Visual field severity indices demonstrate dose-dependent visual loss from Vigabatrin therapy

Purpose: The aims of this study were to develop an algorithm to accurately quantify Vigabatrin (VGB)-induced central visual field loss and to investigate the relationship between visual field loss and maximum daily dose, cumulative dose and duration of dose. Methods: The sample comprised 31 patients (mean age 37.9 years; SD 14.4 years) diagnosed with epilepsy and exposed to VGB. Each participant underwent standard automated static visual field examination of the central visual field. Central visual field loss was determined using continuous scales quantifying severity in terms of area and depth of defect and additionally by symmetry of defect between the two eyes. A simultaneous multiple regression model was used to explore the relationship between these visual field parameters and the drug predictor variables. Results: The regression model indicated that maximum VGB dose was the only factor to be significantly correlated with individual eye severity (right eye: p = 0.020; left eye: p = 0.012) and symmetry of visual field defect (p = 0.024). Conclusions: Maximum daily dose was the single most reliable indicator of those patients likely to exhibit visual field defects due to VGB. These findings suggest that high maximum dose is more likely to result in visual field defects than high cumulative doses or those of long duration.

Visualisation of bioinformatics datasets

Analysing the molecular polymorphism and interactions of DNA, RNA and proteins is of fundamental importance in biology. Predicting functions of polymorphic molecules is important in order to design more effective medicines. Analysing major histocompatibility complex (MHC) polymorphism is important for mate choice, epitope-based vaccine design and transplantation rejection etc. Most of the existing exploratory approaches cannot analyse these datasets because of the large number of molecules with a high number of descriptors per molecule. This thesis develops novel methods for data projection in order to explore high dimensional biological dataset by visualising them in a low-dimensional space. With increasing dimensionality, some existing data visualisation methods such as generative topographic mapping (GTM) become computationally intractable. We propose variants of these methods, where we use log-transformations at certain steps of expectation maximisation (EM) based parameter learning process, to make them tractable for high-dimensional datasets. We demonstrate these proposed variants both for synthetic and electrostatic potential dataset of MHC class-I. We also propose to extend a latent trait model (LTM), suitable for visualising high dimensional discrete data, to simultaneously estimate feature saliency as an integrated part of the parameter learning process of a visualisation model. This LTM variant not only gives better visualisation by modifying the project map based on feature relevance, but also helps users to assess the significance of each feature. Another problem which is not addressed much in the literature is the visualisation of mixed-type data. We propose to combine GTM and LTM in a principled way where appropriate noise models are used for each type of data in order to visualise mixed-type data in a single plot. We call this model a generalised GTM (GGTM). We also propose to extend GGTM model to estimate feature saliencies while training a visualisation model and this is called GGTM with feature saliency (GGTM-FS). We demonstrate effectiveness of these proposed models both for synthetic and real datasets. We evaluate visualisation quality using quality metrics such as distance distortion measure and rank based measures: trustworthiness, continuity, mean relative rank errors with respect to data space and latent space. In cases where the labels are known we also use quality metrics of KL divergence and nearest neighbour classifications error in order to determine the separation between classes. We demonstrate the efficacy of these proposed models both for synthetic and real biological datasets with a main focus on the MHC class-I dataset.

Computação em nuvem no governo: caminhos para a formação de uma agenda governamental

