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.

SLAMF8 Promotes Atherosclerosis by Activating the TLR4 Signaling Pathway in Rheumatoid Arthritis.

Objectives: Rheumatoid Arthritis (RA) can accelerate atherosclerosis (AS) plaque formation. High prevalence of AS has been demonstrated in early-stage RA patients. Therefore, there is an urgent need to investigate what mechanisms and key molecules accelerate AS in RA to improve the management of RA. Methods: We retrieved gene expression data for RA (GSE45291) and atherosclerosis (GSE28829) from Gene Expression Omnibus (GEO). Seventeen key genes were identified, and the top one candidate hub gene was SLAM family member 8 (SLAMF8). To investigate the role of SLAMF8 in AS and RA, U937 cells were differentiated into macrophages using Phorbol 12-myristate 13-acetate (PMA) and further transformed into foam cells by oxidized low-density lipoprotein (ox-LDL) treatment and siRNA was manipulated to knock down SLAMF8. Flow Cytometry was employed to assess cell state. The mRNA and protein expressions of the genes were investigated using western blot and RT-qPCR. Results: SLAMF8 was screened as a key gene by bioinformatic methods. Compared to Mφ, SLAMF8, TLR4 and inflammatory cytokines, tumor necrosis factor α (TNF-α), and Interleukin 6 (IL-6) were noticeably expressed in foam cells. Knockdown of SLAMF8 could remarkably curtail TLR4, TNF-α, and IL-6 protein levels. Antagonizing SLAMF8 could attenuate inflammatory factors and apoptosis of foam cells by inhibiting the TLR4 pathway, thus mitigating the severity of AS in RA. Conclusions: Our work demonstrated that SLAMF8 promoted AS in patients with RA by inducing inflammation and apoptosis of foam cells via TLR4 signaling. Therefore, SLAMF8 could be a possible therapeutic spot for AS in RA patients.

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.

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.

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.

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.

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.

Transcriptome analysis reveals key genes involved in the regulation of nicotine biosynthesis at early time points after topping in tobacco (Nicotiana tabacum L.).

BACKGROUND: Nicotiana tabacum is an important economic crop. Topping, a common agricultural practice employed with flue-cured tobacco, is designed to increase leaf nicotine contents by increasing nicotine biosynthesis in roots. Many genes are found to be differentially expressed in response to topping, particularly genes involved in nicotine biosynthesis, but comprehensive analyses of early transcriptional responses induced by topping are not yet available. To develop a detailed understanding of the mechanisms regulating nicotine biosynthesis after topping, we have sequenced the transcriptomes of Nicotiana tabacum roots at seven time points following topping. RESULTS: Differential expression analysis revealed that 4830 genes responded to topping across all time points. Amongst these, nine gene families involved in nicotine biosynthesis and two gene families involved in nicotine transport showed significant changes during the immediate 24 h period following topping. No obvious preference to the parental species was detected in the differentially expressed genes (DEGs). Significant changes in transcript levels of nine genes involved in nicotine biosynthesis and phytohormone signal transduction were validated by qRT-PCR assays. 549 genes encoding transcription factors (TFs), found to exhibit significant changes in gene expression after topping, formed 15 clusters based on similarities of their transcript level time-course profiles. 336 DEGs involved in phytohormone signal transduction, including genes functionally related to the phytohormones jasmonic acid, abscisic acid, auxin, ethylene, and gibberellin, were identified at the earliest time point after topping. CONCLUSIONS: Our research provides the first detailed analysis of the early transcriptional responses to topping in N. tabacum, and identifies excellent candidates for further detailed studies concerning the regulation of nicotine biosynthesis in tobacco roots.

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.

Stem parasitic plant Cuscuta australis (dodder) transfers herbivory-induced signals among plants.

Cuscuta spp. (i.e., dodders) are stem parasites that naturally graft to their host plants to extract water and nutrients; multiple adjacent hosts are often parasitized by one or more Cuscuta plants simultaneously, forming connected plant clusters. Metabolites, proteins, and mRNAs are known to be transferred from hosts to Cuscuta, and Cuscuta bridges even facilitate host-to-host virus movement. Whether Cuscuta bridges transmit ecologically meaningful signals remains unknown. Here we show that, when host plants are connected by Cuscuta bridges, systemic herbivory signals are transmitted from attacked plants to unattacked plants, as revealed by the large transcriptomic changes in the attacked local leaves, undamaged systemic leaves of the attacked plants, and leaves of unattacked but connected hosts. The interplant signaling is largely dependent on the jasmonic acid pathway of the damaged local plants, and can be found among conspecific or heterospecific hosts of different families. Importantly, herbivore attack of one host plant elevates defensive metabolites in the other systemic Cuscuta bridge-connected hosts, resulting in enhanced resistance against insects even in several consecutively Cuscuta-connected host plants over long distances (> 100 cm). By facilitating plant-to-plant signaling, Cuscuta provides an information-based means of countering the resource-based fitness costs to their hosts.

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.

