Plant Methods

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Background The study of the regulatory mechanisms of evolutionarily conserved Nucleotide-binding leucine-rich repeat (NLR) resistance (R) proteins in animals and plants is of increasing importance due to understanding basic immunity and the value of various crop engineering applications of NLR immune receptors. The importance of temperature is also emerging when applying NLR to crops responding to global climate change. In particular, studies of pathogen effector recognition and autoimmune activity of NLRs in plants can quickly and easily determine their function in tobacco using agro-mediated transient assay. However, there are conditions that should not be overlooked in these cell death-related assays in tobacco. Results Environmental conditions play an important role in the immune response of plants. The system used in this study was to establish conditions for optimal hypertensive response (HR) cell death analysis by using the paired NLR RPS4/RRS1 autoimmune and AvrRps4 effector recognition system. The most suitable greenhouse temperature for growing plants was fixed at 22 °C. In this study, RPS4/RRS1-mediated autoimmune activity, RPS4 TIR domain-dependent cell death, and RPS4/RRS1-mediated HR cell death upon AvrRps4 perception significantly inhibited under conditions of 65% humidity. The HR is strongly activated when the humidity is below 10%. Besides, the leaf position of tobacco is important for HR cell death. Position #4 of the leaf from the top in 4–5 weeks old tobacco plants showed the most effective HR cell death. Conclusions As whole genome sequencing (WGS) or resistance gene enrichment sequencing (RenSeq) of various crops continues, different types of NLRs and their functions will be studied. At this time, if we optimize the conditions for evaluating NLR-mediated HR cell death, it will help to more accurately identify the function of NLRs. In addition, it will be possible to contribute to crop development in response to global climate change through NLR engineering.
Background The construction and application of synthetic genetic circuits is frequently improved if gene expression can be orthogonally controlled, relative to the host. In plants, orthogonality can be achieved via the use of CRISPR-based transcription factors that are programmed to act on natural or synthetic promoters. The construction of complex gene circuits can require multiple, orthogonal regulatory interactions, and this in turn requires that the full programmability of CRISPR elements be adapted to non-natural and non-standard promoters that have few constraints on their design. Therefore, we have developed synthetic promoter elements in which regions upstream of the minimal 35S CaMV promoter are designed from scratch to interact via programmed gRNAs with dCas9 fusions that allow activation of gene expression. Results A panel of three, mutually orthogonal promoters that can be acted on by artificial gRNAs bound by CRISPR regulators were designed. Guide RNA expression targeting these promoters was in turn controlled by either Pol III (U6) or ethylene-inducible Pol II promoters, implementing for the first time a fully artificial Orthogonal Control System (OCS). Following demonstration of the complete orthogonality of the designs, the OCS was tied to cellular metabolism by putting gRNA expression under the control of an endogenous plant signaling molecule, ethylene. The ability to form complex circuitry was demonstrated via the ethylene-driven, ratiometric expression of fluorescent proteins in single plants. Conclusions The design of synthetic promoters is highly generalizable to large tracts of sequence space, allowing Orthogonal Control Systems of increasing complexity to potentially be generated at will. The ability to tie in several different basal features of plant molecular biology (Pol II and Pol III promoters, ethylene regulation) to the OCS demonstrates multiple opportunities for engineering at the system level. Moreover, given the fungibility of the core 35S CaMV promoter elements, the derived synthetic promoters can potentially be utilized across a variety of plant species.
Background Profiling the plant root architecture is vital for selecting resilient crops that can efficiently take up water and nutrients. The high-performance imaging tools available to study root-growth dynamics with the optimal resolution are costly and stationary. In addition, performing nondestructive high-throughput phenotyping to extract the structural and morphological features of roots remains challenging. Results We developed the Multiple XL ab: a modular, mobile, and cost-effective setup to tackle these limitations. The system can continuously monitor thousands of seeds from germination to root development based on a conventional camera attached to a motorized multiaxis-rotational stage and custom-built 3D-printed plate holder with integrated light-emitting diode lighting. We also developed an image segmentation model based on deep learning that allows the users to analyze the data automatically. We tested the Multiple XL ab to monitor seed germination and root growth of Arabidopsis developmental, cell cycle, and auxin transport mutants non-invasively at high-throughput and showed that the system provides robust data and allows precise evaluation of germination index and hourly growth rate between mutants. Conclusion Multiple XL ab provides a flexible and user-friendly root phenotyping platform that is an attractive mobile alternative to high-end imaging platforms and stationary growth chambers. It can be used in numerous applications by plant biologists, the seed industry, crop scientists, and breeding companies.
Design of a synthetic SA hydroxylase expression cassette catalyzing SA to 2,3-DHBA. a Vector map for S3H gene overexpression under S5H promoter. The S5H promoter of Arabidopsis was cloned and constructed into the plant expression vector (pPZP-RCS2) to drive the expression of EGFP-S3H. The map was prepared by SnapGene. b The principle of a feedback loop for SA catabolism to 2,3-DHBA in the S5Hpro::EGFP-S3H transgenic plants. The S5H promoter can be induced by SA and then drive the expression of EGFP-S3H. The expressed S3H enzyme can convert SA into 2,3-DHBA and reduce the SA levels
Quantification of SA, 2,3-DHBA and 2,5-DHBA in the S5Hpro::EGFP-S3H transgenic plants. The levels of free SA (a), total SA (b), total 2,3-DHBA (c), and total 2,5-DHBA (d) in WT, NahG and the single-copy S5Hpro::EGFP-S3H transgenic plants at 28 days after germination (DAG). The data are means ± SE (n = 3 biological replications); FW fresh weight. Statistical differences among replicates are labeled with different letters (P < 0.05, one-way ANOVA and post-hoc Tukey’s test)
Growth and leaf senescence phenotypes of the S5Hpro::EGFP-S3H transgenic plants. a Morphological phenotypes of WT, NahG and S5Hpro::EGFP-S3H transgenic plants at 28 and 35 DAG, Bar = 2 cm. b Quantification of the S3H expression in WT, NahG and S5Hpro::EGFP-S3H transgenic plants at 21 DAG by qPCR. The data are means ± SE (n = 3 biological replications). c Quantification of the rosette leaf diameters from plant of (a) at 28 DAG. The data are presented as means ± SE (n ≥ 10 biological replications). d Quantification of chlorophyll content in the 5–6th leaves from plant of (a) at 35 DAG, the data are means ± SE (n = 4 biological replicates); FW, fresh weight. e Fv/Fm of the 5–6th leaves from plant (a) at 35 DAG, the data are means ± SE (n = 4 biological replicates). e Phenotypes of WT, NahG and S5Hpro::EGFP-S3H transgenic plants grown on 1/2 MS medium with or without 100 μM sodium salicylate. Bar = 1 cm. f Quantification of chlorophyll content from plant of (e), the data are means ± SE (n = 4 biological replications); FW, fresh weight. Statistical differences among replicates are labeled with different letters (P < 0.05, one-way ANOVA and post-hoc Tukey’s test)
Pathogen resistance of the S5Hpro::EGFP-S3H transgenic plants to Pst DC3000. a The disease symptoms of WT, NahG, single-copy S5Hpro::EGFP-S3H transgenic plants at 1 and 3 days after Pst DC3000 infection. b Quantification of the growth of Pst DC3000 in plants of a at 0, 1, 3 days post inoculation (DPI). The data are means ± SE (n = 6 biological replications). The levels of free SA (c) and total SA (d) in WT, NahG and S5Hpro::EGFP-S3H transgenic plants after Pst DC3000 infection. The data are means ± SE (n = 4 biological replications). Quantification of the PR1 (e) and PR2 (f) expression in WT, NahG and S5Hpro::EGFP-S3H transgenic plants after Pst DC3000 infection by qPCR. The data are means ± SE (n = 3 biological replications). Statistical differences among replicates are labeled with different letters (P < 0.05, one-way ANOVA and post-hoc Tukey’s test)
Quantification of SA, 2,3-DHBA and 2,5-DHBA in S5Hpro::EGFP-S3H transgenic plants at T5 generation. Quantification of free SA (a), total SA (b), total 2,3-DHBA (c) and total 2,5-DHBA (d) from WT, NahG, and single-copy S5Hpro::EGFP-S3H transgenic lines at T5 generation at 35 DAG. The data are means ± SE (n = 4 biological replications); FW fresh weight. Statistical differences among replicates are labeled with different letters (P < 0.05, one-way ANOVA and post-hoc Tukey’s test)
Background Salicylic acid (SA) is one of the plant hormones, which plays crucial roles in signaling transduction in plant growth, disease resistance, and leaf senescence. Arabidopsis (Arabidopsis thaliana) SA 3-hydroxylase (S3H) and 5-hydroxylase (S5H) are key enzymes which maintain SA homeostasis by catalyzing SA to 2,3-dihydroxybenzoic acid (DHBA) and 2,5-DHBA, respectively. Results SA deficient transgenic Arabidopsis lines were generated by introducing two binary vectors S5Hpro::EGFP-S3H and 35Spro::EGFP-S3H respectively, in which the expression of S3H is under the control of the S5H promoter or CaMV 35S promoter. Compared with the constitutive expression of S3H gene under the control of 35S promoter, the S3H gene under the native S5H promoter is activated by endogenous SA and results in a dynamic control of SA catabolism in a feedback mode. The SA accumulation, growth, leaf senescence, and pathogen resistance of the S5Hpro::GFP-S3H transgenic plants were investigated in parallel with NahG transgenic plants. The SA levels in the S5Hpro::EGFP-S3H transgenic plants were similar to or slightly lower than those of NahG transgenic Arabidopsis and resulted in SA deficient phenotypes. The low-SA trait of the S5Hpro::EGFP-S3H transgenic lines was inherited stably in the later generations. Conclusions Compared with NahG transgenic lines producing by-product catechol, S5Hpro::EGFP-S3H transgenic lines reduce SA levels by converting SA to a native product 2,3-DHBA for catabolism. Together, we provide new SA-deficient germplasms for the investigations of SA signaling in plant development, leaf senescence, and disease resistance.
Background: Jubaea chilensis (Molina) Baillon, is a uniquely large palm species endemic to Chile. It is under threatened status despite its use as an ornamental species throughout the world. This research seeks to identify the phyllotaxis of the species based on an original combination of non-destructive data acquisition technologies, namely Magnetic Resonance Imaging (MRI) in saplings and young individuals and Terrestrial Laser Scanning (TLS) in standing specimens, and a novel analysis methodology. Results: Two phyllotaxis parameters, parastichy pairs and divergence angle, were determined by analyzing specimens at different developmental stages. Spiral phyllotaxis patterns of J. chilensis progressed in complexity from parastichy pairs (3,2) and (3,5) in juvenile specimens and (5,3), (8,5) and (8,13) for adult specimens. Divergence angle was invariable and averaged 136.9°, close to the golden angle. Phyllotactic pattern changes associated with establishment phase, the adult vegetative and the adult reproductive phases were observed. Both technologies, MRI and TLS proved to be adequate for the proposed analysis. Conclusions: Understanding phyllotactic transitions may assist identification of developmental stages of wild J. chilensis specimens. The proposed methodology may also be useful for the study of other palm species.
Plackett–Burman design of experiments model results for scaled data. The box plot shows the effect of single parameters on the extraction yield. The error bars represent the confidence interval, when the error bar crosses the zero point, the factor does not significantly affect the extraction yield, EtOH - ethanol
Tab. Statistical comparison of setpoints for optimal extraction conditions. A Jarque-Berr test result showing the normal data distribution. B density estimation for obtained data (blue line) and Gaussian normal distribution (green line), C data frequency confirming the normal data distribution, D average extracted amount of quercetin for all tested conditions and difference between maximal and minimal obtained value
Kinetic plots for four tested flow rates 2, 3, 4, and 5 mL/min showing the extracted quercetin amount from 0.5 g of quince fruit versus solvent volume used for the extraction
Chromatograms and MS/MS scan ion spectra of sample No. 13 and standard: A Structure of quercetin and its fragmentation in standard solution, B UHPLC-MS/MS chromatograms, including total ion current (TIC) and reconstructed ion chromatogram (RIC), and C MS/MS scan spectra. ppm parts per million
Quercetin is one of the most important bioflavonoids having positive effects on biological processes and human health. Typically, it is extracted from plant matrices using conventional methods such as maceration, sonification, infusion, and Soxhlet extraction with high solvent consumption. Our study aimed to optimize the environmentally friendly carbon dioxide-based method for the extraction of quercetin from quince fruit with an emphasis on extraction yield, repeatability, and short extraction time. Results: A two-step design of experiments was used for the optimization of the key parameters affecting physico-chemical properties, including CO2/co-solvent ratio, co-solvent type, temperature, and pressure. Finally, gas expanded liquid combining CO2/ethanol/H2O in a ratio of 10/81/9 (v/v/v) provided the best extraction yield. Extraction temperature of 66 °C and pressure 22.3 MPa were the most suitable conditions after careful optimization, although both parameters did not significantly affect the process. It was confirmed by experiments in various pressure and temperature conditions and statistical comparison of obtained data. The optimized extraction procedure at a flow rate of 3 mL/min took 30 min. The repeatability of the extraction method exhibited an RSD of 20.8%. Conclusions: The optimized procedure enabled very fast extraction in 30 min using environmentally friendly sol�vents and it was successfully applied to 16 different plant samples, including 14 bulbs and 2 fruits from South Africa. The quercetin content in extracts was quantified using ultra-high-performance liquid chromatography (UHPLC) with tandem mass spectrometry. UHPLC hyphenated with high-resolution mass spectrometry was used to confirm the chemical identity of quercetin in the analyzed samples. We quantified quercetin in 11 samples of all 16 tested plants. The quercetin was found in Agapanthus praecox from the Amaryllidaceae family and its presence in this specie was reported for the first time.