This work aims to understand how cloud computing contextualizes the IT government and decision agenda, in the light of the multiple streams model, considering the current status of public IT policies, the dynamics of the agenda setting for the area, the interface between the various institutions, and existing initiatives on the use of cloud computing in government. Therefore, a qualitative study was conducted through interviews with a group of policy makers and the other group consists of IT managers. As analysis technique, this work made use of content analysis and analysis of documents, with some results by word cloud. As regards the main results to overregulation to the area, usually scattered in various agencies of the federal government, which hinders the performance of the managers. Identified a lack of knowledge of standards, government programs, regulations and guidelines. Among these he highlighted a lack of understanding of the TI Maior Program, the lack of effectiveness of the National Broadband Plan in view of the respondents, as well as the influence of Internet Landmark as an element that can jam the advances in the use of computing cloud in the Brazilian government. Also noteworthy is the bureaucratization of the acquisition of goods to IT services, limited, in many cases, technological advances. Regarding the influence of the actors, it was not possible to identify the presence of a political entrepreneur, and it was noticed a lack of political force. Political flow was affected only by changes within the government. Fragmentation was a major factor for the theme of weakening the agenda formation. Information security was questioned by the respondents pointed out that the main limitation coupled with the lack of training of public servants. In terms of benefits, resource economy is highlighted, followed by improving efficiency. Finally, the discussion about cloud computing needs to advance within the public sphere, whereas the international experience is already far advanced, framing cloud computing as a responsible element for the improvement of processes, services and economy of public resources

A hora da alfabetização: atores, ideias e instituições na construção do PAIC-CE

The objective of this thesis is to understand how a certain social condition becomes relevant enough to be regarded as an issue worthy of government action and how certain proposed initiatives prevail while others are discarded. More specifically, the goal is to discuss public policy for education and check whether the analytical models employed are significant enough to explain how the literacy issue became part of the policy agenda of the government of the State of Ceará in Brazil, and how the Literacy Program at the Right Age (PAIC) developed over time. From the empirical perspective about public policy for education in Brazil, this is a relevant case when one takes into account that, historically, the literacy policies are focused on teenagers and adults, implying a lack of specific initiatives towards children at the proper age of learning to read and write. In order to understand what drove this issue to the top of the state government agenda, this thesis is primarily based on the literature about public policy analysis, with focus on the agenda setting process and development of proposals. A hybrid approach is used, combining analytical tools from Kingdon’s Multiple Streams Model (1995), the Advocacy Coalition Framework by Sabatier and Jenkins Smith (1993) and the historical new institutionalism lens. The research method is qualitative and based on the single case study method. The data set was assembled from institutional PAIC-related documents, tachygraphy notes from sessions at Ceará’s State House of Representatives, press clippings, academic studies and interviews with key participants from several organizations. The conclusion of this thesis is that, given the complexity of the case in point, the combination of the three analytical methods is adequate and necessary to understanding the multiple drivers for this issue to have entered Ceará’s state government agenda and the design of the PAIC itself. Particularly relevant are the ideas and the policy entrepreneurs, the processes of problem recognition for the composition of a wide coalition and for the specification of alternatives, and the path dependence of the education policy in Ceará. This study adds, as a result, to a better understanding of the stages that make up the agenda setting in public policy, in particular in the field of education.

A hora da alfabetização: atores, ideias e instituições na construção do PAIC-CE

The objective of this thesis is to understand how a certain social condition becomes relevant enough to be regarded as an issue worthy of government action and how certain proposed initiatives prevail while others are discarded. More specifically, the goal is to discuss public policy for education and check whether the analytical models employed are significant enough to explain how the literacy issue became part of the policy agenda of the government of the State of Ceará in Brazil, and how the Literacy Program at the Right Age (PAIC) developed over time. From the empirical perspective about public policy for education in Brazil, this is a relevant case when one takes into account that, historically, the literacy policies are focused on teenagers and adults, implying a lack of specific initiatives towards children at the proper age of learning to read and write. In order to understand what drove this issue to the top of the state government agenda, this thesis is primarily based on the literature about public policy analysis, with focus on the agenda setting process and development of proposals. A hybrid approach is used, combining analytical tools from Kingdon’s Multiple Streams Model (1995), the Advocacy Coalition Framework by Sabatier and Jenkins Smith (1993) and the historical new institutionalism lens. The research method is qualitative and based on the single case study method. The data set was assembled from institutional PAIC-related documents, tachygraphy notes from sessions at Ceará’s State House of Representatives, press clippings, academic studies and interviews with key participants from several organizations. The conclusion of this thesis is that, given the complexity of the case in point, the combination of the three analytical methods is adequate and necessary to understanding the multiple drivers for this issue to have entered Ceará’s state government agenda and the design of the PAIC itself. Particularly relevant are the ideas and the policy entrepreneurs, the processes of problem recognition for the composition of a wide coalition and for the specification of alternatives, and the path dependence of the education policy in Ceará. This study adds, as a result, to a better understanding of the stages that make up the agenda setting in public policy, in particular in the field of education.

