GS Model Research Articles

Differences of stomatal ozone uptake in leaves of mature trees and seedlings of Zelkova serrata

Ozone uptake through the stomata in tree leaves is an important process for improving air quality by urban trees. Stomatal conductance (gs) is a key determinant of stomatal ozone uptake. The parameterization of gs models for estimating stomatal ozone uptake of trees has mainly been carried out using gs data measured in seedling leaves although the leaf traits may differ between mature trees and seedlings. In the present study, we compared stomatal ozone uptake estimated by gs models parameterised with data from mature trees and seedlings of Zelkova serrata. We measured gs in leaves of mature trees and seedlings of Z. serrata using a leaf porometer for 3–4 growing seasons. The Jarvis-type gs model was parameterised with data from mature trees and seedlings, separately. The maximum gs, and the functions of the seedling gs estimation model regarding the response to air temperature, vapour pressure deficit and atmospheric ozone concentration were the factors inducing lower stomatal ozone uptake. In contrast, the function of the seedling gs estimation model regarding the response to irradiance resulted in a higher estimated stomatal ozone uptake. The estimated stomatal ozone uptake for one growing season (April–September) by the seedling gs estimation model was 27% lower than that by the mature tree gs estimation model. These results indicate that leaf gas exchange traits of Z. serrata were different between mature trees and seedlings, and that estimating ozone uptake in mature tree leaves using a model based on seedling gs measurements results in an underestimation.

The impact of environmental variables on surface Conductance: Advancing simulation with a nonlinear Machine learning model

Surface conductance (Gs) is a key factor in the Penman-Monteith (PM) equation; the interaction between environmental variables such as CO2 concentration, air temperature (TA), vapor pressure deficit (VPD), soil water content (SWC), and net radiation (R) affects Gs, evapotranspiration and thus impacts the hydrological cycle. These interactions are highly nonlinear and vary among different vegetation types. However, conventional Gs simulation models use fixed interactions between environmental variables in their equations for all vegetation types. Moreover, the characterisation and parameterisation of conventional Gs models is highly uncertain due to the high spatiotemporal variability in key environmental variables and plant parameters, which inhibits their generalisation. This study investigates whether Gs could be estimated more accurately by nonlinear statistical techniques that capture the multiple interactions between the environmental variables that affect Gs for each vegetation type. We compare mixed generalized additive model (MGAM) for Gs simulation with semi-empirical and empirical models at 20 eddy covariance flux tower sites with four different vegetation types at daily and monthly timescales. The results show that the Nash-Sutcliffe Efficiency (NSE) in Gs simulation increased by up to 50% in MGAM model in comparison to the semi-empirical and empirical models. The MGAM model highlighted the interactive effects of CO2, VPD, and SWC for crops and grasses. The interactive effects of CO2, VPD, and TA were important for trees and grasses. The results from this study expand our understanding of the ability of Gs simulation models to identify and include the interactive effects of crucial environmental variables on plant transpiration and hydrological processes.

Correlation between the sound speed ratio and physical properties of seafloor sediments in the northwestern shelf of the south China sea

Based on a substantial number of seabed sediment samples and corresponding acoustic characteristic and physical property test data collected in the continental shelf area of the northwestern South China Sea, this study employed a sound speed ratio correction model to analyze the correlation between the sound speed ratio of seabed sediments and parameters such as porosity, density, mean grain size, and water content. This study resulted in the development of both single-parameter and double-parameter empirical formulas for the sound speed ratio of seabed sediments in this marine region. The determination coefficients (r2) for the single-parameter empirical formulas based on physical properties, excluding the mean grain size (r2 ≤ 0.75), were all above 0.78, indicating a strong correlation. Additionally, the fitting curve at 100 kHz was slightly greater than that at 50 kHz, consistent with the acoustic dispersion characteristics of sediments. To assess the applicability of the empirical formulas to different maritime areas, the formulas derived from the South Yellow Sea, East China Sea, and South China Sea were compared with those of the study area. The results indicated that the empirical formulas for the sound speed ratio established in this study have general applicability, especially for sediments with low porosity, high density, and low mean grain size. In addition, the determination coefficient of the dual-parameter empirical formula for the sound speed ratio–porosity–mean grain size is superior to that of the single-parameter empirical formula for the sound speed ratio–mean grain size. Through comparative studies with other marine areas, it was found that there are certain differences in the relationships between porosity and mean grain size established in different marine areas. Therefore, based on the rough sphere model, this study transforms the GS model into a single-parameter model built on either porosity or mean grain size. The error analysis results indicate that the single-parameter GS model has a smaller error than the measured data. However, the GS model controlled by a single parameter performs poorly compared to the predicted curves of the sound speed ratio–porosity and the sound speed ratio–mean grain size fitted in this study. This discrepancy may be attributed to the fact that the input parameters of the GS model are reference values, emphasizing the need for further research to determine suitable input parameters for the GS model in this marine area.

