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Abstract The recently‐observed poleward shift in western North Pacific tropical cyclone (TC) genesis has increased the TC threat to East Asia. We find that the poleward shift of TC genesis since 1979 is linked to mega‐ENSO. A downscaling analysis of TC genesis latitude given the constraint of mega‐ENSO using 30 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) show a continued increasing poleward shift with additional warming. We use the dynamic genesis potential index as a TC proxy in future CMIP6 simulations. These simulations show enhanced TC formation in the subtropics and decreased TC formation in the tropics. Modeled TCs in CMIP6 high‐resolution models that well represent mega‐ENSO project future poleward shifts in TC genesis. Both observations and simulations show that extra‐tropical North Pacific sea surface temperature warming associated with mega‐ENSO are the primary driver of the TC genesis poleward shift. Our study provides new insights into climate change‐driven TC migration.

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.

Abstract Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a common airborne wheat disease. The frequent occurrence at a large scale in China has caused significant yield losses and poses a considerable threat to food security. To effectively manage and forecast the disease, a comprehensive understanding of the long-distance migration patterns of Pst is essential. Shaanxi province, situated in close proximity to the northwestern epidemic areas in China, plays a crucial role as a key overwintering region for Pst. However, it remains uncertain whether Pst, after winter reproduction in Shaanxi province, can extend its spread to the primary wheat regions in the North China Plain. In this study, during February and June 2022, a total of 302 Pst samples were collected from Shaanxi province and the North China Plain. Thirteen pairs of simple sequence repeat (SSR) markers were adopted to analyze the population genetic structure. It was observed that both genetic and genotypic diversities exhibited a discernible decline from the Shaanxi to the North China Plain. Moreover, Shaanxi displayed a close genetic relationship with Henan and Shandong, whereas Henan exhibited the most substantial population exchange with Shaanxi. Further analysis revealed that Shaanxi served as the primary inoculum of Pst in the investigated region, and the spread of Pst to Henan and Shandong originated from Shaanxi. As a result, the epidemics in Shandong further led to the prevailing of the disease in Hebei. Our study enhances the understanding of the epidemiological patterns of wheat stripe rust in the springtime prevalent regions of China, and it provides insights for future disease management.

Abstract Tropical cyclone (TC) rainfall is an important component of global precipitation. Based on a 22-year satellite-based record of observed precipitation from 1998 to 2019, we find that composite rainfall rates averaged within 500 km of TCs have increased. However, the background precipitation rate in the TC-affected regions has also increased. Therefore we find, by subtracting the background increase in precipitation rate from the TC precipitation composite, that TC precipitation rates--relative to the background--have decreased, and that TC-related extreme precipitation rates near the TC center have also decreased. Besides TC intensity and environmental sea surface temperature, the TC-induced sea surface cooling, typically formed like a cold wake feature, is indicated to be a potentially important contributor to TC rainfall changes. The cold wake diminishes the upward transport of surface enthalpy flux and leads to a decrease of moisture in the boundary layer, being unfavorable for the rainfall. The relative decreasing rainfall trend of TCs to the background is therefore attributed to the strengthening sea surface cooling induced by TCs. The TC-ocean interaction is suggested to be fully resolved in state-of-the-art global and regional climate models in order to improve TC rainfall simulations and projections.

Abstract Grapevine downy mildew (GDM) caused by Plasmopara viticola is a recurrent disease of wine grapes in the Ningxia Hui Autonomous Region (Ningxia) of northwestern China. However, the primary infectious pathogen in this region has not been thoroughly investigated. In this study, a multiplex real-time PCR assay was utilized to quantify P. viticola in soil, leaf residues, and asymptomatic leaf samples from ten commercial vineyards in two consecutive years to better understand the epidemiological significance of overwintering primary inoculum and its inoculum potential before the appearance of the first visual GDM symptoms. The DNA primers and multiplex real-time PCR assays that had been established exhibited specificity towards P. viticola within the test samples. The majority of the asymptomatic leaves (60%), leaf residues (80%), and soil samples (100%) tested positive for P. viticola. In addition, the amount of primary inoculum of P. viticola was found to be lower in soil than in leaf residues. The area under the disease progress curve in terms of the molecular disease index (AUDPC-MDI) was used to evaluate the overall latent P. viticola infection in asymptomatic leaves. Asymptomatic leaves were found to have different levels of P. viticola infection, and high AUDPC-MDIs correlated with a high AUDPC in terms of disease index (AUDPC-DI), with a significant correlation relationship between them (P

Abstract Although the cold wake of tropical cyclones (TCs) is recognized to be typically rightward biased relative to TC track (in the North Hemisphere), it remains unclear to what extent the rightward bias should be for specified TCs. Based on numerical simulations and observational statistics, this study shows that the distance of cold wake rightward bias is closely related to latitude, translation speed and radius of maximum wind (RMW) of a TC, but is independent from maximum surface wind and radial wind profile outside the RMW, namely TC intensity and size. A semi‐empirical equation is derived to represent the rightward bias extent of cold wakes. For TCs with slower translation speed, smaller RMW, or at higher latitudes, the cold wakes are less asymmetric relative to the TC track. Atmospheric simulations also show that the TC tends to be weaker and more asymmetric as the cold wake is closer to TC center.

