Search

IntroductionSeverity is a key indicator utilized in plant disease monitoring and pathogen-plant interaction phenotyping.MethodsA plant disease severity assessment method based on lesion expansion was proposed in this study to more accurately and quickly assess the severity of plant diseases for which the lesion area ratio of an investigated plant unit at each severity class in the corresponding severity grading standard is not the actual ratio of the lesion area to the area of the whole investigated plant unit. By taking wheat stripe rust caused by Puccinia striiformis f. sp. tritici as an example, after image segmentation operations of single diseased wheat leaves with wheat stripe rust, lesion expansion processing was carried out using nine method combinations of three proposed lesion expansion methods and three proposed lesion expansion coefficient determination methods, and then the severity assessments of single diseased wheat leaves were conducted.ResultsThe results showed that the accuracy of severity assessments of single diseased wheat leaves in each severity class was in the range of 78.00% to 100.00%. No matter which method was used to determine the lesion expansion coefficient/coefficients, the performance of the severity assessments of the single diseased leaves achieved after lesion expansion using lesion expansion method 3 (the lesion expansion method based on an image scaling algorithm) outperformed that achieved after lesion expansion using the other two lesion expansion methods. The performance of the method combination of lesion expansion method 3 and lesion expansion coefficient determination method 1 with a lesion expansion coefficient of 2.74, achieving an accuracy of 96.16% for severity assessments of all the single diseased wheat leaves, was the optimal method among the nine method combinations.DiscussionThe results demonstrated that satisfactory severity assessment results could be achieved using the proposed method based on lesion expansion. The results indicated that the lesion-expansion-based plant disease severity assessment method is feasible, and can be used to solve the severity assessment problem described above. This study provided a new idea and method for accurate severity assessment of plant diseases and provided support for the automatic and intelligent assessment of plant disease severity.

Stripe rust caused by Puccinia striiformis f. sp. tritici and leaf rust caused by Puccinia triticina, are two devastating diseases on wheat, which seriously affect the production safety of wheat. Timely detection and identification of the two diseases are essential for taking effective disease management measures to reduce wheat yield losses. To realize the accurate identification of wheat stripe rust and wheat leaf rust during the different growth stages, in this study, the image-based identification of wheat stripe rust and wheat leaf rust during different growth stages was investigated based on deep learning using image processing technology. Based on the YOLOv5s model, we built identification models of wheat stripe rust and wheat leaf rust during the seedling stage, stem elongation stage, booting stage, inflorescence emergence stage, anthesis stage, milk development stage, and all the growth stages. The models were tested on the different testing sets in the different individual growth stages and in all the growth stages. The results showed that the models performed differently in disease image identification. The model based on the disease images acquired during an individual growth stage was not suitable for the identification of the disease images acquired during the other individual growth stages, except for the model based on the disease images acquired during the milk development stage, which had acceptable identification performance on the testing sets in the anthesis stage and the milk development stage. In addition, the results demonstrated that wheat growth stages had a great influence on the image identification of the two diseases. The model built based on the disease images acquired in all the growth stages produced acceptable identification results. Mean F1 Score values between 64.06% and 79.98% and mean average precision (mAP) values between 66.55% and 82.80% were achieved on each testing set composed of the disease images acquired during an individual growth stage and on the testing set composed of the disease images acquired during all the growth stages. This study provides a basis for the image-based identification of wheat stripe rust and wheat leaf rust during the different growth stages, and it provides a reference for the accurate identification of other plant diseases.

Currently, a reliable and easy-to-use method to monitor flocculation in the water treatment process is highly demanded, especially for small water purification stations. For this problem, in situ images were used to analyze the flocculation process under different conditions via jar tests. A texture feature of the gray level co-occurrence matrix was found to be helpful for monitoring the floc status, such as growth rate and settling velocity. To further verify this finding, we established the correlation between the texture time sequence curve (TTSC) and its corresponding floc status. The slope of the TTSC during the growth phase and during the settling phase can describe the growth rate and the settling velocity, respectively, i.e., the higher the slope, the higher the growth rate and settling velocity. In addition, significant differences between the TTSCs in various abnormal conditions and the normal condition of coagulation can be identified. By using the TTSC for detecting abnormal conditions, we again verified that the texture feature can reliably reflect the flocculation process. Our study helps to develop a low-cost, stable, and simple method for monitoring flocculation and detecting abnormal conditions, which can effectively be used in the operation and management of water treatment plants. HIGHLIGHTS Compared with the morphological features, the texture features are more stable.; The texture time sequence curve (TTSC) can describe flocculation.; Variation characteristics of TTSC can detect abnormal coagulation conditions.; TTSC-based methods can be readily applied online.;

