Description of the genetic structure of malaria parasite populations is central to an understanding of the spread of multiple-locus drug and vaccine resistance. The Plasmodium falciparum mating patterns from madang, Papua New Guinea, where intense transmission of malaria occurs, are described here. A high degree of inbreeding occurs in the absence of detectable linkage disequilibrium. This contrasts with other studies, indicating that the genetic structure of malaria parasite populations is neither clonal nor panmictic but will vary according to the transmission characteristics of the region.
We describe the dynamics of co-infections of Plasmodium falciparum and P. vivax in 28 asymptomatic children by genotyping these species using the polymorphic loci Msp2 and Msp3alpha, respectively. The total number of Plasmodium spp. infections detected using 3 day sampling over 61 days varied between 1 and 14 (mean 6.6). The dynamics of P. falciparum and P. vivax genotypes varied greatly both within and amongst children. Periodicity in the detection of P. falciparum infections is consistent with the synchronous replication of individual genotypes. Replication synchrony of multiple co-infecting genotypes was not detected. In 4-year-old children P. falciparum genotype complexity was reduced and episodes lasted significantly longer (median duration > 60 days) when compared to children aged 5-14 years (median duration 9 days). P. vivax genotype complexity was not correlated with age but the episode duration was also longer for this species in 4-year-olds than in older children but was not as long as P. falciparum episodes. Recurrence of P. falciparum and P. vivax genotypes over weeks was observed. We interpret these major fluctuations in the density of genotypes over time as the result of the mechanism of antigenic variation thought to be present in these Plasmodium species.
We use Bayesian convolutional neural networks and a novel generative model of Galaxy Zoo volunteer responses to infer posteriors for the visual morphology of galaxies. Bayesian CNN can learn from galaxy images with uncertain labels and then, for previously unlabelled galaxies, predict the probability of each possible label. Our posteriors are well-calibrated (e.g. for predicting bars, we achieve coverage errors of 10.6% within 5 responses and 2.9% within 10 responses) and hence are reliable for practical use. Further, using our posteriors, we apply the active learning strategy BALD to request volunteer responses for the subset of galaxies which, if labelled, would be most informative for training our network. We show that training our Bayesian CNNs using active learning requires up to 35-60% fewer labelled galaxies, depending on the morphological feature being classified. By combining human and machine intelligence, Galaxy Zoo will be able to classify surveys of any conceivable scale on a timescale of weeks, providing massive and detailed morphology catalogues to support research into galaxy evolution.
We present Galaxy Zoo DECaLS: detailed visual morphological classifications for Dark Energy Camera Legacy Survey images of galaxies within the SDSS DR8 footprint. Deeper DECaLS images (r = 23.6 versus r = 22.2 from SDSS) reveal spiral arms, weak bars, and tidal features not previously visible in SDSS imaging. To best exploit the greater depth of DECaLS images, volunteers select from a new set of answers designed to improve our sensitivity to mergers and bars. Galaxy Zoo volunteers provide 7.5 million individual classifications over 314 000 galaxies. 140 000 galaxies receive at least 30 classifications, sufficient to accurately measure detailed morphology like bars, and the remainder receive approximately 5. All classifications are used to train an ensemble of Bayesian convolutional neural networks (a state-of-the-art deep learning method) to predict posteriors for the detailed morphology of all 314 000 galaxies. We use active learning to focus our volunteer effort on the galaxies which, if labelled, would be most informative for training our ensemble. When measured against confident volunteer classifications, the trained networks are approximately 99 per cent accurate on every question. Morphology is a fundamental feature of every galaxy; our human and machine classifications are an accurate and detailed resource for understanding how galaxies evolve.
Faint tidal features around galaxies record their merger and interaction histories over cosmic time. Due to their low surface brightnesses and complex morphologies, existing automated methods struggle to detect such features and most work to date has heavily relied on visual inspection. This presents a major obstacle to quantitative study of tidal debris features in large statistical samples, and hence the ability to be able to use these features to advance understanding of the galaxy population as a whole. This paper uses convolutional neural networks (CNNs) with dropout and augmentation to identify galaxies in the CFHTLS-Wide Survey that have faint tidal features. Evaluating the performance of the CNNs against previously-published expert visual classifications, we find that our method achieves high (76%) completeness and low (20%) contamination, and also performs considerably better than other automated methods recently applied in the literature. We argue that CNNs offer a promising approach to effective automatic identification of low surface brightness tidal debris features in and around galaxies. When applied to forthcoming deep wide-field imaging surveys (e.g. LSST, Euclid), CNNs have the potential to provide a several order-of-magnitude increase in the sample size of morphologically-perturbed galaxies and thereby facilitate a much-anticipated revolution in terms of quantitative low surface brightness science.
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