Cost estimation for the monitoring instrumentation of landslide early warning systems

Landslides are socio-natural hazards. In Colombia, for example, these are the most frequent hazards. The interplay of climate change and the mostly informal growth of cities in landslide-prone areas increases the associated risks. Landslide early warning systems (LEWSs) are essential for disaster risk reduction, but the monitoring component is often based on expensive sensor systems. This study presents a data-driven approach to localize landslide-prone areas suitable for low-cost and easy-to-use LEWS instrumentation, as well as to estimate the associated costs. The approach is exemplified in the landslide-prone city of Medellín, Colombia. A workflow that enables decision-makers to balance financial costs and the potential to protect exposed populations is introduced. To achieve this, city-level landslide susceptibility is mapped using data on hazard levels, landslide inventories, geological and topographic factors, and a random forest model. Then, the landslide susceptibility map is combined with a population density map to identify highly exposed areas. Subsequently, a cost function is defined to estimate the cost of LEWS monitoring sensors at the selected sites, using lessons learned from a pilot LEWS in Bello Oriente, a neighbourhood in Medellín. This study estimates that LEWS monitoring sensors could be installed in several landslide-prone areas with a budget ranging from EUR 5 to EUR 41 per person (roughly COP 23 000 to 209 000), improving the resilience of over 190 000 exposed individuals, 81 % of whom are located in precarious neighbourhoods; thus, the systems would particularly reduce the risks of a social group of very high vulnerability. The synopsis of all information allows us to provide recommendations for stakeholders on where to proceed with LEWS instrumentation. These are based on five different cost-effectiveness scenarios. This approach enables decision-makers to prioritize LEWS deployment to protect exposed populations while balancing the financial costs, particularly for those in precarious neighbourhoods. Finally, the limitations, challenges, and opportunities for the successful implementation of a LEWS are discussed.</p

Empiric recommendations for population disaggregation under different data scenarios

High-resolution population mapping is of high relevance for developing and implementing tailored actions in several fields: From decision making in crisis management to urban planning. Earth Observation has considerably contributed to the development of methods for disaggregating population figures with higher resolution data into fine-grained population maps. However, which method is most suitable on the basis of the available data, and how the spatial units and accuracy metrics affect the validation process is not fully known. We aim to provide recommendations to researches that attempt to produce high-resolution population maps using remote sensing and geospatial information in heterogeneous urban landscapes. For this purpose, we performed a comprehensive experimental research on population disaggregation methods with thirty-six different scenarios. We combined five different top-down methods (from basic to complex, i.e., binary and categorical dasymetric, statistical, and binary and categorical hybrid approaches) on different subsets of data with diverse resolutions and degrees of availability (poor, average and rich). Then, the resulting population maps were systematically validated with a two-fold approach using six accuracy metrics. We found that when only using remotely sensed data the combination of statistical and dasymetric methods provide better results, while highly-resolved data require simpler methods. Besides, the use of at least three relative accuracy metrics is highly encouraged since the validation depends on level and method. We also analysed the behaviour of relative errors and how they are affected by the heterogeneity of the urban landscape. We hope that our recommendations save additional efforts and time in future population mapping

Rapid access clinic for unexplained lymphadenopathy and suspected malignancy: prospective analysis of 1000 patients.

Background:In patients presenting with peripheral lymphadenopathy, it is critical to effectively identify those with underlying cancer who require urgent specialist care. Methods:We analyzed a large dataset of 1000 consecutive patients with unexplained lymphadenopathy referred between 2001 and 2009 to the Royal Marsden Hospital (RMH) rapid access lymph node diagnostic clinic (LNDC). Results:Cancer was diagnosed in 14% of patients. Factors predictive for malignant disease were male sex, age, supraclavicular and multiple site involvement. Cancer-associated symptoms were present for a median of 8 weeks. The median time from referral to start of cancer therapy was 53 days. Fine needle aspiration (FNA) was performed in 83% of patients with malignancies. Sensitivity and specificity of FNA were limited (50 and 87%, respectively for any malignancy; 30 and 79%, respectively for lymphoma). The vast majority of cancer patients received diagnostic biopsies on the basis of suspicious clinical and ultrasound findings; the FNA result contributed to establishing the diagnosis in only 4 cases. Conclusions:In conclusion, we demonstrate that Oncologist-led rapid access clinics are successful concepts to assess patients with unexplained lymphadenopathy. Our data suggest that a routine use of FNA should be reconsidered in this setting

Beyond plug and pray : context sensitivity and in silico design of artificial neomycin riboswitches

Gene regulation in prokaryotes often depends on RNA elements such as riboswitches or RNA thermometers located in the 5′ untranslated region of mRNA. Rearrangements of the RNA structure in response, e.g., to the binding of small molecules or ions control translational initiation or premature termination of transcription and thus mRNA expression. Such structural responses are amenable to computational modelling, making it possible to rationally design synthetic riboswitches for a given aptamer. Starting from an artificial aptamer, we construct the first synthetic transcriptional riboswitches that respond to the antibiotic neomycin. We show that the switching behaviour in vivo critically depends not only on the sequence of the riboswitch itself, but also on its sequence context. We therefore developed in silico methods to predict the impact of the context, making it possible to adapt the design and to rescue non-functional riboswitches. We furthermore analyse the influence of 5′ hairpins with varying stability on neomycin riboswitch activity. Our data highlight the limitations of a simple plug-and-play approach in the design of complex genetic circuits and demonstrate that detailed computational models significantly simplify, improve, and automate the design of transcriptional circuits. Our design software is available under a free licence on GitHub (https://github.com/xileF1337/riboswitch_design)