Spatial statistical analysis and selection of genotypes in plant breeding

The objective of this study was to evaluate the efficiency of spatial statistical analysis in the selection of genotypes in a plant breeding program and, particularly, to demonstrate the benefits of the approach when experimental observations are not spatially independent. The basic material of this study was a yield trial of soybean lines, with five check varieties (of fixed effect) and 110 test lines (of random effects), in an augmented block design. The spatial analysis used a random field linear model (RFML), with a covariance function estimated from the residuals of the analysis considering independent errors. Results showed a residual autocorrelation of significant magnitude and extension (range), which allowed a better discrimination among genotypes (increase of the power of statistical tests, reduction in the standard errors of estimates and predictors, and a greater amplitude of predictor values) when the spatial analysis was applied. Furthermore, the spatial analysis led to a different ranking of the genetic materials, in comparison with the non-spatial analysis, and a selection less influenced by local variation effects was obtained.

Portfolio kliinisen eläinlääketieteen erikoistumiskoulutuksen apuvälineenä

Summary: Professional development portfolio as a tool for veterinarians specializing in small animal diseases, equine diseases and production medicine

On the Selection of Non-Invasive Methods Based on Speech Analysis Oriented to Automatic Alzheimer Disease Diagnosis

The work presented here is part of a larger study to identify novel technologies and biomarkers for early Alzheimer disease (AD) detection and it focuses on evaluating the suitability of a new approach for early AD diagnosis by non-invasive methods. The purpose is to examine in a pilot study the potential of applying intelligent algorithms to speech features obtained from suspected patients in order to contribute to the improvement of diagnosis of AD and its degree of severity. In this sense, Artificial Neural Networks (ANN) have been used for the automatic classification of the two classes (AD and control subjects). Two human issues have been analyzed for feature selection: Spontaneous Speech and Emotional Response. Not only linear features but also non-linear ones, such as Fractal Dimension, have been explored. The approach is non invasive, low cost and without any side effects. Obtained experimental results were very satisfactory and promising for early diagnosis and classification of AD patients.

Selection via simulated individual BLUP based on family genotypic effects in sugarcane

The objective of this work was to propose a new selection strategy for the initial stages of sugarcane improvement, based on the methodology 'simulated individual BLUP (BLUPIS)', which promotes a dynamic allocation of individuals selected in each full-sib family, using BLUP as a base for both the genotypic effects of the referred families and plot effects. The method proposed applies to single full-sib families or those obtained from unbalanced or balanced diallel crosses, half-sib families and self-pollinated families. BLUPIS indicates the number of individuals to be selected within each family, the total number of clones to be advanced, and the number of families to contribute with selected individuals. Correlation between BLUPIS and true BLUP was 0.96, by method validation. Additionally, BLUPIS allows the identification of which replication contains the best individuals of each family.

Populations with elevated mutation load do not benefit from the operation of sexual selection.

Theory predicts that if most mutations are deleterious to both overall fitness and condition-dependent traits affecting mating success, sexual selection will purge mutation load and increase nonsexual fitness. We explored this possibility with populations of mutagenized Drosophila melanogaster exhibiting elevated levels of deleterious variation and evolving in the presence or absence of male-male competition and female choice. After 60 generations of experimental evolution, monogamous populations exhibited higher total reproductive output than polygamous populations. Parental environment also affected fitness measures - flies that evolved in the presence of sexual conflict showed reduced nonsexual fitness when their parents experienced a polygamous environment, indicating trans-generational effects of male harassment and highlighting the importance of a common garden design. This cost of parental promiscuity was nearly absent in monogamous lines, providing evidence for the evolution of reduced sexual antagonism. There was no overall difference in egg-to-adult viability between selection regimes. If mutation load was reduced by the action of sexual selection in this experiment, the resultant gain in fitness was not sufficient to overcome the costs of sexual antagonism.

The gendering of excellence through quality criteria: the case of the SNSF Professorships in Switzerland

The Swiss National Science Foundation Professorships Programme is presented as a scientific programme that aims to promote outstanding young scholars to professorial positions. Academic excellence is presented as the main selection criterion. The emphasis put on the research portfolio and on the age of the candidates means that the beneficiaries of these professorships put forward an image of excellence that is more embedded in data-based sciences than in the humanities and social sciences, thus strengthening the domination of a sector scientific activity essentially occupied by men over the sector that has opened up more widely to women. This paper aims to deconstruct the criteria of academic excellence as they appear in this programme and to show that what seem quality criteria are in fact inspired by a specific androcentric model. These biases tend to undermine the gender equality aims of the programme.

