Liverwort bHLH transcription factors and the origin of stomata in plants.

Stomata are distributed in nearly all major groups of land plants, with the only exception being liverworts. Instead of having stomata on sporophytes, many complex thalloid liverworts possess air pores in their gametophytes. At present, whether stomata in land plants are derived from a common origin remains under debate.1,2,3 In Arabidopsis thaliana, a core regulatory module for stomatal development comprises members of the bHLH transcription factor (TF) family, including AtSPCH, AtMUTE, and AtFAMA of subfamily Ia and AtSCRM1/2 of subfamily IIIb. Specifically, AtSPCH, AtMUTE, and AtFAMA each successively form heterodimers with AtSCRM1/2, which in turn regulate the entry, division, and differentiation of stomatal lineages.4,5,6,7 In the moss Physcomitrium patens, two SMF (SPCH, MUTE and FAMA) orthologs have been characterized, one of which is functionally conserved in regulating stomatal development.8,9 We here provide experimental evidence that orthologous bHLH TFs in the liverwort Marchantia polymorpha affect air pore spacing as well as the development of the epidermis and gametangiophores. We found that the bHLH Ia and IIIb heterodimeric module is highly conserved in plants. Genetic complementation experiments showed that liverwort SCRM and SMF genes weakly restored a stomata phenotype in atscrm1, atmute, and atfama mutant backgrounds in A. thaliana. In addition, homologs of stomatal development regulators FLP and MYB88 also exist in liverworts and weakly rescued the stomatal phenotype of atflp/myb88 double mutant. These results provide evidence not only for a common origin of all stomata in extant plants but also for relatively simple stomata in the ancestral plant.

Jasmonate-regulated seed germination and crosstalk with other phytohormones.

Seed plants have evolved mechanisms that maintain the dormancy of mature seeds until the time is appropriate for germination. Seed germination is a critical step in the plant life cycle, and it is an important trait in relation to agricultural production. The process is precisely regulated by various internal and external factors, and in particular by diverse endogenous hormones. Jasmonates (JAs) are one of the main plant hormones that mediate stress responses, and recent studies have provided evidence of their inhibitory effects on seed germination. In this review, we summarize our current understanding of the molecular mechanisms underlying the regulatory roles of JAs during the seed germination stage. We describe the crosstalk between JA and other phytohormones that influence seed germination, such as abscisic acid and gibberellic acid.

Parallel evolution of two AIM24 protein subfamilies and their conserved functions in ER stress tolerance in land plants.

Despite decades of efforts in genome sequencing and functional characterization, some important protein families remain poorly understood. In this study, we report the classification, evolution, and functions of the largely uncharacterized AIM24 protein family in plants, including the identification of a novel subfamily. We show that two AIM24 subfamilies (AIM24-A and AIM24-B) are commonly distributed in major plant groups. These two subfamilies not only have modest sequence similarities and different gene structures but also are of independent bacterial ancestry. We performed comparative functional investigations on the two AIM24 subfamilies using three model plants: the moss Physcomitrium patens, the liverwort Marchantia polymorpha, and the flowering plant Arabidopsis thaliana. Intriguingly, despite their significant differences in sequence and gene structure, both AIM24 subfamilies are involved in ER stress tolerance and the unfolded protein response (UPR). In addition, transformation of the AIM24-A gene from P. patens into the AIM24-B null mutant of A. thaliana could at least partially rescue ER stress tolerance and the UPR. We also discuss the role of AIM24 genes in plant development and other cellular activities. This study provides a unique example of parallel evolution in molecular functions and can serve as a foundation for further investigation of the AIM24 family in plants.

Major episodes of horizontal gene transfer drove the evolution of land plants.

How horizontal gene transfer (HGT) has contributed to the evolution of animals and plants remains a major puzzle. Despite recent progress, defining the overall scale and pattern of HGT events in land plants has been largely elusive. In this study, we performed systematic analyses for acquired genes in different plant groups and throughout land plant evolution. We found that relatively recent HGT events occurred in charophytes and all major land plant groups, but their frequency declined rapidly in seed plants. Two major episodes of HGT events occurred in land plant evolution, corresponding to the early evolution of streptophytes and the origin of land plants, respectively. Importantly, a vast majority of the genes acquired in the two episodes have been retained in descendant groups, affecting numerous activities and processes of land plants. We analyzed some of the acquired genes involved in stress responses, ion and metabolite transport, growth and development, and specialized metabolism, and further assessed the cumulative effects of HGT in land plants.

Vegetable Size Measurement Based on Stereo Camera and Keypoints Detection.