Prediction accuracy of genomic estimated breeding values for fruit traits in cultivated tomato (Solanum lycopersicum L.)

BackgroundGenomic selection (GS) is an efficient breeding strategy to improve quantitative traits. It is necessary to calculate genomic estimated breeding values (GEBVs) for GS. This study investigated the prediction accuracy of GEBVs for five fruit traits including fruit weight, fruit width, fruit height, pericarp thickness, and Brix. Two tomato germplasm collections (TGC1 and TGC2) were used as training populations, consisting of 162 and 191 accessions, respectively.ResultsLarge phenotypic variations for the fruit traits were found in these collections and the 51K Axiom™ SNP array generated confident 31,142 SNPs. Prediction accuracy was evaluated using different cross-validation methods, GS models, and marker sets in three training populations (TGC1, TGC2, and combined). For cross-validation, LOOCV was effective as k-fold across traits and training populations. The parametric (RR-BLUP, Bayes A, and Bayesian LASSO) and non-parametric (RKHS, SVM, and random forest) models showed different prediction accuracies (0.594–0.870) between traits and training populations. Of these, random forest was the best model for fruit weight (0.780–0.835), fruit width (0.791–0.865), and pericarp thickness (0.643–0.866). The effect of marker density was trait-dependent and reached a plateau for each trait with 768−12,288 SNPs. Two additional sets of 192 and 96 SNPs from GWAS revealed higher prediction accuracies for the fruit traits compared to the 31,142 SNPs and eight subsets.ConclusionOur study explored several factors to increase the prediction accuracy of GEBVs for fruit traits in tomato. The results can facilitate development of advanced GS strategies with cost-effective marker sets for improving fruit traits as well as other traits. Consequently, GS will be successfully applied to accelerate the tomato breeding process for developing elite cultivars.

An optimal ensemble of the CoLM for simulating the carbon and water fluxes over typical forests in China

Stomatal conductance (gs) and compensatory water uptake (CWU) are crucial processes in land surface models, as they directly influence the exchange of carbon and water fluxes between terrestrial ecosystems and the atmosphere. In this study, we integrated a new stomatal scheme derived from optimal stomatal theory (Medlyn's gs model), and an empirical CWU scheme into the Common Land Model (CoLM). Assessing the impacts on modeling gross primary productivity (GPP) and latent flux (LE) through observations obtained from eddy covariance (EC) measurements at three forest sites in China. Our results show that replacing the Ball-Berry's gs model (termed BB) with Medlyn's gs model (termed MED) did not bring about significant changes (had neutral impacts) in the performance of CoLM simulations at three forest sites. Considering the climate factors of annual mean precipitation to optimize key fitting parameters in gs exhibited improvement in model simulations. The average coefficient of determination (R2) achieved to 0.65 for GPP and LE at three sites, and the normalized root mean squared error (NRMSE) decreased from 0.83 to 0.77 at those sites. Besides, incorporating CWU into the model improved its performance. The R2 increased to 0.84 and RMSE decreased to 4.84 μmol m−2 s−1 for GPP, and the R2 increased to 0.62 and RMSE decreased to 55.64 W m−2 for LE. Therefore, modifying the model process of both contributed more to enhancing the model simulations than relying solely on one of these functions. Our study highlights that the response of plant functional types (PFTs) to water stress can be effectively represented in gs models when coupled with biochemical capacity to quantify carbon and water fluxes in forest ecosystems or other ecosystems.

