• Plant Methods (IF 3.170) Pub Date : 2020-01-24
Tingting Du; Ping Meng; Jianliang Huang; Shaobing Peng; Dongliang Xiong

Over the past decades, the structural and functional genomics of rice have been deeply studied, and high density of molecular genetic markers have been developed. However, the genetic variation in leaf photosynthesis, the most important trait for rice yield improvement, was rarely studied. The lack of photosynthesis phenotyping tools is one of the bottlenecks, as traditional direct photosynthesis measurements are very low-throughput, and recently developed high-throughput methods are indirect measurements. Hence, there is an urgent need for a fast, accurate and direct measurement approach. We reported a fast photosynthesis measurement (FPM) method for phenotyping photosynthetic capacity of rice, which measures photosynthesis of excised tillers in environment-controlled lab conditions. The light response curves measured using FPM approach coped well with that the curves measured using traditional gas exchange approach. Importantly, the FPM technique achieved an average throughput of 5.4 light response curves per hour, which was 3 times faster than the 1.8 light response curves per hour using the traditional method. Tillers sampled at early morning had the highest photosynthesis, stomatal conductance and the lowest variability. In addition, even 12 h after sampling, there was no significant difference of photosynthesis rate between excised tillers and in situ. We finally investigated the genetic variations of photosynthetic traits across 568 F2 lines using the FPM technique and discussed the logistics of screening several hundred samples per day per instrumental unit using FPM to generate a wealth of photosynthetic phenotypic data, which might help to improve the selection power in large populations of rice with the ultimate aim of improving yield through improved photosynthesis. Here we developed a high-throughput method that can measure the rice leaf photosynthetic capacity approximately 10 times faster than traditional gas exchange approaches. Importantly, this method can overcome measurement errors caused by environmental heterogeneity under field conditions, and it is possible to measure 12 or more hours per day under lab conditions.

更新日期：2020-01-24
• Plant Methods (IF 3.170) Pub Date : 2020-01-23
Kevin G. Falk; Talukder Z. Jubery; Seyed V. Mirnezami; Kyle A. Parmley; Soumik Sarkar; Arti Singh; Baskar Ganapathysubramanian; Asheesh K. Singh

Root system architecture (RSA) traits are of interest for breeding selection; however, measurement of these traits is difficult, resource intensive, and results in large variability. The advent of computer vision and machine learning (ML) enabled trait extraction and measurement has renewed interest in utilizing RSA traits for genetic enhancement to develop more robust and resilient crop cultivars. We developed a mobile, low-cost, and high-resolution root phenotyping system composed of an imaging platform with computer vision and ML based segmentation approach to establish a seamless end-to-end pipeline - from obtaining large quantities of root samples through image based trait processing and analysis. This high throughput phenotyping system, which has the capacity to handle hundreds to thousands of plants, integrates time series image capture coupled with automated image processing that uses optical character recognition (OCR) to identify seedlings via barcode, followed by robust segmentation integrating convolutional auto-encoder (CAE) method prior to feature extraction. The pipeline includes an updated and customized version of the Automatic Root Imaging Analysis (ARIA) root phenotyping software. Using this system, we studied diverse soybean accessions from a wide geographical distribution and report genetic variability for RSA traits, including root shape, length, number, mass, and angle. This system provides a high-throughput, cost effective, non-destructive methodology that delivers biologically relevant time-series data on root growth and development for phenomics, genomics, and plant breeding applications. This phenotyping platform is designed to quantify root traits and rank genotypes in a common environment thereby serving as a selection tool for use in plant breeding. Root phenotyping platforms and image based phenotyping are essential to mirror the current focus on shoot phenotyping in breeding efforts.

更新日期：2020-01-23
• Plant Methods (IF 3.170) Pub Date : 2020-01-21
André Alcântara; Denise Seitner; Fernando Navarrete; Armin Djamei

The unfolded protein response (UPR) is a highly conserved process in eukaryotic organisms that plays a crucial role in adaptation and development. While the most ubiquitous components of this pathway have been characterized, current efforts are focused on identifying and characterizing other UPR factors that play a role in specific conditions, such as developmental changes, abiotic cues, and biotic interactions. Considering the central role of protein secretion in plant pathogen interactions, there has also been a recent focus on understanding how pathogens manipulate their host’s UPR to facilitate infection. We developed a high-throughput screening assay to identify proteins that interfere with UPR signaling in planta. A set of 35 genes from a library of secreted proteins from the maize pathogen Ustilago maydis were transiently co-expressed with a reporter construct that upregulates enhanced yellow fluorescent protein (eYFP) expression upon UPR stress in Nicotiana benthamiana plants. After UPR stress induction, leaf discs were placed in 96 well plates and eYFP expression was measured. This allowed us to identify a previously undescribed fungal protein that inhibits plant UPR signaling, which was then confirmed using the classical but more laborious qRT-PCR method. We have established a rapid and reliable fluorescence-based method to identify heterologously expressed proteins involved in UPR stress in plants. This system can be used for initial screens with libraries of proteins and potentially other molecules to identify candidates for further validation and characterization.

更新日期：2020-01-21
• Plant Methods (IF 3.170) Pub Date : 2020-01-16
Chun-Lian Li; De-Xing Xue; Yi-Han Wang; Zhi-Ping Xie; Christian Staehelin

Plant receptors with lysin motifs (LsyM) recognize microbial signals such as fungal chitin and lipo-chitooligosaccharidic Nod factors of nitrogen-fixing rhizobia. It is generally assumed that ligand-induced dimerization of LysM receptors is an essential step in activation of intracellular kinase domains and downstream signaling. Consequently, genes required for plant defense and establishment of symbiosis are expressed. We recently found that three LysM receptor proteins (namely LYK1, LYK4 and LYK5) of Arabidopsis thaliana form a tripartite receptor complex to perceive chitin. However, constitutive and ligand-induced interactions of LysM receptors generally remain difficult to be characterized. Interactions between ectodomains of LYK1, LYK4 and LYK5 were investigated by a chimeric receptor approach using hairy roots of the legume Lotus japonicus. Synthetic receptor pairs consisting of a LYK ectodomain and the intracellular domain of a L. japonicus Nod factor receptor (NFR1 and NFR5, respectively) were tested for their capacity to activate expression of the symbiotic NIN (nodule inception) gene. The results indicated constitutive (LYK4ED–LYK4ED, LYK4ED–LYK5ED) and chitin-induced interactions (LYK1ED–LYK1ED, LYK1ED–LYK5ED) of the examined ectodomains. We present a method to functionally analyze constitutive and ligand-induced interactions of LysM-type proteins.

更新日期：2020-01-16
• Plant Methods (IF 3.170) Pub Date : 2020-01-04
Paul G. Nevill; Xiao Zhong; Julian Tonti-Filippini; Margaret Byrne; Michael Hislop; Kevin Thiele; Stephen van Leeuwen; Laura M. Boykin; Ian Small

Herbaria are valuable sources of extensive curated plant material that are now accessible to genetic studies because of advances in high-throughput, next-generation sequencing methods. As an applied assessment of large-scale recovery of plastid and ribosomal genome sequences from herbarium material for plant identification and phylogenomics, we sequenced 672 samples covering 21 families, 142 genera and 530 named and proposed named species. We explored the impact of parameters such as sample age, DNA concentration and quality, read depth and fragment length on plastid assembly error. We also tested the efficacy of DNA sequence information for identifying plant samples using 45 specimens recently collected in the Pilbara. Genome skimming was effective at producing genomic information at large scale. Substantial sequence information on the chloroplast genome was obtained from 96.1% of samples, and complete or near-complete sequences of the nuclear ribosomal RNA gene repeat were obtained from 93.3% of samples. We were able to extract sequences for the core DNA barcode regions rbcL and matK from 96 to 93.3% of samples, respectively. Read quality and DNA fragment length had significant effects on sequencing outcomes and error correction of reads proved essential. Assembly problems were specific to certain taxa with low GC and high repeat content (Goodenia, Scaevola, Cyperus, Bulbostylis, Fimbristylis) suggesting biological rather than technical explanations. The structure of related genomes was needed to guide the assembly of repeats that exceeded the read length. DNA-based matching proved highly effective and showed that the efficacy for species identification declined in the order cpDNA >> rDNA > matK >> rbcL. We showed that a large-scale approach to genome sequencing using herbarium specimens produces high-quality complete cpDNA and rDNA sequences as a source of data for DNA barcoding and phylogenomics.

更新日期：2020-01-04
• Plant Methods (IF 3.170) Pub Date : 2020-01-04
Ying Zhang; Liming Ma; Jinglu Wang; Xiaodong Wang; Xinyu Guo; Jianjun Du

Micro-computed tomography (μCT) bring a new opportunity to accurately quantify micro phenotypic traits of maize stem, also provide comparable benchmark to evaluate its dynamic development at the different growth stages. The progressive accumulation of stem biomass brings manifest structure changes of maize stem and vascular bundles, which are closely related with maize varietal characteristics and growth stages. Thus, micro-phenotyping (μPhenotyping) of maize stems is not only valuable to evaluate bio-mechanics and water-transport performance of maize, but also yield growth-based traits for quantitative traits loci (QTL) and functional genes location in molecular breeding. In this study, maize stems of 20 maize cultivars and two growth stages were imaged using μCT scanning technology. According to the observable differences of maize stems from the elongation and tasseling stages, function zones of maize stem were firstly defined to describe the substance accumulation of maize stems. And then a set of image-based μPhenotyping pipelines were implemented to quantify maize stem and vascular bundles at the two stages. The coefficient of determination (R2) of counting vascular bundles was higher than 0.95. Based on the uniform contour representation, intensity-related, geometry-related and distribution-related traits of vascular bundles were respectively evaluated in function zones and structure layers. And growth-related traits of the slice, epidermis, periphery and inner zones were also used to describe the dynamic growth of maize stem. Statistical analysis demonstrated the presented method was suitable to the phenotyping analysis of maize stem for multiple growth stages. The novel descriptors of function zones provide effective phenotypic references to quantify the differences between growth stages; and the detection and identification of vascular bundles based on function zones are more robust to determine the adaptive image analysis pipeline. Developing robust and effective image-based phenotyping method to assess the traits of stem and vascular bundles, is highly relevant for understanding the relationship between maize phenomics and genomics.

更新日期：2020-01-04
• Plant Methods (IF 3.170) Pub Date : 2019-12-30
Xiucheng Cui; Margaret Balcerzak; Johann Schernthaner; Vivijan Babic; Raju Datla; Elizabeth K. Brauer; Natalie Labbé; Rajagopal Subramaniam; Thérèse Ouellet

In the original publication [1], the copyright line was incorrectly published as “© The Author(s) 2019”. The corrected copyright line should read as “© Her Majesty the Queen in Right of Canada as represented by the Minister of Agriculture and Agri-Food Canada, 2019”. The original article has been corrected.

