scholarly journals Prediction of Maize Single Cross Hybrids Using the Total Effects of Associated Markers Approach Assessed by Cross-Validation and Regional Trials

2014 ◽  
Vol 2014 ◽  
pp. 1-9 ◽  
Author(s):  
Wagner Mateus Costa Melo ◽  
Renzo Garcia Von Pinho ◽  
Marcio Balestre
Keyword(s):  
Training Trial ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Field Performance ◽  
Training Population ◽  
Maize Hybrids ◽  
Single Cross ◽  
The Cross ◽  
Predictive Validation ◽  
Single Cross Hybrids

The present study aimed to predict the performance of maize hybrids and assess whether the total effects of associated markers (TEAM) method can correctly predict hybrids using cross-validation and regional trials. The training was performed in 7 locations of Southern Brazil during the 2010/11 harvest. The regional assays were conducted in 6 different South Brazilian locations during the 2011/12 harvest. In the training trial, 51 lines from different backgrounds were used to create 58 single cross hybrids. Seventy-nine microsatellite markers were used to genotype these 51 lines. In the cross-validation method the predictive accuracy ranged from 0.10 to 0.96, depending on the sample size. Furthermore, the accuracy was 0.30 when the values of hybrids that were not used in the training population (119) were predicted for the regional assays. Regarding selective loss, the TEAM method correctly predicted 50% of the hybrids selected in the regional assays. There was also loss in only 33% of cases; that is, only 33% of the materials predicted to be good in training trial were considered to be bad in regional assays. Our results show that the predictive validation of different crop conditions is possible, and the cross-validation results strikingly represented the field performance.

scholarly journals Genomic Prediction of Hybrid Combinations in the Early Stages of a Maize Hybrid Breeding Pipeline

2016 ◽  
Author(s):  
D.C. Kadam ◽  
S.M. Potts ◽  
M.O. Bohn ◽  
A.E. Lipka ◽  
A.J. Lorenz
Keyword(s):  
Genomic Prediction ◽  
Cross Validation ◽  
Hybrid Breeding ◽  
Maize Breeding ◽  
Cross Prediction ◽  
Maize Hybrids ◽  
Early Stages ◽  
Single Cross ◽  
Hybrid Maize ◽  
Single Cross Hybrids

AbstractPrediction of single-cross hybrid performance has been a major goal of plant breeders since the beginning of hybrid breeding. Genomic prediction has shown to be a promising approach, but only limited studies have examined the accuracy of predicting single cross performance. Most of the studies rather focused on predicting top cross performance using single tester to determine the inbred parent’s worth in hybrid combinations. Moreover, no studies have examined the potential of predicting single crosses made among random progenies derived from a series of biparental families, which resembles the structure of germplasm comprising the initial stages of a hybrid maize breeding pipeline. The main objective of this study was to evaluate the potential of genomic prediction for identifying superior single crosses early in the breeding pipeline and optimize its application. To accomplish these objectives, we designed and analyzed a novel population of single-cross hybrids representing the Iowa Stiff Stalk Synthetic/Non-Stiff Stalk heterotic pattern commonly used in the development of North American commercial maize hybrids. The single cross prediction accuracies estimated using cross-validation ranged from 0.40 to 0.74 for grain yield, 0.68 to 0.91 for plant height and 0.54 to 0.94 for staygreen depending on the number of tested parents of the single crosses. The genomic estimated general and specific combining abilities showed a clear advantage over the use of genomic covariances among single crosses, especially when one or both parents of the single cross were untested in hybrid combinations. Overall, our results suggest that genomic prediction of the performance of single crosses made using random progenies from the early stages of the breeding pipeline holds great potential to re-design hybrid breeding and increase its efficiency.

scholarly journals Assesment of recombinant lines of maize hybrids for inbred development

2015 ◽  
Vol 43 (3) ◽  
pp. 363-366
Author(s):  
Uttam Chandel ◽  
BS Mankotia ◽  
KS Thakur
Keyword(s):  
Inbreeding Depression ◽  
Combining Ability ◽  
General Combining Ability ◽  
Specific Combining Ability ◽  
Lower Percentage ◽  
Maize Hybrids ◽  
Mating Design ◽  
Commercial Company ◽  
Single Cross ◽  
Single Cross Hybrids