This work focuses on the problem of non-contact measurement for vegetables in agricultural automation. The application of computer vision in assisted agricultural production significantly improves work efficiency due to the rapid development of information technology and artificial intelligence. Based on object detection and stereo cameras, this paper proposes an intelligent method for vegetable recognition and size estimation. The method obtains colorful images and depth maps with a binocular stereo camera. Then detection networks classify four kinds of common vegetables (cucumber, eggplant, tomato and pepper) and locate six points for each object. Finally, the size of vegetables is calculated using the pixel position and depth of keypoints. Experimental results show that the proposed method can classify four kinds of common vegetables within 60 cm and accurately estimate their diameter and length. The work provides an innovative idea for solving the vegetable's non-contact measurement problems and can promote the application of computer vision in agricultural automation.

The maize single-nucleus transcriptome comprehensively describes signaling networks governing movement and development of grass stomata.

The unique morphology of grass stomata enables rapid responses to environmental changes. Deciphering the basis for these responses is critical for improving food security. We have developed a planta platform of single-nucleus RNA-sequencing by combined fluorescence-activated nuclei flow sorting, and used it to identify cell types in mature and developing stomata from 33,098 nuclei of the maize epidermis-enriched tissues. Guard cells (GCs) and subsidiary cells (SCs) displayed differential expression of genes, besides those encoding transporters, involved in the abscisic acid, CO2, Ca2+, starch metabolism, and blue light signaling pathways, implicating coordinated signal integration in speedy stomatal responses, and of genes affecting cell wall plasticity, implying a more sophisticated relationship between GCs and SCs in stomatal development and dumbbell-shaped guard cell formation. The trajectory of stomatal development identified in young tissues, and by comparison to the bulk RNA-seq data of the MUTE defective mutant in stomatal development, confirmed known features, and shed light on key participants in stomatal development. Our study provides a valuable, comprehensive, and fundamental foundation for further insights into grass stomatal function.

Fluorescence activated cell sorting-A selective tool for plant cell isolation and analysis.

Instrumentation for flow cytometry and sorting is designed around the assumption that samples are single-cell suspensions. However, with few exceptions, higher plants comprise complex multicellular tissues and organs, in which the individual cells are held together by shared cell walls. Single-cell suspensions can be obtained through digestion of the cells walls and release of the so-called protoplasts (plants without their cell wall). Here we describe best practices for protoplast preparation, and for analysis through flow cytometry and cell sorting. Finally, the numerous downstream applications involving sorted protoplasts are discussed.

New insights into the dispersion history and adaptive evolution of taxon Aegilops tauschii in China.

Aegilops tauschii, the wild progenitor of wheat D-genome and a valuable germplasm for wheat improvement, has a wide natural distribution from eastern Turkey to China. However, the phylogenetic relationship and dispersion history of Ae. tauschii in China has not been scientifically clarified. In this study, we genotyped 208 accessions (with 104 in China) using ddRAD sequencing and 55K SNP array, and classified the population into six sublineages. Three possible spreading routes or events were identified, resulting in specific distribution patterns, with four sublineages found in Xinjiang, one in Qinghai, two in Shaanxi and one in Henan. We also established the correlation of SNP-based, karyotype-based and spike-morphology-based techniques to demonstrate the internal classification of Ae. tauschii, and developed consensus dataset with 1245 putative accessions by merging data previously published. Our analysis suggested that eight inter-lineage accessions could be assigned to the putative Lineage 3 and these accessions would help to conserve the genetic diversity of the species. By developing the consensus phylogenetic relationships of Ae. tauschii, our work validated the hypothesis on the dispersal history of Ae. tauschii in China, and contributed to the efficient and comprehensive germplasm-mining of the species.

A Crop Image Segmentation and Extraction Algorithm Based on Mask RCNN.

The wide variety of crops in the image of agricultural products and the confusion with the surrounding environment information makes it difficult for traditional methods to extract crops accurately and efficiently. In this paper, an automatic extraction algorithm is proposed for crop images based on Mask RCNN. First, the Fruits 360 Dataset label is set with Labelme. Then, the Fruits 360 Dataset is preprocessed. Next, the data are divided into a training set and a test set. Additionally, an improved Mask RCNN network model structure is established using the PyTorch 1.8.1 deep learning framework, and path aggregation and features are added to the network design enhanced functions, optimized region extraction network, and feature pyramid network. The spatial information of the feature map is saved by the bilinear interpolation method in ROIAlign. Finally, the edge accuracy of the segmentation mask is further improved by adding a micro-fully connected layer to the mask branch of the ROI output, employing the Sobel operator to predict the target edge, and adding the edge loss to the loss function. Compared with FCN and Mask RCNN and other image extraction algorithms, the experimental results demonstrate that the improved Mask RCNN algorithm proposed in this paper is better in the precision, Recall, Average precision, Mean Average Precision, and F1 scores of crop image extraction results.

