This travel grant would support research into the role of religious and political dissent in the early modern British medical and scientific community. The Banks Manuscripts of the London Natural History Museum holds an extensive and unexplored archive of correspondence centering on Patrick Blair (Scottish physician, Jacobite, and natural historian), John Martyn (English non-Juror, natural historian, and later Professor of Botany at Cambridge), and their circle. A study of their correspondence will further our understanding of the influence of religious and political identity on medical and scientific scholarship in early eighteenth-century Britain.

The Mekong Delta Region (MDR) of Vietnam is vulnerable to climate change which results in more frequent and intense mosquito-borne dengue outbreaks. Current dengue control measures are mostly reactive due to the absence of an early warning system (EWS) tailored to the needs of the local health systems. Local health practitioners and the community are, therefore, not adequately empowered to deploy preventive actions to reduce the impact of a dengue outbreak. We propose to develop and evaluate a digital dengue early warning system (E-DENGUE), based on a prediction model, to assist the local health systems and the local communities affected by dengue to proactively mitigate the impact of outbreaks in the MDR. The specific aims are: i) to build a predictive dengue model that accurately predicts dengue risk, at the district level, two months in advance; ii) to develop E-DENGUE––an open-source software system with a user-friendly web-based and mobile-app interface––aimed at local health practitioners to predict dengue incidence and outbreaks at the district level; iii) to evaluate the effectiveness of E-DENGUE in reducing dengue incidence using a cluster-randomised control trial based in the MDR; iv) To evaluate the cost-effectiveness of E-DENGUE for outbreak prevention in the MDR.

Imagine if we could watch multiple molecules in living cells as they move and interact. This dream may seem years away, but it is now realistic to achieve real-time dynamic super-resolution imaging of multiple tagged proteins in three dimensions (3D) in cells and in tissues. This will allow biologists to discover large-scale patterns involving diverse structures including transport vesicles, ribosomes, and chromatin domains, all previously inaccessible because they lie in the gap between the resolution of electron (1- 2 nm) and light microscopy (200-300 nm). The “big picture” of cellular organization/information processing would emerge, with advances in understanding cell function in health and disease. While we can now do this in 2D, 3D imaging is needed to follow objects as they move out of the plane. Achieving 3D imaging is a major challenge and will require two orders of magnitude more information per cellular volume, and novel algorithms to classify, analyze, and visualize patterns from massive datasets. We propose specific innovations (Table 1) that, should allow us to achieve this over the next five years, given our team’s proven track record of success. Imagine if we could watch multiple molecules in living cells as they move and interact. This dream may seem years away, but it is now realistic to achieve real-time dynamic super-resolution imaging of multiple tagged proteins in three dimensions (3D) in cells and in tissues. This will allow biologists to discover large-scale patterns involving diverse structures including transport vesicles, ribosomes, and chromatin domains, all previously inaccessible because they lie in the gap between the resolution of electron (1- 2 nm) and light microscopy (200-300 nm). The “big picture” of cellular organization/information processing would emerge, with advances in understanding cell function in health and disease. While we can now do this in 2D, 3D imaging is needed to follow objects as they move out of the plane. Achieving 3D imaging is a major challenge and will require two orders of magnitude more information per cellular volume, and novel algorithms to classify, analyze, and visualize patterns from massive datasets. We propose specific innovations (Table 1) that, should allow us to achieve this over the next five years, given our team’s proven track record of success.