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Wheat stripe rust, caused by Puccinia striiformis, is one of the most serious diseases on wheat yield. It is of great significance to timely and accurately detect the disease, in order to monitor and prevent the wheat stripe rust. The stripe rust can infect the internal physical and chemical characteristics and external morphological structure of wheat. Solar-induced chlorophyll fluorescence (SIF) can be expected for the remote sensing detection of crop stress. The red-band sunlight-induced chlorophyll fluorescence (RSIF) has more information about photosystem II (PSII), thus sensitively representing the photosynthetic physiological state of plants. The SIF escape rate is closely related to the canopy geometry, leaf optical properties, and light energy utilization efficiency of vegetation. In this study, field-measured data was used to invert and calculate the SIF and its escape rate (εCP) at different scales (canopy scale SIFCanopy and photosystem scale SIFPS) in the red and far-red band. The contents of four wheat pigments were obtained to combine the leaf area index (LAI) closely related to vegetation growth. The physiological basis of RSIF escape rate (RεCP) was determined to monitor the wheat stripe rust. Subsequently, the response characteristics of RεCP under stripe rust stress were explored to compare with the SIF and its derived parameters (fluorescence yield ФF, apparent SIF yield SIFy) in the red and far-red light bands, the normalized difference vegetation index (NDVI), the MERIS terrestrial chlorophyll index (MTCI) and the simple ratio vegetation index (SR). In addition, a systematic analysis was performed on the response characteristics to SL under different disease severity (SL) and different chlorophyll contents (Chl). The results indicate that the correlations between nitrogen balance index (NBI), Chl, flavonoids (Flav), anthocyanins (Anth), and LAI and SL all reached the P<0.01 level, among which the correlation between Chl and SL was the highest. The correlations of RεCP with NBI, Chl, Flav, and Anth increased by 29.06% and 31.52% on average, respectively, compared with photosystem-scale RSIF and photosystem-scale far-red band SIF (far-red solarinduced chlorophyll fluorescence, FRSIF). The correlation with LAI increased by 15.63%, compared with the canopy-scale FRSIF. RεCP better reflected the variation in the crop physiology and canopy structure caused by disease stress. RεCP shared the highest correlation with SL, which was 60.87%, 42.31%, 17.46%, 39.62%, 34.55%, 5.71%, 13.85%, and 21.31% higher than those of canopy-scale FRSIF (FRSIFCanopy), photosystem-scale FRSIF (FRSIFPS), photosystem-scale RSIF (RSIFPS), apparent SIF yield in the red light band (RSIFy), fluorescence yield in the red light band (RФF), NDVI, MTCI and SR, respectively. In the mild to moderate (0%45%) disease conditions, the correlation between RεCP and SL increased by an average of 56.34% and 53.97%, respectively, compared with SIF and their derived parameters and vegetation index at the P<0.01 level. RεCP was sensitively responded to the variation in SL, which was better than the rest parameters. RεCP was most sensitive to the wheat stripe rust stress under the low (Chl≤30) and medium to high chlorophyll content (Chl>30). The correlation with SL increased by an average of 42.77% and 43.25%, respectively, compared with SIF and their derived parameters and vegetation index at the P<0.01 level. RεCP can serve as a suitable factor for remote sensing monitoring of wheat stripe rust. RεCP can greatly contribute to disease prevention for better yields. The finding can also provide a strong reference and powerful tool for remote sensing monitoring of crops in agricultural production. RSIF and escape rate can be incorporated into the remote sensing monitoring, in order to greatly improve the detection and monitoring of plant health status. [ABSTRACT FROM AUTHOR]

Red solar-induced chlorophyll fluorescence (SIFB) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIFB by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIFB using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PKB) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PKB and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPARchl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIFB by the vegetation canopy. Consequently, we constructed an expression of PKB to modify SIFB. To evaluate the modified SIFB (MSIFB) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIFB, MSIFB, SIFB-VIs (a fusion of the vegetation index and SIFB), and MSIFB-VIs (a fusion of the vegetation index and MSIFB) with the severity level (SL), respectively. The results show that the correlation between MSIFB and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIFB. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIFB, MSIFB, SIFB-VIs, and MSIFB-VIs as independent variables. PKB significantly improves the accuracy and robustness of models based on SIFB and its fusion index SIFB-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIFB-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIFB-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIFB modified by PKB can weaken the effects of chlorophyll reabsorption and canopy architecture on SIFB and improve the ability of SIFB to detect stress information. [ABSTRACT FROM AUTHOR]

In this study, the compound 1-(4-(tert-butyl) phenyl)-3-hydroxy-2-methylpyridine-4 (1H) - one (hereinafter referred to as HAINU-19) was used as the active ingredient to prepare the microemulsion and determine its antifungal activity. First, through the screening solvents, co-solvents, and transparent, the formulation of 2.5% HAINU-19 microemulsion (mass fraction) was determined as follows: 2.5% HAINU-19, 15% ethanol, 10% cyclohexanone, 20% transparent (1601# : 500# : AC1810 = 3 : 3 : 1), with distilled water making up to 100%. All indicators meet national standards. The bioassay result of pot experiments showed that the protective and curative effects of 2.5% HAINU-19 microemulsion on wheat stripe rust were 100% at 1000, 500, and 250-fold dilutions, and curative effects were 76.67%, 81.00%, and 82.33%, respectively. The field trial results showed that the 2.5% HAINU19 microemulsion had a control efficacy of 86.13% on rice sheath blight at a 100-fold dilution and 71.24% at a 200-fold dilution. This study can provide an experimental basis and technical support for the development and application of the antifungal compound HAINU-19. [ABSTRACT FROM AUTHOR]

Wheat contributes one‐fifth of the global food supply with an estimated 29% of global production in low and lower‐middle income countries. As production expands across southern Asia, yields are often negatively impacted by outbreaks of fungal rust diseases. A wheat rust early warning and advisory system comprising surveillance, near real‐time disease risk forecasts and advisory dissemination has been established in two target countries in South Asia, including Nepal and Bangladesh. However, as wheat rust spores can be aerially transmitted over long distances, near real‐time estimates of disease incidence are required from sources of infection in neighbouring regions. To address this challenge, we developed and tested a novel algorithm to generate proxy observations of infection sources using online media reports in two neighbouring countries, India and Pakistan. Media sampling could provide an effective alternative where data from ground surveys are not readily available in near real‐time. Our results show that west Nepal was exposed to a substantial inoculum pressure from aerially dispersed stripe rust spores originating from India and Pakistan. There were no outbreaks of stripe rust disease in Bangladesh with only very low levels of cross‐border dispersion and generally unfavourable environmental conditions for infection. We further describe how proxy observations informed farmer decision‐making in near real‐time in Nepal and filled a knowledge gap in identifying early sources of infection for a major outbreak of stripe rust during 2020 in Nepal. Our results highlight the importance of international cooperation in mitigating transboundary plant pathogens. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most destructive fungal diseases of wheat, and seriously threatens safe production of the crop worldwide. In China, new races historically appeared and rapidly developed to be predominant races and have resulted in ineffectiveness and replacement of wheat resistance cultivars as well as massive reduction in yield. In the present study, the relative parasitic fitness of the two newly-emerged Yr5-virulent races (TSA-6 and TSA-9) were compared with those of four currently predominant Chinese races (CYR31, CYR32, CYR33, and CYR34) based on evaluation on 10 Chinese wheat cultivars. As a result, there were significant differences in the relative parasitic fitness parameters among overall tested races based on multiple comparison (LSD) analysis (PTSA-9 (0.95)>TSA-6 (0.92)>CYR34 (0.29)>CYR31 (–1.54)>CYR33 (–1.77). The results indicated that two Yr5-virulent races TSA-9 and TSA-6 possessed relative parasitic fitness higher than races CYR34, CYR31, and CYR33, but lower than race CYR32, and have potential risks in developing to be predominant races. Therefore, continual monitoring of both Yr5-virulent races, and their variants is needed. The use of wheat cultivars (lines) with Yr5 resistance gene singly in wheat breeding is essential for being avoided, and is suggested to combine with other effective stripe rust resistance genes.