更新日期：2019-12-30
• Plant Methods (IF 3.170) Pub Date : 2019-12-27
Jaspreet Sandhu; Feiyu Zhu; Puneet Paul; Tian Gao; Balpreet K. Dhatt; Yufeng Ge; Paul Staswick; Hongfeng Yu; Harkamal Walia

Recent advances in image-based plant phenotyping have improved our capability to study vegetative stage growth dynamics. However, more complex agronomic traits such as inflorescence architecture (IA), which predominantly contributes to grain crop yield are more challenging to quantify and hence are relatively less explored. Previous efforts to estimate inflorescence-related traits using image-based phenotyping have been limited to destructive end-point measurements. Development of non-destructive inflorescence phenotyping platforms could accelerate the discovery of the phenotypic variation with respect to inflorescence dynamics and mapping of the underlying genes regulating critical yield components. The major objective of this study is to evaluate post-fertilization development and growth dynamics of inflorescence at high spatial and temporal resolution in rice. For this, we developed the Panicle Imaging Platform (PI-Plat) to comprehend multi-dimensional features of IA in a non-destructive manner. We used 11 rice genotypes to capture multi-view images of primary panicle on weekly basis after the fertilization. These images were used to reconstruct a 3D point cloud of the panicle, which enabled us to extract digital traits such as voxel count and color intensity. We found that the voxel count of developing panicles is positively correlated with seed number and weight at maturity. The voxel count from developing panicles projected overall volumes that increased during the grain filling phase, wherein quantification of color intensity estimated the rate of panicle maturation. Our 3D based phenotyping solution showed superior performance compared to conventional 2D based approaches. For harnessing the potential of the existing genetic resources, we need a comprehensive understanding of the genotype-to-phenotype relationship. Relatively low-cost sequencing platforms have facilitated high-throughput genotyping, while phenotyping, especially for complex traits, has posed major challenges for crop improvement. PI-Plat offers a low cost and high-resolution platform to phenotype inflorescence-related traits using 3D reconstruction-based approach. Further, the non-destructive nature of the platform facilitates analyses of the same panicle at multiple developmental time points, which can be utilized to explore the genetic variation for dynamic inflorescence traits in cereals.

更新日期：2019-12-27
• Plant Methods (IF 3.170) Pub Date : 2019-12-26
Julia Schachtsiek; Tajammul Hussain; Khadija Azzouhri; Oliver Kayser; Felix Stehle

The raised demand of cannabis as a medicinal plant in recent years led to an increased interest in understanding the biosynthetic routes of cannabis metabolites. Since there is no established protocol to generate stable gene knockouts in cannabis, the use of a virus-induced gene silencing (VIGS) method, resulting in a gene knockdown, to study gene functions is desirable. For this, a computational approach was employed to analyze the Cannabis sativa L. transcriptomic and genomic resources. Reporter genes expected to give rise to easily scorable phenotypes upon silencing, i.e. the phytoene desaturase (PDS) and magnesium chelatase subunit I (ChlI), were identified in C. sativa. Subsequently, the targets of specific small interfering RNAs (siRNAs) and silencing fragments were predicted and tested in a post-transcriptional gene silencing (PTGS) approach. Here we show for the first time a gene knockdown in C. sativa using the Cotton leaf crumple virus (CLCrV) in a silencing vector system. Plants transiently transformed with the Agrobacterium tumefaciens strain AGL1, carrying the VIGS-vectors, showed the desired phenotypes, spotted bleaching of the leaves. The successful knockdown of the genes was additionally validated by quantitative PCR resulting in reduced expression of transcripts from 70 to 73% for ChlI and PDS, respectively. This is accompanied with the reduction of the chlorophyll a and carotenoid content, respectively. In summary, the data clearly demonstrate the potential for functional gene studies in cannabis using the CLCrV-based vector system. The applied VIGS-method can be used for reverse genetic studies in C. sativa to identify unknown gene functions. This will gain deeper inside into unknown biosynthetic routes and will help to close the gap between available genomic data and biochemical information of this important medicinal plant.

更新日期：2019-12-27
• Plant Methods (IF 3.170) Pub Date : 2019-12-26
Shidong Yue; Yu Zhang; Yi Zhou; Shaochun Xu; Shuai Xu; Xiaomei Zhang; Ruiting Gu

Seagrass meadows are recognized as critical and among the most vulnerable habitats on the planet. The alarming rates of decline in seagrass meadows have attracted the attention globally. There is an urgent need to develop techniques to restore and preserve these vital coastal ecosystems. So far little work has been done to develop effective long-term storage method for seagrass seeds. The seagrass Zostera japonica Asch. & Graebn is an endangered species in its native range. Here we utilized combinations of different storage times, salinities, and temperature to determine the most appropriate conditions for optimal seed storage. Zostera japonica seeds were strongly desiccation sensitive, with a complete loss of viability after 24 h of desiccation. Therefore, long periods of exposure to air should be avoided to minimize seed mortality. In addition, Z. japonica seeds could not endure freezing conditions such as – 5 °C. However, our results indicated that reduced storage temperature to 0 °C could effectively prolong the duration of dormancy of Z. japonica seeds. Seeds stored at 0 °C under a salinity of 40–60 psu showed relatively low seed loss, high seed vigor and fast seed germination, suggesting these to be optimal seed storage conditions. For example, after storage for 540 days (ca. 600 days since the seed collection from reproductive shoots in early October, 2016) at 0 °C under a salinity of 50 psu, seeds still had a considerable vigor, i.e. 57.8 ± 16.8%. Our experiments demonstrated that seeds stored at 0 °C under a salinity of 40–60 psu could effectively prolong the duration of dormancy of Z. japonica seeds. The proposed technique is a simple and effective long-term storage method for Z. japonica seeds, which can then be used to aid future conservation, restoration and management of these sensitive and ecologically important habitat formers. The findings may also serve as useful reference for seed storage of other threatened seagrass species and facilitate their ex situ conservation and habitat restoration.

更新日期：2019-12-27
• Plant Methods (IF 3.170) Pub Date : 2019-12-26
Keiko Hayashi; Tomofumi Yoshida; Yuriko Hayano-Saito

Breeding of rice with panicle resistance to rice blast disease caused by Pyricularia oryzae is a challenge towards sustainable rice production. Methods for accurate estimation of disease severity can support breeding. White head symptoms are a commonly used index of panicle blast in the field. As the development mechanism of this symptom remains unclear, we used cut-flower dye (CFD) solution to visualize the infected panicle tissues. CFD delineated the edge of white head symptoms in rice panicles artificially infected with P. oryzae. Hyphae within the tissues were confirmed through staining with a fluorescent wheat germ agglutinin conjugate. Hyphal density was obviously diminished at the dye edge. Growing hyphae preferred to move along the vascular bundles; infected tissues lost the ability to transport water, leading to white head formation. By marking the edge of the white heads, this simple dyeing technique precisely reveals the extent of infection. Further, digital imaging allowed dried samples to be stored and reassessed later. The CFD detection technique served as a powerful tool for estimating disease severity by color, as it clearly revealed lesions in both the panicles and leaves. Combined with reliable methods for artificial inoculation and observation of infecting hyphae, this technique will advance the research and breeding of panicle blast-resistant rice.

更新日期：2019-12-27
• Plant Methods (IF 3.170) Pub Date : 2019-12-26
Francisca López-Granados; Jorge Torres-Sánchez; Francisco M. Jiménez-Brenes; Octavio Arquero; María Lovera; Ana I. de Castro

Almond is an emerging crop due to the health benefits of almond consumption including nutritional, anti-inflammatory, and hypocholesterolaemia properties. Traditional almond producers were concentrated in California, Australia, and Mediterranean countries. However, almond is currently present in more than 50 countries due to breeding programs have modernized almond orchards by developing new varieties with improved traits related to late flowering (to reduce the risk of damage caused by late frosts) and tree architecture. Almond tree architecture and flowering are acquired and evaluated through intensive field labour for breeders. Flowering detection has traditionally been a very challenging objective. To our knowledge, there is no published information about monitoring of the tree flowering dynamics of a crop at the field scale by using color information from photogrammetric 3D point clouds and OBIA. As an alternative, a procedure based on the generation of colored photogrammetric point clouds using a low cost (RGB) camera on-board an unmanned aerial vehicle (UAV), and an semi-automatic object based image analysis (OBIA) algorithm was created for monitoring the flower density and flowering period of every almond tree in the framework of two almond phenotypic trials with different planting dates. Our method was useful for detecting the phenotypic variability of every almond variety by mapping and quantifying every tree height and volume as well as the flowering dynamics and flower density. There was a high level of agreement among the tree height, flower density, and blooming calendar derived from our procedure on both fields with the ones created from on-ground measured data. Some of the almond varieties showed a significant linear fit between its crown volume and their yield. Our findings could help breeders and researchers to reduce the gap between phenomics and genomics by generating accurate almond tree information in an efficient, non-destructive, and inexpensive way. The method described is also useful for data mining to select the most promising accessions, making it possible to assess specific multi-criteria ranking varieties, which are one of the main tools for breeders.

更新日期：2019-12-27
• Plant Methods (IF 3.170) Pub Date : 2019-12-27
Junfeng Hou; Ying Zhang; Xiuliang Jin; Pengfei Dong; Yanan Guo; Keru Wang; Yinghu Fan; Shaokun Li

High grain breakage rate is the main limiting factor encountered in the mechanical harvest of maize grain. X-ray micro-computed tomography (μCT) scanning technology could be used to obtain the three-dimensional structure of maize grain. Currently, the effect of maize grain structure on the grain breakage rate, determined using X-ray μCT scanning technology, has not been reported. Therefore, the objectives of this study are: (i) to obtain the shape, geometry, and structural parameters related to the breakage rate using X-ray μCT scanning technology; (ii) to explore relationships between these parameters and grain breakage rate. In this study, 28 parameters were determined using X-ray μCT scanning technology. The maize breakage rate was mainly influenced by the grain specific surface area, subcutaneous cavity volume, sphericity, and density. In particular, the breakage rate was directly affected by the subcutaneous cavity volume and density. The maize variety with high density and low subcutaneous cavity volume had a low breakage rate. The specific surface area (r = 0.758*), embryo specific surface area (r = 0.927**), subcutaneous cavity volume ratio (0.581*), and subcutaneous cavity volume (0.589*) of maize grain significantly and positively correlated with breakage rate. The cavity specific surface area (− 0.628*) and grain density (− 0.934**) of maize grain significantly and negatively correlated with grain breakage rates. Grain shape (length, width, thickness, and aspect ratio) positively correlated with grain breakage rate but the correlation did not reach statistical significance. The susceptibility of grain breakage increased when kernel weight decreased (− 0.371), but the effect was not significant. The results indicate that X-ray μCT scanning technology could be effectively used to evaluate maize grain breakage rate. X-ray μCT scanning technology provided a more precise and comprehensive acquisition method to evaluate the shape, geometry, and structure of maize grain. Thus, data gained by X-ray μCT can be used as a guideline for breeding resistant breakage maize varieties. Grain density and subcutaneous cavity volume are two of the most important factors affecting grain breakage rate. Grain density, in particular, plays a vital role in grain breakage and this parameter can be used to predict the breakage rate of maize varieties.

更新日期：2019-12-27
• Plant Methods (IF 3.170) Pub Date : 2019-12-23
Dominik Schneider; Laura S. Lopez; Meng Li; Joseph D. Crawford; Helmut Kirchhoff; Hans-Henning Kunz

Over the last years, several plant science labs have started to employ fluctuating growth light conditions to simulate natural light regimes more closely. Many plant mutants reveal quantifiable effects under fluctuating light despite being indistinguishable from wild-type plants under standard constant light. Moreover, many subtle plant phenotypes become intensified and thus can be studied in more detail. This observation has caused a paradigm shift within the photosynthesis research community and an increasing number of scientists are interested in using fluctuating light growth conditions. However, high installation costs for commercial controllable LED setups as well as costly phenotyping equipment can make it hard for small academic groups to compete in this emerging field. We show a simple do-it-yourself approach to enable fluctuating light growth experiments. Our results using previously published fluctuating light sensitive mutants, stn7 and pgr5, confirm that our low-cost setup yields similar results as top-prized commercial growth regimes. Moreover, we show how we increased the throughput of our Walz IMAGING-PAM, also found in many other departments around the world. We have designed a Python and R-based open source toolkit that allows for semi-automated sample segmentation and data analysis thereby reducing the processing bottleneck of large experimental datasets. We provide detailed instructions on how to build and functionally test each setup. With material costs well below USD\$1000, it is possible to setup a fluctuating light rack including a constant light control shelf for comparison. This allows more scientists to perform experiments closer to natural light conditions and contribute to an emerging research field. A small addition to the IMAGING-PAM hardware not only increases sample throughput but also enables larger-scale plant phenotyping with automated data analysis.