Trust is an essential concept in various scenarios enabled by Information and Communication Technologies (ICT). To facilitate the implementation of trust in these scenarios, different organizations have published a series of trust frameworks. However, most existing works on trust standards only focus on a specific application domain. Unlike these works, in this paper, we provide a comprehensive overview of the current available trust standards related to communication networks and future digital world from several main organizations. We categorize these trust standards into three layers: trust foundation, trust elements, and trust applications. We then analyze these trust standards and discuss their contributions in a systematic way. We also examine the motivations behind each enforced standard, analyze their frameworks and solutions, and present their role and impact on communication works and future digital world. Finally, we offer our suggestions on the trust work that needs to be standardized in the future.

Recent years have witnessed a digital explosion in the deployment of 5G and the proliferation of 5G-enabled innovations. Compared with 5G, 6G is envisioned to achieve a much higher performance and experience a number of paradigm shifts, such as exploiting new spectrum, applying ubiquitous Machine Learning and Artificial Intelligence (ML/AI) technologies and building a space-air-ground-sea integrated network. However, these paradigm shifts may lead to numerous new security and privacy issues, that traditional security measures may not be able to address. Moreover, the expected high performance of 6G also challenges network reliability and energy efficiency. To tackle these issues and build a trustworthy 6G network, we introduce a novel trust framework called SIX-Trust. This framework is composed of three layers with an emphasis on distinct aspects: sustainable trust (S-Trust), with a particular focus on trust in applications of AI, novel trust evaluation methods and modeling of trust relationships; infrastructure trust (I-Trust), which is more focused on the trustworthiness of network infrastructure; and xenogenesis trust (X-Trust) paying special attention to the core technologies which form the backbone of 6G trust. Besides, the importance of each layer varies under different application scenarios of 6G. For each layer, we briefly introduce its related enabling technologies, and demonstrate how these technologies can be applied to enhance the trust and security of the 6G network. Finally, a use case is illustrated and analyzed. In general, SIX-Trust provides a holistic framework for defining and modeling trust in 6G, which can facilitate establishment of a trustworthy 6G network.

Summary: Hepatic inflammation, driven by immune cells such as B and T lymphocytes, is a hallmark feature of metabolic dysfunction-associated steatohepatitis (MASH). Here, we detail a robust cytometry by time-of-flight (CyTOF) procedure to phenotype hepatic lymphocytes from mice with MASH. We employ custom metal conjugation of antibodies, isolation of hepatic lymphocytes, cell surface and intracellular staining, and data acquisition. This protocol overcomes the limitations of traditional flow cytometry by accommodating up to 40 markers for comprehensive immune phenotyping.For complete details on the use and execution of this protocol, please refer to Barrow et al.1 : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics.

To address the issues of excessive heat loss from the roofs of multi-span greenhouses and high energy consumption for heating during winter production, we propose an approach for the external insulation of the roof of multi-span glass greenhouses and have developed an external insulation system (EIS) to practice this approach. The system achieved full coverage of the greenhouse roof through mechanized unfurling and furling of external thermal blankets, thereby achieving energy-saving insulation. This paper describes the overall design and working method of the EIS, providing detailed design and structural parameters for critical components such as the traction rope transmission mechanism and the rail-type sealing structure. Through a system verification experiment, the specifications of the traction rope were determined and the rationality of the EIS’s thermal blanket unfurling and furling time was confirmed. An insulation performance experiment indicated that the average heat flux of the greenhouse roof covered with the external thermal blanket over 14 continuous nights was 54.2 W/m2, compared with 198.6 W/m2 for a single-layer glass roof. Covering the roof with the external thermal blanket reduced heat loss from the glass roof by 72.7%. The average heat flux of the roof of the Venlo-type multi-span greenhouse with double-layer internal insulation was 99.9 W/m2 during the same period, indicating that the heat loss from the roof using external insulation was only 50.3%. This study provides a novel thermal insulation approach and an energy-saving system for multi-span greenhouses.