Wheat stripe rust is globally one of the most important diseases affecting wheat. There is an urgent need to develop environmentally safe and durable biological control options to supplement the control that is achieved with breeding and fungicides. In this study, endophytic bacteria were isolated from healthy wheat through the tissue separation method. Antagonistic endophytic bacteria were screened based on the control effect of urediniospore germination and wheat stripe rust (WSR). The taxonomic status of antagonistic strains was determined based on morphological, physiological, and biochemical characteristics and molecular biological identification (16S rDNA and gyrB gene sequence analysis). Finally, the potential growth-promoting effect of different concentrations of antagonists on wheat seedlings and the biological control effect of WSR were studied. A total of 136 strains of endophytic bacteria belonging to 38 genera were isolated. Pseudomonas was the most common bacterial genus, with 29 isolates (21%). The biological control effect of different isolates was assessed using an urediniospore germination assay. The isolate XD29-G1 of Paenibacillus polymyxa had the best performance, with 85% inhibition of spore germination during primary screening. In the deep screening, the control effect of XD29-G1 on wheat stripe rust was 60%. The antagonist XD29-G1 promoted the germination of wheat seeds and the growth of wheat seedlings at a solution dilution of 10−7 cfu/mL. The pot experiment results showed that different dilution concentrations of the strain had different levels of antibacterial activity against WSR, with the concentration of 10−1 cfu/mL having the best control effect and a control efficiency of 61.19%. XD29-G1 has better biological control potential against wheat stripe rust. [ABSTRACT FROM AUTHOR]

The epidemic of stripe rust, caused by the pathogen Puccinia striiformis f. sp. tritici (Pst), would reduce wheat (Triticum aestivum) yields seriously. Traditional experimental methods are difficult to discover the interaction between wheat and Pst. Multi-omics data analysis provides a new idea for efficiently mining the interactions between host and pathogen. We used 140 wheat-Pst RNA-Seq data to screen for differentially expressed genes (DEGs) between low susceptibility and high susceptibility samples, and carried out Gene Ontology (GO) enrichment analysis. Based on this, we constructed a gene co-expression network, identified the core genes and interacted gene pairs from the conservative modules. Finally, we checked the distribution of Nucleotide-binding and leucine-rich repeat (NLR) genes in the co-expression network and drew the wheat NLR gene co-expression network. In order to provide accessible information for related researchers, we built a web-based visualization platform to display the data. Based on the analysis, we found that resistance-related genes such as TaPR1, TaWRKY18 and HSP70 were highly expressed in the network. They were likely to be involved in the biological processes of Pst infecting wheat. This study can assist scholars in conducting studies on the pathogenesis and help to advance the investigation of wheat-Pst interaction patterns. [ABSTRACT FROM AUTHOR]

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Wheat stripe rust (WSR), a fungal disease capable of inflicting severe crop loss, threatens most of global wheat production. Breeding for genetic resistance is the primary defense against stripe rust infection. Further development of rust-resistant wheat varieties depends on the ability to accurately and rapidly quantify rust resilience. In this study we demonstrate the ability of visible through shortwave infrared reflectance spectroscopy to effectively provide high-throughput classification of wheat stripe rust severity and identify important spectral regions for classification accuracy. Random forest models were developed using both leaf-level and canopy-level hyperspectral reflectance observations collected across a breeding population that was scored for WSR severity using 10 and 5 severity classes, respectively. The models were able to accurately diagnose scored disease severity class across these fine scoring scales between 45-52% of the time, which improved to 79-96% accuracy when allowing scores to be off-by-one. The canopy-level model demonstrated higher accuracy and distinct spectral characteristics relative to the leaf-level models, pointing to the use of this technology for field-scale monitoring. Leaf-level model performance was strong despite clear variation in scoring conducted between wheat growth stages. Two approaches to reduce predictor and model complexity, principal component dimensionality reduction and backward feature elimination, were applied here. Both approaches demonstrated that model classification skill could remain high while simplifying high-dimensional hyperspectral reflectance predictors, with parsimonious models having approximately 10 unique components or wavebands. Through the use of a high-resolution infection severity scoring methodology this study provides one of the most rigorous tests of the use of hyperspectral reflectance observations for WSR classification. We demonstrate that machine learning in combination with a few carefully-selected wavebands can be leveraged for precision remote monitoring and management of WSR to limit crop damage and to aid in the selection of resilient germplasm in breeding programs.

Abstract Wheat contributes one‐fifth of the global food supply with an estimated 29% of global production in low and lower‐middle income countries. As production expands across southern Asia, yields are often negatively impacted by outbreaks of fungal rust diseases. A wheat rust early warning and advisory system comprising surveillance, near real‐time disease risk forecasts and advisory dissemination has been established in two target countries in South Asia, including Nepal and Bangladesh. However, as wheat rust spores can be aerially transmitted over long distances, near real‐time estimates of disease incidence are required from sources of infection in neighbouring regions. To address this challenge, we developed and tested a novel algorithm to generate proxy observations of infection sources using online media reports in two neighbouring countries, India and Pakistan. Media sampling could provide an effective alternative where data from ground surveys are not readily available in near real‐time. Our results show that west Nepal was exposed to a substantial inoculum pressure from aerially dispersed stripe rust spores originating from India and Pakistan. There were no outbreaks of stripe rust disease in Bangladesh with only very low levels of cross‐border dispersion and generally unfavourable environmental conditions for infection. We further describe how proxy observations informed farmer decision‐making in near real‐time in Nepal and filled a knowledge gap in identifying early sources of infection for a major outbreak of stripe rust during 2020 in Nepal. Our results highlight the importance of international cooperation in mitigating transboundary plant pathogens.

ABSTRACT Stripe rust of wheat is caused by the fungal pathogen Puccinia striiformis f. sp. tritici (Pst). Breeding durably resistant wheat varieties by disrupting the susceptibility (S) gene has an important impact on the control of wheat stripe rust. Mingxian169 (MX169) showed strong stripe rust susceptibility to all the races of Pst. However, molecular mechanisms and responsive genes underlying susceptibility of the wheat variety MX169 to Pst have not been elucidated. Here, we utilized next-generation sequencing technology to analyze transcriptomics data of “MX169” and high-resistance wheat “Zhong4” at 24, 48, and 120 h post-inoculation (hpi) with Pst. Comparative transcriptome analysis revealed 3,494, 2,831, and 2,700 differentially expressed genes (DEGs) at different time points. We observed an upregulation of DEGs involved in photosynthesis, flavonoid biosynthesis, pyruvate metabolism, thiamine metabolism, and other biological processes, suggesting their involvement in MX169’s response to Pst. DEGs encoding transcription factors were also identified. Our study suggested the potential susceptibility gene resources in MX169 related to stripe rust response could be valuable for understanding the mechanisms involved in stripe rust susceptibility and for improving wheat resistance to Pst.IMPORTANCEOur study suggests the potential susceptibility gene resources in MX169 related to stripe rust response could be valuable for understanding the mechanisms involved in stripe rust susceptibility and for improving wheat resistance to Pst.

: Stripe rust has posed a serious threat to the wheat yield in recent years. It is crucial to breed the wheat varieties resistant to stripe rust. However, the identification of resistance is single and inefficient in traditional breeding. In this study, an efficient identification was proposed to determine the different resistance grades to the stripe rust using the time series of vegetation index response to wheat canopy. An unmanned aerial vehicle (UAV) was utilized to collect multi-temporal spectral images of the canopy in the naturally occurring breeding populations of colony wheat (600 samples in total, 516 genotypes). Six sensitive features were screened for the severity of stripe rust disease using Random Forest and ReliefF algorithms: normalized pigment chlorophyll index (NPCI), woebbecke index (WI), chlorophyll index rededge (CIrededge), (green atmospherically resistant index GARI), normalized difference vi (NDVI), and chlorophyll index green (CIgreen). These indices were verified as sensitive features. The severity of stripe rust disease incidence was dynamically characterized using the time series of these indices in the test population. The support vector machine (SVM) was used to establish a classification model for the severity grade of stripe rust disease, according to the sensitive features. There was a very small difference in the performance of the test set and the unscreened original features, indicating the effectiveness of the screened sensitive features. The time series of six sensitive traits was observed in the samples of different resistance grades. It was found that there were no significant differences in the CIgreen and CIrededge among the samples with the different resistance grades. This indicated that the saamples were not applicable to categorize the resistance grades to stripe rust. The differences exhibited by GARI, NDVI, NPCI and WI were used to classify the resistance grades to stripe rust. General machine learning cannot capture the smaller differences of feature variation in the samples with the different resistance grades. Therefore, an improved mode was proposed to extract the features from two-dimensional images that transformed vegetation index time series, in order to realize the classification of stripe rust resistance grade. Four time-series vegetation indices (NPCI, GARI, NDVI, and WI) were better distinguished the different disease resistance grades among the sensitive features, and then used to generate the Gramian Angular Summation Field (GASF) images by the Gramian Angular Field. Data augmentation was performed on the dataset to equalize the number of samples in each resistance grade. Each dataset had a total of 1 040 samples, and was then divided into four grades of stripe rust resistance, where each grade contained 260 sample images, while each dataset was divided into training, validation, and testing sets in the ratio of 6:2:2. DenseNet121 model was separately trained using each dataset, in order to classify the various stripe rust resistance. A better performance was achieved in the classification models with the GASF_NPCI and GASF_WI as the input features, compared with the GASF_GARI and GASF_NDVI. The model with the GASF_NPCI as a feature was slightly less effective in distinguishing the samples with the resistance grades R and MR, where the precision and recall were relatively low. There was no difference in the models with the GASF_WI for the precision and recall of the samples that predicted each stripe rust resistance grade. In the F1 scores of the test set, the different vegetation indices on the resistance grades of stripe rust in colony wheat were ranked in the order of NPCI, WI, GARI, NDVI. The classification model with the GASF_NPCI was the most effective in the test set, with an F1 score of up to 0.833. There was a better distinction of differences in the stripe rust resistance grades among different varieties (lines) of population wheat. The grades of wheat stripe rust resistance were fully identified using time series of spectral vegetation index. Meanwhile, the finding can also provide a strong reference for the disease resistance breeding of crops. [ABSTRACT FROM AUTHOR]

