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The aster leafhopper Macrosteles fascifrons is a common insect pest that feeds on rice and other plants and may serve as a vector to transmit various viruses. Here, we discovered a novel virus from M. fascifrons using metagenomic sequencing. We obtained its complete genome sequence by contig assembly and rapid amplification of cDNA ends, and verified the genome sequence by Sanger sequencing of overlapping segments. Based on homology search and phylogenetic analysis, the new virus belongs to the family Iflaviridae and it is tentatively named "Macrosteles fascifrons iflavirus 1" (MfIV1). Excluding the poly(A) tail, the MfIV1 genome is 10,581 nucleotides in length and it is predicted to encode a polyprotein of 3119 amino acids long, which is likely further processed to several polypeptides with conserved domains, including two rhinovirus like (rhv-like) capsid domains, a cricket paralysis virus (CRPV) capsid domain, a helicase domain, and an RNA-dependent RNA polymerase (RdRp) domain. BLAST searches show that the highest amino acid sequence identity between the capsid proteins of MfIV1 and those of other reported iflaviruses is 60.22%, indicating that MfIV1 is a new member in the family Iflaviridae. [ABSTRACT FROM AUTHOR]

The leafhopper Recilia dorsalis (family Cicadellidae, tribe Deltocephalini) is a common pest of rice and a transmitter of various viruses. Here, we discovered a novel virus in an R. dorsalis sample and determined its complete genome sequence by metagenomic sequencing and rapid amplification of cDNA ends. Based on a homology search and phylogenetic analysis, we show that the new virus belongs to the genus Iflavirus, family Iflaviridae, and we have tentatively named it "Recilia dorsalis iflavirus 1" (RdIV1). Excluding the polyA tail, the RdIV1 genome is 10,986 nucleotides in length and is predicted to encode a 3,195-amino-acid-long polyprotein that possesses the typical domains of iflaviruses: two rhinovirus-like (rhv-like) capsid domains, a cricket paralysis virus–like (CRPV-like) capsid domain, a helicase domain, a protease domain, and an RNA-dependent RNA polymerase (RdRp) domain. BLAST searches showed that the RdIV1 genome has the highest amino sequence identity (73.8%) in the coat protein region to Euscelidius variegatus virus 1 (EVV-1), a member of to the genus Iflavirus, indicating that RdIV1 can be classified as a new iflavirus. [ABSTRACT FROM AUTHOR]

Objectives: To evaluate the performance of interpretable machine learning models in predicting breast cancer molecular subtypes. Methods: We retrospectively enrolled 600 patients with invasive breast carcinoma between 2012 and 2019. The patients were randomly divided into a training (n = 450) and a testing (n = 150) set. The five constructed models were trained based on clinical characteristics and imaging features (mammography and ultrasonography). The model classification performances were evaluated using the area under the receiver operating characteristic (ROC) curve (AUC), accuracy, sensitivity, and specificity. Shapley additive explanation (SHAP) technique was used to interpret the optimal model output. Then we choose the optimal model as the assisted model to evaluate the performance of another four radiologists in predicting the molecular subtype of breast cancer with or without model assistance, according to mammography and ultrasound images. Results: The decision tree (DT) model performed the best in distinguishing triple-negative breast cancer (TNBC) from other breast cancer subtypes, yielding an AUC of 0.971; accuracy, 0.947; sensitivity, 0.905; and specificity, 0.941. The accuracy, sensitivity, and specificity of all radiologists in distinguishing TNBC from other molecular subtypes and Luminal breast cancer from other molecular subtypes have significantly improved with the assistance of DT model. In the diagnosis of TNBC versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.090, 0.125, 0.114, and 0.060, 0.090, 0.083, respectively. In the diagnosis of Luminal versus other subtypes, the average sensitivity, average specificity, and average accuracy of less experienced and more experienced radiologists increased by 0.084, 0.152, 0.159, and 0.020, 0.100, 0.048. Conclusions: This study established an interpretable machine learning model to differentiate between breast cancer molecular subtypes, providing additional values for radiologists. Key Points: • Interpretable machine learning model (MLM) could help clinicians and radiologists differentiate between breast cancer molecular subtypes. • The Shapley additive explanations (SHAP) technique can select important features for predicting the molecular subtypes of breast cancer from a large number of imaging signs. • Machine learning model can assist radiologists to evaluate the molecular subtype of breast cancer to some extent. [ABSTRACT FROM AUTHOR]

