Immunohistochemical expression of B and T-lymphocytes and TGF-beta in experimentally transplanted canine venereal tumor

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

The FGM bivariate lifetime copula model: a bayesian approach

In this paper, we propose a bivariate distribution for the bivariate survival times based on Farlie-Gumbel-Morgenstern copula to model the dependence on a bivariate survival data. The proposed model allows for the presence of censored data and covariates. For inferential purpose a Bayesian approach via Markov Chain Monte Carlo (MCMC) is considered. Further, some discussions on the model selection criteria are given. In order to examine outlying and influential observations, we present a Bayesian case deletion influence diagnostics based on the Kullback-Leibler divergence. The newly developed procedures are illustrated via a simulation study and a real dataset.

Spatial modelling and prediction on river networks: up model, down model or hybrid?

Preservation of rivers and water resources is crucial in most environmental policies and many efforts are made to assess water quality. Environmental monitoring of large river networks are based on measurement stations. Compared to the total length of river networks, their number is often limited and there is a need to extend environmental variables that are measured locally to the whole river network. The objective of this paper is to propose several relevant geostatistical models for river modeling. These models use river distance and are based on two contrasting assumptions about dependency along a river network. Inference using maximum likelihood, model selection criterion and prediction by kriging are then developed. We illustrate our approach on two variables that differ by their distributional and spatial characteristics: summer water temperature and nitrate concentration. The data come from 141 to 187 monitoring stations in a network on a large river located in the Northeast of France that is more than 5000 km long and includes Meuse and Moselle basins. We first evaluated different spatial models and then gave prediction maps and error variance maps for the whole stream network.

A Model-Based Approach for Making Ecological Inference from Distance Sampling Data

We consider a fully model-based approach for the analysis of distance sampling data. Distance sampling has been widely used to estimate abundance (or density) of animals or plants in a spatially explicit study area. There is, however, no readily available method of making statistical inference on the relationships between abundance and environmental covariates. Spatial Poisson process likelihoods can be used to simultaneously estimate detection and intensity parameters by modeling distance sampling data as a thinned spatial point process. A model-based spatial approach to distance sampling data has three main benefits: it allows complex and opportunistic transect designs to be employed, it allows estimation of abundance in small subregions, and it provides a framework to assess the effects of habitat or experimental manipulation on density. We demonstrate the model-based methodology with a small simulation study and analysis of the Dubbo weed data set. In addition, a simple ad hoc method for handling overdispersion is also proposed. The simulation study showed that the model-based approach compared favorably to conventional distance sampling methods for abundance estimation. In addition, the overdispersion correction performed adequately when the number of transects was high. Analysis of the Dubbo data set indicated a transect effect on abundance via Akaike’s information criterion model selection. Further goodness-of-fit analysis, however, indicated some potential confounding of intensity with the detection function.

Allometric growth of a common Nassariidae (Gastropoda) in south-east Brazil

The allometric growth of two groups of Nassarius vibex on beds of the bivalve Mytella charruana on the northern coast of the State of Sao Paulo, was evaluated between September 2006 and February 2007 in the bed on Camaroeiro Beach, and from March 2007 to June 2007 at Cidade Beach. The shells from Camaroeiro were longer and wider and had a smaller shell aperture than those from Cidade; a principal components analysis also confirmed different morphometric patterns between the areas. The allometric growth of the two groups showed great variation in the development of individuals. The increase of shell width and height in relation to shell length did not differ between the two areas. Shell aperture showed a contrasting growth pattern, with individuals from Camaroeiro having smaller apertures. The methodology based on Kullback-Leibler information theory and the multi-model inference showed, for N. vibex, that the classic linear allometric growth was not the most suitable explanation for the observed morphometric relationships. The patterns of relative growth observed in the two groups of N. vibex may be a consequence of different growth and variation rates, which modifies the development of the individuals. Other factors such as food resource availability and environmental parameters, which might also differ between the two areas, should also be considered.

