Transcriptome analysis reveals different response of resistant and susceptible rice varieties to rice stripe virus infection

Rice stripe disease,caused by rice stripe virus (RSV) which is transmitted by small brown planthopper (SBPH,Laodelphax striatellus Fallen),resulted in serious losses to rice production during the last 2 decades.Research on the molecular differences between resistant and susceptible rice varieties and the interaction between rice and RSV remains inadequate.In this study,RNA-Seq was used to analyze the transcriptomic differences between the resistant and susceptible rice varieties at different times post RSV infection.Through Gene Ontology (GO) annotation,the differentially expressed genes (DEGs) related to transcription factors,peroxidases,and kinases of 2 varieties at 3 time points were identified.Comparing these 2 varieties,the DEGs associated with these 3 GOs were numerically less in the resistant variety than in the susceptible variety,but the expression showed a significant up-or down-regulation trend under the conditions of|log_2(Fold change)|>0&P_(adj)<0.05 by significance analysis.Then through Kyoto Encyclopedia of Genes and Genomes (KEGG) annotation,DEGs involved in some pathways that have a contribution to disease resistance including plant hormone signal transduction and plant–pathogen interaction were found.The results showed that resistance responses regulated by abscisic acid (ABA) and brassinosteroids (BR) were the same for 2 varieties,but that mediated by salicylic acid (SA) and jasmonic acid (JA)/ethylene (ET) were different.The DEGs in resistant and susceptible varieties at the 3 time points were identified in both PAMP-triggered immunity (PTI) and Effector proteintriggered immunity (ETI),with that most of the unigenes of the susceptible variety were involved in PTI,whereas most of the unigenes of the resistant variety were involved in ETI.These results revealed the different responses of resistant and susceptible varieties in the transcription level to RSV infection.

Visualization for Explanation of Deep Learning-Based Defect Detection Model Using Class Activation Map

Recently,convolutional neural network(CNN)-based visual inspec-tion has been developed to detect defects on building surfaces automatically.The CNN model demonstrates remarkable accuracy in image data analysis;however,the predicted results have uncertainty in providing accurate informa-tion to users because of the“black box”problem in the deep learning model.Therefore,this study proposes a visual explanation method to overcome the uncertainty limitation of CNN-based defect identification.The visual repre-sentative gradient-weights class activation mapping(Grad-CAM)method is adopted to provide visually explainable information.A visualizing evaluation index is proposed to quantitatively analyze visual representations;this index reflects a rough estimate of the concordance rate between the visualized heat map and intended defects.In addition,an ablation study,adopting three-branch combinations with the VGG16,is implemented to identify perfor-mance variations by visualizing predicted results.Experiments reveal that the proposed model,combined with hybrid pooling,batch normalization,and multi-attention modules,achieves the best performance with an accuracy of 97.77%,corresponding to an improvement of 2.49%compared with the baseline model.Consequently,this study demonstrates that reliable results from an automatic defect classification model can be provided to an inspector through the visual representation of the predicted results using CNN models.

Removal of Stripes in Remote Sensing Images Based on Statistics Combined with Image Enhancement

A method to remove stripes from remote sensing images is proposed based on statistics and a new image enhancement method.The overall processing steps for improving the quality of remote sensing images are introduced to provide a general baseline.Due to the differences in satellite sensors when producing images,subtle but inherent stripes can appear at the stitching positions between the sensors.These stitchingstripes cannot be eliminated by conventional relative radiometric calibration.The inherent stitching stripes cause difficulties in downstream tasks such as the segmentation,classification and interpretation of remote sensing images.Therefore,a method to remove the stripes based on statistics and a new image enhancement approach are proposed in this paper.First,the inconsistency in grayscales around stripes is eliminated with the statistical method.Second,the pixels within stripes are weighted and averaged based on updated pixel values to enhance the uniformity of the overall image radiation quality.Finally,the details of the images are highlighted by a new image enhancement method,which makes the whole image clearer.Comprehensive experiments are performed,and the results indicate that the proposed method outperforms the baseline approach in terms of visual quality and radiation correction accuracy.

