A log-extended Weibull regression model

A bathtub-shaped failure rate function is very useful in survival analysis and reliability studies. The well-known lifetime distributions do not have this property. For the first time, we propose a location-scale regression model based on the logarithm of an extended Weibull distribution which has the ability to deal with bathtub-shaped failure rate functions. We use the method of maximum likelihood to estimate the model parameters and some inferential procedures are presented. We reanalyze a real data set under the new model and the log-modified Weibull regression model. We perform a model check based on martingale-type residuals and generated envelopes and the statistics AIC and BIC to select appropriate models. (C) 2009 Elsevier B.V. All rights reserved.

The generalized log-gamma mixture model with covariates: local influence and residual analysis

In a sample of censored survival times, the presence of an immune proportion of individuals who are not subject to death, failure or relapse, may be indicated by a relatively high number of individuals with large censored survival times. In this paper the generalized log-gamma model is modified for the possibility that long-term survivors may be present in the data. The model attempts to separately estimate the effects of covariates on the surviving fraction, that is, the proportion of the population for which the event never occurs. The logistic function is used for the regression model of the surviving fraction. Inference for the model parameters is considered via maximum likelihood. Some influence methods, such as the local influence and total local influence of an individual are derived, analyzed and discussed. Finally, a data set from the medical area is analyzed under the log-gamma generalized mixture model. A residual analysis is performed in order to select an appropriate model.

Double generalized linear model for tissue culture proportion data: a Bayesian perspective

Joint generalized linear models and double generalized linear models (DGLMs) were designed to model outcomes for which the variability can be explained using factors and/or covariates. When such factors operate, the usual normal regression models, which inherently exhibit constant variance, will under-represent variation in the data and hence may lead to erroneous inferences. For count and proportion data, such noise factors can generate a so-called overdispersion effect, and the use of binomial and Poisson models underestimates the variability and, consequently, incorrectly indicate significant effects. In this manuscript, we propose a DGLM from a Bayesian perspective, focusing on the case of proportion data, where the overdispersion can be modeled using a random effect that depends on some noise factors. The posterior joint density function was sampled using Monte Carlo Markov Chain algorithms, allowing inferences over the model parameters. An application to a data set on apple tissue culture is presented, for which it is shown that the Bayesian approach is quite feasible, even when limited prior information is available, thereby generating valuable insight for the researcher about its experimental results.

Spatial pattern of black spot incidence within citrus trees related to disease severity and pathogen dispersal

Guignardia citricarpa, the causal agent of citrus black spot, forms airborne ascospores on decomposing citrus leaves and water-spread conidia on fruits, leaves and twigs. The spatial pattern of diseased fruit in citrus tree canopies was used to assess the importance of ascospores and conidia in citrus black spot epidemics in Sao Paulo State, Brazil. The aggregation of diseased fruit in the citrus tree canopy was quantified by the binomial dispersion index (D) and the binary form of Taylor`s Power Law for 303 trees in six groves. D was significantly greater than 1 in 251 trees. The intercept of the regression line of Taylor`s Power Law was significantly greater than 0 and the slope was not different from 1, implying that diseased fruit was aggregated in the canopy independent of disease incidence. Disease incidence (p) and severity (S) were assessed in 2875 citrus trees. The incidence-severity relationship was described (R-2 = 88.7%) by the model ln(S) = ln(a) + bCLL(p) where CLL = complementary log-log transformation. The high severity at low incidence observed in many cases is also indicative of low distance spread of G. citricarpa spores. For the same level of disease incidence, some trees had most of the diseased fruit with many lesions and high disease severity, whereas other trees had most of the fruit with few lesions and low disease severity. Aggregation of diseased fruit in the trees suggests that splash-dispersed conidia have an important role in increasing the disease in citrus trees in Brazil.

Validation of a root water uptake model to estimate transpiration constraints

Experimental results obtained from a greenhouse trial with common bean (Phaseolus vulgaris L) plants performed to test model hypotheses regarding the onset of limiting hydraulic conditions and the shape of the transpiration reduction curve in the falling rate phase are presented. According to these hypotheses based on simulations with an upscaled single-root model, the matric flux potential at the onset of limiting hydraulic conditions is as a function of root length density and potential transpiration rate, while the relative transpiration in the falling rate phase equals the relative matric flux potential. Transpiration of bean plants in water stressed pots with four different soils was determined daily by weighing and compared to values obtained from non-stressed pots. This procedure allowed determining the onset of the falling rate phase and corresponding soil hydraulic conditions. At the onset of the falling rate phase, the value of matric flux potential M(I) showed to differ in order of magnitude from the model predicted value for three out of four soils. This difference between model and experiment can be explained by the heterogeneity of the root distribution which is not considered by the model. An empirical factor to deal with this heterogeneity should be included in the model to improve predictions. Comparing the predictions of relative transpiration in the falling rate phase using a linear shape with water content, pressure head or matric flux potential, the matric flux potential based reduction function, in agreement with the hypothesis, showed the best performance, while the pressure head based equation resulted in the highest deviations between observed and predicted values of relative transpiration rates. (C) 2010 Elsevier B.V. All rights reserved.