K-r/K-c but not d(N)/d(S) correlates positively with body mass in birds, raising implications for inferring lineage-specific selection

Background: The ratio of the rates of non-synonymous and synonymous substitution (d(N)/d(S)) is commonly used to estimate selection in coding sequences. It is often suggested that, all else being equal, d(N)/d(S) should be lower in populations with large effective size (Ne) due to increased efficacy of purifying selection. As N-e is difficult to measure directly, life history traits such as body mass, which is typically negatively associated with population size, have commonly been used as proxies in empirical tests of this hypothesis. However, evidence of whether the expected positive correlation between body mass and d(N)/d(S) is consistently observed is conflicting. Results: Employing whole genome sequence data from 48 avian species, we assess the relationship between rates of molecular evolution and life history in birds. We find a negative correlation between dN/dS and body mass, contrary to nearly neutral expectation. This raises the question whether the correlation might be a method artefact. We therefore in turn consider non-stationary base composition, divergence time and saturation as possible explanations, but find no clear patterns. However, in striking contrast to d(N)/d(S), the ratio of radical to conservative amino acid substitutions (K-r/K-c) correlates positively with body mass. Conclusions: Our results in principle accord with the notion that non-synonymous substitutions causing radical amino acid changes are more efficiently removed by selection in large populations, consistent with nearly neutral theory. These findings have implications for the use of d(N)/d(S) and suggest that caution is warranted when drawing conclusions about lineage-specific modes of protein evolution using this metric.

Validation of microsatellite markers for assisted selection of soybean resistance to cyst nematode races 3 and 14

The objective of this work was to validate microsatellite markers associated with resistance to soybean cyst nematode (Heterodera glycines Ichinohe) races 3 and 14, in soybean (Glycine max L.) genotypes, for use in marker-assisted selection (MAS) programs. Microsatellites of soybean linkage groups A2, D2 and G were tested in two populations, and their selection efficiencies were determined. The populations were 65 F2:3 families from Msoy8001 (resistant) x Conquista (susceptible) cross, and 66 F2:3 families of S5995 (resistant) x Renascença (susceptible) cross, evaluated for resistance to races 3 and 14, respectively. Families with female index up to 30% were considered moderately resistant. Markers of A2 and G linkage groups were associated with resistance to race 3. Markers Satt309 and GMENOD2B explained the greatest proportion of phenotypic variance in the different groups. The combinations Satt309+GMENOD2B and Satt309+Satt187 presented 100% selection efficiency. Resistance to race 14 was associated with markers of G linkage group, and selection efficiency in the Satt309+Satt356 combination was 100%. The selection differential obtained by phenotypic and marker assisted selection showed that both can result in similar gains.

Strategy selection: An introduction to the modeling challenge

Modeling the mechanisms that determine how humans and other agents choose among different behavioral and cognitive processes-be they strategies, routines, actions, or operators-represents a paramount theoretical stumbling block across disciplines, ranging from the cognitive and decision sciences to economics, biology, and machine learning. By using the cognitive and decision sciences as a case study, we provide an introduction to what is also known as the strategy selection problem. First, we explain why many researchers assume humans and other animals to come equipped with a repertoire of behavioral and cognitive processes. Second, we expose three descriptive, predictive, and prescriptive challenges that are common to all disciplines which aim to model the choice among these processes. Third, we give an overview of different approaches to strategy selection. These include cost‐benefit, ecological, learning, memory, unified, connectionist, sequential sampling, and maximization approaches. We conclude by pointing to opportunities for future research and by stressing that the selection problem is far from being resolved.

Breeding strategies for recurrent selection of maize

The objectives of this work were to analyze theoretical genetic gains of maize due to recurrent selection among full-sib and half-sib families, obtained by Design I, Full-Sib Design and Half-Sib Design, and genotypic variability and gene loss with long term selection. The designs were evaluated by simulation, based on average estimated gains after ten selection cycles. The simulation process was based on seven gene systems with ten genes (with distinct degrees of dominance), three population classes (with different gene frequencies), under three environmental conditions (heritability values), and four selection strategies. Each combination was repeated ten times, amounting to 25, 200 simulations. Full-sib selection is generally more efficient than half-sib selection, mainly with favorable dominant genes. The use of full-sib families derived by Design I is generally more efficient than using progenies obtained by Full-Sib Design. Using Design I with 50 males and 200 females (effective size of 160) did not result in improved populations with minimum genotypic variability. In the populations with lower effective size (160 and 400) the loss of favorable genes was restricted to recessive genes with reduced frequencies.