Objectives: To build and validate deep learning and machine learning fusion models to classify benign, malignant, and intermediate bone tumors based on patient clinical characteristics and conventional radiographs of the lesion. Methods: In this retrospective study, data were collected with pathologically confirmed diagnoses of bone tumors between 2012 and 2019. Deep learning and machine learning fusion models were built to classify tumors as benign, malignant, or intermediate using conventional radiographs of the lesion and potentially relevant clinical data. Five radiologists compared diagnostic performance with and without the model. Diagnostic performance was evaluated using the area under the curve (AUC). Results: A total of 643 patients' (median age, 21 years; interquartile range, 12–38 years; 244 women) 982 radiographs were included. In the test set, the binary category classification task, the radiological model of classification for benign/not benign, malignant/nonmalignant, and intermediate/not intermediate had AUCs of 0.846, 0.827, and 0.820, respectively; the fusion models had an AUC of 0.898, 0.894, and 0.865, respectively. In the three-category classification task, the radiological model achieved a macro average AUC of 0.813, and the fusion model had a macro average AUC of 0.872. In the observation test, the mean macro average AUC of all radiologists was 0.819. With the three-category classification fusion model support, the macro AUC improved by 0.026. Conclusion: We built, validated, and tested deep learning and machine learning models that classified bone tumors at a level comparable with that of senior radiologists. Model assistance may somewhat help radiologists' differential diagnoses of bone tumors. Key Points: • The deep learning model can be used to classify benign, malignant, and intermediate bone tumors. • The machine learning model fusing information from radiographs and clinical characteristics can improve the classification capacity for bone tumors. • The diagnostic performance of the fusion model is comparable with that of senior radiologists and is potentially useful as a complement to radiologists in a bone tumor differential diagnosis. [ABSTRACT FROM AUTHOR]

The evolutionarily conserved target of rapamycin (TOR) kinase acts as a master regulator that coordinates cell proliferation and growth by integrating nutrient, energy, hormone and stress signals in all eukaryotes1,2. Research has focused mainly on TOR-regulated translation, but how TOR orchestrates the global transcriptional network remains unclear. Here we identify ethylene-insensitive protein 2 (EIN2), a central integrator3–5 that shuttles between the cytoplasm and the nucleus, as a direct substrate of TOR in Arabidopsis thaliana. Glucose-activated TOR kinase directly phosphorylates EIN2 to prevent its nuclear localization. Notably, the rapid global transcriptional reprogramming that is directed by glucose–TOR signalling is largely compromised in the ein2-5 mutant, and EIN2 negatively regulates the expression of a wide range of target genes of glucose-activated TOR that are involved in DNA replication, cell wall and lipid synthesis and various secondary metabolic pathways. Chemical, cellular and genetic analyses reveal that cell elongation and proliferation processes that are controlled by the glucose–TOR–EIN2 axis are decoupled from canonical ethylene–CTR1–EIN2 signalling, and mediated by different phosphorylation sites. Our findings reveal a molecular mechanism by which a central signalling hub is shared but differentially modulated by diverse signalling pathways using distinct phosphorylation codes that can be specified by upstream protein kinases.In Arabidopsis, phosphorylation of EIN2 by TOR kinase in the presence of glucose prevents the nuclear localization of EIN2, showing that the glucose–TOR–EIN2 axis regulates the transcriptome independently of ethylene signalling pathways. [ABSTRACT FROM AUTHOR]

