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Although plastic pollution is one of the most noteworthy environmental issues nowadays, there is still a knowledge gap in terms of monitoring the spatial distribution of plastics, which is needed to prevent its negative effects and to plan mitigation actions. Unmanned Aerial Vehicles (UAVs) can provide suitable data for mapping floating plastic, but most of the methods require visual interpretation and manual labeling. The main goals of this paper are to determine the suitability of deep learning algorithms for automatic floating plastic extraction from UAV orthophotos, testing the possibility of differentiating plastic types, and exploring the relationship between spatial resolution and detectable plastic size, in order to define a methodology for UAV surveys to map floating plastic. Two study areas and three datasets were used to train and validate the models. An end-to-end semantic segmentation algorithm based on U-Net architecture using the ResUNet50 provided the highest accuracy to map different plastic materials (F1-score: Oriented Polystyrene (OPS): 0.86; Nylon: 0.88; Polyethylene terephthalate (PET): 0.92; plastic (in general): 0.78), showing its ability to identify plastic types. The classification accuracy decreased with the decrease in spatial resolution, performing best on 4 mm resolution images for all kinds of plastic. The model provided reliable estimates of the area and volume of the plastics, which is crucial information for a cleaning campaign.

The mast-cell sarcoma of a bone is described here for the first time. The tumour presented in a 4-year-old boy, with pain, oedema and deformation of his right lower leg. Radiological findings revealed a destructive tumourous mass. Histopathological examination showed the tumour to be composed of large, atypical cells, with hyperchromatic oval and polygonal nuclei. The cytoplasm around them was eosinophilic with many basophilic and toluidine-blue-positive granules. These atypical mast cells were positive for chloroacetate esterase, c-kit, tryptase and negative for myeloperoxidase. The primary disease quickly progressed to mast-cell leukaemia, and despite intensive chemotherapy the patient died 18 months after first symptoms.

We investigated relationships between spiritual well-being (SWB), intrinsic religiosity (IR), and suicidal behavior in 45 Croatian war veterans with chronic posttraumatic stress disorder and 32 healthy volunteers. Compared with the volunteers, the veterans had significantly lower SWB scores (p = 0.000) and existential well-being (EWB) scores (p = 0.000). Scores on the religious well-being (RWB) subscale (p = 0.108) and the IR scale did not differ significantly between the groups (p = 0.803). Veterans' suicidality inversely correlated with SWB (p = 0.000), EWB (p = 0.000), RWB (p = 0.026), and IR (p = 0.041), with the association being stronger for the EWB subscale than for the RWB subscale. Veterans who had attempted suicide at least once in their lifetime had significantly higher Suicidal Assessment Scale scores and lower EWB scores than veterans who never attempted suicide. Low EWB scores may imply an increased risk of suicidality. Some religious activities were more frequent among the veterans than among the healthy volunteers, possibly reflecting the veterans' increased help-seeking behavior due to poor EWB.

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