Stripe rust, a fungal disease caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most destructive diseases affecting wheat production areas worldwide. In recent years in China, wheat stripe rust has caused huge yield losses throughout the vast Huang-Huai-Hai region, including the eastern coast regions, especially Shandong province. The aim of the present study was to explore the population structure and potential inoculum sources of the pathogen in this region. A total of 234 Pst isolates in 2021 were collected and isolated from seven provinces and identified for virulence phenotypes using 19 Chinese differentials and for genotypes using 17 single-nucleotide polymorphism-based Kompetitive allele-specific PCR markers. The virulence phenotype tests identified predominant races CYR34 (18.0%) and CYR32 (16.0%) in Shandong, which were similar to the results in Henan province, also with the predominant races CYR34 (21.9%) and CYR32 (18.8%). Based on the virulence data of phenolyping, the Pst populations in Shandong, Hubei, and Henan were similar. The genotypic analysis revealed remarkable gene flows among the Shandong, Hubei, Henan, Yunnan, and Guizhou populations, showing a migration of Pst from the southwestern oversummering regions to Shandong through the winter spore production regions. Genetic structure analysis also indicated an additional migration route from the northwestern oversummering regions through winter spore production regions to Shandong. The results are useful for understanding stripe rust epidemiology in the eastern coast region and improving control of the disease throughout the country. [ABSTRACT FROM AUTHOR]

Zhou 8425B is a widely used dwarf large panicle and disease-resistant and stress-resistant Wheat Founder Parent in the wheat production areas of the Yellow-Huaihe-Haihe Rivers regions. The analysis of stripe rust resistance of derived varieties and the genetic transmission information of stripe rust resistance gene carried by Zhou 8425B is of great value for wheat new variety breeding. In this study, we used a highly toxic race of stripe rust, Tiaozhong 34 (CYR34), to identify the resistance of 222 collected Zhou 8425B derived varieties to stripe rust at seedling stage. The hybrid strain mainly CYR34 was used to identify the resistance to stripe rust at adult stage of the derived varieties. Subsequently, molecular markers closely linked to the stripe rust resistance genes (YrZH84, YrZH84.2, Yr30, YrZH22, and Yr9 carried by Zhou 8425B) were used for genotype detection of the derived varieties. The results showed that Zhou 8425B was highly resistant to stripe rust stripe rust at seedling stage and adult stage to the current virulent dominant race CYR34. Among the 222 derivative varieties of Zhou 8425B, 14 of them, including Changmai 9, Jiyanmai 10, Bainong 4199, Saidemai 7, and Zhengmai 103, etc., showed stable disease resistance in two years, accounting for 6.3%; 52 derivative varieties, include Zhoumai 11, Zhoumai 22, Zhoumai 26, Zhoumai 36, Lantian 36, Cunmai 16 and Zhengpinmai 8, etc., had stable disease resistance during the whole growth period, accounting for 23.4%. Many derivative varieties of Zhou 8425B were mainly derived from six offspring, including Zhoumai 11, Zhoumai 12, Zhoumai 13, Zhoumai 15, Zhoumai 16, and Zhoumai 17. In the first generation, Zhoumai 16 and Zhoumai 13 directly derived more varieties because of their good agronomic characters, while Zhoumai 15 and Zhoumai 17 derived fewer varieties. Zhoumai 12 and Zhoumai 13 bred the second generation, Zhoumai 22, and then derived 45 sub-generations, and Zhoumai 11 bred Aikang 58, and then derived 54 sub-generations. The excellent stripe rust resistance gene of Zhou 8425B was continuously separated and polymerized in the process of genetic breeding. The frequencies of YrZH84, YrZH84.2, YrZH22, Yr30, and Yr9 in the derived offspring were 34.7%, 14.9%, 41.9%, 66.2%, and 67.1%, respectively. Among the derivative varieties carrying only one disease resistance gene, the average severity of YrZH84 was the lowest (14.4%). Among the derived varieties that aggregate two disease resistance genes, the average severity of carrying YrZH84+YrZH22 was the lowest (20.0%); among the derived varieties that aggregate three disease resistance genes, the average severity of carrying YrZH84+YrZH22+Yr9 was the lowest (17.2%). Among the derived varieties that aggregate four disease resistance genes, the average severity of carrying YrZH84+YrZH22+Yr30+Yr9 was 16.9%, and the average severity of carrying YrZH84.2+YrZH22+Yr30+Yr9 was 38.4%. At seedling stage, derived varieties that carried the YrZH84 resistance gene or a combination of genes containing YrZH84 during the entire growth period had better disease resistance. These results provide the information of stripe rust gene for the continuous improvement and utilization of the Founder Parent Zhou 8425B in China, identify the new derived germplasm with high resistance and high yield to the highly virulent physiological race CYR34, which providing a reference for the genetic breeding of wheat resistance to stripe rust in China. [ABSTRACT FROM AUTHOR]

Abstract Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a devastating disease that seriously threatens the production of crops worldwide. Triadimefon is the widely-used fungicide for controlling the disease in China; however, as the fungicide targets a single site (position 401 in the 134th codon of the Cyp51 gene), the extensive application imposes a strong selection pressure on the pathogens, which may potentially lose the effect over time. In this study, 176 Pst field isolates sampled from different regions of Xinjiang were determined for their sensitivity to triadimefon because it is the few frequent Pst outbreak and representative area in China. The results showed that the Pst isolates collected from Yili, Xinjiang, exhibited a strong resistance to triadimefon with an average EC50 of 0.263 µg/mL, despite the rest of the isolates maintaining high sensitivity to triadimefon. The triadimefon-resistant and triadimefon-sensitive isolates did not display significant differences in sporulation, but the triadimefon-resistant isolates exhibited weaker adaptive traits in their latent period and urediniospore germination rate than the triadimefon-sensitive isolates. No cross-resistance was found for the other two fungicides, flubeneteram or pyraclostrobin; however, cross-resistance for the demethylation inhibitor (DMI) fungicides, tebuconazole and hexaconazole, was found. Genome sequencing revealed that the Tyrosine (Y) at 134 residue was mutated to Phenylalanine (F) in the Xinjiang isolates. Our study revealed that a natural mutation in Pst led to the efficacy loss of triadimefon to control the disease.