Wheat yield has been constrained by stripe rust disease globally. A wheat landrace (Qishanmai, QSM) consistently showed lower stripe rust severities in multiple year studies than susceptible check varieties including Suwon11 (SW) at the adult plant stage. To detect QTL for reducing the severity in QSM, 1218 recombinant inbred lines (RILs) were developed from SW × QSM. QTL detection was conducted firstly using 112 RILs selected for similarity in pheno-morphological characters. The 112 RILs were assessed for stripe rust severity at the 2nd leaf, 6th leaf and flag leaf stages under field and greenhouse conditions, and genotyping was done primarily with a single nucleotide polymorphism (SNP) array. On the basis of these phenotypic and genotypic data, a major QTL (QYr.cau-1DL) was detected on chromosome 1D at the 6th leaf and flag leaf stages. Further mapping was conducted by genotyping 1218 RILs using new simple sequence repeat (SSR) markers, which were developed by referring to the sequences of the wheat line Chinese Spring (IWGSC RefSeq v1.0). QYr.cau-1DL was mapped within a 0.5 cM (5.2 Mb) interval delimited by the SSR markers 1D-320.58 and 1D-325.79. These markers were applied to select for QYr.cau-1DL by screening F2 or BC4F2 plants of the wheat crosses RL6058 × QSM, Lantian10 × QSM and Yannong21 × QSM. F2:3 or BC4F2:3 families derived from the selected plants were assessed for stripe rust resistance in the fields of two locations and in a greenhouse. Wheat plants carrying the resistant marker haplotype in homozygous state for QYr.cau-1DL showed lower stripe rust severities (by 44% to 48%) than plants lacking this QTL. The trial of RL6058 (a carrier of Yr18) × QSM also indicated that QYr.cau-1DL had larger effect than Yr18 on reducing severity; they acted synergistically, yielding an elevated level of stripe rust resistance.

With the rapid development of blockchain technology in recent years, all kinds of blockchain-based applications have emerged. Among them, the decentralized finance (DeFi) is one of the most successful applications, which is regarded as the future of finance. The great success of DeFi relies on the real-world data which is not directly available on the blockchain. However, due to the deterministic nature of blockchain, the blockchain cannot directly obtain indeterministic data from the outside world (off-chain). Thus, oracles have appeared as a viable solution to feed off-chain data to blockchain applications. In this paper, we carry out a comprehensive study on oracles, especially on DeFi oracles. We first briefly introduce the application scenarios of DeFi oracles, and then we talk about the past of DeFi oracles by categorizing them into several types based on their design features. After that, we introduce five popular DeFi oracles currently in use (such as Chainlink and Band Protocol), with the focus on their system architecture, data validation process, and their incentive mechanisms. Then, we compare these present DeFi oracles from their data trustworthiness, data source trustworthiness and their overall trust models. Finally, we propose a set of metrics for designing trustworthiness DeFi oracles, and propose a potential trust architecture and a few promising techniques for building future trustworthiness oracles.

IntroductionThe accurate severity assessment of wheat stripe rust is the basis for the pathogen-host interaction phenotyping, disease prediction, and disease control measure making.MethodsTo 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.ResultsRegardless 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%.DiscussionThe 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.

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-adjacent-means-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.

Crop fungal diseases are a major threat to crop health and food security worldwide. The epidemiology is the basis for effective and sustainable control of crop fungal diseases. Safe, effective, sustainable, and eco-friendly disease control measures have important economic, ecological, and social significances. This Special Issue, “Epidemiology and Control of Fungal Diseases of Crop Plants”, collected one communication and nine original research articles focusing on the identification and detection of the causal agents of alfalfa Fusarium root rot, strawberry black spot, and barley leaf stripe; the semantic segmentation of wheat stripe rust images; the image-based identification of wheat stripe rust and wheat leaf rust; the image-based identification of the severity of wheat Fusarium head blight; the development process of vanilla Fusarium wilt; the regional migration of wheat leaf rust pathogen; the early prediction of potato early blight; the screening of alternative fungicides for the control of alfalfa Fusarium root rot; and the biocontrol potential of endophytic fungi to control of cumin root rot, presenting the progress of research on the epidemiology and control of crop fungal diseases. The studies contained in this Special Issue facilitated the development of epidemiology of the related crop fungal diseases and provided some basis for control of the diseases, which is conducive to the sustainable management of these diseases.