更新日期：2019-12-23
• Plant Methods (IF 3.170) Pub Date : 2019-12-18
Felicity E. O’Callaghan; Roy Neilson; Stuart A. MacFarlane; Lionel X. Dupuy

Plant feeding, free-living nematodes cause extensive damage to plant roots by direct feeding and, in the case of some trichodorid and longidorid species, through the transmission of viruses. Developing more environmentally friendly, target-specific nematicides is currently impeded by slow and laborious methods of toxicity testing. Here, we developed a bioactivity assay based on the dynamics of light ‘speckle’ generated by living cells and we demonstrate its application by assessing chemicals’ toxicity to different nematode trophic groups. Free-living nematode populations extracted from soil were exposed to methanol and phenyl isothiocyanate (PEITC). Biospeckle analysis revealed differing behavioural responses as a function of nematode feeding groups. Trichodorus nematodes were less sensitive than were bacterial feeding nematodes or non-trichodorid plant feeding nematodes. Following 24 h of exposure to PEITC, bioactivity significantly decreased for plant and bacterial feeders but not for Trichodorus nematodes. Decreases in movement for plant and bacterial feeders in the presence of PEITC also led to measurable changes to the morphology of biospeckle patterns. Biospeckle analysis can be used to accelerate the screening of nematode bioactivity, thereby providing a fast way of testing the specificity of potential nematicidal compounds. With nematodes’ distinctive movement and activity levels being visible in the biospeckle pattern, the technique has potential to screen the behavioural responses of diverse trophic nematode communities. The method discriminates both behavioural responses, morphological traits and activity levels and hence could be used to assess the specificity of nematicidal compounds.

更新日期：2019-12-19
• Plant Methods (IF 3.170) Pub Date : 2019-12-17
Sadiye Hayta; Mark A. Smedley; Selcen U. Demir; Robert Blundell; Alison Hinchliffe; Nicola Atkinson; Wendy A. Harwood

In the original publication of the article [1], a few errors were identified.

更新日期：2019-12-18
• Plant Methods (IF 3.170) Pub Date : 2019-12-17
Niels J. F. De Baerdemaeker; Michiel Stock; Jan Van den Bulcke; Bernard De Baets; Luc Van Hoorebeke; Kathy Steppe

Acoustic emission (AE) sensing is in use since the late 1960s in drought-induced embolism research as a non-invasive and continuous method. It is very well suited to assess a plant’s vulnerability to dehydration. Over the last couple of years, AE sensing has further improved due to progress in AE sensors, data acquisition methods and analysis systems. Despite these recent advances, it is still challenging to detect drought-induced embolism events in the AE sources registered by the sensors during dehydration, which sometimes questions the quantitative potential of AE sensing. In quest of a method to separate embolism-related AE signals from other dehydration-related signals, a 2-year-old potted Fraxinus excelsior L. tree was subjected to a drought experiment. Embolism formation was acoustically measured with two broadband point-contact AE sensors while simultaneously being visualized by X-ray computed microtomography (µCT). A machine learning method was used to link visually detected embolism formation by µCT with corresponding AE signals. Specifically, applying linear discriminant analysis (LDA) on the six AE waveform parameters amplitude, counts, duration, signal strength, absolute energy and partial power in the range 100–200 kHz resulted in an embolism-related acoustic vulnerability curve (VCAE-E) better resembling the standard µCT VC (VCCT), both in time and in absolute number of embolized vessels. Interestingly, the unfiltered acoustic vulnerability curve (VCAE) also closely resembled VCCT, indicating that VCs constructed from all registered AE signals did not compromise the quantitative interpretation of the species’ vulnerability to drought-induced embolism formation. Although machine learning could detect similar numbers of embolism-related AE as µCT, there still is insufficient model-based evidence to conclusively attribute these signals to embolism events. Future research should therefore focus on similar experiments with more in-depth analysis of acoustic waveforms, as well as explore the possibility of Fast Fourier transformation (FFT) to remove non-embolism-related AE signals.

更新日期：2019-12-18
• Plant Methods (IF 3.170) Pub Date : 2019-12-17
Zinan Luo; Kelly R. Thorp; Hussein Abdel-Haleem

Guayule (Parthenium argentatum A. Gray), a plant native to semi-arid regions of northern Mexico and southern Texas in the United States, is an alternative source for natural rubber (NR). Rapid screening tools are needed to replace the current labor-intensive and cost-inefficient method for quantifying rubber and resin contents. Near-infrared (NIR) spectroscopy is a promising technique that simplifies and speeds up the quantification procedure without losing precision. In this study, two spectral instruments were used to rapidly quantify resin and rubber contents in 315 ground samples harvested from a guayule germplasm collection grown under different irrigation conditions at Maricopa, AZ. The effects of eight different pretreatment approaches on improving prediction models using partial least squares regression (PLSR) were investigated and compared. Important characteristic wavelengths that contribute to prominent absorbance peaks were identified. Using two different NIR devices, ASD FieldSpec®3 performed better than Polychromix Phazir™ in improving R2 and residual predicative deviation (RPD) values of PLSR models. Compared to the models based on full-range spectra (750–2500 nm), using a subset of wavelengths (1100–2400 nm) with high sensitivity to guayule rubber and resin contents could lead to better prediction accuracy. The prediction power of the models for quantifying resin content was better than rubber content. In summary, the calibrated PLSR models for resin and rubber contents were successfully developed for a diverse guayule germplasm collection and were applied to roughly screen samples in a low-cost and efficient way. This improved efficiency could enable breeders to rapidly screen large guayule populations to identify cultivars that are high in rubber and resin contents.

更新日期：2019-12-18
• Plant Methods (IF 3.170) Pub Date : 2019-12-16
Kimmo Kivivirta; Denise Herbert; Matthias Lange; Knut Beuerlein; Janine Altmüller; Annette Becker

Plant development is controlled by the action of many, often connected gene regulatory networks. Differential gene expression controlled by internal and external cues is a major driver of growth and time specific differentiation in plants. Transcriptome analysis is the state-of-the-art method to detect spatio-temporal changes in gene expression during development. Monitoring changes in gene expression at early stages or in small plant organs and tissues requires an accurate technique of tissue isolation, which subsequently results in RNA of sufficient quality and quantity. Laser-microdissection enables such accurate dissection and collection of desired tissue from sectioned material at a microscopic level for RNA extraction and subsequent downstream analyses, such as transcriptome, proteome, genome or miRNA. A protocol for laser-microdissection, RNA extraction and RNA-seq was optimized and verified for three distant angiosperm species: Arabidopsis thaliana (Brassicaceae), Oryza sativa (Poaceae) and Eschscholzia californica (Papaveraceae). Previously published protocols were improved in processing speed by reducing the vacuum intensity and incubation time during tissue fixation and incubation time and cryoprotection and by applying adhesive tape. The sample preparation and sectioning of complex and heterogenous flowers produced adequate histological quality and subsequent RNA extraction from micro-dissected gynoecia reliably generated samples of sufficient quality and quantity on all species for RNA-seq. Expression analysis of growth stage specific A. thaliana and O. sativa transcriptomes showed distinct patterns of expression of chromatin remodelers on different time points of gynoecium morphogenesis from the initiation of development to post-meiotic stages. Here we describe a protocol for plant tissue preparation, cryoprotection, cryo-sectioning, laser microdissection and RNA sample preparation for Illumina sequencing of complex plant organs from three phyletically distant plant species. We are confident that this approach is widely applicable to other plant species to enable transcriptome analysis with high spatial resolution in non-model plant species. The protocol is rapid, produces high quality sections of complex organs and results in RNA of adequate quality well suited for RNA-seq approaches. We provide detailed description of each stage of sample preparation with the quality and quantity measurements as well as an analysis of generated transcriptomes.

更新日期：2019-12-17
• Plant Methods (IF 3.170) Pub Date : 2019-12-11
Yu Liu; Ji Qian; Xin Yang; Bao Di; Juan Zhou

Traditional measurements of apple seedling roots often rely on manual measurements and existing root scanners on the market. Manual measurement requires a lot of labor and time, and subjective reasons may cause the uncertainty of data; root scanners have limited scanning size and expensive. In case of fruit roots, coverage and occlusion issues will occur, resulting in inaccurate results, but our research solved this problem. The background plate was selected according to the color of the seedling roots; the image of the roots of the collected apple seedlings was preprocessed with Vision Development Module by combining image and Labview. The root surface area, average root diameter, root length and root volume of apple seedlings were measured by combining root characteristic parameters algorithm. In order to verify the effectiveness of the proposed method, a set of measurement system for root morphology of apple seedlings was designed, and the measurement result was compared with the Canadian root system WinRHIZO 2016 (Canada). With application of SPSS v22.0 analysis, the significance P > 0.01 indicated that the difference was not significant. The relative error of surface area was less than 0.5%. The relative error of the average diameter and length of the root system was less than 0.1%, and the relative error of the root volume was less than 0.2%. It not only proved that the root surface area, average root diameter, root length and root volume of apple seedlings could be accurately measured by the method described herein, which was handy in operation, but also reduced the cost by 80–90% compared with the conventional scanner.

更新日期：2019-12-11
• Plant Methods (IF 3.170) Pub Date : 2019-12-11
Haipeng Xiong; Zhiguo Cao; Hao Lu; Simon Madec; Liang Liu; Chunhua Shen

Grain yield of wheat is greatly associated with the population of wheat spikes, i.e., $$spike~number~\text {m}^{-2}$$. To obtain this index in a reliable and efficient way, it is necessary to count wheat spikes accurately and automatically. Currently computer vision technologies have shown great potential to automate this task effectively in a low-end manner. In particular, counting wheat spikes is a typical visual counting problem, which is substantially studied under the name of object counting in Computer Vision. TasselNet, which represents one of the state-of-the-art counting approaches, is a convolutional neural network-based local regression model, and currently benchmarks the best record on counting maize tassels. However, when applying TasselNet to wheat spikes, it cannot predict accurate counts when spikes partially present. In this paper, we make an important observation that the counting performance of local regression networks can be significantly improved via adding visual context to the local patches. Meanwhile, such context can be treated as part of the receptive field without increasing the model capacity. We thus propose a simple yet effective contextual extension of TasselNet—TasselNetv2. If implementing TasselNetv2 in a fully convolutional form, both training and inference can be greatly sped up by reducing redundant computations. In particular, we collected and labeled a large-scale wheat spikes counting (WSC) dataset, with 1764 high-resolution images and 675,322 manually-annotated instances. Extensive experiments show that, TasselNetv2 not only achieves state-of-the-art performance on the WSC dataset ($$91.01\%$$ counting accuracy) but also is more than an order of magnitude faster than TasselNet (13.82 fps on $$912\times 1216$$ images). The generality of TasselNetv2 is further demonstrated by advancing the state of the art on both the Maize Tassels Counting and ShanghaiTech Crowd Counting datasets. This paper describes TasselNetv2 for counting wheat spikes, which simultaneously addresses two important use cases in plant counting: improving the counting accuracy without increasing model capacity, and improving efficiency without sacrificing accuracy. It is promising to be deployed in a real-time system with high-throughput demand. In particular, TasselNetv2 can achieve sufficiently accurate results when training from scratch with small networks, and adopting larger pre-trained networks can further boost accuracy. In practice, one can trade off the performance and efficiency according to certain application scenarios. Code and models are made available at: https://tinyurl.com/TasselNetv2.

