High-level of viral genomic diversity in cervical cancers: a Brazilian study on human papillomavirus type 16

Invasive cervical cancer (ICC) is the third most frequent cancer among women worldwide and is associated with persistent infection by carcinogenic human papillomaviruses (HPVs). The combination of large populations of viral progeny and decades of sustained infection may allow for the generation of intra-patient diversity, in spite of the assumedly low mutation rates of PVs. While the natural history of chronic HPVs infections has been comprehensively described, within-host viral diversity remains largely unexplored. In this study we have applied next generation sequencing to the analysis of intra-host genetic diversity in ten ICC and one condyloma cases associated to single HPV16 infection. We retrieved from all cases near full-length genomic sequences. All samples analyzed contained polymorphic sites, ranging from 3 to 125 polymorphic positions per genome, and the median probability of a viral genome picked at random to be identical to the consensus sequence in the lesion was only 40%. We have also identified two independent putative duplication events in two samples, spanning the L2 and the L1 gene, respectively. Finally, we have identified with good support a chimera of human and viral DNA. We propose that viral diversity generated during HPVs chronic infection may be fueled by innate and adaptive immune pressures. Further research will be needed to understand the dynamics of viral DNA variability, differentially in benign and malignant lesions, as well as in tissues with differential intensity of immune surveillance. Finally, the impact of intralesion viral diversity on the long-term oncogenic potential may deserve closer attention.

Hierarchical modelling of timber reference properties using probabilistic methods: Maximum Likelihood Method, Bayesian Methods and Probability Networks

In recent decades, an increased interest has been evidenced in the research on multi-scale hierarchical modelling in the field of mechanics, and also in the field of wood products and timber engineering. One of the main motivations for hierar-chical modelling is to understand how properties, composition and structure at lower scale levels may influence and be used to predict the material properties on a macroscopic and structural engineering scale. This chapter presents the applicability of statistic and probabilistic methods, such as the Maximum Likelihood method and Bayesian methods, in the representation of timber’s mechanical properties and its inference accounting to prior information obtained in different importance scales. These methods allow to analyse distinct timber’s reference properties, such as density, bending stiffness and strength, and hierarchically consider information obtained through different non, semi or destructive tests. The basis and fundaments of the methods are described and also recommendations and limitations are discussed. The methods may be used in several contexts, however require an expert’s knowledge to assess the correct statistic fitting and define the correlation arrangement between properties.