O teste em gerações precoces no melhoramento genético de plantas consiste em identificar e selecionar progênies promissoras em gerações iniciais de endogamia, tais como F2 ou F3, e com isso concentrar tempo e recursos somente em populações com potencial para gerar linhas puras superiores. Considerando a ocorrência de relatos conflitantes, este trabalho teve como objetivo avaliar a eficiência do teste precoce no melhoramento genético de soja, utilizando uma população derivada de um cruzamento biparental entre as linhagens 14 e 38, contrastantes para produção de grãos, e selecionadas do cruzamento entre os genitores PI-123439 e PI-239235. Os tratamentos consistiram de uma amostra de 100 progênies de cada tipo: F2:3, F3:4 e F4:5, que foram avaliadas em experimentos em látice 10 x 10 no ano agrícola 2005/6 na área experimental do Departamento de Genética da ESALQ/USP, em Piracicaba, SP. Os experimentos foram avaliados novamente no ano agrícola 2006/7, em dois locais: Piracicaba, SP e Anhumas, SP, utilizando, para isso, os bulks oriundos dos experimentos do ano anterior, isto é, progênies F2:4, F3:5 e F4:6,. A parcela experimental foi constituída de uma linha de 2 m de comprimento, espaçada de 0,5 m, contendo 35 plantas por parcela após o desbaste. Foram avaliados os seguintes caracteres: altura das plantas no florescimento (AF), dias para florescimento (DF), altura das plantas na maturação (AM), dias para maturação (DM) e produção de grãos por parcela (PG). Devido à baixa taxa de germinação das sementes nos experimentos derivados de plantas F3 (F3:4 e F3:5) e, consequentemente à baixa precisão experimental, estes foram excluídos do estudo. Com base nos resultados dos experimentos de 2005/6 foram estimadas as variâncias genéticas e fenotípicas, os coeficientes de herdabilidades e as respostas esperadas com seleção, supondo diferentes intensidades (20%, 30%, 40% e 50%). Com base nos resultados das avaliações de 2006/7 foram obtidas as respostas observadas com seleção, que possibilitaram avaliar a eficiência do teste precoce nas diferentes intensidades de seleção. Observou-se uma boa correspondência entre as respostas preditas e observadas somente para as progênies F4:5, fato este já esperado, considerando o alto grau de heterozigose nas progênies F2:3. Os resultados gerais indicaram que a seleção em geração tão precoces como F2:4 pode ser efetuada, desde que sejam utilizadas intensidades moderados de seleção, da ordem de 40%. / Early generation testing in plant breeding consists of selecting progenies in early generations of selfing such as in F2 or F3, in order to save time and resources. The objective of this work was to assess the early generation testing effectiveness in soybean breeding programs. The base population consisted of a two way cross between inbred lines 14 and 38, derived from the cross between PI-123439 and PI-239235 parents. Entries consisted of a sample of 100 progenies of each generation: F2:3, F3:4 e F4:5, evaluated in lattice 10 x 10 designs in the 2005/6 growing season at Department of Genetics (ESALQ/USP) in Piracicaba, SP. In the 2006/7 growing season new experimental evaluations were performed, using the same experimental designs, in two locations: Piracicaba, SP, and Anhumas, SP, where the entries consisted of bulks derived from 2005/6 entries, i.e., F2:4, F3:5 and F4:6 progenies. Plots consisted of single 2-meter-long rows spaced 0.5 meter apart, with 37 plants after thinning in all experiments. The following traits were evaluated: plant height at flowering (AF), days to flowering (DF), plant height at maturity (AM), days to maturity (DM), and grain yield (PG). Due to the low germination rate and, consequently, the low experimental precision, experiments of F3 derived lines (F3:4 and F3:5) were eliminated. Estimates of genetics variances, heritabilities and expected response to selection, considering four selection intensities (20%, 30%, 40% e 50%) were obtained from 2005/6 evaluations. The observed responses to selection were obtained from the 2006/7 experiments, in order to evaluate the effectiveness of early selection. A good correspondence between estimated and observed responses to selection were observed only for experiments based on F4 derived lines, which is expected, considering the higher degree of heterozigosity of the progenies in as early generations as in F2. General results has shown that selection based in early generation as in F2 can be performed, using moderate intensities of selection, such as 40%.