Wheat lodging has a significant impact on yields and quality, necessitating the accurate acquisition of lodging information for effective disaster assessment and damage evaluation. This study presents a novel approach for wheat lodging detection in large and heterogeneous fields using UAV remote sensing images. A comprehensive dataset spanning an area of 2.3117 km2 was meticulously collected and labeled, constituting a valuable resource for this study. Through a comprehensive comparison of algorithmic models, remote sensing data types, and model frameworks, this study demonstrates that the Deeplabv3+ model outperforms various other models, including U-net, Bisenetv2, FastSCN, RTFormer, Bisenetv2, and HRNet, achieving a noteworthy F1 score of 90.22% for detecting wheat lodging. Intriguingly, by leveraging RGB image data alone, the current model achieves high-accuracy rates in wheat lodging detection compared to models trained with multispectral datasets at the same resolution. Moreover, we introduce an innovative multi-branch binary classification framework that surpasses the traditional single-branch multi-classification framework. The proposed framework yielded an outstanding F1 score of 90.30% for detecting wheat lodging and an accuracy of 86.94% for area extraction of wheat lodging, surpassing the single-branch multi-classification framework by an improvement of 7.22%. Significantly, the present comprehensive experimental results showcase the capacity of UAVs and deep learning to detect wheat lodging in expansive areas, demonstrating high efficiency and cost-effectiveness under heterogeneous field conditions. This study offers valuable insights for leveraging UAV remote sensing technology to identify post-disaster damage areas and assess the extent of the damage.

Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most destructive wheat diseases worldwide. Understanding the effects of cultivar mixture on disease control and population genetic structure of Pst could promote the effective application of cultivar mixtures. This study designed nine cultivar mixtures composed of different component cultivars with field experiments in Zitong County, Sichuan Province in two seasons from 2016 to 2018. Diseased leaves were collected in each plot of treatment in the spring and, in total, 835 and 568 isolates were obtained and genotyped with 10 pairs of simple sequence repeat markers in the 2 years, respectively. The observed and theoretical values of the area under the disease progress curve (AUDPC), wheat yield, Pst genotypic diversity, and the frequency of Pst dominant genetic groups were compared among cultivar mixtures. With the 2 years’ experiments, there were three and two cultivar mixtures, respectively, showing significant effect on the relative reduction of AUDPC; there were no significant effects on the relative yield increase in all mixtures in the 2 years. There were four and five cultivar mixtures showing positive effects on the relative increase of Pst genotypic diversity in the 2 years, respectively. Moreover, all cultivar mixtures could decrease the frequencies of Pst dominant genetic groups on highly susceptible component cultivar compared with their theoretical frequencies.[Graphic: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Abstract Southern corn rust (SCR) caused by Puccinia polysor a Underw is one of the most devastating maize diseases, resulting in substantial yield losses worldwide. The pathogen is an obligate biotrophic parasite that is difficult to culture on artificial media. In recent years, the disease has become prevalent—both globally and in China—and increasing difficult to control because of its wide distribution, long-distance migration, multiple physiological races and fast evolution, all of which have contributed to a considerable increase in the risks of associated epidemics. In this review, we summarize the current knowledge of P. polysora, with emphasis on its global distribution (particularly in China), life and disease cycle, population genetics, migration, physiological races, resistance genes in maize and management. Understanding the underlying factors and processes in SCR epidemics should facilitate management of the disease and breeding for resistant maize varieties.

With the increase of temperature in the winter wheat-growing regions in China, the high-temperature-resistant Blumeria graminis f. sp. tritici (Bgt) isolates developed in the fields. To clarify the key infection stages and the roles of heat shock protein (HSP) genes of high-temperature-resistant Bgt isolates defending high temperature, 3 high-temperature-resistant and 3 sensitive Bgt isolates were selected from 55 isolates after determination of temperature sensitivity. And then they were used to investigate the infection stages and the expression levels of HSP genes, including Bgthsp60, Bgthsp70, Bgthsp90, and Bgthsp104, at 18°C and 25°C. The formation frequency of abnormal appressoria and inhibition rate of haustoria formation of high-temperature-resistant isolates at 25°C were lower than those of high-temperature-sensitive isolates, while major axis of microcolonies of high-temperature-resistant isolates was higher than those of high-temperature-sensitive isolates at 25°C. The results indicated that haustoria formation and hyphal expansion were the key infection stages of defense against heat stress in high-temperature-resistant isolates. Further analyses of HSP genes found the expression levels of Bgthsp60 and Bgthsp70c were upregulated at 24 and 72 h post-inoculation in high-temperature-resistant isolates, while no significant difference was observed for Bgthsp90 and Bgthsp104 genes. Taken together, the basis of high-temperature-resistant Bgt isolates is associated with induced expression of Bgthsp60 and Bgthsp70c response to heat stress in haustoria formation and hyphal expansion stages.

