Issues are regarded as officially published after their release is announced to the table of contents alert mailing list. This review article discusses the importance of systemic regulation, mediated by the plant’s vasculature, in photosynthesis and resource allocation, and offers insights into pathways for crop yield enhancement, by engineering source–sink strength. Recent studies have revealed the impact of plant vasculature-mediated communication on regulating critical agronomic traits, highly correlated with crop yield potential. The plant vascular system mediates resource allocation, between source and sink tissues, and establishes hierarchical signaling networks to regulate adaptive plant development occurring within dynamic environmental changes. INFO: output: data_2. data_2. breeding programs are faced with the need to develop solutions to maintain both crop growth and yields, within deteriorating agricultural environments. INFO: Running model fitting: Ridge regression model implemented in R Build predictor via cross-validation and make prediction INFO: Running model fitting: LASSO model implemented in Python Build predictor via cross-validation and make prediction INFO: Summary of workflow saved to Outcome_Oriented_Report.html INFO: Workflow DAG saved to Outcome_Oriented_011019_2252.dot INFO: Workflow default (ID=1dc69aff03750855) is executed successfully with 26 completed steps. INFO: Running default: Compute and report error estimates in HTML table format INFO: Running evaluation: Evaluate predictors by calculating mean squared error of prediction vs truth (first line of output) and of betahat vs truth (2nd line of output) INFO: Running lasso: LASSO model implemented in Python Build predictor via cross-validation and make prediction INFO: output: data_5.train.csv data_5.test.csv INFO: Running ridge: Ridge regression model implemented in R Build predictor via cross-validation and make prediction INFO: output: data_4.train.csv data_4.test.csv INFO: output: data_3.train.csv data_3.test.csv INFO: output: data_2.train.csv data_2.test.csv INFO: output: data_1.train.csv data_1.test.csv INFO: Running simulation: Simulate sparse data-sets INFO: Summary of workflow saved to Process_Oriented_Report.html INFO: Workflow DAG saved to Process_Oriented_011019_2251.dot INFO: Workflow default (ID=2658daf4cfd433df) is executed successfully with 7 completed steps, 27 completed substeps, 1 ignored step and 5 ignored substeps. INFO: Running default_2: Compute and report error estimates in HTML table format INFO: Running ridge_3: Evaluate predictors by calculating mean squared error of prediction vs truth (first line of output) and of betahat vs truth (2nd line of output) INFO: Running lasso_3: Evaluate predictors by calculating mean squared error of prediction vs truth (first line of output) and of betahat vs truth (2nd line of output) 
INFO: Running ridge_2: Ridge regression model implemented in R Build predictor via cross-validation and make prediction INFO: ridge_1 (index=4) is ignored due to saved signature INFO: ridge_1 (index=3) is ignored due to saved signature INFO: ridge_1 (index=2) is ignored due to saved signature INFO: ridge_1 (index=1) is ignored due to saved signature INFO: ridge_1 (index=0) is ignored due to saved signature INFO: Running ridge_1: Simulate sparse data-sets INFO: Running lasso_2: LASSO model implemented in Python Build predictor via cross-validation and make prediction Here I use numerical indices rather than variable names for input and output to demonstrate alternative syntax for `_input` and `_output`
INFO: output: data_1.train.csv data_1.test.csv. INFO: Running lasso_1: Simulate sparse data-sets INFO: Running default_1: Run default core analysis Prediction vs truth (first line of output) and ofĭefault_2: Compute and report error estimates in HTML table format Lasso_3, ridge_3: Evaluate predictors by calculating mean squared error of Numerical indices rather than variable names for inputĪnd output to demonstrate alternative syntax for Lasso_2: LASSO model implemented in Python Build predictor viaĬross-validation and make prediction Here I use Ridge_2: Ridge regression model implemented in R Build predictor Lasso_1, ridge_1: Simulate sparse data-sets This script is written in process-oriented style Workflow_options: Double-hyphen workflow-specific parametersĪnd summarizes their prediction performance Options: Single-hyphen sos parameters (see "sos run -h" for details) Workflow_name: Single or combined workflows defined in this script
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