of plant growth and development was evaluated by calculating structural similarity index measure (SSIM), mean square error (MSE), and peak signal to noise ratio (PSNR) between the predicted and real plant images. Moreover, the optimal number of time steps and the optimal time interval between steps were determined for the proposed model on the wheat growth and development dataset. Under the optimal setting, the SSIM values surpassed 84% for all time steps. The mean of MSE values was 46.11 and the MSE values were below 68 for all time steps. The mean of PSNR values was 30.67. When the number of prediction steps was set to eight, the prediction model had the best prediction performance on the public Panicoid Phenomap-1 dataset. The SSIM values surpassed 78% for all time steps. The mean of MSE values was 77.78 and the MSE values were below 118 for all time steps. The mean of PSNR values was 29.03. The results showed a high degree of similarity between the predicted images and the real images of plant growth and development and verified the validity, reliability, and feasibility of the proposed model. The study shows the potential to provide the plant phenotyping community with an efficient tool that can perform high-throughput phenotyping and predict future plant growth.
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Amin, Samirkumar, Wonyeong Kang, Amit Gujar, Leigh Maher, Elise Courtois, Paul Robson, Peter Dickinson, Rebecca Packer, Charles Lee, and Roel Verhaak. "TMOD-13. IDENTIFYING DRIVERS IN THE CONVERGING SYNTENIC REGIONS OF SPONTANEOUS CANINE AND PEDIATRIC HIGH-GRADE GLIOMA USING IMAGING BASED CRISPR-CAS9 ARRAY SCREEN." Neuro-Oncology 23, Supplement_6 (November 2, 2021): vi218. http://dx.doi.org/10.1093/neuonc/noab196.874.
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Abstract Gliomas occur in companion dogs at rates comparable to humans, with short-snouted breeds such as boxers being more susceptible than others. The natural progression of cancer in the immuno-competent host allows companion dogs diagnosed with sporadic glioma as an optimal model for preclinical testing of therapeutic approaches with human relevance, including immunotherapies. We have recently performed comprehensive genomic and epigenetic characterization of glioma in dogs to their human counterparts and found strong convergent evolution – shared somatic mutations and aneuploidies - among syntenic regions, including those of known pediatric glioma drivers, e.g., PDGFRA, MYC, PIK3CA. Here, using arrayed CRISPR-Cas9 imaging based phenotypic screen, we will probe potential oncogenic drivers and tumor suppressor genes within syntenic aneuploidies and thus outline functional versus non-functional heterogeneity of cancer aneuploidy. Specifically, we are conducting arrayed knockout screen (one gene per well) of 400+ genes within syntenic aneuploidies across canine (n=2) and pediatric (n=2) high-grade glioma cell lines. We will first capture images by high-speed confocal imaging system at three time points post-transduction of single guide RNAs (2 per gene) targeting each of 400+ genes in their separate wells. Then, using high-throughput image analysis and semi-supervised machine learning methods, we will measure well-based phenotypic features (viability, growth, and morphology) from these images. Genes will be ranked per cross-validated predicted probability in yielding either proliferating or slow-growing cell type based on learned phenotypic features using image data of knockout cells from and across wells. The top ranked genes will then be linked to oncogenes and tumor suppressors based on pathway and ontology analysis. We expect that we will see convergence of the most impactful molecular abnormalities (based on their knockout phenotypes) on mechanisms or candidate signaling pathways for the development of new drugs and repurposing of existing drugs for kids and dogs with high-grade glioma.
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Waqas, Muhammad Ahmed, Xiukang Wang, Syed Adeel Zafar, Mehmood Ali Noor, Hafiz Athar Hussain, Muhammad Azher Nawaz, and Muhammad Farooq. "Thermal Stresses in Maize: Effects and Management Strategies." Plants 10, no. 2 (February 4, 2021): 293. http://dx.doi.org/10.3390/plants10020293.