Xanthomonas causes complex water-soaking symptoms in cassava. A Image of cassava leaf in the field exhibiting water-soaking symptoms characteristic of cassava bacterial blight. Yellow arrows indicate different water-soaked lesions. B Water-soaked symptoms of cassava infiltrated with Xam668 (Xam WT) and a Xam668 deletion mutant lacking the TAL20 effector (XamΔTAL20) at 0, 4, 6, and 9DPI. Mock inoculations of 10 mM MgCl2 at each timepoint were included as controls. Scale bar = 0.5 cm
Manual ImageJ analysis of CBB water-soaking symptoms. A Images of cassava leaves infiltrated with Xam WT, XamΔTAL20, and mock treatments were segmented and analyzed using an ImageJ overlay segmentation method. Overlay segmentation analysis depicted by step using a CBB infected cassava leaf image. Images were taken at 0, 4, 6 and 9 DPI. Leaf lobes were labeled by treatment type: X = Xam WT, T = XamΔTAL20, and M = Mock. White lines point to selected regions of a representative water-soaked lesion at each step of the ImageJ overlay segmentation process. B The variance explained by inoculation type (Xam WT or XamΔTAL20) DPI (4-, 6- and 9-), or the interaction between inoculation type and DPI for ten ImageJ generated measurements. Variances were determined by ANOVA analysis. C Total water-soaked area (pixels, y-axis) for sites infiltrated with each treatment (x-axis). Calculated p-values (Kolmogorov–Smirnov test) shown above the line in each plot. D Negative gray-scale mean (y-axis) of water-soaked lesions for Xam WT and XamΔTAL20 relative to mock inoculated spots (x-axis) within the same leaf. Calculated p-values (Kolmogorov–Smirnov test) shown above the line in each plot. In ImageJ, the gray-scale mean was measured by averaging the mean of each gray-scale value in the RGB channels
Overview of the Support Vector Machine learning segmentation and analysis method. A Images of cassava leaves infiltrated with Xam WT, XamΔTAL20, and mock treatments were segmented and analyzed using a support vector machine learning tool. Images depict steps used to generate a classifier training mask for the machine learning tool. A mask was made by combining representative CBB infected images into one graphic and generating a binary mask in ImageJ. White lines showcase a representative water-soaked lesion within the combined leaf graphic and indicate changes at each step. The mask was used to generate a classifier (YAML) file with PhenotyperCV. B Images depict steps of machine learning processing using a CBB infected cassava leaf image. Images were uploaded into the machine learning tool and processed by gray balance color correction, thresholding, and the inoculated regions of interest were selected and labeled using a color code: Red = Xam WT, Green = XamΔTAL20 and Blue = Mock. White lines showcase a representative water-soaked lesion within the image and indicate changes at each step. C Images exhibit outputs from the machine learning image processing and include the color corrected image (left), a pseudo-colored map of the pixels classified as water-soaked (middle), and a feature prediction image (right). White lines showcase a representative water-soaked lesion within the image and indicate differences in each output image. Text separated files with shapes and color data for each inoculation spot were also generated
Support Vector Machine learning analysis of CBB water-soaked symptoms. A The variance explained by inoculation type (Xam WT or XamΔTAL20), DPI (4-, 6- and 9-), or the interaction between inoculation type and DPI for twelve machine learning generated measurements. Variances were determined by an ANOVA. B Total water-soaked area (pixels, y-axis) for sites infiltrated with each treatment (x-axis). Calculated p-values (Kolmogorov–Smirnov test) shown above the line in each plot. C Negative gray-scale mean (y-axis) of water-soaked lesions for Xam WT and XamΔTAL20 relative to mock inoculated spots (x-axis) within the same leaf. Calculated p-values (Kolmogorov–Smirnov test) shown above the line in each plot. In the machine learning analysis, the gray-scale mean was generated using the average mean of the “L” channel from the LAB color space
Comparison of the ImageJ and machine learning analyses of CBB infected leaves. A Representative images from each timepoint (4-, 6-, and 9- DPI) of a Xam WT (top row) and XamΔTAL20 (bottom row) water-soaked spots were selected, visually inspected, and compared. The original images show the water-soaked spots from the color corrected images without segmentation from the background. The “ImageJ” images show water-soaked spots manually segmented from background and overlaid onto the RGB image. The machine learning images shows water-soaked spots segmented from background and pseudo-colored. Scale bar = 0.5 cm. B Water-soaked area data generated by ImageJ or machine learning were paired by inoculation location and plotted for 4 DPI (left plot), 6 DPI (middle plot), and 9 DPI (right plot). Calculated p-values (F-Variance test) shown in the upper corner of plot. Red = ImageJ Blue = machine learning
Background Methods to accurately quantify disease severity are fundamental to plant pathogen interaction studies. Commonly used methods include visual scoring of disease symptoms, tracking pathogen growth in planta over time, and various assays that detect plant defense responses. Several image-based methods for phenotyping of plant disease symptoms have also been developed. Each of these methods has different advantages and limitations which should be carefully considered when choosing an approach and interpreting the results. Results In this paper, we developed two image analysis methods and tested their ability to quantify different aspects of disease lesions in the cassava- Xanthomonas pathosystem. The first method uses ImageJ, an open-source platform widely used in the biological sciences. The second method is a few-shot support vector machine learning tool that uses a classifier file trained with five representative infected leaf images for lesion recognition. Cassava leaves were syringe infiltrated with wildtype Xanthomonas , a Xanthomonas mutant with decreased virulence, and mock treatments. Digital images of infected leaves were captured overtime using a Raspberry Pi camera. The image analysis methods were analyzed and compared for the ability to segment the lesion from the background and accurately capture and measure differences between the treatment types. Conclusions Both image analysis methods presented in this paper allow for accurate segmentation of disease lesions from the non-infected plant. Specifically, at 4-, 6-, and 9-days post inoculation (DPI), both methods provided quantitative differences in disease symptoms between different treatment types. Thus, either method could be applied to extract information about disease severity. Strengths and weaknesses of each approach are discussed.
Vector construction and PCR-based identification. A Electrophoretic identification of the K167 plasmid. M: Star Marker (DL 2000 Plus); 1–33: Recombinant plasmid to be tested. B Double-enzyme digestion verification of the recombinant plasmid. M: 1 kb Plus; 2–3: NcoI/PmlI enzyme digestion; 4–5: PstI/StuI enzyme digestion. C Schematic diagram of the recombinant plasmid GRP-SHP-GLOX (K167). D PCR identification of transgenic rice. M: Star Marker Plus (D2000 Plus); 1–19: K1—K19. (E). PCR identification of transgenic rice. M: Star Marker (D 2000); 1–2: K20—K21; 3: WT; 4: Plasmid K167
Southern blot, RT–qPCR and Basta smearing identification of rice. A Southern blot detection of rice. M: marker; 1: positive control; 2–8: transgenic rice K1—K7; 9: negative control. B The expression levels of SHP and GLOX genes in rice at the flowering stage. C The expression levels of SHP and GLOX genes in rice at the maturity stage. D Basta smear experiment on rice leaves. A, C: Transgenic rice before and after smear; B, D: Wide-type rice before and after smear
Phenotypes of transgenic rice and wild-type rice. A Phenotypes of grains of transgenic rice. B Phenotypes of grains of wild-type rice. C Comparison of the panicle types between wild-type rice and transgenic rice. A: Wide-type rice; B: transgenic rice. D Comparison of the shapes of wild-type rice and transgenic rice. A: Wild-type rice of the control group; B: transgenic rice
Physiological and biochemical measurements. A Detection of peroxidase activity in transgenic maize plants. B Holocellulose content in transgenic rice and wild-type rice at the blooming stage. C Holocellulose content in transgenic rice and wild-type rice at the mature stage. D Lignin content of transgenic rice and wild-type rice at the blooming stage. E Lignin content in transgenic rice and wild-type rice at the mature stage
Ethanol fermentation experiments using transgenic plants. A Hydrolytic reducing sugar content. B Ethanol concentration during fermentation
Background There is great productivity of rice( Oryza sativa L. spp. japonica ) straw in China, which is a potential source of biomass for biofuel and forage. However, the high levels of lignins in rice straw limited its usage and induced the formation of agricultural waste. In order to modify the lignins contents to improve biofuel production and forage digestibility, we selected Soybean hull peroxidase (SHP) and Glyoxal oxidase (GLOX) as candidate genes to improve quality of rice straw. SHP, a class III plant peroxidase, is derived from multiple sources. It has several advantages, such as high resistance to heat, high stability under acidic and alkaline conditions, and a broad substrate range. SHP is speculated to be useful for lignin degradation. Glyoxal oxidase (GLOX) is an extracellular oxidase that can oxidize glyoxal and methylglyoxal in the extracellular medium to generate H 2 O 2 . Results In the present study, the SHP and GLOX genes in pCAMBIA3301-glycine-rich protein (GRP)-SHP-GLOX, designated the K167 vector, were optimized and introduced into rice embryos using Agrobacterium -mediated transformation. Positive transgenic rice embryos were examined using molecular, physiological, biochemical and fermentation tests. The outcomes suggested that SHP degraded lignin effectively. Conclusions This research has created a rice breeding material with normal growth and yield but stalks that are more amenable to degradation in the later stage for use in breeding rice varieties whose stalks are easily used for energy. Our results will improve the industrial and commercial applications of rice straw.
Background Elaeagnus angustifolia L. is a deciduous tree in the family Elaeagnaceae. It is widely used to study abiotic stress tolerance in plants and to improve desertification-affected land because of its ability to withstand diverse types of environmental stress, such as drought, salt, cold, and wind. However, no studies have examined the mechanisms underlying the resistance of E. angustifolia to environmental stress and its adaptive evolution. Methods Here, we used PacBio, Hi-C, resequencing, and RNA-seq to construct the genome and transcriptome of E. angustifolia and explore its adaptive evolution. Results The reconstructed genome of E. angustifolia was 526.80 Mb, with a contig N50 of 12.60 Mb and estimated divergence time of 84.24 Mya. Gene family expansion and resequencing analyses showed that the evolution of E. angustifolia was closely related to environmental conditions. After exposure to salt stress, GO pathway analysis showed that new genes identified from the transcriptome were related to ATP-binding, metal ion binding, and nucleic acid binding. Conclusion The genome sequence of E. angustifolia could be used for comparative genomic analyses of Elaeagnaceae family members and could help elucidate the mechanisms underlying the response of E. angustifolia to drought, salt, cold, and wind stress. Generally, these results provide new insights that could be used to improve desertification-affected land.
Background On tomato plants, the flowering truss is a group or cluster of smaller stems where flowers and fruit develop, while the growing truss is the most extended part of the stem. Because the state of the growing truss reacts sensitively to the surrounding environment, it is essential to control its growth in the early stages. With the recent development of information and artificial intelligence technology in agriculture, a previous study developed a real-time acquisition and evaluation method for images using robots. Furthermore, we used image processing to locate the growing truss to extract growth information. Among the different vision algorithms, the CycleGAN algorithm was used to generate and transform unpaired images using generated learning images. In this study, we developed a robot-based system for simultaneously acquiring RGB and depth images of the growing truss of the tomato plant. Results The segmentation performance for approximately 35 samples was compared via false negative (FN) and false positive (FP) indicators. For the depth camera image, we obtained FN and FP values of 17.55 ± 3.01% and 17.76 ± 3.55%, respectively. For the CycleGAN algorithm, we obtained FN and FP values of 19.24 ± 1.45% and 18.24 ± 1.54%, respectively. When segmentation was performed via image processing through depth image and CycleGAN, the mean intersection over union (mIoU) was 63.56 ± 8.44% and 69.25 ± 4.42%, respectively, indicating that the CycleGAN algorithm can identify the desired growing truss of the tomato plant with high precision. Conclusions The on-site possibility of the image extraction technique using CycleGAN was confirmed when the image scanning robot drove in a straight line through a tomato greenhouse. In the future, the proposed approach is expected to be used in vision technology to scan tomato growth indicators in greenhouses using an unmanned robot platform.
Background Variety genuineness and purity are essential indices of maize seed quality that affect yield. However, detection methods for variety genuineness are time-consuming, expensive, require extensive training, or destroy the seeds in the process. Here, we present an accurate, high-throughput, cost-effective, and non-destructive method for screening variety genuineness that uses seed phenotype data with machine learning to distinguish between genetically and phenotypically similar seed varieties. Specifically, we obtained image data of seed morphology and hyperspectral reflectance for Jingke 968 and nine other closely-related varieties (non-Jingke 968). We then compared the robustness of three common machine learning algorithms in distinguishing these varieties based on the phenotypic imaging data. Results Our results showed that hyperspectral imaging (HSI) combined with a multilayer perceptron (MLP) or support vector machine (SVM) model could distinguish Jingke 968 from varieties that differed by as few as two loci, with a 99% or higher accuracy, while machine vision imaging provided ~ 90% accuracy. Through model validation and updating with varieties not included in the training data, we developed a genuineness detection model for Jingke 968 that effectively discriminated between genetically similar and distant varieties. Conclusions This strategy has potential for wide adoption in large-scale variety genuineness detection operations for internal quality control or governmental regulatory agencies, or for accelerating the breeding of new varieties. Besides, it could easily be extended to other target varieties and other crops.
Sketch of a typical photoinjection experiment using an inverted microscopic setup and a pulsed laser source. The laser beam is focused onto the sample using an inverted microscope setup. A single laser pulse or a train of pulses facilitates of the cellular membrane and possibly the cell wall. The exact physical process of photoporation depends on the applied laser parameters and will be discussed in the following section. Plasmolyzing the plant cell prior to photoinjection supports the molecular uptake
left: Overview comparing the time scales of different interaction and photopoinjection regimes. Note that the real values and borders vary largely on the respective conditions and can therefore only be regarded as rough estimates. Right: schematic depiction of the different interaction regimes. In the photomechanical regime, typically a single laser pulse with high energy (~ several 10 nJ for fs pulses) is applied, whereas the LDP requires multitudes of pulses with low energy (< 1 nJ) and high repetition rates (~ 80 MHz) to accumulate the photochemical effect. τD(water)\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$${\tau }_{D}(water)$$\end{document}: thermal diffusion time in water for objective with high numerical aperture (NA), LDP: low-density plasma, NIR fs: near infrared femtosecond, CW: continious wave, ROS: reactive oxigen species
high-speed imaging of a photoinjection event in the photodisruptive regime. The focal point is depicted by a white arrow in (a). The generation and progression of a short lived cavitaion bubble can be observed 4 µs (b) and 8 µs (c) after application of a single laser pulse. The differential images shown in the bottom row reflect the dynamics of the photoinjection. (image reprinted from Rukmana et al. [68])
Lasers enable modification of living and non-living matter with submicron precision in a contact-free manner which has raised the interest of researchers for decades. Accordingly, laser technologies have drawn interest across disciplines. They have been established as a valuable tool to permeabilize cellular membranes for molecular delivery in a process termed photoinjection. Laser-based molecular delivery was first reported in 1984, when normal kidney cells were successfully transfected with a frequency-multiplied Nd:YAG laser. Due to the rapid development of optical technologies, far more sophisticated laser platforms have become available. In particular, near infrared femtosecond (NIR fs) laser sources enable an increasing progress of laser-based molecular delivery procedures and opened up multiple variations and applications of this technique. This review is intended to provide a plant science audience with the physical principles as well as the application potentials of laser-based molecular delivery. The historical origins and technical development of laser-based molecular delivery are summarized and the principle physical processes involved in these approaches and their implications for practical use are introduced. Successful cases of laser-based molecular delivery in plant science will be reviewed in detail, and the specific hurdles that plant materials pose will be discussed. Finally, we will give an outlook on current limitations and possible future applications of laser-based molecular delivery in the field of plant science.