An Optimal Model to Improve Genomic Prediction for Protein Content and Test Weight in a Diverse Spring Wheat Panel

In recent years, genomic selection has been widely used in plant breeding to increase genetic gain. Selections are based on breeding values of each genotype estimated using genome-wide markers. The present study developed genomic prediction models for grain protein content (GPC) and test weight (TW) in a diverse panel of 170 spring wheat lines phenotyped in five environments. Five prediction models (GBLUP, RRBLUP, EGBLUP, RF, RKHS) were investigated. The population was genotyped for genome-wide markers with the Infinium iSelect 90 K SNP assay. Environmental variation was adjusted by calculating BLUPs across environments using the complete random effect GxE model. Both GPC and TW showed high heritability of 0.867 and 0.854, respectively. When using the five-fold cross-validation scheme in the five statistical models, we found that the EGBLUP model had the highest mean prediction accuracy (0.743) for GPC, while the RRBLUP model showed the highest mean prediction accuracy (0.650) for TW. Testing various proportions of the training population indicated that a minimum of 100 genotypes were required to train the model for optimum accuracy. Testing the prediction across environments showed that BLUPs outperformed 80% of the tested environments, even though at least one of the environments had higher prediction accuracies for each trait. Thus, the optimized GS model for GPC and TW has the potential to predict trait values accurately. Implementing GS would aid breeding through accurate early generation selection of superior lines, leading to higher genetic gain per breeding cycle.

Realized genomic selection across generations in a reciprocal recurrent selection breeding program of Eucalyptus hybrids.

Genomic selection (GS) experiments in forest trees have largely reported estimates of predictive abilities from cross-validation among individuals in the same breeding generation. In such conditions, no effects of recombination, selection, drift, and environmental changes are accounted for. Here, we assessed the effectively realized predictive ability (RPA) for volume growth at harvest age by GS across generations in an operational reciprocal recurrent selection (RRS) program of hybrid Eucalyptus. Genomic best linear unbiased prediction with additive (GBLUP_G), additive plus dominance (GBLUP_G+D), and additive single-step (HBLUP) models were trained with different combinations of growth data of hybrids and pure species individuals (N = 17,462) of the G1 generation, 1,944 of which were genotyped with ~16,000 SNPs from SNP arrays. The hybrid G2 progeny trial (HPT267) was the GS target, with 1,400 selection candidates, 197 of which were genotyped still at the seedling stage, and genomically predicted for their breeding and genotypic values at the operational harvest age (6 years). Seedlings were then grown to harvest and measured, and their pedigree-based breeding and genotypic values were compared to their originally predicted genomic counterparts. Genomic RPAs ≥0.80 were obtained as the genetic relatedness between G1 and G2 increased, especially when the direct parents of selection candidates were used in training. GBLUP_G+D reached RPAs ≥0.70 only when hybrid or pure species data of G1 were included in training. HBLUP was only marginally better than GBLUP. Correlations ≥0.80 were obtained between pedigree and genomic individual ranks. Rank coincidence of the top 2.5% selections was the highest for GBLUP_G (45% to 60%) compared to GBLUP_G+D. To advance the pure species RRS populations, GS models were best when trained on pure species than hybrid data, and HBLUP yielded ~20% higher predictive abilities than GBLUP, but was not better than ABLUP for ungenotyped trees. We demonstrate that genomic data effectively enable accurate ranking of eucalypt hybrid seedlings for their yet-to-be observed volume growth at harvest age. Our results support a two-stage GS approach involving family selection by average genomic breeding value, followed by within-top-families individual GS, significantly increasing selection intensity, optimizing genotyping costs, and accelerating RRS breeding.