Investigation of Aegilops umbellulata for stripe rust resistance,heading date,and the contents of iron,zinc,and gluten protein

Aegilops umbellulata(UU)is a wheat wild relative that has potential use in the genetic improvement of wheat.In this study,46 Ae.umbellulata accessions were investigated for stripe rust resistance,heading date(HD),and the contents of iron(Fe),zinc(Zn),and seed gluten proteins.Forty-two of the accessions were classified as resistant to stripe rust,while the other four accessions were classified as susceptible to stripe rust in four environments.The average HD of Ae.umbellulata was significantly longer than that of three common wheat cultivars(180.9 d vs.137.0 d),with the exception of PI226500(138.9 d).The Ae.umbellulata accessions also showed high variability in Fe(69.74-348.09 mg kg^(-1))and Zn(49.83-101.65 mg kg^(-1))contents.Three accessions(viz.,PI542362,PI542363,and PI554399)showed relatively higher Fe(230.96-348.09 mg kg^(-1))and Zn(92.46-101.65 mg kg^(-1))contents than the others.The Fe content of Ae.umbellulata was similar to those of Ae.comosa and Ae.markgrafii but higher than those of Ae.tauschii and common wheat.Aegilops umbellulata showed a higher Zn content than Ae.tauschii,Ae.comosa,and common wheat,but a lower content than Ae.markgrafii.Furthermore,Ae.umbellulata had the highest proportion of γ-gliadin among all the species investigated(Ae.umbellulata vs.other species=mean 72.11%vs.49.37%;range:55.33-86.99%vs.29.60-67.91%).These results demonstrated that Ae.umbellulata exhibits great diversity in the investigated traits,so it can provide a potential gene pool for the genetic improvement of these traits in wheat.

Landslide susceptibility mapping(LSM)based on different boosting and hyperparameter optimization algorithms:A case of Wanzhou District,China

Boosting algorithms have been widely utilized in the development of landslide susceptibility mapping(LSM)studies.However,these algorithms possess distinct computational strategies and hyperparameters,making it challenging to propose an ideal LSM model.To investigate the impact of different boosting algorithms and hyperparameter optimization algorithms on LSM,this study constructed a geospatial database comprising 12 conditioning factors,such as elevation,stratum,and annual average rainfall.The XGBoost(XGB),LightGBM(LGBM),and CatBoost(CB)algorithms were employed to construct the LSM model.Furthermore,the Bayesian optimization(BO),particle swarm optimization(PSO),and Hyperband optimization(HO)algorithms were applied to optimizing the LSM model.The boosting algorithms exhibited varying performances,with CB demonstrating the highest precision,followed by LGBM,and XGB showing poorer precision.Additionally,the hyperparameter optimization algorithms displayed different performances,with HO outperforming PSO and BO showing poorer performance.The HO-CB model achieved the highest precision,boasting an accuracy of 0.764,an F1-score of 0.777,an area under the curve(AUC)value of 0.837 for the training set,and an AUC value of 0.863 for the test set.The model was interpreted using SHapley Additive exPlanations(SHAP),revealing that slope,curvature,topographic wetness index(TWI),degree of relief,and elevation significantly influenced landslides in the study area.This study offers a scientific reference for LSM and disaster prevention research.This study examines the utilization of various boosting algorithms and hyperparameter optimization algorithms in Wanzhou District.It proposes the HO-CB-SHAP framework as an effective approach to accurately forecast landslide disasters and interpret LSM models.However,limitations exist concerning the generalizability of the model and the data processing,which require further exploration in subsequent studies.

