scholarly journals Developing a Graphical User Interface (GUI) to Predict the Contamination of GM Corn in Non-GM Corn

2020 ◽  
Vol 36 (1) ◽  
pp. 25-31
Author(s):  
Karthik Salish ◽  
Gretchen A Mosher ◽  
R. P. Kingsly Ambrose
Keyword(s):  
User Interface ◽  
Probability Distribution ◽  
Graphical User Interface ◽  
Genetic Modification ◽  
Genetically Modified ◽  
Threshold Level ◽  
Percentage Error ◽  
List Type ◽  
Tolerance Level ◽  
Adventitious Presence

HighlightsA GUI tool was developed to predict the adventitious presence in non-GM produce.The software calculates tolerance and the probability of GM corn in non-GM corn.Predicted probability of contamination ranged from 0.050 to 0.356 at tolerance levels ranging from 0.1% to 5.0%.Abstract. The current rate of population growth necessitates the use of viable technologies like genetic modification to address estimated global food and feed requirements. However, in recent years, there has been an increase in resistance against the diffusion of genetic modification technology around the world. Many countries have adopted coexistence policies to allow a certain percentage of adventitious presence in non-genetically modified crops. However, the tolerance percentage for adventitious presence has been a bottleneck to free trade in some cases. It is a challenging task to fix a tolerance percentage considering the level of permeation of genetic modification technology in agriculture. This article introduces a software developed to serve as a decision-making tool to predict the probability distribution of genetically modified (GM) contamination in non-GM grain lot using user inputs such as final quantity of processed corn, overall tolerance level, and moisture content. The output from the software includes the mass of corn in each processing stage, the tolerance level and the probability distribution of potential GM contamination. The software predicted the probability of contamination with adventitious presence at tolerance levels of 5.0%, 3.0%, 1.0%, 0.9%, 0.5%, and 0.1% as 0.05, 0.07, 0.11, 0.12, 0.16, and 0.36, respectively. The predictions from the model were compared to a similar study wherein the effect of tolerance levels incurred in the costs of segregation was studied. The mean absolute percentage error for the predictions was found to be 3.07%. This software can be used as a tool in testing GM contamination in non-GM grain against a desired threshold levels in a grain elevator. Keywords: Corn, Genetic modification, Graphical User Interface (GUI), Threshold level.

Meeting the Tolerance: How Successful is Coexistence in Commodity Corn Handling Systems?

2020 ◽  
Vol 36 (5) ◽  
pp. 777-784
Author(s):  
Chad J. Dolphin ◽  
Gretchen A. Mosher ◽  
R.P. Kingsly Ambrose ◽  
Saxon J. Ryan
Keyword(s):  
Monte Carlo Simulation ◽  
Monte Carlo ◽  
Genetically Modified ◽  
List Type ◽  
Genetic Traits ◽  
Tolerance Level ◽  
Gm Maize ◽  
Isolation Distance ◽  
Adventitious Presence ◽  
Handling Practices

HighlightsMeeting the 0.9% tolerance level was challenging under most conditions.Non-GM loads in the simulation were able to meet a 1.5% or 3% tolerance level under specific conditions.Field isolation distance plays a large role in a non-GM load meeting the posted tolerance levelAbstract. The open-air growth environment used in maize production makes it nearly impossible to ensure 100% purity of specified genetic traits. One measure of successful coexistence is a low level of unintended material in seed, grain, and feed or food products, termed “adventitious presence” (AP). To allow the coexistence of genetically modified (GM) and non-genetically modified (non-GM) maize, tolerance levels regulate how much AP of genetically modified corn is allowed in each unit of maize. This research sought to model four factors contributing to levels of adventitious presence: seed purity, field isolation distance, combine cleanout, and grain elevator receipt and handling practices. Monte Carlo simulation was used to test nine scenarios to determine the feasibility of successfully meeting three tolerance levels for adventitious presence (0.9%, 1.5%, and 3.0%). After 50,000 iterations for each model, sensitivity analysis was performed to identify factors that play an important role in whether the load meets the posted tolerance level or not. Results suggest that non-GM maize loads would not meet a tolerance level of 0.9% in most cases. Non-GM maize loads were found to meet tolerance levels of 1.5% and 3.0% in certain cases. The most significant factors affecting the probability of the unit of maize meeting the posted tolerance level were field isolation distance, elevator handling practices, and seed purity. Keywords: Adventitious presence, Coexistence, Identity preservation, Monte Carlo simulation, Transgenic grain.

scholarly journals The GridCAT: A toolbox for automated analysis of human grid cell codes in fMRI