更新日期：2019-12-11
• Plant Methods (IF 3.170) Pub Date : 2019-12-09
Si Chen; Simon Fiil Svane; Kristian Thorup-Kristensen

Deep rooting is one of the most promising plant traits for improving crop yield under water-limited conditions. Most root phenotyping methods are designed for laboratory-grown plants, typically measuring very young plants not grown in soil and not allowing full development of the root system. This study introduced the 15N tracer method to detect genotypic variations of deep rooting and N uptake, and to support the minirhizotron method. The method was tested in a new semifield phenotyping facility on two genotypes of winter wheat, seven genotypes of spring barley and four genotypes of ryegrass grown along a drought stress gradient in four individual experiments. The 15N labeled fertilizer was applied at increasing soil depths from 0.4 to 1.8 m or from 0.7 to 2.8 m through a subsurface tracer supply system, and sampling of aboveground biomass was conducted to measure the 15N uptake. The results confirm that the 15N labeling system could identify the approximate extension of the root system. The results of 15N labeling as well as root measurements made by minirhizotrons showed rather high variation. However, in the spring barley experiment, we did find correlations between root observations and 15N uptake from the deepest part of the root zone. The labeled crop rows mostly had significantly higher 15N enrichment than their neighbor rows. We concluded that the 15N tracer method is promising as a future method for deep root phenotyping because the method will be used for phenotyping for deep root function rather than deep root growth. With some modifications to the injection principle and sampling process to reduce measurement variability, we suggest that the 15N tracer method may be a useful tool for deep root phenotyping. The results demonstrated that the minirhizotrons observed roots of the tested rows rather than their neighboring rows.

更新日期：2019-12-09
• Plant Methods (IF 3.170) Pub Date : 2019-12-05
Beatriz Fernández-Marín; Othmar Buchner; Gerald Kastberger; Federica Piombino; José Ignacio García-Plazaola; Ilse Kranner

Non-invasive procedures for the diagnosis of viability of plant or fungal tissues would be valuable for scientific, industrial and biomonitoring purposes. Previous studies showed that infrared thermography (IRT) enables non-invasive assessment of the viability of individual "orthodox" (i.e. desiccation tolerant) seeds upon water uptake. However, this method was not tested for rehydrating tissues of other desiccation tolerant life forms. Furthermore, evaporative cooling could obscure the effects of metabolic processes that contribute to heating and cooling, but its effects on the shape of the "thermal fingerprints" have not been explored. Here, we further adapted this method using a purpose-built chamber to control relative humidity (RH) and gaseous atmosphere. This enabled us to test (i) the influence of relative humidity on the thermal fingerprints during the imbibition of Pisum sativum (Garden pea) seeds, (ii) whether thermal fingerprints can be correlated with viability in lichens, and (iii) to assess the potential influence of aerobic metabolism on thermal fingerprints by controlling the oxygen concentration in the gaseous atmosphere around the samples. Finally, we developed a method to artificially "age" lichens and validated the IRT-based method to assess lichen viability in three lichen species. Using either 30% or 100% RH during imbibition of pea seeds, we showed that "live" and "dead" seeds produced clearly discernible "thermal fingerprints", which significantly differed by > |0.15| °C in defined time windows, and that RH affected the shape of these thermal fingerprints. We demonstrated that IRT can also be used to assess the viability of the lichens Lobaria pulmonaria, Pseudevernia furfuracea and Peltigera leucophlebia. No clear relationship between aerobic metabolism and the shape of thermal fingerprints was found. Infrared thermography appears to be a promising method for the diagnosis of viability of desiccation-tolerant tissues at early stages of water uptake. For seeds, it is possible to diagnose viability within the first hours of rehydration, after which time they can still be re-dried and stored until further use. We envisage our work as a baseline study for the use of IR imaging techniques to investigate physiological heterogeneity of desiccation tolerant life forms such as lichens, which can be used for biomonitoring, and for sorting live and dead seeds, which is potentially useful for the seed trade.

更新日期：2019-12-05
• Plant Methods (IF 3.170) Pub Date : 2019-12-04
Zhilan Liu; Jue Wang; Ye Tian; Silan Dai

Cultivar recognition is a basic work in flower production, research, and commercial application. Chinese large-flowered chrysanthemum (Chrysanthemum × morifolium Ramat.) is miraculous because of its high ornamental value and rich cultural deposits. However, the complicated capitulum structure, various floret types and numerous cultivars hinder chrysanthemum cultivar recognition. Here, we explore how deep learning method can be applied to chrysanthemum cultivar recognition. We propose deep learning models with two networks VGG16 and ResNet50 to recognize large-flowered chrysanthemum. Dataset A comprising 14,000 images for 103 cultivars, and dataset B comprising 197 images from different years were collected. Dataset A was used to train the networks and determine the calibration accuracy (Top-5 rate of above 98%), and dataset B was used to evaluate the model generalization performance (Top-5 rate of above 78%). Moreover, gradient-weighted class activation mapping (Grad-CAM) visualization and feature clustering analysis were used to explore how the deep learning model recognizes chrysanthemum cultivars. Deep learning method applied to cultivar recognition is a breakthrough in horticultural science with the advantages of strong recognition performance and high recognition speed. Inflorescence edge areas, disc floret areas, inflorescence colour and inflorescence shape may well be the key factors in model decision-making process, which are also critical in human decision-making.

更新日期：2019-12-04
• Plant Methods (IF 3.170) Pub Date : 2019-11-30
Huijuan Wang; Jinxue Hu; Yao Lu; Mancang Zhang; Ning Qin; Ruize Zhang; Yizhe He; Dongdong Wang; Yue Chen; Cuizhu Zhao; Núria S. Coll; Marc Valls; Qin Chen; Haibin Lu

Potato, the third most important crop worldwide, plays a critical role in human food security. Brown rot, one of the most destructive potato diseases caused by Ralstonia solanacearum, results in huge economic losses every year. A quick, stable, low cost and high throughout method is required to meet the demands of identification of germplasm resistance to bacterial wilt in potato breeding programs. Here we present a novel R. solanacearum hydroponic infection assay on potato plants grown in vitro. Through testing wilt symptom appearance and bacterial colonization in aerial part of plants, we found that the optimum conditions for in vitro potato infection were using an OD600 0.01 bacterial solution suspended with tap water for infection, broken potato roots and an open container. Infection using R. solanacearum strains with differential degree of aggressivity demonstrated that this infection system is equally efficient as soil-drench inoculation for assessment of R. solanacearum virulence on potato. A small-scale assessment of 32 potato germplasms identified three varieties highly resistant to the pathogen, which indicates this infection system is a useful method for high-throughout screening of potato germplasm for resistance. Furthermore, we demonstrate the utility of a strain carrying luminescence to easily quantify bacterial colonization and the detection of latent infections in hydroponic conditions, which can be efficiently used in potato breeding programs. We have established a quick and efficient in vitro potato infection system, which may facilitate breeding for new potato cultivars with high resistance to R. solanacearum.

更新日期：2019-11-30
• Plant Methods (IF 3.170) Pub Date : 2019-11-28
Lei Gao; Guojing Shen; Lingdan Zhang; Jinfeng Qi; Cuiping Zhang; Canrong Ma; Jing Li; Lei Wang; Saif Ul Malook; Jianqiang Wu

Insect herbivory poses a major threat to maize. Benzoxazinoids are important anti-insect secondary metabolites in maize, whose biosynthetic pathway has been extensively studied. However, yet little is known about how benzoxazinoids are regulated in maize, partly due to lack of mutant resources and recalcitrance to genetic transformation. Transient systems based on mesophyll- or cultured cell-derived protoplasts have been exploited in several plant species and have become a powerful tool for rapid or high-throughput assays of gene functions. Nevertheless, these systems have not been exploited to study the regulation of secondary metabolites. A protocol for isolation of protoplasts from etiolated maize seedlings and efficient transfection was optimized. Furthermore, a 10-min-run-time and highly sensitive HPLC–MS method was established to rapidly detect and quantify maize benzoxazinoids. Coupling maize protoplast transfection and HPLC–MS, we screened a few genes potentially regulating benzoxazinoid biosynthesis using overexpression or silencing by artificial microRNA technology. Combining the power of maize protoplast transfection and HPLC–MS analysis, this method allows rapid screening for the regulatory and biosynthetic genes of maize benzoxazinoids in protoplasts, before the candidates are selected for in planta functional analyses. This method can also be applied to study the biosynthesis and regulation of other secondary metabolites in maize and secondary metabolites in other plant species, including those not amenable to transformation.

更新日期：2019-11-29
• Plant Methods (IF 3.170) Pub Date : 2019-11-27
Jens Hartung; Juliane Wagener; Reiner Ruser; Hans-Peter Piepho

Observations measured in field and greenhouse experiments always contain errors. These errors can arise from measurement error, local or positional conditions of the experimental units, or from the randomization of experimental units. In statistical analysis errors can be modelled as independent effects or as spatially correlated effects with an appropriate variance–covariance structure. Using a suitable experimental design, a part of the variance can be captured through blocking of the experimental units. If experimental units (e.g. pots within a greenhouse) are mobile, they can be re-arranged during the experiment. This re-arrangement enables a separation of variation due to time-invariant position effects and variation due to the experimental units. If re-arrangement is successful, the time-invariant positional effect can average out for experimental units moved between different positions during the experiment. While re-arrangement is commonly done in greenhouse experiments, data to quantify its usefulness is limited. A uniformity greenhouse experiment with barley (Hordeum vulgare L.) to compare re-arrangement of pots with a range of designs under fixed-position arrangement showed that both methods can reduce the residual variance and the average standard error of a difference. All designs with fixed-position arrangement, which accounted for the known north–south gradient in the greenhouse, outperformed re-arrangement. An α-design with block size four performed best across seven plant growth traits. Blocking with a fixed-position arrangement was more efficient in improving precision of greenhouse experiments than re-arrangement of pots and hence can be recommended for comparable greenhouse experiments.

更新日期：2019-11-28
• Plant Methods (IF 3.170) Pub Date : 2019-11-26
Zhaozhao Dai; Tong Li; Jiadong Li; Zhifei Han; Yonglong Pan; Sha Tang; Xianmin Diao; Meizhong Luo

Large insert paired-end sequencing technologies are important tools for assembling genomes, delineating associated breakpoints and detecting structural rearrangements. To facilitate the comprehensive detection of inter- and intra-chromosomal structural rearrangements or variants (SVs) and complex genome assembly with long repeats and segmental duplications, we developed a new method based on single-molecule real-time synthesis sequencing technology for generating long paired-end sequences of large insert DNA libraries. A Fosmid vector, pHZAUFOS3, was developed with the following new features: (1) two 18-bp non-palindromic I-SceI sites flank the cloning site, and another two sites are present in the skeleton of the vector, allowing long DNA inserts (and the long paired-ends in this paper) to be recovered as single fragments and the vector (~ 8 kb) to be fragmented into 2–3 kb fragments by I-SceI digestion and therefore was effectively removed from the long paired-ends (5–10 kb); (2) the chloramphenicol (Cm) resistance gene and replicon (oriV), necessary for colony growth, are located near the two sides of the cloning site, helping to increase the proportion of the paired-end fragments to single-end fragments in the paired-end libraries. Paired-end libraries were constructed by ligating the size-selected, mechanically sheared pooled Fosmid DNA fragments to the Ampicillin (Amp) resistance gene fragment and screening the colonies with Cm and Amp. We tested this method on yeast and Setaria italica Yugu1. Fosmid-size paired-ends with an average length longer than 2 kb for each end were generated. The N50 scaffold lengths of the de novo assemblies of the yeast and S. italica Yugu1 genomes were significantly improved. Five large and five small structural rearrangements or assembly errors spanning tens of bp to tens of kb were identified in S. italica Yugu1 including deletions, inversions, duplications and translocations. We developed a new method for long paired-end sequencing of large insert libraries, which can efficiently improve the quality of de novo genome assembly and identify large and small structural rearrangements or assembly errors.