Computational prediction of MoRFs based on protein sequences and minimax probability machine.

BACKGROUND: Molecular recognition features (MoRFs) are one important type of disordered segments that can promote specific protein-protein interactions. They are located within longer intrinsically disordered regions (IDRs), and undergo disorder-to-order transitions upon binding to their interaction partners. The functional importance of MoRFs and the limitation of experimental identification make it necessary to predict MoRFs accurately with computational methods. RESULTS: In this study, a new sequence-based method, named as MoRFMPM, is proposed for predicting MoRFs. MoRFMPM uses minimax probability machine (MPM) to predict MoRFs based on 16 features and 3 different windows, which neither relying on other predictors nor calculating the properties of the surrounding regions of MoRFs separately. Comparing with ANCHOR, MoRFpred and MoRFCHiBi on the same test sets, MoRFMPM not only obtains higher AUC, but also obtains higher TPR at low FPR. CONCLUSIONS: The features used in MoRFMPM can effectively predict MoRFs, especially after preprocessing. Besides, MoRFMPM uses a linear classification algorithm and does not rely on results of other predictors which makes it accessible and repeatable.

Origin, evolution and functional characterization of the land plant glycoside hydrolase subfamily GH5_11.

Colonization of the land by plants was a critical event in the establishment of modern terrestrial ecosystems, and many characteristics of land plants originated during this process, including the emergence of rosette terminal cellulose-synthesizing complexes. Cellulases are non-homologous isofunctional enzymes, encoded by glycosyl hydrolase (GH) gene families. Although the plant GH5_11 gene subfamily is presumed to encode a cell-wall degrading enzyme, its evolutionary and functional characteristics remain unclear. In the present study, we report the evolution of the land plant GH5_11 subfamily, and the functions of its members in terms of cellulase activity, through comprehensive phylogenetic analyses and observation of Arabidopsis mutants. Phylogenetic and sequence similarity analyses reveal that the ancestor of land plants acquired the GH5_11 gene from fungi through a horizontal gene transfer (HGT) event. Subsequently, positive selection with massive gene duplication and loss events contributed to the evolution of this subfamily in land plants. In Arabidopsis and rice, expression of GH5_11 genes are regulated by multiple abiotic stresses, the duplicated genes showing different patterns of expression. The Arabidopsis mutants atgh5_11a and atgh5_11c display low levels of cellulase and endoglucanase activities, with correspondingly high levels of cellulose, implying that the encoded proteins may function as endoglucanases. However, atgh5_11a and atgh5_11c also display an enlarged rosette leaf phenotype, and atgh5_11c is late-flowering under short photoperiods. These observations suggest that plant GH5_11s possess more functions beyond being endonucleases. To summarize, we demonstrate that the ancestor of land plants has acquired GH5_11 gene through HGT, which extends the cellulose degradation complexity. Our investigations illuminate features of part of the molecular framework underlying the origin of land plants and provide a focus on the cellulose degradation pathway.

A Geranylfarnesyl Diphosphate Synthase Provides the Precursor for Sesterterpenoid (C25) Formation in the Glandular Trichomes of the Mint Species Leucosceptrum canum.

Plant sesterterpenoids, an important class of terpenoids, are widely distributed in various plants, including food crops. However, little is known about their biosynthesis. Here, we cloned and functionally characterized a plant geranylfarnesyl diphosphate synthase (Lc-GFDPS), the enzyme producing the C25 prenyl diphosphate precursor to all sesterterpenoids, from the glandular trichomes of the woody plant Leucosceptrum canum. GFDPS catalyzed the formation of GFDP after expression in Escherichia coli. Overexpressing GFDPS in Arabidopsis thaliana also gave an extract catalyzing GFDP formation. GFDPS was strongly expressed in glandular trichomes, and its transcript profile was completely in accordance with the sesterterpenoid accumulation pattern. GFDPS is localized to the plastids, and inhibitor studies indicated its use of isoprenyl diphosphate substrates supplied by the 2-C-methyl-D-erythritol 4-phosphate pathway. Application of a jasmonate defense hormone induced GFDPS transcript and sesterterpenoid accumulation, while reducing feeding and growth of the generalist insect Spodoptera exigua, suggesting that these C25 terpenoids play a defensive role. Phylogenetic analysis suggested that GFDPS probably evolved from plant geranylgeranyl diphosphate synthase under the influence of positive selection. The isolation of GFDPS provides a model for investigating sesterterpenoid formation in other species and a tool for manipulating the formation of this group in plants and other organisms.

Multi-Sensor Data Fusion Algorithm Based on Trust Degree and Improved Genetics.

