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Abstract Background Superoxide dismutase (SOD) can greatly scavenge reactive oxygen species (ROS) in plants. SOD activity is highly related to plant stress tolerance that can be improved by overexpression of SOD genes. Identification of SOD activity-related loci and potential candidate genes is essential for improvement of grain quality in wheat breeding. However, the loci and candidate genes for relating SOD in wheat grains are largely unknown. In the present study, grain SOD activities of 309 recombinant inbred lines (RILs) derived from the ‘Berkut’ × ‘Worrakatta’ cross were assayed by photoreduction method with nitro-blue tetrazolium (NBT) in four environments. Quantitative trait loci (QTL) of SOD activity were identified using inclusive composite interval mapping (ICIM) with the genotypic data of 50 K single nucleotide polymorphism (SNP) array. Results Six QTL for SOD activity were mapped on chromosomes 1BL, 4DS, 5AL (2), and 5DL (2), respectively, explaining 2.2 ~ 7.4% of the phenotypic variances. Moreover, QSOD.xjau-1BL, QSOD.xjau-4DS, QSOD.xjau-5 A.1, QSOD.xjau-5 A.2, and QSOD.xjau-5DL.2 identified are likely to be new loci for SOD activity. Four candidate genes TraesCS4D01G059500, TraesCS5A01G371600, TraesCS5D01G299900, TraesCS5D01G343100LC, were identified for QSOD.xjau-4DS, QSOD.xjau-5AL.1, and QSOD.xjau-5DL.1 (2), respectively, including three SOD genes and a gene associated with SOD activity. Based on genetic effect analysis, this can be used to identify desirable alleles and excellent allele variations in wheat cultivars. Conclusion These candidate genes are annotated for promoting SOD production and inhibiting the accumulation of ROS during plant growth. Therefore, lines with high SOD activity identified in this study may be preferred for future wheat breeding.

Rapidly obtaining the chlorophyll content of crop leaves is of great significance for timely diagnosis of crop health and effective field management. Multispectral imagery obtained from unmanned aerial vehicles (UAV) is being used to remotely sense the SPAD (Soil and Plant Analyzer Development) values of wheat crops. However, existing research has not yet fully considered the impact of different growth stages and crop populations on the accuracy of SPAD estimation. In this study, 300 materials from winter wheat natural populations in Xinjiang, collected between 2020 to 2022, were analyzed. UAV multispectral images were obtained in the experimental area, and vegetation indices were extracted to analyze the correlation between the selected vegetation indices and SPAD values. The input variables for the model were screened, and a support vector machine (SVM) model was constructed to estimate SPAD values during the heading, flowering, and filling stages under different water stresses. The aim was to provide a method for the rapid acquisition of winter wheat SPAD values. The results showed that the SPAD values under normal irrigation were higher than those under water restriction. Multiple vegetation indices were significantly correlated with SPAD values. In the prediction model construction of SPAD, the different models had high estimation accuracy under both normal irrigation and water limitation treatments, with correlation coefficients of predicted and measured values under normal irrigation in different environments the value of r from 0.59 to 0.81 and RMSE from 2.15 to 11.64, compared to RE from 0.10% to 1.00%; and under drought stress in different environments, correlation coefficients of predicted and measured values of r was 0.69–0.79, RMSE was 2.30–12.94, and RE was 0.10%–1.30%. This study demonstrated that the optimal combination of feature selection methods and machine learning algorithms can lead to a more accurate estimation of winter wheat SPAD values. In summary, the SVM model based on UAV multispectral images can rapidly and accurately estimate winter wheat SPAD value.

