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Biology subjects

Culmer, P.

Publications and source records attributed to Culmer, P..

2 recordsLinked to original sources

MiGut: a scalable in vitro platform for simulating the human gut microbiome - development, validation, and simulation of antibiotic induced dysbiosis

In vitro models of the human colon have been used extensively in developing understanding of the human gut microbiome and how internal and external factors affect the residing bacterial populations. Such models can be highly predictive of in vivo effects of antibiotics, and indeed more so than animal models. The complexity required by current in vitro models to closely mimic the physiology of the colon poses practical limits on their scalability. MiGut allows considerable expansion of model runs, increasing the capacity to test reproducibility or parameters under investigation. The MiGut platform has been assessed against a well-studied triple-stage chemostat model in a demanding nine-week study, with exposure to multiple antibiotics inducing a state of dysbiosis in the microbiome. A good correlation is found, both between individual MiGut models and against the triple-stage chemostat. Together with high-throughput molecular techniques for sample analysis, it is now conceivable that tens of in vitro models could be run simultaneously, allowing complex microbiome-xenobiotic interactions to be explored in far greater detail. MiGut is a unique platform whereby multiple colonic models can be run simultaneously with minimal added resource and complexity to support our understanding of the cause-effect relationships that govern the gut microbiome. This model system expands the capacity to generate clinically relevant data that can optimize interventions which target the gut microbiome directly or indirectly.

microbiology↗

Exploring Disturbance as a Force for Good in Motor Learning

Disturbance forces facilitate motor learning, but theoretical explanations for this counterintuitive phenomenon are lacking. Smooth arm movements require predictions (inference) about the force-field associated with a workspace. The Free Energy Principle (FEP) suggests that such active inference is driven by surprise. We used these insights to create a formal model that explains why disturbance helps learning. In two experiments, participants undertook a continuous tracking task where they learned how to move their arm in different directions through a novel 3D force field. We compared baseline performance before and after exposure to the novel field to quantify learning. In Experiment 1, the exposure phases (but not the baseline measures) were delivered under three different conditions: (i) robot haptic assistance; (ii) no guidance; (iii) robot haptic disturbance. The disturbance group showed the best learning as our model predicted. Experiment 2 further tested our FEP inspired model. Assistive and/or disturbance forces were applied as a function of performance (low surprise), and compared to a random error manipulation (high surprise). The random group showed the most improvement as predicted by the model. Thus, motor learning can be conceptualised as a process of entropy reduction. Short term motor strategies (e.g. global impedance) can mitigate unexpected perturbations, but continuous movements require active inference about external force-fields in order to create accurate internal models of the external world (motor learning). Our findings reconcile research on the relationship between noise, variability, and motor learning, and show that information is the currency of motor learning.

neuroscience↗