Abstract Southern corn rust is a destructive maize disease caused by Puccinia polysora Underw that can lead to severe yield losses. However, genomic information and microsatellite markers are currently unavailable for this disease. In this study, we generated a total of 27,295,216 high-quality cDNA sequence reads using Illumina sequencing technology. These reads were assembled into 17,496 unigenes with an average length of 1015 bp. The functional annotation indicated that 8113 (46.37%), 1933 (11.04%) and 5516 (31.52%) unigenes showed significant similarity to known proteins in the NCBI Nr, Nt and Swiss-Prot databases, respectively. In addition, 2921 (16.70%) unigenes were assigned to KEGG database categories; 4218 (24.11%), to KOG database categories; and 6,603 (37.74%), to GO database categories. Furthermore, we identified 8,798 potential SSRs among 6653 unigenes. A total of 9 polymorphic SSR markers were developed to evaluate the genetic diversity and population structure of 96 isolates collected from Guangdong Province in China. Clonal reproduction of P. polysora in Guangdong was dominant. The YJ (Yangjiang) population had the highest genotypic diversity and the greatest number of the multilocus genotypes, followed by the HY (Heyuan), HZ (Huizhou) and XY (Xinyi) populations. These results provide valuable information for the molecular genetic analysis of P. polysora and related species.

The impact of tropical cyclones on the characteristics of barrier layer (BL) thickness is investigated in this study in combination with observations and numerical simulations. Statistical results based on Simple Ocean Data Assimilation reanalysis (SODA) data and Argo float measurements reveal a significant effect of TCs on the BL characteristics. The BL thickness increases remarkably with the TC approaching, reaches its maximum 1–5 days after the TC passage, and recovers to its pre-storm state within 30 days on average. The peak increase in BL thickness is slightly rightward biased relative to the TC track, reaching ∼1.31 m (26.93%) relative to the pre-storm state as revealed by SODA data. The changes in BL thickness show a strong correlation with TC characteristics in terms of intensity and translation speed, tending to be more significant with more intense or slower-moving TCs. The thickening of the isothermal layer on the right of TC tracks and shallowing of the mixed layer act together to determine the BL development. The three-dimensional Price–Weller–Pinkel (3DPWP) ocean model is applied to explore the influence of precipitation on BL thickness. The results show that the precipitation tends to decrease the mixed layer depth while the vertical mixing of TCs deepens the isothermal layer depth, both of which act together to increase the BL thickness.

ABSTRACT Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is a crucial disease for wheat worldwide and constantly threatens wheat production in southwestern and northwestern China, where the environment is a good fit for Pst oversummering and overwintering. However, the underlying genetic dynamics of spring epidemic Pst populations across large areas of continuous planting in the southwestern and northwestern regions are poorly understood. A total of 2,103 Pst isolates were sampled in the spring of 2019 from the two agroecosystems and grouped into three horizontal spatial scales (countywide, provincial, and regional subpopulations) and two vertical spatial scales that consisted of elevational and geomorphic subpopulations. A total of 776 multilocus genotypes were identified, with the highest genetic diversity found in the northern and Sichuan populations, particularly in the Ningxia and Sichuan Basins, while the lowest genetic diversity was found in the Yunnan and Guizhou populations. Multivariate discriminant analysis of principal components (DAPC) and STRUCTURE (STRUCTURE 2.3.4) analyses revealed variation in the genotypic compositions of the molecular groups on horizontal and vertical dimensions from north to south or vice versa and from low to high or vice versa, respectively. The regional neighbor-joining tree revealed three large spatial structures consisting of the southwestern, the northwestern, and the Xinjiang regions, while the Tibetan population connected the southwestern and northwestern regions. The isolates of the Sichuan Basin were scattered over the four quartiles by principal coordinate analysis, which indicated frequent genotype interchange with others. Greater genetic differentiation was observed between the southwestern and northwestern regions. Linkage equilibrium (P ≥ 0.05) was detected on different spatial scales, suggesting that Pst populations are using sexual reproduction or mixed reproduction (sexual and clonal reproduction) in southwestern and northwestern China. IMPORTANCE Understanding the epidemiology and population genetics of plant pathogens is crucial to formulate efficient predictions of disease outbreaks and achieve sustainable integrated disease management, especially for pathogens with migratory capability. Here, this study covers the genetic homogeneity and heterogeneity of different geographical Pst populations on broad to fine spatial scales from the key epidemic regions of the two agroecosystems in China, where wheat stripe rust occurs annually. We provide knowledge of the population genetics of Pst and reveal that, for instance, there is greater genetic diversity in northwestern China, there are close genetic relationships between Yunnan and Guizhou and between Gansu-Ningxia and Qinghai, and there are effects of altitude on genetic compositions, etc. All of these findings clarify the genetic relationships and expand the insights into the population dynamics and evolutionary mechanisms of Pst in southwestern and northwestern China, providing a theoretical basis for achieving sustainable control of wheat stripe rust in key epidemic regions.