Surgical Performance During Laparoscopic Radical Nephrectomy Is Improved With Training in a Porcine Model

Background and Purpose: Becoming proficient in laparoscopic surgery is dependent on the acquisition of specialized skills that can only be obtained from specific training. This training could be achieved in various ways using inanimate models, animal models, or live patient surgery-each with its own pros and cons. Currently, there are substantial data that support the benefits of animal model training in the initial learning of laparoscopy. Nevertheless, whether these benefits extent themselves to moderately experienced surgeons is uncertain. The purpose of this study was to determine if training using a porcine model results in a quantifiable gain in laparoscopic skills for moderately experienced laparoscopic surgeons. Materials and Methods: Six urologists with some laparoscopic experience were asked to perform a radical nephrectomy weekly for 10 weeks in a porcine model. The procedures were recorded, and surgical performance was assessed by two experienced laparoscopic surgeons using a previously published surgical performance assessment tool. The obtained data were then submitted to statistical analysis. Results: With training, blood loss was reduced approximately 45% when comparing the averages of the first and last surgical procedures (P = 0.006). Depth perception showed an improvement close to 35% (P = 0.041), and dexterity showed an improvement close to 25% (P = 0.011). Total operative time showed trends of improvement, although it was not significant (P = 0.158). Autonomy, efficiency, and tissue handling were the only aspects that did not show any noteworthy change (P = 0.202, P = 0.677, and P = 0.456, respectively). Conclusions: These findings suggest that there are quantifiable gains in laparoscopic skills obtained from training in an animal model. Our results suggest that these benefits also extend to more advanced stages of the learning curve, but it is unclear how far along the learning curve training with animal models provides a clear benefit for the performance of laparoscopic procedures. Future studies are necessary to confirm these findings and better understand the impact of this learning tool on surgical practice.

A Bayesian analysis of the Conway-Maxwell-Poisson cure rate model

The purpose of this paper is to develop a Bayesian analysis for the right-censored survival data when immune or cured individuals may be present in the population from which the data is taken. In our approach the number of competing causes of the event of interest follows the Conway-Maxwell-Poisson distribution which generalizes the Poisson distribution. Markov chain Monte Carlo (MCMC) methods are used to develop a Bayesian procedure for the proposed model. Also, some discussions on the model selection and an illustration with a real data set are considered.

Maternal obesity in high-risk pregnancies and postpartum infectious complications

Objective: To analyze the association between maternal obesity and postnatal infectious complications in high-risk pregnancies. Methods: Prospective study from August 2009 through August 2010 with the following inclusion criteria: women up to the 5th postpartum day; age L 18 years; high-risk pregnancy; singleton pregnancy with live fetus at labor onset; delivery at the institution; maternal weight measured on day of delivery. The nutritional status in late pregnancy was assessed by the body mass index (BMI), with the application of the Atalah et al. curve. Patients were graded as underweight, adequate weight, overweight, or obese. Postpartum complications investigated during the hospital stay and 30 days post-discharge were: surgical wound infection and/or secretion, urinary infection, postpartum infection, fever, hospitalization, antibiotic use, and composite morbidity (at least one of the complications mentioned). Results: 374 puerperal women were included, graded according to the final BMI as: underweight (n = 54, 14.4%); adequate weight (n = 126, 33.7%); overweight (n = 105, 28.1%); and obese (n = 89, 23.8%). Maternal obesity was shown to have a significant association with the following postpartum complications: surgical wound infection (16.7%, p = 0.042), urinary infection (9.0%, p = 0.004), antibiotic use (12.3%, p < 0.001), and composite morbidity (25.6%, p = 0.016). By applying the logistic regression model, obesity in late pregnancy was found to be an independent variable regardless of the composite morbidity predicted (OR: 2.09; 95% CI: 1.15-3.80, p = 0.015). Conclusion: Maternal obesity during late pregnancy in high-risk patients is independently associated with postpartum infectious complications, which demonstrates the need for a closer follow-up of maternal weight gain in these pregnancies.

