Software Tools to Assist Breeding Decisions

2021 ◽  
pp. 304-327
Keyword(s):  
Prediction Models ◽  
Simulation Analysis ◽  
Molecular Mapping ◽  
Software Tools ◽  
Agricultural Crops ◽  
Software Support ◽  
Phenotype Prediction ◽  
Phenotype Data ◽  
Support Tools ◽  
Simulation Based

Plant breeders are usually faced with the problem of predicting the performance of new individuals with untested gene combinations. Therefore, it is important to follow an integrated breeding approach by combining molecular tools, molecular mapping, and MAS. It is also required to develop tools for modeling and simulation analysis by utilizing all pre-existing and newly generated data. Several software tools have been developed that integrates breeding simulations and phenotype prediction models using genomic information. Reliable phenotype prediction models for the simulation were constructed from actual genotype and phenotype data. Such simulation-based genome-assisted approach to breeding will help optimize plant breeding in all important agricultural crops. Software tools have also been developed for designing target sites or evaluating the outcome of genome/gene editing system. This chapter provides an overview of the key software support tools that will assist the plant breeders in decision making during the process of conducting various breeding program.

scholarly journals Ak-mer-based method for the identification of phenotype-associated genomic biomarkers and predicting phenotypes of sequenced bacteria

2018 ◽  
Author(s):  
Erki Aun ◽  
Age Brauer ◽  
Veljo Kisand ◽  
Tanel Tenson ◽  
Maido Remm
Keyword(s):  
Statistical Model ◽  
Programming Language ◽  
Prediction Models ◽  
Genomic Sequence ◽  
Medical Diagnostics ◽  
Sequencing Data ◽  
Test Set ◽  
Phenotype Prediction ◽  
Python Programming Language ◽  
Python Programming

AbstractWe have developed an easy-to-use and memory-efficient method called PhenotypeSeeker that (a) generates ak-mer-based statistical model for predicting a given phenotype and (b) predicts the phenotype from the sequencing data of a given bacterial isolate. The method was validated on 167Klebsiella pneumoniaeisolates (virulence), 200Pseudomonas aeruginosaisolates (ciprofloxacin resistance) and 460Clostridium difficileisolates (azithromycin resistance). The phenotype prediction models trained from these datasets performed with 88% accuracy on theK. pneumoniaetest set, 88% on theP. aeruginosatest set and 96.5% on theC. difficiletest set. Prediction accuracy was the same for assembled sequences and raw sequencing data; however, building the model from assembled genomes is significantly faster. On these datasets, the model building on a mid-range Linux server takes approximately 3 to 5 hours per phenotype if assembled genomes are used and 10 hours per phenotype if raw sequencing data are used. The phenotype prediction from assembled genomes takes less than one second per isolate. Thus, PhenotypeSeeker should be well-suited for predicting phenotypes from large sequencing datasets.PhenotypeSeeker is implemented in Python programming language, is open-source software and is available at GitHub (https://github.com/bioinfo-ut/PhenotypeSeeker/).SummaryPredicting phenotypic properties of bacterial isolates from their genomic sequences has numerous potential applications. A good example would be prediction of antimicrobial resistance and virulence phenotypes for use in medical diagnostics. We have developed a method that is able to predict phenotypes of interest from the genomic sequence of the isolate within seconds. The method uses statistical model that can be trained automatically on isolates with known phenotype. The method is implemented in Python programming language and can be run on low-end Linux server and/or on laptop computers.

Experiment and Simulation Analysis of the Pressure Carrying Capacity of X80 Pipe with Metal Loss Defect on the Girth Weld

2021 ◽  
Vol 1035 ◽  
pp. 813-818
Author(s):  
Zheng Long Li ◽  
Lin Chen ◽  
Zhi Hong Li ◽  
Guo Shuai Yan ◽  
Wei Li
Keyword(s):  
Sensitivity Analysis ◽  
Carrying Capacity ◽  
Simulation Analysis ◽  
Water Pressure ◽  
Metal Loss ◽  
Factors Affecting ◽  
Element Simulation ◽  
Simulation Based ◽  
Blasting Test ◽  
Water Pressure Blasting

In order to study the pressure carrying capacity of X80 pipe with metal loss defect on the girth weld the water-pressure blasting test of the pipe with metal loss defect was analyzed by experiment and finite element simulation. Based on this, the sensitivity analysis of the factors affecting the pressure carrying of the pipeline, such as the circular size, the axial size, and the depth of the metal loss defect, was carried out. The research results show that the circular size of the metal loss defect on the girth weld had little impact to the pressure carrying capacity of the pipe while it reduced with the increasing of the axial size and the depth of the metal loss defect.

