Replication and active partition of integrative and conjugative elements (ICEs) of the SXT/R391 family : the line between ICEs and conjugative plasmids is getting thinner

Integrative and Conjugative Elements (ICEs) of the SXT/R391 family disseminate multidrug resistance among pathogenic Gammaproteobacteria such as Vibrio cholerae. SXT/R391 ICEs are mobile genetic elements that reside in the chromosome of their host and eventually self-transfer to other bacteria by conjugation. Conjugative transfer of SXT/R391 ICEs involves a transient extrachromosomal circular plasmid-like form that is thought to be the substrate for single-stranded DNA translocation to the recipient cell through the mating pore. This plasmid-like form is thought to be non-replicative and is consequently expected to be highly unstable. We report here that the ICE R391 of Providencia rettgeri is impervious to loss upon cell division. We have investigated the genetic determinants contributing to R391 stability. First, we found that a hipAB-like toxin/antitoxin system improves R391 stability as its deletion resulted in a tenfold increase of R391 loss. Because hipAB is not a conserved feature of SXT/R391 ICEs, we sought for alternative and conserved stabilization mechanisms. We found that conjugation itself does not stabilize R391 as deletion of traG, which abolishes conjugative transfer, did not influence the frequency of loss. However, deletion of either the relaxase-encoding gene traI or the origin of transfer (oriT) led to a dramatic increase of R391 loss correlated with a copy number decrease of its plasmid-like form. This observation suggests that replication initiated at oriT by TraI is essential not only for conjugative transfer but also for stabilization of SXT/R391 ICEs. Finally, we uncovered srpMRC, a conserved locus coding for two proteins distantly related to the type II (actin-type ATPase) parMRC partitioning system of plasmid R1. R391 and plasmid stabilization assays demonstrate that srpMRC is active and contributes to reducing R391 loss. While partitioning systems usually stabilizes low-copy plasmids, srpMRC is the first to be reported that stabilizes a family of ICEs

The Transcriptional Regulator Rok Binds A+T-Rich DNA and Is Involved in Repression of a Mobile Genetic Element in Bacillus subtilis

The rok gene of Bacillus subtilis was identified as a negative regulator of competence development. It also controls expression of several genes not related to competence. We found that Rok binds to extended regions of the B. subtilis genome. These regions are characterized by a high A+T content and are known or believed to have been acquired by horizontal gene transfer. Some of the Rok binding regions are in known mobile genetic elements. A deletion of rok resulted in higher excision of one such element, ICEBs1, a conjugative transposon found integrated in the B. subtilis genome. When expressed in the Gram negative E. coli, Rok also associated with A+T-rich DNA and a conserved C-terminal region of Rok contributed to this association. Together with previous work, our findings indicate that Rok is a nucleoid associated protein that serves to help repress expression of A+T-rich genes, many of which appear to have been acquired by horizontal gene transfer. In these ways, Rok appears to be functionally analogous to H-NS, a nucleoid associated protein found in Gram negative bacteria and Lsr2 of high G+C Mycobacteria

Apprentissage a contrario et architecture efficace pour la détection d'évènements visuels significatifs