Full textAbstract:
Climate change can decrease the global maize productivity and grain quality. Maize crop requires an optimal temperature for better harvest productivity. A suboptimal temperature at any critical stage for a prolonged duration can negatively affect the growth and yield formation processes. This review discusses the negative impact of temperature extremes (high and low temperatures) on the morpho-physiological, biochemical, and nutritional traits of the maize crop. High temperature stress limits pollen viability and silks receptivity, leading to a significant reduction in seed setting and grain yield. Likewise, severe alterations in growth rate, photosynthesis, dry matter accumulation, cellular membranes, and antioxidant enzyme activities under low temperature collectively limit maize productivity. We also discussed various strategies with practical examples to cope with temperature stresses, including cultural practices, exogenous protectants, breeding climate-smart crops, and molecular genomics approaches. We reviewed that identified quantitative trait loci (QTLs) and genes controlling high- and low temperature stress tolerance in maize could be introgressed into otherwise elite cultivars to develop stress-tolerant cultivars. Genome editing has become a key tool for developing climate-resilient crops. Moreover, challenges to maize crop improvement such as lack of adequate resources for breeding in poor countries, poor communication among the scientists of developing and developed countries, problems in germplasm exchange, and high cost of advanced high-throughput phenotyping systems are discussed. In the end, future perspectives for maize improvement are discussed, which briefly include new breeding technologies such as transgene-free clustered regularly interspaced short palindromic repeat (CRISPR)/CRISPR-associated (Cas)-mediated genome editing for thermo-stress tolerance in maize.
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Li, Baohua, Michelle Tang, Céline Caseys, Ayla Nelson, Marium Zhou, Xue Zhou, Siobhan M. Brady, and Daniel J. Kliebenstein. "Epistatic Transcription Factor Networks Differentially Modulate Arabidopsis Growth and Defense." Genetics 214, no. 2 (December 18, 2019): 529–41. http://dx.doi.org/10.1534/genetics.119.302996.
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Plants integrate internal and external signals to finely coordinate growth and defense for maximal fitness within a complex environment. A common model suggests that growth and defense show a trade-offs relationship driven by energy costs. However, recent studies suggest that the coordination of growth and defense likely involves more conditional and intricate connections than implied by the trade-off model. To explore how a transcription factor (TF) network may coordinate growth and defense, we used a high-throughput phenotyping approach to measure growth and flowering in a set of single and pairwise mutants previously linked to the aliphatic glucosinolate (GLS) defense pathway. Supporting a link between growth and defense, 17 of the 20 tested defense-associated TFs significantly influenced plant growth and/or flowering time. The TFs’ effects were conditional upon the environment and age of the plant, and more critically varied across the growth and defense phenotypes for a given genotype. In support of the coordination model of growth and defense, the TF mutant’s effects on short-chain aliphatic GLS and growth did not display a simple correlation. We propose that large TF networks integrate internal and external signals and separately modulate growth and the accumulation of the defensive aliphatic GLS.
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Waititu, Joram Kiriga, Chunyi Zhang, Jun Liu, and Huan Wang. "Plant Non-Coding RNAs: Origin, Biogenesis, Mode of Action and Their Roles in Abiotic Stress." International Journal of Molecular Sciences 21, no. 21 (November 9, 2020): 8401. http://dx.doi.org/10.3390/ijms21218401.
Full textAbstract:
As sessile species, plants have to deal with the rapidly changing environment. In response to these environmental conditions, plants employ a plethora of response mechanisms that provide broad phenotypic plasticity to allow the fine-tuning of the external cues related reactions. Molecular biology has been transformed by the major breakthroughs in high-throughput transcriptome sequencing and expression analysis using next-generation sequencing (NGS) technologies. These innovations have provided substantial progress in the identification of genomic regions as well as underlying basis influencing transcriptional and post-transcriptional regulation of abiotic stress response. Non-coding RNAs (ncRNAs), particularly microRNAs (miRNAs), short interfering RNAs (siRNAs), and long non-coding RNAs (lncRNAs), have emerged as essential regulators of plants abiotic stress response. However, shared traits in the biogenesis of ncRNAs and the coordinated cross-talk among ncRNAs mechanisms contribute to the complexity of these molecules and might play an essential part in regulating stress responses. Herein, we highlight the current knowledge of plant microRNAs, siRNAs, and lncRNAs, focusing on their origin, biogenesis, modes of action, and fundamental roles in plant response to abiotic stresses.
