Objects of Daydreaming

Daydreaming offers time to detach and slow down, to stretch and move inwards. For a handful of seconds to minutes on end, I float between experiences and imaginings of once familiar ground. Within the open space of the daydream, fragments of memories rise to the surface which then become touchstones, full of potential for future work. Full of sensory detail, these memories whether from a walk pierced by a bird call, a meal with family, or a day at the beach control how new objects take shape. An atmospheric light, a deeply rooted dining table, and lounge chairs all invite idle reverie for others

Resource Allocation for Maximizing Prediction Accuracy and Genetic Gain of Genomic Selection in Plant Breeding: A Simulation Experiment

Allocating resources between population size and replication affects both genetic gain through phenotypic selection and quantitative trait loci detection power and effect estimation accuracy for marker-assisted selection (MAS). It is well known that because alleles are replicated across individuals in quantitative trait loci mapping and MAS, more resources should be allocated to increasing population size compared with phenotypic selection. Genomic selection is a form of MAS using all marker information simultaneously to predict individual genetic values for complex traits and has widely been found superior to MAS. No studies have explicitly investigated how resource allocation decisions affect success of genomic selection. My objective was to study the effect of resource allocation on response to MAS and genomic selection in a single biparental population of doubled haploid lines by using computer simulation. Simulation results were compared with previously derived formulas for the calculation of prediction accuracy under different levels of heritability and population size. Response of prediction accuracy to resource allocation strategies differed between genomic selection models (ridge regression best linear unbiased prediction [RR-BLUP], BayesCp) and multiple linear regression using ordinary least-squares estimation (OLS), leading to different optimal resource allocation choices between OLS and RR-BLUP. For OLS, it was always advantageous to maximize population size at the expense of replication, but a high degree of flexibility was observed for RR-BLUP. Prediction accuracy of doubled haploid lines included in the training set was much greater than of those excluded from the training set, so there was little benefit to phenotyping only a subset of the lines genotyped. Finally, observed prediction accuracies in the simulation compared well to calculated prediction accuracies, indicating these theoretical formulas are useful for making resource allocation decisions

Resource Allocation for Maximizing Prediction Accuracy and Genetic Gain of Genomic Selection in Plant Breeding: A Simulation Experiment

Allocating resources between population size and replication affects both genetic gain through phenotypic selection and quantitative trait loci detection power and effect estimation accuracy for marker-assisted selection (MAS). It is well known that because alleles are replicated across individuals in quantitative trait loci mapping and MAS, more resources should be allocated to increasing population size compared with phenotypic selection. Genomic selection is a form of MAS using all marker information simultaneously to predict individual genetic values for complex traits and has widely been found superior to MAS. No studies have explicitly investigated how resource allocation decisions affect success of genomic selection. My objective was to study the effect of resource allocation on response to MAS and genomic selection in a single biparental population of doubled haploid lines by using computer simulation. Simulation results were compared with previously derived formulas for the calculation of prediction accuracy under different levels of heritability and population size. Response of prediction accuracy to resource allocation strategies differed between genomic selection models (ridge regression best linear unbiased prediction [RR-BLUP], BayesCp) and multiple linear regression using ordinary least-squares estimation (OLS), leading to different optimal resource allocation choices between OLS and RR-BLUP. For OLS, it was always advantageous to maximize population size at the expense of replication, but a high degree of flexibility was observed for RR-BLUP. Prediction accuracy of doubled haploid lines included in the training set was much greater than of those excluded from the training set, so there was little benefit to phenotyping only a subset of the lines genotyped. Finally, observed prediction accuracies in the simulation compared well to calculated prediction accuracies, indicating these theoretical formulas are useful for making resource allocation decisions

The quantitative determination of phytate and available phosphorus for maize (Zea mays L.) breeding

The prevalence of phytate, an unavailable form of phosphorus (P), in maize grain is a source of environmental and nutritional problems. Because many of these issues are not due to the total amount of P concentration in grain per se and available P is an essential nutrient for livestock and humans, it would be desirable to reduce phytate and increase available P levels of maize. This study involved the design of rapid and simple protocols for phytate and available P measurement to use in a maize breeding program. A set of inbred lines (50) and 90 SI families were evaluated using these modified assays. Correlations between phytate, available P, and other important agronomic traits were estimated to identify any secondary selection responses. Selection indices involving these P traits and standard agronomic traits were also constructed to ascertain the practicality of involving these traits in a breeding program. The broad-sense heritability and repeatability of available P suggested that an assay typically used for qualitative purposes can be used quantitatively as well. However, the lower field repeatabilities of phytate indicate that either there was a lack of genetic variance in the genetic material evaluated or the phytate assay is in need of further refinement to increase precision. Positive phytate:protein correlations were detected and are in accordance with previously published literature. Together with the phytate:protein correlation, negative correlations between phytate and starch and phytate and kernel weight suggest that selection on phytate may alter the germ to endosperm ratio. The selection differentials from the selection indices constructed suggest that progress may be made for phytate, available P, yield, moisture, and root and stalk lodging in the desired directions, but long-term selection or more diverse genetic material may be needed for noticeable progress. This study provides an optimistic start to the improvement of traits not typically included in breeding programs

Lyrics and the law : the constitution of law in music.

