Background: High-throughput technologies enable the cost-effective collection and analysis of DNA methylation data throughout the human genome. This naturally entails missing values management that can complicate the analysis of the data. Several general and specific imputation methods are suitable for DNA methylation data. However, there are no detailed studies of their performances under different missing data mechanisms -(completely) at random or not- and different representations of DNA methylation levels (beta andM-value).

The massive production of biological data by means of highly parallel devices like microarrays for gene expression has paved the way to new possible approaches in molecular genetics. Among them the possibility of inferring biological answers by querying large amounts of expression data. Based on this principle, we present here TOM, a web-based resource for the efficient extraction of candidate genes for hereditary diseases. The service requires the previous knowledge of at least another gene responsible for the disease and the linkage area, or else of two disease associated genetic intervals.

Electrostimulation is the object of the study of a variety of clinical approaches, ranging from bioelectronic medicine where the aim is to elicit the activity of the autonomic nervous system (ANS), to electroacupuncture with the general objective to restore homeostasis, to transcutaneous electrical nerve stimulation (TENS) to control pain and degeneration, to name a few. Among the numerous obstacles preventing from a clear adoption or rejection of these approaches in mainstream clinical practice, is the difficulty in standardizing experimental systems for testing and validation.

Mathematical models based on non-linear integral and integro-differential equations are gaining increasing attention in mathematical epidemiology due to their ability to incorporate the past infection dynamic into its current development. This property is particularly suitable to represent the evolution of diseases where the dependence of infectivity on the time since becoming infected plays a crucial role.

Inflammaging is a low-grade inflammatory state generated by the aging process that can contribute to frailty and age-related diseases in the elderly. However, it can have distinct effects in the elderly living in endemic areas for infectious diseases. An increased inflammatory response may confer protection against infectious agents in these areas, although this advantage can cause accelerating epigenetic aging. In this study, we evaluated the inflammatory profile and the epigenetic age of infected and noninfected individuals from an endemic area in Brazil.

We describe preliminary results from a multiobjective graph matching algorithm, in the coarsening step of an aggregation-based Algebraic MultiGrid (AMG) preconditioner, for solving large and sparse linear systems of equations on highend parallel computers. We have two objectives. First, we wish to improve the convergence behavior of the AMG method when applied to highly anisotropic problems. Second, we wish to extend the parallel package PSCToolkit to exploit multi-threaded parallelism at the node level on multi-core processors.