Aiming at the problems of low data fusion precision and poor stability in greenhouse wireless sensor networks (WSNs), a multi-sensor data fusion algorithm based on trust degree and improved genetics is proposed. The original data collected by the sensor nodes are sent to the gateway through the sink node, and data preprocessing based on cubic exponential smoothing is performed at the gateway to eliminate abnormal data and noise data. In fuzzy theory, the range of membership functions is determined, according to this feature, the data fusion algorithm based on exponential trust degree is used to fuse the smooth data to avoid the absolute degree of mutual trust between data. In this paper, we have improved the crossover and mutation operations in the standard genetic algorithm, the variation is separated from the intersection, the chaotic sequence is used to determine the intersection, and the weakest single-point intersection is implemented to improve the convergence accuracy of the algorithm, weaken and avoid jitter problems during optimization. The chaotic sequence is used to mutate multiple genes in the chromosome to avoid premature algorithm maturity. Finally, the improved genetic algorithm is used to optimize the fusion estimation value. The experimental results show that the cubic exponential smoothing can significantly reduce the data fluctuation and improve the stability of the system. Compared with the commonly used data fusion algorithms such as arithmetic average method and adaptive weighting method, the data fusion algorithm based on trust degree and improved genetics has higher fusion precision. At the same time, the execution time of the algorithm is greatly reduced.

Low-Energy Data Collection in Wireless Sensor Networks Based on Matrix Completion.

Sparse sensing schemes based on matrix completion for data collection have been proposed to reduce the power consumption of data-sensing and transmission in wireless sensor networks (WSNs). While extensive efforts have been made to improve the recovery accuracy from the sparse samples, it is usually at the cost of running time. Moreover, most data-collection methods are difficult to implement with low sampling ratio because of the communication limit. In this paper, we design a novel data-collection method including a Rotating Random Sparse Sampling method and a Fast Singular Value Thresholding algorithm. With the proposed method, nodes are in the sleep mode most of the time, and the sampling ratio varies over time slots during the sampling process. From the samples, a corresponding algorithm with Nesterov technique is given to recover the original data accurately and fast. With two real-world data sets in WSNs, simulations verify that our scheme outperforms other schemes in terms of energy consumption, reconstruction accuracy, and rate. Moreover, the proposed sampling method enhances the recovery algorithm and prolongs the lifetime of WSNs.

Extraction and Research of Crop Feature Points Based on Computer Vision.

Based on computer vision technology, this paper proposes a method for identifying and locating crops in order to successfully capture crops in the process of automatic crop picking. This method innovatively combines the YOLOv3 algorithm under the DarkNet framework with the point cloud image coordinate matching method, and can achieve the goal of this paper very well. Firstly, RGB (RGB is the color representing the three channels of red, green and blue) images and depth images are obtained by using the Kinect v2 depth camera. Secondly, the YOLOv3 algorithm is used to identify the various types of target crops in the RGB images, and the feature points of the target crops are determined. Finally, the 3D coordinates of the feature points are displayed on the point cloud images. Compared with other methods, this method of crop identification has high accuracy and small positioning error, which lays a good foundation for the subsequent harvesting of crops using mechanical arms. In summary, the method used in this paper can be considered effective.

Prediction of MoRFs in Protein Sequences with MLPs Based on Sequence Properties and Evolution Information.

Molecular recognition features (MoRFs) are one important type of intrinsically disordered proteins functional regions that can undergo a disorder-to-order transition through binding to their interaction partners. Prediction of MoRFs is crucial, as the functions of MoRFs are associated with many diseases and can therefore become the potential drug targets. In this paper, a method of predicting MoRFs is developed based on the sequence properties and evolutionary information. To this end, we design two distinct multi-layer perceptron (MLP) neural networks and present a procedure to train them. We develop a preprocessing process which exploits different sizes of sliding windows to capture various properties related to MoRFs. We then use the Bayes rule together with the outputs of two trained MLP neural networks to predict MoRFs. In comparison to several state-of-the-art methods, the simulation results show that our method is competitive.

Aphid (Myzus persicae) feeding on the parasitic plant dodder (Cuscuta australis) activates defense responses in both the parasite and soybean host.

Dodders (Cuscuta spp.) are shoot holoparasites, whose haustoria penetrate host tissues to enable fusion between the parasite and host vascular systems, allowing Cuscuta to extract water, nutrients and other molecules from hosts. Aphids are piercing-sucking herbivores that use specialized stylets to feed on phloem sap. Aphids are known to feed on Cuscuta, but how Cuscuta and its host plant respond to aphids attacking the parasite was unknown. Phytohormone quantification, transcriptomic analysis and bioassays were performed to determine the responses of Cuscuta australis and its soybean (Glycine max) hosts to the feeding of green peach aphid (GPA; Myzus persicae) on C. australis. Decreased salicylic acid levels and 172 differentially expressed genes (DEGs) were found in GPA-attacked C. australis, and the soybean hosts exhibited increased jasmonic acid contents and 1015 DEGs, including > 100 transcription factor genes. Importantly, GPA feeding on C. australis increased the resistance of the soybean host to subsequent feeding by the leafworm Spodoptera litura and soybean aphid Aphis glycines, resulting in 21% decreased leafworm mass and 41% reduced aphid survival rate. These data strongly suggest that GPA feeding on Cuscuta induces a systemic signal, which is translocated to hosts and activates defense against herbivores.