Development of Ramulosis Disease of Cotton Under Controlled Environment and Field Conditions

Colletotrichum gossypii var. cephalosporioides, the fungus that causes ramulosis disease of cotton, is widespread in Brazil and can cause severe yield loss. Because weather conditions greatly affect disease development, the objective of this work was to develop weather-based models to assess disease favorability. Latent period, incidence, and severity of ramulosis symptoms were evaluated in controlled environment experiments using factorial combinations of temperature (15, 20, 25, 30, and 35 degrees C) and leaf wetness duration (0, 4, 8, 16, 32, and 64 h after inoculation). Severity was modeled as an exponential function of leaf wetness duration and temperature. At the optimum temperature of disease development, 27 degrees C, average latent period was 10 days. Maximum ramulosis severity occurred from 20 to 30 degrees C, with sharp decreases at lower and higher temperatures. Ramulosis severity increased as wetness periods were increased from 4 to 32 h. In field experiments at Piracicaba, Sao Paulo State, Brazil, cotton plots were inoculated (10(5) conidia ml(-1)) and ramulosis severity was evaluated weekly. The model obtained from the controlled environment study was used to generate a disease favorability index for comparison with disease progress rate in the field. Hourly measurements of solar radiation, temperature, relative humidity, leaf wetness duration, rainfall, and wind speed were also evaluated as possible explanatory variables. Both the disease favorability model and a model based on rainfall explained ramulosis growth rate well, with R(2) of 0.89 and 0.91, respectively. They are proposed as models of ramulosis development rate on cotton in Brazil, and weather-disease relationships revealed by this work can form the basis of a warning system for ramulosis development.

Parametric correlation functions to model the structure of permanent environmental (co)variances in milk yield random regression models

The objective of the present study was to estimate milk yield genetic parameters applying random regression models and parametric correlation functions combined with a variance function to model animal permanent environmental effects. A total of 152,145 test-day milk yields from 7,317 first lactations of Holstein cows belonging to herds located in the southeastern region of Brazil were analyzed. Test-day milk yields were divided into 44 weekly classes of days in milk. Contemporary groups were defined by herd-test-day comprising a total of 2,539 classes. The model included direct additive genetic, permanent environmental, and residual random effects. The following fixed effects were considered: contemporary group, age of cow at calving (linear and quadratic regressions), and the population average lactation curve modeled by fourth-order orthogonal Legendre polynomial. Additive genetic effects were modeled by random regression on orthogonal Legendre polynomials of days in milk, whereas permanent environmental effects were estimated using a stationary or nonstationary parametric correlation function combined with a variance function of different orders. The structure of residual variances was modeled using a step function containing 6 variance classes. The genetic parameter estimates obtained with the model using a stationary correlation function associated with a variance function to model permanent environmental effects were similar to those obtained with models employing orthogonal Legendre polynomials for the same effect. A model using a sixth-order polynomial for additive effects and a stationary parametric correlation function associated with a seventh-order variance function to model permanent environmental effects would be sufficient for data fitting.

A Log-Linear Regression Model for the Beta-Weibull Distribution

We introduce the log-beta Weibull regression model based on the beta Weibull distribution (Famoye et al., 2005; Lee et al., 2007). We derive expansions for the moment generating function which do not depend on complicated functions. The new regression model represents a parametric family of models that includes as sub-models several widely known regression models that can be applied to censored survival data. We employ a frequentist analysis, a jackknife estimator, and a parametric bootstrap for the parameters of the proposed model. We derive the appropriate matrices for assessing local influences on the parameter estimates under different perturbation schemes and present some ways to assess global influences. Further, for different parameter settings, sample sizes, and censoring percentages, several simulations are performed. In addition, the empirical distribution of some modified residuals are displayed and compared with the standard normal distribution. These studies suggest that the residual analysis usually performed in normal linear regression models can be extended to a modified deviance residual in the proposed regression model applied to censored data. We define martingale and deviance residuals to evaluate the model assumptions. The extended regression model is very useful for the analysis of real data and could give more realistic fits than other special regression models.

