Maize Tassel Research Articles

Detection of the Progression of Anthesis in Field-Grown Maize Tassels: A Case Study.

The tassel of the maize plant is responsible for the production and dispersal of pollen for subsequent capture by the silk (stigma) and fertilization of the ovules. Both the amount and timing of pollen shed are physiological traits that impact the production of a hybrid seed. This study describes an automated end-to-end pipeline that combines deep learning and image processing approaches to extract tassel flowering patterns from time-lapse camera images of plants grown under field conditions. Inbred lines from the SAM and NAM diversity panels were grown at the Curtiss farm at Iowa State University, Ames, IA, during the summer of 2016. Using a set of around 500 pole-mounted cameras installed in the field, images of plants were captured every 10 minutes of daylight hours over a three-week period. Extracting data from imaging performed under field conditions is challenging due to variabilities in weather, illumination, and the morphological diversity of tassels. To address these issues, deep learning algorithms were used for tassel detection, classification, and segmentation. Image processing approaches were then used to crop the main spike of the tassel to track reproductive development. The results demonstrated that deep learning with well-labeled data is a powerful tool for detecting, classifying, and segmenting tassels. Our sequential workflow exhibited the following metrics: mAP for tassel detection was 0.91, F1 score obtained for tassel classification was 0.93, and accuracy of semantic segmentation in creating a binary image from the RGB tassel images was 0.95. This workflow was used to determine spatiotemporal variations in the thickness of the main spike—which serves as a proxy for anthesis progression.

Understanding Ustilago maydis Infection of Multiple Maize Organs.

Ustilago maydis is a smut fungus that infects all aerial maize organs, namely, seedling leaves, tassels, and ears. In all organs, tumors are formed by inducing hypertrophy and hyperplasia in actively dividing cells; however, the vast differences in cell types and developmental stages for different parts of the plant requires that U. maydis have both general and organ-specific strategies for infecting maize. In this review, we summarize how the maize–U. maydis interaction can be studied using mutant U. maydis strains to better understand how individual effectors contribute to this interaction, either through general or specific expression in a cell type, tissue, or organ. We also examine how male sterile maize mutants that do not support tumor formation can be used to explore key features of the maize anthers that are required for successful infection. Finally, we discuss key unanswered questions about the maize–U. maydis interaction and how new technologies can potentially be used to answer them.

TasselNetV2+: A Fast Implementation for High-Throughput Plant Counting From High-Resolution RGB Imagery.

Plant counting runs through almost every stage of agricultural production from seed breeding, germination, cultivation, fertilization, pollination to yield estimation, and harvesting. With the prevalence of digital cameras, graphics processing units and deep learning-based computer vision technology, plant counting has gradually shifted from traditional manual observation to vision-based automated solutions. One of popular solutions is a state-of-the-art object detection technique called Faster R-CNN where plant counts can be estimated from the number of bounding boxes detected. It has become a standard configuration for many plant counting systems in plant phenotyping. Faster R-CNN, however, is expensive in computation, particularly when dealing with high-resolution images. Unfortunately high-resolution imagery is frequently used in modern plant phenotyping platforms such as unmanned aerial vehicles, engendering inefficient image analysis. Such inefficiency largely limits the throughput of a phenotyping system. The goal of this work hence is to provide an effective and efficient tool for high-throughput plant counting from high-resolution RGB imagery. In contrast to conventional object detection, we encourage another promising paradigm termed object counting where plant counts are directly regressed from images, without detecting bounding boxes. In this work, by profiling the computational bottleneck, we implement a fast version of a state-of-the-art plant counting model TasselNetV2 with several minor yet effective modifications. We also provide insights why these modifications make sense. This fast version, TasselNetV2+, runs an order of magnitude faster than TasselNetV2, achieving around 30 fps on image resolution of 1980 × 1080, while it still retains the same level of counting accuracy. We validate its effectiveness on three plant counting tasks, including wheat ears counting, maize tassels counting, and sorghum heads counting. To encourage the use of this tool, our implementation has been made available online at https://tinyurl.com/TasselNetV2plus.