Image-based phenomic prediction can provide valuable decision support in wheat breeding

Key messageGenotype-by-environment interactions of secondary traits based on high-throughput field phenotyping are less complex than those of target traits, allowing for a phenomic selection in unreplicated early generation trials.Traditionally, breeders’ selection decisions in early generations are largely based on visual observations in the field. With the advent of affordable genome sequencing and high-throughput phenotyping technologies, enhancing breeders’ ratings with such information became attractive. In this research, it is hypothesized that GtimesE interactions of secondary traits (i.e., growth dynamics’ traits) are less complex than those of related target traits (e.g., yield). Thus, phenomic selection (PS) may allow selecting for genotypes with beneficial response-pattern in a defined population of environments. A set of 45 winter wheat varieties was grown at 5 year-sites and analyzed with linear and factor-analytic (FA) mixed models to estimate GtimesE interactions of secondary and target traits. The dynamic development of drone-derived plant height, leaf area and tiller density estimations was used to estimate the timing of key stages, quantities at defined time points and temperature dose–response curve parameters. Most of these secondary traits and grain protein content showed little GtimesE interactions. In contrast, the modeling of GtimesE for yield required a FA model with two factors. A trained PS model predicted overall yield performance, yield stability and grain protein content with correlations of 0.43, 0.30 and 0.34. While these accuracies are modest and do not outperform well-trained GS models, PS additionally provided insights into the physiological basis of target traits. An ideotype was identified that potentially avoids the negative pleiotropic effects between yield and protein content.

Stomatal conductance parameters of tomatoes are regulated by reducing osmotic potential and pre-dawn leaf water potential via increasing ABA under salt stress

The opening and closing of stomata control the diffusion of CO2 and water vapor, and accurate modeling of stomatal conductance (gs) is essential for predicting global water and CO2 fluxes. The Ball-Berry and Medlyn models with key slope parameters (m or g1) have been widely used to estimate gs. However, there are few studies on quantifying the model parameter values of different tomato genotypes and analyzing their relationship with physiological traits under salt stress. Here, we fit m and g1 of tomatoes using stomatal response curves and analyzed their correlations with physiological characteristics under five genotypes and two salt treatments. m was significantly different while g1 was more stable among genotypes, and salt stress significantly reduced them. By testing differences in regression slopes or intercepts, the relationships between leaf water status, and biochemical and stomatal development are found to be uniform among tomato genotypes. Osmotic potential, pre-dawn leaf water potential and abscisic acid (ABA) are driving factors for gs model parameters. Structural equation modeling showed that ABA directly modulates model parameters, not through leaf morphology or stomatal development. Overall, this study quantified gs model parameters and identified driving factors for different tomato genotypes under salt stress, providing a practical approach to model water and carbon fluxes under salt stress.

Improved representation of plant physiology in the JULES-vn5.6 land surface model: photosynthesis, stomatal conductance and thermal acclimation