Red solar-induced chlorophyll fluorescence (SIFB) is closely related to the photosynthetically active radiation absorbed by chlorophyll. The scattering and reabsorption of SIFB by the vegetation canopy significantly change the spectral intensity and shape of SIF, which affects the relationship between SIF and crop stress. To address this, we propose a method of modifying SIFB using SIF spectral shape characteristic parameters to reduce this influence. A red pseudokurtosis (PKB) parameter that can characterize spectral shape features was calculated using full-spectrum SIF data. On this basis, we analyzed the photosynthetic physiological mechanism of PKB and found that it significantly correlates with both the fraction of photosynthetically active radiation absorbed by chlorophyll(fPARchl) and the red SIF escape rate (fesc680); thus, it is closely related to the scattering and reabsorption of SIFB by the vegetation canopy. Consequently, we constructed an expression of PKB to modify SIFB. To evaluate the modified SIFB (MSIFB) in monitoring the severity of wheat stripe rust, we analyzed the correlations between SIFB, MSIFB, SIFB-VIs (a fusion of the vegetation index and SIFB), and MSIFB-VIs (a fusion of the vegetation index and MSIFB) with the severity level (SL), respectively. The results show that the correlation between MSIFB and the severity of wheat stripe rust increased by an average of 25.6% and at least 16.95% compared with that for SIFB. In addition, we constructed remote sensing monitoring models for wheat stripe rust using linear regression methods, with SIFB, MSIFB, SIFB-VIs, and MSIFB-VIs as independent variables. PKB significantly improves the accuracy and robustness of models based on SIFB and its fusion index SIFB-VIs in the constructed testing set. The R-value between the predicted SL and the measured SL of the remote sensing monitoring model for wheat stripe rust was established using MSIFB-VIs as the independent variable, and it was improved by an average of 39.49% compared with the model using SIFB-VIs. The RMSE was reduced by an average of 18.22%. Therefore, the SIFB modified by PKB can weaken the effects of chlorophyll reabsorption and canopy architecture on SIFB and improve the ability of SIFB to detect stress information.

Wheat cultivar Xiaoyan 6 (XY6) has high‐temperature seedling‐plant (HTSP) resistance to Puccinia striiformis f. sp. tritici (Pst). However, the molecular mechanism of Pst effectors involved in HTSP resistance remains unclear. In this study, we determined the interaction between two Pst effectors, PstCEP1 and PSTG_11208, through yeast two‐hybrid (Y2H), bimolecular fluorescence complementation (BiFC), and pull‐down assays. Transient overexpression of PSTG_11208 enhanced HTSP resistance in different temperature treatments. The interaction between PstCEP1 and PSTG_11208 inhibited the resistance enhancement by PSTG_11208. Furthermore, the wheat apoplastic thaumatin‐like protein 1 (TaTLP1) appeared to recognize Pst invasion by interacting with PSTG_11208 and initiate the downstream defence response by the pathogenesis‐related protein TaPR1. Silencing of TaTLP1 and TaPR1 separately or simultaneously reduced HTSP resistance to Pst in XY6. Moreover, we found that PstCEP1 targeted wheat ferredoxin 1 (TaFd1), a homologous protein of rice OsFd1. Silencing of TaFd1 affected the stability of photosynthesis in wheat plants, resulting in chlorosis on the leaves and reducing HTSP resistance. Our findings revealed the synergistic mechanism of effector proteins in the process of pathogen infection. [ABSTRACT FROM AUTHOR]

Wheat yellow (stripe) rust (Puccinia striiformis f. sp. tritici) is a dominant type of winter wheat disease. Developing new, highly productive varieties with increased immunological indicators helps to minimize the threat of rust spread. The progressive study searched the sources of resistance to the Pst populations and determined the effectiveness of Yr genes in Southeast Kazakhstan. Immunological studies ensued during 2018-2022 at the Kazakh Research Institute of Agriculture and Plant growing, Almaty, Kazakhstan. Wheat's 23 isogenic lines and 193 winter wheat genotypes attained evaluation for their reactions against an artificially infectious background of infection mixed with Pst pathotypes. Determining the intensity of virulence, the effectiveness of Yr genes, and the resistance of genotypes to the Pst population transpired in the said region. During the vegetation period, based on weather conditions, the accumulated flow of the source, and the period of infection, wheat genotypes responded differently to the rust disease manifestation. The wheat genotypes found resistant to P. striiformis and promising for selection with immunity reached nomination. Their practical use centered on increasing the immunological potential of the new winter wheat cultivars for creation and further reducing the large-scale use of fungicides and the negative environmental consequences. [ABSTRACT FROM AUTHOR]

Wheat stripe rust poses a serious threat to the quality and yield of wheat crops. Typically, the occurrence data of wheat stripe rust is characterized by small sample sizes, and the current research on severity identification lacks high-precision methods for small sample data. Additionally, the irregular edges of wheat stripe rust lesions make it challenging to draw samples. In this study, we propose a method for wheat stripe rust severity identification that combines SLIC superpixel segmentation and a random forest algorithm. This method first employs SLIC to segment subregions of wheat stripe rust, automatically constructs and augments a dataset of wheat stripe rust samples based on the segmented patches. Then, a random forest model is used to classify the segmented subregion images, achieving fine-grained extraction of wheat stripe rust lesions. By merging the extracted subregion images and using pixel statistics, the percentage of lesion area is calculated, ultimately enabling the identification of the severity of wheat stripe rust. The results show that our method outperforms unsupervised classification algorithms such as watershed segmentation and K-Means clustering in terms of lesion extraction when using the segmented subregion dataset of wheat stripe rust. Compared to the K-Means segmentation method, the mean squared error is reduced by 1.2815, and compared to the watershed segmentation method, it is reduced by 2.0421. When compared to human visual inspection as the ground truth, the perceptual loss for lesion area extraction is 0.064. This method provides a new approach for the intelligent extraction of wheat stripe rust lesion areas and fading green areas, offering important theoretical reference for the precise prevention and control of wheat stripe rust. [ABSTRACT FROM AUTHOR]

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a foliar disease that affects both winter and spring wheat crops in Xinjiang, China, which is linked to Central Asia. Race identification of Pst from spring wheat in Xinjiang was not done before. In this study, a total of 216 isolates were recovered from stripe rust samples of spring wheat in the region in 2021 and multiplied using the susceptible cultivar Mingxian 169. These isolates were tested on the Chinese set of 19 wheat differential lines for identifying Pst races. A total of 46 races were identified. Races Suwon-11-1, Suwon11-12, and CYR32 had high frequencies in the spring wheat region. The frequencies of virulence factors on differentials “Fulhard” and “Early Premium” were high (>95%), whereas the virulence factor to differential “Triticum spelta var. Album” (Yr5) was not detected, while virulence to other differentials showed variable frequency within different counties. The predominant races in winter wheat in the same season were also detected from spring wheat cultivars, indicating Pst spreading from winter wheat to spring wheat crops. Deploying resistance genes in spring and winter wheat cultivars is critical for control stripe rust. [ABSTRACT FROM AUTHOR]

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a devastating disease that seriously threatens the production of crops worldwide. Triadimefon is the widely-used fungicide for controlling the disease in China; however, as the fungicide targets a single site (position 401 in the 134th codon of the Cyp51 gene), the extensive application imposes a strong selection pressure on the pathogens, which may potentially lose the effect over time. In this study, 176 Pst field isolates sampled from different regions of Xinjiang were determined for their sensitivity to triadimefon because it is the few frequent Pst outbreak and representative area in China. The results showed that the Pst isolates collected from Yili, Xinjiang, exhibited a strong resistance to triadimefon with an average EC50 of 0.263 µg/mL, despite the rest of the isolates maintaining high sensitivity to triadimefon. The triadimefon-resistant and triadimefon-sensitive isolates did not display significant differences in sporulation, but the triadimefon-resistant isolates exhibited weaker adaptive traits in their latent period and urediniospore germination rate than the triadimefon-sensitive isolates. No cross-resistance was found for the other two fungicides, flubeneteram or pyraclostrobin; however, cross-resistance for the demethylation inhibitor (DMI) fungicides, tebuconazole and hexaconazole, was found. Genome sequencing revealed that the Tyrosine (Y) at 134 residue was mutated to Phenylalanine (F) in the Xinjiang isolates. Our study revealed that a natural mutation in Pst led to the efficacy loss of triadimefon to control the disease. [ABSTRACT FROM AUTHOR]

Wheat leaf rust and stripe rust are important diseases worldwide. Breeding resistant cultivars is an effective means to control wheat leaf and stripe rust. Wheat line L224-3 currently has high resistance to wheat leaf and stripe rust at the field. In this study, 166 recombinant inbred lines (RILs) derived from the L224-3 × Zhengzhou 5389 cross were used to map quantitative trait locus (QTL) for leaf and stripe rust resistance. The RILs and two parents were phenotyped for leaf rust severity at Baoding in Hebei province and Zhoukou in Henan province in the 2015/2016 and 2016/2017 cropping seasons, and for stripe rust severity at Baoding in Hebei Province and Mianyang in Sichuan Province in the 2015/2016 and 2016/2017 growth seasons. All the RILs and parents were also genotyped with the 660 K SNP array and simple sequence repeat (SSR) markers to screen for potential polymorphic markers associated with rust resistance. Four QTLs on chromosomes 1A, 2A, 4B and 7B, were detected using inclusive composite interval mapping (IciMapping). QLr.hbau-1A/QYr.hbau-1A, derived from susceptible parent Zhengzhou 5389, was pleiotropic for both leaf rust and stripe rust resistance and maybe a novel QTL. The second QTL, QLr.hbau-2A/QYr.hbau-2A derived from L224-3 for leaf rust and stripe rust resistance is possibly Lr37/Yr17. QLr.hbau-4B/QYr.hbau-4B might be a new locus for leaf rust and stripe rust resistance. The fourth QTL, QYr.hbau-7B is possibly a new QTL. The QTL identified in the present study with their flanking markers might be used for candidate gene mining and marker-assisted selection (MAS) in wheat breeding programs for rust resistance. [ABSTRACT FROM AUTHOR]

