Morphological and structural analysis in the Anaga offshore massif, Canary Islands: fractures and debris avalanches relationships

As part of the ‘National Hydrographic and Oceanographic Research Plan for the Spanish Exclusive Economic Zone’, multibeam bathymetry and seismic reflection profiles were obtained in the Canary Islands aboard the R/V Hespérides. The submarine flanks of the Anaga offshore extension of Tenerife Island are here studied to analyze its geomorphology. In the north sector of the Anaga submarine massif, the extension of the Anaga Debris Avalanche has been mapped for the first time, and a volume of 36 km3 was calculated. The relationship between the Anaga and Orotava Debris Avalanches is also described. Faulting has been recognized as a key process for the occurrence of debris avalanches and the growth of volcanic lineaments. Moreover, faulting affects previous structures and the channelling of debris flows. Structural analysis shows the typical radial pattern of an oceanic island. In addition, a NE-SW dominant direction of faulting was obtained, consistent with the Tenerife Island structural trend seen in the Anaga Massif and Cordillera Dorsal. NW-SE and E-W are two other main trends seen in the area. Special interest is manifest in two long faults: ‘Santa Cruz Fault’ bounds the southern edge of Anaga offshore Massif with a length of 50 km and a direction that changes from NE-SW to almost E-W. The Güimar Debris Avalanche was probably channeled by this fault. The ‘Guayotá Fault’ was recognized in several seismic profiles with a N-S direction that changes towards NW-SE at its southern end. This fault affects the more recent sediments with a vertical offset of 25–30 m, along 60 km. It has been interpreted as a transpressive strike-slip fault

Transcription-based prediction of response to IFNbeta using supervised computational methods

Changes in cellular functions in response to drug therapy are mediated by specific transcriptional profiles resulting from the induction or repression in the activity of a number of genes, thereby modifying the preexisting gene activity pattern of the drug-targeted cell(s). Recombinant human interferon beta (rIFNbeta) is routinely used to control exacerbations in multiple sclerosis patients with only partial success, mainly because of adverse effects and a relatively large proportion of nonresponders. We applied advanced data-mining and predictive modeling tools to a longitudinal 70-gene expression dataset generated by kinetic reverse-transcription PCR from 52 multiple sclerosis patients treated with rIFNbeta to discover higher-order predictive patterns associated with treatment outcome and to define the molecular footprint that rIFNbeta engraves on peripheral blood mononuclear cells. We identified nine sets of gene triplets whose expression, when tested before the initiation of therapy, can predict the response to interferon beta with up to 86% accuracy. In addition, time-series analysis revealed potential key players involved in a good or poor response to interferon beta. Statistical testing of a random outcome class and tolerance to noise was carried out to establish the robustness of the predictive models. Large-scale kinetic reverse-transcription PCR, coupled with advanced data-mining efforts, can effectively reveal preexisting and drug-induced gene expression signatures associated with therapeutic effects