Assessment of triticale varieties for swine feeding performance, late planting

Triticale holds potential as a third grain crop in Iowa. This project studied different cultivars to assess their suitability for production and use as swine feed

Optimal Taylor–Couette flow: radius ratio dependence

Taylor–Couette flow with independently rotating inner (i) and outer (o) cylinders is explored numerically and experimentally to determine the effects of the radius ratio η on the system response. Numerical simulations reach Reynolds numbers of up to Rei=9.5×10^3 and Reo=5×10^3, corresponding to Taylor numbers of up to Ta=10^8 for four different radius ratios η=ri/ro between 0.5 and 0.909. The experiments, performed in the Twente Turbulent Taylor–Couette (T3C) set-up, reach Reynolds numbers of up to Rei=2×10^6 and Reo=1.5×10^6, corresponding to Ta=5×10^12 for η=0.714--0.909. Effective scaling laws for the torque Jω(Ta) are found, which for sufficiently large driving Ta are independent of the radius ratio η. As previously reported for η=0.714, optimum transport at a non-zero Rossby number Ro=ri|ωi−ωo|/[2(ro−ri)ωo] is found in both experiments and numerics. Here Roopt is found to depend on the radius ratio and the driving of the system. At a driving in the range between Ta∼3×10^8 and Ta∼10^10, Roopt saturates to an asymptotic η-dependent value. Theoretical predictions for the asymptotic value of Roopt are compared to the experimental results, and found to differ notably. Furthermore, the local angular velocity profiles from experiments and numerics are compared, and a link between a flat bulk profile and optimum transport for all radius ratios is reported

Inter-molecular structure factors of macromolecules in solution: integral equation results

The inter-molecular structure of semidilute polymer solutions is studied theoretically. The low density limit of a generalized Ornstein-Zernicke integral equation approach to polymeric liquids is considered. Scaling laws for the dilute-to-semidilute crossover of random phase (RPA) like structure are derived for the inter-molecular structure factor on large distances when inter-molecular excluded volume is incorporated at the microscopic level. This leads to a non-linear equation for the excluded volume interaction parameter. For macromolecular size-mass scaling exponents, $\nu$, above a spatial-dimension dependent value, $\nu_c=2/d$, mean field like density scaling is recovered, but for $\nu<\nu_c$ the density scaling becomes non-trivial in agreement with field theoretic results and justifying phenomenological extensions of RPA. The structure of the polymer mesh in semidilute solutions is discussed in detail and comparisons with large scale Monte Carlo simulations are added. Finally a new possibility to determine the correction to scaling exponent $\omega_{12}$ is suggested.Comment: 11 pages, 5 figures; to be published in Phys. Rev. E (1999

New Method for Phase transitions in diblock copolymers: The Lamellar case

A new mean-field type theory is proposed to study order-disorder transitions (ODT) in block copolymers. The theory applies to both the weak segregation (WS) and the strong segregation (SS) regimes. A new energy functional is proposed without appealing to the random phase approximation (RPA). We find new terms unaccounted for within RPA. We work out in detail transitions to the lamellar state and compare the method to other existing theories of ODT and numerical simulations. We find good agreements with recent experimental results and predict that the intermediate segregation regime may have more than one scaling behavior.Comment: 23 pages, 8 figure

Prospects for Genomic Selection in Cassava Breeding

Article purchased; Published online: 28 Sept 2017Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross-validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross-population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within-generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one-third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden.Bill & Melinda Gates FoundationUKaidCGIAR Research Program on Roots, Tubers and BananasPeer Revie

Effectiveness of Genomic Prediction of Maize Hybrid Performance in Different Breeding Populations and Environments

Genomic prediction is expected to considerably increase genetic gains by increasing selection intensity and accelerating the breeding cycle. In this study, marker effects estimated in 255 diverse maize (Zea mays L.) hybrids were used to predict grain yield, anthesis date, and anthesis-silking interval within the diversity panel and testcross progenies of 30 F(2)-derived lines from each of five populations. Although up to 25% of the genetic variance could be explained by cross validation within the diversity panel, the prediction of testcross performance of F(2)-derived lines using marker effects estimated in the diversity panel was on average zero. Hybrids in the diversity panel could be grouped into eight breeding populations differing in mean performance. When performance was predicted separately for each breeding population on the basis of marker effects estimated in the other populations, predictive ability was low (i.e., 0.12 for grain yield). These results suggest that prediction resulted mostly from differences in mean performance of the breeding populations and less from the relationship between the training and validation sets or linkage disequilibrium with causal variants underlying the predicted traits. Potential uses for genomic prediction in maize hybrid breeding are discussed emphasizing the need of (1) a clear definition of the breeding scenario in which genomic prediction should be applied (i.e., prediction among or within populations), (2) a detailed analysis of the population structure before performing cross validation, and (3) larger training sets with strong genetic relationship to the validation set

