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Learning to distinguish between predators and non-predators: understanding the critical role of diet cues and predator odours in generalisation.

ABSTRACT: It is critical for prey to recognise predators and distinguish predators from non-threatening species. Yet, we have little understanding of how prey develop effective predator recognition templates. Recent studies suggest that prey may actually learn key predator features which can be used to recognise novel species with similar characteristics. However, non-predators are sometimes mislabelled as predators when generalising recognition. Here, we conduct the first comprehensive investigation of how prey integrate information on predator odours and predator diet cues in generalisation, allowing them to discriminate between predators and non-predators. We taught lemon damselfish to recognise a predator fed a fish diet, and tested them for their response to the known predator and a series of novel predators (fed fish diet) and non-predators (fed squid diet) distributed across a phylogenetic gradient. Our findings show that damselfish distinguish between predators and non-predators when generalising recognition. Additional experiments revealed that generalised recognition did not result from recognition of predator odours or diet cues, but that damselfish based recognition on what they learned during the initial conditioning. Incorporating multiple sources of information enables prey to develop highly plastic and accurate recognition templates that will increase survival in patchy environments where they have little prior knowledge.

SUBMITTER: Mitchell MD

PROVIDER: S-EPMC4566087 | biostudies-other | 2015 Sep

REPOSITORIES: biostudies-other

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Project description:ObjectiveThis work aimed to derive a machine learning (ML) model for the differentiation between ischemic cardiomyopathy (ICM) and non-ischemic cardiomyopathy (NICM) on non-contrast cardiovascular magnetic resonance (CMR).MethodsThis retrospective study evaluated CMR scans of 107 consecutive patients (49 ICM, 58 NICM), including atrial and ventricular strain parameters. We used these data to compare an explainable tree-based gradient boosting additive model with four traditional ML models for the differentiation of ICM and NICM. The models were trained and internally validated with repeated cross-validation according to discrimination and calibration. Furthermore, we examined important variables for distinguishing between ICM and NICM.ResultsA total of 107 patients and 38 variables were available for the analysis. Of those, 49 were ICM (34 males, mean age 60 ± 9 years) and 58 patients were NICM (38 males, mean age 56 ± 19 years). After 10 repetitions of the tenfold cross-validation, the proposed model achieved the highest area under curve (0.82, 95% CI [0.47-1.00]) and lowest Brier score (0.19, 95% CI [0.13-0.27]), showing competitive diagnostic accuracy and calibration. At the Youden's index, sensitivity was 0.72 (95% CI [0.68-0.76]), the highest of all. Analysis of predictions revealed that both atrial and ventricular strain CMR parameters were important for the identification of ICM patients.ConclusionThe current study demonstrated that using a ML model, multi chamber myocardial strain, and function on non-contrast CMR parameters enables the discrimination between ICM and NICM with competitive diagnostic accuracy.Clinical relevance statementA machine learning model based on non-contrast cardiovascular magnetic resonance parameters may discriminate between ischemic and non-ischemic cardiomyopathy enabling wider access to cardiovascular magnetic resonance examinations with lower costs and faster imaging acquisition.Key points• The exponential growth in cardiovascular magnetic resonance examinations may require faster and more cost-effective protocols. • Artificial intelligence models can be utilized to distinguish between ischemic and non-ischemic etiologies. • Machine learning using non-contrast CMR parameters can effectively distinguish between ischemic and non-ischemic cardiomyopathies.

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Learning to distinguish between predators and non-predators: understanding the critical role of diet cues and predator odours in generalisation.

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