The plant hormone auxin has crucial roles in almost all aspects of plant growth and development. Concentrations of auxin vary across different tissues, mediating distinct developmental outcomes and contributing to the functional diversity of auxin. However, the mechanisms that underlie these activities are poorly understood. Here we identify an auxin signalling mechanism, which acts in parallel to the canonical auxin pathway based on the transport inhibitor response1 (TIR1) and other auxin receptor F-box (AFB) family proteins (TIR1/AFB receptors)1,2, that translates levels of cellular auxin to mediate differential growth during apical-hook development. This signalling mechanism operates at the concave side of the apical hook, and involves auxin-mediated C-terminal cleavage of transmembrane kinase 1 (TMK1). The cytosolic and nucleus-translocated C terminus of TMK1 specifically interacts with and phosphorylates two non-canonical transcriptional repressors of the auxin or indole-3-acetic acid (Aux/IAA) family (IAA32 and IAA34), thereby regulating ARF transcription factors. In contrast to the degradation of Aux/IAA transcriptional repressors in the canonical pathway, the newly identified mechanism stabilizes the non-canonical IAA32 and IAA34 transcriptional repressors to regulate gene expression and ultimately inhibit growth. The auxin–TMK1 signalling pathway originates at the cell surface, is triggered by high levels of auxin and shares a partially overlapping set of transcription factors with the TIR1/AFB signalling pathway. This allows distinct interpretations of different concentrations of cellular auxin, and thus enables this versatile signalling molecule to mediate complex developmental outcomes. In Arabidopsis thaliana, a newly identified auxin signalling pathway that involves TMK1 protein cleavage and IAA32 and IAA34 transcriptional repressors mediates complex developmental outcomes by allowing distinct interpretations of varying concentrations of cellular auxin. [ABSTRACT FROM AUTHOR]

Circle detection is significant in image processing and pattern recognition. We present a new algorithm for detecting circles, which is based on the global geometric symmetry of circles. First, the horizontal and vertical midpoint histograms of the edge image are obtained by using scan lines. Then, we apply the peak-finding algorithm to the midpoint histograms to look for the center of the circle. The normalized radius histogram is finally used to verify the existence of the circle and extract its radius. Synthetic images with different levels of pepper noise and real images containing several circles have been taken to test the performance. Experimental results demonstrate that the proposed algorithm has the advantage of computational efficiency as compared with the randomized Hough transform and some other algorithms. [ABSTRACT FROM AUTHOR]

Abiotic stresses such as drought, cold, and high salinity are among the most adverse factors that affect plant growth and yield in the field. MicroRNAs are small RNA molecules that regulate gene expression in a sequence-specific manner and play an important role in plant stress response. Identifying abiotic stress-associated microRNAs and understanding their function will help develop new strategies for improvement of plant stress tolerance. Here we highlight recent advances in our understanding of abiotic stress-associated miRNAs in various plants, with focus on their discovery, expression analysis, and evolution. [ABSTRACT FROM AUTHOR]

MicroRNAs (miRNAs) are short RNAs with essential roles in gene regulation in various organisms including higher plants. In contrast to the vast information on miRNAs from many economically important plants, almost nothing has been reported on the identification or analysis of miRNAs from rubber tree ( Hevea brasiliensis L.), the most important natural rubber-producing crop. To identify miRNAs and their target genes in rubber tree, high-throughput sequencing combined with a computational approach was performed. Four small RNA libraries were constructed for deep sequencing from mature and young leaves of two rubber tree clones, PB 260 and PB 217, which provide high and low latex yield, respectively. 115 miRNAs belonging to 56 known miRNA families were identified, and northern hybridization validated miRNA expression and revealed developmental stage-dependent and clone-specific expression for some miRNAs. We took advantage of the newly released rubber tree genome assembly and predicted 20 novel miRNAs. Further, computational analysis uncovered potential targets of the known and novel miRNAs. Predicted target genes included not only transcription factors but also genes involved in various biological processes including stress responses, primary and secondary metabolism, and signal transduction. In particular, genes with roles in rubber biosynthesis are predicted targets of miRNAs. This study provides a basic catalog of miRNAs and their targets in rubber tree to facilitate future improvement and exploitation of rubber tree. [ABSTRACT FROM AUTHOR]

