Selection of the N-Acylhomoserine Lactone-Degrading Bacterium Alteromonas stellipolaris PQQ-42 and of Its Potential for Biocontrol in Aquaculture

The production of virulence factors by many pathogenic microorganisms depends on the intercellular communication system called quorum sensing, which involves the production and release of signal molecules known as autoinducers. Based on this, new-therapeutic strategies have emerged for the treatment of a variety of infections, such as the enzymatic degradation of signaling molecules, known as quorum quenching (QQ). In this study, we present the screening of QQ activity amongst 450 strains isolated from a bivalve hatchery in Granada (Spain), and the selection of the strain PQQ-42, which degrades a wide range of N-acylhomoserine lactones (AHLs). The selected strain, identified as Alteromonas stellipolaris, degraded the accumulation of AHLs and reduced the production of protease and chitinase and swimming motility of a Vibrio species in co-cultivation experiments in vitro. In the bio-control experiment, strain PQQ-42 significantly reduced the pathogenicity of Vibrio mediterranei VibC-Oc-097 upon the coral Oculina patagonica showing a lower degree of tissue damage (29.25 ± 14.63%) in its presence, compared to when the coral was infected with V. mediterranei VibC-Oc-097 alone (77.53 ± 13.22%). Our results suggest that this AHL-degrading bacterium may have biotechnological applications in aquaculture.

3D Visual Data-Driven Spatiotemporal Deformations for Non-Rigid Object Grasping Using Robot Hands

Sensing techniques are important for solving problems of uncertainty inherent to intelligent grasping tasks. The main goal here is to present a visual sensing system based on range imaging technology for robot manipulation of non-rigid objects. Our proposal provides a suitable visual perception system of complex grasping tasks to support a robot controller when other sensor systems, such as tactile and force, are not able to obtain useful data relevant to the grasping manipulation task. In particular, a new visual approach based on RGBD data was implemented to help a robot controller carry out intelligent manipulation tasks with flexible objects. The proposed method supervises the interaction between the grasped object and the robot hand in order to avoid poor contact between the fingertips and an object when there is neither force nor pressure data. This new approach is also used to measure changes to the shape of an object’s surfaces and so allows us to find deformations caused by inappropriate pressure being applied by the hand’s fingers. Test was carried out for grasping tasks involving several flexible household objects with a multi-fingered robot hand working in real time. Our approach generates pulses from the deformation detection method and sends an event message to the robot controller when surface deformation is detected. In comparison with other methods, the obtained results reveal that our visual pipeline does not use deformations models of objects and materials, as well as the approach works well both planar and 3D household objects in real time. In addition, our method does not depend on the pose of the robot hand because the location of the reference system is computed from a recognition process of a pattern located place at the robot forearm. The presented experiments demonstrate that the proposed method accomplishes a good monitoring of grasping task with several objects and different grasping configurations in indoor environments.

μ-MAR: Multiplane 3D Marker based Registration for depth-sensing cameras

Many applications including object reconstruction, robot guidance, and. scene mapping require the registration of multiple views from a scene to generate a complete geometric and appearance model of it. In real situations, transformations between views are unknown and it is necessary to apply expert inference to estimate them. In the last few years, the emergence of low-cost depth-sensing cameras has strengthened the research on this topic, motivating a plethora of new applications. Although they have enough resolution and accuracy for many applications, some situations may not be solved with general state-of-the-art registration methods due to the signal-to-noise ratio (SNR) and the resolution of the data provided. The problem of working with low SNR data, in general terms, may appear in any 3D system, then it is necessary to propose novel solutions in this aspect. In this paper, we propose a method, μ-MAR, able to both coarse and fine register sets of 3D points provided by low-cost depth-sensing cameras, despite it is not restricted to these sensors, into a common coordinate system. The method is able to overcome the noisy data problem by means of using a model-based solution of multiplane registration. Specifically, it iteratively registers 3D markers composed by multiple planes extracted from points of multiple views of the scene. As the markers and the object of interest are static in the scenario, the transformations obtained for the markers are applied to the object in order to reconstruct it. Experiments have been performed using synthetic and real data. The synthetic data allows a qualitative and quantitative evaluation by means of visual inspection and Hausdorff distance respectively. The real data experiments show the performance of the proposal using data acquired by a Primesense Carmine RGB-D sensor. The method has been compared to several state-of-the-art methods. The results show the good performance of the μ-MAR to register objects with high accuracy in presence of noisy data outperforming the existing methods.

Pencil it in: pencil drawn electrochemical sensing platforms

Inspired by recent reports concerning the utilisation of hand drawn pencil macroelectrodes (PDEs), we report the fabrication, characterisation (physicochemical and electrochemical) and implementation (electrochemical sensing) of various PDEs drawn upon a flexible polyester substrate. Electrochemical characterisation reveals that there are no quantifiable electrochemical responses upon utilising these PDEs with an electroactive analyte that requires an electrochemical oxidation step first, therefore the PDEs have been examined towards the electroactive redox probes hexaammineruthenium(III) chloride, potassium ferricyanide and ammonium iron(II) sulfate. For the first time, characterisation of the number of drawn pencil layers and the grade of pencil are examined; these parameters are commonly overlooked when utilising PDEs. It is demonstrated that a PDE drawn ten times with a 6B pencil presented the most advantageous electrochemical platform, in terms of electrochemical reversibility and peak height/analytical signal. In consideration of the aforementioned limitation, analytes requiring an electrochemical reduction as the first process were solely analysed. We demonstrate the beneficial electroanalytical capabilities of these PDEs towards p-benzoquinone and the simultaneous detection of heavy metals, namely lead(II) and cadmium(II), all of which are explored for the first time utilising PDEs. Initially, the detection limits of this system were higher than desired for electroanalytical platforms, however upon implementation of the PDEs in a back-to-back configuration (in which two PDEs are placed back-to-back sharing a single connection to the potentiostat), the detection limits for lead(II) and cadmium(II) correspond to 10 μg L−1 and 98 μg L−1 respectively within model aqueous (0.1 M HCl) solutions.