No abstract

Epileptic seizure detection and prediction by using noninvasive measurements such as scalp EEG signals or invasive, intracranial recordings, has been at the heart of epilepsy studies for at least three decades. To this end, the most common approach has been to consider short-length recordings (several seconds to a few minutes) around a seizure, aiming to identify significant changes that occur before or during seizures. An inherent assumption in this approach is the presence of a relatively constant EEG activity in the interictal period, which is interrupted by seizure occurrence. Here, we examine this assumption by using long-duration scalp EEG data (21-94 hr) in nine patients with epilepsy, based on which we construct functional brain networks. Our results reveal that these networks vary over time in a periodic fashion, exhibiting multiple peaks at periods ranging between 1 and 24 hr. The effects of seizure onset on the functional brain network properties were found to be considerably smaller in magnitude compared to the changes due to these inherent periodic cycles. Importantly, the properties of the identified network periodic components (instantaneous phase) were found to be strongly correlated to seizure onset, especially for the periodicities around 3 and 5 hr. These correlations were found to be largely absent between EEG signal periodicities and seizure onset, suggesting that higher specificity may be achieved by using network-based metrics. In turn, this implies that more robust seizure detection and prediction can be achieved if longer term underlying functional brain network periodic variations are taken into account. K E Y W O R D S brain networks, epilepsy, periodicities, scalp EEG

We investigated functional epigenetic changes that occur in primary human T lymphocytes during entry into the cell cycle and mapped these at the single-nucleosome level by ChIP-chip on tiling arrays for chromosomes 1 and 6. We show that nucleosome loss and flanking active histone marks define active transcriptional start sites (TSSs). Moreover, these signatures are already set at many inducible genes in quiescent cells prior to cell stimulation. In contrast, there is a dearth of the inactive histone mark H3K9me3 at the TSS, and under-representation of H3K9me2 and H3K9me3 defines the body of active genes. At the DNA level, cytosine methylation (meC) is enriched for nucleosomes that remain at the TSS, whereas in general there is a dearth of meC at TSSs. Furthermore, a drop in meC also marks 3′ transcription termination, and a peak of meC occurs at stop codons. This mimics the 3′ nucleosomal distribution in yeast, which we show does not occur in human T cells.

An integer array y = y[1..n] is said to be feasible if and only if y[1] = n and, for every i ∈ 2..n, i ≤ i+y[i] ≤ n+1. A string is said to be indeterminate if and only if at least one of its elements is a subset of cardinality greater than one of a given alphabet Σ; otherwise it is said to be regular. A feasible array y is said to be regular if and only if it is the prefix array of some regular string. We show using a graph model that every feasible array of integers is a prefix array of some (indeterminate or regular) string, and for regular strings corresponding to y, we use the model to provide a lower bound on the alphabet size. We show further that there is a 1-1 correspondence between labelled simple graphs and indeterminate strings, and we show how to determine the minimum alphabet size σ of an indeterminate string x based on its associated graph G x . Thus, in this sense, indeterminate strings are a more natural object of combinatorial interest than the strings on elements of Σ that have traditionally been studied.