The Tandem Diabetes Care Basal-IQ PLGS system significantly reduced hypoglycemia without rebound hyperglycemia, indicating that the system can benefit adults and youth with type 1 diabetes in improving glycemic control.

[1] The 26 December 2004 Sumatra earthquake (M w = 9.1) initiated around 30 km depth and ruptured 1300 km of the Indo-Australian -Sunda plate boundary. During the Sumatra-OBS (ocean bottom seismometer) survey, a wide-angle seismic profile was acquired across the epicentral region. A seismic velocity model was obtained from combined travel time tomography and forward modeling. Together with reflection seismic data from the SeaCause II cruise, the deep structure of the source region of the great earthquake is revealed. Four to five kilometers of sediments overlie the oceanic crust at the trench, and the subducting slab can be imaged down to a depth of 35 km. We find a crystalline backstop 120 km from the trench axis, below the fore-arc basin. A high-velocity zone at the lower landward limit of the ray-covered domain, at 22 km depth, marks a shallow continental Moho, 170 km from the trench. The deep structure obtained from the seismic data was used to construct a thermal model of the fore arc in order to predict the limits of the seismogenic zone along the plate boundary fault. Assuming 100°-150°C as its updip limit, the seismogenic zone is predicted to begin 5-30 km from the trench. The downdip limit of the 2004 rupture as inferred from aftershocks is within the 350°-450°C temperature range, but this limit is 210-250 km from the trench axis and is much deeper than the fore-arc Moho. The deeper part of the rupture occurred along the contact between the mantle wedge and the downgoing plate.

International audienceCurrent models predict that the seismogenic zone along subduction thrusts, where the largest earthquakes nucleate and propagate, does not extend to the forearc mantle below the crust of the upper plate. Stable sliding conditions have been shown to prevail there, particularly along several circum-Pacific margins that underwent great megathrust earthquakes (Mw > 8.5) during the twentieth century. Based on geophysical investigation, we show that the great 2004 Sumatra-Andaman earthquake (Mw = 9–9.3) contradicts these models: not only did it propagate downdip along the interface between the forearc mantle and the subducting plate, but it actually nucleated along this reportedly aseismic part of the interplate contact. Petrological models can therefore underestimate the downdip extent of rupture zones to be expected in megathrust earthquakes, and need to be revised to account for this observation, albeit unusual

There are a number of methodological issues involved in assessing damage caused by natural hazards. The first is the lack of data, due to the rarity of events and the widely different circumstances in which they occur. Thus, historical data, albeit scarce, should not be neglected when seeking to build ex-ante risk management models. This article analyses the input of insurance data for two recent severe coastal storm events, to examine what causal relationships may exist between hazard characteristics and the level of damage incurred by residential buildings. To do so, data was collected at two levels: from lists of about 4000 damage records, 358 loss adjustment reports were consulted, constituting a detailed damage database. The results show that for flooded residential buildings, over 75% of reconstruction costs are associated with interior elements, with damage to structural components remaining very localised and negligible. Further analysis revealed a high scatter between costs and water depth, suggesting that uncertainty remains high in drawing up damage functions with insurance data alone. Due to the paper format of the loss adjustment reports, and the lack of harmonisation between their contents, the collection stage called for a considerable amount of work. For future events, establishing a standardised process for archiving damage information could significantly contribute to the production of such empirical damage functions. Nevertheless, complementary sources of data on hazards and asset vulnerability parameters will definitely still be necessary for damage modelling; multivariate approaches, crossing insurance data with external material, should also be investigated more deeply