Development of Machine Learning Methods for Accurate Prediction of Plant Disease Resistance

The traditional method of screening plants for disease resistance phenotype is both time-consuming and costly.Genomic selection offers a potential solution to improve efficiency,but accurately predicting plant disease resistance remains a challenge.In this study,we evaluated eight different machine learning(ML)methods,including random forest classification(RFC),support vector classifier(SVC),light gradient boosting machine(lightGBM),random forest classification plus kinship(RFC_K),support vector classification plus kinship(SVC_K),light gradient boosting machine plus kinship(lightGBM_K),deep neural network genomic prediction(DNNGP),and densely connected convolutional networks(DenseNet),for predicting plant disease resistance.Our results demonstrate that the three plus kinship(K)methods developed in this study achieved high prediction accuracy.Specifically,these methods achieved accuracies of up to 95%for rice blast(RB),85%for rice black-streaked dwarf virus(RBSDV),and 85%for rice sheath blight(RSB)when trained and applied to the rice diversity panel I(RDPI).Furthermore,the plus K models performed well in predicting wheat blast(WB)and wheat stripe rust(WSR)diseases,with mean accuracies of up to 90%and 93%,respectively.To assess the generalizability of our models,we applied the trained plus K methods to predict RB disease resistance in an independent population,rice diversity panel II(RDPII).Concurrently,we evaluated the RB resistance of RDPII cultivars using spray inoculation.Comparing the predictions with the spray inoculation results,we found that the accuracy of the plus K methods reached 91%.These findings highlight the effectiveness of the plus K methods(RFC_K,SVC_K,and lightGBM_K)in accurately predicting plant disease resistance for RB,RBSDV,RSB,WB,and WSR.The methods developed in this study not only provide valuable strategies for predicting disease resistance,but also pave the way for using machine learning to streamline genome-based crop breeding.

Deformation resistance prediction of tandem cold rolling based on grey wolf optimization and support vector regression

In the traditional rolling force model of tandem cold rolling mills,the calculation of the deformation resistance of the strip head does not consider the actual size and mechanical properties of the incoming material,which results in a mismatch between the deformation resistance setting and the actual state of the incoming material and thus affects the accuracy of the rolling force during the low-speed rolling process of the strip head.The inverse calculation of deformation resistance was derived to obtain the actual deformation resistance of the strip head in the tandem cold rolling process,and the actual process parameters of the strip in the hot and cold rolling processes were integrated to create the cross-process dataset as the basis to establish the support vector regression(SVR)model.The grey wolf optimization(GWO)algorithm was used to optimize the hyperparameters in the SVR model,and a deformation resistance prediction model based on GWO–SVR was established.Compared with the traditional model,the GWO–SVR model shows different degrees of improvement in each stand,with significant improvement in stands S3–S5.The prediction results of the GWO–SVR model were applied to calculate the head rolling setting of a 1420 mm tandem rolling mill.The head rolling force had a similar degree of improvement in accuracy to the deformation resistance,and the phenomenon of low head rolling force setting from stands S3 to S5 was obviously improved.Meanwhile,the thickness quality and shape quality of the strip head were improved accordingly,and the application results were consistent with expectations.

Molecular Drug Resistance Prediction for Acetohydroxyacid Synthase Mutants Against Chlorsulfuron Using MB-QSAR

Chlorsulfuron is the first commercialized sulfonylurea herbicide, which targets acetohydroxyacid synthase （AHAS）. Mutations in AHAS have caused serious herbicide resistance to chlorsulfuron. Quantitative description of the herbicide resistance in molecular level will benefit the understanding of the resistance mechanism and aid the design of resistance-evading herbicide. We have recently established a MB-QSAR （Mutation-dependent Biomac- romolecular Quantitative Structure-Activity Relationship） method to conduct the 3D-QSAR study in biomacro- molecules. Herein, based on the herbicide resistance data measured for a series of AHAS mutants against chlorsul- furon, we constructed MB-QSAR models to quantitatively predict the herbicide resistance and interpret the struc- ture resistance relationships for AHAS mutants against chlorsulfuron. Quite well correlations between the experi- mental and the predicted pKi values were achieved for MB-QSAR/CoMFA （q^2=0.705, r^2=0.918, r^2pred=0.635） and MB-QSAR/CoMSIA （q^2=0.558, r^2=0.940, r^2pred=0.527） models, and interpretation of the MB-QSAR models gave chemical intuitive information to guide the resistance-evading herbicide design.

