WHO global research priorities for antimicrobial resistance in human health

The WHO research agenda for antimicrobial resistance (AMR) in human health has identified 40 research priorities to be addressed by the year 2030. These priorities focus on bacterial and fungal pathogens of crucial importance in addressing AMR, including drug-resistant pathogens causing tuberculosis. These research priorities encompass the entire people-centred journey, covering prevention, diagnosis, and treatment of antimicrobial-resistant infections, in addition to addressing the overarching knowledge gaps in AMR epidemiology, burden and drivers, policies and regulations, and awareness and education. The research priorities were identified through a multistage process, starting with a comprehensive scoping review of knowledge gaps, with expert inputs gathered through a survey and open call. The priority setting involved a rigorous modified Child Health and Nutrition Research Initiative approach, ensuring global representation and applicability of the findings. The ultimate goal of this research agenda is to encourage research and investment in the generation of evidence to better understand AMR dynamics and facilitate policy translation for reducing the burden and consequences of AMR

The influence of elevation uncertainty on derivation of topographic indices

Digital elevation models at a variety of resolutions are increasingly being used in geomorphology, for example in comparing the hypsometric properties of multiple catchments. A considerable bodyof research has investigated the sensitivity of topographic indices to resolution and algorithms, but little work has been done to address the impact of DEM uncertainty and elevation value error on derived products. By using higher resolution data from the Shuttle Radar Topography Mission - of supposed higher accuracy - for comparison with the widely used GLOBE 1km data set, error surfaces for three mountainous regions were calculated. Correlation analysis showed that error surfaces related to a range of topographic variables for all three regions, namely roughness, minimum and mean extremity and aspect. This correlation of error with local topography was used to develop a model of uncertainty including a stochastic component, permitting Monte Carlo Simulations. These suggest that global statistics for a range of topographic indices are robust to the introduction of uncertainty. However, the derivation of watersheds and related statistics per watershed (e.g. hypsometry) is shown to vary significantly as a result of the introduced uncertainty