With the fast development of digital technologies, we are running into a digital world. The relationship among people and the connections among things become more and more complex, and new challenges arise. To tackle these challenges, trust — a soft security mechanism — is considered a promising technology. Thus, in this survey, we do a comprehensive study on trust and trust modelling for the future digital world. We revisit the definitions and properties of trust, analyse the trust theories, and discuss their impact on digital trust modelling. We analyse the digital world and its corresponding environment where people, things, and infrastructure connect with each other. We detail the challenges that require trust in these digital scenarios. Under our analysis of trust and the digital world, we define different types of trust relationships and find out the factors that are needed to ensure a fully representative model. Next, to meet the challenges of digital trust modelling, comprehensive trust model evaluation criteria are proposed, and potential security and privacy issues of trust modelling are analysed. Finally, we provide a wide-ranging analysis of different methodologies, mathematical theories, and how they can be applied to trust modelling.

Ring rot caused by Botryosphaeria dothidea and anthracnose caused by Colletotrichum gloeosporioides are two important apple fruit diseases. It is critical to conduct timely and accurate distinction and diagnosis of the two diseases for apple disease management and apple quality control. The automatic distinction between the two diseases was investigated based on image processing technology in this study. The acquired disease images were preprocessed via image scaling, color image contrast stretching, and morphological opening and closing reconstruction. Then, two lesion segmentation methods based on circle fitting were proposed and used to conduct lesion segmentation. After comparison with the manual segmentation results obtained via the software Adobe Photoshop CC, Lesion segmentation method 1 was chosen for further disease image processing. The gray images on the nine components in the RGB, HSI, and L*a*b* color spaces of the segmented lesion images were filtered by using multi-scale block local binary pattern operators with the sizes of pixel blocks of 1 × 1, 2 × 2, and 3 × 3, respectively, and the corresponding local binary pattern (LBP) histogram vectors were calculated as the features of the lesion images. Subsequently, support vector machine (SVM) models and random forest models were built based on individual LBP histogram features or different LBP histogram feature combinations for distinguishing the diseases. The optimal SVM model with the distinction accuracies of the training and testing sets equal to 100 and 95.12% and the optimal random forest model with the distinction accuracies of the training and testing sets equal to 100 and 90.24% were achieved. The results indicated that the distinction between the two diseases could be implemented with high accuracy by using the proposed method. In this study, a method based on image processing technology was provided for the distinction of ring rot and anthracnose on apple fruits.

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.

Objective: Non-alcoholic fatty liver disease (NAFLD) covers a wide spectrum of liver pathology ranging from simple fatty liver to non-alcoholic steatohepatitis (NASH). Notably, immune cell-driven inflammation is a key mechanism in the transition from fatty liver to the more serious NASH. Although exercise training is effective in ameliorating obesity-related diseases, the underlying mechanisms of the beneficial effects of exercise remain unclear. It is unknown whether there is an optimal modality and intensity of exercise to treat NAFLD. The objective of this study was to determine whether high-intensity interval training (HIIT) or moderate-intensity continuous training (MIT) is more effective at ameliorating the progression of NASH. Methods: Wild-type mice were fed a high-fat, high-carbohydrate (HFHC) diet for 6 weeks and left sedentary (SED) or assigned to either an MIT or HIIT regimen using treadmill running for an additional 16 weeks. MIT and HIIT groups were pair-fed to ensure that energy intake was similar between the exercise cohorts. To determine changes in whole-body metabolism, we performed insulin and glucose tolerance tests, indirect calorimetry, and magnetic resonance imaging. NASH progression was determined by triglyceride accumulation, expression of inflammatory genes, and histological assessment of fibrosis. Immune cell populations in the liver were characterized by cytometry by time-of-flight mass spectrometry, and progenitor populations within the bone marrow were assessed by flow cytometry. Finally, we analyzed the transcriptional profile of the liver by bulk RNA sequencing. Results: Compared with SED mice, both HIIT and MIT suppressed weight gain, improved whole-body metabolic parameters, and ameliorated the progression of NASH by reducing hepatic triglyceride levels, inflammation, and fibrosis. However, HIIT was superior to MIT at reducing adiposity, improving whole-body glucose tolerance, and ameliorating liver steatosis, inflammation, and fibrosis, without any changes in body weight. Improved NASH progression in HIIT mice was accompanied by a substantial decrease in the frequency of pro-inflammatory infiltrating, monocyte-derived macrophages in the liver and reduced myeloid progenitor populations in the bone marrow. Notably, an acute bout of MIT or HIIT exercise had no effect on the intrahepatic and splenic immune cell populations. In addition, bulk mRNA sequencing of the entire liver tissue showed a pattern of gene expression confirming that HIIT was more effective than MIT in improving liver inflammation and lipid biosynthesis. Conclusions: Our data suggest that exercise lessens hepatic inflammation during NASH by reducing the accumulation of hepatic monocyte-derived inflammatory macrophages and bone marrow precursor cells. Our findings also indicate that HIIT is superior to MIT in ameliorating the disease in a dietary mouse model of NASH.