Maize (Zea mays L.) breeders currently exploit genetically narrow-base populations by deriving the recombination lines from F2 of commercial single cross hybrids. A mating design was proposed for maize hybrid evaluation as source germplasm. The commercial single cross hybrids, Hi Shell, DKC 7074 and PMZ 4, developed by the commercial company, Monsanto, were evaluated for their usefulness as germplasm. According to mating design three criteria were used: the percentage of inbreeding depression, the general combining ability and the specific combining ability. PMZ 4 had a lower percentage (21.9) of inbreeding depression, which was also combined with positive general combining ability (7.5) and negative specific combining ability. The estimated percentage of inbreeding depression was greater in DKC 7074 (31.4) and in Hi Shell (25.3). DKC 7074 also had negative general combining ability (35.5), while Hi Shell had positive specific combining ability (75.0). Therefore, evaluation through mating design showed PMZ 4 possesses more desirable genes and that it’s F2 may be a more profitable germplasm for developing elite inbred lines DOI: http://dx.doi.org/10.3329/bjb.v43i3.21615 Bangladesh J. Bot. 43(3): 363-366, 2014 (December)

scholarly journals Estimation of heterosis in yield and yield attributing traits in single cross hybrids of maize

2016 ◽  
Vol 2 (1) ◽  
pp. 123-132 ◽  
Author(s):  
Hari Prasad Sharma ◽  
Krishna Hari Dhakal ◽  
Raju Kharel ◽  
Jiban Shrestha
Keyword(s):  
Grain Yield ◽  
Block Design ◽  
Winter Season ◽  
Maize Production ◽  
Yield Attributes ◽  
Maize Hybrids ◽  
Randomized Complete Block Design ◽  
Single Cross ◽  
Standard Heterosis ◽  
Single Cross Hybrids

A field experiment was conducted at National Maize Research Program, Rampur, Chitwan, Nepal during winter season from 6th October, 2015 to 5th March 2016 to estimate different heterosis on single cross maize hybrids . Thirteen maize hybrids were tested randomized complete block design with three replications. Hybrid namely RML-98/RL-105 gave the highest standard heterosis (57.5%) for grain yield over CP-666 followed by RML-4/NML-2 (32.6%), RML-95/RL-105 (29%) and RML-5/RL-105 (20.6%). The hybrid RML-98/RL-105 produced the highest standard heterosis (75.1%) for grain yield over Rajkumar followed by RML-4/NML-2(50.2%), RML-95/RL-105(46.6%), RML-5/RL-105 and (35.7%). Mid and better parent heterosis were significantly higher for yield and yield attributes viz. ear length, ear diameter, no of kernel row per ear, no of kernel per row and test weight. The highest positive mid-parent heterosis for grain yield was found in RML-98/RL-105 followed by RML-5/RL-105, RML-95/RL-105, and RML-4/NML-2. For the grain yield the better parent heterosis was the highest in RML-98/RL-105, followed by RML-5/RL-105, RML-95/RL-105, and RML-4/NML-2. These results suggested that maize production can be maximized by cultivating hybrids namely RML-98/RL-105, RML-5/RL-105, RML-95/RL-105, and RML-4/NML-2.

scholarly journals Ant Colony-Based Hyperparameter Optimisation in Total Variation Reconstruction in X-ray Computed Tomography

Sensors ◽  
2021 ◽  
Vol 21 (2) ◽  
pp. 591
Author(s):  
Manasavee Lohvithee ◽  
Wenjuan Sun ◽  
Stephane Chretien ◽  
Manuchehr Soleimani
Keyword(s):  
Computed Tomography ◽  
Total Variation ◽  
Cross Validation ◽  
Ant Colony ◽  
Reference Image ◽  
X Ray ◽  
X Ray Computed ◽  
Hyperparameter Selection ◽  
The Cross ◽  
Optimal Set