Wheat stripe rust is globally one of the most important diseases affecting wheat. There is an urgent need to develop environmentally safe and durable biological control options to supplement the control that is achieved with breeding and fungicides. In this study, endophytic bacteria were isolated from healthy wheat through the tissue separation method. Antagonistic endophytic bacteria were screened based on the control effect of urediniospore germination and wheat stripe rust (WSR). The taxonomic status of antagonistic strains was determined based on morphological, physiological, and biochemical characteristics and molecular biological identification (16S rDNA and gyrB gene sequence analysis). Finally, the potential growth-promoting effect of different concentrations of antagonists on wheat seedlings and the biological control effect of WSR were studied. A total of 136 strains of endophytic bacteria belonging to 38 genera were isolated. Pseudomonas was the most common bacterial genus, with 29 isolates (21%). The biological control effect of different isolates was assessed using an urediniospore germination assay. The isolate XD29-G1 of Paenibacillus polymyxa had the best performance, with 85% inhibition of spore germination during primary screening. In the deep screening, the control effect of XD29-G1 on wheat stripe rust was 60%. The antagonist XD29-G1 promoted the germination of wheat seeds and the growth of wheat seedlings at a solution dilution of 10−7 cfu/mL. The pot experiment results showed that different dilution concentrations of the strain had different levels of antibacterial activity against WSR, with the concentration of 10−1 cfu/mL having the best control effect and a control efficiency of 61.19%. XD29-G1 has better biological control potential against wheat stripe rust.

The fungus Puccinia striiformis f. sp. tritici (Pst) is an important threat to wheat production because it can cause wheat stripe rust. The present study aimed to identify new stripe rust resistance genes and to provide a theoretical and practical basis for breeding wheat varieties with broad spectrum, stable, and durable resistance. Wheat leaves inoculated with wheat stripe rust fungus Chinese yellow rust 34 were collected at different time points for transcriptomic analysis based on the wheat stripe rust susceptible varieties AVOCET S (AVS) and AVSYr15NIL [near-isogenic line (NIL) derived from AVS]. The results showed that the number of upregulated genes in the two varieties was 294, 364, 398, and 604, and the number of downregulated genes was 520, 178, 570, and 345 on the 1st, 3rd, 5th, and 7th days post inoculation, respectively. Gene Ontology and Kyoto Encyclopedia of Gene and Genomes enrichment analyses found enrichment of differentially expressed genes in the peroxisome proliferators-activated receptor signaling pathways, plant-pathogen interaction, and styrene acrylic acid biosynthesis that encoded protein kinases, signal transduction, transcription factors, and functional protein components. Differentially expressed genes were randomly selected for quantitative reverse transcription PCR analysis, and the change trend was the same as in the transcriptome data. The results of this study suggest that genes in AVSYr15NIL related to the stripe rust response could be valuable for understanding the mechanisms involved in stripe rust resistance.

Abstract Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a devastating disease threatening global wheat production. Biocontrol by beneficial microorganisms is considered an alternative to synthetic fungicide applications. This study aimed to investigate the mechanisms involved in the biocontrol of wheat stripe rust by streptomycetes. A streptomycete strain XF, isolated from the rhizospheric soil of peony, was identified as Streptomyces tauricus based on morphological characteristics and phylogenetic analysis. We determined the inhibitory effect of XF on Pst and biocontrol effect on the disease using XF fermentation filtrate (FL) and actinomycete cell suspension (AC). Results revealed that FL inhibited urediniospore germination by up to 99% and rendered a lethality rate of 61.47% against urediniospores. Additionally, crude extract of ethyl acetate phase of FL caused cytoplasm releases from urediniospores and the deformation of germ tubes. Furthermore, histochemical analyses revealed that treatments of plants with AC and FL increased reactive oxygen species, inhibited haustorium formation, and reduced the biomass of Pst in leaves. Electron microscopy showed that XF mycelium was able to colonize the leaf surface. Moreover, gene expression assays revealed that AC and FL treatments induced the expression of a number of pathogenesis-related genes in wheat leaves. Besides, in the greenhouse experiments, the control effects of AC and FL reached 65.48% and 68.25%, respectively. In the field, application of XF fermentation broth significantly reduced the disease indices of stripe rust by 53.83%. These findings suggest that XF is a potential biocontrol agent for managing wheat stripe rust disease.

Realizing the high-precision monitoring of wheat stripe rust over a large area is of great significance in ensuring the safety of wheat production. Existing studies have mostly focused on the fusion of multi-source data and the construction of key monitoring features to improve the accuracy of disease monitoring, with less consideration for the regional distribution characteristics of the disease. In this study, based on the occurrence and spatial distribution patterns of wheat stripe rust in the experimental area, we constructed a multi-source monitoring feature set, then utilized geographical detectors for feature selection that integrates the spatial-distribution differences of the disease. The research results show that the optimal monitoring feature set selected by the geographical detectors has a higher monitoring accuracy. Based on the Random Forest (RF), eXtreme Gradient Boosting (XGBoost), and Support Vector (SVM) models, the disease monitoring results demonstrate that the monitoring feature set constructed in this study has an overall accuracy in its disease monitoring that is 3.2%, 2.7%, and 4.3% higher, respectively, than that of the ReliefF method, with Kappa coefficient higher by 0.064, 0.044, and 0.087, respectively. Furthermore, the optimal monitoring feature set obtained by the geographical detectors method exhibits a higher stability, and the spatial distribution of wheat stripe rust in the monitoring results generated by the different models demonstrates good consistency. In contrast, the features selected by the ReliefF method exhibit significant spatial-distribution differences in the wheat stripe rust among the different monitoring results, indicating poor stability and consistency. Overall, incorporating information on disease spatial-distribution differences in stripe-rust monitoring can improve the accuracy and stability of disease monitoring, and it can provide data and methodological support for regional stripe-rust detection and accurate preventions. [ABSTRACT FROM AUTHOR]

[Objectives] The urediniospores of wheat stripe rust pathogen is an important reason for the outbreak of wheat stripe rust. Aiming at the problems of low detection precision and low segmentation precision in the identification of microscopic image of urediniospores, an automatic detection method for urediniospores of wheat stripe rust pathogen based on spin Unet++ network was proposed. [Methods] According to the characteristics of dense and conglutination of the urediniospores in the microscopic image, a spinning connection structure was proposed by improving the feature extraction network of Unet ++, which optimized the segmentation precision of Unet ++ network and improved the segmentation rate of detection. According to the morphological characteristics of spores, a weighted mapping rectangle calculation formula was proposed to calculate its rectangular thermogram, so as to improve the precision of spore detection. [Results] The algorithm test results showed that the average precision of the improved algorithm reached 99.03%, and the segmentation rate was 86.45%, which met the requirements of accurate detection. Compared with the original CenterNet model, the overlap rate increased by 10.35 percentage points, the precision increased by 0.46 percentage points, and the memory usage reduced by 66.09%. [Conclusions] The model in this paper not only ensured a high precision of detection of urediniospores, but also ensured a high spore segmentation rate, which provided an effective method for the early warning of wheat stripe rust. [ABSTRACT FROM AUTHOR]