Radiotherapy Treatment Assessment using DCE-MRI

Abstract

The goal of modern radiotherapy is to precisely deliver a prescribed radiation dose to delineated target volumes that contain a significant amount of tumor cells while sparing the surrounding healthy tissues/organs. Precise delineation of treatment and avoidance volumes is the key for the precision radiation therapy. In recent years, considerable clinical and research efforts have been devoted to integrate MRI into radiotherapy workflow motivated by the superior soft tissue contrast and functional imaging possibility. Dynamic contrast-enhanced MRI (DCE-MRI) is a noninvasive technique that measures properties of tissue microvasculature. Its sensitivity to radiation-induced vascular pharmacokinetic (PK) changes has been preliminary demonstrated. In spite of its great potential, two major challenges have limited DCE-MRI’s clinical application in radiotherapy assessment: the technical limitations of accurate DCE-MRI imaging implementation and the need of novel DCE-MRI data analysis methods for richer functional heterogeneity information.

This study aims at improving current DCE-MRI techniques and developing new DCE-MRI analysis methods for particular radiotherapy assessment. Thus, the study is naturally divided into two parts. The first part focuses on DCE-MRI temporal resolution as one of the key DCE-MRI technical factors, and some improvements regarding DCE-MRI temporal resolution are proposed; the second part explores the potential value of image heterogeneity analysis and multiple PK model combination for therapeutic response assessment, and several novel DCE-MRI data analysis methods are developed.

I. Improvement of DCE-MRI temporal resolution. First, the feasibility of improving DCE-MRI temporal resolution via image undersampling was studied. Specifically, a novel MR image iterative reconstruction algorithm was studied for DCE-MRI reconstruction. This algorithm was built on the recently developed compress sensing (CS) theory. By utilizing a limited k-space acquisition with shorter imaging time, images can be reconstructed in an iterative fashion under the regularization of a newly proposed total generalized variation (TGV) penalty term. In the retrospective study of brain radiosurgery patient DCE-MRI scans under IRB-approval, the clinically obtained image data was selected as reference data, and the simulated accelerated k-space acquisition was generated via undersampling the reference image full k-space with designed sampling grids. Two undersampling strategies were proposed: 1) a radial multi-ray grid with a special angular distribution was adopted to sample each slice of the full k-space; 2) a Cartesian random sampling grid series with spatiotemporal constraints from adjacent frames was adopted to sample the dynamic k-space series at a slice location. Two sets of PK parameters’ maps were generated from the undersampled data and from the fully-sampled data, respectively. Multiple quantitative measurements and statistical studies were performed to evaluate the accuracy of PK maps generated from the undersampled data in reference to the PK maps generated from the fully-sampled data. Results showed that at a simulated acceleration factor of four, PK maps could be faithfully calculated from the DCE images that were reconstructed using undersampled data, and no statistically significant differences were found between the regional PK mean values from undersampled and fully-sampled data sets. DCE-MRI acceleration using the investigated image reconstruction method has been suggested as feasible and promising.