Climate change affects crops development, pathogens survival rates and pathogenicity, leading to more severe disease epidemics. There are few reports on early, simple, large-scale quantitative detection technology for wheat diseases against climate change. A new technique for detecting wheat stripe rust (WSR) during the latent period based on hyperspectral technology is proposed. Canopy hyperspectral data of WSR was obtained; meanwhile, duplex PCR was used to measure the content of Puccinia striiformis f.sp. tritici (Pst) in the same canopy section. The content of Pst corresponded to its spectrum as the classification label of the model, which is established by discriminant partial least squares (DPLS) and support vector machine (SVM) algorithm. In the spectral region of 325–1075 nm, the model’s average recognition accuracy was between 75% and 80%. In the sub-band of 325–1075 nm, the average recognition accuracy of the DPLS was 80% within the 325–474 nm. The average recognition accuracy of the SVM was 83% within the 475–624 nm. Correlation analysis showed that the disease index of WSR was positively correlated with soil nitrogen nutrition, indicating that the soil nitrogen nutrition would affect the severity of WSR during the latent period.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most important fungal diseases affecting wheat (Triticum aestivum L.) worldwide. In this study, the genetic diversity and population structure of Pst isolates were analyzed using 15 microsatellite markers. Isolates were collected from five wheat cultivars with different levels of resistance from Yanting county and Fucheng district, Mianyang city, Sichuan province, China. The aim of this study was to investigate whether Pst populations are differentiated by wheat genotype or geographic origin. Seventy-six multilocus genotypes (MLGs) were identified from all 289 single uredinial isolates. In general, the genotypic diversity of Pst populations from five wheat cultivars in Fucheng was higher than that in Yanting. In addition, the genetic diversity was highest in the Pst populations from Mianmai 367, a cultivar considered to be highly resistant. The unweighted pair group method with arithmetic mean (UPGMA) phylogenetic tree, Bayesian clustering analysis, and minimum spanning network for the MLGs revealed two major genetic clusters based on geographical location. Greater differentiation was observed between the populations from the two sampling locations than between the populations from different hosts in the same location. The results suggest that geographic and environmental differences could partially explain the genetic differentiation of Pst more than wheat genotype. This study provides novel insight into the interactions between Pst populations and their hosts. The results could be helpful in designing more effective management strategies for stripe rust in wheat production.

Wheat stripe rust (WSR) is a foliar disease that causes destructive damage in the wheat production context. Accurately estimating the severity of WSR in the autumn growing stage can help to objectively monitor the disease incidence level of WSR and predict the nationwide disease incidence in the following year, which have great significance for controlling its nationwide spread and ensuring the safety of grain production. In this study, to address the low accuracy and the efficiency of disease index estimation by traditional methods, WSR-diseased areas are segmented based on Segformer, and the macro disease index (MDI) is automatically calculated for the measurement of canopy-scale disease incidence. The results obtained with different semantic segmentation algorithms, loss functions, and data sets are compared for the segmentation effect, in order to address the severe class imbalance in disease region segmentation. We find that: (1) The results of the various models differed significantly, with Segformer being the best algorithm for WSR segmentation (rust class F1 score = 72.60%), based on the original data set; (2) the imbalanced nature of the data has a significant impact on the identification of the minority class (i.e., the rust class), for which solutions based on loss functions and re-weighting of the minority class are ineffective; (3) data augmentation of the minority class or under-sampling of the original data set to increase the proportion of the rust class greatly improved the F1-score of the model (rust class F1 score = 86.6%), revealing that re-sampling is a simple and effective approach to alleviating the class imbalance problem. Finally, the MDI was used to evaluate the models based on the different data sets, where the model based on the augmented data set presented the best performance (R2 = 0.992, RMSE = 0.008). In conclusion, the deep-learning-based semantic segmentation method, and the corresponding optimization measures, applied in this study allow us to achieve pixel-level accurate segmentation of WSR regions on wheat leaves, thus enabling accurate assessment of the degree of WSR disease under complex backgrounds in the field, consequently providing technical support for field surveys and calculation of the disease level.

