Hglm: A package for fitting hierarchical generalized linear models

We present the hglm package for fitting hierarchical generalized linear models. It can be used for linear mixed models and generalized linear mixed models with random effects for a variety of links and a variety of distributions for both the outcomes and the random effects. Fixed effects can also be fitted in the dispersion part of the model.

Genetic heterogeneity of residual variance : estimation of variance components using double hierarchical generalized linear models

Background: The sensitivity to microenvironmental changes varies among animals and may be under genetic control. It is essential to take this element into account when aiming at breeding robust farm animals. Here, linear mixed models with genetic effects in the residual variance part of the model can be used. Such models have previously been fitted using EM and MCMC algorithms. Results: We propose the use of double hierarchical generalized linear models (DHGLM), where the squared residuals are assumed to be gamma distributed and the residual variance is fitted using a generalized linear model. The algorithm iterates between two sets of mixed model equations, one on the level of observations and one on the level of variances. The method was validated using simulations and also by re-analyzing a data set on pig litter size that was previously analyzed using a Bayesian approach. The pig litter size data contained 10,060 records from 4,149 sows. The DHGLM was implemented using the ASReml software and the algorithm converged within three minutes on a Linux server. The estimates were similar to those previously obtained using Bayesian methodology, especially the variance components in the residual variance part of the model. Conclusions: We have shown that variance components in the residual variance part of a linear mixed model can be estimated using a DHGLM approach. The method enables analyses of animal models with large numbers of observations. An important future development of the DHGLM methodology is to include the genetic correlation between the random effects in the mean and residual variance parts of the model as a parameter of the DHGLM.

Endogenous wage compression and aggregation boundaries in a model of hierarchical human capital

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Processing of hierarchical syntactic strusture in music

Hierarchical structure with nested nonlocal dependencies is a key feature of human language and can be identified theoretically in most pieces of tonal music. However, previous studies have argued against the perception of such structures in music. Here, we show processing of nonlocal dependencies in music. We presented chorales by J. S. Bach and modified versions inwhich the hierarchical structure was rendered irregular whereas the local structure was kept intact. Brain electric responses differed between regular and irregular hierarchical structures, in both musicians and nonmusicians. This finding indicates that, when listening to music, humans apply cognitive processes that are capable of dealing with longdistance dependencies resulting from hierarchically organized syntactic structures. Our results reveal that a brain mechanism fundamental for syntactic processing is engaged during the perception of music, indicating that processing of hierarchical structure with nested nonlocal dependencies is not just a key component of human language, but a multidomain capacity of human cognition.

Evaluation of an average numerator relationship matrix model and a Bayesian hierarchical model for growth traits in Nellore cattle with uncertain paternity

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Self-Organizing Maps as a New Tool for Classification of Plants at Lower Hierarchical Levels

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Mating system parameters at hierarchical levels of fruits, individuals and populations in the Brazilian insect-pollinated tropical tree, Tabebuia roseo-alba (Bignoniaceae)

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Hierarchical status and colour preference in Nile tilapia (Oreochromis niloticus)

We studied the colour preference of isolated Nile tilapia (Oreochromis niloticus) and whether previous residence or body size can affect environmental colour choice. In the first phase, a cylindrical tank was divided into five differently coloured compartments (yellow, blue, green, white and red), a single fish was introduced into the tank and the frequency at which this fish visited each compartment was recorded over a 2-day study period. An increasingly larger fish (approx +2 cm in length each time) was then added into the tank on each of days 3, 5 and 7 (=four fish in the tank by day 7), and the frequency at which each fish visited the different compartments of the tank was observed twice a day to obtain visit frequency data on the differently sized fishes. This experiment was replicated six times. In the first phase, the solitary fish established residence inside the yellow compartment on the first and second days. Following the introduction of a larger fish, the smaller fish was displaced from the occupied compartment. Nile tilapia possibly shows this preference for yellow as a function of its visual spectral sensitivity and/or the spectral characteristics of its natural environment. Moreover, body size is an important factor in determining hierarchical dominance and territorial defence, and dominant fish chose the preferred environmental colour compartment as their territory.

Toward a hierarchical concept of plant stress

A number of attempts have been made to obtain a clear definition of biological stress. However, in spite of the efforts, some controversies on the concept of plant stress remain. The current versions are centered either on the cause (stress factor) or on the effect (stress response) of environmental stress. The objective of this study was to contribute to the definition of stress, using a hierarchical approach. Thus, we have performed an analysis of the most usual stress concepts and tested the relevance of considering different observation scales in a study on plant response to water deficit. Seedlings of Eucalyptus grandis were grown in vitro at water potentials ranging from -0.16 to -0.6 MPa, and evaluated according to growth and biochemical parameters. Data were analyzed through principal component analysis (PCA), which pointed to a hierarchical organization in plant responses to environmental disturbances. Growth parameters (height and dry weight) are more sensitive to water deficit than biochemical ones (sugars, proline, and protein), suggesting that higher hierarchical levels were more sensitive to environmental constraints than lower hierarchical ones. We suggest that before considering an environmental fluctuation as stressful, it is necessary to take into account different levels of plant response, and that the evaluation of the effects of environmental disturbances on an organism depends on the observation scale being used. Hence, a more appropriate stress concept should consider the hierarchical organization of the biological systems, not only for a more adequate theoretical approach, but also for the improvement of practical studies on plants under stress.

Synergistic effect in the catalytic activity of lipase Rhizomucor miehei immobilized on zeolites for the production of biodiesel

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

The hierarchical model of branch planning with random coefficients

We consider the management branch model where the random resources of the subsystem are given by the exponential distributions. The determinate equivalent is a block structure problem of quadratic programming. It is solved effectively by means of the decomposition method, which is based on iterative aggregation. The aggregation problem of the upper level is resolved analytically. This overcomes all difficulties concerning the large dimension of the main problem.

Classification and characterization of places for mapping of environment using hierarchical neural network and omnivision

Mobile robots need autonomy to fulfill their tasks. Such autonomy is related whith their capacity to explorer and to recognize their navigation environments. In this context, the present work considers techniques for the classification and extraction of features from images, using artificial neural networks. This images are used in the mapping and localization system of LACE (Automation and Evolutive Computing Laboratory) mobile robot. In this direction, the robot uses a sensorial system composed by ultrasound sensors and a catadioptric vision system equipped with a camera and a conical mirror. The mapping system is composed of three modules; two of them will be presented in this paper: the classifier and the characterizer modules. Results of these modules simulations are presented in this paper.

Design of hierarchical porous aluminas by using one-pot synthesis and different calcination temperatures

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)

HIERARCHICAL VERSUS NONHIERARCHICAL PATTERNS OF GENETIC DISTANCES AMONG POPULATIONS - A SIMULATION STUDY

A simulation study was made of the effects of mixing two evolutionary forces (natural selection and random genetic drift), combined in a single data matrix of gene frequencies, on the resulting genetic distances among populations. Twenty-one, kinds of simulated gene frequencies surfaces, for 15 populations linearly distributed over geographic space, were used to construct 21 data matrices, combining different proportions of two types of surfaces (gradients and random surfaces). These matrices were analysed by Unweighted Pair-Group Method - Arithmetic Averages (UPGMA), clustering and Principal Coordinate Analysis. The results obtained show that ordination is more accurate than UPGMA in revealing the spatial patterns in the genetic distances, in comparison with results obtained using the Mantel test comparing directly genetic and geographic distances.