A new isolation device for shortening gene flow distance in small-scale transgenic maize breeding

The transmission of pollen is the main cause of maize gene flow. Under the compulsory labeling system for genetically modified (GM) products in China, isolation measures are crucial. At present, there is no effective isolation device for preventing and controlling the short-range flow of GM maize pollen. The purposes of the present experiments were to overcome the deficiencies of existing technology and to demonstrate a new isolation device for decreasing the gene flow distance of GM maize. The isolation device we invented was shown to be more robust than traditional isolation methods, and it can be disassembled and repeatedly reused. The most important point was that the frequency of gene flow could be greatly reduced using this device. When the distance from the isolation device was more than 1 m, the gene flow rate could be decreased to less than 1%, and when the distance from the isolation device was more than 10 m, the gene flow rate could be reduced to less than 0.1%. When the isolation device was adopted to isolate GM maize in conjunction with bagging the tassels of GM maize at the pollination stage, the gene flow could be controlled to less than 0.1% when the distance from the isolation device was more than 1 m. This device was, however, only applicable for small plots and can shorten the isolation distance of GM maize planting and improve the purity of seeds, all while meeting the needs of close isolation breeding. The use of this device represents a feasible method for risk prevention and control of GM crops.

Automatic Detection of Maize Tassels from UAV Images by Combining Random Forest Classifier and VGG16

The tassel development status and its branch number in maize flowering stage are the key phenotypic traits to determine the growth process, pollen quantity of different maize varieties, and detasseling arrangement for seed maize production fields. Rapid and accurate detection of tassels is of great significance for maize breeding and seed production. However, due to the complex planting environment in the field, such as unsynchronized growth stage and tassels vary in size and shape caused by varieties, the detection of maize tassel remains challenging problem, and the existing methods also cannot distinguish the early tassels. In this study, based on the time series unmanned aerial vehicle (UAV) RGB images with maize flowering stage, we proposed an algorithm for automatic detection of maize tassels which is suitable for complex scenes by using random forest (RF) and VGG16. First, the RF was used to segment UAV images into tassel regions and non-tassel regions, and then extracted the potential tassel region proposals by morphological method; afterwards, false positives were removed through VGG16 network with the ratio of training set to validation set was 7:3. To demonstrate the performance of the proposed method, 50 plots were selected from UAV images randomly. The precision, recall rate and F1-score were 0.904, 0.979 and 0.94 respectively; 50 plots were divided into early, middle and late tasseling stages according to the proportion of tasseling plants and the morphology of tassels. The result of tassels detection was late tasseling stage > middle tasseling stage > early tasseling stage, and the corresponding F1-score were 0.962, 0.914 and 0.863, respectively. It was found that the model error mainly comes from the recognition of leaves vein and reflective leaves as tassels. Finally, to show the morphological characteristics of tassel directly, we proposed an endpoint detection method based on the tassel skeleton, and further extracted the tassel branch number. The method proposed in this paper can well detect tassels of different development stages, and support large scale tassels detection and branch number extraction.

Kinetics, Isotherm, and Thermodynamic Studies of the Adsorption Mechanism of PFOS and PFOA Using Inactivated and Chemically Activated Maize Tassel