In this paper, a computer-aided training method for hyperparameter selection of limited data X-ray computed tomography (XCT) reconstruction was proposed. The proposed method employed the ant colony optimisation (ACO) approach to assist in hyperparameter selection for the adaptive-weighted projection-controlled steepest descent (AwPCSD) algorithm, which is a total-variation (TV) based regularisation algorithm. During the implementation, there was a colony of artificial ants that swarm through the AwPCSD algorithm. Each ant chose a set of hyperparameters required for its iterative CT reconstruction and the correlation coefficient (CC) score was given for reconstructed images compared to the reference image. A colony of ants in one generation left a pheromone through its chosen path representing a choice of hyperparameters. Higher score means stronger pheromones/probabilities to attract more ants in the next generations. At the end of the implementation, the hyperparameter configuration with the highest score was chosen as an optimal set of hyperparameters. In the experimental results section, the reconstruction using hyperparameters from the proposed method was compared with results from three other cases: the conjugate gradient least square (CGLS), the AwPCSD algorithm using the set of arbitrary hyperparameters and the cross-validation method.The experiments showed that the results from the proposed method were superior to those of the CGLS algorithm and the AwPCSD algorithm using the set of arbitrary hyperparameters. Although the results of the ACO algorithm were slightly inferior to those of the cross-validation method as measured by the quantitative metrics, the ACO algorithm was over 10 times faster than cross—Validation. The optimal set of hyperparameters from the proposed method was also robust against an increase of noise in the data and can be applicable to different imaging samples with similar context. The ACO approach in the proposed method was able to identify optimal values of hyperparameters for a dataset and, as a result, produced a good quality reconstructed image from limited number of projection data. The proposed method in this work successfully solves a problem of hyperparameters selection, which is a major challenge in an implementation of TV based reconstruction algorithms.

scholarly journals Assessing elderly’s functional balance and mobility via analyzing data from waist-mounted tri-axial wearable accelerometers in timed up and go tests

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Lisha Yu ◽  
Yang Zhao ◽  
Hailiang Wang ◽  
Tien-Lung Sun ◽  
Terrence E. Murphy ◽  
...  
Keyword(s):  
Predictive Models ◽  
Cross Validation ◽  
Motor Coordination ◽  
Predictive Accuracy ◽  
Short Form ◽  
Community Dwelling ◽  
Regularized Regression ◽  
Balance Assessment ◽  
Timed Up And Go ◽  
Functional Balance

Abstract Background Poor balance has been cited as one of the key causal factors of falls. Timely detection of balance impairment can help identify the elderly prone to falls and also trigger early interventions to prevent them. The goal of this study was to develop a surrogate approach for assessing elderly’s functional balance based on Short Form Berg Balance Scale (SFBBS) score. Methods Data were collected from a waist-mounted tri-axial accelerometer while participants performed a timed up and go test. Clinically relevant variables were extracted from the segmented accelerometer signals for fitting SFBBS predictive models. Regularized regression together with random-shuffle-split cross-validation was used to facilitate the development of the predictive models for automatic balance estimation. Results Eighty-five community-dwelling older adults (72.12 ± 6.99 year) participated in our study. Our results demonstrated that combined clinical and sensor-based variables, together with regularized regression and cross-validation, achieved moderate-high predictive accuracy of SFBBS scores (mean MAE = 2.01 and mean RMSE = 2.55). Step length, gender, gait speed and linear acceleration variables describe the motor coordination were identified as significantly contributed variables of balance estimation. The predictive model also showed moderate-high discriminations in classifying the risk levels in the performance of three balance assessment motions in terms of AUC values of 0.72, 0.79 and 0.76 respectively. Conclusions The study presented a feasible option for quantitatively accurate, objectively measured, and unobtrusively collected functional balance assessment at the point-of-care or home environment. It also provided clinicians and elderly with stable and sensitive biomarkers for long-term monitoring of functional balance.