更新日期：2019-11-27
• Plant Methods (IF 3.170) Pub Date : 2019-11-23
Yu Jiang; Changying Li; Andrew H. Paterson; Jon S. Robertson

Plant population density is an important factor for agricultural production systems due to its substantial influence on crop yield and quality. Traditionally, plant population density is estimated by using either field assessment or a germination-test-based approach. These approaches can be laborious and inaccurate. Recent advances in deep learning provide new tools to solve challenging computer vision tasks such as object detection, which can be used for detecting and counting plant seedlings in the field. The goal of this study was to develop a deep-learning-based approach to count plant seedlings in the field. Overall, the final detection model achieved F1 scores of 0.727 (at $$IOU_{all}$$) and 0.969 (at $$IOU_{0.5}$$) on the $$Seedling_{All}$$ testing set in which images had large variations, indicating the efficacy of the Faster RCNN model with the Inception ResNet v2 feature extractor for seedling detection. Ablation experiments showed that training data complexity substantially affected model generalizability, transfer learning efficiency, and detection performance improvements due to increased training sample size. Generally, the seedling counts by the developed method were highly correlated ($$R^2$$ = 0.98) with that found through human field assessment for 75 test videos collected in multiple locations during multiple years, indicating the accuracy of the developed approach. Further experiments showed that the counting accuracy was largely affected by the detection accuracy: the developed approach provided good counting performance for unknown datasets as long as detection models were well generalized to those datasets. The developed deep-learning-based approach can accurately count plant seedlings in the field. Seedling detection models trained in this study and the annotated images can be used by the research community and the cotton industry to further the development of solutions for seedling detection and counting.

更新日期：2019-11-26
• Plant Methods (IF 3.170) Pub Date : 2019-11-19
Fabiana Freitas Moreira; Anthony Ahau Hearst; Keith Aric Cherkauer; Katy Martin Rainey

In the early stages of plant breeding programs high-quality phenotypes are still a constraint to improve genetic gain. New field-based high-throughput phenotyping (HTP) platforms have the capacity to rapidly assess thousands of plots in a field with high spatial and temporal resolution, with the potential to measure secondary traits correlated to yield throughout the growing season. These secondary traits may be key to select more time and most efficiently soybean lines with high yield potential. Soybean average canopy coverage (ACC), measured by unmanned aerial systems (UAS), is highly heritable, with a high genetic correlation with yield. The objective of this study was to compare the direct selection for yield with indirect selection using ACC and using ACC as a covariate in the yield prediction model (Yield|ACC) in early stages of soybean breeding. In 2015 and 2016 we grew progeny rows (PR) and collected yield and days to maturity (R8) in a typical way and canopy coverage using a UAS carrying an RGB camera. The best soybean lines were then selected with three parameters, Yield, ACC and Yield|ACC, and advanced to preliminary yield trials (PYT). We found that for the PYT in 2016, after adjusting yield for R8, there was no significant difference among the mean performances of the lines selected based on ACC and Yield. In the PYT in 2017 we found that the highest yield mean was from the lines directly selected for yield, but it may be due to environmental constraints in the canopy growth. Our results indicated that PR selection using Yield|ACC selected the most top-ranking lines in advanced yield trials. Our findings emphasize the value of aerial HTP platforms for early stages of plant breeding. Though ACC selection did not result in the best performance lines in the second year of selections, our results indicate that ACC has a role in the effective selection of high-yielding soybean lines.

更新日期：2019-11-19
• Plant Methods (IF 3.170) Pub Date : 2019-11-19
Xinjia Yang; Jialin Peng; Junmin Pan

Chlamydomonas reinhardtii is a unicellular green alga, which is a most commonly used model organism for basic research and biotechnological applications. Generation of transgenic strains, which usually requires selectable markers, is instrumental in such studies/applications. Compared to other organisms, the number of selectable markers is limited in this organism. Nourseothricin (NTC) N-acetyl transferase (NAT) has been reported as a selectable marker in a variety of organisms but not including C. reinhardtii. Thus, we investigated whether NAT was useful and effective for selection of transgenic strains in C. reinhardtii. The successful use of NAT would provide alterative choice for selectable markers in this organism and likely in other microalgae. C. reinhardtii was sensitive to NTC at concentrations as low as 5 µg/ml. There was no cross-resistance to nourseothricin in strains that had been transformed with hygromycin B and/or paromomycin resistance genes. A codon-optimized NAT from Streptomyces noursei was synthesized and assembled into different expression vectors followed by transformation into Chlamydomonas. Around 500 transformants could be obtained by using 50 ng DNA on selection with 10 µg/ml NTC. The transformants exhibited normal growth rate and were stable at least for 10 months on conditions even without selection. We successfully tested that NAT could be used as a selectable marker for ectopic expression of IFT54-HA in strains with paromomycin and hygromycin B resistance markers. We further showed that the selection rate for IFT54-HA positive clones was greatly increased by fusing IFT54-HA to NAT and processing with the FMDV 2A peptide. This work represents the first demonstration of stable expression of NAT in the nuclear genome of C. reinhardtii and provides evidence that NAT can be used as an effective selectable marker for transgenic strains. It provides alterative choice for selectable markers in C. reinhardtii. NAT is compatible with paromomycin and hygromycin B resistance genes, which allows for multiple selections.

更新日期：2019-11-19
• Plant Methods (IF 3.170) Pub Date : 2019-11-18
Yong-Fang Li; Miao Zhao; Menglei Wang; Junqiang Guo; Li Wang; Jie Ji; Zongbo Qiu; Yun Zheng; Ramanjulu Sunkar

Post-transcriptional gene regulation is one of the critical layers of overall gene expression programs and microRNAs (miRNAs) play an indispensable role in this process by guiding cleavage on the messenger RNA targets. The transcriptome-wide cleavages on the target transcripts can be identified by analyzing the degradome or PARE or GMUCT libraries. However, high-throughput sequencing of PARE or degradome libraries using Illumina platform, a widely used platform, is not so straightforward. Moreover, the currently used degradome or PARE methods utilize MmeI restriction site in the 5′ RNA adapter and the resulting fragments are only 20-nt long, which often poses difficulty in distinguishing between the members of the same target gene family or distinguishing miRNA biogenesis intermediates from the primary miRNA transcripts belonging to the same miRNA family. Consequently, developing a method which can generate longer fragments from the PARE or degradome libraries which can also be sequenced easily using Illumina platform is ideal. In this protocol, 3′ end of the 5′RNA adaptor of TruSeq small RNA library is modified by introducing EcoP15I recognition site. Correspondingly, the double-strand DNA (dsDNA) adaptor sequence is also modified to suit with the ends generated by the restriction enzyme EcoP15I. These modifications allow amplification of the degradome library by primer pairs used for small RNA library preparation, thus amenable for sequencing using Illumina platform, like small RNA library. Degradome library generated using this improved protocol can be sequenced easily using Illumina platform, and the resulting tag length is ~ 27-nt, which is longer than the MmeI generated fragment (20-nt) that can facilitate better accuracy in validating target transcripts belonging to the same gene family or distinguishing miRNA biogenesis intermediates of the same miRNA family. Furthermore, this improved method allows pooling and sequencing degradome libraries and small RNA libraries simultaneously using Illumina platform.

更新日期：2019-11-18
• Plant Methods (IF 3.170) Pub Date : 2019-11-18
Yahui Liu; Song Lu; Kefu Liu; Sheng Wang; Luqi Huang; Lanping Guo

In recent years, mass spectrometry-based proteomics has provided scientists with the tremendous capability to study plants more precisely than previously possible. Currently, proteomics has been transformed from an isolated field into a comprehensive tool for biological research that can be used to explain biological functions. Several studies have successfully used the power of proteomics as a discovery tool to uncover plant resistance mechanisms. There is growing evidence that indicates that the spatial proteome and post-translational modifications (PTMs) of proteins directly participate in the plant immune response. Therefore, understanding the subcellular localization and PTMs of proteins is crucial for a comprehensive understanding of plant responses to biotic stress. In this review, we discuss current approaches to plant proteomics that use mass spectrometry, with particular emphasis on the application of spatial proteomics and PTMs. The purpose of this paper is to investigate the current status of the field, discuss recent research challenges, and encourage the application of proteomics techniques to further research.

更新日期：2019-11-18
• Plant Methods (IF 3.170) Pub Date : 2019-11-18
Saeid Jamshidi; Abbas Yadollahi; Mohammad Mehdi Arab; Mohammad Soltani; Maliheh Eftekhari; Hamed Sabzalipoor; Abdollatif Sheikhi; Jalal Shiri

Predicting impact of plant tissue culture media components on explant proliferation is important especially in commercial scale for optimizing efficient culture media. Previous studies have focused on predicting the impact of media components on explant growth via conventional multi-layer perceptron neural networks (MLPNN) and Multiple Linear Regression (MLR) methods. So, there is an opportunity to find more efficient algorithms such as Radial Basis Function Neural Network (RBFNN) and Gene Expression Programming (GEP). Here, a novel algorithm, i.e. GEP which has not been previously applied in plant tissue culture researches was compared to RBFNN and MLR for the first time. Pear rootstocks (Pyrodwarf and OHF) were used as case studies on predicting the effect of minerals and some hormones in the culture medium on proliferation indices. Generally, RBFNN and GEP showed extremely higher performance accuracy than the MLR. Moreover, GEP models as the most accurate models were optimized using genetic algorithm (GA). The improvement was mainly due to the RBFNN and GEP strong estimation capability and their superior tolerance to experimental noises or improbability. GEP as the most robust and accurate prospecting procedure to achieve the highest proliferation quality and quantity has also the benefit of being easy to use.

更新日期：2019-11-18
• Plant Methods (IF 3.170) Pub Date : 2019-11-18
Wei Lu; Ye Li; Yiming Deng

The root phenotypes of different vigorous maize seeds vary a lot. Imaging roots of growing maize is a non-invasive, affordable and high throughput approach. However, it’s difficult to get integral root images because of the block of the soil. The paper proposed an algorithm to repair incomplete root images for maize root fast non-invasive phenotyping detection. A two-layer transparent stress growth device with two concentric cylinders was developed as mesocosms and the maize seeds were planted in the annulus of it. The maize roots grow in soil against two acrylic plastic surfaces due to the press of the small growing area to acquire more root details during roots visualization and imaging. Even though, parts of the roots are occluded which means that it’s tough to extract the information of root general physical construction. For recovering gaps from disconnected root segments, Progressive Corrosion Joining (PCJ) algorithm was proposed based on the physiological characteristics of hydrotropism, geostrophic and continuity with three steps which are root image thinning, progressive corrosion and joining processing respectively. The experiments indicate that maize phenotyping parameters are negative correlation with seed aging days. And specifically, Root Number (RTN), Root Length (RTL), Root Width (RTW) and Root Extension Length (REL) of unaged and 14-day-aged maize seeds are decreased from 15.40, 82.40 mm, 1.53 mm and 82.20 mm to 4.58, 38.6 mm, 1.35 mm and 55.20 mm, and the growing speed of them are changed from 1.68 per day, 8.80 mm/d, 0.06 mm/d, 9.0 mm/d to 0.70 per day, 4.3 mm/d, 0.05 mm/d and 5.70 mm/d respectively. Whereas Root Extension Angle (REA) is basically irrelevant with the level of maize seed aging. The developed double-layer Annular Root Phenotyping Container (ARPC) can satisfy the general physical construction of maize as well as push each root growing along the inner wall of the container which help to acquire more root information. The presented novel PCJ algorithm can recover the missing parts, even for big gaps, of maize roots effectively according to root morphological properties. The experiments show that the proposed method can be applied to evaluate the vigor of maize seeds which has vast application prospect in high throughput root phenotyping area.