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Amin, Samirkumar B., Amit Gujar, Eunhee Yi, Wonyeong Kang, Megan Costa, Greg Sjogren, Paul Gabriel, et al. "Abstract 3106: Identifying drivers in the converging syntenic aneuploidies of spontaneous canine and pediatric high-grade glioma using imaging-based an arrayed CRISPR-Cas9 phenotypic screen." Cancer Research 82, no. 12_Supplement (June 15, 2022): 3106. http://dx.doi.org/10.1158/1538-7445.am2022-3106.
Full textAbstract:
Abstract Gliomas occur in companion dogs at rates comparable to humans, with short-snouted breeds such as boxers being more susceptible than others. The natural progression of cancer in the immuno-competent host allows companion dogs diagnosed with sporadic glioma as an optimal model for preclinical testing of therapeutic approaches with human relevance, including immunotherapies. We have recently performed comprehensive genomic and epigenetic characterization of glioma in dogs to their human counterparts and found strong convergent evolution - shared somatic mutations and aneuploidies - among syntenic regions, including those of known pediatric glioma drivers, e.g., PDGFRA, MYC, PIK3CA. Here, using arrayed CRISPR-Cas9 imaging based phenotypic screen, we will probe potential oncogenic drivers and tumor suppressor genes within syntenic aneuploidies and thus outline functional versus non-functional heterogeneity of cancer aneuploidy. Specifically, we are conducting arrayed knockout screen (one gene per well) of 400+ genes within syntenic aneuploidies across primary cultured cells of canine glioma (n=2) and pediatric high-grade glioma cell lines (n=2). We will first capture images by high-speed confocal imaging system at three time points post-transduction of single guide RNAs (2 per gene) targeting each of 400+ genes in their separate wells. Then, using high-throughput image analysis and semi-supervised machine learning methods, we will measure well-based phenotypic features (viability, growth, and morphology) from these images. Genes will be ranked per cross-validated predicted probability in yielding either proliferating or slow-growing cell type based on learned phenotypic features using image data of knockout cells from and across wells. The top ranked genes will then be linked to oncogenes and tumor suppressors based on pathway and ontology analysis as well as further functional in vitro and in vivo (PDX) validation. We expect to find convergence of the most impactful molecular abnormalities (based on their knockout phenotypes) on candidate signaling pathways for the development of new drugs and repurposing of existing drugs for children and dogs with high-grade glioma. Citation Format: Samirkumar B. Amin, Amit Gujar, Eunhee Yi, Wonyeong Kang, Megan Costa, Greg Sjogren, Paul Gabriel, Leigh Maher, Peter Dickinson, Rebecca Packer, Elise Courtois, Paul Robson, Charles Lee, Roel Verhaak. Identifying drivers in the converging syntenic aneuploidies of spontaneous canine and pediatric high-grade glioma using imaging-based an arrayed CRISPR-Cas9 phenotypic screen [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 3106.
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Leveridge, Melanie, Chun-Wa Chung, Jeffrey W. Gross, Christopher B. Phelps, and Darren Green. "Integration of Lead Discovery Tactics and the Evolution of the Lead Discovery Toolbox." SLAS DISCOVERY: Advancing the Science of Drug Discovery 23, no. 9 (June 6, 2018): 881–97. http://dx.doi.org/10.1177/2472555218778503.
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There has been much debate around the success rates of various screening strategies to identify starting points for drug discovery. Although high-throughput target-based and phenotypic screening has been the focus of this debate, techniques such as fragment screening, virtual screening, and DNA-encoded library screening are also increasingly reported as a source of new chemical equity. Here, we provide examples in which integration of more than one screening approach has improved the campaign outcome and discuss how strengths and weaknesses of various methods can be used to build a complementary toolbox of approaches, giving researchers the greatest probability of successfully identifying leads. Among others, we highlight case studies for receptor-interacting serine/threonine-protein kinase 1 and the bromo- and extra-terminal domain family of bromodomains. In each example, the unique insight or chemistries individual approaches provided are described, emphasizing the synergy of information obtained from the various tactics employed and the particular question each tactic was employed to answer. We conclude with a short prospective discussing how screening strategies are evolving, what this screening toolbox might look like in the future, how to maximize success through integration of multiple tactics, and scenarios that drive selection of one combination of tactics over another.