BackgroundMolecular studies have suggested that the true diversity of Leucocytozoon (Apicomplexa: Haemospororida) species well exceeds the approximately 35 currently described taxa. Further, the degree of host-specificity may vary substantially among lineages. Parasite distribution can be influenced by the ability of the parasite to infect a host, vector preferences for certain avian hosts, or other factors such as microhabitat requirements that increase the probability that vertebrate hosts and vectors are in frequent contact with each other. Whereas most studies of haemosporidians have focused on passerine hosts, sampling vectors in the same habitats may allow the detection of other lineages affecting other hosts.MethodsWe sampled abundant, ornithophilic black flies (Simuliidae) across a variety of sites and habitats in the Colorado Rocky Mountains throughout the summer of 2007. Black flies were screened with PCR using Leucocytozoon-specific primers that amplify a portion of the cytochrome b gene, and the sequences were compared to the haplotypes in the MalAvi database. Infections of Leucocytozoon from birds sampled in the same area were also included.ResultsWe recovered 33 unique haplotypes from the black flies in this study area, which represented a large phylogenetic diversity of Leucocytozoon parasites. However, there were no clear patterns of avian host species or geography for the distribution of Leucocytozoon haplotypes in the phylogeny.ConclusionsSampling host-seeking vectors is a useful way to obtain a wide variety of avian haemosporidian haplotypes from a given area and may prove useful for understanding the global patterns of host, parasite, and vector associations of these ubiquitous and diverse parasites.Electronic supplementary materialThe online version of this article (doi:10.1186/s13071-015-0952-9) contains supplementary material, which is available to authorized users.

Automated classification of earthquake damage in remotely-sensed imagery using machine learning techniques depends on training data, or data examples that are labeled correctly by a human expert as containing damage or not. Mislabeled training data are a major source of classifier error due to the use of imprecise digital labeling tools and crowdsourced volunteers who are not adequately trained on or invested in the task. The spatial nature of remote sensing classification leads to the consistent mislabeling of classes that occur in close proximity to rubble, which is a major byproduct of earthquake damage in urban areas. In this study, we look at how mislabeled training data, or label noise, impact the quality of rubble classifiers operating on high-resolution remotely-sensed images. We first study how label noise dependent on geospatial proximity, or geospatial label noise, compares to standard random noise. Our study shows that classifiers that are robust to random noise are more susceptible to geospatial label noise. We then compare the effects of label noise on both pixel-and object-based remote sensing classification paradigms. While object-based classifiers are known to outperform their pixel-based counterparts, this study demonstrates that they are more susceptible to geospatial label noise. We also introduce a new labeling tool to enhance precision and image coverage. This work has important implications for the Sendai framework as autonomous damage classification will ensure rapid disaster assessment and contribute to the minimization of disaster risk.

Revised! EENY115, a 13-page illustrated fact sheet by J. Howard Frank and Michael C. Thomas, is part of the Featured Creatures collection. It introduces the Florida representatives of this large, diverse, and important family of beetles — description, life cycle and habits, and subfamilies in Florida. Includes references. Published by the UF Department of Entomology and Nematology, January 2010. EENY115/IN272: Rove Beetles of Florida, Staphylinidae (Insecta: Coleoptera: Staphylinidae) (ufl.edu)

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