Background The identification of tropical African wood species based on microscopic imagery is a challenging problem due to the heterogeneous nature of the composition of wood combined with the vast number of candidate species. Image classification methods that rely on machine learning can facilitate this identification, provided that sufficient training material is available. Despite the fact that the three main anatomical sections contain information that is relevant for species identification, current methods only rely on transverse sections. Additionally, commonly used procedures for evaluating the performance of these methods neglect the fact that multiple images often originate from the same tree, leading to an overly optimistic estimate of the performance. Results We introduce a new image dataset containing microscopic images of the three main anatomical sections of 77 Congolese wood species. A dedicated multi-view image classification method is developed and obtains an accuracy (computed using the naive but common approach) of 95%, outperforming the single-view methods by a large margin. An in-depth analysis shows that naive accuracy estimates can lead to a dramatic over-prediction, of up to 60%, of the accuracy. Conclusions Additional images from non-transverse sections can boost the performance of machine-learning-based wood species identification methods. Additionally, care should be taken when evaluating the performance of machine-learning-based wood species identification methods to avoid an overestimation of the performance.
Background The interaction between canopy and droplets is very important in the process of crop spraying. During the actual air-assisted application process, air-mist flow inevitably disturbs the leaves before droplets reaching them, which will also affect the final deposition state of the droplets on the leaf. Currently, researches on the interaction between droplets and the target leaf surface mainly focuses on the deposition behaviour on the surface of stationary target leaves rather than the dynamic leaves. Therefore, the deposition characteristics after the collision between the droplets and dynamic leaves are important for practical application and worth further study. Results Computational fluid dynamics simulations were performed to characterise the surface roughness, contact angle, and mechanical vibration. The interaction platform between the droplet and the vibrating pear leaf was built for experimental verification under laboratory conditions. The simulation results are in good agreement with the experimental results, which revealed the main reason for the droplet spreading and sliding was the inertial force generated by the relative velocity. It also indicated that the pear leaf vibration can improve the deposition of low-velocity and small droplets, which is different from that of static pear leaves. Conclusion The deposition effect of droplets in vibrating pear leaves was investigated. This study also provides a simulation method for the collision between a vibrating leaf and moving droplets, and provides reference for the study of droplet deposition characteristics under the vibration of fruit trees.
The novel, whole-tree in situ passive heating structure (WTHS). A Drone image of the prototype built in the Cerrado around an individual of D. elliptica. B Diagram depicting shape, size and materials
Four-sided WTHSs in situ in the Cerrado. A Aerial image of WTHSs S2 and S3 in situ in the Cerrado, with the position of control individuals C2 and C3 marked with umbrellas. B Photograph of WTHS S2 partially opened for taking measurements for the analysis of plant responses to temperature
Mean diurnal patterns of A temperature; B relative humidity (RH); and C vapour pressure deficit (VPD); calculated from data recorded at treatment individuals inside each of the four WTHSs (the prototype, S1, S2 and S3; red lines), and their relative controls (blue lines), with the differences calculated between the two (orange lines). In A temperature difference is given on the right-hand axis for greater definition, and the dashed line indicates the target of 3 °C
Mean differences in temperature and RH between inside each WTHS (prototype, S1, S2, S3) and their relative controls during given time periods, including average values for all four WTHSs together. Mean differences were calculated for A 09:00 to 17:00 (period of strongest heating), B daytime (06:30 to 18:30), and C night-time (18:30 to 06:30). Red error bars are the average (over all days of measurement) of the daily standard deviation of the mean difference in temperature or RH. Black error bars are the standard deviation of the daily mean differences over all days of measurement
Climatic conditions and example results from the heating experiment on E. suberosum. A Mean daytime (06:30–18:30) temperature and RH experienced by treatment and control groups in relation to the number of days before or after heating began. B1–B4 Results of the photosynthesis and respiration parameters B1 Tmax, B2 Topt, B3 Aopt, and B4 R45, estimated from leaf measurements taken at 0, 7, 14 and 21 days after experiment initiation for each individual. Each point represents the results from one leaf on one day. Lines show average results for treatment and control groups
Background Many significant ecosystems, including important non-forest woody ecosystems such as the Cerrado (Brazilian savannah), are under threat from climate change, yet our understanding of how increasing temperatures will impact native vegetation remains limited. Temperature manipulation experiments are important tools for investigating such impacts, but are often constrained by access to power supply and limited to low-stature species, juvenile individuals, or heating of target organs, perhaps not fully revealing how entire or mature individuals and ecosystems will react to higher temperatures. Results We present a novel, modified open top chamber design for in situ passive heating of whole individuals up to 2.5 m tall (but easily expandable) in remote field environments with strong solar irradiance. We built multiple whole-tree heating structures (WTHSs) in an area of Cerrado around native woody species Davilla elliptica and Erythroxylum suberosum to test the design and its effects on air temperature and humidity, while also studying the physiological responses of E. suberosum to short-term heating. The WTHSs raised internal air temperature by approximately 2.5 °C above ambient during the daytime. This increased to 3.4 °C between 09:00 and 17:00 local time when thermal impact was greatest, and during which time mean internal temperatures corresponded closely with maximum ambient temperatures. Heating was consistent over time and across WTHSs of variable size and shape, and they had minimal effect on humidity. E. suberosum showed no detectable response of photosynthesis or respiration to short-term experimental heating, but some indication of acclimation to natural temperature changes. Conclusions Our WTHSs produced a consistent and reproducible level of daytime heating in line with mid-range climate predictions for the Cerrado biome by the end of the century. The whole-tree in situ passive heating design is flexible, low-cost, simple to build using commonly available materials, and minimises negative impacts associated with passive chambers. It could be employed to investigate the high temperature responses of many understudied species in a range of complex non-forest environments with sufficient solar irradiance, providing new and important insights into the possible impacts of our changing climate.
Background The superposition of COVID-19 and climate change has brought great challenges to global food security. As a major economic crop in the world, studying its phenotype to cultivate high-quality wheat varieties is an important way to increase grain yield. However, most of the existing phenotyping platforms have the disadvantages of high construction and maintenance costs, immobile and limited in use by climatic factors, while the traditional climate chambers lack phenotypic data acquisition, which makes crop phenotyping research and development difficult. Crop breeding progress is slow. At present, there is an urgent need to develop a low-cost, easy-to-promote, climate- and site-independent facility that combines the functions of crop cultivation and phenotype acquisition. We propose a movable cabin-type intelligent artificial climate chamber, and build an environmental control system, a crop phenotype monitoring system, and a crop phenotype acquisition system. Result We selected two wheat varieties with different early vigor to carry out the cultivation experiments and phenotype acquisition of wheat under different nitrogen fertilizer application rates in an intelligent artificial climate chamber. With the help of the crop phenotype acquisition system, images of wheat at the trefoil stage, pre-tillering stage, late tillering stage and jointing stage were collected, and then the phenotypic information including wheat leaf area, plant height, and canopy temperature were extracted by the crop type acquisition system. We compared systematic and manual measurements of crop phenotypes for wheat phenotypes. The results of the analysis showed that the systematic measurements of leaf area, plant height and canopy temperature of wheat in four growth periods were highly correlated with the artificial measurements. The correlation coefficient (r) is positive, and the determination coefficient ( R 2 ) is greater than 0.7156. The root mean square error ( RSME ) is less than 2.42. Among them, the crop phenotype-based collection system has the smallest measurement error for the phenotypic characteristics of wheat trefoil stage. The canopy temperature RSME is only 0.261. The systematic measurement values of wheat phenotypic characteristics were significantly positively correlated with the artificial measurement values, the fitting degree was good, and the errors were all within the acceptable range. The experiment showed that the phenotypic data obtained with the intelligent artificial climate chamber has high accuracy. We verified the feasibility of wheat cultivation and phenotype acquisition based on intelligent artificial climate chamber. Conclusion It is feasible to study wheat cultivation and canopy phenotype with the help of intelligent artificial climate chamber. Based on a variety of environmental monitoring sensors and environmental regulation equipment, the growth environment factors of crops can be adjusted. Based on high-precision mechanical transmission and multi-dimensional imaging sensors, crop images can be collected to extract crop phenotype information. Its use is not limited by environmental and climatic factors. Therefore, the intelligent artificial climate chamber is expected to be a powerful tool for breeders to develop excellent germplasm varieties.
Background In India, raw peanuts are obtained by aggregators from smallholder farms in the form of whole pods and the price is based on a manual estimation of basic peanut pod and kernel characteristics. These methods of raw produce evaluation are slow and can result in procurement irregularities. The procurement delays combined with the lack of storage facilities lead to fungal contaminations and pose a serious threat to food safety in many regions. To address this gap, we investigated whether X-ray technology could be used for the rapid assessment of the key peanut qualities that are important for price estimation. Results We generated 1752 individual peanut pod 2D X-ray projections using a computed tomography (CT) system (CTportable160.90). Out of these projections we predicted the kernel weight and shell weight, which are important indicators of the produce price. Two methods for the feature prediction were tested: (i) X-ray image transformation (XRT) and (ii) a trained convolutional neural network (CNN). The prediction power of these methods was tested against the gravimetric measurements of kernel weight and shell weight in diverse peanut pod varieties ¹ . Both methods predicted the kernel mass with R ² > 0.93 (XRT: R ² = 0.93 and mean error estimate (MAE) = 0.17, CNN: R ² = 0.95 and MAE = 0.14). While the shell weight was predicted more accurately by CNN (R ² = 0.91, MAE = 0.09) compared to XRT (R ² = 0.78; MAE = 0.08). Conclusion Our study demonstrated that the X-ray based system is a relevant technology option for the estimation of key peanut produce indicators (Figure 1). The obtained results justify further research to adapt the existing X-ray system for the rapid, accurate and objective peanut procurement process. Fast and accurate estimates of produce value are a necessary pre-requisite to avoid post-harvest losses due to fungal contamination and, at the same time, allow the fair payment to farmers. Additionally, the same technology could also assist crop improvement programs in selecting and developing peanut cultivars with enhanced economic value in a high-throughput manner by skipping the shelling of the pods completely. This study demonstrated the technical feasibility of the approach and is a first step to realize a technology-driven peanut production system transformation of the future.
The epitope-tagged minimal RNA-binding protein ‘FLAG-BD’ binds the SOC1 mRNA. A The Arabidopsis BRN1 protein contains three RRM domains (1–3) that bind the SOC1 mRNA 3’UTR [18]. Only two RRM domains (1–2) are necessary for Bruno-like proteins to bind their targets [29]. The BRN1 protein inhibits SOC1 translation [18], and this is thought to be mediated via the protein region between RRM 2 and 3. We generated a FLAG epitope-tagged (asterisk) truncated BRN1 protein with only RRM domains 1 and 2 (FLAG-BD, bottom). Figure created with BioRender. B Western blot of the FLAG-immunoprecipitation in plants with and without the FLAG-BD transgene. The three wt Col and FLAG-BD samples are biological replicates. PEP is an unrelated protein used as a loading control. FT = Flow Through fraction unbound to the FLAG antibody. Arrowheads mark the predicted size of the protein detected. C FLAG-IP followed by RNA extraction and qRT-PCR of samples from (B). AT2G20610 is an unrelated gene used as a negative control. Each biological replicate is shown as a circle. The bar represents the average and error bars represent the standard deviation between three or more biological replicates. P-value is calculated by using an unpaired t-test with Welch's correction. The RIP experiment was repeated twice (Rep 1 / Rep 2) using distinct biological replicate plants
FLAG-BD binding does not alter SOC1 regulation. A Flowering time is measured as the number of leaves generated at the time the first flower opens. Gray points are individual plants, and the red box plots represent the 25th and 75th percentiles of the sample population, with the center bar representing the median and whiskers at the 10th and 90th percentile. P-values are comparisons to the wt Col in the same growth replicate, calculated by using unpaired t-test. ns = not statistically significant. B qRT-PCR of SOC1 mRNA levels in plants with and without FLAG-BD. Three or more biological replicates for each genotype were used (shown as red points), the height of the bar represents their average and the error bars represent the standard deviation. P-value is calculated by using unpaired t-test with Welch’s correction. C Western blot displaying SOC1 protein levels between biological replicates with and without FLAG-BD. D SOC1 protein quantification from the Western blot in part (C). Biological replicate data points are shown as blue points, the height of the bar represents their average and error bars represent the standard deviation. P-value is calculated by using unpaired t-test with Welch’s correction. E Accumulation of siRNAs from wt Col and FLAG-BD lines. TAS3 is a trans-acting siRNA producing locus shown as a positive control for siRNA accumulation. AT2G20610 is an unrelated gene without small RNA production used as a negative control. Two biological replicates are shown as points, and their average is the height of the bar. RPM = reads per million sequenced small RNAs
RNA-binding proteins interact with FLAG-BD. A Volcano plots of anti-FLAG immunoprecipitation followed by Mass Spectrometry (IP-MS) of four biological replicates (Rep1-4). The x-axis shows the log2 fold change between each FLAG-IP sample and the mock-IP control, and the y-axis depicts the p-value of Fisher exact test in a negative log scale. The gray shaded region represents the region of statistical significance (fold change ≥ 2, p ≤ 0.05), while the pink shaded region represents proteins that accumulated in the FLAG-IP but did not accumulate in the mock-IP, making their enrichment value infinite. The green point indicates the bait protein BRN1. Red points indicate the significantly enriched proteins. Blue points indicate the proteins significantly enriched in at least 2 of the 4 biological replicates. Gels of the IP protein sample before Mass Spectrometry are shown as Additional file 1: Figure S2. B Stacked bar graph showing the RNA-interaction annotation of the 20 proteins enriched in at least 2 of the 4 biological replicates from part (A ). These are compared to the annotation of the proteins identified in the mock-IP (FLAG-BD mock), the annotation of the entire Arabidopsis genome, or the genes annotated as expressed in rosette or cauline leaves. Bold indicates the percent of genes, with the total number of genes in parentheses. The hypergeometric test results are shown above each bar as fold enrichments (top number) and p-value in parentheses. C Table showing the 11 proteins previously designated as ‘RNA-binding’ enriched in at least 2 of the 4 FLAG-BD IPs from part (B ). The weighted spectral count (number of spectra associated with only a specific protein group plus the apportioned number of spectra shared with other proteins) is indicated for each protein. The total spectral count and total unique peptide count is listed below. Additional results from the IP-MS experiment are shown in Additional file 2: Table S1
Proof-of-principle tethering of an RNA decay enzyme function to the SOC1 mRNA. A Translational fusion between FLAG-BD and the CAF1a deadenylase protein generates the BD + D protein. B Flowering time of BD + D plants two (T2) and three (T3) generations after transformation. + T plants inherited the BD + D transgene, -T plants are siblings that did not inherit the transgene. Box plots and statistics are the same as in Fig. 2A. C qRT-PCR of SOC1 polyadenylated mRNA. Three biological replicates of each genotype are shown as red points. Bar height, error bars and statistics are the same as Fig. 2B. D qRT-PCR of SOC1 nascent transcripts (unspliced and not polyadenylated). Three or more biological replicates of each genotype are shown as red points. Bar height, error bars and statistics are the same as Fig. 2B. E ePAT assay to determine the poly(A) tail length of the SOC1 mRNA. n = the number of clones Sanger sequenced. TVN is a control where the reverse transcription primer is anchored at the most 3’ nucleotide before the poly(A) tail begins. Box plot organization is the same as Fig. 2A. P-value is calculated by using an unpaired t-test with Welch’s correction. F Quantification of SOC1 protein accumulation in the BD + D line. Individual biological replicates are down as blue points. Bar height, error bars and statistics are the same as Fig. 2B
Tethering of a ribosomal protein translation factor to the SOC1 mRNA boosts protein production. A Translational fusion between FLAG-BD and the RPS6 ribosomal protein generates the BD + R protein. B Flowering time of T2 BD + R plants grown side-by-side with wt Col. + T plants inherited the BD + R transgene, -T plants are siblings that did not inherit the transgene. C Representative images of T2 BD + R (+ T) plants grown side-by-side with wt Col taken 24 days after germination. D Western blot of SOC1 protein accumulation in seven biological replicates of early-flowering plants of the BD + R line compared to wt Col. PEP is an unrelated protein used as a loading control. E Quantification of SOC1 protein accumulation in the BD + R line from part (D). Individual biological replicates are shown as blue points. Bar height, error bars and statistics are the same as Fig. 2B. F qRT-PCR of SOC1 polyadenylated mRNA. Three biological replicates of each genotype are shown as red points. Bar height, error bars and statistics are the same as Fig. 2B. G qRT-PCR of SOC1 nascent transcripts (unspliced and not polyadenylated). Three biological replicates of each genotype are shown as red points. Bar height, error bars and statistics are the same as Fig. 2B
The sorting of RNA transcripts dictates their ultimate post-transcriptional fates, such as translation, decay or degradation by RNA interference (RNAi). This sorting of RNAs into distinct fates is mediated by their interaction with RNA-binding proteins. While hundreds of RNA binding proteins have been identified, which act to sort RNAs into different pathways is largely unknown. Particularly in plants, this is due to the lack of reliable protein-RNA artificial tethering tools necessary to determine the mechanism of protein action on an RNA in vivo. Here we generated a protein-RNA tethering system which functions on an endogenous Arabidopsis RNA that is tracked by the quantitative flowering time phenotype. Unlike other protein-RNA tethering systems that have been attempted in plants, our system circumvents the inadvertent triggering of RNAi. We successfully in vivo tethered a protein epitope, deadenylase protein and translation factor to the target RNA, which function to tag, decay and boost protein production, respectively. We demonstrated that our tethering system (1) is sufficient to engineer the downstream fate of an RNA, (2) enables the determination of any protein’s function upon recruitment to an RNA, and (3) can be used to discover new interactions with RNA-binding proteins.