Early Detection of Colletotrichum Kahawae Disease in Coffee Cherry Based on Computer Vision Techniques

Colletotrichum kahawae(Coffee Berry Disease)spreads through spores that can be carried by wind,rain,and insects affecting coffee plantations,and causes 80%yield losses and poor-quality coffee beans.The deadly disease is hard to control because wind,rain,and insects carry spores.Colombian researchers utilized a deep learning system to identify CBD in coffee cherries at three growth stages and classify photographs of infected and uninfected cherries with 93%accuracy using a random forest method.If the dataset is too small and noisy,the algorithm may not learn data patterns and generate accurate predictions.To overcome the existing challenge,early detection of Colletotrichum Kahawae disease in coffee cherries requires automated processes,prompt recognition,and accurate classifications.The proposed methodology selects CBD image datasets through four different stages for training and testing.XGBoost to train a model on datasets of coffee berries,with each image labeled as healthy or diseased.Once themodel is trained,SHAP algorithmto figure out which features were essential formaking predictions with the proposed model.Some of these characteristics were the cherry’s colour,whether it had spots or other damage,and how big the Lesions were.Virtual inception is important for classification to virtualize the relationship between the colour of the berry is correlated with the presence of disease.To evaluate themodel’s performance andmitigate excess fitting,a 10-fold cross-validation approach is employed.This involves partitioning the dataset into ten subsets,training the model on each subset,and evaluating its performance.In comparison to other contemporary methodologies,the model put forth achieved an accuracy of 98.56%.

Dynamic Forecasting of Traffic Event Duration in Istanbul:A Classification Approach with Real-Time Data Integration

Today,urban traffic,growing populations,and dense transportation networks are contributing to an increase in traffic incidents.These incidents include traffic accidents,vehicle breakdowns,fires,and traffic disputes,resulting in long waiting times,high carbon emissions,and other undesirable situations.It is vital to estimate incident response times quickly and accurately after traffic incidents occur for the success of incident-related planning and response activities.This study presents a model for forecasting the traffic incident duration of traffic events with high precision.The proposed model goes through a 4-stage process using various features to predict the duration of four different traffic events and presents a feature reduction approach to enable real-time data collection and prediction.In the first stage,the dataset consisting of 24,431 data points and 75 variables is prepared by data collection,merging,missing data processing and data cleaning.In the second stage,models such as Decision Trees(DT),K-Nearest Neighbour(KNN),Random Forest(RF)and Support Vector Machines(SVM)are used and hyperparameter optimisation is performed with GridSearchCV.In the third stage,feature selection and reduction are performed and real-time data are used.In the last stage,model performance with 14 variables is evaluated with metrics such as accuracy,precision,recall,F1-score,MCC,confusion matrix and SHAP.The RF model outperforms other models with an accuracy of 98.5%.The study’s prediction results demonstrate that the proposed dynamic prediction model can achieve a high level of success.

Yr5-virulent races of Puccinia striiformis f.sp.tritici possess relative parasitic fitness higher than current main predominant races and potential risk

Wheat stripe rust,caused by Puccinia striiformis f.sp.tritici(Pst),is one of the most destructive fungal diseases of wheat,and seriously threatens safe production of the crop worldwide.In China,new races historically appeared and rapidly developed to be predominant races and have resulted in ineffectiveness and replacement of wheat resistance cultivars as well as massive reduction in yield.In the present study,the relative parasitic fitness of the two newlyemerged Yr5-virulent races(TSA-6 and TSA-9)were compared with those of four currently predominant Chinese races(CYR31,CYR32,CYR33,and CYR34)based on evaluation on 10 Chinese wheat cultivars.As a result,there were significant differences in the relative parasitic fitness parameters among overall tested races based on multiple comparison(LSD)analysis(P<0.05).The principal component analysis(PCA)of overall parasitic fitness parameters indicated that the sporulation ability,infection and spore survivability,expansion capacity,and potential pathogenicity were the most important parasitic fitness attributes of the tested races.Based on the establishment of extracted three principal components and a comprehensive factor score mathematical models,evaluations of the parasitic fitness attributes of tested races showed that the level of relative parasitic fitness of the tested six races was:CYR32(1.15)>TSA-9(0.95)>TSA-6(0.92)>CYR34(0.29)>CYR31(–1.54)>CYR33(–1.77).The results indicated that two Yr5-virulent races TSA-9 and TSA-6 possessed relative parasitic fitness higher than races CYR34,CYR31,and CYR33,but lower than race CYR32,and have potential risks in developing to be predominant races.Therefore,continual monitoring of both Yr5-virulent races,and their variants is needed.The use of wheat cultivars(lines)with Yr5 resistance gene singly in wheat breeding is essential for being avoided,and is suggested to combine with other effective stripe rust resistance genes.