Uncertainty Analysis in CFD for Resistance

Based on the Latin square design of statistics, the thickness of first boundary layer, the turbulence model and the cell number were taken as the three main factors of uncertainty in CFD （computational fluid dynamics）. Total resistance of hull was calculated and the flow field around the hull was simulated by CFD method. Then, the influence of uncertainty factors on the hull resistance was discussed by regression analysis with trimmed mesh and overset mesh. Through a series of calculation and analysis, the optimal calculation method was put forward, and the relevant parameters of the calculation were determined. Thirdly, the total resistance of different speed was calculated by using these two kinds of grids, which were in good agreement with the experimental results. Finally, according to the ITTC recommended procedures, uncertainty analysis in CFD was carried out with the numerical results of the total resistance by three sets of grids with uniform refinement ratio rG = √2. Then the modified resistance was compared with the experimental result, which improved the accuracy of the resistance prediction.

Experimental study on uplift mechanism of pipeline buried in sand using high-resolution fiber optic strain sensing nerves

Reliable assessment of uplift capacity of buried pipelines against upheaval buckling requires a valid failure mechanism and a reliable real-time monitoring technique.This paper presents a sensing solution for evaluating uplift capacity of pipelines buried in sand using fiber optic strain sensing(FOSS)nerves.Upward pipe-soil interaction(PSI)was investigated through a series of scaled tests,in which the FOSS and image analysis techniques were used to capture the failure patterns.The published prediction models were evaluated and modified according to observations in the present study as well as a database of 41 pipe loading tests assembled from the literature.Axial strain measurements of FOSS nerves horizontally installed above the pipeline were correlated with the failure behavior of the overlying soil.The test results indicate that the previous analytical models could be further improved regarding their estimations in the failure geometry and mobilization distance at the peak uplift resistance.For typical slip plane failure forms,inclined shear bands star from the pipe shoulder,instead of the springline,and have not yet reached the ground surface at the peak resistance.The vertical inclination of curved shear bands decreases with increasing uplift displacements at the post-peak periods.At large displacements,the upward movement is confined to the deeper ground,and the slip plane failure progressively changes to the flow-around.The feasibility of FOSS in pipe uplift resistance prediction was validated through the comparison with image analyses.In addition,the shear band locations can be identified using fiber optic strain measurements.Finally,the advantages and limits of the FOSS system are discussed in terms of different levels in upward PSI assessment,including failure identification,location,and quantification.

Experimental and computational correlation and prediction on herbicide resistance for acetohydroxyacid synthase mutants to Bispyribac

Bispyribac is a widely used herbicide that targets the acetohydroxyacid synthase （AHAS） enzyme. Mutations in AHAS have caused serious herbicide resistance that threatened the continued use of the herbicide. So far, a unified model to decipher herb- icide resistance in molecular level with good prediction is still lacking. In this paper, we have established a new QSAR method to construct a prediction model for AHAS mutation resistance to herbicide Bispyribac. A series of AHAS mutants concerned with the herbicide resistance were constructed, and the inhibitory properties of Bispyribac against these mutants were meas- ured. The 3D-QSAR method has been transformed to process the AHAS mutants and proposed as mutation-dependent biom- acromolecular QSAR （MB-QSAR）. The excellent correlation between experimental and computational data gave the MB-QSAR/CoMFA model （q2 = 0.615, P = 0.921, F2pred = 0.598） and the MB-QSAR/CoMSIA model （q2 = 0.446, r2 = 0.929, r2pred = 0.612）, which showed good prediction for the inhibition properties of Bispyribac against AHAS mutants. Such MB-QSAR models, containing the three-dimensional molecular interaction diagram, not only disclose to us for the first time the detailed three-dimensional information about the structure-resistance relationships, but may also provide further guidance to resistance mutation evolution. Also, the molecular interaction diagram derived from MB-QSAR models may aid the resistance-evading herbicide design.

Modeling hot strip rolling process under framework of generalized additive model

This research develops a new mathematical modeling method by combining industrial big data and process mechanism analysis under the framework of generalized additive models(GAM)to generate a practical model with generalization and precision.Specifically,the proposed modeling method includes the following steps.Firstly,the influence factors are screened using mechanism knowledge and data-mining methods.Secondly,the unary GAM without interactions including cleaning the data,building the sub-models,and verifying the sub-models.Subsequently,the interactions between the various factors are explored,and the binary GAM with interactions is constructed.The relationships among the sub-models are analyzed,and the integrated model is built.Finally,based on the proposed modeling method,two prediction models of mechanical property and deformation resistance for hot-rolled strips are established.Industrial actual data verification demonstrates that the new models have good prediction precision,and the mean absolute percentage errors of tensile strength,yield strength and deformation resistance are 2.54%,3.34%and 6.53%,respectively.And experimental results suggest that the proposed method offers a new approach to industrial process modeling.