The extreme persistence, bioaccumulative, and toxicity tendencies of per- and polyfluoroalkyl substances (PFAS) have contributed to the ever-increasing quest for effective and low-cost technologies for removing PFAS from aqueous solutions. Therefore, in the present study, maize tassel (MT) was activated using phosphoric acid. The chemically activated maize tassel (CAMT) was thereafter applied as an adsorbent for removing perfluorooctanoate (PFOA) and perfluorooctanesulfonate (PFOS) from aqueous media in comparison with the inactivated MT. The adsorption behaviors of PFOS and PFOA onto MT and CAMT were investigated via adsorption experiments. It was found that the isothermal data fit better with the Freundlich isotherm model than with the Langmuir isotherm model. Maximum adsorption capacities of 1552.5 mg g−1 (3.10 mmol g−1) and 380.32 mg g−1 (0.92 mmol g−1) were recorded for PFOS and PFOA, respectively, using CAMT, when the Freundlich model was applied. Equilibrium was attained within 60 min using both MT and CAMT. The pseudo-second-order kinetics model suited the kinetics data better. It was observed from the thermodynamic studies that the adsorption mechanism of PFOS and PFOA on MT and CAMT is spontaneous and feasible. All the values for the Gibb’s free energy change (ΔG0, kJ mol−1) obtained in the present study were below − 20 kJ mol−1. The adsorption of PFOS and PFOA using CAMT is exothermic in nature (enthalpy, ΔH0 was negative) whereas the adsorption of PFOS and PFOA using MT is endothermic. The PFOS and PFOA adsorption processes using both MT and CAMT were greatly influenced by electrostatic and hydrophobic interactions.

Sowing date and maize response to the splitting of nitrogen side-dressing fertilization

The splitting of nitrogen side-dressing fertilization with the application of a nitrogen dose at maize tasseling may increase grain yield, mainly when the crop is sown at the most favorable date to achieve high productivity. This research was carried out to evaluate the effect of sowing date on the response of maize to the splitting of nitrogen side-dressing fertilization at different growth stages. Two sowing dates were tested: preferential (10/15/2015 and 09/20/2016) and late (12/5/2015 and 12/5/2016). Six nitrogen side-dressing management treatments were evaluated at each sowing date: control without N; complete nitrogen rate applied at phenological stages V5, V10 and VT; ½ N rate at V5 + ½ N rate at V10; and ⅓ N rate at V5 + ⅓ N rate at V10 + ⅓ N rate at VT. The N rate was equivalent to 300 kg ha-1. Grain yield ranged from 8.9 to 15.3 t ha-1 in Lages and from 7.4 to 16.4 t ha-1 in Atalanta. There were no significant differences in grain yield and agronomic efficiency of nitrogen use (AE) among the treatments with the entire N side-dressing fertilization at V5 and V10 and those when the nutrient was split into two or three times, regardless of sowing date. AE was higher when the crop was sown at the preferential sowing date than in the late sowing date, regardless of N side-dressing application. The splitting of nitrogen fertilization up to the crop tasseling did not increase maize grain yield or AE.

Maize tassels detection: a benchmark of the state of the art

BackgroundThe population of plants is a crucial indicator in plant phenotyping and agricultural production, such as growth status monitoring, yield estimation, and grain depot management. To enhance the production efficiency and liberate labor force, many automated counting methods have been proposed, in which computer vision-based approaches show great potentials due to the feasibility of high-throughput processing and low cost. In particular, with the success of deep learning, more and more deeper learning-based approaches are introduced to deal with agriculture automation. Since different detection- and regression-based counting models have distinct characteristics, how to choose an appropriate model given the target task at hand remains unexplored and is important for practitioners.ResultsTargeting in-field maize tassels as a representative case study, the goal of this work is to present a comprehensive benchmark of state-of-the-art object detection and object counting methods, including Faster R-CNN, YOLOv3, FaceBoxes, RetinaNet, and the leading counting model of maize tassels—TasselNet. We create a Maize Tassel Detection Counting (MTDC) dataset by supplementing bounding box annotations to the Maize Tassels Counting (MTC) dataset to allow the training of detection models. We investigate key factors effecting the practical applications of the models, such as convergence behavior, scale robustness, speed-accuracy trade-off, as well as parameter sensitivity. Based on our benchmark, we summarise the advantages and limitations of each method and suggest several possible directions to improve current detection- and regression-based counting approaches to benefit next-generation intelligent agriculture.ConclusionsCurrent state-of-the-art detection- and regression-based counting approaches can all achieve a relatively high degree of accuracy when dealing with in-field maize tassels, with at least 0.85 R^2 values and 28.2% rRMSE error. While detection-based methods are more robust than regression-based methods in scale variations and can infer extra information (e.g., object positions and sizes), the latter ones have significantly faster convergence behaviors and inference speed. To choose an appropriate in-filed plant counting method, accuracy, robustness, speed and some other algorithm-specific factors should be taken into account with the same priority. This work sheds light on different aspects of existing detection and counting approaches and provides guidance on how to tackle in-field plant counting. The MTDC dataset is made available at https://git.io/MTDC

