Detection of Salmonella enterica in Magellanic penguins (Spheniscus magellanicus) of Chilean Patagonia: evidences of inter-species transmission.

Patagonia in southern South America is among the few world regions where direct human impact is still limited but progressively increasing, mainly represented by tourism, farming, fishing and mining activities. The sanitary condition of Patagonian wildlife is unknown, in spite of being critical for the assessment of anthropogenic effects there. The aim of this study was the characterization of Salmonella enterica strains isolated from wild colonies of Magellanic penguins (Spheniscus magellanicus) located in Magdalena Island and Otway Sound, in Chilean Patagonia. Eight isolates of Salmonella were found, belonging to Agona and Enteritidis serotypes, with an infection rate of 0·38%. Resistance to ampicillin, cefotaxime, ceftiofur and tetracycline antimicrobials were detected, and some of these strains showed genotypic similarity with Salmonella strains isolated from humans and gulls, suggesting inter-species transmission cycles and strengthening the role of penguins as sanitary sentinels in the Patagonian ecosystem.

Non-stationary distributed source approximation: an alternative to improve localization procedures.

Localization of the generators of the scalp measured electrical activity is particularly difficult when a large number of brain regions are simultaneously active. In this study, we describe an approach to automatically isolate scalp potential maps, which are simple enough to expect reasonable results after applying a distributed source localization procedure. The isolation technique is based on the time-frequency decomposition of the scalp-measured data by means of a time-frequency representation. The basic rationale behind the approach is that neural generators synchronize during short time periods over given frequency bands for the codification of information and its transmission. Consequently potential patterns specific for certain time-frequency pairs should be simpler than those appearing at single times but for all frequencies. The method generalizes the FFT approximation to the case of distributed source models with non-stationary time behavior. In summary, the non-stationary distributed source approximation aims to facilitate the localization of distributed source patterns acting at specific time and frequencies for non-stationary data such as epileptic seizures and single trial event related potentials. The merits of this approach are illustrated here in the analysis of synthetic data as well as in the localization of the epileptogenic area at seizure onset in patients. It is shown that time and frequency at seizure onset can be precisely detected in the time-frequency domain and those localization results are stable over seizures. The results suggest that the method could also be applied to localize generators in single trial evoked responses or spontaneous activity.