Genomic insights into the fast growth of paulownias and the formation of Paulownia witches' broom.

Paulownias are among the fastest growing trees in the world, but they often suffer tremendous loss of wood production due to infection by Paulownia witches' broom (PaWB) phytoplasmas. In this study, we have sequenced and assembled a high-quality nuclear genome of Paulownia fortunei, a commonly cultivated paulownia species. The assembled genome of P. fortunei is 511.6 Mb in size, with 93.2% of its sequences anchored to 20 pseudo-chromosomes, and it contains 31 985 protein-coding genes. Phylogenomic analyses show that the family Paulowniaceae is sister to a clade composed of Phrymaceae and Orobanchaceae. Higher photosynthetic efficiency is achieved by integrating C3 photosynthesis and the crassulacean acid metabolism pathway, which may contribute to the extremely fast growth habit of paulownia trees. Comparative transcriptome analyses reveal modules related to cambial growth and development, photosynthesis, and defense responses. Additional genome sequencing of PaWB phytoplasma, combined with functional analyses, indicates that the effector PaWB-SAP54 interacts directly with Paulownia PfSPLa, which in turn causes the degradation of PfSPLa by the ubiquitin-mediated pathway and leads to the formation of witches' broom. Taken together, these results provide significant insights into the biology of paulownias and the regulatory mechanism for the formation of PaWB.

Fully Learnable Model for Task-Driven Image Compressed Sensing.

This study primarily investigates image sensing at low sampling rates with convolutional neural networks (CNN) for specific applications. To improve the image acquisition efficiency in energy-limited systems, this study, inspired by compressed sensing, proposes a fully learnable model for task-driven image-compressed sensing (FLCS). The FLCS, based on Deep Convolution Generative Adversarial Networks (DCGAN) and Variational Auto-encoder (VAE), divides the image-compressed sensing model into three learnable parts, i.e., the Sampler, the Solver and the Rebuilder. To be specific, a measurement matrix suitable for a type of image is obtained by training the Sampler. The Solver calculates the image's low-dimensional representation with the measurements. The Rebuilder learns a mapping from the low-dimensional latent space to the image space. All the mentioned could be trained jointly or individually for a range of application scenarios. The pre-trained FLCS reconstructs images with few iterations for task-driven compressed sensing. As indicated from the experimental results, compared with existing approaches, the proposed method could significantly improve the reconstructed images' quality while decreasing the running time. This study is of great significance for the application of image-compressed sensing at low sampling rates.

Introgressing the Aegilops tauschii genome into wheat as a basis for cereal improvement.

Increasing crop production is necessary to feed the world's expanding population, and crop breeders often utilize genetic variations to improve crop yield and quality. However, the narrow diversity of the wheat D genome seriously restricts its selective breeding. A practical solution is to exploit the genomic variations of Aegilops tauschii via introgression. Here, we established a rapid introgression platform for transferring the overall genetic variations of A. tauschii to elite wheats, thereby enriching the wheat germplasm pool. To accelerate the process, we assembled four new reference genomes, resequenced 278 accessions of A. tauschii and constructed the variation landscape of this wheat progenitor species. Genome comparisons highlighted diverse functional genes or novel haplotypes with potential applications in wheat improvement. We constructed the core germplasm of A. tauschii, including 85 accessions covering more than 99% of the species' overall genetic variations. This was crossed with elite wheat cultivars to generate an A. tauschii-wheat synthetic octoploid wheat (A-WSOW) pool. Laboratory and field analysis with two examples of the introgression lines confirmed its great potential for wheat breeding. Our high-quality reference genomes, genomic variation landscape of A. tauschii and the A-WSOW pool provide valuable resources to facilitate gene discovery and breeding in wheat.

Deep Shearlet Residual Learning Network for Single Image Super-Resolution.

Recently, the residual learning strategy has been integrated into the convolutional neural network (CNN) for single image super-resolution (SISR), where the CNN is trained to estimate the residual images. Recognizing that a residual image usually consists of high-frequency details and exhibits cartoon-like characteristics, in this paper, we propose a deep shearlet residual learning network (DSRLN) to estimate the residual images based on the shearlet transform. The proposed network is trained in the shearlet transform-domain which provides an optimal sparse approximation of the cartoon-like image. Specifically, to address the large statistical variation among the shearlet coefficients, a dual-path training strategy and a data weighting technique are proposed. Extensive evaluations on general natural image datasets as well as remote sensing image datasets show that the proposed DSRLN scheme achieves close results in PSNR to the state-of-the-art deep learning methods, using much less network parameters.