更新日期：2019-11-18
• Plant Methods (IF 3.170) Pub Date : 2019-11-18
Adnan Zahid; Hasan T. Abbas; Aifeng Ren; Ahmed Zoha; Hadi Heidari; Syed A. Shah; Muhammad A. Imran; Akram Alomainy; Qammer H. Abbasi

The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time–frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring.

更新日期：2019-11-18
• Plant Methods (IF 3.170) Pub Date : 2019-11-16
Jorge Victorino; Francisco Gómez

Unfortunately, the original version of the article [1] contained an error in figure 7. The names of species Umus minor and Acer campestre were interchanged. The corrected figure 7 is given below:

更新日期：2019-11-17
• Plant Methods (IF 3.170) Pub Date : 2019-11-16
Erich-Christian Oerke; Marlene Leucker; Ulrike Steiner

Due to its high damaging potential, Cercospora leaf spot (CLS) caused by Cercospora beticola is a continuous threat to sugar beet production worldwide. Breeding for disease resistance is hampered by the quantitative nature of resistance which may result from differences in penetration, colonization, and sporulation of the pathogen on sugar beet genotypes. In particular, problems in the quantitative assessment of C. beticola sporulation have resulted in the common practice to assess field resistance late in the growth period as quantitative resistance parameter. Recently, hyperspectral sensors have shown potential to assess differences in CLS severity. Hyperspectral microscopy was used for the quantification of C. beticola sporulation on sugar beet leaves in order to characterize the host plant suitability / resistance of genotypes for decision-making in breeding for CLS resistance. Assays with attached and detached leaves demonstrated that vital plant tissue is essential for the full potential of genotypic mechanisms of disease resistance and susceptibility. Spectral information (400 to 900 nm, 160 wavebands) of CLSs recorded before and after induction of C. beticola sporulation allowed the identification of sporulating leaf spot sub-areas. A supervised classification and quantification of sporulation structures was possible, but the necessity of genotype-specific reference spectra restricts the general applicability of this approach. Fungal sporulation could be quantified independent of the host plant genotype by calculating the area under the difference reflection spectrum from hyperspectral imaging before and with sporulation. The overall relationship between sensor-based and visual quantification of C. beticola sporulation on five genotypes differing in CLS resistance was R2 = 0.81; count-based differences among genotypes could be reproduced spectrally. For the first time, hyperspectral imaging was successfully tested for the quantification of sporulation as a fungal activity depending on host plant suitability. The potential of this non-invasive and non-destructive approach for the quantification of fungal sporulation in other host–pathogen systems and for the phenotyping of crop traits complex as sporulation resistance is discussed.

更新日期：2019-11-17
• Plant Methods (IF 3.170) Pub Date : 2019-11-08
Jonathan Matheka; Jaindra Nath Tripathi; Ibsa Merga; Endale Gebre; Leena Tripathi

Enset (Ensete ventricosum), also known as Ethiopian banana, is a food security crop for more than 20 million people in Ethiopia. As conventional breeding of enset is very challenging, genetic engineering is an alternative option to introduce important traits such as enhanced disease resistance and nutritional value. Genetic transformation and subsequent regeneration of transgenic enset has never been reported mainly due to challenges in developing transformation protocols for this tropical species. Agrobacterium-mediated transformation could be a practical tool for the genetic improvement of enset. However, the efficiency of the transformation system depends on several parameters such as plant regeneration, genotype, explant, selection agent and Agrobacterium strains. As a first step towards the development of transgenic enset, a simple and rapid plant regeneration system was developed using multiple buds as explants. Induction and proliferation of multiple buds from shoot tip explants was achieved on Murashige and Skoog (MS) medium supplemented with 5 and 10 mg/l of 6-benzylaminopurine (BAP), respectively. Shoots were regenerated from multiple buds on MS media containing 2 mg/l BAP and 0.2% activated charcoal. Based on the optimized regeneration protocol, an Agrobacterium-mediated transformation method was developed using multiple buds as explants and the binary plasmid pCAMBIA2300-GFP containing the green florescent protein (gfp) reporter gene and neomycin phosphotransferase II (nptII) selection marker gene. Transgenic plantlets were obtained within 4 months at a frequency of about 1.25%. The transgenic lines were validated by PCR analysis using primers specific to the nptII gene. To obtain uniformly transformed plantlets, chimerism was diluted by subculturing and regenerating the transgenic shoots on a selective medium containing kanamycin (150 mg/l) for five cycles. The uniformity of the transgenic plants was confirmed by Southern blot hybridization and RT-PCR analyses on different tissues such as leaf, pseudostem and root of same transgenic plant. In the present study, we report a simple Agrobacterium-mediated transformation system for generating transgenic events of enset. To the best of our knowledge, this is the first report on the stable transformation and regeneration of transgenic events of enset. The transformation system established in this study can be used for the generation of transgenic enset with important traits such as disease resistance.

更新日期：2019-11-11
• Plant Methods (IF 3.170) Pub Date : 2019-11-09
Michael Gomez Selvaraj; Maria Elker Montoya-P; John Atanbori; Andrew P. French; Tony Pridmore

Root and tuber crops are becoming more important for their high source of carbohydrates, next to cereals. Despite their commercial impact, there are significant knowledge gaps about the environmental and inherent regulation of storage root (SR) differentiation, due in part to the innate problems of studying storage roots and the lack of a suitable model system for monitoring storage root growth. The research presented here aimed to develop a reliable, low-cost effective system that enables the study of the factors influencing cassava storage root initiation and development. We explored simple, low-cost systems for the study of storage root biology. An aeroponics system described here is ideal for real-time monitoring of storage root development (SRD), and this was further validated using hormone studies. Our aeroponics-based auxin studies revealed that storage root initiation and development are adaptive responses, which are significantly enhanced by the exogenous auxin supply. Field and histological experiments were also conducted to confirm the auxin effect found in the aeroponics system. We also developed a simple digital imaging platform to quantify storage root growth and development traits. Correlation analysis confirmed that image-based estimation can be a surrogate for manual root phenotyping for several key traits. The aeroponic system developed from this study is an effective tool for examining the root architecture of cassava during early SRD. The aeroponic system also provided novel insights into storage root formation by activating the auxin-dependent proliferation of secondary xylem parenchyma cells to induce the initial root thickening and bulking. The developed system can be of direct benefit to molecular biologists, breeders, and physiologists, allowing them to screen germplasm for root traits that correlate with improved economic traits.

更新日期：2019-11-11
• Plant Methods (IF 3.170) Pub Date : 2019-11-07
Umut Rende; Totte Niittylä; Thomas Moritz

Sugar phosphates are important intermediates of central carbon metabolism in biological systems, with roles in glycolysis, the pentose–phosphate pathway, tricarboxylic acid (TCA) cycle, and many other biosynthesis pathways. Understanding central carbon metabolism requires a simple, robust and comprehensive analytical method. However, sugar phosphates are notoriously difficult to analyze by traditional reversed phase liquid chromatography. Here, we show a two-step derivatization of sugar phosphates by methoxylamine and propionic acid anhydride after chloroform/methanol (3:7) extraction from Populus leaf and developing wood that improves separation, identification and quantification of sugar phosphates by ultra high performance liquid chromatography–electrospray ionization–mass spectrometry (UHPLC–ESI–MS). Standard curves of authentic sugar phosphates were generated for concentrations from pg to ng/μl with a correlation coefficient R2 > 0.99. The method showed high sensitivity and repeatability with relative standard deviation (RSD) < 20% based on repeated extraction, derivatization and detection. The analytical accuracy for Populus leaf extracts, determined by a two-level spiking approach of selected metabolites, was 79–107%. The results show the reliability of combined reversed phase liquid chromatography–tandem mass spectrometry for sugar phosphate analysis and demonstrate the presence of two unknown sugar phosphates in Populus extracts.

更新日期：2019-11-07
• Plant Methods (IF 3.170) Pub Date : 2019-11-07
María Dolores Fariñas; Daniel Jimenez-Carretero; Domingo Sancho-Knapik; José Javier Peguero-Pina; Eustaquio Gil-Pelegrín; Tomás Gómez Álvarez-Arenas

Non-contact resonant ultrasound spectroscopy (NC-RUS) has been proven as a reliable technique for the dynamic determination of leaf water status. It has been already tested in more than 50 plant species. In parallel, relative water content (RWC) is highly used in the ecophysiological field to describe the degree of water saturation in plant leaves. Obtaining RWC implies a cumbersome and destructive process that can introduce artefacts and cannot be determined instantaneously. Here, we present a method for the estimation of RWC in plant leaves from non-contact resonant ultrasound spectroscopy (NC-RUS) data. This technique enables to collect transmission coefficient in a [0.15–1.6] MHz frequency range from plant leaves in a non-invasive, non-destructive and rapid way. Two different approaches for the proposed method are evaluated: convolutional neural networks (CNN) and random forest (RF). While CNN takes the entire ultrasonic spectra acquired from the leaves, RF only uses four relevant parameters resulted from the transmission coefficient data. Both methods were tested successfully in Viburnum tinus leaf samples with Pearson’s correlations between 0.92 and 0.84. This study showed that the combination of NC-RUS technique with deep learning algorithms is a robust tool for the instantaneous, accurate and non-destructive determination of RWC in plant leaves.

更新日期：2019-11-07
• Plant Methods (IF 3.170) Pub Date : 2019-11-07
Yadi Song; Shang Chen; Xiujuan Wang; Rui Zhang; Lichan Tu; Tianyuan Hu; Xihong Liu; Yifeng Zhang; Luqi Huang; Wei Gao

Tripterygium wilfordii Hook. f. (T. wilfordii) is an important medicinal plant with anti-inflammatory, immunosuppressive and anti-tumor activities. The main bioactive ingredients are diterpenoids and triterpenoids, such as triptolide, triptophenolide and celastrol. However, the production of terpenoids from original plants, hairy roots and dedifferentiated cells (DDCs) are not satisfactory for clinical applications. To find a new way to further improve the production of terpenoids, we established a new culture system of cambial meristematic cells (CMCs) with stem cell-like properties, which had strong vigor and high efficiency to produce large amounts of terpenoids of T. wilfordii. CMCs of T. wilfordii were isolated and cultured for the first time. CMCs were characterized consistent with stem cell identities based on their physiological and molecular analysis, including morphology of CMCs, hypersensitivity to zeocin, thin cell wall and orthogonal partial least square-discriminant analysis, combination of transcriptional data analysis. After induction with methyl jasmonate (MJ), the maximal production of triptolide, celastrol and triptophenolide in CMCs was 312%, 400% and 327% higher than that of control group, respectively. As for medium, MJ-induced CMCs secreted 231% triptolide and 130% triptophenolide at the maximum level into medium higher than that of control group. Maximal celastrol production of induced CMCs medium was 48% lower than that of control group. Long-term induction significantly enhanced the production of terpenoids both in cells and medium. The reason for increasing the yield of terpenoids was that expression levels of 1-deoxy-d-xylulose-5-phosphate synthase (DXS), 1-deoxy-d-xylulose-5-phosphate reductoisomerase (DXR) and hydroxymethylglutaryl-CoA synthase (HMGS) were upregulated in CMCs after induction. For the first time, CMCs of T. wilfordii were isolated, cultured, characterized and applied. Considering the significant enrichment of terpenoids in CMCs of T. wilfordii, CMCs could provide an efficient and controllable platform for sustainable production of terpenoids, which can be a better choice than DDCs.