3D genome architecture coordinates trans and cis regulation of differentially expressed ear and tassel genes in maize

BackgroundMaize ears and tassels are two separate types of inflorescence which are initiated by similar developmental processes but gradually develop distinct architectures. However, coordinated trans and cis regulation of differentially expressed genes determining ear and tassel architecture within the 3D genome context is largely unknown.ResultsWe identify 56,055 and 52,633 open chromatin regions (OCRs) in developing maize ear and tassel primordia using ATAC-seq and characterize combinatorial epigenome features around these OCRs using ChIP-seq, Bisulfite-seq, and RNA-seq datasets. Our integrative analysis of coordinated epigenetic modification and transcription factor binding to OCRs highlights the cis and trans regulation of differentially expressed genes in ear and tassel controlling inflorescence architecture. We further systematically map chromatin interactions at high-resolution in corresponding tissues using in situ digestion-ligation-only Hi-C (DLO Hi-C). The extensive chromatin loops connecting OCRs and genes provide a 3D view on cis- and trans-regulatory modules responsible for ear- and tassel-specific gene expression. We find that intergenic SNPs tend to locate in distal OCRs, and our chromatin interaction maps provide a potential mechanism for trait-associated intergenic SNPs that may contribute to phenotypic variation by influencing target gene expression through chromatin loops.ConclusionsOur comprehensive epigenome annotations and 3D genome maps serve as valuable resource and provide a deep understanding of the complex regulatory mechanisms of genes underlying developmental and morphological diversities between maize ear and tassel.

In-field automatic detection of maize tassels using computer vision

The heading stage of maize is an important period during its growth and development and indicates the beginning of its pollination. In this regard, an automated method for maize tassel detection is highly important to monitor maize growth. However, the recognition of maize heading stage mainly relies on visual evaluation. This method presents some limitations, such as expensive and subjective. This work proposed a novel method for automatic tassel detection. In the proposed algorithm, a color attenuation prior model was used to model the scene depth of saturation graph to remove image saturation. An Itti visual attention detection algorithm was used to detect the area of interest. Texture features and vegetation indices were used to develop a classification model to eliminate false positives. Pictures were captured using a commercial camera for two years to verify the stability of the proposed algorithm. Three indices were calculated to quantitatively assess and rate the algorithms. Experimental results show that the proposed method outperforms other existing methods, and its recall, precision, and F1 measure values are 86.30%, 91.44%, and 88.36%, respectively. Results indicate that the proposed method can effectively detect maize tassels in field images and remain stable with time.

High expression in maize pollen correlates with genetic contributions to pollen fitness as well as with coordinated transcription from neighboring transposable elements.