Second, for high temporal resolution DCE-MRI, a new PK model fitting method was developed to solve PK parameters for better calculation accuracy and efficiency. This method is based on a derivative-based deformation of the commonly used Tofts PK model, which is presented as an integrative expression. This method also includes an advanced Kolmogorov-Zurbenko (KZ) filter to remove the potential noise effect in data and solve the PK parameter as a linear problem in matrix format. In the computer simulation study, PK parameters representing typical intracranial values were selected as references to simulated DCE-MRI data for different temporal resolution and different data noise level. Results showed that at both high temporal resolutions (<1s) and clinically feasible temporal resolution (~5s), this new method was able to calculate PK parameters more accurate than the current calculation methods at clinically relevant noise levels; at high temporal resolutions, the calculation efficiency of this new method was superior to current methods in an order of 102. In a retrospective of clinical brain DCE-MRI scans, the PK maps derived from the proposed method were comparable with the results from current methods. Based on these results, it can be concluded that this new method can be used for accurate and efficient PK model fitting for high temporal resolution DCE-MRI.

II. Development of DCE-MRI analysis methods for therapeutic response assessment. This part aims at methodology developments in two approaches. The first one is to develop model-free analysis method for DCE-MRI functional heterogeneity evaluation. This approach is inspired by the rationale that radiotherapy-induced functional change could be heterogeneous across the treatment area. The first effort was spent on a translational investigation of classic fractal dimension theory for DCE-MRI therapeutic response assessment. In a small-animal anti-angiogenesis drug therapy experiment, the randomly assigned treatment/control groups received multiple fraction treatments with one pre-treatment and multiple post-treatment high spatiotemporal DCE-MRI scans. In the post-treatment scan two weeks after the start, the investigated Rényi dimensions of the classic PK rate constant map demonstrated significant differences between the treatment and the control groups; when Rényi dimensions were adopted for treatment/control group classification, the achieved accuracy was higher than the accuracy from using conventional PK parameter statistics. Following this pilot work, two novel texture analysis methods were proposed. First, a new technique called Gray Level Local Power Matrix (GLLPM) was developed. It intends to solve the lack of temporal information and poor calculation efficiency of the commonly used Gray Level Co-Occurrence Matrix (GLCOM) techniques. In the same small animal experiment, the dynamic curves of Haralick texture features derived from the GLLPM had an overall better performance than the corresponding curves derived from current GLCOM techniques in treatment/control separation and classification. The second developed method is dynamic Fractal Signature Dissimilarity (FSD) analysis. Inspired by the classic fractal dimension theory, this method measures the dynamics of tumor heterogeneity during the contrast agent uptake in a quantitative fashion on DCE images. In the small animal experiment mentioned before, the selected parameters from dynamic FSD analysis showed significant differences between treatment/control groups as early as after 1 treatment fraction; in contrast, metrics from conventional PK analysis showed significant differences only after 3 treatment fractions. When using dynamic FSD parameters, the treatment/control group classification after 1st treatment fraction was improved than using conventional PK statistics. These results suggest the promising application of this novel method for capturing early therapeutic response.

The second approach of developing novel DCE-MRI methods is to combine PK information from multiple PK models. Currently, the classic Tofts model or its alternative version has been widely adopted for DCE-MRI analysis as a gold-standard approach for therapeutic response assessment. Previously, a shutter-speed (SS) model was proposed to incorporate transcytolemmal water exchange effect into contrast agent concentration quantification. In spite of richer biological assumption, its application in therapeutic response assessment is limited. It might be intriguing to combine the information from the SS model and from the classic Tofts model to explore potential new biological information for treatment assessment. The feasibility of this idea was investigated in the same small animal experiment. The SS model was compared against the Tofts model for therapeutic response assessment using PK parameter regional mean value comparison. Based on the modeled transcytolemmal water exchange rate, a biological subvolume was proposed and was automatically identified using histogram analysis. Within the biological subvolume, the PK rate constant derived from the SS model were proved to be superior to the one from Tofts model in treatment/control separation and classification. Furthermore, novel biomarkers were designed to integrate PK rate constants from these two models. When being evaluated in the biological subvolume, this biomarker was able to reflect significant treatment/control difference in both post-treatment evaluation. These results confirm the potential value of SS model as well as its combination with Tofts model for therapeutic response assessment.