Feature selection is the process of automatically selecting important features from data. It is an essential part of machine learning, artificial intelligence, data mining, and modelling in general. There are many feature selection algorithms available and the appropriate choice can be difficult. The aim of this thesis was to compare feature selection algorithms in order to provide an experimental basis for which algorithm to choose. The first phase involved assessing which algorithms are most common in the scientific community, through a systematic literature study in the two largest reference databases: Scopus and Web of Science. The second phase involved constructing and implementing a benchmark pipeline to compare 31 algorithms’ performance on 50 data sets.The selected features were used to construct classification models and their predictive performances were compared, as well as the runtime of the selection process. The results show a small overall superiority of embedded type algorithms, especially types that involve Decision Trees. However, there is no algorithm that is significantly superior in every case. The pipeline and data from the experiments can be used by practitioners in determining which algorithms to apply to their respective problems. / Variabelselektion är en process där relevanta variabler automatiskt selekteras i data. Det är en essentiell del av maskininlärning, artificiell intelligens, datautvinning och modellering i allmänhet. Den stora mängden variabelselektionsalgoritmer kan göra det svårt att avgöra vilken algoritm som ska användas. Målet med detta examensarbete är att jämföra variabelselektionsalgoritmer för att ge en experimentell bas för valet av algoritm. I första fasen avgjordes vilka algoritmer som är mest förekommande i vetenskapen, via en systematisk litteraturstudie i de två största referensdatabaserna: Scopus och Web of Science. Den andra fasen bestod av att konstruera och implementera en experimentell mjukvara för att jämföra algoritmernas prestanda på 50 data set. De valda variablerna användes för att konstruera klassificeringsmodeller vars prediktiva prestanda, samt selektionsprocessens körningstid, jämfördes. Resultatet visar att inbäddade algoritmer i viss grad är överlägsna, framför allt typer som bygger på beslutsträd. Det finns dock ingen algoritm som är signifikant överlägsen i varje sammanhang. Programmet och datan från experimenten kan användas av utövare för att avgöra vilken algoritm som bör appliceras på deras respektive problem.

Many plant species recognize kin and respond with changes in functional traits. Researchers hypothesize that siblings compete less than strangers. However, no study has directly tested whether siblings are less competitive. Measuring natural selection on kin recognition responses in root allocation and other destructively measured traits is challenging, since trait and fitness cannot be measured on the same individual. Here, a methodology using family-level selection is developed, measuring the trait on one individual and measuring its fitness value using another related individual. Three greenhouse pot experiments were conducted using six Brassica oleracea cultivars at two nutrient levels. We investigated whether root allocation and morphological traits were under natural selection. We tested whether or not there was cultivar recognition or resource partitioning in B. oleracea. We found that putative competitive traits (size, height, emergence and root allocation) had expected patterns of individual selection for an increase in each trait and group selection for a reduction of each trait. There was no indication that resource partitioning was occurring or that B. oleracea could recognize cultivars. However, plants were experiencing competitive interactions within pots. In conclusion, we demonstrate that using family-level selection estimates the fitness consequences of root allocation and morphological traits. / Thesis / Master of Science (MSc)