Abstract. Carbon and water cycle dynamics of vegetation are controlled primarily by photosynthesis and stomatal conductance (gs). Our goal is to improve the representation of these key physiological processes within the JULES land surface model, with a particular focus on refining the temperature sensitivity of photosynthesis, impacting modelled carbon, energy and water fluxes. We test (1) an implementation of the Farquhar et al. (1980) photosynthesis scheme and associated plant functional type-dependent photosynthetic temperature response functions, (2) the optimality-based gs scheme from Medlyn et al. (2011) and (3) the Kattge and Knorr (2007) photosynthetic capacity thermal acclimation scheme. New parameters for each model configuration are adopted from recent large observational datasets that synthesise global experimental data. These developments to JULES incorporate current physiological understanding of vegetation behaviour into the model and enable users to derive direct links between model parameters and ongoing measurement campaigns that refine such parameter values. Replacement of the original Collatz et al. (1991) C3 photosynthesis model with the Farquhar scheme results in large changes in GPP for the current day, with ∼ 10 % reduction in seasonal (June–August, JJA, and December–February, DJF) mean GPP in tropical forests and ∼ 20 % increase in the northern high-latitude forests in JJA. The optimality-based gs model decreases the latent heat flux for the present day (∼ 10 %, with an associated increase in sensible heat flux) across regions dominated by needleleaf evergreen forest in the Northern Hemisphere summer. Thermal acclimation of photosynthesis coupled with the Medlyn gs scheme reduced tropical forest GPP by up to 5 % and increased GPP in the high-northern-latitude forests by between 2 % and 5 %. Evaluation of simulated carbon and water fluxes by each model configuration against global data products shows this latter configuration generates improvements in these key areas. Thermal acclimation of photosynthesis coupled with the Medlyn gs scheme improved modelled carbon fluxes in tropical and high-northern-latitude forests in JJA and improved the simulation of evapotranspiration across much of the Northern Hemisphere in JJA. Having established good model performance for the contemporary period, we force this new version of JULES offline with a future climate scenario corresponding to rising atmospheric greenhouse gases (Shared Socioeconomic Pathway (SSP5), Representative Concentration Pathway 8.5 (RCP8.5)). In particular, these calculations allow for understanding of the effects of long-term warming. We find that the impact of thermal acclimation coupled with the optimality-based gs model on simulated fluxes increases latent heat flux (+50 %) by the year 2050 compared to the JULES model configuration without acclimation. This new JULES configuration also projects increased GPP across tropical (+10 %) and northern-latitude regions (+30 %) by 2050. We conclude that thermal acclimation of photosynthesis with the Farquhar photosynthesis scheme and the new optimality-based gs scheme together improve the simulation of carbon and water fluxes for the current day and have a large impact on modelled future carbon cycle dynamics in a warming world.

Evaluation of genomic selection for high salinity tolerance traits in Pacific white shrimp Litopenaeus vannamei

The Pacific white shrimp (Litopenaeus vannamei) can tolerate wide range of salinity from 0.5 to 40. However, the growth and survival rate of shrimp is relatively low when they are cultured in high salinity seawater (>40). There is a large area of high salinity water which could be utilized for shrimp aquaculture. It is necessary to select new varieties of shrimp with high salinity tolerance, which can be cultured in these areas. In this study, the genetic parameters and genomic prediction accuracy on high salinity tolerance traits of Litopenaeus vannamei were evaluated using genome-wide SNPs. The survival time, lethal salinity and survival status of shrimp during high salinity treatments were recorded as phenotypes for each individual. The heritabilities of three phenotypes including survival time, lethal salinity and survival status were 0.38 ± 0.1, 0.40 ± 0.1, 0.5 ± 0.1, respectively. Among different GS models, GBLUP and BayesB showed similar prediction accuracy. The prediction accuracy of GBLUP for survival time, lethal salinity, and survival state of shrimp were 0.62 ± 0.03, 0.64 ± 0.04, and 0.76 ± 0.01, respectively. Compared with traditional pedigree selection (PBLUP), GS models showed a higher prediction accuracy by up to 12.1%. In addition, the optimal training population size was predicted to be around 1000, in which the prediction accuracy could rise to around 0.9. These results indicate that the heritability of high salinity tolerance traits of shrimp is medium to high heritability, and the genomic selection is superior to traditional selection approach. This study provides important guidance for genomic selection of shrimp tolerant to high salinity.

Genome-wide association study and genomic prediction for yield and grain quality traits of hybrid rice.

The online version contains supplementary material available at 10.1007/s11032-022-01289-6.