Abstract Wheat stripe rust, caused by Puccinia striiformis f. sp. tritici , is one of the most destructive diseases of wheat in China. Conjunction area of Gansu, Sichuan and Shaanxi, acting as over-summering and over-wintering regions for the pathogen, plays a unique and critical role in epidemics of this disease in China. Because of the complexity in terrains and environmental conditions within this conjunction area, studies on the population structure, gene flow between local subpopulations and maintenance of genetic structure over time within this area are important to understand the epidemiology of this disease in China, and have practical significance in management of this disease at national scale. In this study, 461 isolates of Pst were collected from the junction area of Gansu, Sichuan and Shaanxi from 2013 spring to 2014 spring, and genotyped with amplified fragment length polymorphism markers. Results revealed that genotypic and genetic diversity were consistently high in Gansu and Shaanxi, but low in Sichuan, and a closer genetic relationship was found between Gansu and Sichuan than between them and Shaanxi illustrated by φpt, shared genotypes, Bayesian and nonparametric clustering methods. Genetic differentiation existed among autumn subpopulations, and genetic barriers were detected, although spring subpopulations were less differentiated. Subpopulations in Gangu of Gansu and Longxian of Shaanxi remained stable over the seasons studied. Potential migration events occurred at the junction area between successive seasons. The estimated frequency of sexual reproduction was 0.970 (s) (i.e. 97% of individuals being sexually derived during the yearly sexual cycle), suggesting the existence of sexual reproduction in this region. The main conclusions of this study are that genetic barriers exist at the junction area, and subpopulations in Gansu and Shaanxi are stable, and population exchange occurs mainly between Gansu and Sichuan.

Maize is one of the most important crops in China, and it is under a serious, ever-increasing threat from southern corn rust (SCR). The identification of wheat rust based on hyperspectral data has been proved effective, but little research on detecting maize rust has been reported. In this study, full-range hyperspectral data (350~2500 nm) were collected under solar illumination, and spectra collected under solar illumination (SCUSI) were separated into several groups according to the disease severity, measuring height and leaf curvature (the smoothness of the leaf surface). Ten indices were selected as candidate indicators for SCR classification, and their sensitivities to the disease severity, measuring height and leaf curvature, were subjected to analysis of variance (ANOVA). The better-performing indices according to the ANOVA test were applied to a random forest classifier, and the classification results were evaluated by using a confusion matrix. The results indicate that the PRI was the optimal index for SCR classification based on the SCUSI, with an overall accuracy of 81.30% for mixed samples. The results lay the foundation for SCR detection in the incubation period and reveal potential for SCR detection based on UAV and satellite imageries, which may provide a rapid, timely and cost-effective detection method for SCR monitoring.

The reduction in biodiversity owing to agricultural intensification has brought negative effects on sustainable crop production in the agro-environment. Plant diversity can help regulate insect pests; however, research that demonstrates that genotypic diversity of multiple varieties with prominent agroecological practices to suppress insect pests and benefit crop yield at agricultural landscapes is limited. To examine the effects of diversity of wheat varieties on aphids and crop productivity, we performed field experiments by designing plots of single (resistant/susceptible) and cultivar mixtures in 2018–2020. The effects of population abundance of cereal aphids (Sitobion miscanthi and Rhopalosiphum padi) and crop yield on resistant and susceptible wheat varieties and mixture of wheat cultivars were determined to reveal the most dominant mixed broadcasting mode in Kaifeng of China. The results showed that cultivar mixture significantly decreased aphid abundance compared with the single varieties (resistant/susceptible). Cultivar mixture had a higher yield compared with the single varieties (resistant/susceptible). Additionally, cultivar mixture had significantly higher thousand-grain weights than susceptible and resistant wheat varieties in 2019 and 2020. Cultivar mixture enhanced wheat yield by decreasing cereal aphids, indicating a bottom-up or top-down effect from genetic diversity to pest abundance. Our results indicated that appropriate cultivar mixtures could manage insect pests to some extent and stabilize crop yield.

The most common cause of pulmonary artery filling defects on computed tomography pulmonary angiography or magnetic resonance imaging is pulmonary thromboembolism, but not infrequently, the presentation of this finding lacks specificity. Given that the morbidity and mortality associated with pulmonary thromboembolism is high, proper diagnosis of the condition is essential. Unusual or more rarely encountered etiologies must be considered when clinical manifestations and imaging findings are inconsistent. With this review, our purpose is to describe possible causes of pulmonary arterial filling defects. We aim to provide clinicians with a comprehensive list of differential diagnoses to facilitate a measured approach to the assessment of pulmonary arterial filling defects on computed tomography pulmonary angiography or magnetic resonance imaging.

Wheat stripe rust, caused by the fungal pathogen Puccinia striiformis f. sp. tritici (Pst), is a destructive wheat disease in China. The Gansu–Ningxia region (GN) is a key area for pathogen over-summering in China, and northwestern Hubei (HB) is an important region for pathogen over-wintering, serving as a source of inoculum in spring epidemic regions. The spatiotemporal population genetic structure of Pst in HB and the pathogen population exchanges between GN and HB are important for estimating the risk of interregional epidemics. Here, 567 isolates from GN and HB were sampled from fall 2016 to spring 2018 and were genotyped using simple sequence repeat markers. The genotypic and genetic diversity of Pst subpopulations in HB varied among seasons and locations. Greater genetic diversification levels were found in the spring compared with fall populations using principal coordinate analysis and Bayesian assignments. In total, there were 17 common genotypes among the 208 determined, as shown by a small overlap of genotypes in the principal coordinate analysis and dissimilar Bayesian assignments in both regions, which revealed the limited genotype exchange between the populations of GN and HB.

