BackgroundPlants have evolved multifaceted defence mechanisms to resist pathogen infection. Production of the pathogenesis-related (PR) proteins in response to pathogen attack has been implicated in plant disease resistance specialized in systemic-acquired resistance (SAR). Our earlier studies have reported that a full length TaLr35PR5 gene, encoding a protein exhibiting amino acid and structural similarity to a sweet protein thaumatin, was isolated from wheat near-isogenic line TcLr35. The present study aims to understand the function of TaLr35PR5 gene in Lr35-mediated adult resistance to Puccinia triticina.ResultsWe determined that the TaLr35PR5 protein contained a functional secretion peptide by utilizing the yeast signal sequence trap system. Using a heterologous expression assay on onion epidermal cells we found that TaLr35PR5 protein was secreted into the apoplast of onion cell. Expression of TaLr35PR5 was significantly reduced in BSMV-induced gene silenced wheat plants, and pathology test on these silenced plants revealed that Lr35-mediated resistance phenotype was obviously altered, indicating that Lr35-mediated resistance was compromised.ConclusionsAll these findings strongly suggest that TaLr35PR5 is involved in Lr35-mediated adult wheat defense in response to leaf rust attack.Electronic supplementary materialThe online version of this article (10.1186/s12870-018-1297-2) contains supplementary material, which is available to authorized users.

Disease progression prediction and therapeutic drug target discovery for Coronavirus disease 2019 (COVID-19) are particularly important, as there is still no effective strategy for severe COVID-19 patient treatment. Herein, we performed multi-platform omics analysis of serial plasma and urine samples collected from patients during the course of COVID-19. Integrative analyses of these omics data revealed several potential therapeutic targets, such as ANXA1 and CLEC3B. Molecular changes in plasma indicated dysregulation of macrophage and suppression of T cell functions in severe patients compared to those in non-severe patients. Further, we chose 25 important molecular signatures as potential biomarkers for the prediction of disease severity. The prediction power was validated using corresponding urine samples and plasma samples from new COVID-19 patient cohort, with AUC reached to 0.904 and 0.988, respectively. In conclusion, our omics data proposed not only potential therapeutic targets, but also biomarkers for understanding the pathogenesis of severe COVID-19.

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Cataracts, which are lenticular opacities that may occur at different lens locations, are the leading cause of visual impairment worldwide. Accurate and timely diagnosis can improve the quality of life of cataract patients. In this paper, a feature extraction-based method for grading cataract severity using retinal images is proposed. To obtain more appropriate features for the automatic grading, the Haar wavelet is improved according to the characteristics of retinal images. Retinal images of non-cataract, as well as mild, moderate, and severe cataracts, are automatically recognized using the improved Haar wavelet. A hierarchical strategy is used to transform the four-class classification problem into three adjacent two-class classification problems. Three sets of two-class classifiers based on a neural network are trained individually and integrated together to establish a complete classification system. The accuracies of the two-class classification (cataract and non-cataract) and four-class classification are 94.83% and 85.98%, respectively.The performance analysis demonstrates that the improved Haar wavelet feature achieves higher accuracy than the original Haar wavelet feature, and the fusion of three sets of two-class classifiers is superior to a simple four-class classifier. The discussion indicates that the retinal image-based method offers significant potential for cataract detection.