Diversity of microbial communities at shallow-hydrothermal vents off Dominica, Lesser Antilles

Hydrothermal vents are often compared to desert oases, because of the presence of highly diverse and abundant biotic communities inhabiting these extreme environments. Nevertheless, the microbial communities associated with shallow-hydrothermal systems have been poorly studied. Hydrothermal activity at Dominica Island is quite well known under the geological and geochemical aspects, but no previous information existed about the microbial communities associated to this area. This thesis is therefore targeting the microbiology of hydrothermal sediments combining geochemical and molecular biological investigations, focusing on differences between hydrothermal vents and background (i.e. control) areas, and between hydrothermal sites. It was also intended to assess relationship between geochemical parameters and microbial diversity at the two hydrothermally impacted sites. Two shallow-sea hydrothermal vents located south-west off Dominica Island (Lesser Antilles) have been investigated in this study: Champagne Hot Springs and Soufriere Bay offshore vent. During this study, sediments for geochemical and molecular analyses were collected every 2 cm from the two impacted areas and from two control sites not associated with hydrothermal activity; in situ temperatures measurements were also taken every 5 cm deep in the sediment for all the sites. A geochemical characterization of the sediment porewater was performed through the analysis of several elements’ concentrations (i.e. H2S, Cl-, Br-, SO42-, Fe2+, Na+, K+, B+, Si+). Microbial communities at the different sites were studied by Automated Intergenic Spacer Analysis (ARISA). Inspection of the operational taxonomic units (OTUs) distribution was performed, as well as statistical analyses for communities’ structure and composition differences, and for changes of β-diversity along with sediment geochemistry. Data suggested that mixing between hydrothermal fluids and seawater results in distinct different environmental gradients and potential ecological niches between the two investigated hydrothermal vents, reflecting a difference in microbial community structures between them.

Development of wearable electrochemical sensors for the determination of biological analytes

Wearable biosensors are attracting interest due to their potential to provide continuous, real-time physiological information via dynamic, non-invasive measurements of biochemical markers in biofluids, such as interstitial fluid (ISF). One notable example of their applications is for glycemic monitoring in diabetic patients, which is typically carried out either by direct measurement of blood glucose via finger pricking or by wearable sensors that can continuously monitor glucose in ISF by sampling it from below the skin with a microneedle. In this context, the development of a new and minimally invasive multisensing tattoo-based platform for the monitoring of glucose and other analytes in ISF extracted through reverse iontophoresis in proposed by the GLUCOMFORT project. This elaborate describes the in-vitro development of flexible electrochemical sensors based on inkjet-printed PEDOT:PSS and metal inks that are capable of determining glucose and chloride at biologically relevant concentrations, making them good candidates for application in the GLUCOMFORT platform. In order to make PEDOT:PSS sensitive to glucose at micromolar concentrations, a biocompatible functionalization based on immobilized glucose oxidase and electrodeposited platinum was developed. This functionalization was successfully applied to bulk and flexible amperometric devices, the design of which was also optimized. Using the same strategy, flexible organic electrochemical transistors (OECTs) for glucose sensing were also made and successfully tested. For the sensing of chloride ions, an organic charge-modulated field-effect transistor (OCMFET) featuring a silver/silver chloride modified floating gate electrode was developed and tested.

Deep learning based style transfer for low altitude aerial imagery

Unmanned Aerial Vehicle (UAVs) equipped with cameras have been fast deployed to a wide range of applications, such as smart cities, agriculture or search and rescue applications. Even though UAV datasets exist, the amount of open and quality UAV datasets is limited. So far, we want to overcome this lack of high quality annotation data by developing a simulation framework for a parametric generation of synthetic data. The framework accepts input via a serializable format. The input specifies which environment preset is used, the objects to be placed in the environment along with their position and orientation as well as additional information such as object color and size. The result is an environment that is able to produce UAV typical data: RGB image from the UAVs camera, altitude, roll, pitch and yawn of the UAV. Beyond the image generation process, we improve the resulting image data photorealism by using Synthetic-To-Real transfer learning methods. Transfer learning focuses on storing knowledge gained while solving one problem and applying it to a different - although related - problem. This approach has been widely researched in other affine fields and results demonstrate it to be an interesing area to investigate. Since simulated images are easy to create and synthetic-to-real translation has shown good quality results, we are able to generate pseudo-realistic images. Furthermore, object labels are inherently given, so we are capable of extending the already existing UAV datasets with realistic quality images and high resolution meta-data. During the development of this thesis we have been able to produce a result of 68.4% on UAVid. This can be considered a new state-of-art result on this dataset.