Images and average reflectance profiles of tomato seeds included in this study. Photos of tomato seeds from two varieties, A and B, and five subsamples for each variety (a). Average reflectance profiles of five subsamples of tomato seed variety 1 (b) and 2 (c) included in this study
Average reflectance profiles from germinating and non-germinating seeds. Average profiles of non-germinating and germinating tomato seeds from variety 1 and 2 (a), and relative effects of germination (germination / non-germination) of variety 1 and 2 (b)
Results from experimental performance assessments of classification models. Training data set from tomato variety 1 was manipulated in three different ways, and for each manipulation, we examined the effect on accuracy of linear discriminant (LDA) and support vector machine (SVM) classification models (based on ten-fold cross validation). Object assignment error: effect of individual seeds being assigned to the wrong class (a). Spectral repeatability: effect of introducing known levels of stochastic noise to individual reflectance values (b). Size of training data set: effect of randomly reducing the number of observations in the training data set (c)
Correlations between observed and predicted seed germination (%) based on linear discriminant (LDA) and support vector machine (SVM) classification models. Validation data (see Table 1) were used to predict tomato seed germination (%) in five seed subsamples from two varieties. We performed validations of both linear discriminant (LDA) and support vector machine (SVM) classification models. Seed germination percentages obtained from the seed company are presented as colored circles and considered “known germination”. Blue circles represent germination percentages of samples, which were used as training data. Red colored circles represent germination percentages of validation samples (not included in training data). Colored squares represent predicted germination percentages of training (blue squares) and validation (red squares) samples. Each colored symbol represents germination percentage based on 96 individual seeds
Background Optical sensing solutions are being developed and adopted to classify a wide range of biological objects, including crop seeds. Performance assessment of optical classification models remains both a priority and a challenge. Methods As training data, we acquired hyperspectral imaging data from 3646 individual tomato seeds (germination yes/no) from two tomato varieties. We performed three experimental data manipulations: (1) Object assignment error: effect of individual object in the training data being assigned to the wrong class. (2) Spectral repeatability: effect of introducing known ranges (0–10%) of stochastic noise to individual reflectance values. (3) Size of training data set: effect of reducing numbers of observations in training data. Effects of each of these experimental data manipulations were characterized and quantified based on classifications with two functions [linear discriminant analysis (LDA) and support vector machine (SVM)]. Results For both classification functions, accuracy decreased linearly in response to introduction of object assignment error and to experimental reduction of spectral repeatability. We also demonstrated that experimental reduction of training data by 20% had negligible effect on classification accuracy. LDA and SVM classification algorithms were applied to independent validation seed samples. LDA-based classifications predicted seed germination with RMSE = 10.56 (variety 1) and 26.15 (variety 2), and SVM-based classifications predicted seed germination with RMSE = 10.44 (variety 1) and 12.58 (variety 2). Conclusion We believe this study represents the first, in which optical seed classification included both a thorough performance evaluation of two separate classification functions based on experimental data manipulations, and application of classification models to validation seed samples not included in training data. Proposed experimental data manipulations are discussed in broader contexts and general relevance, and they are suggested as methods for in-depth performance assessments of optical classification models.
Background Triticum aestivum is the most important staple food grain of the world. In recent years, the outbreak of a major seed-borne disease, common bunt, in wheat resulted in reduced quality and quantity of the crop. The disease is caused by two fungal pathogens, Tilletia caries and Tilletia laevis , which show high similarity to each other in terms of life cycle, germination, and disease symptoms. The host–pathogen protein–protein interactions play a crucial role in initiating the disease infection mechanism as well as in plant defense responses. Due to the availability of limited information on Tilletia species, the elucidation of infection mechanisms is hampered. Results We constructed a database WeCoNET ( ), providing functional annotations of the pathogen proteins and various tools to exploit host–pathogen interactions and other relevant information. The database implements a host–pathogen interactomics tool to predict protein–protein interactions, followed by network visualization, BLAST search tool, advanced ‘keywords-based’ search module, etc. Other features in the database include various functional annotations of host and pathogen proteins such as gene ontology terms, functional domains, and subcellular localization. The pathogen proteins that serve as effector and secretory proteins have also been incorporated in the database, along with their respective descriptions. Additionally, the host proteins that serve as transcription factors were predicted, and are available along with the respective transcription factor family and KEGG pathway to which they belong. Conclusion WeCoNET is a comprehensive, efficient resource to the molecular biologists engaged in understanding the molecular mechanisms behind the common bunt infection in wheat. The data integrated into the database can also be beneficial to the breeders for the development of common bunt-resistant cultivars.
Workflow for VIGS in switchgrass. Fragments of the target genes inserted into the foxtail mosaic vector (FoMV) system are first transfected into Nicotiana benthamiana leaves for multiplication of the virus. After PCR verification of successful transformation, leaf sap is extracted and used to mechanically rub-inoculate switchgrass leaves, followed by PCR-verification of transformants and gene expression analysis of target genes via RT-PCR
RT-PCR analysis of construct presence and integrity in N. benthamiana and P. virgatum. RT-PCR assays of systemic N. benthamiana (A) and P. virgatum (B) leaves and roots verifying the presence of the constructs FoMV:PDS, FoMV:ChlI or FoMV:ChlD as compared to wild type plants (no virus) and FoMV transfection only. Expected amplicon size is 314 bp for FoMV, 410 bp for FoMV:PDS, 424 bp for FoMV:ChlI, and 369 bp for FoMV:ChlD. Oligonucleotide sequences are listed in Additional file 1: Table S3
Phenotypic differences among older and younger switchgrass leaves 4 weeks post-inoculation (A) in younger plants that were infected at the E1 stage, (B) in older plants that were infected at the E3 stage. WT: wildtype/untreated plants; FoMV: empty vector control; ChlD: plants infected with FoMV:ChlD; ChlI: plants infected with FoMV:ChlI; PDS: plants infected with FoMV:PDS
Gene expression levels 28 days after inoculation in leaves and roots for all three constructs and controls of switchgrass that was infected at the E1 stage. Expression of ChlD in control and FoMV:ChlD (A) leaves and (B) roots; expression of ChlI in control and FoMV:ChlI (C) leaves and (D) roots; expression of PDS in control and FoMV:PDS (E) leaves and (F) roots; n = 6 per biological sample group; no inoculated leaves but only newly emerged leaves were sampled for gene expression analysis; p ≦ 0.05 *; p ≦ 0.01 **; p ≦ 0.001***. WT, untreated wild type plants
Gene expression levels 28 days after inoculation in leaves and roots for all three constructs and controls of switchgrass plants that were infected at the E3 stage. Expression of ChlD in control and FoMV:ChlD (A) leaves and (B) roots; expression of ChlI in control and FoMV:ChlI (C) leaves and (D) roots; expression of PDS in control and FoMV:PDS (E) leaves and (F) roots; n = 6 per biological sample group; no inoculated leaves but only newly emerged leaves were sampled for gene expression analysis; p ≦ 0.05 *; p ≦ 0.01 **; p ≦ 0.001***. WT, untreated wild type plants
Background Although the genome for the allotetraploid bioenergy crop switchgrass ( Panicum virgatum ) has been established, limitations in mutant resources have hampered in planta gene function studies toward crop optimization. Virus-induced gene silencing (VIGS) is a versatile technique for transient genetic studies. Here we report the implementation of foxtail mosaic virus (FoMV)-mediated gene silencing in switchgrass in above- and below-ground tissues and at different developmental stages. Results The study demonstrated that leaf rub-inoculation is a suitable method for systemic gene silencing in switchgrass. For all three visual marker genes, Magnesium chelatase subunit D ( ChlD ) and I ( ChlI ) as well as phytoene desaturase ( PDS ), phenotypic changes were observed in leaves, albeit at different intensities. Gene silencing efficiency was verified by RT-PCR for all tested genes. Notably, systemic gene silencing was also observed in roots, although silencing efficiency was stronger in leaves (~ 63–94%) as compared to roots (~ 48–78%). Plants at a later developmental stage were moderately less amenable to VIGS than younger plants, but also less perturbed by the viral infection. Conclusions Using FoMV-mediated VIGS could be achieved in switchgrass leaves and roots, providing an alternative approach for studying gene functions and physiological traits in this important bioenergy crop.
A Scheme of the experimental setup and feedback loop. B control diagram of the feedback loop controlling leaf water content (LWC) by using the LWC sensor's output to control the pump in the drying bypass loop. Biological regulators of leaf water homeostasis (green boxes) modify the effect of vapour pressure deficit (VPD) on LWC. Stomatal responses determine the effect of VPD on transpiration while hydraulic conductivity and osmotic adjustment modulate the effect of transpiration on the leaf water balance and thus LWC. The varying effect of these biological influences is compensated for by continuous adjustment of VPD
Cuvette for control of leaf water content of the enclosed leaf. The cuvette A is installed on the motorized stage of an inverted fluorescence microscope B. The enclosed leaf C is attached to the transparent D lid of the cuvette with double sided tape and microscopically observed through the glass bottom made of cover slide glass. A leaf water content sensor comprised of a dual IR-LED light source E and a photo-diode (not visible) below the leaf-surface continuously tracks LWC. Its output is fed into a feedback algorithm which controls vapour pressure deficit (VPD) of the air passing over the leaf surface. Alternatively VPD can be feed-back-controlled according to the output of a air humidity sensor F. Illumination is provided by a switchable white LED light source under the control of the microscope central unit, which can be switched off for about 120 ms during fluorescence measurements
Measurement of leaf water content (LWC) and vapour pressure deficit (VPD) during an experiment using first (0–120 min) feedback-control of air humidity to perform a step change in air humidity and (120–280 min) feedback control of LWC to perform step changes of LWC. In the first part, leaf water content fluctuates dynamically as a result of changing leaf water balance. In the second part, LWC is fixed, and VPD is varied by feedback control as required to clamp LWC at constant set-points
A Recording of apoplastic pH of the lower leaf epidermis by fluorescence ratio imaging using OregonGreen under feedback-controlled leaf water content (LWC). LWC set-point was varied by a programmed ramp from 100% full saturation under low VPD to 98.5% saturation within 40 min. and in reverse to achieve constant rates of change of LWC. B Relation between LWC and apopastic pH. Same data as panel A. Here the the dose response relation observed during slowly changing LWC is visualized
Background Leaf hydration is controlled by feedback mechanisms, e.g. stomatal responses, adjustments of osmotic potential and hydraulic conductivity. Leaf water content thus is an input into related feedback-loops controlling the balance of water uptake and loss. Apoplastic alkalisation upon leaf dehydration is hypothesized to be involved together and in interaction with abscisic acid (ABA) in water stress related signaling on tissue level. However, important questions are still unresolved, e.g. the mechanisms leading to pH changes and the exact nature of its interaction with ABA. When studying these mechanisms and their intermediate signaling steps, an experimenter has only poor means to actually control the central experimental variable, leaf water content (LWC), because it is not only dependent on external variables (e.g. air humidity), which are under experimental control, but is also governed by the biological influences controlling transpiration and water uptake. Those are often unknown in their magnitude, unpredictable and fluctuating throughout an experiment and will prevent true repetitions of an experiment. The goal of the method presented here is to experimentally control and manipulate leaf water content (LWC) of attached intact leaves enclosed in a cuvette while simultaneously measuring physiological parameters like, in this case, apoplastic pH. Results An experimental setup was developed where LWC is measured by a sensor based on IR-transmission and its signal processed to control a pump which circulates air from the cuvette through a cold trap. Hereby a feedback-loop is formed, which by adjusting vapour pressure deficit (VPD) and consequently leaf transpiration can precisely control LWC. This technique is demonstrated here in a combination with microscopic fluorescence imaging of apoplastic pH (pH apo ) as indicated by the excitation ratio of the pH sensitive dye OregonGreen. Initial results indicate that pH apo of the adaxial epidermis of Vicia faba is linearly related to reductions in LWC. Conclusions Using this setup, constant LWC levels, step changes or ramps can be experimentally applied while simultaneously measuring physiological responses. The example experiments demonstrate that bringing LWC under experimental control in this way allows better controlled and more repeatable experiments to probe quantitative relationships between LWC and signaling and regulatory processes.