In silico genotyping of the maize nested association mapping population

Nested Association Mapping (NAM) has been proposed as a means to combine the power of linkage mapping with the resolution of association mapping. It is enabled through sequencing or array genotyping of parental inbred lines while using low-cost, low-density genotyping technologies for their segregating progenies. For purposes of data analyses of NAM populations, parental genotypes at a large number of Single Nucleotide Polymorphic (SNP) loci need to be projected to their segregating progeny. Herein we demonstrate how approximately 0.5 million SNPs that have been genotyped in 26 parental lines of the publicly available maize NAM population can be projected onto their segregating progeny using only 1,106 SNP loci that have been genotyped in both the parents and their 5,000 progeny. The challenge is to estimate both the genotype and genetic location of the parental SNP genotypes in segregating progeny. Both challenges were met by estimating their expected genotypic values conditional on observed flanking markers through the use of both physical and linkage maps. About 90%, of 500,000 genotyped SNPs from the maize HapMap project, were assigned linkage map positions using linear interpolation between the maize Accessioned Gold Path (AGP) and NAM linkage maps. Of these, almost 70% provided high probability estimates of genotypes in almost 5,000 recombinant inbred lines

Prospects for Genomic Selection in Cassava Breeding

Article purchased; Published online: 28 Sept 2017Cassava (Manihot esculenta Crantz) is a clonally propagated staple food crop in the tropics. Genomic selection (GS) has been implemented at three breeding institutions in Africa to reduce cycle times. Initial studies provided promising estimates of predictive abilities. Here, we expand on previous analyses by assessing the accuracy of seven prediction models for seven traits in three prediction scenarios: cross-validation within populations, cross-population prediction and cross-generation prediction. We also evaluated the impact of increasing the training population (TP) size by phenotyping progenies selected either at random or with a genetic algorithm. Cross-validation results were mostly consistent across programs, with nonadditive models predicting of 10% better on average. Cross-population accuracy was generally low (mean = 0.18) but prediction of cassava mosaic disease increased up to 57% in one Nigerian population when data from another related population were combined. Accuracy across generations was poorer than within-generation accuracy, as expected, but accuracy for dry matter content and mosaic disease severity should be sufficient for rapid-cycling GS. Selection of a prediction model made some difference across generations, but increasing TP size was more important. With a genetic algorithm, selection of one-third of progeny could achieve an accuracy equivalent to phenotyping all progeny. We are in the early stages of GS for this crop but the results are promising for some traits. General guidelines that are emerging are that TPs need to continue to grow but phenotyping can be done on a cleverly selected subset of individuals, reducing the overall phenotyping burden

Extension of the bayesian alphabet for genomic selection

<p>Abstract</p> <p>Background</p> <p>Two Bayesian methods, BayesC<it>π </it>and BayesD<it>π</it>, were developed for genomic prediction to address the drawback of BayesA and BayesB regarding the impact of prior hyperparameters and treat the prior probability <it>π </it>that a SNP has zero effect as unknown. The methods were compared in terms of inference of the number of QTL and accuracy of genomic estimated breeding values (GEBVs), using simulated scenarios and real data from North American Holstein bulls.</p> <p>Results</p> <p>Estimates of <it>π </it>from BayesC<it>π</it>, in contrast to BayesD<it>π</it>, were sensitive to the number of simulated QTL and training data size, and provide information about genetic architecture. Milk yield and fat yield have QTL with larger effects than protein yield and somatic cell score. The drawback of BayesA and BayesB did not impair the accuracy of GEBVs. Accuracies of alternative Bayesian methods were similar. BayesA was a good choice for GEBV with the real data. Computing time was shorter for BayesC<it>π </it>than for BayesD<it>π</it>, and longest for our implementation of BayesA.</p> <p>Conclusions</p> <p>Collectively, accounting for computing effort, uncertainty as to the number of QTL (which affects the GEBV accuracy of alternative methods), and fundamental interest in the number of QTL underlying quantitative traits, we believe that BayesC<it>π </it>has merit for routine applications.</p