Wheat stripe rust is a fungal disease caused by Puccinia striiformis f. sp. Tritici (Pst). It significantly impacts wheat yields in Xinjiang, China. Breeding and promoting disease-resistant cultivars carrying disease-resistance genes remains the most cost-effective strategy with which to control the disease. In this study, 17 molecular markers were used to identify Yr5, Yr9, Yr10, Yr15, Yr17, Yr18, Yr26, Yr41, Yr44, and Yr50 in 82 wheat cultivars from Xinjiang. According to the differences in SNP loci, the KASP markers for Yr30, Yr52, Yr78, Yr80, and Yr81 were designed and detected in the same set of 82 wheat cultivars. The results showed that there was a diverse distribution of Yr genes across all wheat cultivars in Xinjiang, and the detection rates of Yr5, Yr15, Yr17, Yr26, Yr41, and Yr50 were the highest, ranging from 74.39% to 98.78%. In addition, Yr5 and Yr15 were prevalent in spring wheat cultivars, with detection rates of 100% and 97.56%, respectively. A substantial 85.37% of wheat cultivars carried at least six or more different combinations of Yr genes. The cultivar Xindong No.15 exhibited the remarkable presence of 11 targeted Yr genes. The pedigree analysis results showed that 33.33% of Xinjiang wheat cultivars shared similar parentage, potentially leading to a loss of resistance against Pst. The results clarified the Yr gene distribution of the Xinjiang wheat cultivars and screened out varieties with a high resistance against Pst. [ABSTRACT FROM AUTHOR]

Wheat stripe rust (WSR) is an airborne disease that causes severe damage to wheat. The rapid and early detection of WSR is essential for the prevention and control of this disease. The minimum detection limit (MDL) is one of the most important characteristics of quantitative methods that can be used to determine the scope and applicability of a measurement technique. Three wheat cultivars were inoculated with Puccinia striiformis f.sp. tritici (Pst), and a spectrometer was used to collect the canopy hyperspectral data, and the Pst content was obtained via a duplex real-time polymerase chain reaction (PCR) during the latent period, respectively. The disease index (DI) and molecular disease index (MDI) were calculated. The regression tree algorithm was used to determine the MDL of the Pst based on hyperspectral feature parameters. The logistic, IBK, and random committee algorithms were used to construct the classification model based on the MDL. The results showed that when the MDL was 0.7, IBK had the best recognition accuracy. The optimal model, which used the spectral feature R_2nd.dv ((the second derivative of the original hyperspectral value)) and the modeling ratio 2:1, had an accuracy of 91.67% on the testing set and 90.67% on the 10-fold cross-validation. Thus, during the latent period, the MDL of Pst was determined using hyperspectral technology as 0.7. [ABSTRACT FROM AUTHOR]

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Stripe rust is one of the most common diseases challenging the safe production of wheat. Rapid identification and analysis of urediospores, responsible for disease transmission, are the key to preventing and controlling stripe rust. The current spore detection is time-consuming and requires auxiliary equipment, but many optical detection methods and sensors with high efficiency, sensitivity, and detection ability have been developed and widely used. Thus, the investigation of optical parameters is the prerequisite for optical sensitive detection and is detailed in this study. Firstly, the microscopic images and surface elements of wheat stripe rust urediospores were obtained using a high-resolution electron microscope and an energy spectrum analyzer. The reflectivity and absorptivity spectra in the near-infrared band of active/inactivate urediospores were measured. Further, the optical parameters, such as reflection phase shift, complex refractive index, and dielectric constant, of the urediospores were analyzed based on electromagnetic theory and Kramers–Kronig relation. The results revealed that the urediospores have a strong tolerance to near-infrared light, and the real part of the complex refractive index n was between 1.0 and 1.4. These observations indicate that whether the spores are active or not has little effect on the real part and dielectric constant of the complex refractive index but has a significant impact on the imaginary part and extinction coefficient of the complex refractive index, and the corresponding relationship between the optical properties of urediospores and biological components was established. These findings were verified with HPLC-MS and Near-Infrared Spectroscopy Analysis Technology and lay a theoretical foundation for detecting urediospores of wheat stripe rust by using optical sensors. The study provides a reference for the analysis of optical characteristics of other biological samples. [ABSTRACT FROM AUTHOR]

Abstract Wheat rusts, including stripe, leaf, and stem rusts, are severe wheat diseases and cause huge yield loss in China annually. Benefiting from utilizing the genetic resistance wheat varieties, wheat stem rust has been effectively controlled since the 1970s; however, the wheat stripe and leaf rusts are still threating the wheat production in China due to lack of effective agricultural regulations. This review summarizes the research advances on wheat rust physiology, epidemiology, and fungicide resistance in China. In addition, the corresponding field management strategies for the integrated control of rust diseases are also discussed.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a devastating disease threatening global wheat production. Biocontrol by beneficial microorganisms is considered an alternative to synthetic fungicide applications. This study aimed to investigate the mechanisms involved in the biocontrol of wheat stripe rust by streptomycetes. A streptomycete strain XF, isolated from the rhizospheric soil of peony, was identified as Streptomyces tauricus based on morphological characteristics and phylogenetic analysis. We determined the inhibitory effect of XF on Pst and biocontrol effect on the disease using XF fermentation filtrate (FL) and actinomycete cell suspension (AC). Results revealed that FL inhibited urediniospore germination by up to 99% and rendered a lethality rate of 61.47% against urediniospores. Additionally, crude extract of ethyl acetate phase of FL caused cytoplasm releases from urediniospores and the deformation of germ tubes. Furthermore, histochemical analyses revealed that treatments of plants with AC and FL increased reactive oxygen species, inhibited haustorium formation, and reduced the biomass of Pst in leaves. Electron microscopy showed that XF mycelium was able to colonize the leaf surface. Moreover, gene expression assays revealed that AC and FL treatments induced the expression of a number of pathogenesis-related genes in wheat leaves. Besides, in the greenhouse experiments, the control effects of AC and FL reached 65.48% and 68.25%, respectively. In the field, application of XF fermentation broth significantly reduced the disease indices of stripe rust by 53.83%. These findings suggest that XF is a potential biocontrol agent for managing wheat stripe rust disease. [ABSTRACT FROM AUTHOR]

Wheat stripe rust caused by Puccinia striiformis f. sp. tritici is a destructive disease. Its pathogen frequently adapts to newly invaded regions and overcomes resistance in wheat cultivars. This disease is especially important in China due to its favorable conditions for the stripe rust epidemic and the recombination population structure of pathogens. Xinjiang is a vast epidemic region in China, but very limited research on this disease has been performed in this region. In this study, we identified 25 races from 129 isolates collected from winter wheat fields from five different regions (Nileke, Xinyuan, Gongliu, Huocheng, and Qapqal) of Yili, Xinjiang, using the Chinese set of 19 differential wheat lines. All isolates were virulent on the differentials Fulhad and Early Premium, but no isolates were virulent on Yr5. Among the 25 races, Suwon11-1 was the most prevalent, followed by CYR34. Both races were found in four out of the five locations. It is important to continue monitoring stripe rust and its pathogen races in this region, as it forms a pathway between China and Central Asia. Collaborative research is essential for controlling stripe rust in this region, other regions in China, and neighboring countries. [ABSTRACT FROM AUTHOR]

The wheat canopy reflectance spectrum is affected by many internal and external factors such as diseases and growth stage. Separating the effects of disease stress on the crop from the observed mixed signals is crucial for increasing the precision of remote sensing monitoring of wheat stripe rust. The canopy spectrum of winter wheat infected by stripe rust was processed with the difference-in-differences (DID) algorithm used in econometrics. The monitoring accuracies of wheat stripe rust before and after processing with the DID algorithm were compared in the presence of various external factors, disease severity, and several simulated satellite sensors. The correlation between the normalized difference vegetation index processed by the DID algorithm (NDVI-DID) and the disease severity level (SL) increased in comparison with the NDVI before processing. The increase in precision in the natural disease area in the field in the presence of large differences in growth stage, growth, planting, and management of the crop was greater than that in the controlled experiment. For low disease levels (SL 40%). According to the measured hyperspectral data, the spectral reflectance of three satellite sensor levels was simulated. The wide-band NDVI was calculated. Compared with the wide-band NDVI and vegetation indexes (VIS) before DID processing, there were increases in the correlation between SL and the various types of VIS-DID, as well as in the correlation between SL and NDVI-DID. It is feasible to apply the DID algorithm to multispectral satellite data and diverse types of VIS for monitoring wheat stripe rust. Our results improve the quantification of independent effects of stripe rust infection on canopy reflectance spectrum, increase the precision of remote sensing monitoring of wheat stripe rust, and provide a reference for remote sensing monitoring of other crop diseases.

Abstract Wheat is one of the most important cereal crops, and it is essential for worldwide food security. However, wheat production is threatened by various diseases, including wheat stripe rust caused by the fungus Puccinia striiformis f. sp. tritici (Pst). The development of plant resistance against disease is usually challenged by potential reduction in crop yield due to the enhancement of plant immunity. In a recent article, Wang et al. found that TaPsIPK1 is a susceptibility gene targeted by rust effectors. Editing of TaPsIPK1 increases resistance to stripe rust without any developmental effects or yield penalty, providing an exceptional resource for developing disease resistance in wheat.