Stripe rust, caused by Puccinia striiformis f. sp. tritici (Pst), is one of the most important diseases threatening the yield and stability of wheat production in China and many other countries. Identification and utilization of new genes for durable stripe rust resistance are important for ongoing control of this disease. The objectives of this study were to identify quantitative trait loci (QTL) associated with adult-plant stripe rust resistance in the Chinese wheat landrace Yibinzhuermai (YBZR) and to provide wheat breeders with new sources of potentially durable resistance. A total of 117 recombinant inbred lines (RILs) (F5:8) derived from a cross between YBZR and highly susceptible cultivar Taichung 29 (TC29) were assessed for stripe rust severity in field experiments at Wenjiang in 2016 and 2017 and Chongzhou in 2016, 2017, 2018, and 2019 in Sichuan following inoculation with a mixture of current Pst races. The RILs were genotyped using the Wheat55K single nucleotide polymorphism (SNP) array. Three QTL were identified on chromosome arms 6AL, 5BL and 7DS. QYr.YBZR-6AL and QYr.YBZR-7DS conferred major effects in all field environments, explaining 10.6% to 14.7% and 11.5% to 21.2% of phenotypic variation, respectively. The QTL on 5BL and 7DS likely correspond to previously known QTL, whereas QYr.YBZR-6AL is probably novel. Haplotype analysis revealed that the resistance allele at QYr.YBZR-6AL was present in 2.8% of 324 Chinese wheat landraces. SNP markers closely linked with QYr.YBZR-6AL were converted to kompetitive allele-specific PCR markers and validated in the RIL population and a subset of 92 wheat cultivars. QYr.YBZR-6AL and its markers should be useful in breeding programs to improve the level and durability of stripe rust resistance.

Abstract Background Stripe rust, caused by the fungal pathogen Puccinia striiformis f. sp. tritici (Pst), is a serious foliar disease of wheat. Identification of novel stripe rust resistance genes and cultivation of resistant cultivars are considered to be the most effective approaches to control this disease. In this study, we evaluated the infection type (IT), disease severity (DS) and area under the disease progress curve (AUDPC) of 143 Chinese wheat landrace accessions for stripe rust resistance. Assessments were undertaken in five environments at the adult-plant stage with Pst mixture races under field conditions. In addition, IT was assessed at the seedling stage with two prevalent Pst races (CYR32 and CYR34) under a controlled greenhouse environment. Results Seventeen accessions showed stable high-level resistance to stripe rust across all environments in the field tests. Four accessions showed resistance to the Pst races CYR32 and CYR34 at the seedling stage. Combining phenotypic data from the field and greenhouse trials with 6404 markers that covered the entire genome, we detected 17 quantitative trait loci (QTL) on 11 chromosomes for IT associated with seedling resistance and 15 QTL on seven chromosomes for IT, final disease severity (FDS) or AUDPC associated with adult-plant resistance. Four stable QTL detected on four chromosomes, which explained 9.99–23.30% of the phenotypic variation, were simultaneously associated with seedling and adult-plant resistance. Integrating a linkage map of stripe rust resistance in wheat, 27 QTL overlapped with previously reported genes or QTL, whereas four and one QTL conferring seedling and adult-plant resistance, respectively, were mapped distantly from previously reported stripe rust resistance genes or QTL and thus may be novel resistance loci. Conclusions Our results provided an integrated overview of stripe rust resistance resources in a wheat landrace diversity panel from the southern autumn-sown spring wheat zone of China. The identified resistant accessions and resistance loci will be useful in the ongoing effort to develop new wheat cultivars with strong resistance to stripe rust.