2017 ◽  
Author(s):  
Matthias Stangl ◽  
Jonathan Shine ◽  
Thomas Wolbers
Keyword(s):  
User Interface ◽  
Open Source ◽  
Graphical User Interface ◽  
Cognitive Processes ◽  
Source Code ◽  
Research Area ◽  
List Type ◽  
Grid Cells ◽  
Code Analysis ◽  
Open Source Code

AbstractHuman fMRI studies examining the putative firing of grid cells (i.e., the grid code) suggest that this cellular mechanism supports not only spatial navigation, but also more abstract cognitive processes. This research area, however, remains relatively unexplored, perhaps us to the complexities of data analysis. To overcome this, we have developed the Matlab-based Grid Code Analysis Toolbox (GridCAT), providing a graphical user interface, and open-source code, for the analysis of fMRI data. The GridCAT performs all analyses, from estimation and fitting of the grid code in the general linear model, to the generation of grid code metrics and plots. Moreover, it is flexible in allowing the specification of bespoke analysis pipelines; example data are provided to demonstrate the GridCAT’s main functionality. We believe the GridCAT is essential to opening this research area to the imaging community, and helping to elucidate the role of human grid codes in higher-order cognitive processes.HighlightsThe putative firing of grid cells (i.e., the grid code) can be examined using fMRINecessary steps for grid code analysis are reviewedThe Matlab-based grid code analysis toolbox (GridCAT) is introducedAutomated grid code analysis can be conducted either via a graphical user interface or open-source codeA detailed manual and an example dataset are provided

Hardware and Software Design for Premixing In-Line Injection System Attached to Variable-Rate Orchard Sprayer

2020 ◽  
Vol 63 (4) ◽  
pp. 823-821
Author(s):  
Zhihong Zhang ◽  
Heping Zhu ◽  
Chengsong Hu
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Touch Screen ◽  
Environmental Stewardship ◽  
Injection System ◽  
List Type ◽  
Variable Rate ◽  
Spray Application ◽  
Injection Line ◽  
Buffer Tank

.HighlightsA premixing in-line injection system was designed as a retrofit attachment to a laser-guided variable-rate orchard sprayer.A graphical user interface with touchscreen functions was incorporated into the system to facilitate field applications.Preliminary tests demonstrated that simulated pesticide and water could be accurately dispensed and discharged separately into the injection line and mixed well in the buffer tank.This premixing in-line injection system has great potential to further reduce pesticide waste and improve environmental stewardship for conventional and precision variable-rate sprayers.Abstract To eliminate the problems associated with leftover tank mixture in pesticide applications, a premixing in-line injection system was designed as an attachment to a laser-guided variable-rate orchard sprayer. The primary components of the system consisted of a chemical metering pump, a water pump, a two-stage static mixer, a premixing tank, a buffer tank, an electric shut-off valve, a chemical container, electronic control boards, a graphical user interface, and an embedded computer with a touch screen. Liquid level sensors were mounted in all tanks and the chemical container to control the fluid discharge and prevent overflows. The graphical user interface on the touch screen was designed for operators to communicate with the system and monitor the system status. During spray applications, the system performed with automatic loops in dispensing, mixing, and transferring the desired amounts of water and chemical concentrates to maintain the spray mixture at a constant concentration for use with variable-rate nozzles. The system was rinsed automatically when the spray application task was completed. Test results showed that simulated pesticide and water could be accurately delivered into the injection line and could be mixed well in the buffer tank before the spray mixture was discharged to the nozzles. The premixing in-line injection system is a potential technique to further reduce pesticide waste and improve environmental stewardship for both conventional and precision variable-rate orchard sprayers. Keywords: Automation, Environmental protection, Intelligent sprayer, Pesticide spray application, Precision agriculture, Tank mixture leftover.

HOW TO USE CROP GROWTH MODEL WOFOST FOR FORECASTING GROWTH AND YIELD OF A CROP

2016 ◽  
Vol 3 (1) ◽  
Author(s):  
LAL SINGH ◽  
PARMEET SINGH ◽  
RAIHANA HABIB KANTH ◽  
PURUSHOTAM SINGH ◽  
SABIA AKHTER ◽  
...  
Keyword(s):  
User Interface ◽  
Graphical User Interface ◽  
Input Data ◽  
Growth And Yield ◽  
Weather Data ◽  
Data Sets ◽  
Crop Growth Model ◽  
Control Center ◽  
Field Crops ◽  
Crop Calendar

WOFOST version 7.1.3 is a computer model that simulates the growth and production of annual field crops. All the run options are operational through a graphical user interface named WOFOST Control Center version 1.8 (WCC). WCC facilitates selecting the production level, and input data sets on crop, soil, weather, crop calendar, hydrological field conditions, soil fertility parameters and the output options. The files with crop, soil and weather data are explained, as well as the run files and the output files. A general overview is given of the development and the applications of the model. Its underlying concepts are discussed briefly.

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