Comparative homology analysis of target gene sequences of 22 Cucumber green mottle mosaic virus (CGMMV) isolates using DNAStar software. As seen in the figure, the homology between 22 CGMMV isolates and DY13 isolate ranged from 97% to 99.9%
Cucumber green mottle mosaic virus (CGMMV) was detected by improved one-step pre-amplification RT-qPCR. CGMMV-infected single seed sample; CGMMV reference material; simulated CGMMV-contaminated seed powder base material; cucumber mosaic virus (CMV); zucchini yellow mosaic virus (ZYMV); melon yellow spot virus (MYSV). Tests were performed using total RNA extracted from leaves and seeds infected with one of the four viruses studied. For each assay, RNA extracted from uninfected seeds was used as negative control, along with a non-template control (NTC). Amplification plots show the normalized fluorescence values (ΔRn) versus the amplification cycle number, and horizontal lines denote the threshold limit of the test. Samples were tested in duplicate. Amplification plots correspond to improved one-step pre-amplification RT-qPCR
Background Seeds were an important medium for long-distance transmission of plant viruses. Therefore, appropriate, more sensitive methods for detecting low concentrations of virus-infected in seeds were crucial to ensure the quality of seed lots. In this study, we have developed a one-step pre-amplification reverse transcription quantitative PCR (RT-qPCR) assay based on the TaqMan technology to detect Cucumber green mottle mosaic virus (CGMMV) in zucchini seeds. Result Seed powder samples with simulated CGMMV-infected at a low concentration were prepared (the mass ratio 1:900 and 1:1000), and their uniformity were verified using one-step pre-amplification RT-qPCR. We used one-step pre-amplification RT-qPCR to detect CGMMV in low-concentration virus-infected seeds and compared this method with universal RT-qPCR and double antibody sandwich–enzyme-linked immunosorbent (DAS–ELISA) assay, the main methods used for virus detection in seeds. The minimum limit of detection (LOD) of the improved one-step pre-amplification RT-qPCR assays for simulated CGMMV-infected seeds in large lots seeds samples were 0.1%. Conclusions One-step pre-amplification RT-qPCR assays could reliably and stably detected a single CGMMV-infected seed in 1000 seeds and demonstrated a higher detection sensitivity than universal RT-qPCR (infected seeds versus healthy seeds 1:900) and DAS–ELISA assay (infected seeds versus healthy seeds 1:500). Our improved one-step pre-amplification RT-qPCR assay have proved to be very suitable for the analysis of large seed lots. Graphical Abstract
Background The bimolecular fluorescence complementation (BiFC) assay has emerged as one of the most popular methods for analysing protein–protein interactions (PPIs) in plant biology. This includes its increasing use as a tool for dissecting the molecular mechanisms of chloroplast function. However, the construction of chloroplast fusion proteins for BiFC can be difficult, and the availability and selection of appropriate controls is not trivial. Furthermore, the challenges of performing BiFC in restricted cellular compartments has not been specifically addressed. Results Here we describe the development of a flexible modular cloning-based toolkit for BiFC (MoBiFC) and proximity labelling in the chloroplast and other cellular compartments using synthetic biology principles. We used pairs of chloroplast proteins previously shown to interact (HSP21/HSP21 and HSP21/PTAC5) and a negative control (HSP21/ΔPTAC5) to develop standardised Goldengate-compatible modules for the assembly of protein fusions with fluorescent protein (FP) fragments for BiFC expressed from a single multigenic T-DNA. Using synthetic biology principles and transient expression in Nicotiana benthamiana, we iteratively improved the approach by testing different FP fragments, promoters, reference FPs for ratiometric quantification, and cell types. A generic negative control (mCHERRY) was also tested, and modules for the identification of proximal proteins by Turbo-ID labelling were developed and validated. Conclusions MoBiFC facilitates the cloning process for organelle-targeted proteins, allows robust ratiometric quantification, and makes available model positive and negative controls. Development of MoBiFC underlines how Goldengate cloning approaches accelerate the development and enrichment of new toolsets, and highlights several potential pitfalls in designing BiFC experiments including the choice of FP split, negative controls, cell type, and reference FP. We discuss how MoBiFC could be further improved and extended to other compartments of the plant cell and to high throughput cloning approaches.
Background Leaf Area Index (LAI) is half of the amount of leaf area per unit horizontal ground surface area. Consequently, accurate vegetation extraction in remote sensing imagery is critical for LAI estimation. However, most studies do not fully exploit the advantages of Unmanned Aerial Vehicle (UAV) imagery with high spatial resolution, such as not removing the background (soil and shadow, etc.). Furthermore, the advancement of multi-sensor synchronous observation and integration technology allows for the simultaneous collection of canopy spectral, structural, and thermal data, making it possible for data fusion. Methods To investigate the potential of high-resolution UAV imagery combined with multi-sensor data fusion in LAI estimation. High-resolution UAV imagery was obtained with a multi-sensor integrated MicaSense Altum camera to extract the wheat canopy's spectral, structural, and thermal features. After removing the soil background, all features were fused, and LAI was estimated using Random Forest and Support Vector Machine Regression. Results The results show that: (1) the soil background reduced the accuracy of the LAI prediction of wheat, and soil background could be effectively removed by taking advantage of high-resolution UAV imagery. After removing the soil background, the LAI prediction accuracy improved significantly, R² raised by about 0.27, and RMSE fell by about 0.476. (2) The fusion of multi-sensor synchronous observation data could achieve better accuracy (R² = 0.815 and RMSE = 1.023), compared with using only one data; (3) A simple LAI prediction method could be found, that is, after selecting a few features by machine learning, high prediction accuracy can be obtained only by simple multiple linear regression (R² = 0.679 and RMSE = 1.231), providing inspiration for rapid and efficient LAI prediction of wheat. Conclusions The method of this study can be transferred to other sites with more extensive areas or similar agriculture structures, which will facilitate agricultural production and management.
Background The chlorophyll content is a vital indicator for reflecting the photosynthesis ability of plants and it plays a significant role in monitoring the general health of plants. Since the chlorophyll content and the soil–plant analysis development (SPAD) value are positively correlated, it is feasible to predict the SPAD value by calculating the vegetation indices (VIs) through hyperspectral images, thereby evaluating the severity of plant diseases. However, current indices simply adopt few wavelengths of the hyperspectral information, which may decrease the prediction accuracy. Besides, few researches explored the applicability of VIs over rice under the bacterial blight disease stress. Methods In this study, the SPAD value was predicted by calculating the spectral fractal dimension index (SFDI) from a hyperspectral curve (420 to 950 nm). The correlation between the SPAD value and hyperspectral information was further analyzed for determining the sensitive bands that correspond to different disease levels. In addition, a SPAD prediction model was built upon the combination of selected indices and four machine learning methods. Results The results suggested that the SPAD value of rice leaves under different disease levels are sensitive to different wavelengths. Compared with current VIs, a stronger positive correlation was detected between the SPAD value and the SFDI, reaching an average correlation coefficient of 0.8263. For the prediction model, the one built with support vector regression and SFDI achieved the best performance, reaching R ² , RMSE, and RE at 0.8752, 3.7715, and 7.8614%, respectively. Conclusions This work provides an in-depth insight for accurately and robustly predicting the SPAD value of rice leaves under the bacterial blight disease stress, and the SFDI is of great significance for monitoring the chlorophyll content in large-scale fields non-destructively.
Background Although quantitative single-cell analysis is frequently applied in animal systems, e.g. to identify novel drugs, similar applications on plant single cells are largely missing. We have exploited the applicability of high-throughput microscopic image analysis on plant single cells using tobacco leaf protoplasts, cell-wall free single cells isolated by lytic digestion. Protoplasts regenerate their cell wall within several days after isolation and have the potential to expand and proliferate, generating microcalli and finally whole plants after the application of suitable regeneration conditions. Results High-throughput automated microscopy coupled with the development of image processing pipelines allowed to quantify various developmental properties of thousands of protoplasts during the initial days following cultivation by immobilization in multi-well-plates. The focus on early protoplast responses allowed to study cell expansion prior to the initiation of proliferation and without the effects of shape-compromising cell walls. We compared growth parameters of wild-type tobacco cells with cells expressing the antiapoptotic protein Bcl2-associated athanogene 4 from Arabidopsis ( AtBAG4). Conclusions AtBAG4 -expressing protoplasts showed a higher proportion of cells responding with positive area increases than the wild type and showed increased growth rates as well as increased proliferation rates upon continued cultivation. These features are associated with reported observations on a BAG4-mediated increased resilience to various stress responses and improved cellular survival rates following transformation approaches. Moreover, our single-cell expansion results suggest a BAG4-mediated, cell-independent increase of potassium channel abundance which was hitherto reported for guard cells only. The possibility to explain plant phenotypes with single-cell properties, extracted with the single-cell processing and analysis pipeline developed, allows to envision novel biotechnological screening strategies able to determine improved plant properties via single-cell analysis.
Background Quinoa is an increasingly popular seed crop frequently studied for its tolerance to various abiotic stresses as well as its susceptibility to heat. Estimations of quinoa pollen viability through staining methods have resulted in conflicting results. A more effective alternative to stains is to estimate pollen viability through in vitro germination. Here we report a method for in vitro quinoa pollen germination that could be used to understand the impact of various stresses on quinoa fertility and therefore seed yield or to identify male-sterile lines for breeding. Results A semi-automated method to count germinating pollen was developed in PlantCV, which can be widely used by the community. Pollen collected on day 4 after first anthesis at zeitgeber time 5 was optimum for pollen germination with an average germination of 68% for accession QQ74 (PI 614886). The optimal length of pollen incubation was found to be 48 h, because it maximizes germination rates while minimizing contamination. The pollen germination medium’s pH, boric acid, and sucrose concentrations were optimized. The highest germination rates were obtained with 16% sucrose, 0.03% boric acid, 0.007% calcium nitrate, and pH 5.5. This medium was tested on quinoa accessions QQ74, and cherry vanilla with 68%, and 64% germination efficiencies, respectively. Conclusions We provide an in vitro pollen germination method for quinoa with average germination rates of 64 and 68% on the two accessions tested. This method is a valuable tool to estimate pollen viability in quinoa, and to test how stress affects quinoa fertility. We also developed an image analysis tool to semi-automate the process of counting germinating pollen. Quinoa produces many new flowers during most of its panicle development period, leading to significant variation in pollen maturity and viability between different flowers of the same panicle. Therefore, collecting pollen at 4 days after first anthesis is very important to collect more uniformly developed pollen and to obtain high germination rates.
Background The number of banana plants is closely related to banana yield. The diameter and height of the pseudo-stem are important morphological parameters of banana plants, which can reflect the growth status and vitality. To address the problems of high labor intensity and subjectivity in traditional measurement methods, a fast measurement method for banana plant count, pseudo-stem diameter, and height based on terrestrial laser scanning (TLS) was proposed. Results First, during the nutritional growth period of banana, three-dimensional (3D) point cloud data of two measured fields were obtained by TLS. Second, the point cloud data was preprocessed. And the single plant segmentation of the canopy closed banana plant point cloud was realized furtherly. Finally, the number of banana plants was obtained by counting the number of pseudo-stems, and the diameter of pseudo-stems was measured using a cylindrical segmentation algorithm. A sliding window recognition method was proposed to determine the junction position between leaves and pseudo-stems, and the height of the pseudo-stems was measured. Compared with the measured value of artificial point cloud, when counting the number of banana plants, the precision,recall and percentage error of field 1 were 93.51%, 94.02%, and 0.54% respectively; the precision,recall and percentage error of field 2 were 96.34%, 92.00%, and 4.5% respectively; In the measurement of pseudo-stem diameter and height of banana, the root mean square error (RMSE) of pseudo-stem diameter and height of banana plant in field 1 were 0.38 cm and 0.2014 m respectively, and the mean absolute percentage error (MAPE) were 1.30% and 5.11% respectively; the RMSE of pseudo-stem diameter and height of banana plant in field 2 were 0.39 cm and 0.2788 m respectively, and the MAPE were 1.04% and 9.40% respectively. Conclusion The results show that the method proposed in this paper is suitable for the field measurement of banana count, pseudo-stem diameter, and height and can provide a fast field measurement method for banana plantation management.
