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Macquarie University

Country: Australia

Macquarie University

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17 Projects, page 1 of 4
  • Funder: UKRI Project Code: NE/N010434/1
    Funder Contribution: 458,273 GBP

    The biology of animals is in part a function of the microbes they interact with. During digestion, for instance, food is broken down both by enzymes secreted by our digestive system and those secreted by the microbes that live within the gut. In many insects, microbe-host interactions are even more developed. Bacterial symbionts live inside the cells of the insect body, and these are passed from a female to her offspring via her eggs: heritable symbiosis. We know a lot about how these symbionts affect the individual they infect. One particularly interesting impact is male-killing, where the bacterium passes from a female to her eggs, and kills those which develop as males. We know male-killing bacteria are common: they are present in many species, and, where they are present, can be present in the majority of individuals - this produces insect populations where males are rare. However, we know little about how these bacteria affect insect ecology or evolution. A variety of researchers believe these symbionts may drive changes in the way male and female insects are formed during development, sex determination. The hypothesis is simple - where symbionts target males only, natural selection counteracts this by favouring new ways of making a male that escape male-killing. This study will examine this theory for a recent case of evolution of the blue moon butterfly to avoid the action of male-killing bacterium called Wolbachia. We have documented the spread of a mutation that rescues male blue moon butterflies from Wolbachia-induced death. This project will establish what this mutation is, whether it involves changes in a gene called 'doublesex', which defines male and female characteristics in insects. A second aspect of male-killers is that they may drive very strong natural selection to rescue males. The intensity of selection is such that the changes that occur to rescue males may be otherwise deleterious. A second aim of the project is to establish if this is true, and whether the mutation (beyond rescuing males) degrades male and female function. In completing this project, we will present the first direct test of the theory that the processes that make males/females different can be driven by microbes. This is an enigmatic link that would make clear the interdependence of insect and microbe evolution.

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  • Funder: UKRI Project Code: BB/S020411/1
    Funder Contribution: 30,082 GBP

    Abstracts are not currently available in GtR for all funded research. This is normally because the abstract was not required at the time of proposal submission, but may be because it included sensitive information such as personal details.

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  • Funder: UKRI Project Code: EP/V012436/1
    Funder Contribution: 232,927 GBP

    Thousands of years ago, early Mesopotamian people discovered that a mixture of mud and straw creates strong durable buildings, what we call today a composite material. Composites are far better than the sum of their parts, for example they can be stronger and cheaper. Similar experiments with mixing fluids and gases led to the discovery of complex fluids. Composites, complex fluids, and powders can all be examples of particulate materials. These materials led to advances in food science and healthcare (emulsions, colloids, powders); automobile, aerospace, and construction (composites, cement), among many others. Although these materials are highly valuable, we do not have accurate and simple ways to measure their structure. This is due to their complex microstructure, which is a random mix of different types of particles. However, measuring is the first step to automation and perfecting any product. When using a powder for a chemical reaction, or producing an emulsion, the particles will constantly change size and properties. To automate these processes, we need to monitor the particle properties. In many cases the particle properties are simply unknown. For example, the pores (which are a type of particle) in bones. Measuring these pores would help diagnose and treat osteoporosis. The end goal is to develop new sensing methods for dense particulates using ultrasound. To achieve this the first step is to understand how a sound wave reflects from these materials? To develop new sensing methods requires a team with engineers and mathematicians working together to develop: the maths of sound waves, consider how these sensors will be installed in industry, and use machine learning to deal with the complex microstructure of particulate materials.

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  • Funder: UKRI Project Code: EP/S030964/1
    Funder Contribution: 953,584 GBP