Doctor of Philosophy / Department of Agronomy / Allan Fritz / Wheat breeders are constantly working to develop new wheat varieties with improved performance for agronomically important traits such as yield and disease resistance. Identifying better ways of phenotyping germplasm, developing methods for predicting performance based on genetic information, and identifying novel sources of genetic disease resistance can all improve the efficiency of breeding efforts. Three studies relating to these research interests were conducted. Synthetic hexaploid wheat lines were screened for resistance to root-lesion nematodes, an economically important pest of wheat. This resulted in the identification of three lines resistant to the root-lesion nematode species Pratylenchus thornei. Grain yield data from multi-location yield trials and average yields for counties in Kansas were used to identify wheat production areas in Kansas. Knowledge obtained from this study is useful for both interpreting data from yield trials and deciding where to place them in order to identify new higher yielding varieties. These data also aided the final research study, developing a genomic selection (GS) model for yield in the Kansas State University wheat breeding program. This model was used to assess the accuracy of GS in conditions experienced in a breeding project. Available measurements of GS have been constructed using simulations or using conditions not typical of those experienced in a wheat breeding program. The estimate of accuracy determined in this study was less than many of the reported measurements. This measure of accuracy will aid in determining if GS is a cost efficient tool for use in wheat breeding.

Genomic selection (GS) has been adopted by the dairy cattle breeding industry and the opportunity exists to implement this technology in UK beef and sheep breeding. However, these sectors do not appear so readily predisposed to GS implementation. Following an introduction to GS in Chapter 1, Chapter 2 investigated the structure of the little-studied UK beef breeding sector. This provided estimates of key commercial and pedigree population parameters, for use in modelling genetic gain from GS. Terminal traits were found to be the dominant selection goals, with 85% of beef-sired commercial matings resulting in progeny being slaughtered at a prime age. Pedigree bulls disseminated the majority of genes in the sector via natural service. The correlation between the terminal selection index (TI) and the sale price of breeding bulls was moderate, suggesting a modest uptake of genetic technology in the sector. Chapter 3 estimated selection intensity for TI, generation interval and the dissemination rate of improved genes in the pedigree Limousin population. In order to predict the genetic gain achievable in using GS in beef and sheep breeding, Chapters 4 to 6 undertook deterministic selection index simulations, incorporating genomic information as correlated traits. In Chapter 4, GS was modelled for terminal beef traits, when incorporating carcass information and accounting for likely genotype by environment interaction. Using a training population of 2000 sires, this concept was predicted to offer 40% greater genetic gain than existing BLUP selection using pedigree phenotypes. Gene flow methodology projected the commercial value of this gain to offer a substantial return net of genotyping costs. Chapter 5 explored GS for maternal beef traits within the concept of a nucleus breeding scheme. Whilst greater genetic gain was predicted with GS than with conventional BLUP, the economic value of this gain was projected to be too low to justify such a scheme in the UK. Chapter 6 proposed a synergy between computer tomography (CT) phenotypes and GS in sheep breeding. Developing a genomic predictor from male selection candidates with CT phenotypes and conventional performance records was predicted to increase genetic gain by 55% over BLUP selection without CT traits. Introducing GBV contributed most of the accuracy in this scenario, suggesting that the existing performance recording structure in UK sheep breeding could in the future be replaced by GS using CT. In the general discussion, the potential for GS in other beef and sheep traits was considered in the light of the outcomes of these simulations. Given the lack of vertical integration in UK beef and sheep sectors, the drivers for implementation of GS are examined. Finally, the options for international cooperation and the possibilities offered by future genotyping technology are considered. It was concluded that implementation of GS incorporating beef carcass phenotypes was merited and could provide a platform for future GS implementation in other novel traits. Sheep GS with CT traits was considered a higher risk strategy, due to the lack of evidence for uptake of existing EBV technology.

Several statistical problems can be described as estimation problem, where the goal is to learn a set of parameters, from some data, by maximizing a criterion. These type of problems are typically encountered in a supervised learning setting, where we want to relate an output (or many outputs) to multiple inputs. The relationship between these outputs and these inputs can be complex, and this complexity can be attributed to the high dimensionality of the space containing the inputs and the outputs; the existence of a structural prior knowledge within the inputs or the outputs that if ignored may lead to inefficient estimates of the parameters; and the presence of a non-trivial noise structure in the data. In this thesis we propose new statistical methods to achieve model selection and estimation when there are more predictors than observations. We also design a new set of algorithms to efficiently solve the proposed statistical models. We apply the implemented methods to genetic data sets of cancer patients and to some economics data.

