Large-scale risk assessment on snow avalanche hazard in alpine regions

Snow avalanches are recurring natural hazards that affect the population and infrastructure in mountainous regions, such as in the recent avalanche winters of 2018 and 2019, when considerable damage was caused by avalanches throughout the Alps. Hazard decision makers need detailed information on the spatial distribution of avalanche hazards and risks to prioritize and apply appropriate adaptation strategies and mitigation measures and thus minimize impacts. Here, we present a novel risk assessment approach for assessing the spatial distribution of avalanche risk by combining large-scale hazard mapping with a state-of-the-art risk assessment tool, where risk is understood as the product of hazard, exposure and vulnerability. Hazard disposition is modeled using the large-scale hazard indication mapping method RAMMS::LSHIM (Rapid Mass Movement Simulation::Large-Scale Hazard Indication Mapping), and risks are assessed using the probabilistic Python-based risk assessment platform CLIMADA, developed at ETH Zurich. Avalanche hazard mapping for scenarios with a 30-, 100- and 300-year return period is based on a high-resolution terrain model, 3 d snow depth increase, automatically determined potential release areas and protection forest data. Avalanche hazard for 40 000 individual snow avalanches is expressed as avalanche intensity, measured as pressure. Exposure is represented by a detailed building layer indicating the spatial distribution of monetary assets. The vulnerability of buildings is defined by damage functions based on the software EconoMe, which is in operational use in Switzerland. The outputs of the hazard, exposure and vulnerability analyses are combined to quantify the risk in spatially explicit risk maps. The risk considers the probability and intensity of snow avalanche occurrence, as well as the concentration of vulnerable, exposed buildings. Uncertainty and sensitivity analyses were performed to capture inherent variability in the input parameters. This new risk assessment approach allows us to quantify avalanche risk over large areas and results in maps displaying the spatial distribution of risk at specific locations. Large-scale risk maps can assist decision makers in identifying areas where avalanche hazard mitigation and/or adaption is needed.ISSN:1561-8633ISSN:1684-998

ROS1 rearrangements in lung adenocarcinoma : prognostic impact, therapeutic options and genetic variability

Background: While recent data show that crizotinib is highly effective in patients with ROS1 rearrangement, few data is available about the prognostic impact, the predictive value for different treatments, and the genetic heterogeneity of ROS1- positive patients. Patients and methods: 1137 patients with adenocarcinoma of the lung were analyzed regarding their ROS1 status. In positive cases, next-generation sequencing (NGS) was performed. Clinical characteristics, treatments and outcome of these patients were assessed. Overall survival (OS) was compared with genetically defined subgroups of ROS1-negative patients. Results: 19 patients of 1035 evaluable (1.8%) had ROS1-rearrangement. The median OS has not been reached. Stage IV patients with ROS1-rearrangement had the best OS of all subgroups (36.7 months, p < 0.001). 9 of 14 (64.2%) patients had at least one response to chemotherapy. Estimated mean OS for patients receiving chemotherapy and crizotinib was 5.3 years. Ten patients with ROS1-rearrangement (52.6%) harbored additional aberrations. Conclusion: ROS1-rearangement is not only a predictive marker for response to crizotinib, but also seems to be the one of the best prognostic molecular markers in NSCLC reported so far. In stage IV patients, response to chemotherapy was remarkable high and overall survival was significantly better compared to other subgroups including EGFR-mutated and ALK-fusion-positive NSCLC