Predicting Phenotypes From High-Dimensional Genomes Using Gradient Boosting Decision Trees

xsxsGenomic selection (GS) is an emerging technique for predicting unknown phenotypes using genome-wide marker coverage, allowing the use of efficient computational models to select individuals with high phenotypic values as candidate breeding populations. However, GS remains challenging inefficient crop breeding due to the limited size of training populations, the nature of genotype-environment interactions, and the complex interaction patterns between molecular markers. In this study, we use ensemble learning algorithms to construct gradient boosted decision tree (GBDT) models to achieve the prediction of phenotypic values from genotypic markers. We trained GBDT using the wheat GS dataset and compared the predictive performance with six other widely used GS models. The mean normalized discounted cumulative gain (MNDCG) method was used to evaluate the ability of each model to select individuals with high phenotypic values. The results of the study show that: (1) Bayesian models converge and reach a steady-state only when a sufficient number of iterations are set. As the number of iterations increases, the prediction accuracy of the Bayesian model increases, but the computational efficiency of the model decreases significantly. When 200,000 iterations are performed, the prediction performance of the five Bayesian models is similar and converges to a smooth state, and their prediction accuracy is 7.60% better than the GBDT model overall, and the computational efficiency of the GBDT model is 70 times that of the Bayesian model. (2) Overall, the overall prediction performance of the RRBLUP model was the best, but for some traits, the GBDT model still had a higher ability to select individuals with high phenotypic values than the RRBLUP and Bayesian models. (3) The prediction accuracy of GBDT and RRBLUP models was influenced by the subset of markers, and the higher the number of markers the higher the prediction accuracy of the models, so the reasonable selection of genetic marker data of appropriate size could improve the prediction performance of the models.

Inventory calculation for commercial production of cereal grain flour using particle size distribution model.

In India cerealgrain sector reflects increasing demand with increasingly affluent population.Wheat, jowar (sorghum), and corn (maize) are commonly consumed cereal grains in Indian subcontinent and hence produced abundantly. These cereals are consumed in the form of flour (Atta) obtained fromeither in domestic or commercial mills, which have increasingglobal market demand. The particle size distribution of cerealgrain flour decides the quality of final product. Thus study of particle size distribution of flour obtainedduring milling is one of the essential parameter for the large scale production. In the present research work grinding of wheat, jowar (sorghum) and corn(maize) using flour mills (Buhrstone mill) and their particle size distribution using suitable mathematical models (Gaudin-Schuhmann and Rosin-Rammler-Bennett) have been studied. Further the best fit model equation is utilized to determine the inventory required for the large scale production of cereal flour. Upon applying different models it was found that GS model fits well to cereal grain flour making which is evident from high coefficient of determination of 0.9221. The model can be used to compute inventory requirement for production of flour of desired size of required cereal grain. For example to produce 1 ton of 1362µm size of wheat flour one needs 1.4 ton of raw material.

Multi-Trait Multi-Environment Genomic Prediction of Agronomic Traits in Advanced Breeding Lines of Winter Wheat.

Genomic prediction is a promising approach for accelerating the genetic gain of complex traits in wheat breeding. However, increasing the prediction accuracy (PA) of genomic prediction (GP) models remains a challenge in the successful implementation of this approach. Multivariate models have shown promise when evaluated using diverse panels of unrelated accessions; however, limited information is available on their performance in advanced breeding trials. Here, we used multivariate GP models to predict multiple agronomic traits using 314 advanced and elite breeding lines of winter wheat evaluated in 10 site-year environments. We evaluated a multi-trait (MT) model with two cross-validation schemes representing different breeding scenarios (CV1, prediction of completely unphenotyped lines; and CV2, prediction of partially phenotyped lines for correlated traits). Moreover, extensive data from multi-environment trials (METs) were used to cross-validate a Bayesian multi-trait multi-environment (MTME) model that integrates the analysis of multiple-traits, such as G × E interaction. The MT-CV2 model outperformed all the other models for predicting grain yield with significant improvement in PA over the single-trait (ST-CV1) model. The MTME model performed better for all traits, with average improvement over the ST-CV1 reaching up to 19, 71, 17, 48, and 51% for grain yield, grain protein content, test weight, plant height, and days to heading, respectively. Overall, the empirical analyses elucidate the potential of both the MT-CV2 and MTME models when advanced breeding lines are used as a training population to predict related preliminary breeding lines. Further, we evaluated the practical application of the MTME model in the breeding program to reduce phenotyping cost using a sparse testing design. This showed that complementing METs with GP can substantially enhance resource efficiency. Our results demonstrate that multivariate GS models have a great potential in implementing GS in breeding programs.