The number of studies estimating neighbourhood effects has increased rapidly during the last two decades. Although results from these studies vary, a majority find at least small effects. But to what extent can we trust these estimates? Neighbourhood effect studies face many serious methodological challenges, of which some are related to the fact that people move. The mobility of individuals may cause neighbourhoods to change over time, result in exposure times that are too short and seriously bias estimates. These methodological problems have not been given enough attention in the neighbourhood effect literature: no study controls for them all, and implications of mobility are rarely included in theoretical discussions of neighbourhood effects. In a comprehensive summary and five different papers, I argue that the two scholarly fields of residential mobility and neighbourhood effect studies are intrinsically connected and that any arbitrary separation between the two is both conceptually problematic and risks leading to erroneous conclusions. Studies of neighbourhood effects must address the problems caused by mobility, before it can be convincingly argued that results actually show neighbourhood effects. To do this, longitudinal data are necessary. Furthermore, the connection between the two fields may also have implications for studies of residential mobility.

The purpose of this study is to find out how three teachers in Sweden work with literature and fiction in school. The main focus of this study is to examine for what reasons the teachers use literature and fiction as a pedagogic tool. Research shows that using fiction in school as an education tool can develop pupils writing and linguistic improvement. The teachers who were interviewed in this study seem to have a positive attitude to fiction.  They also believe that fiction is increasing the pupils´ vocabulary and that group discussions about reading are important because the pupils learn to reflect reading. The interwied teachers´-mention that fiction has a positive effect on pupils’ general language development. The method which was used in this essay is qualitative interviews. The participants have between 3 to 16 years of experience in their work field and the interviews were recorded and took place in their class room.

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Doctor of Philosophy / Genetics Interdepartmental Program - Plant Pathology / Jesse A. Poland / A major challenge for 21st century plant geneticists is to predict plant performance based on genetic information. This is a daunting challenge, especially when there are thousands of genes that control complex traits as well as the extreme variation that results from the environment where plants are grown. Rapid advances in technology are assisting in overcoming the obstacle of connecting the genotype to phenotype. Next generation sequencing has provided a wealth of genomic information resulting in numerous completely sequenced genomes and the ability to quickly genotype thousands of individuals. The ability to pair the dense genotypic data with phenotypic data, the observed plant performance, will culminate in successfully predicting cultivar performance. While genomics has advanced rapidly, phenomics, the science and ability to measure plant phenotypes, has slowly progressed, resulting in an imbalance of genotypic to phenotypic data. The disproportion of high-throughput phenotyping (HTP) data is a bottleneck to many genetic and association mapping studies as well as genomic selection (GS). To alleviate the phenomics bottleneck, an affordable and portable phenotyping platform, Phenocart, was developed and evaluated. The Phenocart was capable of taking multiple types of georeferenced measurements including normalized difference vegetation index and canopy temperature, throughout the growing season. The Phenocart performed as well as existing manual measurements while increasing the amount of data exponentially. The deluge of phenotypic data offered opportunities to evaluate lines at specific time points, as well as combining data throughout the season to assess for genotypic differences. Finally in an effort to predict crop performance, the phenotypic data was used in GS models. The models combined molecular marker data from genotyping-by-sequencing with high-throughput phenotyping for plant phenotypic characterization. Utilizing HTP data, rather than just the often measured yield, increased the accuracy of GS models. Achieving the goal of connecting genotype to phenotype has direct impact on plant breeding by allowing selection of higher yielding crops as well as selecting crops that are adapted to local environments. This will allow for a faster rate of improvement in crops, which is imperative to meet the growing global population demand for plant products.