A split-pot experiment with sorghum to test a root water uptake partitioning model

Correct modeling of root water uptake partitioning over depth is an important issue in hydrological and crop growth models. Recently a physically based model to describe root water uptake was developed at single root scale and upscaled to the root system scale considering a homogeneous distribution of roots per soil layer. Root water uptake partitioning is calculated over soil layers or compartments as a function of respective soil hydraulic conditions, specifically the soil matric flux potential, root characteristics and a root system efficiency factor to compensate for within-layer root system heterogeneities. The performance of this model was tested in an experiment performed in two-compartment split-pot lysimeters with sorghum plants. The compartments were submitted to different irrigation cycles resulting in contrasting water contents over time. The root system efficiency factor was determined to be about 0.05. Release of water from roots to soil was predicted and observed on several occasions during the experiment; however, model predictions suggested root water release to occur more often and at a higher rate than observed. This may be due to not considering internal root system resistances, thus overestimating the ease with which roots can act as conductors of water. Excluding these erroneous predictions from the dataset, statistical indices show model performance to be of good quality.

Nutrition in bean plants and anthracnose intensity in function of silicon and copper application.

Nutrition in bean plants and anthracnose intensity in function of silicon and copper application. The objective of this work was to evaluate the effect of calcium silicate and copper sulfate on anthracnose intensity and nutrition of bean plants. The experiment was conducted using an experimental design in randomized blocks following a 4 x 4 factorial arrangement , (four levels of calcium silicate and four levels of copper sulfate) and two additional treatments (plants without inoculation and plants sprinkled with Benomyl). Four evaluations of the incidence and severity of anthracnose were done, in addition to measuring, total leaf area. At the end of the evaluations, incidence: and data were integrated over time, obtaining the area under disease progress curve (AUDPC). Contents of N, P, K, Ca, Mg, B, Cu, Fe, Mn, Zn, Si and lignin were determined in the aerial Part. A linear decrease of the intensity AUDPC was observed with the increase of the doses of calcium silicate. The severity AUDPC was influenced by the doses of copper, obtaining a reduction of 35% on the higher dosage. The supply of silicon and copper altered the content of the K, mg, S, Zn, Ca and Si in the aerial part of the bean plants.

Study of lymphocyte subpopulations in bone marrow in a model of protein-energy malnutrition

Objective: Protein-energy malnutrition (PEM) is an important public health problem affecting millions of people worldwide. Hematopoietic tissue requires a high nutrient supply, and a reduction in leukocytes, especially lymphocytes, suggests that some nutritional deficiencies might be altering bone marrow function and decreasing its ability to produce lymphocytes. In this study, we evaluated the effect that PEM has on lymphocyte subtypes and the cell cycle of CD5(+) cells. Methods: Swiss mice were subjected to PEM using a low-protein diet containing 4% protein. When the experimental group had lost about 20% of their original body weight, we collected blood and bone marrow cells and evaluated the hemogram, the myelogram, bone marrow lymphoid markers using flow cytometry, and the cell cycle in CD5(+) bone marrow. Results: Malnourished animals presented anemia, reticulocytopenia, and leukopenia with lymphopenia. The bone marrow was hypocellular, and flow cytometric analyses of bone marrow cells showed cells that were CD45(+) (91.2%), CD2(+) (84.9%), CD5(+) (37.3%), CD3(+) (23.5%), CD19(+) (43.3%), CD22(+) (34.7%), CD19(+)/CD2(+) (51.2%), CD19(+)/CD3(+)(24.0%), CD19(+)/CD5(+) (13.2%), CD22(+)/CD2(+) (40.1%), CD22(+)/CD3(+) (30.3%), and CD22(+)/CD5(+) (1.1%) in malnourished animals and CD45(+) (97.5%), CD2(+) (42.9%), CD5(+) (91.5%), CD3(+) (92.0%), CD19(+) (52.0%), CD22(+) (75.6%), CD19(+)/CD2(+) (62.0%), CD19(+)/CD3(+) (55.4%), CD19(+)/CO5(+) (6.7%), CD22(+)/CD2(+) (70.3%), CD22(+)/CD3(+) (55.9%), and CD22(+)/ CD5(+) (8.4%) in control animals. Malnourished animals also presented more CD5(+) cells in the G0 phase of cell cycle development. Conclusion: Malnourished animals presented bone marrow hypoplasia, maturation interruption, prominent lymphopenia with depletion in the lymphoid lineage, and changes in cellular development. We suggest that these changes are some of the primary causes of lymphopenia in cases of PEM and partly explain the increase in susceptibility to infections found in malnourished individuals. Published by Elsevier Inc.

