The role of peptidoglycan in chlamydial cell division: towards resolving the chlamydial anomaly

Chlamydiales are obligate intracellular bacteria including some important pathogens causing trachoma, genital tract infections and pneumonia, among others. They share an atypical division mechanism, which is independent of an FtsZ homologue. However, they divide by binary fission, in a process inhibited by penicillin derivatives, causing the formation of an aberrant form of the bacteria, which is able to survive in the presence of the antibiotic. The paradox of penicillin sensitivity of chlamydial cells in the absence of detectable peptidoglycan (PG) was dubbed the chlamydial anomaly, since no PG modified by enzymes (Pbps) that are the usual target of penicillin could be detected in Chlamydiales. We review here the recent advances in this field with the first direct and indirect evidences of PG-like material in both Chlamydiaceae and Chlamydia-related bacteria. Moreover, PG biosynthesis is required for proper localization of the newly described septal proteins RodZ and NlpD. Taken together, these new results set the stage for a better understanding of the role of PG and septal proteins in the division mechanism of Chlamydiales and illuminate the long-standing chlamydial anomaly. Moreover, understanding the chlamydial division mechanism is critical for the development of new antibiotics for the treatment of chlamydial chronic infection

The VODKA sensor: a bio-inspired hyperacute optical position sensing device

International audienceWe have designed and built a simple optical sensor called Vibrating Optical Device for the Kontrol of Autonomous robots (VODKA), that was inspired by the "tremor" eye movements observed in many vertebrate and invertebrate animals. In the initial version presented here, the sensor relies on the repetitive micro-translation of a pair of photoreceptors set behind a small lens, and on the processing designed to locate a target from the two photoreceptor signals. The VODKA sensor, in which retinal micro-scanning movements are performed via a small piezo-bender actuator driven at a frequency of 40Hz, was found to be able to locate a contrasting edge with an outstandingly high resolution 900-fold greater than its static resolution (which is constrained by the interreceptor angle), regardless of the scanning law imposed on the retina. Hyperacuity is thus obtained at a very low cost, thus opening new vistas for the accurate visuo-motor control of robotic platforms. As an example, the sensor was mounted onto a miniature aerial robot that became able to track a moving target accurately by exploiting the robot's uncontrolled random vibrations as the source of its ocular microscanning movement. The simplicity, small size, low mass and low power consumption of this optical sensor make it highly suitable for many applications in the fields of metrology, astronomy, robotics, automotive, and aerospace engineering. The basic operating principle may also shed new light on the whys and wherefores of the tremor eye movements occurring in both animals and humans

A Review of Non-Destructive Methods for Detection of Insect Infestation in Fruits and Vegetables

Insect damage in fruits and vegetables cause major production and economic losses in the agriculture and food industry worldwide. Monitoring of internal quality and detection of insect infestation in fruits and vegetables is critical for sustainable agriculture. Early detection of an infestation in fruits can facilitate the control of insects and the quarantine operations through proper post-harvest management strategies and can improve productivity. The present review recognizes the need for developing a rapid, cost-effective, and reliable insect infestation monitoring system that would lead to advancements in agriculture and food industry. In this paper, an overview of non-destructive detection insect damages in fruits and vegetables was presented, and the research and applications were discussed. This paper elaborated all of the post-harvest fruit infestation detection methods which are based on the following technologies: optical properties, machine vision technique, sonic properties, magnetic resonance imaging (MRI), thermal imaging, x-ray computed tomography and chemical chromatography. Also, the main challenges and limitations of non-destructive detection methods in the agricultural products quality assessment were also elucidated

Insect inspired visual motion sensing and flying robots

International audienceFlying insects excellently master visual motion sensing techniques. They use dedicated motion processing circuits at a low energy and computational costs. Thanks to observations obtained on insect visual guidance, we developed visual motion sensors and bio-inspired autopilots dedicated to flying robots. Optic flow-based visuomotor control systems have been implemented on an increasingly large number of sighted autonomous robots. In this chapter, we present how we designed and constructed local motion sensors and how we implemented bio-inspired visual guidance scheme on-board several micro-aerial vehicles. An hyperacurate sensor in which retinal micro-scanning movements are performed via a small piezo-bender actuator was mounted onto a miniature aerial robot. The OSCAR II robot is able to track a moving target accurately by exploiting the microscan-ning movement imposed to its eye's retina. We also present two interdependent control schemes driving the eye in robot angular position and the robot's body angular position with respect to a visual target but without any knowledge of the robot's orientation in the global frame. This "steering-by-gazing" control strategy, which is implemented on this lightweight (100 g) miniature sighted aerial robot, demonstrates the effectiveness of this biomimetic visual/inertial heading control strategy

Deep learning in the wild

Invited paperDeep learning with neural networks is applied by an increasing number of people outside of classic research environments, due to the vast success of the methodology on a wide range of machine perception tasks. While this interest is fueled by beautiful success stories, practical work in deep learning on novel tasks without existing baselines remains challenging. This paper explores the specific challenges arising in the realm of real world tasks, based on case studies from research & development in conjunction with industry, and extracts lessons learned from them. It thus fills a gap between the publication of latest algorithmic and methodical developments, and the usually omitted nitty-gritty of how to make them work. Specifically, we give insight into deep learning projects on face matching, print media monitoring, industrial quality control, music scanning, strategy game playing, and automated machine learning, thereby providing best practices for deep learning in practice