In summary, this study addressed two problems of DCE-MRI application in radiotherapy assessment. In the first part, a method of accelerating DCE-MRI acquisition for better temporal resolution was investigated, and a novel PK model fitting algorithm was proposed for high temporal resolution DCE-MRI. In the second part, two model-free texture analysis methods and a multiple-model analysis method were developed for DCE-MRI therapeutic response assessment. The presented works could benefit the future DCE-MRI routine clinical application in radiotherapy assessment.

Diversity and path coefficient analysis of Southern African maize hybrids

Detailed knowledge on genetic diversity among germplasm is important for hybrid maize ( Zea mays L.) breeding. The objective of the study was to determine genetic diversity in widely grown hybrids in Southern Africa, and compare effectiveness of phenotypic analysis models for determining genetic distances between hybrids. Fifty hybrids were evaluated at one site with two replicates. The experiment was a randomized complete block design. Phenotypic and genotypic data were analyzed using SAS and Power Marker respectively. There was significant (p < 0.01) variation and diversity among hybrid brands but small within brand clusters. Polymorphic Information Content (PIC) ranged from 0.07 to 0.38 with an average of 0.34 and genetic distance ranged from 0.08 to 0.50 with an average of 0.43. SAH23 and SAH21 (0.48) and SAH33 and SAH3 (0.47) were the most distantly related hybrids. Both single nucleotide polymorphism (SNP) markers and phenotypic data models were effective for discriminating genotypes according to genetic distance. SNP markers revealed nine clusters of hybrids. The 12-trait phenotypic analysis model, revealed eight clusters at 85%, while the five-trait model revealed six clusters. Path analysis revealed significant direct and indirect effects of secondary traits on yield. Plant height and ear height were negatively correlated with grain yield meaning shorter hybrids gave high yield. Ear weight, days to anthesis, and number of ears had highest positive direct effects on yield. These traits can provide good selection index for high yielding maize hybrids. Results confirmed that diversity of hybrids is small within brands and also confirm that phenotypic trait models are effective for discriminating hybrids.

Organizational configuration, fit and performance: The case of the pharmaceutical industry in Portugal

Miles e Snow’s configurational theory has received a great deal of attention from many investigators. Framing the Miles e Snow Typology with the organizational configuration concept, the main purpose of this paper is to make an empirical evaluation of what configurational theories postulate: higher organizational performance is associated to the resemblance to one of the ideal types defined. However, as it is often assumed that an organization can increase performance by selecting the adjustable hybrid type to its own exogenous environment, the relation between the organization’s effectiveness and the hybrid configuration alignment to the respective specific environment types was also analyzed. The assumption of equifinality was also considered because the configurational theory assumes that all the ideal types can potentially achieve the same performance level. A multiple regression model was made to confirm if the misfit related to the ideal and hybrid types has significant impact on the organizational effectiveness. The analysis of variance and the Kruskal-Wallis test were used to verify the equality of performance between the different organization types. In short, the empirical results obtained confirm what is postulated in the theory.

Relationship between subjective wellbeing and weight perception in children and adolescents: Gender and age differences

The relation between weight status (Body Mass Index - BMI), weight perception and subjective wellbeing remains unclear. Several studies conclude that discrepancies can be found between weight status and weight perception, among children and adolescents. The present study aims at investigating the associations between subjective wellbeing and individual characteristics, among children and adolescents. The sample included 1200 children and adolescents (51.7 % girls, aged 9 to 17). Their mean age was 12.55 years (SD = 1.61). The questionnaire was completed in school context, asking about the subjective wellbeing, use of self-regulation, eating behavior awareness/care, weight perception and sociodemographic questions such as age, gender and BMI. The study found a strong association between BMI and weight perception, although subjective wellbeing was better explained by weight perception than by BMI. Eating awareness and self-regulation also played an important role in subjective controlling for age and gender. Age and gender interfere in the relation between subjective wellbeing and other variables. The multiple regression model is more robust and explicative for girls and older children. Psychological factors related to weight, such as weight perception, self-regulation and eating awareness have a stronger explicative impact in subjective wellbeing compared to physical aspects, such as Body Mass Index. The relation between subjective wellbeing and weight is influence by age and gender.