To obtain the canopy chlorophyll content of winter wheat in a rapid and non-destructive high-throughput manner, the study was conducted on winter wheat in Xinjiang Manas Experimental Base in 2021, and the multispectral images of two water treatments' normal irrigation (NI) and drought stress (DS) in three key fertility stages (heading, flowering, and filling) of winter wheat were obtained by DJI P4M unmanned aerial vehicle (UAV). The flag leaf chlorophyll content (CC) data of different genotypes in the field were obtained by SPAD-502 Plus chlorophyll meter. Firstly, the CC distribution of different genotypes was studied, then, 13 vegetation indices, combined with the Random Forest algorithm and correlation evaluation of CC, and 14 vegetation indices were used for vegetation index preference. Finally, preferential vegetation indices and nine machine learning algorithms, Ridge regression with cross-validation (RidgeCV), Ridge, Adaboost Regression, Bagging_Regressor, K_Neighbor, Gradient_Boosting_Regressor, Random Forest, Support Vector Machine (SVM), and Least absolute shrinkage and selection operator (Lasso), were preferentially selected to construct the CC estimation models under two water treatments at three different fertility stages, which were evaluated by correlation coefficient (r), root means square error (RMSE) and the normalized root mean square error (NRMSE) to select the optimal estimation model. The results showed that the CC values under normal irrigation were higher than those underwater limitation treatment at different fertility stages; several vegetation indices and CC values showed a highly significant correlation, with the highest correlation reaching.51; in the prediction model construction of CC values, different models under normal irrigation and water limitation treatment had high estimation accuracy, among which the model with the highest prediction accuracy under normal irrigation was at the heading stage. The highest precision of the model prediction under normal irrigation was in the RidgeCV model (r = 0.63, RMSE = 3.28, NRMSE = 16.2%) and the highest precision of the model prediction under water limitation treatment was in the SVM model (r = 0.63, RMSE = 3.47, NRMSE = 19.2%).

Abstract Background As one of the most important food crops in the world, increasing wheat (Triticum aestivum L.) yield is an urgent task for global food security under the continuous threat of stripe rust (caused by Puccinia striiformis f. sp. tritici) in many regions of the world. Molecular marker-assisted breeding is one of the most efficient ways to increase yield. Here, we identified loci associated to multi-environmental yield-related traits under stripe rust stress in 244 wheat accessions from Sichuan Province through genome-wide association study (GWAS) using 44,059 polymorphic markers from the 55 K single nucleotide polymorphism (SNP) chip. Results A total of 13 stable quantitative trait loci (QTLs) were found to be highly associating to yield-related traits, including 6 for spike length (SL), 3 for thousand-kernel weight (TKW), 2 for kernel weight per spike (KWPS), and 2 for both TKW and KWPS, in at least two test environments under stripe rust stress conditions. Of them, ten QTLs were overlapped or very close to the reported QTLs, three QTLs, QSL.sicau-1AL, QTKW.sicau-4AL, and QKWPS.sicau-4AL.1, were potentially novel through the physical location comparison with previous QTLs. Further, 21 candidate genes within three potentially novel QTLs were identified, they were mainly involved in the regulation of phytohormone, cell division and proliferation, meristem development, plant or organ development, and carbohydrate transport. Conclusions QTLs and candidate genes detected in our study for yield-related traits under stripe rust stress will facilitate elucidating genetic basis of yield-related trait and could be used in marker-assisted selection in wheat yield breeding.

Abstract Background Stripe rust (also called yellow rust) is a common and serious fungal disease of wheat (Triticum aestivum L.) caused by Puccinia striiformis f. sp. tritici. The narrow genetic basis of modern wheat cultivars and rapid evolution of the rust pathogen have been responsible for periodic and devastating epidemics of wheat rust diseases. In this study, we conducted a genome-wide association study with 44,059 single nucleotide polymorphism markers to identify loci associated with resistance to stripe rust in 244 Sichuan wheat accessions, including 79 landraces and 165 cultivars, in six environments. Results In all the field assessments, 24 accessions displayed stable high resistance to stripe rust. Significant correlations among environments were observed for both infection (IT) and disease severity (DS), and high heritability levels were found for both IT and DS. Using mixed linear models, 12 quantitative trait loci (QTLs) significantly associated with IT and/or DS were identified. Two QTLs were mapped on chromosomes 5AS and 5AL and were distant from previously identified stripe rust resistance genes or QTL regions, indicating that they may be novel resistance loci. Conclusions Our results revealed that resistance alleles to stripe rust were accumulated in Sichuan wheat germplasm, implying direct or indirect selection for improved stripe rust resistance in elite wheat breeding programs. The identified stable QTLs or favorable alleles could be important chromosome regions in Sichuan wheat that controlled the resistance to stripe rust. These markers can be used molecular marker-assisted breeding of Sichuan wheat cultivars, and will be useful in the ongoing effort to develop new wheat cultivars with strong resistance to stripe rust.