更新日期：2019-11-07
• Plant Methods (IF 3.170) Pub Date : 2019-11-02
Jing-Wei Li; Xiao-Chen Zhang; Min-Rui Wang; Wen-Lu Bi; M. Faisal; Jaime A. Teixeira da Silva; Gayle M. Volk; Qiao-Chun Wang

Lilium is one of the most popular flower crops worldwide, and some species are also used as vegetables and medicines. The availability of and easy access to diverse Lilium genetic resources are essential for plant genetic improvements. Cryopreservation is currently considered as an ideal means for the long-term preservation of plant germplasm. Over the last two decades, great efforts have been exerted in studies of Lilium cryopreservation and progress has been made in the successful cryopreservation of pollen, seeds and shoot tips in Lilium. Genes that exist in Lilium, including those that regulate flower shape, color and size, and that are resistant to cold stress and diseases caused by fungi and viruses, provide a rich source of valuable genetic resources for breeding programs to create novel cultivars required by the global floriculture and ornamental markets. Successful cryopreservation of Lilium spp. is a way to preserve these valuable genes. The present study provides updated and comprehensive information about the development of techniques that have advanced Lilium cryopreservation. Further ideas are proposed to better direct future studies on Lilium cryobiotechnology.

更新日期：2019-11-04
• Plant Methods (IF 3.170) Pub Date : 2019-11-04
Federica Brunoni; Silvio Collani; Jan Šimura; Markus Schmid; Catherine Bellini; Karin Ljung

Plants rely on concentration gradients of the native auxin, indole-3-acetic acid (IAA), to modulate plant growth and development. Both metabolic and transport processes participate in the dynamic regulation of IAA homeostasis. Free IAA levels can be reduced by inactivation mechanisms, such as conjugation and degradation. IAA can be conjugated via ester linkage to glucose, or via amide linkage to amino acids, and degraded via oxidation. Members of the UDP glucosyl transferase (UGT) family catalyze the conversion of IAA to indole-3-acetyl-1-glucosyl ester (IAGlc); by contrast, IAA is irreversibly converted to indole-3-acetyl-l-aspartic acid (IAAsp) and indole-3-acetyl glutamic acid (IAGlu) by Group II of the GRETCHEN HAGEN3 (GH3) family of acyl amido synthetases. Dioxygenase for auxin oxidation (DAO) irreversibly oxidizes IAA to oxindole-3-acetic acid (oxIAA) and, in turn, oxIAA can be further glucosylated to oxindole-3-acetyl-1-glucosyl ester (oxIAGlc) by UGTs. These metabolic pathways have been identified based on mutant analyses, in vitro activity measurements, and in planta feeding assays. In vitro assays for studying protein activity are based on producing Arabidopsis enzymes in a recombinant form in bacteria or yeast followed by recombinant protein purification. However, the need to extract and purify the recombinant proteins represents a major obstacle when performing in vitro assays. In this work we report a rapid, reproducible and cheap method to screen the enzymatic activity of recombinant proteins that are known to inactivate IAA. The enzymatic reactions are carried out directly in bacteria that produce the recombinant protein. The enzymatic products can be measured by direct injection of a small supernatant fraction from the bacterial culture on ultrahigh-performance liquid chromatography coupled to electrospray ionization tandem spectrometry (UHPLC–ESI-MS/MS). Experimental procedures were optimized for testing the activity of different classes of IAA-modifying enzymes without the need to purify recombinant protein. This new method represents an alternative to existing in vitro assays. It can be applied to the analysis of IAA metabolites that are produced upon supplementation of substrate to engineered bacterial cultures and can be used for a rapid screening of orthologous candidate genes from non-model species.

更新日期：2019-11-04
• Plant Methods (IF 3.170) Pub Date : 2019-11-01
Jiating Li; Arun-Narenthiran Veeranampalayam-Sivakumar; Madhav Bhatta; Nicholas D. Garst; Hannah Stoll; P. Stephen Baenziger; Vikas Belamkar; Reka Howard; Yufeng Ge; Yeyin Shi

Automated phenotyping technologies are continually advancing the breeding process. However, collecting various secondary traits throughout the growing season and processing massive amounts of data still take great efforts and time. Selecting a minimum number of secondary traits that have the maximum predictive power has the potential to reduce phenotyping efforts. The objective of this study was to select principal features extracted from UAV imagery and critical growth stages that contributed the most in explaining winter wheat grain yield. Five dates of multispectral images and seven dates of RGB images were collected by a UAV system during the spring growing season in 2018. Two classes of features (variables), totaling to 172 variables, were extracted for each plot from the vegetation index and plant height maps, including pixel statistics and dynamic growth rates. A parametric algorithm, LASSO regression (the least angle and shrinkage selection operator), and a non-parametric algorithm, random forest, were applied for variable selection. The regression coefficients estimated by LASSO and the permutation importance scores provided by random forest were used to determine the ten most important variables influencing grain yield from each algorithm. Both selection algorithms assigned the highest importance score to the variables related with plant height around the grain filling stage. Some vegetation indices related variables were also selected by the algorithms mainly at earlier to mid growth stages and during the senescence. Compared with the yield prediction using all 172 variables derived from measured phenotypes, using the selected variables performed comparable or even better. We also noticed that the prediction accuracy on the adapted NE lines (r = 0.58–0.81) was higher than the other lines (r = 0.21–0.59) included in this study with different genetic backgrounds. With the ultra-high resolution plot imagery obtained by the UAS-based phenotyping we are now able to derive more features, such as the variation of plant height or vegetation indices within a plot other than just an averaged number, that are potentially very useful for the breeding purpose. However, too many features or variables can be derived in this way. The promising results from this study suggests that the selected set from those variables can have comparable prediction accuracies on the grain yield prediction than the full set of them but possibly resulting in a better allocation of efforts and resources on phenotypic data collection and processing.

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2019-11-01
Bo Duan; Yating Liu; Yan Gong; Yi Peng; Xianting Wu; Renshan Zhu; Shenghui Fang

The accurate estimation of rice LAI is particularly important to monitor rice growth status. Remote sensing, as a non-destructive measurement technology, has been proved to be useful for estimating vegetation growth parameters, especially at large scale. With the development of unmanned aerial vehicles (UAVs), this novel remote sensing platform has been widely used to provide remote sensing images which have much higher spatial resolution. Previous reports have shown that the spectral feature of remote sensing images could be an effective indicator to estimate vegetation growth parameters. However, the texture feature of high-resolution remote sensing images is rarely employed for this purpose. Besides, the physical mechanism between the texture feature and vegetation growth parameters is still unclear. In this study, a Fourier spectrum texture based on the UAV Image was developed to estimate rice LAI. And the relationship between Fourier spectrum texture and rice LAI was also analyzed. The results showed that Fourier spectrum texture could improve the accuracy of rice LAI estimation. In conclusion, the texture feature of high-resolution remote sensing images may be more effective in rice LAI estimation than the spectral feature.

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2019-10-31
Wei Wu; Tao Liu; Ping Zhou; Tianle Yang; Chunyan Li; Xiaochun Zhong; Chengming Sun; Shengping Liu; Wenshan Guo

The number grain per panicle of rice is an important phenotypic trait and a significant index for variety screening and cultivation management. The methods that are currently used to count the number of grains per panicle are manually conducted, making them labor intensive and time consuming. Existing image-based grain counting methods had difficulty in separating overlapped grains. In this study, we aimed to develop an image analysis-based method to quickly quantify the number of rice grains per panicle. We compared the counting accuracy of several methods among different image acquisition devices and multiple panicle shapes on both Indica and Japonica subspecies of rice. The linear regression model developed in this study had a grain counting accuracy greater than 96% and 97% for Japonica and Indica rice, respectively. Moreover, while the deep learning model that we used was more time consuming than the linear regression model, the average counting accuracy was greater than 99%. We developed a rice grain counting method that accurately counts the number of grains on a detached panicle, and believe this method can be a huge asset for guiding the development of high throughput methods for counting the grain number per panicle in other crops.

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2018-12-07
S V G N Priyadarshani,Bingyan Hu,Weimin Li,Hina Ali,Haifeng Jia,Lihua Zhao,Simon Peter Ojolo,Syed Muhammad Azam,Junjie Xiong,Maokai Yan,Zia Ur Rahman,Qingsong Wu,Yuan Qin

[This corrects the article DOI: 10.1186/s13007-018-0365-9.].

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2018-11-06
S V G N Priyadarshani,Bingyan Hu,Weimin Li,Hina Ali,Haifeng Jia,Lihua Zhao,Simon Peter Ojolo,Syed Muhammad Azam,Junjie Xiong,Maokai Yan,Zia Ur Rahman,Qingsong Wu,Yuan Qin

Background An efficient transformation protocol is a primary requisite to study and utilize the genetic potential of any plant species. A quick transformation system is also crucial for the functional analysis of genes along with the study of proteins and their interactions in vivo. Presently, however, quick and effective transformation systems are still lacking for many plant species including pineapple. This has limited the full exploration of the genetic repository of pineapple as well as the study of its genes, protein localization and protein interactions. Results To address the above limitations, we have developed an efficient system for protoplast isolation and subcellular localization of desired proteins using pineapple plants derived from tissue culture. A cocktail of 1.5% (W/V) Cellulase R-10 and 0.5% (W/V) Macerozyme R-10 resulted in 51% viable protoplasts with 3 h digestion. Compared to previously reported protocols, our protoplast isolation method is markedly faster (saving 4.5 h), requires only a small quantity of tissue sample (1 g of leaves) and has high yield (6.5 × 105). The quality of the isolated protoplasts was verified using organelle localization in protoplasts with different organelle markers. Additionally, colocalization analysis of two pineapple Mg2+ transporter genes in pineapple protoplasts was consistent with the results in a tobacco transient expression system, confirming that the protoplast isolation method can be used to study subcellular localization. Further findings showed that the system is also suitable for protein-protein interaction studies. Conclusion Based on our findings, the presently described method is an efficient and effective strategy for pineapple protoplast isolation and transformation; it is convenient and time saving and provides a greater platform for transformation studies.

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2018-07-14
Abelardo Montesinos-López,Osval A Montesinos-López,Gustavo de Los Campos,José Crossa,Juan Burgueño,Francisco Javier Luna-Vazquez

[This corrects the article DOI: 10.1186/s13007-018-0314-7.].

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2017-11-28
Marvin Lüpke,Rainer Steinbrecher,Michael Leuchner,Annette Menzel

[This corrects the article DOI: 10.1186/s13007-017-0166-6.].

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2018-06-28
R Makanza,M Zaman-Allah,J E Cairns,J Eyre,J Burgueño,Ángela Pacheco,C Diepenbrock,C Magorokosho,A Tarekegne,M Olsen,B M Prasanna

Background Grain yield, ear and kernel attributes can assist to understand the performance of maize plant under different environmental conditions and can be used in the variety development process to address farmer's preferences. These parameters are however still laborious and expensive to measure. Results A low-cost ear digital imaging method was developed that provides estimates of ear and kernel attributes i.e., ear number and size, kernel number and size as well as kernel weight from photos of ears harvested from field trial plots. The image processing method uses a script that runs in a batch mode on ImageJ; an open source software. Kernel weight was estimated using the total kernel number derived from the number of kernels visible on the image and the average kernel size. Data showed a good agreement in terms of accuracy and precision between ground truth measurements and data generated through image processing. Broad-sense heritability of the estimated parameters was in the range or higher than that for measured grain weight. Limitation of the method for kernel weight estimation is discussed. Conclusion The method developed in this work provides an opportunity to significantly reduce the cost of selection in the breeding process, especially for resource constrained crop improvement programs and can be used to learn more about the genetic bases of grain yield determinants.