A Correction to this paper has been published: https://doi.org/10.1038/s41467-021-22150-y [ABSTRACT FROM AUTHOR]

The tea plant (Camellia sinensis) presents an excellent system to study evolution and diversification of the numerous classes, types and variable contents of specialized metabolites. Here, we investigate the relationship among C. sinensis phylogenetic groups and specialized metabolites using transcriptomic and metabolomic data on the fresh leaves collected from 136 representative tea accessions in China. We obtain 925,854 high-quality single-nucleotide polymorphisms (SNPs) enabling the refined grouping of the sampled tea accessions into five major clades. Untargeted metabolomic analyses detect 129 and 199 annotated metabolites that are differentially accumulated in different tea groups in positive and negative ionization modes, respectively. Each phylogenetic group contains signature metabolites. In particular, CSA tea accessions are featured with high accumulation of diverse classes of flavonoid compounds, such as flavanols, flavonol mono-/di-glycosides, proanthocyanidin dimers, and phenolic acids. Our results provide insights into the genetic and metabolite diversity and are useful for accelerated tea plant breeding. The molecular basis for the unique taste and aroma of tea cultivars is largely unknown, but is critical for breeding new cultivars. Here the authors use transcriptomics and metabolomics to study the relationship among phylogenetic groups and specialized metabolites from 136 tea accessions in China. [ABSTRACT FROM AUTHOR]

Wheat spike development is a coordinated process of cell proliferation and differentiation with distinctive phases and architecture changes. However, the dynamic alteration of gene expression in this process remains enigmatic. Here, we characterized and dissected bread wheat spike into six developmental stages, and used genome-wide gene expression profiling, to investigate the underlying regulatory mechanisms. High gene expression correlations between any two given stages indicated that wheat early spike development is controlled by a small subset of genes. Throughout, auxin signaling increased, while cytokinin signaling decreased. Besides, many genes associated with stress responses highly expressed during the double ridge stage. Among the differentially expressed genes (DEGs), were identified 375 transcription factor (TF) genes, of which some homologs in rice or Arabidopsis are proposed to function in meristem maintenance, flowering time, meristem initiation or transition, floral organ development or response to stress. Gene expression profiling demonstrated that these genes had either similar or distinct expression pattern in wheat. Several genes regulating spike development were expressed in the early spike, of which Earliness per se 3 (Eps-3) was found might function in the initiation of spikelet meristem. Our study helps uncover important genes associated with apical meristem morphology and development in wheat. [ABSTRACT FROM AUTHOR]

Cucumber (Cucumis sativus L.) fruit is a type of fleshy fruit that is harvested immaturely. Early fruit development directly determines the final fruit length and diameter, and consequently the fruit yield and quality. Different cucumber varieties display huge variations of fruit length, but how fruit length is determined at the molecular level remains poorly understood. To understand the genes and gene networks that regulate fruit length in cucumber, high throughout RNA-Seq data were used to compare the transcriptomes of early fruit from two near isogenic lines with different fruit lengths. 3955 genes were found to be differentially expressed, among which 2368 genes were significantly up-regulated and 1587 down-regulated in the line with long fruit. Microtubule and cell cycle related genes were dramatically activated in the long fruit, and transcription factors were implicated in the fruit length regulation in cucumber. Thus, our results built a foundation for dissecting the molecular mechanism of fruit length control in cucumber, a key agricultural trait of significant economic importance. [ABSTRACT FROM AUTHOR]