PURPOSE:We aimed to define multiparametric magnetic resonance imaging (MRI) findings to differentiate between pulmonary artery sarcoma (PAS) and pulmonary thromboembolism (PTE).METHODS:Eleven patients with suspected PTE were prospectively included to undergo pulmonary MRI before surgery or biopsy. MRI protocol included an unenhanced sequence, diffusion-weighted imaging (DWI, b=800 s/mm2) and a dynamic contrast-enhanced sequence. Morphologic characteristics including distribution, filling defect, and intensity were observed on T1-, T2-, and fat-suppressed T2-weighted imaging, DWI, and contrast-enhanced MRI. Apparent diffusion coefficient (ADC) values were calculated.RESULTS:Six patients were pathologically diagnosed as PAS and the other five as chronic PTE. There were no significant differences in age, gender, presenting symptoms, D-dimer, and N-terminal pro-brain natriuretic peptide between the two groups (P > 0.05). Among MRI findings that were tested for their ability to diagnose PAS, area under the curve (AUC) was significantly higher than 0.5 for main pulmonary artery involvement (AUC, 0.83±0.13; P = 0.011), hyperintensity on fat-suppressed T2-weighted imaging (AUC, 0.82±0.14; P = 0.025), hyperintensity on DWI (AUC, 0.88±0.12; P = 0.002), contrast enhancement (AUC, 0.92±0.10; P < 0.001) and pleural effusion (AUC, 0.82±0.14; P = 0.025). Moreover, grape-like appearance in distal pulmonary artery and cardiac invasion had 100% specificity for diagnosis of PAS. However, ADC value of PAS was not significantly different than that of chronic PTE (U, 12.00; P = 0.584).CONCLUSION:Hyperintense filling defect in main pulmonary artery on fat-suppressed T2-weighted imaging and DWI and contrast enhancement may help to discriminate PAS from PTE.

The authors wish to make the following corrections to this paper: [...]

The degradation mechanism of mid-power GaN-based white LEDs were investigated by using the in-situ multi-functional accelerated aging tests. The changes of the luminous flux and the chromaticity shift during the stress time show some correlations. To quantitatively analyze the degradation behavior, a composite model considering the luminous flux increasing and decreasing mechanisms was proposed and the results agree well with the experiments in the entire aging time. Furthermore, different analytical technologies have been used to understand the cause of luminous flux degradation and chromaticity shift. The results show that the chromaticity shift was mainly due to the phosphors deterioration, while the serious degradation of luminous flux was the overall effects from the package, including the phosphors deterioration and oxidation of silicone encapsulant.

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is a devastating wheat disease worldwide. Potential application of near-infrared spectroscopy (NIRS) in detection of pathogen amounts in latently Pst-infected wheat leaves was investigated for disease prediction and control. A total of 300 near-infrared spectra were acquired from the Pst-infected leaf samples in an incubation period, and relative contents of Pst DNA in the samples were obtained using duplex TaqMan real-time PCR arrays. Determination models of the relative contents of Pst DNA in the samples were built using quantitative partial least squares (QPLS), support vector regression (SVR), and a method integrated with QPLS and SVR. The results showed that the kQPLS-SVR model built with a ratio of training set to testing set equal to 3 : 1 based on the original spectra, when the number of the randomly selected wavelength points was 700, the number of principal components was 8, and the number of the built QPLS models was 5, was the best. The results indicated that quantitative detection of Pst DNA in leaves in the incubation period could be implemented using NIRS. A novel method for determination of latent infection levels of Pst and early detection of stripe rust was provided.

PURPOSEWe aimed to determine the correlation between flow characteristics of the proximal pulmonary arteries and vena cava obtained by 3.0 T phase-contrast magnetic resonance imaging (MRI) and hemodynamic characteristics by right heart catheterization in patients with chronic thromboembolic pulmonary hypertension.MATERIALS AND METHODSTwenty consecutive patients with chronic thromboembolic pulmonary hypertension and 20 sex- and age-matched healthy volunteers were included prospectively. All patients and controls underwent phase-contrast MRI to determine the flow characteristics including peak velocity, mean velocity, and mean blood flow of the proximal pulmonary artery and vena cava. All patients underwent right heart catheterization to determine the hemodynamics.RESULTSPeak velocity and mean velocity of the proximal pulmonary artery were significantly lower in the patient group. In patients, both peak velocity and mean blood flow were sequentially decreased in the main pulmonary artery, left and right pulmonary arteries, and left and right interlobar pulmonary arteries. Inferior vena cava had higher peak velocity, mean velocity, and mean blood flow than superior vena cava. Peak velocity of the main pulmonary artery correlated with mean and diastolic pulmonary artery pressure. Peak velocity of both inferior and superior vena cava strongly correlated with the pulmonary vascular resistance index (PVRI) (r=-0.68, P < 0.001 and r=-0.74, P < 0.001, respectively). Mean velocity of the main pulmonary artery and right pulmonary artery strongly correlated with PVRI and mean pulmonary artery pressure. Mean velocity of the superior vena cava and mean blood flow of the main pulmonary artery strongly correlated with PVRI and right cardiac work index.CONCLUSIONBlood flow in the proximal pulmonary artery and vena cava evaluated by phase-contrast MRI correlate with hemodynamic parameters of right heart catheterization and can be used to noninvasively evaluate the severity of chronic thromboembolic pulmonary hypertension and, potentially, to follow up the treatment response.