Factores asociados al perfil de lípidos sanguíneos en los pacientes del área de salud de Montes de Oca, Costa Rica

En este estudio se analiza la asociación entre la exposición a diversos factores —de biotipo, socioeconómicos y patológicos— y la alteración del perfil de lípidos sanguíneos (dislipidemia) en pacientes adscritos al Área de Salud de Montes de Oca. Se realizó un estudio de caso-control, con un total de 135 casos e igual cantidad de controles, entre 20 y 65 años, a los que se les hizo un perfil de lípidos sanguíneos durante el año 2006. Las variables estudiadas fueron: edad, sexo, índice de masa corporal, tipo de aseguramiento, estado de portador de hipertensión arterial, de diabetes mellitus o de ambas patologías crónicas a la vez. Se realizó un análisis univariado, seguido de un análisis multivariado, mediante un modelo logístico múltiple. La única variable asociada con la dislipidemia fue el índice de masa corporal, tanto en el análisis univariado como en el multivariado; las variables restantes no mostraron asociación estadística. Aquellos pacientes con mayor índice de masa corporal presentan un mayor riesgo de tener un perfil alterado de lípidos sanguíneos.

Trait contributions to fish community assembly emerge from trophic interactions in an individual-based model

Community ecology seeks to understand and predict the characteristics of communities that can develop under different environmental conditions, but most theory has been built on analytical models that are limited in the diversity of species traits that can be considered simultaneously. We address that limitation with an individual-based model to simulate assembly of fish communities characterized by life history and trophic interactions with multiple physiological tradeoffs as constraints on species performance. Simulation experiments were carried out to evaluate the distribution of 6 life history and 4 feeding traits along gradients of resource productivity and prey accessibility. These experiments revealed that traits differ greatly in importance for species sorting along the gradients. Body growth rate emerged as a key factor distinguishing community types and defining patterns of community stability and coexistence, followed by egg size and maximum body size. Dominance by fast-growing, relatively large, and fecund species occurred more frequently in cases where functional responses were saturated (i.e. high productivity and/or prey accessibility). Such dominance was associated with large biomass fluctuations and priority effects, which prevented richness from increasing with productivity and may have limited selection on secondary traits, such as spawning strategies and relative size at maturation. Our results illustrate that the distribution of species traits and the consequences for community dynamics are intimately linked and strictly dependent on how the benefits and costs of these traits are balanced across different conditions. © 2012 Elsevier B.V.

A mixed model QTL analysis for sugarcane multiple-harvest-location trial data

Sugarcane-breeding programs take at least 12 years to develop new commercial cultivars. Molecular markers offer a possibility to study the genetic architecture of quantitative traits in sugarcane, and they may be used in marker-assisted selection to speed up artificial selection. Although the performance of sugarcane progenies in breeding programs are commonly evaluated across a range of locations and harvest years, many of the QTL detection methods ignore two- and three-way interactions between QTL, harvest, and location. In this work, a strategy for QTL detection in multi-harvest-location trial data, based on interval mapping and mixed models, is proposed and applied to map QTL effects on a segregating progeny from a biparental cross of pre-commercial Brazilian cultivars, evaluated at two locations and three consecutive harvest years for cane yield (tonnes per hectare), sugar yield (tonnes per hectare), fiber percent, and sucrose content. In the mixed model, we have included appropriate (co)variance structures for modeling heterogeneity and correlation of genetic effects and non-genetic residual effects. Forty-six QTLs were found: 13 QTLs for cane yield, 14 for sugar yield, 11 for fiber percent, and 8 for sucrose content. In addition, QTL by harvest, QTL by location, and QTL by harvest by location interaction effects were significant for all evaluated traits (30 QTLs showed some interaction, and 16 none). Our results contribute to a better understanding of the genetic architecture of complex traits related to biomass production and sucrose content in sugarcane.

