Assessment of vector/host contact: comparison of animal-baited traps and UV-light/suction trap for collecting Culicoides biting midges (Diptera: Ceratopogonidae), vectors of Orbiviruses

<p>Abstract</p> <p>Background</p> <p>The emergence and massive spread of bluetongue in Western Europe during 2006-2008 had disastrous consequences for sheep and cattle production and confirmed the ability of Palaearctic <it>Culicoides </it>(Diptera: Ceratopogonidae) to transmit the virus. Some aspects of <it>Culicoides </it>ecology, especially host-seeking and feeding behaviors, remain insufficiently described due to the difficulty of collecting them directly on a bait animal, the most reliable method to evaluate biting rates.</p> <p>Our aim was to compare typical animal-baited traps (drop trap and direct aspiration) to both a new sticky cover trap and a UV-light/suction trap (the most commonly used method to collect <it>Culicoides</it>).</p> <p>Methods/results</p> <p>Collections were made from 1.45 hours before sunset to 1.45 hours after sunset in June/July 2009 at an experimental sheep farm (INRA, Nouzilly, Western France), with 3 replicates of a 4 sites × 4 traps randomized Latin square using one sheep per site. Collected <it>Culicoides </it>individuals were sorted morphologically to species, sex and physiological stages for females. Sibling species were identified using a molecular assay. A total of 534 <it>Culicoides </it>belonging to 17 species was collected. Abundance was maximal in the drop trap (232 females and 4 males from 10 species) whereas the diversity was the highest in the UV-light/suction trap (136 females and 5 males from 15 species). Significant between-trap differences abundance and parity rates were observed.</p> <p>Conclusions</p> <p>Only the direct aspiration collected exclusively host-seeking females, despite a concern that human manipulation may influence estimation of the biting rate. The sticky cover trap assessed accurately the biting rate of abundant species even if it might act as an interception trap. The drop trap collected the highest abundance of <it>Culicoides </it>and may have caught individuals not attracted by sheep but by its structure. Finally, abundances obtained using the UV-light/suction trap did not estimate accurately <it>Culicoides </it>biting rate.</p

Monthly variation in the probability of presence of adult Culicoides populations in nine European countries and the implications for targeted surveillance

Background: Biting midges of the genus Culicoides (Diptera: Ceratopogonidae) are small hematophagous insects responsible for the transmission of bluetongue virus, Schmallenberg virus and African horse sickness virus to wild and domestic ruminants and equids. Outbreaks of these viruses have caused economic damage within the European Union. The spatio-temporal distribution of biting midges is a key factor in identifying areas with the potential for disease spread. The aim of this study was to identify and map areas of neglectable adult activity for each month in an average year. Average monthly risk maps can be used as a tool when allocating resources for surveillance and control programs within Europe. Methods : We modelled the occurrence of C. imicola and the Obsoletus and Pulicaris ensembles using existing entomological surveillance data from Spain, France, Germany, Switzerland, Austria, Denmark, Sweden, Norway and Poland. The monthly probability of each vector species and ensembles being present in Europe based on climatic and environmental input variables was estimated with the machine learning technique Random Forest. Subsequently, the monthly probability was classified into three classes: Absence, Presence and Uncertain status. These three classes are useful for mapping areas of no risk, areas of high-risk targeted for animal movement restrictions, and areas with an uncertain status that need active entomological surveillance to determine whether or not vectors are present. Results: The distribution of Culicoides species ensembles were in agreement with their previously reported distribution in Europe. The Random Forest models were very accurate in predicting the probability of presence for C. imicola (mean AUC = 0.95), less accurate for the Obsoletus ensemble (mean AUC = 0.84), while the lowest accuracy was found for the Pulicaris ensemble (mean AUC = 0.71). The most important environmental variables in the models were related to temperature and precipitation for all three groups. Conclusions: The duration periods with low or null adult activity can be derived from the associated monthly distribution maps, and it was also possible to identify and map areas with uncertain predictions. In the absence of ongoing vector surveillance, these maps can be used by veterinary authorities to classify areas as likely vector-free or as likely risk areas from southern Spain to northern Sweden with acceptable precision. The maps can also focus costly entomological surveillance to seasons and areas where the predictions and vector-free status remain uncertain

Joint Future Semantic and Instance Segmentation Prediction

International audienceThe ability to predict what will happen next from observing the past is a key component of intelligence. Methods that forecast future frames were recently introduced towards better machine intelligence. However, predicting directly in the image color space seems an overly complex task, and predicting higher level representations using semantic or instance segmentation approaches were shown to be more accurate. In this work, we introduce a novel prediction approach that encodes instance and semantic segmentation information in a single representation based on distance maps. Our graph-based modeling of the instance segmentation prediction problem allows us to obtain temporal tracks of the objects as an optimal solution to a watershed algorithm. Our experimental results on the Cityscapes dataset present state-of-the-art semantic segmentation predictions, and instance segmentation results outperforming a strong baseline based on optical flow

Incremental algorithm for hierarchical minimum spanning forests and saliency of watershed cuts

International audienceWe study hierarchical segmentations that are optimal in the sense of minimal spanning forests of the original image. We introduce a region-merging operation called uprooting, and we prove that optimal hierarchical segmentations are equivalent to the ones given by uprooting a watershed-cut based segmentation. Based on those theoretical results, we propose an efficient algorithm to compute such hierarchies, as well as the first saliency map algorithm compatible with the morphological filtering framework