Inheritance pattern and selection criteria for resistance to soybean cyst nematode races 3 and 9

The objective of this work was to determine soybean resistance inheritance to Heterodera glycines Ichinohe (soybean cyst nematode - SCN) races 3 and 9, as well as to evaluate the efficiency of direct and indirect selection in a soybean population of 112 recombinant inbred lines (RIL) derived from the resistant cultivar Hartwig. The experiment was conducted in a completely randomized design, in Londrina, PR, Brazil. The estimated narrow-sense heritabilities for resistance to races 3 and 9 were 80.67 and 77.97%. The genetic correlation coefficient (r g = 0.17; p<0.01) shows that some genetic components of resistance to these two races are inherited together. The greatest genetic gain by indirect selection was obtained to race 9, selecting to race 3 due to simpler inheritance of resistance to race 9 and not because these two races share common resistance genes. The resistance of cultivar Hartwig to races 3 and 9 is determined by 4 and 2 genes, respectively. One of these genes confers resistance to both races, explaining a fraction of the significant genetic correlation found between resistance to these SCN races. The inheritance pattern described indicates that selection for resistance to SCN must be performed for each race individually.

Allohexaploid pearl millet x elephantgrass population potential for a recurrent selection program

The objective of this work was to evaluate the potential of allohexaploid pearl millet x elephantgrass (HGL) population for a recurrent selection program through open-pollinated progenies. Seventy-eight progenies, one representative sample of the population, and two commercial cultivars, Pioneiro and Paraíso, were evaluated in a 9x9 triple lattice design, in two sites. Plant height and dry matter yield were evaluated in three and four cuts, respectively. For plant height, the 17 best progenies were similar to both commercial controls, while for dry matter yield they were higher than 'Paraíso' and lower than 'Pioneiro'. The correlation between progenies and cuts indicated that the fourth cut represents the mean of all cuts, and the possibility of using early selection. Heritability estimates considering cuts and sites were 56.9% for plant height and 58.8% for dry matter yield, and the expected response to selection was 23.4% for dry matter yield and 18.1% for plant height. These results demonstrate the promising HGL population potential for a recurrent selection program.

The branch-site test of positive selection is surprisingly robust but lacks power under synonymous substitution saturation and variation in GC

Positive selection is widely estimated from protein coding sequence alignments by the nonsynonymous-to-synonymous ratio omega. Increasingly elaborate codon models are used in a likelihood framework for this estimation. Although there is widespread concern about the robustness of the estimation of the omega ratio, more efforts are needed to estimate this robustness, especially in the context of complex models. Here, we focused on the branch-site codon model. We investigated its robustness on a large set of simulated data. First, we investigated the impact of sequence divergence. We found evidence of underestimation of the synonymous substitution rate for values as small as 0.5, with a slight increase in false positives for the branch-site test. When dS increases further, underestimation of dS is worse, but false positives decrease. Interestingly, the detection of true positives follows a similar distribution, with a maximum for intermediary values of dS. Thus, high dS is more of a concern for a loss of power (false negatives) than for false positives of the test. Second, we investigated the impact of GC content. We showed that there is no significant difference of false positives between high GC (up to similar to 80%) and low GC (similar to 30%) genes. Moreover, neither shifts of GC content on a specific branch nor major shifts in GC along the gene sequence generate many false positives. Our results confirm that the branch-site is a very conservative test.

Iowa Pavement Asset Management Decision-Making Framework & Pavement Treatment Selection Tool for Local Agencies, TR-651, 2015

Most local agencies in Iowa currently make their pavement treatment decisions based on their limited experience due primarily to lack of a systematic decision-making framework and a decision-aid tool. The lack of objective condition assessment data of agency pavements also contributes to this problem. This study developed a systematic pavement treatment selection framework for local agencies to assist them in selecting the most appropriate treatment and to help justify their maintenance and rehabilitation decisions. The framework is based on an extensive literature review of the various pavement treatment techniques in terms of their technical applicability and limitations, meaningful practices of neighboring states, and the results of a survey of local agencies. The treatment selection framework involves three different steps: pavement condition assessment, selection of technically feasible treatments using decision trees, and selection of the most appropriate treatment considering the return-on-investment (ROI) and other non-economic factors. An Excel-based spreadsheet tool that automates the treatment selection framework was also developed, along with a standalone user guide for the tool. The Pavement Treatment Selection Tool (PTST) for Local Agencies allows users to enter the severity and extent levels of existing distresses and then, recommends a set of technically feasible treatments. The tool also evaluates the ROI of each feasible treatment and, if necessary, it can also evaluate the non-economic value of each treatment option to help determine the most appropriate treatment for the pavement. It is expected that the framework and tool will help local agencies improve their pavement asset management practices significantly and make better economic and defensible decisions on pavement treatment selection.