To ensure the robustness of a detection algorithm, it is important to get a close control of the false alarms it may produce. Because of the great variability of natural images, this task is very difficult in computer vision, and most methods have to rely on a priori chosen parameters. This limits the validity and applicability of the resulting algorithms. Recently, by searching for structures for which some properties are very unlikely to be due to chance, the a contrario statistical approach has proved successful to provide parameterless detection algorithms with a bounded expected number of false alarms. However, existing applications rely on a purely analytical framework that requires a big modeling effort, makes it difficult to use heterogeneous features and limits the use of data-driven search heuristics. In this thesis, we propose to overcome these restrictions by using statistical learning for quantities that cannot be computed analytically. The interest of this approach is demonstrated through three applications : segment detection, segmentation into homogeneous regions, and object matching from a database of pictures. For the two first ones, we show that robust decision thresholds can be learned from white noise images. For the last one, we show that only a few examples of natural images that do not contain the database objects are sufficient to learn accurate decision thresholds. Finally, we notice that the monotonicity of a contrario reasoning enables an incremental integration of partial data. This property leads us to propose an architecture for object detection which has an "anytime" behavior : it provides results all along its execution, the most salient first, and thus can be constrained to run in limited time.Pour assurer la robustesse d'un algorithme de détection, il est nécessaire de maîtriser son point de fonctionnement, et en particulier son taux de fausses alarmes. Cette tâche est particulièrement difficile en vision artificielle à cause de la grande variabilité des images naturelles, qui amène généralement à introduire des paramètres choisis a priori qui limitent la portée et la validité des algorithmes. Récemment, l'approche statistique a contrario a montré sa capacité à détecter des structures visuelles sans autre paramètre libre que le nombre moyen de fausses alarmes tolérées, en recherchant des entités dont certaines propriétés sont statistiquement trop improbables pour être le fruit du hasard. Les applications existantes reposent toutefois sur un cadre purement analytique qui requiert un travail important de modélisation, rend difficile l'utilisation de caractéristiques multiples et limite l'utilisation d'heuristiques de recherche dirigées par les données. Nous proposons dans cette thèse d'assouplir ces restrictions en ayant recours à de l'apprentissage pour les quantités non calculables analytiquement. Nous illustrons l'intérêt de la démarche à travers trois applications : la détection de segments, la segmentation en régions homogènes et la détection d'objets à partir d'une base de photos. Pour les deux premières applications, nous montrons que des seuils de détection robustes peuvent être appris à partir d'images de bruit blanc. Pour la dernière, nous montrons que quelques exemples d'images naturelles ne contenant pas d'objets de la base suffisent pour obtenir un algorithme de détection fiable. Enfin, nous remarquons que la monotonicité du raisonnement a contrario permet d'intégrer incrémentalement des informations partielles. Cette propriété nous conduit à proposer une architecture "anytime" pour la détection d'objets, c'est-à-dire capable de fournir des détections progressivement au cours de son exécution, en commençant par les objets les plus saillants

Apprentissage a contrario et architecture efficace pour la détection d'évènements visuels significatifs

PARIS-BIUSJ-Thèses (751052125) / SudocPARIS-BIUSJ-Mathématiques rech (751052111) / SudocSudocFranceF

Smart retina as a contour-based visual interface

International audienceThe purpose of this work is to provide a robust vision-based input device. In our system, a programmable retina is looking at the user who sends commands by moving his hand. The fusion between the acquisition and the processing functions of the retina allows a close adaptation to the lighting conditions and to the dynamic range of the scene. Thanks to its optical input and massive parallelism, the retina computes efficiently the contours of the moving objects. This feature has nice properties in terms of motion detection capabilities and allows a dramatic reduction in the volume of data to be output of the retina. An external low-power processor then performs global computations on the output data, such as extreme points or geometric moments, which are temporally filtered to generate a command

Smart retina as a contour-based visual interface

International audienceThe purpose of this work is to provide a robust vision-based input device. In our system, a programmable retina is looking at the user who sends commands by moving his hand. The fusion between the acquisition and the processing functions of the retina allows a close adaptation to the lighting conditions and to the dynamic range of the scene. Thanks to its optical input and massive parallelism, the retina computes efficiently the contours of the moving objects. This feature has nice properties in terms of motion detection capabilities and allows a dramatic reduction in the volume of data to be output of the retina. An external low-power processor then performs global computations on the output data, such as extreme points or geometric moments, which are temporally filtered to generate a command

Bottom-up and top-down object matching using asynchronous agents and a contrario principles

International audienceWe experiment a vision architecture for object matching based on a hierarchy of independent agents running asynchronously in parallel. Agents communicate through bidirectional signals, enabling the mix of top-down and bottom-up influences. Following the so-called a contrario principle, each signal is given a strength according to the statistical relevance of its associated visual data. By handling most important signals first, the system focuses on most promising hypotheses and provides relevant results as soon as possible. Compared to an equivalent feed-forward and sequential algorithm, our architecture is shown capable of handling more visual data and thus reach higher detection rates in less time