Non-Gaussian outcomes are often modeled using members of the so-called exponential family. Notorious members are the Bernoulli model for binary data, leading to logistic regression, and the Poisson model for count data, leading to Poisson regression. Two of the main reasons for extending this family are (1) the occurrence of overdispersion, meaning that the variability in the data is not adequately described by the models, which often exhibit a prescribed mean-variance link, and (2) the accommodation of hierarchical structure in the data, stemming from clustering in the data which, in turn, may result from repeatedly measuring the outcome, for various members of the same family, etc. The first issue is dealt with through a variety of overdispersion models, such as, for example, the beta-binomial model for grouped binary data and the negative-binomial model for counts. Clustering is often accommodated through the inclusion of random subject-specific effects. Though not always, one conventionally assumes such random effects to be normally distributed. While both of these phenomena may occur simultaneously, models combining them are uncommon. This paper proposes a broad class of generalized linear models accommodating overdispersion and clustering through two separate sets of random effects. We place particular emphasis on so-called conjugate random effects at the level of the mean for the first aspect and normal random effects embedded within the linear predictor for the second aspect, even though our family is more general. The binary, count and time-to-event cases are given particular emphasis. Apart from model formulation, we present an overview of estimation methods, and then settle for maximum likelihood estimation with analytic-numerical integration. Implications for the derivation of marginal correlations functions are discussed. The methodology is applied to data from a study in epileptic seizures, a clinical trial in toenail infection named onychomycosis and survival data in children with asthma. 1 2 MOLENBERGHS, VERBEKE, DEMÉTRIO AND VIEIRA

Brazilian Santa Inês (SI) sheep are very well-adapted to the tropical conditions of Brazil and are an important source of animal protein. A high rate of twin births was reported in some SI flocks. Growth and Differentiation Factor 9 (GDF9) and Bone Morphogenetic Protein 15 (BMP15) are the first two genes expressed by the oocyte to be associated with an increased ovulation rate in sheep. All GDF9 and BMP15 variants characterized, until now, present the same phenotype: the heterozygote ewes have an increased ovulation rate and the mutated homozygotes are sterile. In this study, we have found a new allele of GDF9, named FecG(E) (Embrapa), which leads to a substitution of a phenylalanine with a cysteine in a conservative position of the mature peptide. Homozygote ewes presenting the FecG(E) allele have shown an increase in their ovulation rate (82%) and prolificacy (58%). This new phenotype can be very useful in better understanding the genetic control of follicular development; the mechanisms involved in the control of ovulation rate in mammals; and for the improvement of sheep production.

The preslaughter handling and transport of broilers are stressful operations that might affect welfare and meat quality and could increase numbers of deaths before slaughter. However, the influence of thermal factors during transportation and lairage at slaughterhouses is complex in subtropical regions, where increasing temperature and high RH are the major concerns regarding animal survival before slaughter. In this study we assessed the influence of a controlled lairage environment on preslaughter mortality rates of broiler chickens that were transported during different seasons of the year and had varying lairage times in the subtropical climate. Preslaughter data from 13,937 broiler flocks were recorded daily during 2006 in a commercial slaughterhouse in southeastern Brazil. The main factors that influenced daily mortality rate were mean dry bulb temperature and RH, lairage time, daily periods, density of broilers per crate, season of the year, stocking density per lorry, transport time, and distance between farms and slaughterhouse. A holding area at the slaughterhouse with environmental control was assessed. Using a double GLM for mean and dispersion modeling, the seasons were found to have significant effects (P < 0.05) on average mortality rates. The highest incidence was observed in summer (0.42%), followed by spring (0.39%), winter (0.28%), and autumn (0.23%). A decrease of preslaughter mortality of broilers during summer (P < 0.05) was observed when the lairage time was increased, mainly after 1 h of exposure to a controlled environment. Thus, lairage for 3 to 4 h in a controlled lairage environment during the summer and spring is necessary to reduce the thermal load of broiler chickens.

Biological control of pests is an important branch of entomology, providing environmentally friendly forms of crop protection. Bioassays are used to find the optimal conditions for the production of parasites and strategies for application in the field. In some of these assays, proportions are measured and, often, these data have an inflated number of zeros. In this work, six models will be applied to data sets obtained from biological control assays for Diatraea saccharalis , a common pest in sugar cane production. A natural choice for modelling proportion data is the binomial model. The second model will be an overdispersed version of the binomial model, estimated by a quasi-likelihood method. This model was initially built to model overdispersion generated by individual variability in the probability of success. When interest is only in the positive proportion data, a model can be based on the truncated binomial distribution and in its overdispersed version. The last two models include the zero proportions and are based on a finite mixture model with the binomial distribution or its overdispersed version for the positive data. Here, we will present the models, discuss their estimation and compare the results.

Visceral leishmaniasis (VL) is a chronic and often fatal protozoal disease that is endemic in Belo Horizonte (State of Minas Gerais, Brazil). Leishmania sp. is an intracellular obligatory parasite of macrophages that can naturally infect several mammalian species. Non-human primates (NHP) have been used as experimental models for infection with Leishmania of the donovani complex. The present report describes a case of visceral leishmaniasis in a black-fronted titi. Among 41 primates kept in captivity in a zoo in Belo Horizonte (State of Minas Gerais, Brazil), one animal, a black-fronted titi (Callicebus nigrifrons), was positive for Leishmania chagasi infection by PCR and immunohistochemistry, and developed a fatal disease with clinical signs and lesions compatible with VL. Other 17 NHP, including six black-fronted titis (C. nigrifrons), one howler monkey (Alouatta guariba), three golden-bellied capuchins (Cebus xanthosternos), one golden-headed lion tamarin (Leontopithecus crysomelas), one black-headed owl monkey (Aotus nigriceps), two Rio Tapajós sakis (Pithecia irrorata) and three emperor tamarins (Saguinus imperator) had blood samples that tested positive for amplification of Leishmania kDNA by PCR, although these NPH had no clinical signs of the disease.