The intensive use of groundwater in agriculture under the current climate conditions leads to acceleration of soil salinization. Given that almond is a salt-sensitive crop, selection of salt-tolerant rootstocks can help maintain productivity under salinity stress. Selection for tolerant rootstocks at an early growth stage can reduce the investment of time and resources. However, salinity-sensitive markers and salinity tolerance mechanisms of almond species to assist this selection process are largely unknown. We established a microscopy-based approach to investigate mechanisms of stress tolerance in and identified cellular, root anatomical, and molecular traits associated with rootstocks exhibiting salt tolerance. We characterized three almond rootstocks: Empyrean-1 (E1), Controller-5 (C5), and Krymsk-86 (K86). Based on cellular and molecular evidence, our results show that E1 has a higher capacity for salt exclusion by a combination of upregulating ion transporter expression and enhanced deposition of suberin and lignin in the root apoplastic barriers, exodermis, and endodermis, in response to salt stress. Expression analyses revealed differential regulation of cation transporters, stress signaling, and biopolymer synthesis genes in the different rootstocks. This foundational study reveals the mechanisms of salinity tolerance in almond rootstocks from cellular and structural perspectives across a root developmental gradient and provides insights for future screens targeting stress response.

Public health emergencies are more related to the safety and health of the public. For the management of the public health emergencies, all parties’ cooperation is the key to preventing and controlling the emergencies. Based on the assumption of bounded rationality, we formulate a tripartite evolutionary game model, involving the local government, the enterprises, and the public, for the public health emergency, e.g., COVID-19. The evolutionary stable strategies under different conditions of the tripartite evolutionary game are explored, and the effect from different factors on the decision-makings of participants for public health emergencies is also analyzed. Numerical analysis results show that formulating reasonable subsidy measures, encouraging the participation of the public, and enforcing the punishment to enterprises for their negative behaviors can prompt three parties to cooperate in fighting against the epidemic. Our work enriches an understanding of the governance for the public health emergency and provides theoretical support for the local government and related participants to make proper decisions in public health emergencies.