    We will bring together world leaders in insect biology and neuroscience with world leaders in biorobotic modelling and computational neuroscience to create a partnership that will be transformative in understanding active learning and selective attention in insects, robots and autonomous systems in artificial intelligence (AI). By considering how brains, behaviours and the environment interact during natural animal behaviour, we will develop new algorithms and methods for rapid, robust and efficient learning for autonomous robotics and AI for dynamic real world applications. Recent advances in AI and notably in deep learning, have proven incredibly successful in creating solutions to specific complex problems (e.g. beating the best human players at Go, and driving cars through cities). But as we learn more about these approaches, their limitations are becoming more apparent. For instance, deep learning solutions typically need a great deal of computing power, extremely long training times and very large amounts of labeled training data which are simply not available for many tasks. While they are very good at solving specific tasks, they can be quite poor (and unpredictably so) at transferring this knowledge to other, closely related tasks. Finally, scientists and engineers are struggling to understand what their deep learning systems have learned and how well they have learned it. These limitations are particularly apparent when contrasted to the naturally evolved intelligence of insects. Insects certainly cannot play Go or drive cars, but they are incredibly good at doing what they have evolved to do. For instance, unlike any current AI system, ants learn how to forage effectively with limited computing power provided by their tiny brains and minimal exploration of their world. We argue this difference comes about because natural intelligence is a property of closed loop brain-body-environment interactions. Evolved innate behaviours in concert with specialised sensors and neural circuits extract and encode task-relevant information with maximal efficiency, aided by mechanisms of selective attention that focus learning on task-relevant features. This focus on behaving embodied agents is under-represented in present AI technology but offers solutions to the issues raised above, which can be realised by pursuing research in AI in its original definition: a description and emulation of biological learning and intelligence that both replicates animals' capabilities and sheds light on the biological basis of intelligence. This endeavour entails studying the workings of the brain in behaving animals as it is crucial to know how neural activity interacts with, and is shaped by, environment, body and behaviour and the interplay with selective attention. These experiments are now possible by combining recent advances in neural recordings of flies and hoverflies which can identify neural markers of selective attention, in combination with virtual reality experiments for ants; techniques pioneered by the Australian team. In combination with verification of emerging hypotheses on large-scale neural models on-board robotic platforms in the real world, an approach pioneered by the UK team, this project represents a unique and timely opportunity to transform our understanding of learning in animals and through this, learning in robots and AI systems. We will create an interdisciplinary collaborative research environment with a "virtuous cycle" of experiments, analysis and computational and robotic modelling. New findings feed forward and back around this virtuous cycle, each discipline informing the others to yield a functional understanding of how active learning and selective attention enable small-brained insects to learn a complex world. Through this understanding, we will develop ActiveAI algorithms which are efficient in learning and final network configuration, robust to real-world conditions and learn rapidly.

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  • Funder: UKRI Project Code: NE/I029927/1
    Funder Contribution: 52,421 GBP

    In October and November 2010, Merapi volcano (Java, Indonesia) had its biggest eruption since 1872. Merapi, which literally means "Fire Mountain" in Javanese, is one of Indonesia's most active and dangerous volcanoes with a history of deadly eruptions. Before 2010, these eruptions have usually been characterised by several months of viscous lava effusion at the summit of the steep-sided volcano, forming lava domes which, when big enough, collapse gravitationally generating relatively small pyroclastic flows. These flows are mixtures of lava dome fragments, smaller volcanic particles (ash) and hot gases that travel down the flanks of the volcano at high velocities of > 100 km/hour and, in the case of Merapi, typically reach distances of a few kilometres from the volcano. With a few exceptions only, this eruptive behaviour has been so typical of Merapi for at least the last two centuries that the pyroclastic flows generated by the gravitational failure of lava domes are often referred to in the literature as Merapi-type nuées ardentes (glowing clouds). In 2010, the eruptive behaviour of Merapi has changed. The unforeseen, large-magnitude explosive events were very different to previous episodes that followed Merapi's usual pattern of dome growth and collapse. On 26 October 2010, pyroclastic flows, generated during explosive eruption phases, swept down the flanks of the volcano, killing at least 34 people. The events were preceded by enhanced levels of seismicity and summit deformation that started in early September 2010. After days of high level activity, with glowing avalanches from a newly formed lava dome, pyroclastic flows and sporadic explosions generating a 7-km-high, sustained eruption column on 4 November, an unusually large explosive eruption on 5 November generated pyroclastic flows that extended up to 15 km from the volcano. Associated surges swept across Merapi's south flank, devastating villages and causing more fatalities. Since then, the death toll has risen to > 300 people, making this eruption the worst volcanic disaster at Merapi in 80 years. This project seeks to exploit a "once-in-a-century" opportunity to capitalise on these unexpected events at Merapi through a detailed investigation of the rocks formed during the 2010 eruption. These rocks preserve a record of the sub-surface processes that operated inside the volcano before the eruption occurred. Through the use of modern micro-analytical techniques and measurements of different radioactive isotopes that decay quickly within months, decades or millennia in the rocks, we can unravel these processes (which are the driving forces behind the unusual explosive behaviour of Merapi in 2010) and their timescales. The shortest radionuclide, 210Po, has a half-life of only 138 days and can tell us about the degassing of the magma and other processes that occurred in the weeks and months before the eruption. Because of its short half-life, 210Po must be analysed quickly after the eruption and before it has decayed completely to its daughter isotope 206Pb. Once we have established where in the crust beneath Merapi the magma feeding the 2010 eruption has come from and the processes of pre- and syn-eruptive crystallisation and degassing during magma ascent to the surface, we will compare the results with analytical data we have already collected on rock samples from the preceding eruptive episode in 2006, which followed Merapi "normal" (i.e. less explosive) eruptive behaviour. Ultimately, we will attempt to link the results obtained by analysing the rocks from the eruptions to the surface manifestations of these processes (e.g., seismic signals, ground deformation, gas flux) recorded through continuous geophysical monitoring of the volcano by our Indonesian colleagues.

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