Stability and in vitro penetration study of rutin incorporated in a cosmetic emulsion through an alternative model biomembrane

Rutin is employed as antioxidant and to prevent the capillary fragility and, when incorporated in cosmetic emulsions, it must target the action site. In vitro cutaneous penetration studies through human skin is the ideal situation, however, there are difficulties to obtain and to maintain this tissue viability. Among the membrane models, shed snake skin presents itself as pure stratum corneum, providing barrier function similar to human and it is obtained without the animal sacrifice. The objectives of this research were the development and stability evaluation of a cosmetic emulsion containing rutin and propylene glycol (penetration enhancer) and the evaluation or rutin in vitro cutaneous penetration and retention from the emulsion, employing an alternative model biomembrane. Emulsion was developed with rutin and propylene glycol, both at 5.0% w/w. Active substance presented on the formulation was quantified by a validated spectrophotometric method at 361.0 nm. Rutin Rutin cutaneous penetration and retention was performed in vertical diffusion cells with shed snake skin of Crotalus durissus, as alternative model biomembrane, and distilled water and ethanol 99.5% (1:1), as receptor fluid. The experiment was conducted for six hours, at 37.0 +/- 0.5 degrees C with constant stirring of 300 rpm. Spectrophotometry at 410.0 nm, previously validated, determined the active substance after cutaneous penetration/ retention. Emulsion did not promote rutin cutaneous penetration through C. durissus skin, retaining 0.931 +/- 0.0391 mu g rutin/mg shed snake skin. The referred formulation was chemically stable for 30 days after stored at 25.0 +/- 2.0 degrees C, 5.0 +/- 0.5 degrees C and 45.0 +/- 0.5 degrees C. In conclusion, it has not been verified the active cutaneous penetration through the model biomembrane, but only its retention on the Crotalus durissus stratum corneum, condition considered stable for 30 days.

Excised Porcine Cornea Integrity Evaluation in an in vitro Model of Iontophoretic Ocular Research

Background/Aims: It is a challenge to adapt traditional in vitro diffusion experiments to ocular tissue. Thus, the aim of this work was to present experimental evidence on the integrity of the porcine cornea, barrier function and maintenance of electrical properties for 6 h of experiment when the tissue is mounted on an inexpensive and easy-to-use in vitro model for ocular iontophoresis. Methods: A modified Franz diffusion cell containing two ports for the insertion of the electrodes and a receiving compartment that does not need gassing with carbogen was used in the studies. Corneal electron transmission microscopy images were obtained, and diffusion experiments with fluorescent markers were performed to examine the integrity of the barrier function. The preservation of the negatively charged corneal epithelium was verified by the determination of the electro-osmotic flow of a hydrophilic and non-ionized molecule. Results: The diffusion cell was able to maintain the temperature, homogenization, porcine epithelial corneal structure integrity, barrier function and electrical characteristics throughout the 6 h of permeation experiment, without requiring CO(2) gassing when the receiving chamber was filled with 25 m M of HEPES buffer solution. Conclusion: The system described here is inexpensive, easy to handle and reliable as an in vitro model for iontophoretic ocular delivery studies. Copyright (C) 2010 S. Karger AG, Basel

Entanglement between a qubit and the environment in the spin-boson model

The quantitative description of the quantum entanglement between a qubit and its environment is considered. Specifically, for the ground state of the spin-boson model, the entropy of entanglement of the spin is calculated as a function of α, the strength of the ohmic coupling to the environment, and ɛ, the level asymmetry. This is done by a numerical renormalization group treatment of the related anisotropic Kondo model. For ɛ=0, the entanglement increases monotonically with α, until it becomes maximal for α→1-. For fixed ɛ>0, the entanglement is a maximum as a function of α for a value, α=αM

Weibull model selection for reliability modelling

A large number of models have been derived from the two-parameter Weibull distribution and are referred to as Weibull models. They exhibit a wide range of shapes for the density and hazard functions, which makes them suitable for modelling complex failure data sets. The WPP and IWPP plot allows one to determine in a systematic manner if one or more of these models are suitable for modelling a given data set. This paper deals with this topic.