Efficiency of B. napus-RC cotyledons and hypocotyls in producing calli and shoots in vitro. Proportion of B. napus-RC cotyledons and hypocotyls producing calli a and shoots b, in tissue culture media containing plant growth regulators NAA (0.1–0.3 mg/L), BAP (0.5–1.5 mg/L), GA3 (0.01 mg/L) and the ethylene antagonist, AgNO3 (5 mg/L). The error bars represent standard error of mean; means that do not share the same letter are significantly different from each other according to Fisher’s Least Significance Difference (protected) test at the 5% confidence level (n = 10, r = 18). c Representative calli (arrow) arising from cotyledon petioles following incubation on callus induction medium. d Representative shoots (arrow) from cotyledons on shoot induction medium where small shoots/plantlets bud off from developed calli. e Elongation of shoots (arrow) from hypocotyls on shoot elongation medium. f Root intitials (arrow) from plantlets in root induction medium. Scale bar, 1 cm
Generation of AtACBP6-overexpressing B. napus-RC shoots cultured in vitro on medium containing kanamycin (25 mg/L) applied two weeks after transformation to differentiate transformed from untransformed shoots; and subsequent PCR screening. a On selective shoot media, non-transgenic shoots are pink or pale-yellow (red arrows) whereas transgenic shoots are green (blue arrow). b Putative transformed green shoots (arrows). c Close-up of representative yellow cotyledon explant with an untransformed purple shoot (arrow). d Close-up of a cotyledon explant with a putatively transformed green shoot (arrow). Black scale bar, 1 cm; white scale bar, 5 mm. e PCR screening of regenerated shoots using an AtACBP6-specific primer pair ML838 and ML750. The expected amplicon size (0.37 kb) is arrowed. M, 1 kb plus DNA ladder; P, plasmid pAT593 positive control; W, water control; Putative transformed green shoot samples, 1–11; UT, untransformed B. napus-RC
Detection of AtACBP6 protein in transgenic B. napus-RC and Westar lines by western blot analysis with AtACBP6 specific antibodies. The top half of each panel shows the protein blot with 10.4-kD AtACBP6 cross-reacting band (red arrow), and the bottom panel shows an identically loaded gel stained with Coomassie blue. Lane 1, AtACBP6-overexpressing B. napus-RC (RC) line 01; Lanes 2–6, AtACBP6-overexpressing Westar (W) lines × 1, 01, 02, 03 and 04; Lane 7, A. thaliana (AT) Col-0; Lane 8, wild-type B. napus (WT); Lane 9, AtACBP6-overexpressing Westar line 05; Lane 10 to 15, transgenic B. napus-RC plants lines 81, 05, 06, 109, 110, 111
Recovery of AtACBP6-overexpressing B. napus-RC after freezing/frost treatment a Average number of shoots after freezing-without-frosting treatment. b Average number of flowers after freezing-without-frosting treatment. c Average number of shoots after freezing-with-frosting treatment. d Average number of flowers after freezing-with-frosting treatment during the recovery period. To provide statistical differences in shoot and flower numbers, the total area under the lines was calculated for each plant and the average values presented in Table 3. e Comparison of germination ability of wild type (left punnets) and AtACBP6-overexpressing seeds after freezing/frost treatment (right punnets) with B. napus-RC lines 81, 1 and 109 with 20 seeds/punnet
Background Rapid-cycling Brassica napus ( B. napus -RC) has potential as a rapid trait testing system for canola ( B. napus ) because its life cycle is completed within 2 months while canola usually takes 4 months, and it is susceptible to the same range of diseases and abiotic stress as canola. However, a rapid trait testing system for canola requires the development of an efficient transformation and tissue culture system for B. napus -RC. Furthermore, effectiveness of this system needs to be demonstrated by showing that a particular trait can be rapidly introduced into B. napus -RC plants. Results An in-vitro regeneration protocol was developed for B. napus -RC using 4-day-old cotyledons as the explant. High regeneration percentages, exceeding 70%, were achieved when 1-naphthaleneacetic acid (0.10 mg/L), 6-benzylaminopurine (1.0 mg/L), gibberellic acid (0.01 mg/L) and the ethylene antagonist silver nitrate (5 mg/L) were included in the regeneration medium. An average transformation efficiency of 16.4% was obtained using Agrobacterium -mediated transformation of B. napus -RC cotyledons using Agrobacterium strain GV3101 harbouring a plasmid with an NPTII (kanamycin-selectable) marker gene and the Arabidopsis thaliana cDNA encoding ACYL-COA-BINDING PROTEIN6 (AtACBP6). Transgenic B. napus -RC overexpressing AtACBP6 displayed better tolerance to freezing/frost than the wild type, with enhanced recovery from cellular membrane damage at both vegetative and flowering stages. AtACBP6 -overexpressing B. napus -RC plants also exhibited lower electrolyte leakage and improved recovery following frost treatment, resulting in higher yields than the wild type. Ovules from transgenic AtACBP6 lines were better protected from frost than those of the wild type, while the developing embryos of frost-treated AtACBP6 -overexpressing plants showed less freezing injury than the wild type. Conclusions This study demonstrates that B. napus -RC can be successfully regenerated and transformed from cotyledon explants and has the potential to be an effective trait testing platform for canola. Additionally, AtACBP6 shows potential for enhancing cold tolerance in canola however, larger scale studies will be required to further confirm this outcome.
Background The accurate estimation of leaf hydraulic conductance ( K leaf ) is important for revealing leaf physiological characteristics and function. However, the K leaf values are largely incomparable in previous studies for a given species indicating some uncertain influencing factors in K leaf measurement. Result We investigated the potential impacts of plant sampling method, measurement setup, environmental factors, and transpiration steady state identification on K leaf estimation in Oryza sativa and Cinnamomum camphora using evaporation flux method (EFM). The effects of sampling and rehydration time, the small gravity pressure gradients between water sources and leaves, and water degassing on K leaf estimation were negligible. As expected, the estimated steady flow rate ( E ) was significantly affected by multiple environmental factors including airflow around leaf, photosynthetically active radiation (PARa) on leaf surfaces and air temperature. K leaf decreased by 40% when PARa declined from 1000 to 500 µmol m ⁻² s ⁻¹ and decreased by 15.1% when air temperature increased from 27 to 37 °C. In addition, accurate steady-state flow rate identification and leaf water potential measurement were important for K leaf estimation. Conclusions Based on the analysis of influencing factors, we provided a format for reporting the metadata of EFM-based K leaf to achieve greater comparability among studies and interpretation of differences.
Background Despite the advances in the techniques of indirect estimation of leaf area, the destructive measurement approaches have still remained as the reference and the most accurate methods. However, even utilizing the modern sensors and applications usually requires the laborious and time-consuming practice of unfolding and analyzing the single leaves, separately. In the present study, a volumetric approach was tested to determine the pile leaf area based on the ratio of leaf volume divided by thickness. For this purpose, the suspension technique was used for volumetry, which is based on the simple practice and calculations of the Archimedes’ principle. Results Wheat volumetric leaf area (VLA), had a high agreement and approximately 1:1 correlation with the conventionally measured optical leaf area (OLA). Exclusion of the midrib volume from calculations, did not affect the estimation error (NRMSE < 2.61%); however, improved the slope of the linear model by about 6%, and also reduced the bias between the methods. The error of sampling for determining mean leaf thickness of the pile, was also less than 2% throughout the season. Besides, a more practical and facilitated version of pile volumetry was tested using Specific Gravity Bench (SGB), which is currently available as a laboratory equipment. As an important observation, which was also expectable according to the leaf 3D expansion (i.e., in a given 2D plane), it was evidenced that the variations in the OLA exactly follows the pattern of the changes in the leaf volume. Accordingly, it was suggested that the relative leaf areas of various experimental treatments might be compared directly based on volume, and independently of leaf thickness. Furthermore, no considerable difference was observed among the OLAs measured using various image resolutions (NRMSE < 0.212%); which indicates that even the superfast scanners with low resolutions as 200 dpi may be used for a precision optical measurement of leaf area. Conclusions It is expected that utilizing the reliable and simple concept of volumetric leaf area, based on which the measurement time might be independent of sample size, facilitate the laborious practice of leaf area measurement; and consequently, improve the precision of field experiments.
Background There is a demand for non-destructive systems in plant phenotyping which could precisely measure plant traits for growth monitoring. In this study, the growth of chilli plants (Capsicum annum L.) was monitored in outdoor conditions. A non-destructive solution is proposed for growth monitoring in 3D using a single mobile phone camera based on a structure from motion algorithm. A method to measure leaf length and leaf width when the leaf is curled is also proposed. Various plant traits such as number of leaves, stem height, leaf length, and leaf width were measured from the reconstructed and segmented 3D models at different plant growth stages. Results The accuracy of the proposed system is measured by comparing the values derived from the 3D plant model with manual measurements. The results demonstrate that the proposed system has potential to non-destructively monitor plant growth in outdoor conditions with high precision, when compared to the state-of-the-art systems. Conclusions In conclusion, this study demonstrated that the methods proposed to calculate plant traits can monitor plant growth in outdoor conditions.
Background Leaf chlorophyll content plays an important role in indicating plant stresses and nutrient status. Traditional approaches for the quantification of chlorophyll content mainly include acetone ethanol extraction, spectrophotometry and high-performance liquid chromatography. Such destructive methods based on laboratory procedures are time consuming, expensive, and not suitable for high-throughput analysis. High throughput imaging techniques are now widely used for non-destructive analysis of plant phenotypic traits. In this study three imaging modules (RGB, hyperspectral, and fluorescence imaging) were, separately and in combination, used to estimate chlorophyll content of sorghum plants in a greenhouse environment. Color features, spectral indices, and chlorophyll fluorescence intensity were extracted from these three types of images, and multiple linear regression models and PLSR (partial least squares regression) models were built to predict leaf chlorophyll content (measured by a handheld leaf chlorophyll meter) from the image features. Results The models with a single color feature from RGB images predicted chlorophyll content with R ² ranging from 0.67 to 0.88. The models using the three spectral indices extracted from hyperspectral images (Ration Vegetation Index, Normalized Difference Vegetation Index, and Modified Chlorophyll Absorption Ratio Index) predicted chlorophyll content with R ² ranging from 0.77 to 0.78. The model using the fluorescence intensity extracted from fluorescence images predicted chlorophyll content with R ² of 0.79. The PLSR model that involved all the image features extracted from the three different imaging modules exhibited the best performance for predicting chlorophyll content, with R ² of 0.90. It was also found that inclusion of SLW (Specific Leaf Weight) into the image-based models further improved the chlorophyll prediction accuracy. Conclusion All three imaging modules (RGB, hyperspectral, and fluorescence) tested in our study alone could estimate chlorophyll content of sorghum plants reasonably well. Fusing image features from different imaging modules with PLSR modeling significantly improved the predictive performance. Image-based phenotyping could provide a rapid and non-destructive approach for estimating chlorophyll content in sorghum.
Background Mean grain weight (MGW) is among the most frequently measured parameters in wheat breeding and physiology. Although in the recent decades, various wheat grain analyses (e.g. counting, and determining the size, color, or shape features) have been facilitated, thanks to the automated image processing systems, MGW estimations have been limited to using few number of image-derived indices; i.e. mainly the linear or power models developed based on the projected area (Area). Following a preliminary observation which indicated the potential of grain width in improving the predictions, the present study was conducted to explore more efficient indices for increasing the precision of image-based MGW estimations. For this purpose, an image archive of the grains was processed, which were harvested from a 2-year field experiment carried out with 3 replicates under two irrigation conditions and included 15 cultivar mixture treatments (so the archive was consisted of 180 images including more than 72,000 grains). Results It was observed that among the more than 30 evaluated indices of grain size and shape, indicators of grain width (i.e. Minor & MinFeret) along with 8 other empirical indices had a higher correlation with MGW, compared with Area. The most precise MGW predictions were obtained using the Area × Circularity, Perimeter × Circularity, and Area/Perimeter indices. Furthermore, it was found that (i) grain width and the Area/Perimeter ratio were the common factors in the structure of the superior predictive indices; and (ii) the superior indices had the highest correlation with grain width, rather than with their mathematical components. Moreover, comparative efficiency of the superior indices almost remained stable across the 4 environmental conditions. Eventually, using the selected indices, ten simple linear models were developed and validated for MGW prediction, which indicated a relatively higher precision than the current Area-based models. The considerable effect of enhancing image resolution on the precision of the models has been also evidenced. Conclusions It is expected that the findings of the present study, along with the simple predictive linear models developed and validated using new image-derived indices, could improve the precision of the image-based MGW estimations, and consequently facilitate wheat breeding and physiological assessments.
Background China has a unique cotton planting pattern. Cotton is densely planted in alternating wide and narrow rows to increase yield in Xinjiang, China, causing the difficulty in the accurate estimation of cotton yield using remote sensing in such field with branches occluded and overlapped. Results In this study, unmanned aerial vehicle (UAV) imaging and deep convolutional neural networks (DCNN) were used to estimate densely planted cotton yield. Images of cotton fields were acquired by the UAV at an altitude of 5 m. Cotton bolls were manually harvested and weighed afterwards. Then, a modified DCNN model (CD-SegNet) was constructed for pixel-level segmentation of cotton boll images by reorganizing the encoder-decoder and adding dilated convolutions. Besides, linear regression analysis was employed to build up the relationship between cotton boll pixels ratio and cotton yield. Finally, the estimated yield for four cotton fields were verified by weighing harvested cotton. The results showed that CD-SegNet outperformed the other tested models, including SegNet, support vector machine (SVM), and random forest (RF). The average error in yield estimates of the cotton fields was as low as 6.2%. Conclusions Overall, the estimation of densely planted cotton yields based on low-altitude UAV imaging is feasible. This study provides a methodological reference for cotton yield estimation in China.
Background Stalk lodging (breaking of agricultural plant stalks prior to harvest) is a multi-billion dollar a year problem. Stalk lodging occurs when high winds induce bending moments in the stalk which exceed the bending strength of the plant. Previous biomechanical models of plant stalks have investigated the effect of cross-sectional morphology on stalk lodging resistance (e.g., diameter and rind thickness). However, it is unclear if the location of stalk failure along the length of stem is determined by morphological or compositional factors. It is also unclear if the crops are structurally optimized, i.e., if the plants allocate structural biomass to create uniform and minimal bending stresses in the plant tissues. The purpose of this paper is twofold: (1) to investigate the relationship between bending stress and failure location of maize stalks, and (2) to investigate the potential of phenotyping for internode-level bending stresses to assess lodging resistance. Results 868 maize specimens representing 16 maize hybrids were successfully tested in bending to failure. Internode morphology was measured, and bending stresses were calculated. It was found that bending stress is highly and positively associated with failure location. A user-friendly computational tool is presented to help plant breeders in phenotyping for internode-level bending stress. Phenotyping for internode-level bending stresses could potentially be used to breed for more biomechanically optimal stalks that are resistant to stalk lodging. Conclusions Internode-level bending stress plays a potentially critical role in the structural integrity of plant stems. Equations and tools provided herein enable researchers to account for this phenotype, which has the potential to increase the bending strength of plants without increasing overall structural biomass.