Nonalcoholic fatty liver disease (NAFLD) has become the most common chronic liver disease worldwide. A significant proportion of patients with NAFLD develop a progressive inflammatory condition termed nonalcoholic steatohepatitis (NASH), which may eventually advance to cirrhosis and hepatocellular carcinoma (HCC). NASH is characterized by steatosis, hepatocyte ballooning, and lobular inflammation. Heightened immune cell infiltration is a hallmark of NASH, yet the mechanisms whereby hepatic inflammation occurs in NASH and how it contributes to disease initiation and progression remain incompletely understood. Emerging evidence indicates that intrahepatic T cell immune mechanisms play an integral role in the pathogenesis of NASH and its transition to HCC. In this review, we summarize the current knowledge regarding the T cell-mediated mechanisms of inflammation in NASH. We highlight recent preclinical and human studies implicating various subsets of conventional and innate-like T cells in the onset and progression of NASH and HCC. Finally, we discuss the potential therapeutic strategies targeting T cell-mediated responses for the treatment of NASH.

The emergence of new physiological races of Puccinia striiformis f. sp. tritici (Pst) causing wheat stripe rust can lead to the loss of resistance of wheat cultivars to stripe rust, thus resulting in severe losses in wheat yield. In this study, after the germination of urediospores of three Pst strains including the original strain (CYR32, a dominant physiological race of Pst in China) and two virulence-mutant strains (CYR32-5 and CYR32-61) acquired from CYR32 via UV-B radiation, proteomic analysis based on isobaric tags for relative and absolute quantification (iTRAQ) technology was performed on the strains. A total of 2,271 proteins were identified, and 59, 74, and 64 differentially expressed proteins (DEPs) were acquired in CYR32-5 vs. CYR32, CYR32-61 vs. CYR32, and CYR32-61 vs. CYR32-5, respectively. The acquired DEPs were mainly involved in energy metabolism, carbon metabolism, and cellular substance synthesis. Furthermore, quantitative reverse transcription PCR assays were used to determine the relative expression of the 6, 7, and 1 DEPs of CYR32-5 vs. CYR32, CYR32-61 vs. CYR32, and CYR32-61 vs. CYR32-5, respectively, at the transcriptional level. The relative expression levels of one, five, and one gene, respectively, encoding the DEPs, were consistent with the corresponding protein abundance determined by iTRAQ technology. Compared with CYR32, the DEPs associated with energy metabolism and stress—including E3JWK6, F4S0Z3, and A8N2Q4—were up-regulated in the mutant strains. The results indicated that the virulence-mutant strains CYR32-5 and CYR32-61 had more tolerance to stress than the original strain CYR32. The results obtained in this study are of great significance for exploring the virulence variation mechanisms of Pst, monitoring the changes in Pst populations, breeding new disease-resistant wheat cultivars, and managing wheat stripe rust sustainably.