An Assessment of the Factors Influencing the Prediction Accuracy of Genomic Prediction Models Across Multiple Environments.

The effects of climate change create formidable challenges for breeders striving to produce sufficient food quantities in rapidly changing environments. It is therefore critical to investigate the ability of multi-environment genomic prediction (GP) models to predict genomic estimated breeding values (GEBVs) in extreme environments. Exploration of the impact of training set composition on the accuracy of such GEBVs is also essential. Accordingly, we examined the influence of the number of training environments and the use of environmental covariates (ECs) in GS models on four subsets of n = 500 lines of the soybean nested association mapping (SoyNAM) panel grown in nine environments in the US-North Central Region. The ensuing analyses provided insights into the influence of both of these factors for predicting grain yield in the most and the least extreme of these environments. We found that only a subset of the available environments was needed to obtain the highest observed prediction accuracies. The inclusion of ECs in the GP model did not substantially increase prediction accuracies relative to competing models, and instead more often resulted in negative prediction accuracies. Combined with the overall low prediction accuracies for grain yield in the most extreme environment, our findings highlight weaknesses in current GP approaches for prediction in extreme environments, and point to specific areas on which to focus future research efforts.

Accuracy and Clinical Utility of a Tumor Grade- and Stage-based Predictive Model in Localized Upper Tract Urothelial Carcinoma

BackgroundAmong various clinicopathologic factors used to identify low-risk upper tract urothelial carcinoma (UTUC), tumor grade and stage are of utmost importance. The clinical value added by inclusion of other risk factors remains unproven. ObjectiveTo assess the performance of a tumor grade- and stage-based (GS) model to identify patients with UTUC for whom kidney-sparing surgery (KSS) could be attempted. Design, setting, and participantsIn this international study, we reviewed the medical records of 1240 patients with UTUC who underwent radical nephroureterectomy. Complete data needed for risk stratification according to the European Association of Urology (EAU) and National Comprehensive Cancer Network (NCCN) guidelines were available for 560 patients. Outcome measurements and statistical analysisUnivariable and multivariable logistic regression analyses were performed to determine if risk factors were associated with the presence of localized UTUC. The sensitivity, specificity, positive predictive value, negative predictive value, and accuracy of the GS, EAU, and NCCN models in predicting pathologic stage were calculated. Results and limitationsOverall, 198 patients (35%) had clinically low-grade, noninvasive tumors, and 283 (51%) had ≤pT1disease. On multivariable analyses, none of the EAU and NCCN risk factors were associated with the presence of non–muscle-invasive UTUC among patients with low-grade and low-stage UTUC. The GS model exhibited the highest accuracy, sensitivity, and negative predictive value among all three models. According to the GS, EAU, and NCCN models, the proportion of patients eligible for KSS was 35%, 6%, and 4%, respectively. Decision curve analysis revealed that the net benefit of the three models was similar within the clinically reasonable range of probability thresholds. ConclusionsThe GS model showed favorable predictive accuracy and identified a greater number of KSS-eligible patients than the EAU and NCCN models. A decision-making algorithm that weighs the benefits of avoiding unnecessary kidney loss against the risk of undertreatment in case of advanced carcinoma is necessary for individualized treatment for UTUC patients. Patient summaryWe assessed the ability of three models to predict low-grade, low-stage disease in patients with cancer of the upper urinary tract. No risk factors other than grade assessed on biopsy and stage assessed from scans were associated with better prediction of localized cancer. A model based on grade and stage may help to identify patients who could benefit from kidney-sparing treatment of their cancer.