Mapping Arctic clam abundance using multiple datasets, models, and a spatially explicit accuracy assessment

2019 ◽  
Vol 76 (7) ◽  
pp. 2349-2361
Author(s):  
Benjamin Misiuk ◽  
Trevor Bell ◽  
Alec Aitken ◽  
Craig J Brown ◽  
Evan N Edinger
Keyword(s):  
Spatial Autocorrelation ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Accuracy Assessment ◽  
Small Scale ◽  
Suitable Habitat ◽  
Distribution Models ◽  
Management Tools ◽  
Seabed Topography ◽  
Leave One Out

Abstract Species distribution models are commonly used in the marine environment as management tools. The high cost of collecting marine data for modelling makes them finite, especially in remote locations. Underwater image datasets from multiple surveys were leveraged to model the presence–absence and abundance of Arctic soft-shell clam (Mya spp.) to support the management of a local small-scale fishery in Qikiqtarjuaq, Nunavut, Canada. These models were combined to predict Mya abundance, conditional on presence throughout the study area. Results suggested that water depth was the primary environmental factor limiting Mya habitat suitability, yet seabed topography and substrate characteristics influence their abundance within suitable habitat. Ten-fold cross-validation and spatial leave-one-out cross-validation (LOO CV) were used to assess the accuracy of combined predictions and to test whether this was inflated by the spatial autocorrelation of transect sample data. Results demonstrated that four different measures of predictive accuracy were substantially inflated due to spatial autocorrelation, and the spatial LOO CV results were therefore adopted as the best estimates of performance.

Pharmacokinetics of Propofol in Adult Patients Undergoing Coronary Revascularization

1996 ◽  
Vol 84 (6) ◽  
pp. 1288-1297 ◽  
Author(s):  
James M. Bailey ◽  
Christina T. Mora ◽  
Stephen L. Shafer ◽  
Keyword(s):  
Cardiac Surgery ◽  
Cross Validation ◽  
Predictive Accuracy ◽  
Coronary Revascularization ◽  
Artery Bypass ◽  
Bypass Graft ◽  
Adult Patients ◽  
Pharmacokinetic Parameters ◽  
Surgery Patient ◽  
Prospective Comparison

Background Propofol is increasingly used for cardiac anesthesia and for perioperative sedation. Because pharmacokinetic parameters vary among distinct patient populations, rational drug dosing in the cardiac surgery patient is dependent on characterization of the drug's pharmacokinetic parameters in patients actually undergoing cardiac procedures and cardiopulmonary bypass (CPB). In this study, the pharmacokinetics of propofol was characterized in adult patients undergoing coronary revascularization. Methods Anesthesia was induced and maintained by computer-controlled infusions of propofol and alfentanil, or sufentanil, in 41 adult patients undergoing coronary artery bypass graft surgery. Blood samples for determination of plasma propofol concentrations were collected during the predefined study periods and assayed by high-pressure liquid chromatography. Three-compartment model pharmacokinetic parameters were determined by nonlinear extended least-squares regression of pooled data from patients receiving propofol throughout the perioperative period. The effect of CPB on propofol pharmacokinetics was modeled by allowing the parameters to change with the institution and completion of extracorporeal circulation and selecting the optimal model on the basis of the logarithm of the likelihood. Predicted propofol concentrations were calculated by convolving the infusion rates with unit disposition functions using the estimated parameters. The predictive accuracy of the parameters was evaluated by cross-validation and by a prospective comparison of predicted and measured levels in a subset of patients. Results Optimal pharmacokinetic parameters were: central compartment volume = 6.0 l; second compartment volume = 49.5 l; third compartment volume = 429.3 l; Cl1 (elimination clearance) = 0.68 l/min; Cl2 (distribution clearance) = 1.97 l/min1; and Cl3 (distribution clearance) = 0.70 l/min. The effects of CPB were optimally modeled by step changes in V1 and Cl1 to values of 15.9 and 1.95, respectively, with the institution of CPB. Median absolute prediction error was 18% in the cross-validation assessment and 19% in the prospective evaluation. There was no evidence for nonlinear kinetics. Previously published propofol pharmacokinetic parameter sets poorly predicted the observed concentrations in cardiac surgical patients. Conclusions The pharmacokinetics of propofol in adult patients undergoing cardiac surgery with CPB are dissimilar from those reported for other adult patient populations. The effect of CPB was best modeled by an increase in V1 and Cl1. Predictive accuracy of the derived pharmacokinetic parameters was excellent as measured by cross-validation and a prospective test.

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