tural methods, plant resistance potentially provides the most economic and environmentally effective means of The sugarcane borer [Diatraea saccharalis (Frabricius)] causes control available to the production industry (Hensley, significant damage to sugarcane (Saccharum spp.), rendering cultivar resistance important. Researchers assess borer-induced damage using 1981). Hence, cultivar characterization and selection for up to five different measures: percentage bored internodes, percentage resistance plays an important part of the evaluation exited internodes, pupation success, moth production, and a damage process. rating. The inheritance of the different damage measures and the best Cultivars express resistance via complex contribuapproach to integrate the different variables into a simplified damagetions and interactions of several components (Kyle and resistance assessment has not been well studied. Furthermore, the Hensley, 1970; Coburn and Hensley, 1972; Martin et al., relationships of the damage traits to sugar production have not been 1975; White and Hensley, 1987; Sosa, 1988; Bessin et comparatively assessed. We planted a replicated, two-location test of al., 1990; White, 1993). Thus, all of the recognized mech-28 clones typical of the selection stage screened for borer resistance anisms of resistance (antibiosis, antizenosis, and tolerin the Louisiana sugarcane breeding programs. We recorded the five measures together with sucrose production and its components. Using ance) may be expressed in cane's resistance to the sugarappropriate variance components, the heritability, expected response cane borer (Painter, 1951). to selection, and genetic correlations among the traits were used to Sugarcane is a clonally propagated crop with two construct selection indices of all combinations of the five damage breeding programs in Louisiana; one is conducted by traits studied. We used the regression coefficients of the damage traits the Louisiana Agricultural Experiment Station (LAES) on sucrose production as economic weights. The indices indicated near St. Gabriel, LA, and the other is conducted by that percentage bored internodes was the most effective single trait the USDA-ARS-SRRC Sugarcane Research Unit in to reduce sugarcane borer damage. If data collection costs were consid-Houma, LA. Experimental clones are routinely screened ered, then the subjectively assessed damage rating was the most expeby each program starting ≈6 yr after initial clonal plantditious of the traits examined. High correlation values among several of the traits lead to the observation that inclusion of more than the ing (White, 1993;Reagan et al., 1999). Clones are evalubored internode, exited internodes, and the damage rating in an index ated in similar fashion among the programs, but methwere unnecessary.
This chapter discusses applications of mixed model theory to predict cross performance and to analyse multienvironmental trials (METs).
The accurate quantification of tumor-infiltrating immune cells turns crucial to uncover their role in tumor immune escape, to determine patient prognosis and to predict response to immune checkpoint blockade. Current state-of-the-art methods that quantify immune cells from tumor biopsies using gene expression data apply computational deconvolution methods that present multicollinearity and estimation errors resulting in the overestimation or underestimation of the diversity of infiltrating immune cells and their quantity. To overcome such limitations, we developed MIXTURE, a new ν-support vector regression-based noise constrained recursive feature selection algorithm based on validated immune cell molecular signatures. MIXTURE provides increased robustness to cell type identification and proportion estimation, outperforms the current methods, and is available to the wider scientific community. We applied MIXTURE to transcriptomic data from tumor biopsies and found relevant novel associations between the components of the immune infiltrate and molecular subtypes, tumor driver biomarkers, tumor mutational burden, microsatellite instability, intratumor heterogeneity, cytolytic score, programmed cell death ligand 1 expression, patients’ survival and response to anti-cytotoxic T-lymphocyte-associated antigen 4 and anti-programmed cell death protein 1 immunotherapy.
Corn (Zea mays L.) nitrogen (N) management requires monitoring plant N concentration (Nc) with remote sensing tools to improve N use, increasing both profitability and sustainability. This work aims to predict the corn Nc during the growing cycle from Sentinel-2 and Sentinel-1 (C-SAR) sensor data fusion. Eleven experiments using five fertilizer N rates (0, 60, 120, 180, and 240 kg N ha−1) were conducted in the Pampas region of Argentina. Plant samples were collected at four stages of vegetative and reproductive periods. Vegetation indices were calculated with new combinations of spectral bands, C-SAR backscatters, and sensor data fusion derived from Sentinel-1 and Sentinel-2. Predictive models of Nc with the best fit (R2 = 0.91) were calibrated with spectral band combinations and sensor data fusion in six experiments. During validation of the models in five experiments, sensor data fusion predicted corn Nc with lower error (MAPE: 14%, RMSE: 0.31 %Nc) than spectral band combination (MAPE: 20%, RMSE: 0.44 %Nc). The red-edge (704, 740, 740 nm), short-wave infrared (1375 nm) bands, and VV backscatter were all necessary to monitor corn Nc. Thus, satellite remote sensing via sensor data fusion is a critical data source for predicting changes in plant N status.
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