In flowering plants, gene expression in the haploid male gametophyte (pollen) is essential for sperm delivery and double fertilization. Pollen also undergoes dynamic epigenetic regulation of expression from transposable elements (TEs), but how this process interacts with gene expression is not clearly understood. To explore relationships among these processes, we quantified transcript levels in four male reproductive stages of maize (tassel primordia, microspores, mature pollen, and sperm cells) via RNA-seq. We found that, in contrast with vegetative cell-limited TE expression in Arabidopsis pollen, TE transcripts in maize accumulate as early as the microspore stage and are also present in sperm cells. Intriguingly, coordinate expression was observed between highly expressed protein-coding genes and their neighboring TEs, specifically in mature pollen and sperm cells. To investigate a potential relationship between elevated gene transcript level and pollen function, we measured the fitness cost (male-specific transmission defect) of GFP-tagged coding sequence insertion mutations in over 50 genes identified as highly expressed in the pollen vegetative cell, sperm cell, or seedling (as a sporophytic control). Insertions in seedling genes or sperm cell genes (with one exception) exhibited no difference from the expected 1:1 transmission ratio. In contrast, insertions in over 20% of vegetative cell genes were associated with significant reductions in fitness, showing a positive correlation of transcript level with non-Mendelian segregation when mutant. Insertions in maize gamete expressed2 (Zm gex2), the sole sperm cell gene with measured contributions to fitness, also triggered seed defects when crossed as a male, indicating a conserved role in double fertilization, given the similar phenotype previously demonstrated for the Arabidopsis ortholog GEX2. Overall, our study demonstrates a developmentally programmed and coordinated transcriptional activation of TEs and genes in pollen, and further identifies maize pollen as a model in which transcriptomic data have predictive value for quantitative phenotypes.

Detection of Maize Tassels from UAV RGB Imagery with Faster R-CNN

Maize tassels play a critical role in plant growth and yield. Extensive RGB images obtained using unmanned aerial vehicle (UAV) and the prevalence of deep learning provide a chance to improve the accuracy of detecting maize tassels. We used images from UAV, a mobile phone, and the Maize Tassel Counting dataset (MTC) to test the performance of faster region-based convolutional neural network (Faster R-CNN) with residual neural network (ResNet) and a visual geometry group neural network (VGGNet). The results showed that the ResNet, as the feature extraction network, was better than the VGGNet for detecting maize tassels from UAV images with 600 × 600 resolution. The prediction accuracy ranged from 87.94% to 94.99%. However, the prediction accuracy was less than 87.27% from the UAV images with 5280 × 2970 resolution. We modified the anchor size to [852, 1282, 2562] in the region proposal network according to the width and height of pixel distribution to improve detection accuracy up to 89.96%. The accuracy reached up to 95.95% for mobile phone images. Then, we compared our trained model with TasselNet without training their datasets. The average difference of tassel number was 1.4 between the calculations with 40 images for the two methods. In the future, we could further improve the performance of the models by enlarging datasets and calculating other tassel traits such as the length, width, diameter, perimeter, and the branch number of the maize tassels.

TasselGAN: An Application of the Generative Adversarial Model for Creating Field-Based Maize Tassel Data.

Machine learning-based plant phenotyping systems have enabled high-throughput, non-destructive measurements of plant traits. Tasks such as object detection, segmentation, and localization of plant traits in images taken in field conditions need the machine learning models to be developed on training datasets that contain plant traits amidst varying backgrounds and environmental conditions. However, the datasets available for phenotyping are typically limited in variety and mostly consist of lab-based images in controlled conditions. Here, we present a new method called TasselGAN, using a variant of a deep convolutional generative adversarial network, to synthetically generate images of maize tassels against sky backgrounds. Both foreground tassel images and background sky images are generated separately and merged together to form artificial field-based maize tassel data to aid the training of machine learning models, where there is a paucity of field-based data. The effectiveness of the proposed method is demonstrated using quantitative and perceptual qualitative experiments.

TasselNetv2: in-field counting of wheat spikes with context-augmented local regression networks

BackgroundGrain 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.ResultsIn 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 912times 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.ConclusionsThis 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.

Pest categorisation of Diabrotica barberi.