Background Classification and phenotype identification of lettuce leaves urgently require fine quantification of their multi-semantic traits. Different components of lettuce leaves undertake specific physiological functions and can be quantitatively described and interpreted using their observable properties. In particular, petiole and veins determine mechanical support and material transport performance of leaves, while other components may be closely related to photosynthesis. Currently, lettuce leaf phenotyping does not accurately differentiate leaf components, and there is no comparative evaluation for positive-back of the same lettuce leaf. In addition, a few traits of leaf components can be measured manually, but it is time-consuming, laborious, and inaccurate. Although several studies have been on image-based phenotyping of leaves, there is still a lack of robust methods to extract and validate multi-semantic traits of large-scale lettuce leaves automatically. Results In this study, we developed an automated phenotyping pipeline to recognize the components of detached lettuce leaves and calculate multi-semantic traits for phenotype identification. Six semantic segmentation models were constructed to extract leaf components from visible images of lettuce leaves. And then, the leaf normalization technique was used to rotate and scale different leaf sizes to the “size-free” space for consistent leaf phenotyping. A novel lamina-based approach was also utilized to determine the petiole, first-order vein, and second-order veins. The proposed pipeline contributed 30 geometry-, 20 venation-, and 216 color-based traits to characterize each lettuce leaf. Eleven manually measured traits were evaluated and demonstrated high correlations with computation results. Further, positive-back images of leaves were used to verify the accuracy of the proposed method and evaluate the trait differences. Conclusions The proposed method lays an effective strategy for quantitative analysis of detached lettuce leaves' fine structure and components. Geometry, color, and vein traits of lettuce leaf and its components can be comprehensively utilized for phenotype identification and breeding of lettuce. This study provides valuable perspectives for developing automated high-throughput phenotyping application of lettuce leaves and the improvement of agronomic traits such as effective photosynthetic area and vein configuration.
Background From an economic perspective, cotton is one of the most important crops in the world. The fertility of male reproductive organs is a key determinant of cotton yield. Anther dehiscence or indehiscence directly determines the probability of fertilization in cotton. Thus, rapid and accurate identification of cotton anther dehiscence status is important for judging anther growth status and promoting genetic breeding research. The development of computer vision technology and the advent of big data have prompted the application of deep learning techniques to agricultural phenotype research. Therefore, two deep learning models (Faster R-CNN and YOLOv5) were proposed to detect the number and dehiscence status of anthers. Result The single-stage model based on YOLOv5 has higher recognition speed and the ability to deploy to the mobile end. Breeding researchers can apply this model to terminals to achieve a more intuitive understanding of cotton anther dehiscence status. Moreover, three improvement strategies are proposed for the Faster R-CNN model, where the improved model has higher detection accuracy than the YOLOv5 model. We have made three improvements to the Faster R-CNN model and after the ensemble of the three models and original Faster R-CNN model, R² of “open” reaches to 0.8765, R² of “close” reaches to 0.8539, R² of “all” reaches to 0.8481, higher than the prediction results of either model alone, which are completely able to replace the manual counting results. We can use this model to quickly extract the dehiscence rate of cotton anthers under high temperature (HT) conditions. In addition, the percentage of dehiscent anthers of 30 randomly selected cotton varieties were observed from the cotton population under normal conditions and HT conditions through the ensemble of the Faster R-CNN model and manual counting. The results show that HT decreased the percentage of dehiscent anthers in different cotton lines, consistent with the manual method. Conclusions Deep learning technology have been applied to cotton anther dehiscence status recognition instead of manual methods for the first time to quickly screen HT–tolerant cotton varieties. Deep learning can help to explore the key genetic improvement genes in the future, promoting cotton breeding and improvement.
Comparison of characteristic peaks of elements in LIBS257-447 nm Camellia oleifera leaves, a 257–262 nm, b 279–281 nm, c 393–398 nm, d 422–446 nm, a–d represent the elements in the camellia samples detected by LIBS in the 257–446 nm spectral segment
Spectra of 0.6–1.8THz absorption coefficients
Comparison of the accuracy of SVM prediction set and MSC preprocessing prediction set, a LIBS-CARS prediction set and MSC-prediction set, b THz-LIBS-CARS prediction set and MSC- prediction set, c THz (CARS)-LIBS(CARS) prediction set and MSC-prediction set
Plane classification diagram of linear discriminant analysis model of modeling set samples, a THz-LIBS-CARS, b THz-LIBS-MSC-CARS, c THz-LIBS-UVE, d THz-LIBS-MSC-UVE
PLS-DA classification diagram of Camellia oleifera leaves detected by different spectra, a LIBS-THz modeling set, b LIBS-THz prediction set, c LIBS-THz-CARS modeling set, d LIBS-THz-CARS prediction set, e THz(CARS)-LIBS(CARS) modeling set, f THz(CARS)-LIBS(CARS) prediction set
Background Anthracnose of Camellia oleifera is a very destructive disease that commonly occurs in the Camellia oleifera industry, which severely restricts the development of the Camellia oleifera industry. In the early stage of the Camellia oleifera suffering from anthracnose, only the diseased parts of the tree need to be repaired in time. With the aggravation of the disease, the diseased branches need to be eradicated, and severely diseased plants should be cut down in time. At present, aiming at the problems of complex experiments and low accuracy in detecting the degree of anthracnose of Camellia oleifera, a method is proposed to detect the degree of anthracnose of Camellia oleifera leaves by using terahertz spectroscopy (THz) combined with laser-induced breakdown spectroscopy (LIBS), so as to realize the rapid, efficient, non-destructive and high-precision determination of the degree of anthracnose of Camellia oleifera. Results Mn, Ca, Ca II, Fe and other elements in the LIBS spectrum of healthy and infected Camellia oleifera leaves with different degrees of anthracnose are significantly different, and the Terahertz absorption spectra of healthy Camellia oleifera leaves, and Camellia oleifera leaves with different degrees of anthracnose there are also significant differences. Partial least squares discriminant analysis (PLS-DA), support vector machine (SVM), and linear discriminant analysis (LDA) are used to establish the fusion spectrum anthracnose classification model of Camellia oleifera. Among them, the Root mean square error of prediction (RMSEP) and the prediction determination coefficient R²p of THz-LIBS-CARS-PLS-DA of prediction set are 0.110 and 0.995 respectively, and the misjudgment rate is 1.03%; The accuracy of the modeling set of THz (CARS)-LIBS (CARS)-SVM is 100%, and the accuracy of prediction set is 100%, after preprocessing of the multivariate scattering correction (MSC), the accuracy of the THz-LIBS-MSC-CARS modeling set is 100%, and the accuracy of prediction set is 100%; The accuracy rate of THz-LIBS-MSC-CARS-LDA of modeling set is 98.98%, and the accuracy rate of the prediction set is 96.87%. Conclusion The experimental results show that: the SVM model has higher qualitative analysis accuracy and is more stable than the PLS-DA and LDA models. The results showed that: the THz spectrum combined with the LIBS spectrum could be used to separate healthy Camellia oleifera leaves from various grades of anthracnose Camellia oleifera leaves non-destructively, quickly and accurately.
Visualization of organellar microcapture in Carya ovata under transmitted light (A, C) and fluorescence microscopy (B, D). A An empty micropipette prior to insertion into the target parenchyma cell—note that the amyloplasts (a) in the target cell and adjacent cells are readily visible, but the nucleus (arrow) is less distinctly resolvable. B The same frame as A, with the DAPI-stained target nucleus (arrow) easily discernible, and the parenchyma cell walls exhibiting lignin autofluorescence. C Amyloplasts and the target nucleus successfully aspirated into the micropipette. D The same frame as C, confirming that the nucleus is among the captured organelles. Scale bars 20 µ
Radial sections (A, B) in DAPI-stained Picea sp. Fluorescent micrographs of intact nuclei (A) and dispersed chromatin (B) presumably as a result of disruption of the nuclear envelope. Scale bars 30 µm
Radial sections (A, B, Carya wood) and longitudinal sections (C, D, Brassica leaf midrib cortical tissue) showing plastids. Amyloplasts are abundant in the ray parenchyma cells (A), and in B a plastid (arrow) is about to be aspirated into the tip of the pipette. Abundant chloroplasts (C) in a slightly plasmolyzed cell (arrow) are aspirated to the pipette tip (D), recovering the entire protoplast, ensuring a high copy number of plastids for PCR. Scale bars 50 µm in A, B, and 100 µm in C, D
Schematic overview of organellar microcapture protocol
Background Illegal logging is a global crisis with significant environmental, economic, and social consequences. Efforts to combat it call for forensic methods to determine species identity, provenance, and individual identification of wood specimens throughout the forest products supply chain. DNA-based methodologies are the only tools with the potential to answer all three questions and the only ones that can be calibrated “non-destructively” by using leaves or other plant tissue and take advantage of publicly available DNA sequence databases. Despite the potential that DNA-based methods represent for wood forensics, low DNA yield from wood remains a limiting factor because, when compared to other plant tissues, wood has few living DNA-containing cells at functional maturity, it often has PCR-inhibiting extractives, and industrial processing of wood degrades DNA. To overcome these limitations, we developed a technique—organellar microcapture—to mechanically isolate intact nuclei and plastids from wood for subsequent DNA extraction, amplification, and sequencing. Results Here we demonstrate organellar microcapture wherein we remove individual nuclei from parenchyma cells in wood (fresh and aged) and leaves of Carya ovata and Tilia americana, amyloplasts from Carya wood, and chloroplasts from kale (Brassica sp.) leaf midribs. ITS (773 bp), ITS1 (350 bp), ITS2 (450 bp), and rbcL (620 bp) were amplified via polymerase chain reaction, sequenced, and heuristic searches against the NCBI database were used to confirm that recovered DNA corresponded to each taxon. Conclusion Organellar microcapture, while too labor-intensive for routine extraction of many specimens, successfully recovered intact nuclei from wood samples collected more than sixty-five years ago, plastids from fresh sapwood and leaves, and presents great potential for DNA extraction from recalcitrant plant samples such as tissues rich in secondary metabolites, old specimens (archaeological, herbarium, and xylarium specimens), or trace evidence previously considered too small for analysis.
Bcakground The dry root and rhizome of Salvia miltiorrhiza are used to treat cardiovascular diseases, chronic pain, and thoracic obstruction over 2000 years in Asian countries. For high quality, Sichuan Zhongjiang is regarded as the genuine producing area of S. miltiorrhiza. Given its abnormal pollen development, S. miltiorrhiza from Sichuan ( S.m. -SC) relies on root reproduction and zymad accumulation; part of diseased plants present typical viral disease symptoms and seed quality degeneration. This study aim to detected unknown viruses from mosaic-diseased plants and establish a highly efficient virus-free regeneration system to recover germplasm properties. Results Tobacco mosaic virus (TMV) and cucumber mosaic virus (CMV) were detected from mosaic-diseased plants. Primary apical meristem with two phyllo podium in 0.15–0.5 mm peeled from diseased plants were achieved 73.33% virus-free rate. The results showed that the medium containing MS, 0.5 mg/L 6-BA, 0.1 mg/L NAA, 0.1 mg/L GA 3 , 30 g/L sucrose and 7.5 g/L agar can achieve embryonic-tissue (apical meristem, petiole and leaf callus) high efficient organogenesis. For callus induction, the optimal condition was detected on the medium containing MS, 2 mg/L TDZ, 0.1 mg/L NAA by using secondary petiole of virus-free plants under 24 h dark/d condition for 21 d. The optimal system for root induction was the nutrient solution with 1/2 MS supplemented with 1 mg/L NAA. After transplant, the detection of agronomic metric and salvianolic acid B content confirmed the great germplasm properties of S.m.-SC virus-free plants. Conclusions A highly efficient virus-free regeneration system of S.m.- SC was established based on the detected viruses to recover superior seed quality. The proposed system laid support to control disease spread, recover good germplasm properties in S.m.- SC.
Background Rice bacterial blight (BB) has caused serious damage in rice yield and quality leading to huge economic loss and food safety problems. Breeding disease resistant cultivar becomes the eco-friendliest and most effective alternative to regulate its outburst, since the propagation of pathogenic bacteria is restrained. However, the BB resistance cultivar selection suffers tremendous labor cost, low efficiency, and subjective human error. And dynamic rice BB phenotyping study is absent from exploring the pattern of BB growth with different genotypes. Results In this paper, with the aim of alleviating the labor burden of plant breeding experts in the resistant cultivar screening processing and exploring the disease resistance phenotyping variation pattern, visible/near-infrared (VIS–NIR) hyperspectral images of rice leaves from three varieties after inoculation were collected and sent into a self-built deep learning model LPnet for disease severity assessment. The growth status of BB lesion at the time scale was fully revealed. On the strength of the attention mechanism inside LPnet, the most informative spectral features related to lesion proportion were further extracted and combined into a novel and refined leaf spectral index. The effectiveness and feasibility of the proposed wavelength combination were verified by identifying the resistant cultivar, assessing the resistant ability, and spectral image visualization. Conclusions This study illustrated that informative VIS–NIR spectrums coupled with attention deep learning had great potential to not only directly assess disease severity but also excavate spectral characteristics for rapid screening disease resistant cultivars in high-throughput phenotyping.