Assessment of variance components in nonlinear mixed-effects elliptical models

The issue of assessing variance components is essential in deciding on the inclusion of random effects in the context of mixed models. In this work we discuss this problem by supposing nonlinear elliptical models for correlated data by using the score-type test proposed in Silvapulle and Silvapulle (1995). Being asymptotically equivalent to the likelihood ratio test and only requiring the estimation under the null hypothesis, this test provides a fairly easy computable alternative for assessing one-sided hypotheses in the context of the marginal model. Taking into account the possible non-normal distribution, we assume that the joint distribution of the response variable and the random effects lies in the elliptical class, which includes light-tailed and heavy-tailed distributions such as Student-t, power exponential, logistic, generalized Student-t, generalized logistic, contaminated normal, and the normal itself, among others. We compare the sensitivity of the score-type test under normal, Student-t and power exponential models for the kinetics data set discussed in Vonesh and Carter (1992) and fitted using the model presented in Russo et al. (2009). Also, a simulation study is performed to analyze the consequences of the kurtosis misspecification.

Bayesian Analysis for Errors in Variables with Changepoint Models

Changepoint regression models have originally been developed in connection with applications in quality control, where a change from the in-control to the out-of-control state has to be detected based on the avaliable random observations. Up to now various changepoint models have been suggested for differents applications like reliability, econometrics or medicine. In many practical situations the covariate cannot be measured precisely and an alternative model are the errors in variable regression models. In this paper we study the regression model with errors in variables with changepoint from a Bayesian approach. From the simulation study we found that the proposed procedure produces estimates suitable for the changepoint and all other model parameters.

MUC1 gene polymorphism in three Nelore lines selected for growth and its association with growth and carcass traits

The objective of this study was to describe the VNTR polymorphism of the mucin 1 gene (MUC1) in three Nelore lines selected for yearling weight to determine whether allele and genotype frequencies of this polymorphism were affected by selection for growth. In addition, the effects of the polymorphism on growth and carcass traits were evaluated. Birth, weaning and yearling weights, rump height, Longissimus muscle area, backfat thickness, and rump fat thickness, were analyzed. A total of 295 Nelore heifers from the Beef Cattle Research Center, Instituto de Zootecnia de Sertozinho, were used, including 41 of the control line, 102 of the selection line and 152 of the traditional. The selection and traditional lines comprise animals selected for higher yearling weight, whereas control line animals are selected for yearling weight close to the average. Five alleles were identified, with allele 1 being the most frequent in the three lines, especially in the lines selected for higher means for yearling weight. Heterozygosity was significantly higher in the control line. Association analyses showed significant effects of allele 1 on birth weight and weaning weight while the allele 3 exert significant effects on yearling weight and back fat thickness. Despite these findings, application of this marker to marker-assisted selection requires more consistent results based on the genotyping of a larger number of animals in order to increase the accuracy of the statistical analyses.

Costs and benefits of temporary brood desertion in a Neotropical harvestman (Arachnida: Opiliones)

Brood desertion is a life history strategy that allows parents to minimize costs related to parental care and increase their future fecundity. The harvestman Neosadocus maximus is an interesting model organism to study costs and benefits of temporary brood desertion because females abandon their clutches periodically and keep adding eggs to their clutches for some weeks. In this study, we tested if temporary brood desertion (a) imposes a cost to caring females by increasing the risk of egg predation and (b) offers a benefit to caring females by increasing fecundity as a result of increased foraging opportunities. With intensive field observations followed by a model selection approach, we showed that the proportion of consumed eggs was very low during the day and it was not influenced by the frequency of brood desertion. The proportion of consumed eggs was higher at night and it was negatively related to the frequency of brood desertion. However, frequent brood desertion did not result in higher fecundity, measured both as the number of eggs added to the current clutch and the probability of laying a second clutch over the course of the reproductive season. Considering that harvestmen are sensitive to dehydration, brood desertion during the day may attenuate the physiological stress of remaining exposed on the vegetation. Moreover, since brood desertion is higher during the day, when egg predation pressure is lower, caring females could be adjusting their maternal effort to the temporal variation in predation risk, which is regarded as the main cost of brood desertion in ectotherms.

