Lysogeny with Shiga Toxin 2-Encoding Bacteriophages Represses Type III Secretion in Enterohemorrhagic Escherichia coli

Lytic or lysogenic infections by bacteriophages drive the evolution of enteric bacteria. Enterohemorrhagic Escherichia coli (EHEC) have recently emerged as a significant zoonotic infection of humans with the main serotypes carried by ruminants. Typical EHEC strains are defined by the expression of a type III secretion (T3S) system, the production of Shiga toxins (Stx) and association with specific clinical symptoms. The genes for Stx are present on lambdoid bacteriophages integrated into the E. coli genome. Phage type (PT) 21/28 is the most prevalent strain type linked with human EHEC infections in the United Kingdom and is more likely to be associated with cattle shedding high levels of the organism than PT32 strains. In this study we have demonstrated that the majority (90%) of PT 21/28 strains contain both Stx2 and Stx2c phages, irrespective of source. This is in contrast to PT 32 strains for which only a minority of strains contain both Stx2 and 2c phages (28%). PT21/28 strains had a lower median level of T3S compared to PT32 strains and so the relationship between Stx phage lysogeny and T3S was investigated. Deletion of Stx2 phages from EHEC strains increased the level of T3S whereas lysogeny decreased T3S. This regulation was confirmed in an E. coli K12 background transduced with a marked Stx2 phage followed by measurement of a T3S reporter controlled by induced levels of the LEE-encoded regulator (Ler). The presence of an integrated Stx2 phage was shown to repress Ler induction of LEE1 and this regulation involved the CII phage regulator. This repression could be relieved by ectopic expression of a cognate CI regulator. A model is proposed in which Stx2-encoding bacteriophages regulate T3S to co-ordinate epithelial cell colonisation that is promoted by Stx and secreted effector proteins

Guidelines for management of ischaemic stroke and transient ischaemic attack 2008

This article represents the update of the European Stroke Initiative Recommendations for Stroke Management. These guidelines cover both ischaemic stroke and transient ischaemic attacks, which are now considered to be a single entity. The article covers referral and emergency management, Stroke Unit service, diagnostics, primary and secondary prevention, general stroke treatment, specific treatment including acute management, management of complications, and rehabilitation

Adaptive filtering methods for identifying cross-frequency couplings in human EEG.

Oscillations have been increasingly recognized as a core property of neural responses that contribute to spontaneous, induced, and evoked activities within and between individual neurons and neural ensembles. They are considered as a prominent mechanism for information processing within and communication between brain areas. More recently, it has been proposed that interactions between periodic components at different frequencies, known as cross-frequency couplings, may support the integration of neuronal oscillations at different temporal and spatial scales. The present study details methods based on an adaptive frequency tracking approach that improve the quantification and statistical analysis of oscillatory components and cross-frequency couplings. This approach allows for time-varying instantaneous frequency, which is particularly important when measuring phase interactions between components. We compared this adaptive approach to traditional band-pass filters in their measurement of phase-amplitude and phase-phase cross-frequency couplings. Evaluations were performed with synthetic signals and EEG data recorded from healthy humans performing an illusory contour discrimination task. First, the synthetic signals in conjunction with Monte Carlo simulations highlighted two desirable features of the proposed algorithm vs. classical filter-bank approaches: resilience to broad-band noise and oscillatory interference. Second, the analyses with real EEG signals revealed statistically more robust effects (i.e. improved sensitivity) when using an adaptive frequency tracking framework, particularly when identifying phase-amplitude couplings. This was further confirmed after generating surrogate signals from the real EEG data. Adaptive frequency tracking appears to improve the measurements of cross-frequency couplings through precise extraction of neuronal oscillations