Abstract Planting resistant cultivars is the most economical and effective measure to control wheat stripe rust caused by Puccinia striiformis f. sp. tritici (Pst), but the cultivars often lose their resistance due to the emergence of new physiological races. The UV‐B‐irradiated urediospores of the Pst physiological race CYR32 in China were inoculated on wheat cultivar Guinong 22 for screening virulence‐mutant strains. CYR32 and mutant strains (CYR32‐5 and CYR32‐61) before and after UV‐B radiation were used to conduct urediospore morphological and histopathological observations and an investigation of epidemiological components. The results showed that UV‐B radiation affected the urediospore morphology of each strain. UV‐B radiation inhibited urediospore invasion and hyphal elongation, which mainly manifested as decreases in germination rate, quantities of hyphal branches, haustorial mother cells and haustoria and hyphal length. After wheat cultivar Mingxian 169 was inoculated with the UV‐B‐irradiated urediospores, the incubation period was prolonged, and the infection efficiency, lesion expansion rate, total sporulation quantity and area under the disease progress curve were reduced. The results demonstrated that CYR32‐5 and CYR32‐61 may have more tolerance to UV‐B radiation than CYR32. The results are significant for understanding mechanisms of Pst virulence variations and implementing sustainable management of wheat stripe rust.

Identifying plant pathogens for disease diagnosis and disease control strategy making is of great significance. In this study, based on near-infrared spectroscopy, a method for identifying three kinds of pathogens causing wheat smuts, including Tilletia foetida, Ustilago tritici, and Urocystis tritici, was investigated. Based on the acquired near-infrared spectral data of the teliospore samples of the three pathogens, pathogen identification models were built in different spectral regions using distinguished partial least squares (DPLS), backpropagation neural network (BPNN), and support vector machine (SVM). Satisfactory identification results were achieved using the DPLS, BPNN, and SVM models built in each of the 22 spectral regions. By contrast, the modeling effects of DPLS and SVM were better than those of BPNN. The modeling ratio of the training set to the testing set affected the identification results of the BPNN models more than those obtained using the DPLS and SVM models. In this study, a rapid, accurate, and nondestructive method was provided for plant pathogen identification, and some basis was provided for disease diagnosis, pathogen monitoring, and disease control. Moreover, some methodological references and supports were provided for identification of quarantine wheat smut fungi in plant quarantine.

To track recent thymic emigrants (RTEs) or study T cell development in the thymus, intra-thymic injection of a cellular tag or precursor cells for various T cell lineages is often desired. However, the traditional surgical approach to expose the thymus for intra-thymic injection is time-consuming and can cause a high level of pain and stress to animals, which might disrupt immune homeostasis, potentially confounding the results. Here, we introduce an ultrasound guided intra-thymic injection procedure, which is non-surgical and minimally invasive to animals. This technique is relatively easy to learn and offers an efficient and accurate tool to track RTEs or perform intra-thymic transfer of various cell types to investigate their differentiation.

The precise steps of iNKT subset differentiation in the thymus and periphery have been controversial. We demonstrate here that the small proportion of thymic iNKT and mucosal associated invariant T cells that express CCR7 represent a multi-potent progenitor pool that gives rise to effector subsets within the thymus. Using intra-thymic labeling, we also showed that CCR7+ iNKT cells emigrate from the thymus in a Klf2 dependent manner, and undergo further maturation after reaching the periphery. Ccr7 deficiency impaired differentiation of iNKT effector subsets and localization to the medulla. Parabiosis and intra-thymic transfer showed that thymic NKT1 and NKT17 were resident—they were not derived from and did not contribute to the peripheral pool. Finally, each thymic iNKT effector subset produces distinct factors that influence T cell development. Our findings demonstrate how the thymus is both a source of iNKT progenitors and a unique site of tissue dependent effector cell differentiation.

In contrast to peptide-recognizing T cells, invariant natural killer T (iNKT) cells express a semi-invariant T cell receptor that specifically recognizes self- or foreign-lipids presented by CD1d molecules. There are three major functionally distinct effector states for iNKT cells. Owning to these innate-like effector states, iNKT cells have been implicated in early protective immunity against pathogens. Yet, growing evidence suggests that iNKT cells play a role in tissue homeostasis as well. In this review, we discuss current knowledge about the underlying mechanisms that regulate the effector states of iNKT subsets, with a highlight on the roles of a variety of transcription factors and describe how each subset influences different facets of thymus homeostasis.

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.

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.

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.

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.

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.