Structure of the stem in barley by light microscopy and MRI. A The fragment of the stem with leaf and spike at the top. B Light microscopic section through the stem. Numerous vascular bundles of different sizes are clearly visible (arrowed) in the parenchyma tissue of stem. C The structure of a single vascular bundle showing the localization of phloem and xylem inside of the vascular bundle. D NMR image shows an axial cross section through the stem. E NMR-based model showing the 3D arrangement of vascular bundles (blue) inside the stem
Scheme and application of the pDCE-MRI for monitoring of CA in Barley stem. A Scheme of MRI acquisition for pDCE. B Brightening of the areas corresponding to the vascular bundles due to gadolinium inflow after the CA administration. The time course is exemplified by the T1-weighted images of an axial cross section (lowest of the five measured slices) through the sample. C Color-coded images representing the T¹-maps calculated on the basis of the saturation recovery T¹-map (shown in Fig. 3A) and the T¹-weigthed images (shown here in B) by using the pDCE method. A decrease of T¹ over time can be observed. D Calculated concentration maps of the contrast agent based on the T1-maps from C; background is the 1H-NMR image acquired before CA administration. Accumulation of CA (color-coded) in the areas of the vascular bundles is clearly distinguishable
Concentration of CA in four individual vascular bundles of barley stem. A Color-coded image representing the standard saturation recovery T1-map (cross section through the stem) before CA administration. B Fragment of the concentration map after 14 h monitoring showing the four regions of interest at the lowest slice (ROIs, in black). The temporal courses of CA concentration are correspondingly depicted in D. C Histological image of a slice analyzed by pDCE-MRI (dissected after the monitoring experiment). D The time course of the CA concentration in selected vascular bundles (a–d)
Estimation of CA velocity in individual bundles. A The monitoring of individual vascular bundle at the lowest slice over the entire experiment (upper panel). Simultaneous monitoring of the same vascular bundle at the highest slice (low panel). The distance between the lowest and highest slice was 1.1 cm. The temporal delay of the CA arrival at highest slice is clearly distinguishable (arrowed). This is exploited to calculate the vertical transport velocities of the CA along the vascular bundles. B–D Analogous monitoring data are shown for other bundles (b, c and d, as pictured in Fig. 3B). E The velocity value as estimated using monitoring data
Observation of the horizontal movement of the CA within one axial slice. A–E A section of the histology image of stem (after monitoring, see blue box in Fig. 3C), overlaid by the corresponding concentration images (pDCE-MRI) for different time points can be seen. The accumulation and spreading of the CA around the vascular bundles are detectable (F) Representation of the temporal accumulation of the CA along the yellow line marked in the image of E. ph: phloem; xy: xylem; bsc: bundle sheat cells; sc: sclerenchyma
Background Studying dynamic processes in living organisms with MRI is one of the most promising research areas. The use of paramagnetic compounds as contrast agents (CA), has proven key to such studies, but so far, the lack of appropriate techniques limits the application of CA-technologies in experimental plant biology. The presented proof-of-principle aims to support method and knowledge transfer from medical research to plant science. Results In this study, we designed and tested a new approach for plant Dynamic Contrast Enhanced Magnetic Resonance Imaging (pDCE-MRI). The new approach has been applied in situ to a cereal crop ( Hordeum vulgare ). The pDCE-MRI allows non-invasive investigation of CA allocation within plant tissues. In our experiments, gadolinium-DTPA, the most commonly used contrast agent in medical MRI, was employed. By acquiring dynamic T 1 -maps, a new approach visualizes an alteration of a tissue-specific MRI parameter T 1 (longitudinal relaxation time) in response to the CA. Both, the measurement of local CA concentration and the monitoring of translocation in low velocity ranges (cm/h) was possible using this CA-enhanced method. Conclusions A novel pDCE-MRI method is presented for non-invasive investigation of paramagnetic CA allocation in living plants. The temporal resolution of the T 1 -mapping has been significantly improved to enable the dynamic in vivo analysis of transport processes at low-velocity ranges, which are common in plants. The newly developed procedure allows to identify vascular regions and to estimate their involvement in CA allocation. Therefore, the presented technique opens a perspective for further development of CA-aided MRI experiments in plant biology.
Background Optimizing plant tissue culture media is a complicated process, which is easily influenced by genotype, mineral nutrients, plant growth regulators (PGRs), vitamins and other factors, leading to undesirable and inefficient medium composition. Facing incidence of different physiological disorders such as callusing, shoot tip necrosis (STN) and vitrification (Vit) in walnut proliferation, it is necessary to develop prediction models for identifying the impact of different factors involving in this process. In the present study, three machine learning (ML) approaches including multi-layer perceptron neural network (MLPNN), k -nearest neighbors (KNN) and gene expression programming (GEP) were implemented and compared to multiple linear regression (MLR) to develop models for prediction of in vitro proliferation of Persian walnut ( Juglans regia L.). The accuracy of developed models was evaluated using coefficient of determination (R ² ), root mean square error (RMSE) and mean absolute error (MAE). With the aim of optimizing the selected prediction models, multi-objective evolutionary optimization algorithm using particle swarm optimization (PSO) technique was applied. Results Our results indicated that all three ML techniques had higher accuracy of prediction than MLR, for example, calculated R ² of MLPNN, KNN and GEP vs. MLR was 0.695, 0.672 and 0.802 vs. 0.412 in Chandler and 0.358, 0.377 and 0.428 vs. 0.178 in Rayen, respectively. The GEP models were further selected to be optimized using PSO. The comparison of modeling procedures provides a new insight into in vitro culture medium composition prediction models. Based on the results, hybrid GEP-PSO technique displays good performance for modeling walnut tissue culture media, while MLPNN and KNN have also shown strong estimation capability. Conclusion Here, besides MLPNN and GEP, KNN also is introduced, for the first time, as a simple technique with high accuracy to be used for developing prediction models in optimizing plant tissue culture media composition studies. Therefore, selection of the modeling technique to study depends on the researcher’s desire regarding the simplicity of the procedure, obtaining clear results as entire formula and/or less time to analyze.
Background Plant growth devices, for example, rhizoponics, rhizoboxes, and ecosystem fabrication (EcoFAB), have been developed to facilitate studies of plant root morphology and plant-microbe interactions in controlled laboratory settings. However, several of these designs are suitable only for studying small model plants such as Arabidopsis thaliana and Brachypodium distachyon and therefore require modification to be extended to larger plant species like crop plants. In addition, specific tools and technical skills needed for fabricating these devices may not be available to researchers. Hence, this study aimed to establish an alternative protocol to generate a larger, modular and reusable plant growth device based on different available resources. Results Root-TRAPR (Root-Transparent, Reusable, Affordable three-dimensional Printed Rhizo-hydroponic) system was successfully developed. It consists of two main parts, an internal root growth chamber and an external structural frame. The internal root growth chamber comprises a polydimethylsiloxane (PDMS) gasket, microscope slide and acrylic sheet, while the external frame is printed from a three-dimensional (3D) printer and secured with nylon screws. To test the efficiency and applicability of the system, industrial hemp ( Cannabis sativa ) was grown with or without exposure to chitosan, a well-known plant elicitor used for stimulating plant defense. Plant root morphology was detected in the system, and plant tissues were easily collected and processed to examine plant biological responses. Upon chitosan treatment, chitinase and peroxidase activities increased in root tissues (1.7- and 2.3-fold, respectively) and exudates (7.2- and 21.6-fold, respectively). In addition, root to shoot ratio of phytohormone contents were increased in response to chitosan. Within 2 weeks of observation, hemp plants exhibited dwarf growth in the Root-TRAPR system, easing plant handling and allowing increased replication under limited growing space. Conclusion The Root-TRAPR system facilitates the exploration of root morphology and root exudate of C. sativa under controlled conditions and at a smaller scale. The device is easy to fabricate and applicable for investigating plant responses toward elicitor challenge. In addition, this fabrication protocol is adaptable to study other plants and can be applied to investigate plant physiology in different biological contexts, such as plant responses against biotic and abiotic stresses.
Variation in flavonoid contents of sweet tea samples among different locations
Raw NIR spectra of sweet tea leaf samples
The distribution of all the samples under different locations
Spectral curves after different preprocessing
Reference (measured) and predicted values of three constituents in sweet tea leaves
Background Sweet tea, which functions as tea, sugar and medicine, was listed as a new food resource in 2017. Flavonoids are the main medicinal components in sweet tea and have significant pharmacological activities. Therefore, the quality of sweet tea is related to the content of flavonoids. Flavonoid content in plants is normally determined by time-consuming and expensive chemical analyses. The aim of this study was to develop a methodology to measure three constituents of flavonoids, namely, total flavonoids, phloridin and trilobatin, in sweet tea leaves using near-infrared spectroscopy (NIR). Results In this study, we demonstrated that the combination of principal component analysis (PCA) and NIR spectroscopy can distinguish sweet tea from different locations. In addition, different spectral preprocessing methods are used to establish partial least squares (PLS) models between spectral information and the content of the three constituents. The best total flavonoid prediction model was obtained with NIR spectra preprocessed with Savitzky–Golay combined with second derivatives (SG + D2) (R P ² = 0.893, and RMSEP = 0.131). For trilobatin, the model with the best performance was developed with raw NIR spectra (R P ² = 0.902, and RMSEP = 2.993), and for phloridin, the best model was obtained with NIR spectra preprocessed with standard normal variate (SNV) (R P ² = 0.818, and RMSEP = 1.085). The coefficients of determination for all calibration sets, validation sets and prediction sets of the best PLS models were higher than 0.967, 0.858 and 0.818, respectively. Conclusions The conclusion indicated that NIR spectroscopy has the ability to determine the flavonoid content of sweet tea quickly and conveniently.
Background Wild rocket ( Diplotaxis tenuifolia ) is prone to soil-borne stresses under intensive cultivation systems devoted to ready-to-eat salad chain, increasing needs for external inputs. Early detection of the abiotic and biotic stresses by using digital reflectance-based probes may allow optimization and enhance performances of the mitigation strategies. Methods Hyperspectral image analysis was applied to D. tenuifolia potted plants subjected, in a greenhouse experiment, to five treatments for one week: a control treatment watered to 100% water holding capacity, two biotic stresses: Fusarium wilting and Rhizoctonia rotting, and two abiotic stresses: water deficit and salinity. Leaf hyperspectral fingerprints were submitted to an artificial intelligence pipeline for training and validating image-based classification models able to work in the stress range. Spectral investigation was corroborated by pertaining physiological parameters. Results Water status was mainly affected by water deficit treatment, followed by fungal diseases, while salinity did not change water relations of wild rocket plants compared to control treatment. Biotic stresses triggered discoloration in plants just in a week after application of the treatments, as evidenced by the colour space coordinates and pigment contents values. Some vegetation indices, calculated on the bases of the reflectance data, targeted on plant vitality and chlorophyll content, healthiness, and carotenoid content, agreed with the patterns of variations observed for the physiological parameters. Artificial neural network helped selection of VIS (492–504, 540–568 and 712–720 nm) and NIR (855, 900–908 and 970 nm) bands, whose read reflectance contributed to discriminate stresses by imaging. Conclusions This study provided significative spectral information linked to the assessed stresses, allowing the identification of narrowed spectral regions and single wavelengths due to changes in photosynthetically active pigments and in water status revealing the etiological cause.
Crosslinking efficiency and physical shearing chromatin analyses. a Bud and mesocarp tissues were crosslinked in buffers containing increasing amounts of formaldehyde (0, 1, and 3%). Samples were subjected or not to a reverse crosslinking phase (decrosslinked sample + and −, respectively), and DNA was isolated using phenol/chloroform extraction as described in “Materials and methods” section. While DNA is efficiently isolated from samples that were not crosslinked (lanes indicated with 0%), a decrosslinking procedure is required for the isolation of DNA from cross-linked samples (with 1% indicating the relative concentration of formaldehyde used in testing analyses, which resulted in a better yield of signal). b Chromatin shearing check after the application of 60% amplitude with several 10 s shearing rounds (25 for FB and 15 for FM) followed by a reverse crosslinking phase and a DNA isolated using phenol/chloroform extraction
Chromatin marks analysis by X-ChIP method for peach mesocarp tissue. Histone modification analysis on chromatin extracted from FAN mesocarp tissue at 83, 104, and 111 DAFB. The ‘TSS around-’ (dark bars, questioned with primer set A) and ‘gene body-’ (white bars, questioned with primer set C) subregions of FLESHY genomic locus, were investigated by real-time PCR quantification on ChIPed DNA immunoprecipitated with α-H3K4me3 (a), and α-H3K27me3 (b). Data are reported as a percentage of chromatin input (% IP), normalized on background signal (No Ab serum control sample, measured by omitting antibody during ChIP procedure). Three PCR repetitions for each ChIP assay. Standard errors are reported. Asterisks indicate statistically significant changes: * = p ˂ 0.05, ** = p ˂ 0.01. DAFB Days after full bloom
H3K4me3 distribution on selected target genes and their relative expression levels in dormant floral buds. Integrative Genomics Viewer (IGV) screenshot for H3K4me3 signals across PRUPE_6G011600, PRUPE_8G183700, and PRUPE_8G062800 genes compared with those of RNA-Seq (green reads) performed in dormant flower buds collected at 0, 475, and 770CU along with their corresponding inputs. Gene structures are represented by blue rectangles. Arrows represent the TSS, Transcription Start Site, and indicate the gene orientation on the genome. Red boxes represent the differentially expressed peaks
H3K27me3 distribution on selected target genes and their relative expression levels in dormant floral buds. Integrative Genomics Viewer (IGV) screenshot for H3K27me3 signals across PRUPE_2G042400, PRUPE_8G216300, and PRUPE_6G322900 genes compared with those of RNA-Seq (green reads) performed in dormant flower buds collected at 0, 475, and 770CU along with their corresponding inputs. Gene structures are represented by blue rectangles. Arrows represent the TSS, Transcription Start Site, and indicate the gene orientation on the genome. Red boxes represent the differentially expressed peaks
Schematic workflow of ChIP protocol. The protocol phases are reported on the left of the picture and the main relative improvements applied are described on the right
Background Perennial fruit trees display a growth behaviour characterized by annual cycling between growth and dormancy, with complex physiological features. Rosaceae fruit trees represent excellent models for studying not only the fruit growth/patterning but also the progression of the reproductive cycle depending upon the impact of climate conditions. Additionally, current developments in high‐throughput technologies have impacted Rosaceae tree research while investigating genome structure and function as well as (epi)genetic mechanisms involved in important developmental and environmental response processes during fruit tree growth. Among epigenetic mechanisms, chromatin remodelling mediated by histone modifications and other chromatin-related processes play a crucial role in gene modulation, controlling gene expression. Chromatin immunoprecipitation is an effective technique to investigate chromatin dynamics in plants. This technique is generally applied for studies on chromatin states and enrichment of post-transcriptional modifications (PTMs) in histone proteins. Results Peach is considered a model organism among climacteric fruits in the Rosaceae family for studies on bud formation, dormancy, and organ differentiation. In our work, we have primarily established specific protocols for chromatin extraction and immunoprecipitation in reproductive tissues of peach ( Prunus persica ). Subsequently, we focused our investigations on the role of two chromatin marks, namely the trimethylation of histone H3 at lysine in position 4 (H3K4me3) and trimethylation of histone H3 at lysine 27 (H3K27me3) in modulating specific gene expression. Bud dormancy and fruit growth were investigated in a nectarine genotype called Fantasia as our model system. Conclusions We present general strategies to optimize ChIP protocols for buds and mesocarp tissues of peach and analyze the correlation between gene expression and chromatin mark enrichment/depletion. The procedures proposed may be useful to evaluate any involvement of histone modifications in the regulation of gene expression during bud dormancy progression and core ripening in fruits.
Top-cited authors
Roger P Hellens
Arthur Korte
  • University of Wuerzburg
Ashley Farlow
  • Gregor Mendel Institute of Molecular Plant Biology (GMI)
Rongmei Wu
  • Plant and Food Research
Marion Wood
  • Plant and Food Research