Malaria risk in Corsica, former hot spot of malaria in France

Background: The prevalence of Plasmodium falciparum and Plasmodium vivax malaria was very high in Corsica just before the Second World War. The last outbreak was in 1972 and the most recent indigenous case was in 2006. Results: Analysis of historical data shows that anopheline vectors were abundant. Recent surveys demonstrated that potential vectors are still present in Corsica, despite the likely disappearance of Anopheles sacharovi. Moreover, P. falciparum can develop experimentally into these mosquitoes, notably Anopheles labranchiae, which is locally abundant, and parasites are regularly introduced into the island. Discussion, Conclusions: The presence of vectors, the introduction of parasites and the conducive climate raise questions about the possibility of malaria re-emerging and becoming re-established in Corsica. Analysis of historic and current parasitological and entomological data shows that the current theoretical risk of indigenous cases or malaria foci is negligible, particularly since there is very little contact between humans and Anopheles mosquitoes, Plasmodium carriers are reliably treated and there is a widespread vector control on the island

A Machine-Learning Approach for Classifying Defects on Tree Trunks using Terrestrial LiDAR

International audienceThree-dimensional data are increasingly prevalent in forestry thanks to terrestrial LiDAR. This work assesses the feasibility for an automated recognition of the type of local defects present on the bark surface. These sin-gularities are frequently external markers of inner defects affecting wood quality, and their type, size, and frequency are major components of grading rules. The proposed approach assigns previously detected abnormalities in the bark roughness to one of the defect types: branches, branch scars, epi-cormic shoots, burls, and smaller defects. Our machine learning approach is based on random forests using potential defects shape descriptors, including Hu invariant moments, dimensions, and species. The results of our experiments involving different French commercial species, oak, beech, fir, and pine showed that most defects were well classified with an average F 1 score of 0.86

Tree Defect Segmentation using Geometric Features and CNN

International audienceEstimating the quality of standing trees or roundwood after felling is a crucial step in forest production trading. The ongoing revolution in the forest sector resulting from the use of 3D sensors can also contribute to this step. Among them the terrestrial lidar scanning is a reference descriptive method offering the possibility to segment defects. In this paper, we propose a new reproducible method allowing to automatically segment the defects. It is based on the construction of a relief map inspired from a previous strategy and combining with a convolutional neural network to improve the resulting segmentation quality. The proposed method outperforms the previous results and the source code is publicly available with an online demonstration allowing to test the defect detection without any software installation

Segmentation of defects on log surface from terrestrial lLidar data

International audienceSegmentation of defects on the tree log surface remains a challenge due to the unclear seperation between the foreground and the background and the high variability of the tree surface. Even if some first works exist to process specific tree species, a generic method robust to various species is missing. We propose a new approach for segmenting defects on log surface based on the tabular object analysis. We firstly compute the log centerline by surface normal accumulation and then threshold the point cloud by the the difference between the distance to the centerline and the reference distance estimated from a patch of neighbors. The performance of the proposed approach was experimented and compared on ten logs recovered from different species. The results showed that our approach outperformed other method based on cylinder detection and was robust to several tree species. The results can be reproduced and compared on an online demonstration

CNN-based Method for Segmenting Tree Surface Singularites

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Algorithms and Implementation for Segmenting Tree Log Surface Defects

International audienceThis paper focuses on the algorithms and implementation details of a published segmentation method defined to identify the defects of tree log surface. Such a method overcomes the difficulty of the high variability of the tree log surface and allows to segment the defects from the tree bark. All the algorithms used in this method are described in link to their source code which guarantees a full reproducible method associated to an online demonstration

Evidence of Chara fibrosa Agardh ex Bruzelius, an alien species in South France

The paper describes the discovery of a species of genus Chara, hitherto unknown in France. The morphological features of the plants and oospores are shown and identify the plant as Chara fibrosa ssp. benthamii, a tropical taxon, non-native to the European Charophyte flora. The ecological conditions of this particular find correspond to an artificial temporary pond located in the Crau plain, north of the Camargue. In contrast to the normal hydrological cycle ruled by local precipitation during winter, the studied pond is flooded from irrigation water in late spring and during summer. This context allowed C. fibrosa to out-compete the indigenous Chara species, Chara vulgaris and Chara globularis. The accidental introduction of C. fibrosa is attributed to original contamination from rice seed material imported to the nearby Camargue, rather than to dispersal by migratory water birds. Although the species has formed a very large population in that pond within a few years, it might not be classified as “invasive” because this occurrence is linked to a particular man-made habitat. However, spread of the species in the future, as a function of global warming related to climate change, cannot be excluded

Trace metal extraction and biomass production by spontaneous vegetation in temporary Mediterranean stormwater highway retention ponds: Freshwater macroalgae (Chara spp.) vs. cattails (Typha spp.)

International audienceA field study on the capacity of spontaneous vegetation of four motorway ponds to extract four trace metals (Cd, Cu, Zn and Pb) was carried out. High biomasses (ca. 400 g m (2) DW) of freshwater macroalgae Chara spp. were found in all ponds. However, higher biomasses (ca. 900 and 1600 g m (2) DW for aerial and root parts, respectively) were found for broadleaf cattails Typha spp. with perennial structures. Biomasses of Chara were mainly controlled by seasonal variations and water level of the ponds. Metal contents in Chara vulgaris beds were higher than those in Chara globularis and, with less than half of the biomass corresponded to the same range than those found in Typha. For the latter, the highest contents of metals were found in the root system. Sediments of the ponds were moderately contaminated and the concentrations of the four metals were very low in the water (often under detection level). Translocation factors of the contaminants from sediment to plants were very low (parts per thousand) for all species and phytoextraction of metals from the sediment seemed not to be the main effect of the spontaneous vegetation. However, this latter prevent pollutant transfers provided that the dry and crumbly Chara are collected during summer before being blown outside the pond by strong winds. (C) 2015 Elsevier B.V. All rights reserved