The characteristics of moist static energy (MSE) and its budget in a simulated tropical cyclone (TC) are examined in this study. Results demonstrate that MSE in a TC system is enhanced as the storm strengthens, primarily because of two mechanisms: upward transfer of surface heat fluxes and subsequent warming of the upper troposphere. An inspection of the interchangeable approximation between MSE and equivalent potential temperature (θe) suggests that although MSE is capable of capturing overall structures of θe, some important features will still be distorted, specifically the low-MSE pool outside the eyewall. In this low-MSE region, from the budget analysis, the discharge of MSE in the boundary layer may even surpass the recharge of MSE from the ocean. Unlike the volume-averaged MSE, the mass-weighted MSE in a fixed volume following the TC shows no apparent increase as the TC intensifies, because the atmosphere becomes continually thinner accompanying the warming of the storm. By calculating a mass-weighted volume MSE budget, the TC system is found to export MSE throughout its lifetime, since the radial outflow overwhelms the radial inflow. Moreover, the more intensified the TC is, the more export of MSE there tends to be. The input of MSE by surface heat fluxes is roughly balanced by the combined effects of radiation and lateral export, wherein a great majority of the imported MSE is reduced by radiation, while the export of MSE from the TC system to the environment accounts for only a small portion.

The band structure, the density of states and optical absorption properties of Cu-doped ZnO were studied by the first-principles generalized gradient approximation plane-wave pseudopotential method based on density functional theory. For the Zn1-xCuxO (x = 0, x = 0.0278, x = 0.0417) original structure, geometric optimization and energy calculations were performed and compared with experimental results. With increasing Cu concentration, the band gap of the Zn1-xCuxO decreased due to the shift of the conduction band. Since the impurity level was introduced after Cu doping, the conduction band was moved downwards. Additionally, it was shown that the insertion of a Cu atom leads to a red shift of the optical absorption edge, which was consistent with the experimental results.

This study explored the influence of choosing a nonhydrostatic dynamical core or a hydrostatic dynamical core in the weather research and forecasting (WRF) model on the intensity and structure of simulated tropical cyclones (TCs). A comparison of cloud-resolving simulations using each core revealed significant differences in the TC simulations. In comparison with the nonhydrostatic simulation, the hydrostatic simulation produced a stronger and larger TC, associated with stronger convective activity. A budget analysis of the vertical momentum equation was conducted to investigate the underlying mechanisms. Although the hydrostatic dynamical core was used, the vertical motion was not in strict hydrostatic balance because of the existence of the vertical perturbation pressure gradient force, local buoyancy force, water loading, and sum of the Coriolis and diffusion effects. The contribution of the enhanced vertical perturbation pressure gradient force was found to be more important for stronger upward acceleration in the eyewall in the hydrostatic simulation than in the nonhydrostatic simulation. This is because it leads to intensified convection in the eyewall that releases more latent heat, which induces a larger low-level radial pressure gradient and inflow motion, and eventually leads to a stronger storm.

It is important to implement detection and assessment of plant diseases based on remotely sensed data for disease monitoring and control. Hyperspectral data of healthy leaves, leaves in incubation period and leaves in diseased period of wheat stripe rust and wheat leaf rust were collected under in-field conditions using a black-paper-based measuring method developed in this study. After data preprocessing, the models to identify the diseases were built using distinguished partial least squares (DPLS) and support vector machine (SVM), and the disease severity inversion models of stripe rust and the disease severity inversion models of leaf rust were built using quantitative partial least squares (QPLS) and support vector regression (SVR). All the models were validated by using leave-one-out cross validation and external validation. The diseases could be discriminated using both distinguished partial least squares and support vector machine with the accuracies of more than 99%. For each wheat rust, disease severity levels were accurately retrieved using both the optimal QPLS models and the optimal SVR models with the coefficients of determination (R2) of more than 0.90 and the root mean square errors (RMSE) of less than 0.15. The results demonstrated that identification and severity evaluation of stripe rust and leaf rust at the leaf level could be implemented based on the hyperspectral data acquired using the developed method. A scientific basis was provided for implementing disease monitoring by using aerial and space remote sensing technologies.