Abstract Puccinia striiformis f. sp. tritici (Pst), the cause of wheat stripe rust, is a biotrophic and heteroecious rust fungus with five spore types. Cool and humid climatic conditions are conducive for the development of wheat stripe rust, and in turn, temperatures above 22 °C limit or even cease the disease. The survival of Pst during summer after wheat harvest is responsible for the infection on autumn-sown wheat to maintain disease cycle all the year round. Teliospores formed at late stage of wheat growth are essential for initiating sexual reproduction in spring. Although Chinese native grasses have been experimentally testified as susceptible hosts for Pst and teliospores produced on wheat have been shown to be potential inoculum sources causing infection on alternate hosts (mainly Berberis) in spring, the roles of grass hosts in harboring the survival of urediospores and teliospores of Pst and promoting the emergence of diverse races under field conditions in China have not been known. Herein, Gansu, as a hotspot and an important oversummering region for Pst, was exemplified to demonstrate these roles of grass hosts. As a result, 63 Pst isolates, derived from 2184 uredial samples collected from grass hosts during harvesting period (from mid-June to mid-to-late July) and seeding period (mid to late September) in 2012 and 2013, were identified as 52 diverse phenotypes (82.5%) on the Chinese differential hosts. Subsequently, after inoculation of barberry, 52 Pst isolates with high infection type, 20 known races and 32 new races, were recovered from 1,712 single aecium, which are derived from 35 telial samples of grass hosts. Our experiments showed that Pst urediospores can oversummer on grass species in Gansu or other oversummering regions with similar ecological and climatic conditions. Pst teliospores, which are produced on these grass hosts, are important inoculum source for barberry infection in spring. Therefore, treatment of grasses hosts should be taken into consideration in management of wheat stripe rust.

Introduction: The accurate severity assessment of wheat stripe rust is the basis for the pathogen-host interaction phenotyping, disease prediction, and disease control measure making. Methods: To realize the rapid and accurate severity assessment of the disease, the severity assessment methods of the disease were investigated based on machine learning in this study. Based on the actual percentages of the lesion areas in the areas of the corresponding whole single diseased wheat leaves of each severity class of the disease, obtained after the image segmentation operations on the acquired single diseased wheat leaf images and the pixel statistics operations on the segmented images by using image processing software, under two conditions of considering healthy single wheat leaves or not, the training and testing sets were constructed by using two modeling ratios of 4:1 and 3:2, respectively. Then, based on the training sets, two unsupervised learning methods including K-means clustering algorithm and spectral clustering and three supervised learning methods including support vector machine, random forest, and K-nearest neighbor were used to build severity assessment models of the disease, respectively. Results: Regardless of whether the healthy wheat leaves were considered or not, when the modeling ratios were 4:1 and 3:2, satisfactory assessment performances on the training and testing sets can be achieved by using the optimal models based on unsupervised learning and those based on supervised learning. In particular, the assessment performances obtained by using the optimal random forest models were the best, with the accuracies, precisions, recalls, and F1 scores for all the severity classes of the training and testing sets equal to 100.00% and the overall accuracies of the training and testing sets equal to 100.00%. Discussion: The simple, rapid, and easy-to-operate severity assessment methods based on machine learning were provided for wheat stripe rust in this study. This study provides a basis for the automatic severity assessment of wheat stripe rust based on image processing technology, and provides a reference for the severity assessments of other plant diseases. [ABSTRACT FROM AUTHOR]

Wheat rusts, including stripe, leaf, and stem rusts, are severe wheat diseases and cause huge yield loss in China annually. Benefiting from utilizing the genetic resistance wheat varieties, wheat stem rust has been effectively controlled since the 1970s; however, the wheat stripe and leaf rusts are still threating the wheat production in China due to lack of effective agricultural regulations. This review summarizes the research advances on wheat rust physiology, epidemiology, and fungicide resistance in China. In addition, the corresponding field management strategies for the integrated control of rust diseases are also discussed. [ABSTRACT FROM AUTHOR]

Sun-induced chlorophyll fluorescence (SIF) has shown potential in quantifying plant responses to environmental changes by which abiotic drivers are dominated. However, SIF is a mixed signal influenced by factors such as leaf physiology, canopy structure, and sun-sensor geometry. Whether the physiological information contained in SIF can better quantify crop disease stresses dominated by biological drivers, and clearly explain the physiological variability of stressed crops, has not yet been sufficiently explored. On this basis, we took winter wheat naturally infected with stripe rust as the research object and conducted a study on the responses of physiological signals and reflectivity spectrum signals to crop disease stress dominated by biological drivers, based on in situ canopy-scale and leaf-scale data. Physiological signals include SIF, SIFyield (normalized by absorbed photosynthetically active radiation), fluorescence yield (ΦF) retrieved by NIRvP (non-physiological components of canopy SIF) and relative fluorescence yield (ΦF-r) retrieved by near-infrared radiance of vegetation (NIRvR). Reflectance spectrum signals include normalized difference vegetation index (NDVI) and near-infrared reflectance of vegetation (NIRv). At the canopy scale, six signals reached extremely significant correlations (P < 0.001) with disease severity levels (SL) under comprehensive experimental conditions (SL without dividing the experimental samples) and light disease conditions (SL < 20%). The strongest correlation between NDVI and SL (R = 0.69) was observed under the comprehensive experimental conditions, followed by NIRv (R = 0.56), ΦF-r (R = 0.53) and SIF (R = 0.51), and the response of ΦF (R = 0.45) and SIFyield (R = 0.34) to SL was weak. Under lightly diseased conditions, ΦF-r (R = 0.62) showed the strongest response to disease, followed by SIFyield (R = 0.60), SIF (R = 0.56) and NIRv (R = 0.54). The weakest correlation was observed between ΦF and SL (R = 0.51), which also showed a result approximating NDVI (R = 0.52). In the case of a high level of crop disease severity, NDVI showed advantages in disease monitoring. In the early stage of crop diseases, which we pay more attention to, compared with SIF and reflectivity spectrum signals, ΦF-r estimated by the newly proposed 'NIRvR approach' (which uses SIF together with NIRvR (i.e., SIF/ NIRvR) as a substitute for ΦF) showed superior ability to monitor crop physiological stress, and was more sensitive to plant physiological variation. At the leaf scale, the response of SIF to SL was stronger than that of NDVI. These results validate the potential of ΦF-r estimated by the NIRvR approach to monitoring disease stress dominated by biological drivers, thus providing a new research avenue for quantifying crop responses to disease stress. [ABSTRACT FROM AUTHOR]

The timely and accurate identification of stripe rust and leaf rust is essential in effective disease control and the safe production of wheat worldwide. To investigate methods for identifying the two diseases on different wheat varieties based on image processing technology, single-leaf images of the diseases on different wheat varieties, acquired under field and laboratory environmental conditions, were processed. After image scaling, median filtering, morphological reconstruction, and lesion segmentation on the images, 140 color, texture, and shape features were extracted from the lesion images; then, feature selections were conducted using methods including ReliefF, 1R, correlation-based feature selection, and principal components analysis combined with support vector machine (SVM), back propagation neural network (BPNN), and random forest (RF), respectively. For the individual-variety disease identification SVM, BPNN, and RF models built with the optimal feature combinations, the identification accuracies of the training sets and the testing sets on the same individual varieties acquired under the same image acquisition conditions as the training sets used for modeling were 87.18–100.00%, but most of the identification accuracies of the testing sets for other individual varieties were low. For the multi-variety disease identification SVM, BPNN, and RF models built with the merged optimal feature combinations based on the multi-variety disease images acquired under field and laboratory environmental conditions, identification accuracies in the range of 82.05–100.00% were achieved on the training set, the corresponding multi-variety disease image testing set, and all the individual-variety disease image testing sets. The results indicated that the identification of images of stripe rust and leaf rust could be greatly affected by wheat varieties, but satisfactory identification performances could be achieved by building multi-variety disease identification models based on disease images from multiple varieties under different environments. This study provides an effective method for the accurate identification of stripe rust and leaf rust and could be a useful reference for the automatic identification of other plant diseases. [ABSTRACT FROM AUTHOR]

