5 resultados para Regulatory Models

em Repositório da Produção Científica e Intelectual da Unicamp


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Prosopis rubriflora and Prosopis ruscifolia are important species in the Chaquenian regions of Brazil. Because of the restriction and frequency of their physiognomy, they are excellent models for conservation genetics studies. The use of microsatellite markers (Simple Sequence Repeats, SSRs) has become increasingly important in recent years and has proven to be a powerful tool for both ecological and molecular studies. In this study, we present the development and characterization of 10 new markers for P. rubriflora and 13 new markers for P. ruscifolia. The genotyping was performed using 40 P. rubriflora samples and 48 P. ruscifolia samples from the Chaquenian remnants in Brazil. The polymorphism information content (PIC) of the P. rubriflora markers ranged from 0.073 to 0.791, and no null alleles or deviation from Hardy-Weinberg equilibrium (HW) were detected. The PIC values for the P. ruscifolia markers ranged from 0.289 to 0.883, but a departure from HW and null alleles were detected for certain loci; however, this departure may have resulted from anthropic activities, such as the presence of livestock, which is very common in the remnant areas. In this study, we describe novel SSR polymorphic markers that may be helpful in future genetic studies of P. rubriflora and P. ruscifolia.

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In acquired immunodeficiency syndrome (AIDS) studies it is quite common to observe viral load measurements collected irregularly over time. Moreover, these measurements can be subjected to some upper and/or lower detection limits depending on the quantification assays. A complication arises when these continuous repeated measures have a heavy-tailed behavior. For such data structures, we propose a robust structure for a censored linear model based on the multivariate Student's t-distribution. To compensate for the autocorrelation existing among irregularly observed measures, a damped exponential correlation structure is employed. An efficient expectation maximization type algorithm is developed for computing the maximum likelihood estimates, obtaining as a by-product the standard errors of the fixed effects and the log-likelihood function. The proposed algorithm uses closed-form expressions at the E-step that rely on formulas for the mean and variance of a truncated multivariate Student's t-distribution. The methodology is illustrated through an application to an Human Immunodeficiency Virus-AIDS (HIV-AIDS) study and several simulation studies.

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Sugarcane is a monocot plant that accumulates sucrose to levels of up to 50% of dry weight in the stalk. The mechanisms that are involved in sucrose accumulation in sugarcane are not well understood, and little is known with regard to factors that control the extent of sucrose storage in the stalks. UDP-glucose pyrophosphorylase (UGPase; EC 2.7.7.9) is an enzyme that produces UDP-glucose, a key precursor for sucrose metabolism and cell wall biosynthesis. The objective of this work was to gain insights into the ScUGPase-1 expression pattern and regulatory mechanisms that control protein activity. ScUGPase-1 expression was negatively correlated with the sucrose content in the internodes during development, and only slight differences in the expression patterns were observed between two cultivars that differ in sucrose content. The intracellular localization of ScUGPase-1 indicated partial membrane association of this soluble protein in both the leaves and internodes. Using a phospho-specific antibody, we observed that ScUGPase-1 was phosphorylated in vivo at the Ser-419 site in the soluble and membrane fractions from the leaves but not from the internodes. The purified recombinant enzyme was kinetically characterized in the direction of UDP-glucose formation, and the enzyme activity was affected by redox modification. Preincubation with H2O2 strongly inhibited this activity, which could be reversed by DTT. Small angle x-ray scattering analysis indicated that the dimer interface is located at the C terminus and provided the first structural model of the dimer of sugarcane UGPase in solution.

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Often in biomedical research, we deal with continuous (clustered) proportion responses ranging between zero and one quantifying the disease status of the cluster units. Interestingly, the study population might also consist of relatively disease-free as well as highly diseased subjects, contributing to proportion values in the interval [0, 1]. Regression on a variety of parametric densities with support lying in (0, 1), such as beta regression, can assess important covariate effects. However, they are deemed inappropriate due to the presence of zeros and/or ones. To evade this, we introduce a class of general proportion density, and further augment the probabilities of zero and one to this general proportion density, controlling for the clustering. Our approach is Bayesian and presents a computationally convenient framework amenable to available freeware. Bayesian case-deletion influence diagnostics based on q-divergence measures are automatic from the Markov chain Monte Carlo output. The methodology is illustrated using both simulation studies and application to a real dataset from a clinical periodontology study.