The EFSA Panel on Plant Health performed a pest categorisation of Diabrotica barberi (Coleoptera: Chrysomelidae), the northern corn rootworm, for the EU. D. barberi is a univoltine species occurring in mid‐western and eastern USA and Canada, where it reproduces on maize (Zea mays), the preferred larval host. A small proportion of individuals can develop to a lesser extent on spelt (Triticum spelta), rice (Oryza sativa), millet (Panicum miliaceum) and a few North American wild grasses. Eggs are laid in the soil of maize fields, where they overwinter and can enter a diapause which can extend for more than one winter. Larvae hatch in late spring and early summer. Adult emergence peaks in the summer to feed on maize tassels, silks and ear tips. Adults abandon maize fields looking for other feeding hosts and return to maize for oviposition during late summer and autumn. D. barberi is considered a key pest of maize, together with other rootworm species of the same genus. D. barberi is regulated in the EU by Directive 2000/29/EC (Annex IAI). Within this Directive, a general prohibition of soil from most third countries prevents the entry of D. barberi larvae. However, adults carried on sweetcorn or green maize are potential pathways for entry into the EU. Climatic conditions and the wide availability of maize provide conditions to support establishment in the EU. Following establishment, impact on maize yields is anticipated. Phytosanitary measures are available to inhibit entry of this pest. D. barberi satisfies the criteria, which are within the remit of EFSA to assess for it to be regarded as a potential Union quarantine pest. D. barberi does not meet the criteria of occurring in the EU nor plants for planting being the principal means of spread for it to be regarded as a potential Union regulated non‐quarantine pest.

Tasselseed5 encodes a cytochrome C oxidase that functions in sex determination by affecting jasmonate catabolism in maize.

Maize (Zea mays L.) is a monoecious grass plant in which mature male and female florets form the tassel and ear, respectively. Maize is often used as a model plant to study flower development. Several maize tassel seed mutants, such as the recessive mutants tasselseed1 (ts1) and tasselseed2 (ts2), exhibit a reversal in sex determination, which leads to the generation of seeds in tassels. The phenotype of the dominant mutant, Tasselseed5 (Ts5), is similar to that of ts2. Here, we positionally cloned the underlying gene of Ts5 and characterized its function. We show that the GRMZM2G177668 gene is overexpressed in Ts5. This gene encodes a cytochrome C oxidase, which catalyzes the transformation of jasmonoyl-L-isoleucine (JA-Ile) to 12OH-JA-Ile during jasmonic acid catabolism. Consistent with this finding, no JA-Ile peak was detected in Ts5 tassels during the sex determination period, unlike in the wild type. Transgenic maize plants overexpressing GRMZM2G177668 exhibited a tassel-seed phenotype similar to that of Ts5. These results indicate that the JA-Ile peak in tassels is critical for sex determination and that the Ts5 mutant phenotype results from the disruption of this peak in tassels during sex determination.

Reconstructing the Transcriptional Ontogeny of Maize and Sorghum Supports an Inverse Hourglass Model of Inflorescence Development.

Assembling meaningful comparisons between species is a major limitation in studying the evolution of organismal form. To understand development in maize and sorghum, closely related species with architecturally distinct inflorescences, we collected RNA-seq profiles encompassing inflorescence body-plan specification in both species. We reconstructed molecular ontogenies from 40 B73 maize tassels and 47 BTx623 sorghum panicles and separated them into transcriptional stages. To discover new markers of inflorescence development, we used random forest machine learning to determine stage by RNA-seq. We used two descriptions of transcriptional conservation to identify hourglass-like stages during inflorescence development. Despite a relatively short 12 million years since their last common ancestor, we found maize and sorghum inflorescences are most different during their hourglass-like stages of development, following an inverse-hourglass model of development. We discuss whether agricultural selection may account for the rapid divergence signatures in these species and the observed separation of evolutionary pressure and developmental reprogramming.

Egg-Laying Pattern of Helicoverpa zea1 in Sweet Corn: A New Pest Management Prospect

The corn earworm, Helicoverpa zea (Boddie), is a major insect pest of maize (Zea mays L.), cotton (Gossypium hirsutum L.), and vegetable crops. Maize silk is a favorite egg-laying site of H. zea. However, other parts of the maize plant might be even more attractive for oviposition by H. zea. Under field conditions and in insect-proof cages in a greenhouse, egg-laying distribution patterns were assessed on a late and an early sweet corn cultivar, i.e., Java (Technisem) and Garrison (Syngenta), respectively. Maize tassels on both cultivars were the main egg-laying sites of the pest. In insect-proof cages, at the maize silking stage, more than 60% of the total number of eggs collected were on tassels. In the field, oviposition by H. zea began at tassel emergence, and more than 80% of the total number of eggs were collected from tassels. Egg distribution on leaves and silk varied according to the sweet corn cultivar. The findings offer new prospects to control H. zea. The tassel emergence stage is the best period for controlling H. zea in maize fields and the best phenological stage to use maize as a trap crop for the pest.