High-throughput phenotypic identification is a prerequisite for large-scale identification and gene mining of important traits. However, existing work has rarely leveraged high-throughput phenotypic identification into quantitative trait locus (QTL) acquisition in wheat crops. Clarifying the feasibility and effectiveness of high-throughput phenotypic data obtained from UAV multispectral images in gene mining of important traits is an urgent problem to be solved in wheat. In this paper, 309 lines of the spring wheat Worrakatta × Berkut recombinant inbred line (RIL) were taken as materials. First, we obtained the leaf area index (LAI) including flowering, filling, and mature stages, as well as the flag leaf chlorophyll content (CC) including heading, flowering, and filling stages, from multispectral images under normal irrigation and drought stress, respectively. Then, on the basis of the normalized difference vegetation index (NDVI) and green normalized difference vegetation index (GNDVI), which were determined by multispectral imagery, the LAI and CC were comprehensively estimated through the classification and regression tree (CART) and cross-validation algorithms. Finally, we identified the QTLs by analyzing the predicted and measured values. The results show that the predicted values of determination coefficient (R2) ranged from 0.79 to 0.93, the root-mean-square error (RMSE) ranged from 0.30 to 1.05, and the relative error (RE) ranged from 0.01 to 0.18. Furthermore, the correlation coefficients of predicted and measured values ranged from 0.93 to 0.94 for CC and from 0.80 to 0.92 for LAI at different wheat growth stages under normal irrigation and drought stress. Additionally, a linkage map of this RIL population was constructed by 11,375 SNPs; eight QTLs were detected for LAI on wheat chromosomes 1BL, 2BL (four QTLs), 3BL, 5BS, and 5DL, and three QTLs were detected for CC on chromosomes 1DS (two QTLs) and 3AL. The closely linked QTLs formed two regions on chromosome 2BL (from 54 to 56 cM and from 96 to 101 cM, respectively) and one region on 1DS (from 26 to 27 cM). Each QTL explained phenotypic variation for LAI from 2.5% to 13.8% and for CC from 2.5% to 5.8%. For LAI, two QTLs were identified at the flowering stage, two QTLs were identified at the filling stage, and three QTLs were identified at the maturity stage, among which QLAI.xjau-5DL-pre was detected at both filling and maturity stages. For CC, two QTLs were detected at the heading stage and one QTL was identified at the flowering stage, among which QCC.xjau-1DS was detected at both stages. Three QTLs (QLAI.xjau-2BL-pre.2, QLAI.xjau-2BL.2, and QLAI.xjau-3BL-pre) for LAI were identified under drought stress conditions. Five QTLs for LAI and two QTLs for CC were detected by imagery-predicted values, while four QTLs for LAI and two QTLs for CC were identified by manual measurement values. Lastly, investigations of these QTLs on the wheat reference genome identified 10 candidate genes associated with LAI and three genes associated with CC, belonging to F-box family proteins, peroxidase, GATA transcription factor, C2H2 zinc finger structural protein, etc., which are involved in the regulation of crop growth and development, signal transduction, and response to drought stress. These findings reveal that UAV sensing technology has relatively high reliability for phenotyping wheat LAI and CC, which can play an important role in crop genetic improvement.

Stripe rust (also known as yellow rust), caused by the pathogen Puccinia striiformis f. sp. tritici (Pst), is a common and serious fungal disease of wheat (Triticum aestivum L.) worldwide. To identify effective stripe rust resistance loci, a genome-wide association study was performed using 152 wheat landraces from the Yellow and Huai River Valleys in China based on Diversity Arrays Technology and simple sequence repeat markers. Phenotypic evaluation of the degree of resistance to stripe rust at the adult-plant stage under field conditions was carried out in five environments. In total, 19 accessions displayed stable, high degrees of resistance to stripe rust development when exposed to mixed races of Pst at the adult-plant stage in multi-environment field assessments. A marker–trait association analysis indicated that 51 loci were significantly associated with adult-plant resistance to stripe rust. These loci included 40 quantitative trait loci (QTL) regions for adult-plant resistance. Twenty identified resistance QTL were linked closely to previously reported yellow rust resistance genes or QTL regions, which were distributed across chromosomes 1B, 1D, 2A, 2B, 3A, 3B, 4A, 4B, 5B, 6B, 7A, 7B, and 7D. Six multi-trait QTL were detected on chromosomes 1B, 1D, 2B, 3A, 3B, and 7D. Twenty QTL were mapped to chromosomes 1D, 2A, 2D, 4B, 5B, 6A, 6B, 6D, 7A, 7B, and 7D, distant from previously identified yellow rust resistance genes. Consequently, these QTL are potentially novel loci for stripe rust resistance. Among the 20 potentially novel QTL, five (QDS.sicau-2A, QIT.sicau-4B, QDS.sicau-4B.2, QDS.sicau-6A.3, and QYr.sicau-7D) were associated with field responses at the adult-plant stage in at least two environments, and may have large effects on stripe rust resistance. The novel effective QTL for adult-plant resistance to stripe rust will improve understanding of the genetic mechanisms that control the spread of stripe rust, and will aid in the molecular marker-assisted selection-based breeding of wheat for stripe rust resistance.