Trait Contributions to Fish Community Assembly Emerge From Trophic Interactions in an Individual-Based Model

Community ecology seeks to understand and predict the characteristics of communities that can develop under different environmental conditions, but most theory has been built on analytical models that are limited in the diversity of species traits that can be considered simultaneously. We address that limitation with an individual-based model to simulate assembly of fish communities characterized by life history and trophic interactions with multiple physiological tradeoffs as constraints on species performance. Simulation experiments were carried out to evaluate the distribution of 6 life history and 4 feeding traits along gradients of resource productivity and prey accessibility. These experiments revealed that traits differ greatly in importance for species sorting along the gradients. Body growth rate emerged as a key factor distinguishing community types and defining patterns of community stability and coexistence, followed by egg size and maximum body size. Dominance by fast-growing, relatively large, and fecund species occurred more frequently in cases where functional responses were saturated (i.e. high productivity and/or prey accessibility). Such dominance was associated with large biomass fluctuations and priority effects, which prevented richness from increasing with productivity and may have limited selection on secondary traits, such as spawning strategies and relative size at maturation. Our results illustrate that the distribution of species traits and the consequences for community dynamics are intimately linked and strictly dependent on how the benefits and costs of these traits are balanced across different conditions.

Distributed power control algorithm for multiple access systems based on Verhulst model

In this paper the continuous Verhulst dynamic model is used to synthesize a new distributed power control algorithm (DPCA) for use in direct sequence code division multiple access (DS-CDMA) systems. The Verhulst model was initially designed to describe the population growth of biological species under food and physical space restrictions. The discretization of the corresponding differential equation is accomplished via the Euler numeric integration (ENI) method. Analytical convergence conditions for the proposed DPCA are also established. Several properties of the proposed recursive algorithm, such as Euclidean distance from optimum vector after convergence, convergence speed, normalized mean squared error (NSE), average power consumption per user, performance under dynamics channels, and implementation complexity aspects, are analyzed through simulations. The simulation results are compared with two other DPCAs: the classic algorithm derived by Foschini and Miljanic and the sigmoidal of Uykan and Koivo. Under estimated errors conditions, the proposed DPCA exhibits smaller discrepancy from the optimum power vector solution and better convergence (under fixed and adaptive convergence factor) than the classic and sigmoidal DPCAs. (C) 2010 Elsevier GmbH. All rights reserved.

Quantitative Trait Loci Mapping and The Genetic Basis of Heterosis in Maize and Rice

Despite its importance to agriculture, the genetic basis of heterosis is still not well understood. The main competing hypotheses include dominance, overdominance, and epistasis. NC design III is an experimental design that. has been used for estimating the average degree of dominance of quantitative trait 106 (QTL) and also for studying heterosis. In this study, we first develop a multiple-interval mapping (MIM) model for design III that provides a platform to estimate the number, genomic positions, augmented additive and dominance effects, and epistatic interactions of QTL. The model can be used for parents with any generation of selling. We apply the method to two data sets, one for maize and one for rice. Our results show that heterosis in maize is mainly due to dominant gene action, although overdominance of individual QTL could not completely be ruled out due to the mapping resolution and limitations of NC design III. For rice, the estimated QTL dominant effects could not explain the observed heterosis. There is evidence that additive X additive epistatic effects of QTL could be the main cause for the heterosis in rice. The difference in the genetic basis of heterosis seems to be related to open or self pollination of the two species. The MIM model for NC design III is implemented in Windows QTL Cartographer, a freely distributed software.