Theoretically predicted innovative palladium stripe dopingcobalt(111) surface with excellent catalytic performance for carbonmonoxide oxidative coupling to dimethyl oxalate

Pd-based catalysts are extensively employed to catalyze CO oxidative coupling to generate DMO,while the expensive price and high usage of Pd hinder its massive application in industrial production.Designing Pd-based catalysts with high efficiency and low Pd usage as well as expounding the catalytic mechanisms are significant for the reaction.In this study,we theoretically predict that Pd stripe doping Co(111)surface exhibits excellent performance than pure Pd(111),Pd monolayer supporting on Co(111)and Pd single atom doping Co(111)surface,and clearly expound the catalytic mechanisms through the density functional theory(DFT)calculation and micro-reaction kinetic model analysis.It is obtained that the favorable reaction pathway is COOCH_(3)-COOCH_(3)coupling pathway over these four catalysts,while the rate-controlling step is COOCH_(3)+CO+OCH_(3)→2COOCH_(3)on Pd stripe doping Co(111)surface,which is different from the case(2COOCH_(3)→DMO)on pure Pd(111),Pd monolayer supporting on Co(111)and Pd single atom doping Co(111)surface.This study can contribute a certain reference value for developing Pd-based catalysts with high efficiency and low Pd usage for CO oxidative coupling to DMO.

Screening of Wheat Cultivars for High Temperature Resistance to Stripe Rust from Wheat Resources in Huanghuai Growth Area

[Objective] The study aimed to screen wheat cultivars with high temperature resistance to stripe rust from the wheat resources in Huanghuai growth area. [Method] Seedlings of 165 wheat cultivars from Huanghuai growth area were identified by wheat stripe rust under high temperature; then the wheat cultivars showing stripe rust at seedling stage were further used to identify the same resistance in field. [Results] 13 cultivars were proved to be stripe rust resistant under high temperature, and the expression stages of stripe rust in the 13 cultivars were revealed. The field identification results confirmed the identification results at seedling stage via inoculation of mixed stripe rust of physiological races. The stripe resistances of wheat cultivars were also proved to be non-race-specific. [Conclusion] Wheat resources in Huanghuai growth area are abundant in wheat cultivars with high temperature resistance to stripe rust.

Physiological and Biochemical Characteristic of a Stripe Mutant with Abnormal Flower Organs in Rice

[Objective] The experiment aimed to explore physiological and biochemical changes of leaves after plants were mutated. [Method] A rice double mutant with stripes on stems, leaves and spikelets were taken as experimental materials to study the enzyme activity changes in different growth stages and amino acid variation in rice. [ Result] The SOD activity in mutant was higher than that in wild plant at tillering metaphase, but lower than that in wild type before heading stage and late flowering; the POD activity in three stages increased firstly then declined and the activity showed highest maximal activity at before heading stage. However, the POD activity in wild type showed the opposite change trend; the CAT activity presented degression at three stages, especially high at tillering metaphase, but reverse changes in wild type; the MDA activity decreased at three stages, but it was still higher than that in wild type, besides, the soluble sugar content of mutant was lower, but total amino acid content was increased. [ Conclusion] The expression of mutant characteristics was correlated with SOD, POD, CAT and MDA activity Changes and these changes made the mutant survive and rice quality change at last.