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2018-11-22
Md Mehedi Hasan,Joshua P Chopin,Hamid Laga,Stanley J Miklavcic

Background Field phenotyping by remote sensing has received increased interest in recent years with the possibility of achieving high-throughput analysis of crop fields. Along with the various technological developments, the application of machine learning methods for image analysis has enhanced the potential for quantitative assessment of a multitude of crop traits. For wheat breeding purposes, assessing the production of wheat spikes, as the grain-bearing organ, is a useful proxy measure of grain production. Thus, being able to detect and characterize spikes from images of wheat fields is an essential component in a wheat breeding pipeline for the selection of high yielding varieties. Results We have applied a deep learning approach to accurately detect, count and analyze wheat spikes for yield estimation. We have tested the approach on a set of images of wheat field trial comprising 10 varieties subjected to three fertilizer treatments. The images have been captured over one season, using high definition RGB cameras mounted on a land-based imaging platform, and viewing the wheat plots from an oblique angle. A subset of in-field images has been accurately labeled by manually annotating all the spike regions. This annotated dataset, called SPIKE, is then used to train four region-based Convolutional Neural Networks (R-CNN) which take, as input, images of wheat plots, and accurately detect and count spike regions in each plot. The CNNs also output the spike density and a classification probability for each plot. Using the same R-CNN architecture, four different models were generated based on four different datasets of training and testing images captured at various growth stages. Despite the challenging field imaging conditions, e.g., variable illumination conditions, high spike occlusion, and complex background, the four R-CNN models achieve an average detection accuracy ranging from 88 to 94 % across different sets of test images. The most robust R-CNN model, which achieved the highest accuracy, is then selected to study the variation in spike production over 10 wheat varieties and three treatments. The SPIKE dataset and the trained CNN are the main contributions of this paper. Conclusion With the availability of good training datasets such us the SPIKE dataset proposed in this article, deep learning techniques can achieve high accuracy in detecting and counting spikes from complex wheat field images. The proposed robust R-CNN model, which has been trained on spike images captured during different growth stages, is optimized for application to a wider variety of field scenarios. It accurately quantifies the differences in yield produced by the 10 varieties we have studied, and their respective responses to fertilizer treatment. We have also observed that the other R-CNN models exhibit more specialized performances. The data set and the R-CNN model, which we make publicly available, have the potential to greatly benefit plant breeders by facilitating the high throughput selection of high yielding varieties.

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2018-11-06
Sadiye Hayta,Mark A Smedley,Jinhong Li,Wendy A Harwood,Philip M Gilmartin

Background Genetic transformation is a valuable tool and an important procedure in plant functional genomics contributing to gene discovery, allowing powerful insights into gene function and genetically controlled characteristics. Primulaceae species provide one of the best-known examples of heteromorphic flower development, a breeding system which has attracted considerable attention, including that of Charles Darwin. Molecular approaches, including plant transformation give the best opportunity to define and understand the role of genes involved in floral heteromorphy in the common primrose, Primula vulgaris, along with other Primula species. Results Two transformation systems have been developed in P. vulgaris. The first system, Agrobacterium-mediated vacuum infiltration of seedlings, enables the rapid testing of transgenes, transiently in planta. GUS expression was observed in the cotyledons, true leaves, and roots of Primula seedlings. The second system is based on Agrobacterium tumefaciens infection of pedicel explants with an average transformation efficiency of 4.6%. This transformation system, based on regeneration and selection of transformants within in vitro culture, demonstrates stable transgene integration and transmission to the next generation. Conclusion The two transformation systems reported here will aid fundamental research into important traits in Primula. Although, stable integration of transgenes is the ultimate goal for such analyses, transient gene expression via Agrobacterium-mediated DNA transfer, offers a simple and fast method to analyse transgene functions. The second system describes, for the first time, stable Agrobacterium-mediated transformation of Primula vulgaris, which will be key to characterising the genes responsible for the control of floral heteromorphy.

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2017-11-10
Nathan Hughes,Karen Askew,Callum P Scotson,Kevin Williams,Colin Sauze,Fiona Corke,John H Doonan,Candida Nibau

Background Wheat is one of the most widely grown crop in temperate climates for food and animal feed. In order to meet the demands of the predicted population increase in an ever-changing climate, wheat production needs to dramatically increase. Spike and grain traits are critical determinants of final yield and grain uniformity a commercially desired trait, but their analysis is laborious and often requires destructive harvest. One of the current challenges is to develop an accurate, non-destructive method for spike and grain trait analysis capable of handling large populations. Results In this study we describe the development of a robust method for the accurate extraction and measurement of spike and grain morphometric parameters from images acquired by X-ray micro-computed tomography (μCT). The image analysis pipeline developed automatically identifies plant material of interest in μCT images, performs image analysis, and extracts morphometric data. As a proof of principle, this integrated methodology was used to analyse the spikes from a population of wheat plants subjected to high temperatures under two different water regimes. Temperature has a negative effect on spike height and grain number with the middle of the spike being the most affected region. The data also confirmed that increased grain volume was correlated with the decrease in grain number under mild stress. Conclusions Being able to quickly measure plant phenotypes in a non-destructive manner is crucial to advance our understanding of gene function and the effects of the environment. We report on the development of an image analysis pipeline capable of accurately and reliably extracting spike and grain traits from crops without the loss of positional information. This methodology was applied to the analysis of wheat spikes can be readily applied to other economically important crop species.

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2018-08-14
Victoria Ruiz-Hernández,María José Roca,Marcos Egea-Cortines,Julia Weiss

Background Full scent profiles emitted by living tissues can be screened by using total ion chromatograms generated in full scan mode and gas chromatography-mass spectrometry technique using Headspace Sorptive Extraction. This allows the identification of specific compounds and their absolute quantification or relative abundance. Quantifications ideally should be based on calibration curves using standards for each compound. However, the unpredictable composition of Volatile Organic Compounds (VOCs) and lack of standards make this approach difficult. Researchers studying scent profiles therefore concentrate on identifying specific scent footprints i.e. relative abundance rather than absolute quantities. We compared several semi-quantitative methods: external calibration curves generated in the sampling system and by liquid addition of standards to stir bars, total integrated peak area per fresh weight (FW), normalized peak area per FW, semi-quantification based on internal standard abundance, semi-quantification based on the nearest n-alkane and percentage of emission. Furthermore, we explored the usage of nearest components and single calibrators for semi-quantifications. Results Any of the semi-quantification methods based on a standard produced similar or even identical results compared to quantification by a true-standard for a compound, except for the method based on standard addition. Each method beholds advantages and disadvantages regarding level of accuracy, experimental variability, acceptance and retrieved quantities. Conclusions Our data shows that, except for the method of standard addition to the biological sample, the rest of the semi-quantification methods studied give highly similar statistical results. Any of the methodologies presented here can therefore be considered as valid for scent profiling. Regarding relative proportions of VOCs, the generation of calibration curves for each compound analysed is not necessary.

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2018-06-09
Wenxian Liang,Xiaoxing Zou,Rebeca Carballar-Lejarazú,Lingjiao Wu,Weihong Sun,Xueyuan Yuan,Songqing Wu,Pengfei Li,Hui Ding,Lin Ni,Wei Huang,Shuangquan Zou

Background Quantitative real-time reverse transcription-polymerase chain reaction has been widely used in gene expression analysis, however, to have reliable and accurate results, reference genes are necessary to normalize gene expression under different experimental conditions. Several reliable reference genes have been reported in plants of Traditional Chinese Medicine, but none have been identified for Euscaphis konishii Hayata. Results In this study, 12 candidate reference genes, including 3 common housekeeping genes and 9 novel genes based on E. konishii Hayata transcriptome data were selected and analyzed in different tissues (root, branch, leaf, capsule and seed), capsule and seed development stages. Expression stability was calculated using geNorm and NormFinder, the minimal number of reference genes required for accurate normalization was calculated by Vn/Vn + 1 using geNorm. EkEEF-5A-1 and EkADF2 were the two most stable reference genes for all samples, while EkGSTU1 and EkGAPDH were the most stable reference genes for tissue samples. For seed development stages, EkGAPDH and EkEEF-5A-1 were the most stable genes, whereas EkGSTU1 and EkGAPDH were identified as the two most stable genes in the capsule development stages. Two reference genes were sufficient to normalize gene expression across all sample sets. Conclusion Results of this study revealed that suitable reference genes should be selected for different experimental samples, and not all the common reference genes are suitable for different tissue samples and/or experimental conditions. In this study, we present the first data of reference genes selection for E. konishii Hayata based on transcriptome data, our data will facilitate further studies in molecular biology and gene function on E. konishii Hayata and other closely related species.

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2018-01-30
Jordan Ubbens,Mikolaj Cieslak,Przemyslaw Prusinkiewicz,Ian Stavness

Deep learning presents many opportunities for image-based plant phenotyping. Here we consider the capability of deep convolutional neural networks to perform the leaf counting task. Deep learning techniques typically require large and diverse datasets to learn generalizable models without providing a priori an engineered algorithm for performing the task. This requirement is challenging, however, for applications in the plant phenotyping field, where available datasets are often small and the costs associated with generating new data are high. In this work we propose a new method for augmenting plant phenotyping datasets using rendered images of synthetic plants. We demonstrate that the use of high-quality 3D synthetic plants to augment a dataset can improve performance on the leaf counting task. We also show that the ability of the model to generate an arbitrary distribution of phenotypes mitigates the problem of dataset shift when training and testing on different datasets. Finally, we show that real and synthetic plants are significantly interchangeable when training a neural network on the leaf counting task.

更新日期：2019-11-01
• Plant Methods (IF 3.170) Pub Date : 2017-11-21
Michael Rzanny,Marco Seeland,Jana Wäldchen,Patrick Mäder

Background Automated species identification is a long term research subject. Contrary to flowers and fruits, leaves are available throughout most of the year. Offering margin and texture to characterize a species, they are the most studied organ for automated identification. Substantially matured machine learning techniques generate the need for more training data (aka leaf images). Researchers as well as enthusiasts miss guidance on how to acquire suitable training images in an efficient way. Methods In this paper, we systematically study nine image types and three preprocessing strategies. Image types vary in terms of in-situ image recording conditions: perspective, illumination, and background, while the preprocessing strategies compare non-preprocessed, cropped, and segmented images to each other. Per image type-preprocessing combination, we also quantify the manual effort required for their implementation. We extract image features using a convolutional neural network, classify species using the resulting feature vectors and discuss classification accuracy in relation to the required effort per combination. Results The most effective, non-destructive way to record herbaceous leaves is to take an image of the leaf's top side. We yield the highest classification accuracy using destructive back light images, i.e., holding the plucked leaf against the sky for image acquisition. Cropping the image to the leaf's boundary substantially improves accuracy, while precise segmentation yields similar accuracy at a substantially higher effort. The permanent use or disuse of a flash light has negligible effects. Imaging the typically stronger textured backside of a leaf does not result in higher accuracy, but notably increases the acquisition cost. Conclusions In conclusion, the way in which leaf images are acquired and preprocessed does have a substantial effect on the accuracy of the classifier trained on them. For the first time, this study provides a systematic guideline allowing researchers to spend available acquisition resources wisely while yielding the optimal classification accuracy.

更新日期：2019-11-01
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