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Bebbington, A.

Publications and source records attributed to Bebbington, A..

2 recordsLinked to original sources

Doctoral Students as Carbon Accountants: Calculating Carbon Costs of a PhD in Neuroscience

Research is an energy and resource-demanding activity. However, despite increasing awareness and emerging sustainability initiatives, a paucity of data and methodological inconsistency continue to hamper effective and accountable emissions mitigation. With > 250,000 doctoral students graduating annually across all academic disciplines, empowering PhD students to engage in carbon accounting could provide a sizable and robust source of carbon data alongside a powerful generational force for decarbonisation. Here, we demonstrate how doctoral students and other researchers can consistently measure the carbon footprint of their work, using one PhD students research in a neuroscience Drosophila lab as our case study. We present a comprehensive life-cycle assessment of the equivalent carbon dioxide emissions (CO2e) generated by the students research activities, including measurement of scope 1 emissions associated with Drosophila husbandry; calculation of time- and region-specific scope 2 emissions produced by widely used techniques including calcium imaging, electrophysiology, and optogenetics; and estimation of scope 3 emissions associated with procurement and research-related travel. We found that research-related travel and procurement of laboratory supplies were responsible for the majority of annual emissions, up to 1942 kg CO2e and 543 kg CO2e respectively after accounting for aircraft radiative forcing. Using NESOs open-source Carbon Intensity API to account for temporal and geographical variation in the carbon intensity of UK National Grid energy, we found that persistent laboratory energy consumption released 10.99 kg CO2e, with an additional 3.56 kg CO2e scope 2 and 3.6 kg CO2e scope 1 emissions underpinning direct research activities. Finally, we discuss the challenges of accurately carbon foot printing research across disciplines in the UK and beyond, highlighting the value of regionally precise open-source energy mix data and the need for data openness within research supply chains. Overall, we present a common framework for including carbon footprint analyses as Carbon Appendices to PhD theses to generate carbon footprint data across disciplines. Beyond the benefits of such data for informed emissions mitigation, we envision doctoral students carrying insights from carbon appendices forward into academia and industry to catalyse a community-driven decarbonisation of the research sector.

neuroscience↗

An active matter model captures the spatial dynamics of actomyosin oscillations during morphogenesis

The apicomedial actomyosin network is crucial for generating mechanical forces in cells. Oscillatory behaviour of this contractile network is commonly observed before or during significant morphogenetic events. For instance, during the development of the Drosophila adult abdominal epidermis, larval epithelial cells (LECs) undergo pulsed contractions before being replaced by histoblasts. These contractions involve the formation of contracted regions of concentrated actin and myosin. However, the emergence and control of pulsed contractions are not fully understood. Here, we combined in vivo 4D microscopy with numerical simulations of an active elastomer model applied to realistic cell geometries and boundary conditions to study LEC actomyosin dynamics. The active elastomer model was able to reproduce in vivo observations quantitatively. We also characterised the relationship between cell shape, cell polarity, and actomyosin network parameters with the spatiotemporal characteristics of the contractile network both in vivo and in simulations. Our results show that cell geometry, accompanied by boundary conditions which reflect the cells polarity, is essential to understand the dynamics of the apicomedial actomyosin network. Moreover, our findings support the notion that spatiotemporal oscillatory behaviour of the actomyosin network is an emergent property of the actomyosin network, rather than driven by upstream signalling.

biophysics↗