Genome-wide identification of AGO18b-bound miRNAs and phasiRNAs in maize by cRIP-seq

BackgroundArgonaute proteins (AGOs) are important players in the regulation of plant development by directing sRNAs to target mRNAs. In maize (Zea mays), AGO18b is a tassel-enriched and grass-specific AGO. Previous studies have shown that AGO18b is highly expressed in tassels during meiosis and negatively regulates determinacy of spikelet meristems. However, binding profile on RNAs and acting mechanisms of AGO18b remain unknown.ResultsIn this study, we explored the binding profile of AGO18b in maize tassel by UV cross-linking RNA immunoprecipitation, followed by deep sequencing of these cDNA libraries (cRIP-seq), and systematically studied AGO18b-associated small RNAs and mRNAs by bioinformatics analysis. By globally analyzing the phased small-interfering RNA (phasiRNA) and miRNA abundance bound by AGO18b, we found AGO18b primarily binds to 21-nt phasiRNAs/miRNAs with a 5′-uridine and binds less strongly to 24-nt phasiRNAs with a 5′-adenosine in the premeiotic tassels. The abundance profile of AGO18b-associated miRNAs was different from their expression profile. Moreover, AGO18b strongly binds to miR166a-3p. We then obtained the AGO18b-bound mRNA targets of miR166a-3p by cRIP-seq, and confirmed the molecular function of AGO18b in regulating spikelet meristems.ConclusionsOur results indicated that AGO18b binds to phasiRNAs with obvious 5 prime end bias under different sRNA length. MiRNAs and their target mRNAs associated with AGO18b indicated the molecular mechanisms of AGO18b as a negative regulator of inflorescence meristem and tassel development through integrating both phasiRNAs and miRNA pathways, which extended our view of sRNA regulation in flower development and provided potential methods to control pollination in the future.

Response surface methodology for optimization of Cd(II) adsorption from wastewaters by fabricated tartaric acid-maize tassel magnetic hybrid sorbent

Maize tassels (MT), an agro-based biomass waste was carbonised followed by thermo-chemical modification using tartaric acid. The functionalized activated carbon was further modified to yield a magnetic hybrid composite adsorbent. The adsorbent was characterized using Fourier transform infrared (FTIR) spectroscopy, X-ray diffraction (XRD) and Scanning Electron Microscopy (SEM). The adsorbent was evaluated for its efficiency to remove Cd(II) ions from aqueous solutions through batch adsorption studies following a Central Composite Design. Effects of solution pH, contact time, adsorbent dosage, initial metal concentration and temperature on Cd(II) adsorption were investigated. Optimization of the adsorption process was done using desirability function on the Design Expert V11 software. The desirability function showed that the optimum parameters were pH 5.29, contact time (67.50 min), dosage (0.575 g) and initial concentration (152.50 mg/L). The adsorption process was analysed using kinetic and isotherm models. The kinetics of the adsorption process followed the pseudo-second-order model (lowest sum of square error (SSE) values and correlation coefficients (R2) > 0.999) in addition to the intraparticle diffusion model. The isotherm data were consistent with the Langmuir isotherm as evidenced by the highest correlation coefficient (R2= 0.998). The thermodynamic parameters showed that the process was endothermic and spontaneous in nature. The adsorption capacity of the adsorbent was found to be 188.68 mg/g at 20 ⁰C which is higher than that of the previously reported magnetic maize tassel hybrid (52.05 mg/g). The adsorbent showed good removal efficiency on real effluent samples.