New Technologies in Personalized Decision Support to Enhance Patient Choice

2012 ◽  
pp. 220-235 ◽  
Author(s):  
Nananda F. Col
Keyword(s):  
Risk Factors ◽  
Decision Support ◽  
Individual Differences ◽  
New Technologies ◽  
Prediction Models ◽  
Decision Aids ◽  
Behavioral Risk ◽  
Decision Support Tools ◽  
Support Tools ◽  
Average Patient

Medical decisions are difficult when there are two or more reasonable options and each option has good and bad features that different people may value differently because of differences in health, risk factors, preferences, or values. Personalized decision support tools are being developed in many areas, but two particularly promising areas are patient decision aids and Risk Prediction Models (RPMs). These personalized decision support tools can help patients and/or providers make better decisions about preventing, managing, or treating disease, taking into consideration specific aspects of an individual patient that distinguish them from an ’average’ patient or the population at large. Decision aids tend to focus on individual differences in preferences and values, whereas RPM’s focus on individual differences in clinical, biological, and behavioral risk factors. There are tremendous opportunities with both approaches, and both have been shown to be able to improve clinical judgment and decision making. Decision support tools are needed that provide personalized service that addresses important individual differences in biology, values, and preferences, and that targets the provider-patient dyad.

Simulation Analysis of the Pressure Carrying Capacity of X80 Pipe with Plain Dents

2019 ◽  
Vol 944 ◽  
pp. 835-840
Author(s):  
Peng Song ◽  
Zheng Long Li ◽  
Yu Ran Fan ◽  
Lei Guo ◽  
Xi Xi Zhang ◽  
...  
Keyword(s):  
Sensitivity Analysis ◽  
Finite Element ◽  
Internal Pressure ◽  
Carrying Capacity ◽  
Formation Process ◽  
Simulation Analysis ◽  
Hydraulic Test ◽  
Factors Affecting ◽  
Element Simulation ◽  
Simulation Based

In order to study the pressure carrying capacity of X80 pipe with plain dents, the formation process and the hydraulic test were analyzed by finite element simulation. Based on this, the sensitivity analysis of the factors affecting the pressure carrying capacity of the pipeline, such as the internal pressure, the confinement state and the material performance, is carried out. Research results show that springback amount of the pipeline decreases due to the initial internal pressure, and constraint state has little effect on the pressure carrying capacity while increases with the increasing of the material tensile properties. When the depth of the dent is less than 6% pipe diameter or the strain of the dent is less than 6%, the dent has little impact to the pressure carrying capacity of the pipe.

Quantifying effects of accidents by fuzzy-logic- and simulation-based analysis

2006 ◽  
Vol 33 (3) ◽  
pp. 219-226 ◽  
Author(s):  
Sangyoub Lee ◽  
Daniel W Halpin ◽  
Hoon Chang
Keyword(s):  
Fuzzy Logic ◽  
Simulation Analysis ◽  
Productivity Loss ◽  
Indirect Costs ◽  
Safety Factors ◽  
Logic Simulation ◽  
Training And Supervision ◽  
Simulation Based ◽  
The Relationship ◽  
Productive Time

This study quantifies the effects of accidents by defining one of the indirect costs, the productive time lost owing to accidents in utility trenching operations. The probability of accidents, estimated by fuzzy-logic-based analysis of the performance of the factors (training, supervision, and preplanning) affecting safety in utility trenching operations, was used to quantify, based on simulation analysis, the productivity loss due to process delays resulting from accidents during excavation and pipe installation. It was determined that the productivity loss resulting from accidents during excavation is greater than that resulting from accidents during pipe installation. During excavation, the "very poor" condition of preplanning is most critical to productivity loss due to accidents, whereas during pipe installation, the condition of training and supervision affects the productivity loss more than that of preplanning. This paper provides insights into the relationship between the condition of the safety factors and the possible productivity loss by concomitant probability of accidents to quantify the effects of the accidents.Key words: effect of accidents, probability of accidents, productivity, fuzzy logic, simulation.

