Mouse-to-Human Transmission of Variant Lymphocytic Choriomeningitis Virus

A case of lymphocytic choriomeningitis virus (LCMV) infection led to investigation of the reservoir. LCMV was detected in mice trapped at the patient's home, and 12 isolates were recovered. Genetic analysis showed that human and mouse LCMVs were identical and that this LCMV strain was highly divergent from previously characterized LCMV

Arénavirus à potentiel bioterroriste (génomique, évolution et diagnostic)

AIX-MARSEILLE2-BU Méd/Odontol. (130552103) / SudocPARIS-BIUP (751062107) / SudocSudocFranceF

A rapid and specific real time RT-PCR assay for diagnosis of Toscana virus infection.

International audienceTo scan a virus (TOSV) belongs to the Phlebovirus genus within the Bunyaviridae family. TOSV is an arbovirus transmitted by sandflies. In Mediterranean countries, TOSV is one of the major viral pathogens involved in aseptic meningitis and meningoencephalitis. Development and assessment of a new sensitive and specific real-time RT-PCR assay for TOSV diagnosis. TOSV-specific primers and probe targeting the S-segment of the genome were designed, based on recent TOSV sequences available in public databases. Sensitivity was assessed using 10-fold serial dilutions of a RNA transcript and serial dilutions of TOSV strains isolated from infected human beings. Specificity was determined by testing RNA extracts from closely related Phleboviruses. The assay was then used for TOSV infection diagnosis in 971 clinical samples and for TOSV detection in 2000 sandflies. The real-time RT-PCR assay exhibited a sensitivity of under 257 copies per reaction for the RNA transcripts and 0.0056 and 0.014 TCID50 of Italian and Spanish TOSV genotypes per reaction, respectively. No other close Phleboviruses were detected. TOSV was identified in 17 clinical samples and in 3 sandflies. The assay described is a rapid, robust and reliable real-time RT-PCR test for accurate diagnosis of human TOSV infection as well as for the surveillance of TOSV in vector populations

An Adjusted Nearest Neighbor Algorithm Maximizing the F-Measure from Imbalanced Data

International audienceIn this paper, we address the challenging problem of learning from imbalanced data using a Nearest-Neighbor (NN) algorithm. In this setting, the minority examples typically belong to the class of interest requiring the optimization of specific criteria, like the F-Measure. Based on simple geometrical ideas, we introduce an algorithm that reweights the distance between a query sample and any positive training example. This leads to a modification of the Voronoi regions and thus of the decision boundaries of the NN algorithm. We provide a theoretical justification about the weighting scheme needed to reduce the False Negative rate while controlling the number of False Positives. We perform an extensive experimental study on many public imbalanced datasets, but also on large scale non public data from the French Ministry of Economy and Finance on a tax fraud detection task, showing that our method is very effective and, interestingly, yields the best performance when combined with state of the art sampling methods

End-to-end Learning for Early Classification of Time Series

Classification of time series is a topical issue in machine learning. While accuracy stands for the most important evaluation criterion, some applications require decisions to be made as early as possible. Optimization should then target a compromise between earliness, i.e., a capacity of providing a decision early in the sequence, and accuracy. In this work, we propose a generic, end-to-end trainable framework for early classification of time series. This framework embeds a learnable decision mechanism that can be plugged into a wide range of already existing models. We present results obtained with deep neural networks on a diverse set of time series classification problems. Our approach compares well to state-of-the-art competitors while being easily adaptable by any existing neural network topology that evaluates a hidden state at each time step

A Nearest Neighbor Algorithm for Imbalanced Classification

International audienceDue to the inability of the accuracy-driven methods to address the challenging problem of learning from imbalanced data, several alternative measures have been proposed in the literature, like the Area Under the ROC Curve (AUC), the Average Precision (AP), the F-measure, the G-Mean, etc. However, these latter measures are neither smooth, convex nor separable, making their direct optimization hard in practice. In this paper, we tackle the challenging problem of imbalanced learning from a nearest-neighbor (NN) classification perspective, where the minority examples typically belong to the class of interest. Based on simple geometrical ideas, we introduce an algorithm that rescales the distance between a query sample and any positive training example. This leads to a modification of the Voronoi regions and thus of the decision boundaries of the NN classifier. We provide a theoretical justification about this scaling scheme which inherently aims at reducing the False Negative rate while controlling the number of False Positives. We further formally establish a link between the proposed method and cost-sensitive learning. An extensive experimental study is conducted on many public imbalanced datasets showing that our method is very effective with respect to popular Nearest-Neighbor algorithms, comparable to state-of-the-art sampling methods and even yields the best performance when combined with them

End-to-end learned early classification of time series for in-season crop type mapping

International audienc

Une version corrigée de l’algorithme des plus proches voisins pour l’optimisation de la F-mesure dans un contexte déséquilibré

International audienceDans le présent papier, nous proposons une approche basée sur l’algorithme des plus proches voisins pour de l’apprentissage dans un contexte déséquilibré. Dans un tel contexte, les exemples de la classe minoritaire sont au centre de l’attention et nécessitent des critères d’optimisation spécifiques pour nous permettre de les détecter, comme la F-mesure. Reposant sur des fondements géométriques, nous présentons un algorithme qui pondère la distance entre un nouvel exemple et les exemples positifs de la classe minoritaire. Cela entraı̂ne une modification des régions de Voronoı̈ et donc de la frontière de décision. Une analyse théorique de cette pondération explique comment il est possible de réduire le taux de faux négatifs tout en contrôlant le taux de faux positifs. Les expériences menées sur plusieurs jeux de données publiques, ainsi que sur de grands jeux de données du Ministère de l’Economie et des Finances sur la détection de fraude à l’impôt, mettent en évidence l’efficacité de la méthode en dépit de sa simplicité. En outre, elle se révèle d’autant plus intéressante et performante lorsque qu’elle est combinée à des méthodes d’échantillonage

Phylogeny and evolution of old world arenaviruses

The intention of this study was to investigate the genomics, phylogeny and evolution of the Old World arenaviruses based on sequence data representing the four viral genes. To achieve this aim, we sequenced the complete S and L RNA segments of Ippy virus (IPPYV), Mobala virus (MOBV) and Mopeia virus (MOPV). Full-length sequences of the NP, GPC, Z and L genes were used to reconstruct phylogenetic relationships and to compare resulting tree topologies. Each of the five Old World arenavinis species (namely Lassa virus [LASV], IPPYV MOBV MOPV and Lymphocytic choriomeningitis virus [LCMV]) are monophyletic; seven selected strains of LASV showed a similar topology, regardless of the gene under analysis; IPPYV rooted the three other African arenaviruses; the four African arenaviruses are rooted by the ubiquitous LCMV; and the tree topologies of the three African arenaviruses other than LASV are identical regardless of the gene used for analysis. No evidence for significant evolutionary events such as intra- or intersegmental recombination was obtained. (c) 2006 Elsevier Inc. All rights reserved