Phytoremediation of PAH- and Cu-Contaminated Soil by Cannabis sativa L.: Preliminary Experiments on a Laboratory Scale

This study proposes the phytoremediation of phenanthrene (PHE)-, pyrene (PYR)-, and copper (Cu)-contaminated soil by Cannabis sativa L. The experimental campaign was conducted in 300 mL volume pots over a 50 d period using different initial polycyclic aromatic hydrocarbon (PAH) concentrations, i.e., 100 (PC1), 200 (PC2), and 300 (PC3) mg ƩPAHs kg−1 dry weight of soil, while maintaining a constant Cu concentration of 350 mg∙kg−1. PHE and PYR removal was 93 and 61%, 98 and 48%, and 97 and 36% in PC1, PC2, and PC3, respectively, in the greenhouse condition. The highest Cu extraction amounted to 58 mg∙kg−1. In general, the growth of C. sativa L. under the PC1, PC2, and PC3 conditions decreased by approximately 25, 65, and 71% (dry biomass), respectively, compared to the uncontaminated control. The present study is aimed at highlighting the phytoremediation potential of C. sativa L. and providing the preliminary results necessary for future field-scale investigations

A Comparative Evaluation of Deep Learning Techniques for Photovoltaic Panel Detection From Aerial Images

Solar energy production has significantly increased in recent years in the European Union (EU), accounting for 12% of the total in 2022. The growth in solar energy production can be attributed to the increasing adoption of solar photovoltaic (PV) panels, which have become cost-effective and efficient means of energy production, supported by government policies and incentives. The maturity of solar technologies has also led to a decrease in the cost of solar energy, making it more competitive with other energy sources. As a result, there is a growing need for efficient methods for detecting and mapping the locations of PV panels. Automated detection can in fact save time and resources compared to manual inspection. Moreover, the resulting information can also be used by governments, environmental agencies and other companies to track the adoption of renewable sources or to optimize energy distribution across the grid. However, building effective models to support the automated detection and mapping of solar photovoltaic (PV) panels presents several challenges, including the availability of high-resolution aerial imagery and high-quality, manually-verified labels and annotations. In this study, we address these challenges by first constructing a dataset of PV panels using very-high-resolution (VHR) aerial imagery, specifically focusing on the region of Piedmont in Italy. The dataset comprises 105 large-scale images, providing more than 9,000 accurate and detailed manual annotations, including additional attributes such as the PV panel category. We first conduct a comprehensive evaluation benchmark on the newly constructed dataset, adopting various well-established deep-learning techniques. Specifically, we experiment with instance and semantic segmentation approaches, such as Rotated Faster RCNN and Unet, comparing strengths and weaknesses on the task at hand. Second, we apply ad-hoc modifications to address the specific issues of this task, such as the wide range of scales of the installations and the sparsity of the annotations, considerably improving upon the baseline results. Last, we introduce a robust and efficient post-processing polygonization algorithm that is tailored to PV panels. This algorithm converts the rough raster predictions into cleaner and more precise polygons for practical use. Our benchmark evaluation shows that both semantic and instance segmentation techniques can be effective for detecting and mapping PV panels. Instance segmentation techniques are well-suited for estimating the localization of panels, while semantic solutions excel at surface delineation. We also demonstrate the effectiveness of our ad-hoc solutions and post-processing algorithm, which can provide an improvement up to +10% on the final scores, and can accurately convert coarse raster predictions into usable polygons

Indocyanine green clearance test in liver transplantation: defining cut-off levels for graft viability assessment during organ retrieval and for the prediction of post-transplant graft function recovery - the Liver Indocyanine Green (LivInG) Trial Study Protocol

Introduction Viability assessment of the graft is essential to lower the risk of liver transplantation (LT) failure and need for emergency retransplantation, however, this still relies mainly on surgeon's experience. Post-LT graft function recovery assessment is also essential to aid physicians in the management of LT recipients and guide them through challenging decision making. This study aims to trial the use of indocyanine green clearance test (IGT) in the donor as an objective tool to assess graft viability and in the recipient to assess graft function recovery after LT.Methods and analysis This is an observational prospective single-centre study on consecutive liver transplant donors and recipients.Primary objective To determine the capability of IGT of predicting graft viability at the time of organ retrieval. Indocyanine green will be administered to the donor and the plasma disappearance rate (PDR) measured using the pulsidensitometric method. Some 162 IGT donor procedures will be required (alpha, 5%; beta, 20%) using an IGT-PDR cut-off value of 13% to achieve a significant discrimination between viable and non-viable grafts.Secondary objective IGT-PDR will be measured at different time-points in the LT recipient: during the anhepatic phase, after graft reperfusion, at 24 hours, on day 3 and day 7 after LT. The slope of IGT values from the donor to the recipient will be evaluated for correlation with the development of early allograft dysfunction.Ethics and dissemination This research protocol was approved by Fondazione Policlinico Universitario Agostino Gemelli IRCCS Ethics Committee (reference number: 0048466/20, study ID: 3656) and by the Italian National Transplant Center (CNT) (reference number: Prot.11/ CNT2021). Liver recipients will be required to provide written informed consent. Results will be published in international peer-reviewed scientific journals and presented in congresses