Forecast Model for Occurrence Degree of Wheat Stripe Rust Using Meteorological Data

[Objective] The aim of this study is to establish the model for forecasting wheat stripe rust occurrence condition using meteorological factors. [Method] Based on the data of wheat stripe rust occurrence degrees in its past prevalent years and the meteorological data at corresponding periods, the methods of grey correlation analysis and fuzzy mathematics were employed to establish the forecast model for four pathogenesis indices according to the time sequence before winter, Early March, Early April and Middle May. Thus, the criterion for forecasting the occurrence degree of wheat stripe rust was obtained based on the distribution method of arithmetic progression. [Result] The model corresponding to meteorological conditions for forecasting wheat stripe rust was successfully established. According to the verification, the forecasting results before winter and in Early Mar. were more severer than the real occurrence condition, while the forecasting results in Early Apr. and Middle May were basically consistent with real values. [Conclusion] The results of the present study may avail the control of wheat stripe rust in Henan Province.

Control of Wheat Stripe Rust and Ensuring High Yield of Crops under Planting of Wheat Variety Mixtures

Wheat stripe rust has become the most dangerous disease which threaten safe yield of wheat in Sichuan Province. It is meaningful to provide technique support for integrated disease control by exploring the effective control measures of wheat stripe rust. Wheat stripe rust dynamic developments of all-planting and mixed-planting have been systematically investigated in this study by taking different mixed-planting combinations among 6 wheat varieties with different resistance levels. The results of this experiment show that the mixed-plantings of 4 and 6 wheat varieties can delay the occurance of wheat stripe rust,slow the speed of disease and decline the damage of disease as well as stabilize yield of wheats.

Molecular Detection of Resistance Genes to Stripe Rust and Powdery Mildew in Common Wheat Cultivar Yunmai52

Yunmai52, developed by crossing with common wheat-Haynaldia villosa6AL/6VS translocation line 92R149 as a resistant parent in 1992, was a common wheat cultivar approved and released in 2007 in Yunnan Province, China, which is characterized by high resistance to powdery mildew and stripe rust. In this study,an F_2 population derived from a cross K78S/Yunmai52 was constructed to investigate the resistance genes, where K78 S is a wheat male sterile line susceptible to powdery mildew and stripe rust. Phenotypic identification of the parents, F_1 and F_2 populations and chi-square analyses showed that F_1 population was immune to stripe rust and powdery mildew; the segregation ratio of resistance and susceptibility to powdery mildew（χ~2=1.10χ~2_（1,0.05）=3.84） and stripe rust（χ~2=0.15χ~2_（1,0.05）=3.84） fit to a 3：1 ratio in F_2 population, indicating that Yunmai52 harbors a dominant stripe rust resistance gene and a dominant powdery mildew resistance gene. The individuals were further detected with a marker co-segregated with Pm21（SCAR_（1400）） and two markers closely linked with Yr26（XWe173 and Xbarc181）. The results showed that polymorphic bands could be amplified between the parents and between resistance and susceptibility gene pools at the same locus. Randomly 96 individuals of F_2 population were selected for verification. The results showed that the phenotype was significantly correlated with the genotype. The detection accuracy of markers SCAR_（1400）, XWe173 and Xbarc181 was 100%, 97.91% and 92.70%, respectively.Yunmai52 harbored powdery mildew resistance gene Pm21 and stripe rust resistance gene Yr26, which were both derived from 6AL/6VS translocation line 92R149.In addition, the results also demonstrate that Pm21 and Yr26 are two genes conferring durable resistance to powdery mildew and stripe rust in wheat.

Study on Epidemic Characteristics and Its Causes of Wheat Stripe Rust in Nanchong City

On basis of the research result of stripe rust for 16 years since 1999,the epidemic characteristics and trend of stripe rust in the city were determined.Namely,the earlier the initial stage appeared,the heavier the disease would be.Furthermore,stripe rust has two introduction infection peaks,of which the first peak plays a key role.In farmlands,there are one to three epidemic peaks,and the infection area of the first peak plays the key role on the epidemic area of that year.In addition,the accumulated areas of late January was in significantly positive correlation with annually total area,with a correlation coefficient of 0.769 2.In recent 16 years,the frequency of severe stripe rust was as high as 81.25% which was 50% higher than that before 1995.The slight stripe rust became just in 2013,with a frequency of 6.3%,which indicated that the city has become a region hit by severe stripe rust.The internal reason is the reduction or loss of wheat variety＇s resistance to tripe rust for a new physiological race of rust is becoming pathogenic stronger and be the major race.Big fluctuation of temperatures in warm winter and spring,foggy and dew days slants much would be the external reason.