This is a study of music as a form of jurisprudence. Political science scholars have focused on researching what they view as political music. Sociolegal scholars have done scant research from a theoretical perspective in looking at law having a musical rhythm. Rather than connecting the themes within the music to law, I begin with the perspective that the American law creates and maintains inequality. This premise allows for a dissection of the lyrics of various songs to be connected to each other through grand themes. I argue that various musical artists have created their own form of jurisprudence, often more about justice than present U.S. law. Once establishing what justice entails, and the shortcomings that exist within the system, I work through various themes such as race, gender, and class to demonstrate the comparisons between both political scientists and sociolegal scholars with the musicians. Classic legal theories are incorporated to understand the musicians use of judicial interpretation. Several methodologies are used in this research. Historical analysis establishes the foundation that is necessary to discuss slavery and the genesis of specific genres of music, namely reggae music from Jamaica. While reggae music may be a constant in the project, it is not the sole genre that will be studied. Content analysis of the lyrics, through the prism of the literature, will explain how and why the musicians shape legal discourse. In addition, Supreme Court case law will be studied, specifically relating to First and Fourth Amendment issues. Content analysis in the form of speeches and various interviews will be essential to understanding the goals and actions of the musicians and their impact on legal culture. The result will be an amalgamation of literature that equates musicians with scholars and demonstrates how musicians respond to law

Prospects of genomic prediction in the USDA Soybean Germplasm Collection: Historical data creates robust models for enhancing selection of accessions

The identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K SNP data collected on 18,480 domesticated soybean (G. max) accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained an appreciable amount of the variation in accession performance in independent validation trials, with correlations between predicted and observed reaching up to 0.92 for oil and protein and 0.79 for yield. The optimization of training set design was explored using a series of cross-validation schemes. It was found that the target population and environment need to be well represented in the training set. Secondly, genomic prediction training sets appear to be robust to the presence of data from diverse geographical locations and genetic clusters. This finding, however, depends on the influence of shattering and lodging, and may be specific to soybean with its presence of maturity groups. The distribution of 7,608 non-phenotyped accessions was examined through the application of genomic prediction models. The distribution of predictions of phenotyped accessions was representative of the distribution of predictions for non-phenotyped accessions, with no non-phenotyped accessions being predicted to fall far outside the range of predictions of phenotyped accessions

Prospects of genomic prediction in the USDA Soybean Germplasm Collection: Historical data creates robust models for enhancing selection of accessions

The identification and mobilization of useful genetic variation from germplasm banks for use in breeding programs is critical for future genetic gain and protection against crop pests. Plummeting costs of next-generation sequencing and genotyping is revolutionizing the way in which researchers and breeders interface with plant germplasm collections. An example of this is the high density genotyping of the entire USDA Soybean Germplasm Collection. We assessed the usefulness of 50K SNP data collected on 18,480 domesticated soybean (G. max) accessions and vast historical phenotypic data for developing genomic prediction models for protein, oil, and yield. Resulting genomic prediction models explained an appreciable amount of the variation in accession performance in independent validation trials, with correlations between predicted and observed reaching up to 0.92 for oil and protein and 0.79 for yield. The optimization of training set design was explored using a series of cross-validation schemes. It was found that the target population and environment need to be well represented in the training set. Secondly, genomic prediction training sets appear to be robust to the presence of data from diverse geographical locations and genetic clusters. This finding, however, depends on the influence of shattering and lodging, and may be specific to soybean with its presence of maturity groups. The distribution of 7,608 non-phenotyped accessions was examined through the application of genomic prediction models. The distribution of predictions of phenotyped accessions was representative of the distribution of predictions for non-phenotyped accessions, with no non-phenotyped accessions being predicted to fall far outside the range of predictions of phenotyped accessions

Conflict-Based Model Predictive Control for Scalable Multi-Robot Motion Planning

This paper presents a scalable multi-robot motion planning algorithm called Conflict-Based Model Predictive Control (CB-MPC). Inspired by Conflict-Based Search (CBS), the planner leverages a similar high-level conflict tree to efficiently resolve robot-robot conflicts in the continuous space, while reasoning about each agent's kinematic and dynamic constraints and actuation limits using MPC as the low-level planner. We show that tracking high-level multi-robot plans with a vanilla MPC controller is insufficient, and results in unexpected collisions in tight navigation scenarios. Compared to other variations of multi-robot MPC like joint, prioritized, and distributed, we demonstrate that CB-MPC improves the executability and success rate, allows for closer robot-robot interactions, and reduces the computational cost significantly without compromising the solution quality across a variety of environments. Furthermore, we show that CB-MPC combined with a high-level path planner can effectively substitute computationally expensive full-horizon multi-robot kinodynamic planners

Response Surface Analysis of Genomic Prediction Accuracy Values Using Quality Control Covariates in Soybean

An important and broadly used tool for selection purposes and to increase yield and genetic gain in plant breeding programs is genomic prediction (GP). Genomic prediction is a technique where molecular marker information and phenotypic data are used to predict the phenotype (eg, yield) of individuals for which only marker data are available. Higher prediction accuracy can be achieved not only by using efficient models but also by using quality molecular marker and phenotypic data. The steps of a typical quality control (QC) of marker data include the elimination of markers with certain level of minor allele frequency (MAF) and missing marker values and the imputation of missing marker values. In this article, we evaluated how the prediction accuracy is influenced by the combination of 12 MAF values, 27 different percentages of missing marker values, and 2 imputation techniques (IT; naïve and Random Forest (RF)). We constructed a response surface of prediction accuracy values for the two ITs as a function of MAF and percentage of missing marker values using soybean data from the University of Nebraska–Lincoln Soybean Breeding Program. We found that both the genetic architecture of the trait and the IT affect the prediction accuracy implying that we have to be careful how we perform QC on the marker data. For the corresponding combinations MAF-percentage of missing values we observed that implementing the RF imputation increased the number of markers by 2 to 5 times than the simple naïve imputation method that is based on the mean allele dosage of the non-missing values at each loci. We conclude that there is not a unique strategy (combination of the QCs and imputation method) that outperforms the results of the others for all traits