In order to explore the environmental and meteorological factors affecting the wheat stripe rust occurrence grade in Yuncheng city, in this study, the methods of mathematical statistics and multiple logistic regression analysis were used to analyze the data such as infection time and occurrence grade in Yuncheng from 1990 to 2020, and the meteorological factors which were significantly correlated to wheat stripe rust occurrence grade were selected to establish the grade forecasting model of wheat stripe rust. The results showed that the epidemic degree of wheat stripe rust in Yuncheng was mainly affected by the quantity of pathogen in spring, the infection time, and environmental and meteorological conditions; the stripe rust generally first appeared in the southwest of Yuncheng city in late April, such as Ruicheng and Yongji. When the meteorological conditions were not suitable, the activity of stripe rust pathogen was inhibited and died; When the meteorological conditions were suitable, the pathogen propagated rapidly. The epidemic stage was entered after May. The earlier the infection time was, and the higher the epidemic level in the later stage was, meanwhile, the later the infection was, the lower the epidemic level in the later stage was. The occurrence grade of stripe rust in Yuncheng city was significantly positively correlated with sunshine hours in early April, relative humidity from late March to early April, and precipitation in the middle of May. By analyzing the environmental and meteorological data and the grade of wheat stripe rust, the prediction model(y=-6.415+0.051x1+0.047x2+0.022x3+0.019x4) was established and verified by 30-year historical data, and the historical fitting accuracy was more than 80%. Prediction of the grade of wheat stripe rust by the model in Yuncheng in 2021 had high accuracy. [ABSTRACT FROM AUTHOR]

Glucose-6-phosphate dehydrogenase (G6PDH), the rate-limiting enzyme of the pentose phosphate pathway (PPP), plays a pivotal role in plant stress responses. However, the function and mechanism of G6PDHs in crop plants challenged by fungal pathogens remain poorly understood. In this study, a wheat G6DPH gene responding to infection by Puccinia striiformis f. sp. tritici (Pst), designated TaG6PDH2, was cloned and functionally identified. TaG6PDH2 expression was significantly upregulated in wheat leaves inoculated with Pst or treated with abiotic stress factors. Heterologous mutant complementation and enzymatic properties indicate that TaG6PDH2 encodes a G6PDH protein. The transient expression of TaG6PDH2 in Nicotiana benthamiana leaves and wheat protoplasts revealed that TaG6PDH2 is a chloroplast-targeting protein. Silencing TaG6PDH2 via the barley stripe mosaic virus (BSMV)-induced gene silencing (VIGS) system led to compromised wheat resistance to the Pst avirulent pathotype CYR23, which is implicated in weakened H2O2 accumulation and cell death. In addition, TaG6PDH2 was confirmed to interact with the wheat glutaredoxin TaGrxS4. These results demonstrate that TaG6PDH2 endows wheat with increased resistance to stripe rust by regulating reactive oxygen species (ROS) production. [ABSTRACT FROM AUTHOR]

Wheat stripe rust poses a serious threat to the quality and yield of wheat crops. Typically, the occurrence data of wheat stripe rust is characterized by small sample sizes, and the current research on severity identification lacks high-precision methods for small sample data. Additionally, the irregular edges of wheat stripe rust lesions make it challenging to draw samples. In this study, we propose a method for wheat stripe rust severity identification that combines SLIC superpixel segmentation and a random forest algorithm. This method first employs SLIC to segment subregions of wheat stripe rust, automatically constructs and augments a dataset of wheat stripe rust samples based on the segmented patches. Then, a random forest model is used to classify the segmented subregion images, achieving fine-grained extraction of wheat stripe rust lesions. By merging the extracted subregion images and using pixel statistics, the percentage of lesion area is calculated, ultimately enabling the identification of the severity of wheat stripe rust. The results show that our method outperforms unsupervised classification algorithms such as watershed segmentation and K-Means clustering in terms of lesion extraction when using the segmented subregion dataset of wheat stripe rust. Compared to the K-Means segmentation method, the mean squared error is reduced by 1.2815, and compared to the watershed segmentation method, it is reduced by 2.0421. When compared to human visual inspection as the ground truth, the perceptual loss for lesion area extraction is 0.064. This method provides a new approach for the intelligent extraction of wheat stripe rust lesion areas and fading green areas, offering important theoretical reference for the precise prevention and control of wheat stripe rust.

Wheat stripe rust-damaged leaves present challenges to automatic disease index calculation, including high similarity between spores and spots, and difficulty in distinguishing edge contours. In actual field applications, investigators rely on the naked eye to judge the disease extent, which is subjective, of low accuracy, and essentially qualitative. To address the above issues, this study undertook a task of semantic segmentation of wheat stripe rust damage images using deep learning. To address the problem of small available datasets, the first large-scale open dataset of wheat stripe rust images from Qinghai province was constructed through field and greenhouse image acquisition, screening, filtering, and manual annotation. There were 33,238 images in our dataset with a size of 512 × 512 pixels. A new segmentation paradigm was defined. Dividing indistinguishable spores and spots into different classes, the task of accurate segmentation of the background, leaf (containing spots), and spores was investigated. To assign different weights to high- and low-frequency features, we used the Octave-UNet model that replaces the original convolutional operation with the octave convolution in the U-Net model. The Octave-UNet model obtained the best benchmark results among four models (PSPNet, DeepLabv3, U-Net, Octave-UNet), the mean intersection over a union of the Octave-UNet model was 83.44%, the mean pixel accuracy was 94.58%, and the accuracy was 96.06%, respectively. The results showed that the state-of-art Octave-UNet model can better represent and discern the semantic information over a small region and improve the segmentation accuracy of spores, leaves, and backgrounds in our constructed dataset. [ABSTRACT FROM AUTHOR]

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However, at higher outbreak disease levels, >1% of the total possible infections at the outbreak onset, the power law dispersal kernel projections suggested that the outbreak and at-risk population susceptibility properties may exert a roughly equal contribution to the end epidemic severity and at higher outbreak disease levels the at-risk population was projected to have a greater influence than the outbreak (Figure 2A vs. Keywords: disease gradient; disease outbreak; Puccinia; wheat stripe rust; plant epidemic; dispersal ecology EN disease gradient disease outbreak Puccinia wheat stripe rust plant epidemic dispersal ecology 1727 11 11/17/22 20221101 NES 221101 1. Regardless, when applying a modified power law dispersal kernel, which empirical data strongly supports over an exponential dispersal kernel [[17], [32]], it is clear that the most biologically accurate of the two dispersal kernels yields unintuitive WSR disease projections even though there was no anisotropic disease dispersal (a well-known feature of wind dispersed pathogens [[49], [57]]), homogenous host distribution, and host plants with invariant physiological states (both of which are not biologically true). [Extracted from the article]

Copyright of Journal of Northwest A & F University - Natural Science Edition is the property of Editorial Department of Journal of Northwest A&F University (Natural Science Edition) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Accurate severity assessment of wheat stripe rust caused by Puccinia striiformis f. sp. tritici is of great significance for phenotypic determination, prediction, and control of the disease. To achieve accurate severity assessment of the disease based on the actual percentages of lesion areas in the areas of the corresponding whole diseased leaves, two new methods were proposed for severity assessment of the disease. In the Adobe Photoshop 2022 software, the acquired images of single diseased leaves of each severity class of the disease were manually segmented, and the numbers of the leaf region pixels and lesion pixels of each diseased leaf were obtained by pixel statistics. After calculation of the actual percentages of lesion areas in the areas of the corresponding whole diseased leaves based on the obtained pixel numbers, the training sets and testing sets were constructed for each severity class by using the system sampling method with two sampling ratios of 4:1 and 3:2. Then the mean and standard deviation of the actual percentages of lesion areas contained in each training set were calculated, respectively. For each sampling ratio, two methods, one based on the midpoint value of the means of the actual percentages of lesion areas corresponding to two adjacent severity classes and the other based on the distribution range of most of the actual percentages of lesion areas, were used to determine the midpoint-of-two-adjacentmeans-based actual percentage reference range and the 90%, 95%, and 99% reference ranges of the actual percentages of lesion areas for each severity class. According to the determined reference ranges, the severity of each diseased leaf in the training sets and testing sets was assessed. The results showed that high assessment accuracies (not lower than 85%) for the training sets and testing sets were achieved, demonstrating that the proposed methods could be used to conduct severity assessment of wheat stripe rust based on the actual percentages of lesion areas. This study provides a reference for accurate severity assessments of plant diseases. [ABSTRACT FROM AUTHOR]

Copyright of Mycosystema is the property of Mycosystema Editorial Board and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)