Common leaf spot (caused by Pseudopeziza medicaginis), rust (caused by Uromyces striatus), Leptosphaerulina leaf spot (caused by Leptosphaerulina briosiana) and Cercospora leaf spot (caused by Cercospora medicaginis) are the four common types of alfalfa leaf diseases. Timely and accurate diagnoses of these diseases are critical for disease management, alfalfa quality control and the healthy development of the alfalfa industry. In this study, the identification and diagnosis of the four types of alfalfa leaf diseases were investigated using pattern recognition algorithms based on image-processing technology. A sub-image with one or multiple typical lesions was obtained by artificial cutting from each acquired digital disease image. Then the sub-images were segmented using twelve lesion segmentation methods integrated with clustering algorithms (including K_means clustering, fuzzy C-means clustering and K_median clustering) and supervised classification algorithms (including logistic regression analysis, Naive Bayes algorithm, classification and regression tree, and linear discriminant analysis). After a comprehensive comparison, the segmentation method integrating the K_median clustering algorithm and linear discriminant analysis was chosen to obtain lesion images. After the lesion segmentation using this method, a total of 129 texture, color and shape features were extracted from the lesion images. Based on the features selected using three methods (ReliefF, 1R and correlation-based feature selection), disease recognition models were built using three supervised learning methods, including the random forest, support vector machine (SVM) and K-nearest neighbor methods. A comparison of the recognition results of the models was conducted. The results showed that when the ReliefF method was used for feature selection, the SVM model built with the most important 45 features (selected from a total of 129 features) was the optimal model. For this SVM model, the recognition accuracies of the training set and the testing set were 97.64% and 94.74%, respectively. Semi-supervised models for disease recognition were built based on the 45 effective features that were used for building the optimal SVM model. For the optimal semi-supervised models built with three ratios of labeled to unlabeled samples in the training set, the recognition accuracies of the training set and the testing set were both approximately 80%. The results indicated that image recognition of the four alfalfa leaf diseases can be implemented with high accuracy. This study provides a feasible solution for lesion image segmentation and image recognition of alfalfa leaf disease.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the important wheat diseases worldwide. In this study, the spectral data were collected from wheat canopy during the latent period inoculated with three different concentrations of urediniospores and classification models based on discriminant partial least squares (DPLS) were built to differentiate leaves with and without infection of the stripe rust pathogen. The effects of different spectra features, wavebands, and the number of the samples used in modeling on the performances of the models were assessed. The results showed that, in the spectral region of 325–1075 nm, the model with the spectral feature of 2nd derivative of Pseudoabsorption index had better accuracy than others. The average accuracy rate was 97.28% for the training set and 92.98% for the testing set. In the waveband of 925–1075 nm, the model with the spectral feature of 1st derivative Pseudoabsorption index had better accuracy than other models, and the average accuracy rates were 98.27% and 94.33% for the training and testing sets, respectively. The results demonstrated that wheat stripe rust in latent period can be qualitatively identified based on the canopy spectral detection. Thus, the method can be used for early monitoring of infections of wheat stripe rust.

Stripe rust caused by Puccinia striiformis f. sp. tritici (Pst) is an important disease on wheat. In this study, quantitative determination of germinability of Pst urediospores was investigated by using near infrared reflectance spectroscopy (NIRS) combined with quantitative partial least squares (QPLS) and support vector regression (SVR). The near infrared spectra of the urediospore samples were acquired using FT-NIR MPA spectrometer and the germination rate of each sample was measured using traditional spore germination method. The best QPLS model was obtained with vector correction as the preprocessing method of the original spectra and 4000–12000 cm−1 as the modeling spectral region while the modeling ratio of the training set to the testing set was 4 : 1. The best SVR model was built when vector normalization was used as the preprocessing method, the modeling ratio was 5 : 1 and the modeling spectral region was 8000–11000 cm−1. The results showed that the effect of the best model built using QPLS or SVR was satisfactory. This indicated that quantitative determination of germinability of Pst urediospores using near infrared spectroscopy technology is feasible. A new method based on NIRS was provided for rapid, automatic, and nondestructive determination of germinability of Pst urediospores.

Stripe rust and leaf rust with similar symptoms are two important wheat diseases. In this study, to investigate a method to identify and assess the two diseases, the canopy hyperspectral data of healthy wheat, wheat in incubation period, and wheat in diseased period of the diseases were collected, respectively. After data preprocessing, three support vector machine (SVM) models for disease identification and six support vector regression (SVR) models for disease index (DI) inversion were built. The results showed that the SVM model based on wavelet packet decomposition coefficients with the overall identification accuracy of the training set equal to 99.67% and that of the testing set equal to 82.00% was better than the other two models. To improve the identification accuracy, it was suggested that a combination model could be constructed with one SVM model and two models built using K-nearest neighbors (KNN) method. Using the DI inversion SVR models, the satisfactory results were obtained for the two diseases. The results demonstrated that identification and DI inversion of stripe rust and leaf rust can be implemented based on hyperspectral data at the canopy level.