An EEG signal generator with variable time delayed output

Mutations in the HPV16 genome induced by APOBEC3 are associated with viral clearance

HPV16 causes half of cervical cancers worldwide; for unknown reasons, most infections resolve within two years. Here, we analyze the viral genomes of 5,328 HPV16-positive case-control samples to investigate mutational signatures and the role of human APOBEC3-induced mutations in viral clearance and cervical carcinogenesis. We identify four de novo mutational signatures, one of which matches the COSMIC APOBEC-associated signature 2. The viral genomes of the precancer/cancer cases are less likely to contain within-host somatic HPV16 APOBEC3-induced mutations (Fisher's exact test, P = 6.2 x 10-14), and have a 30% lower nonsynonymous APOBEC3 mutation burden compared to controls. We replicate the low prevalence of HPV16 APOBEC3-induced mutations in 1,749 additional cases. APOBEC3 mutations also historically contribute to the evolution of HPV16 lineages. We demonstrate that cervical infections with a greater burden of somatic HPV16 APOBEC3-induced mutations are more likely to be benign or subsequently clear, suggesting they may reduce persistence, and thus progression, within the host.

Software simulation of the EEG

The literature contains many examples of digital procedures for the analytical treatment of electroencephalograms, but there is as yet no standard by which those techniques may be judged or compared. This paper proposes one method of generating an EEG, based on a computer program for Zetterberg's simulation. It is assumed that the statistical properties of an EEG may be represented by stationary processes having rational transfer functions and achieved by a system of software filters and random number generators. The model represents neither the neurological mechanism response for generating the EEG, nor any particular type of EEG record; transient phenomena such as spikes, sharp waves and alpha bursts also are excluded. The basis of the program is a valid 'partial' statistical description of the EEG; that description is then used to produce a digital representation of a signal which, if plotted sequentially, might or might not by chance resemble an EEG, that is unimportant. What is important is that the statistical properties of the series remain those of a real EEG; it is in this sense that the output is a simulation of the EEG. There is considerable flexibility in the form of the output, i.e. its alpha, beta and delta content, which may be selected by the user, the same selected parameters always producing the same statistical output. The filtered outputs from the random number sequences may be scaled to provide realistic power distributions in the accepted EEG frequency bands and then summed to create a digital output signal, the 'stationary EEG'.(ABSTRACT TRUNCATED AT 250 WORDS)