scholarly journals Numerical and Experimental Study on the Performance of Thermoelectric Radiant Panel for Space Heating

Materials ◽  
2020 ◽  
Vol 13 (3) ◽  
pp. 550 ◽  
Author(s):  
Hansol Lim ◽  
Jae-Weon Jeong
Keyword(s):  
Energy Consumption ◽  
Prediction Models ◽  
Thermoelectric Module ◽  
Coefficient Of Performance ◽  
Joule Effect ◽  
Heating Capacity ◽  
Simulation Based ◽  
Radiant Panel ◽  
And Performance ◽  
Heating Mode

The purpose of this study is to investigate the suitable operation and performance of a thermoelectric radiant panel (TERP) in the heating operation. First, the hypothesis was suggested that the heating operation of TERP can operate without a heat source at the cold side according to theoretical considerations. To prove this hypothesis, the thermal behavior of the TERP was investigated during the heating operation using a numerical simulation based on the finite difference method. The results indicated that it is possible to heat the radiant panel using a thermoelectric module without fan operation via the Joule effect. A mockup model of the TERP was constructed, and the numerical model and hypothesis were validated in experiment 1. Moreover, experiment 2 was performed to evaluate the necessity of fan operation in the heating operation of TERP regarding energy consumption. The results revealed that the TERP without fan operation showed the higher coefficient of performance (COP) in the heating season. After determining the suitable heating operation of the TERP, prediction models for the heating capacity and power consumption of the TERP were developed using the response surface methodology. Both models exhibited good R2 values of >0.94 and were validated within 10% error bounds in experimental cases. These prediction models are expected to be utilized in whole-building simulation programs for estimating the energy consumption of TERPs in the heating mode.

scholarly journals MINIMAR (MINimum Information for Medical AI Reporting): Developing reporting standards for artificial intelligence in health care

2020 ◽  
Vol 27 (12) ◽  
pp. 2011-2015 ◽  
Author(s):  
Tina Hernandez-Boussard ◽  
Selen Bozkurt ◽  
John P A Ioannidis ◽  
Nigam H Shah
Keyword(s):  
Artificial Intelligence ◽  
Health Care ◽  
Clinical Decision Making ◽  
Prediction Models ◽  
Clinical Decision ◽  
Digital Data ◽  
Reporting Standards ◽  
Minimum Information ◽  
Support Tools ◽  
Hidden Biases

Abstract The rise of digital data and computing power have contributed to significant advancements in artificial intelligence (AI), leading to the use of classification and prediction models in health care to enhance clinical decision-making for diagnosis, treatment and prognosis. However, such advances are limited by the lack of reporting standards for the data used to develop those models, the model architecture, and the model evaluation and validation processes. Here, we present MINIMAR (MINimum Information for Medical AI Reporting), a proposal describing the minimum information necessary to understand intended predictions, target populations, and hidden biases, and the ability to generalize these emerging technologies. We call for a standard to accurately and responsibly report on AI in health care. This will facilitate the design and implementation of these models and promote the development and use of associated clinical decision support tools, as well as manage concerns regarding accuracy and bias.

Thermal Errors Simulation and Modeling of Motorized Spindle

2010 ◽  
Vol 154-155 ◽  
pp. 1305-1309 ◽  
Author(s):  
Liang Yu Cui ◽  
Da Wei Zhang ◽  
Wei Guo Gao ◽  
Xiang Yang Qi ◽  
Yu Shen
Keyword(s):  
Neural Network ◽  
Prediction Models ◽  
Simulation Analysis ◽  
Rbf Neural Network ◽  
Thermal Characteristics ◽  
Cnc Machine ◽  
Motorized Spindle ◽  
Neural Network Method ◽  
Thermal Errors ◽  
Network Method

Thermal errors of motorized spindle are of great importance to affect final machining precision of CNC machine tool. Thermal characteristics simulation analysis of motorized spindle is realized by ANSYS; thermal errors test measurement is completed based on 5-point method; and prediction models of thermal errors are constructed by multiple linear regression (MLR) method, Back Propagation (BP) neural network method and Radial Basis Function (RBF) neural network method respectively. The results of simulation and experiments illustrate that simulation results can represent thermal characteristics of motorized spindle, whose degree of confidence mainly depending on setting of thermal load and boundary conditions properly or not; RBF neural network model has highest prediction precision for thermal errors of motorized spindle based on test data.

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