Machine learning techniques to characterize functional traits of plankton from image data.

Plankton imaging systems supported by automated classification and analysis have improved ecologists' ability to observe aquatic ecosystems. Today, we are on the cusp of reliably tracking plankton populations with a suite of lab-based and in situ tools, collecting imaging data at unprecedentedly fine spatial and temporal scales. But these data have potential well beyond examining the abundances of different taxa; the individual images themselves contain a wealth of information on functional traits. Here, we outline traits that could be measured from image data, suggest machine learning and computer vision approaches to extract functional trait information from the images, and discuss promising avenues for novel studies. The approaches we discuss are data agnostic and are broadly applicable to imagery of other aquatic or terrestrial organisms.

A Magnetic Crawler System for Autonomous Long-Range Inspection and Maintenance on Large Structures.

The inspection and maintenance of large-scale industrial structures are major challenges that require time-efficient and reliable solutions to ensure the healthy condition of structures during operation. Autonomous robots may provide a promising solution for this purpose. In particular, they could lead to faster and more reliable inspection and maintenance without direct intervention from human operators. In this paper, we present a custom magnetic crawler system, and sensor suit and sensing modalities to enable such robotic operation. We first describe a localization framework based on a mesh created from a point cloud coupled with Inertial Measurement Unit (IMU) and Ultra-Wide Band (UWB) readings. Next, we introduce a mapping framework that relies on a 3D laser, and explicitly state how autonomous navigation and obstacle avoidance can be developed. Lastly, we present how ultrasonic guided waves (UGWs) are integrated into the system to provide accurate robot localization and structural feature mapping by relying on acoustic reflections in combination with the other systems. It is envisioned that long-range inspection capabilities that are not yet available in current industrial mobile platforms could emerge from the designed robotic system.

Learning the propagation properties of rectangular metal plates for Lamb wave-based mapping.

The inspection of sizeable plate-based metal structures such as storage tanks or marine vessel hulls is a significant stake in the industry, which necessitates reliable and time-efficient solutions. Although Lamb waves have been identified as a promising solution for long-range non-destructive testing, and despite the substantial progress made in autonomous navigation and environment sensing, a Lamb-wave-based robotic system for extensive structure monitoring is still lacking. Following previous work on ultrasonic Simultaneous Localization and Mapping (SLAM), we introduce a method to achieve plate geometry inference without prior knowledge of the material propagation properties, which may be lacking during a practical inspection task in challenging and outdoor environments. Our approach combines focalization to adjust the propagation model parameters and beamforming to infer the plate boundaries location by relying directly on acoustic measurements acquired along the mobile unit trajectory. For each candidate model, the focusing ability of the corresponding beamformer is assessed over high-pass filtered beamforming maps to further improve the robustness of the plate geometry estimates. We then recover the optimal space-domain beamformer through a simulated annealing optimization process. We evaluate our method on three sets of experimental data acquired in different conditions and show that accurate plate geometry inference can be achieved without any prior propagation model. Finally, the results show that the optimal beamformer outperforms the beamformer resulting from the predetermined propagation model in non-nominal acquisition conditions.

Identification of antibiotics triggering the dissemination of antibiotic resistance genes by SXT/R391 elements using a dedicated high-throughput whole-cell biosensor assay.

BACKGROUND: Mobile genetic elements (MGEs) are widely involved in the dissemination of antibiotic resistance genes and some of them, such as the integrative and conjugative element SXT, are even induced by specific antibiotics at sub-lethal concentrations. OBJECTIVES: This work explores collateral effects of a broad range of antibiotics on the mobility of the SXTMO10 element using a specifically designed high-throughput screening test. METHODS: Twenty-five promoters involved in the mobility of SXT and six artificial constitutive promoters were transcriptionally fused to luxCDABE bioluminescent genes and introduced into Escherichia coli strains with or without SXT to build whole-cell biosensors for a large-scale screening involving 48 antibiotics. A bioluminescent assay implementing a classical agar diffusion approach was coupled to an automated data processing pipeline developed to extract and analyse luminescence data from over 2000 antibiotic/biosensor combination profiles. RESULTS: In addition to quinolones previously reported as inducing the expression of SXT mobility genes, we found that specific antibiotics belonging to other classes, such as imipenem and azithromycin, also behave as inducers. The use of a control set of constitutive biosensors also revealed an unexpected intricate relationship between cell respiration and light production that allowed the identification of antibiotics interfering with the respiration process. CONCLUSIONS: The effect of antibiotics goes beyond the interaction with their primary cell targets and may lead to adverse effects such as triggering the dissemination of resistance by MGEs, sometimes in unpredictable ways. Identifying such MGE-triggering antibiotics is of prime importance for better controlling collateral effects during therapy.

Design and implementation of a biomimetic turtle hydrofoil for an autonomous underwater vehicle.

This paper presents the design and implementation of a turtle hydrofoil for an Autonomous Underwater Vehicle (AUV). The final design of the AUV must have navigation performance like a turtle, which has also been the biomimetic inspiration for the design of the hydrofoil and propulsion system. The hydrofoil design is based on a National Advisory Committee for Aeronautics (NACA) 0014 hydrodynamic profile. During the design stage, four different propulsion systems were compared in terms of propulsion path, compactness, sealing and required power. The final implementation is based on a ball-and-socket mechanism because it is very compact and provides three degrees of freedom (DoF) to the hydrofoil with very few restrictions on the propulsion path. The propulsion obtained with the final implementation of the hydrofoil has been empirically evaluated in a water channel comparing different motion strategies. The results obtained have confirmed that the proposed turtle hydrofoil controlled with a mechanism with three DoF generates can be used in the future implementation of the planned AUV.