The importance of mesofauna and decomposition environment on leaf decomposition in three forests in southeastern Brazil

We examined the effects of soil mesofauna and the litter decomposition environment (above and belowground) on leaf decomposition rates in three forest types in southeastern Brazil. To estimate decomposition experimentally, we used litterbags with a standard substrate in a full-factorial experimental design. We used model selection to compare three decomposition models and also to infer the importance of forest type, decomposition environment, mesofauna, and their interactions on the decomposition process. Rather than the frequently used simple and double-exponential models, the best model to describe our dataset was the exponential deceleration model, which assumed a single organic compartment with an exponential decrease of the decomposition rate. Decomposition was higher in the wet than in the seasonal forest, and the differences between forest types were stronger aboveground. Regarding litter decomposition environment, decomposition was predominantly higher below than aboveground, but the magnitude of this effect was higher in the seasonal than in wet forests. Mesofauna exclusion treatments had slower decomposition, except aboveground into the Semi-deciduous Forest, where the mesofauna presence did not affect decomposition. Furthermore, the effect of mesofauna was stronger in the wet forests and belowground. Overall, our results suggest that, in a regional scale, both decomposers activity and the positive effect of soil mesofauna in decomposition are constrained by abiotic factors, such as moisture conditions.

Superdiffusion in a non-Markovian random walk model with a Gaussian memory profile

Most superdiffusive Non-Markovian random walk models assume that correlations are maintained at all time scales, e. g., fractional Brownian motion, Levy walks, the Elephant walk and Alzheimer walk models. In the latter two models the random walker can always "remember" the initial times near t = 0. Assuming jump size distributions with finite variance, the question naturally arises: is superdiffusion possible if the walker is unable to recall the initial times? We give a conclusive answer to this general question, by studying a non-Markovian model in which the walker's memory of the past is weighted by a Gaussian centered at time t/2, at which time the walker had one half the present age, and with a standard deviation sigma t which grows linearly as the walker ages. For large widths we find that the model behaves similarly to the Elephant model, but for small widths this Gaussian memory profile model behaves like the Alzheimer walk model. We also report that the phenomenon of amnestically induced persistence, known to occur in the Alzheimer walk model, arises in the Gaussian memory profile model. We conclude that memory of the initial times is not a necessary condition for generating (log-periodic) superdiffusion. We show that the phenomenon of amnestically induced persistence extends to the case of a Gaussian memory profile.

A Bayesian destructive weighted Poisson cure rate model and an application to a cutaneous melanoma data

In this article, we propose a new Bayesian flexible cure rate survival model, which generalises the stochastic model of Klebanov et al. [Klebanov LB, Rachev ST and Yakovlev AY. A stochastic-model of radiation carcinogenesis - latent time distributions and their properties. Math Biosci 1993; 113: 51-75], and has much in common with the destructive model formulated by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)]. In our approach, the accumulated number of lesions or altered cells follows a compound weighted Poisson distribution. This model is more flexible than the promotion time cure model in terms of dispersion. Moreover, it possesses an interesting and realistic interpretation of the biological mechanism of the occurrence of the event of interest as it includes a destructive process of tumour cells after an initial treatment or the capacity of an individual exposed to irradiation to repair altered cells that results in cancer induction. In other words, what is recorded is only the damaged portion of the original number of altered cells not eliminated by the treatment or repaired by the repair system of an individual. Markov Chain Monte Carlo (MCMC) methods are then used to develop Bayesian inference for the proposed model. Also, some discussions on the model selection and an illustration with a cutaneous melanoma data set analysed by Rodrigues et al. [Rodrigues J, de Castro M, Balakrishnan N and Cancho VG. Destructive weighted Poisson cure rate models. Technical Report, Universidade Federal de Sao Carlos, Sao Carlos-SP. Brazil, 2009 (accepted in Lifetime Data Analysis)] are presented.