Modelling Occurrence of Invasive Water Hyacinth (Eichhornia crassipes) in Wetlands

Eichhornia crassipes is one of the most ubiquitous invasive aquatic species in the world that negatively impact on native fauna and flora. The information on the occurrence of such invasive plant is crucial for the river/wetland management. The aim of the present study is to apply classification tree model (testing with the highest level of pruning confidence factor) integrated with an optimizer technique (greedy stepwise search algorithm) to predict the occurrence of this exotic species based on water quality and physical-habitat parameters. The ten sites (in the Anzali wetland, located in northern Iran) were monthly measured where the exotic species was present in 50 % of sampling sites and it was absent in the remaining of the sites. In total, 120 samples of E. crassipes (60 presence and 60 absence instances) were monthly measured together with 12 environmental variables during 1-year study period (2017–2018). For the model, two-third of datasets (80 instances) was employed for the training and the remainder for the validation set (40 instances). Before model optimizing (with the pruning confidence factor, PCF = 0.01), six variables were predicted by the model in three folds confirming that the non-occurrence of the exotic species might be associated with increasing flow velocity, depth of ecosystem, water turbidity, bicarbonate and dissolved oxygen concentration, while the occurrence of the exotic species (in terms of the abundance) might show an increase with increasing the concentration of nutrients such as phosphate. After model optimizing (with PCF = 0.01), the model selected five variables in three folds (flow velocity, depth, phosphate, bicarbonate, and nitrate) so that except nitrate, other selected variables were in common before and after variable selection. CT integrated with GS model thus proved to have a high potential when applied for decision-making in the context of wetland management.

Dissecting the Contribution of Environmental Influences, Plant Phenology, and Disease Resistance to Improving Genomic Predictions for Fusarium Head Blight Resistance in Wheat

Environmental factors like temperature and humidity are presumed to greatly influence Fusarium head blight FHB infections in wheat. Anther retention AR, on the other hand, is a morphologically neutral trait that shares a common genetic basis with FHB resistance. In this study, our aims were to: (i) Evaluate two types of corrections of FHB severity scores, namely method-1 via linear regression on flowering time (FT), and method-2 via a best-subset multiple linear regression analysis comprising FT plus accumulated thermal time variables; and (ii) assess the performance of multi-trait genomic selection (MT.GS) models for FHB severity assisted by AR. The forward prediction scenarios where GS models were trained with data from the previous years revealed average prediction accuracies (PA) of 0.28, 0.33, and 0.36 for FHB severity scores that were uncorrected or corrected by method-1 and method-2, respectively. FHB severity scores free from the influences of both environment and phenology seemed to be the most efficient trait to be predicted across different seasons. Average PA increments up to 1.9-fold were furthermore obtained for the MT.GS models, evidencing the feasibility of using AR as an assisting trait to improve the genomic selection of FHB resistance breeding lines.

Genomic selection of forage agronomic traits in winter wheat

AbstractGenomic selection (GS) can improve genetic gain of complex traits in plant breeding. Phenotyping agronomic traits of winter wheat (Triticum aestivum L.) for dual‐purpose use is expensive and time‐consuming. In this study, we compared the prediction accuracies of four GS models (RR‐BLUP, GBLUP, GAUSS, and BL) for forage yield (FY), plant height (PH) and heading date (HD) of the hard winter wheat diversity panel (n = 298) using random and stratified sampling methods. In addition, we determined the appropriate training population (TP) size and marker density for GS of the traits. Moderate to high prediction accuracies ranging from 0.66 to 0.69 for FY, 0.46 to 0.49 for PH, and 0.71 to 0.74 for HD were observed for the GS models. However, the sampling method had little or no impact on prediction accuracy. The RR‐BLUP, GBLUP, and GAUSS models produced slightly greater prediction accuracies than BL for all traits studied. Prediction accuracies increased with increasing TP size and marker density in all the GS models tested. However, increase of prediction accuracy started to plateau at nTP = 180 lines and 1,000; 1,500; or 3,000 SNPs suggesting that the minimum TP size and marker density were about 180 lines and 1,000 or more SNPs, depending on the model and trait. The impact of TP size on prediction accuracy was greater for RR‐BLUP, GAUSS, and GBLUP than for BL model. This study suggests that RR‐BLUP, GBLUP, and GAUSS are viable models for selecting the forage agronomic traits during dual‐purpose wheat breeding.