Flow Cytometry and Sorting in Arabidopsis.

Flow cytometry and sorting represents a valuable and mature experimental platform for the analysis of cellular populations. Applications involving higher plants started to emerge around 40 years ago and are now widely employed both to provide unique information regarding basic and applied questions in the biosciences and to advance agricultural productivity in practical ways. Further development of this platform is being actively pursued, and this promises additional progress in our understanding of the interactions of cells within complex tissues and organs. Higher plants offer unique challenges in terms of flow cytometric analysis, first since their organs and tissues are, almost without exception, three-dimensional assemblies of different cell types held together by tough cell walls, and, second, because individual plant cells are generally larger than those of mammals.This chapter, which updates work last reviewed in 2014 [Galbraith DW (2014) Flow cytometry and sorting in Arabidopsis. In: Sanchez Serrano JJ, Salinas J (eds) Arabidopsis Protocols, 3rd ed. Methods in molecular biology, vol 1062. Humana Press, Totowa, pp 509-537], describes the application of techniques of flow cytometry and sorting to the model plant species Arabidopsis thaliana, in particular emphasizing (a) fluorescence labeling in vivo of specific cell types and of subcellular components, (b) analysis using both conventional cytometers and spectral analyzers, (c) fluorescence-activated sorting of protoplasts and nuclei, and (d) transcriptome analyses using sorted protoplasts and nuclei, focusing on population analyses at the level of single protoplasts and nuclei. Since this is an update, details of new experimental methods are emphasized.

Research on Image Reconstruction of Compressed Sensing Based on a Multi-Feature Residual Network.

In order to solve the problem of how to quickly and accurately obtain crop images during crop growth monitoring, this paper proposes a deep compressed sensing image reconstruction method based on a multi-feature residual network. In this method, the initial reconstructed image obtained by linear mapping is input to a multi-feature residual reconstruction network, and multi-scale convolution is used to autonomously learn different features of the crop image to realize deep reconstruction of the image, and complete the inverse solution of compressed sensing. Compared with traditional image reconstruction methods, the deep learning-based method relaxes the assumptions about the sparsity of the original crop image and converts multiple iterations into deep neural network calculations to obtain higher accuracy. The experimental results show that the compressed sensing image reconstruction method based on the multi-feature residual network proposed in this paper can improve the quality of crop image reconstruction.

Author Correction: A mycorrhizae-like gene regulates stem cell and gametophore development in mosses.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Resolving complicated relationships of the Panax bipinnatifidus complex in southwestern China by RAD-seq data.

The P. binpinnatifidus complex included most of the Panax species distributed in Sino-Himalaya regions except for P. pseudoginseng, P. stipuleanatus and P. notoginseng. However, the delimitation and identification of these taxa within the species complex are very difficult due to the existence of morphological intermediates, and their evolutionary relationships remain unresolved despite several studies have been carried out based on traditional DNA markers. The taxonomic uncertainty hinders the identification, conservation and exploration of these wild populations of Panax. To study this species complex, we employed ddRAD-seq data of these taxa from 18 different localities of southwestern China, using two RAD analysis pipelines, STACKS and pyRAD. Based on the results of phylogenetic analysis, the species complex was divided into four clades with high supports, which largely agreed with morphologically described species. Two clades, corresponding to P. vietnamensis and P. zingiberensis, respectively, were sister groups, indicating that these two species had a closer genetic relationship; the third clade was consisted of samples with bamboo-like rhizomes named as P. wangianus clade, and the fourth one with moniliform rhizomes was named as P. bipinnatifidus clade. The population genetic structure analysis and D-statistics test showed the localized admixture among these species, which indicated that introgression had occurred among the related lineages continuously distributed in southeastern Yunnan and adjacent regions.

A mycorrhizae-like gene regulates stem cell and gametophore development in mosses.

Plant colonization of land has been intimately associated with mycorrhizae or mycorrhizae-like fungi. Despite the pivotal role of fungi in plant adaptation, it remains unclear whether and how gene acquisition following fungal interaction might have affected the development of land plants. Here we report a macro2 domain gene in bryophytes that is likely derived from Mucoromycota, a group that includes some mycorrhizae-like fungi found in the earliest land plants. Experimental and transcriptomic evidence suggests that this macro2 domain gene in the moss Physcomitrella patens, PpMACRO2, is important in epigenetic modification, stem cell function, cell reprogramming and other processes. Gene knockout and over-expression of PpMACRO2 significantly change the number and size of gametophores. These findings provide insights into the role of fungal association and the ancestral gene repertoire in the early evolution of land plants.