Fast estimation of fundamental matrix based on stripe constraints

In order to improve the performance of estimating the fundamental matrix, a key problem arising in stereo vision, a novel method based on stripe constraints is presented. In contrast to traditional methods based on algebraic least-square algorithms, the proposed approach aims to minimize a cost function that is derived from the minimum radius of the Hough transform. In a structured-light system with a particular stripe code pattern, there are linear constraints that the points with the same code are on the same surface. Using the Hough transform, the pixels with the same code map to the Hough space, and the radius of the intersections can be defined as the evaluation function in the optimization progress. The global optimum solution of the fundamental matrix can be estimated using a Levenberg- Marquardt optimization iterative process based on the Hough transform radius. Results illustrate the validity of this algorithm, and prove that this method can obtain good performance with high efficiency.

Screening of Rice Stripe Virus(RSV)-resistant Germplasm from Landraces of Rice(Oryza sativa L.)

[Objective] The aim of this study was to screen rice strip virus （RSV）-resistant landraces. [Method] The resistance of 119 rice landraces to rice stripe virus was identified in field spontaneously infected with smal plant-hopper. [Result] There were 55 landraces resistant to rice strip disease in 56 indica rice landraces, but on-ly two resistant to rice strip disease in 63 japonica rice landraces. [Conclusion] The results revealed that there were abundant rice landscapes resistant to RSV in Chi-na, and these varieties can be used to develop more genes resistant to RSV.

Study on the control of rice stripe disease with Bilken virusicide

The paper reviewed the function mechanism of Bilken virusicide against rice stipe disease, and then introduced its control effects in field test as well as its application method.

xStripeMerge:基于纠删码存储的高效宽条带生成方法

为了解决纠删码存储系统的已有宽条带生成方法中扩容方案将产生大量宽条带生成带宽,合并方案受限于双条带的问题,提出了一种针对多条带合并下的宽条带生成问题的高效宽条带生成方法。定义了多条带合并过程的2个关键算子,并将宽条带生成问题建模为组合优化问题,提出了优先寻找具有小的奇偶校验块传输成本的窄条带组合方案的高效宽条带生成方法xStripeMerge。实验结果表明,与目前最优的存储扩容方法相比,xStripeMerge可以减少75%宽条带生成带宽。xStripeMerge的时间和空间复杂度远优于扩展的双条带合并方法,xStripeMerge可以在更短的时间内获得与其性能相近的宽条带生成方案,并且xStripeMerge可以适用于大规模存储系统。

基于Shapley additive explanations(SHAP)分析乳腺X线检查假阴性乳腺癌的特征

目的:基于Shapley additive explanations(SHAP)分析乳腺X线摄影(MG)检查假阴性乳腺癌的特征。方法:回顾性收集我院2018年1月—2022年5月首诊乳腺癌患者,评估临床病理和影像学表现。通过logistic回归分析确定MG假阴性乳腺癌的独立危险因素。应用SHAP分析各独立危险因素的贡献价值。结果:1505个病灶纳入研究,MG假阴性乳腺癌373个。较小的病灶、致密或不均匀致密的MG密度、无可疑钙化、非肿块样强化(NME)、原位癌和腋窝淋巴结阴性与MG假阴性乳腺癌独立相关,上述参数构建的联合模型具有良好的预测效能(AUC=0.871)。根据SHAP分析,病灶大小、MG密度和病灶MRI强化类型是导致MG假阴性的主要因素;钙化状态、病理类型和腋窝淋巴结状态是重要补充。结论:MG假阴性乳腺癌具有一定的特征,此类患者需补充乳腺MRI检查以提高癌灶的检出率。