A rapid and bidirectional reporter of neural activity reveals neural correlates of social behaviors in Drosophila.

Neural activity is modulated over different timescales encompassing subseconds to hours, reflecting changes in external environment, internal state and behavior. Using Drosophila as a model, we developed a rapid and bidirectional reporter that provides a cellular readout of recent neural activity. This reporter uses nuclear versus cytoplasmic distribution of CREB-regulated transcriptional co-activator (CRTC). Subcellular distribution of GFP-tagged CRTC (CRTC::GFP) bidirectionally changes on the order of minutes and reflects both increases and decreases in neural activity. We established an automated machine-learning-based routine for efficient quantification of reporter signal. Using this reporter, we demonstrate mating-evoked activation and inactivation of modulatory neurons. We further investigated the functional role of the master courtship regulator gene fruitless (fru) and show that fru is necessary to ensure activation of male arousal neurons by female cues. Together, our results establish CRTC::GFP as a bidirectional reporter of recent neural activity suitable for examining neural correlates in behavioral contexts.

Using parallel geocoding to analyse the spatial characteristics of road traffic injury occurrences across Lagos, Nigeria.

While efforts to understand and mitigate road traffic injury (RTI) occurrence have long been underway in high-income countries, similar projects in low/middle-income countries (LMICs) are frequently hindered by institutional and informational obstacles. Technological advances in geospatial analysis provide a pathway to overcome a subset of these barriers, and in doing so enable researchers to create actionable insights in the pursuit of mitigating RTI-associated negative health outcomes. This analysis develops a parallel geocoding workflow to improve investigation of low-fidelity datasets common in LMICs. Subsequently, this workflow is applied to and evaluated on an RTI dataset from Lagos State, Nigeria, minimising positional error in geocoding by incorporating outputs from four commercially available geocoders. The concordance between outputs from these geocoders is evaluated, and spatial visualisations are generated to provide insight into the distribution of RTI occurrence within the analysis region. This study highlights the implications of geospatial data analysis in LMICs facilitated by modern technologies on health resource allocation, and ultimately, patient outcomes.