SEMANTIC SEGMENTATION of BENTHIC COMMUNITIES from ORTHO-MOSAIC MAPS

Visual sampling techniques represent a valuable resource for a rapid, non-invasive data acquisition for underwater monitoring purposes.Long-term monitoring projects usually requires the collection of large quantities of data, and the visual analysis of a human expertoperator remains, in this context, a very time consuming task. It has been estimated that only the 1-2%of the acquired images are lateranalyzed by scientists (Beijbom et al., 2012). Strategies for the automatic recognition of benthic communities are required to effectivelyexploit all the information contained in visual data. Supervised learning methods, the most promising classification techniques in thisfield, are commonly affected by two recurring issues: the wide diversity of marine organism, and the small amount of labeled data.In this work, we discuss the advantages offered by the use of annotated high resolution ortho-mosaics of seabed to classify and segmentthe investigated specimens, and we suggest several strategies to obtain a considerable per-pixel classification performance although theuse of a reduced training dataset composed by a single ortho-mosaic. The proposed methodology can be applied to a large number ofdifferent species, making the procedure of marine organism identification an highly adaptable tas

SEMANTIC SEGMENTATION OF BENTHIC COMMUNITIES FROM ORTHO-MOSAIC MAPS

Visual sampling techniques represent a valuable resource for a rapid, non-invasive data acquisition for underwater monitoring purposes. Long-term monitoring projects usually requires the collection of large quantities of data, and the visual analysis of a human expert operator remains, in this context, a very time consuming task. It has been estimated that only the 1-2% of the acquired images are later analyzed by scientists (Beijbom et al., 2012). Strategies for the automatic recognition of benthic communities are required to effectively exploit all the information contained in visual data. Supervised learning methods, the most promising classification techniques in this field, are commonly affected by two recurring issues: the wide diversity of marine organism, and the small amount of labeled data. In this work, we discuss the advantages offered by the use of annotated high resolution ortho-mosaics of seabed to classify and segment the investigated specimens, and we suggest several strategies to obtain a considerable per-pixel classification performance although the use of a reduced training dataset composed by a single ortho-mosaic. The proposed methodology can be applied to a large number of different species, making the procedure of marine organism identification an highly adaptable task.</p

Long-term trends of PM10-bound arsenic, cadmium, nickel, and lead across the Veneto region (NE Italy)

Since the mid-90s, the European Community has adopted increasingly stringent air quality standards. Consequently, air quality has generally improved across Europe. However, current EU standards are still breached in some European hotspots. The Veneto region (NE Italy) lies in the eastern part of the Po Valley, a major European hotspot for air pollution, where EU standards for particulate matter, nitrogen oxides and ozone are still breached at some sites. This study aims to analyse the PM10-bound arsenic, cadmium, nickel, and lead concentrations over a 10 years-long period (2010-2020) in the Veneto Region by using data collected by the local environmental protection agency (ARPAV) in 20 sampling stations mostly distributed across the plain areas of the region and categorized as rural (RUR), urban (URB), and suburban (SUB) background, industrial (IND) and traffic (TRA) hotspots (Figure 1). The comprehensive dataset discussed in this study was statistically investigated to detect the seasonal trends, their relationship with other air pollutants and meteorological parameters and their spatial variations at a regional scale. This study completes previous air quality studies over the Veneto region for gaseous pollutants and bulk PM10 (Masiol et al. 2017). Samplings were carried out according to CEN EN 12341:1998 standard on quartz fibre filters and were continuous for 24 h, starting at midnight. The gravimetric determination of PM10 mass was measured following the CEN EN 12341:2014 standard. The elemental analysis was performed using an ICP-MS (Agilent 7700) after acid digestion (EN 14902:2005). The trends were analysed using different approaches on the monthly-averaged data. The shape of trends and their seasonal variations were assessed through the seasonal-trend decomposition time series procedure based on “Loess” (STL). The linear trends were computed by the Mann-Kendall trend test (p &lt; 0.05) and the Theil-Sen nonparametric estimator of slope (MK-TS). Since this latter analysis assumes monotonic linear trends and does not consider the shape of trends, the presence of possible breakpoints was investigated using the piecewise regression. Generally, monthly patterns of all analysed elements show higher concentrations during winter, following PM10 concentrations. Some exceptions were detected and discussed. Results of trend analysis indicate statistically significant negative (decreasing) or null linear trends in almost all stations. A few positive (increasing) but not statistically significant trends were also detected. Some sites showed rapid decreases occurred in short periods and linked to peculiar events or local causes. Among others, several sites across the Venice area showed significant drops of arsenic concentrations after the REACH (Registration Evaluation Authorisation of Chemicals) implementation (Formenton et al., 2021). Data used in this study are provided by ARPAV (Agenzia Regionale per la Prevenzione e Protezione Ambientale del Veneto, https://www.arpa.veneto.it/)

The Natural Compound Fucoidan From New Zealand Undaria Pinnatifida Synergizes With the ERBB Inhibitor Lapatinib Enhancing Melanoma Growth Inhibition

Melanoma remains one of the most aggressive and therapy-resistant cancers. Finding new treatments to improve patient outcomes is an ongoing effort. We previously demonstrated that melanoma relies on the activation of ERBB signaling, specifically of the ERBB3/ERBB2 cascade. Here we show that melanoma tumor growth is inhibited by 60% over controls when treated with lapatinib, a clinically approved inhibitor of ERBB2/EGFR. Importantly, tumor growth is further inhibited to 85% when the natural compound fucoidan from New Zealand U. pinnatifida is integrated into the treatment regimen. Fucoidan not only enhances tumor growth inhibition, it counteracts the morbidity associated with prolonged lapatinib treatment. Fucoidan doubles the cell killing capacity of lapatinib. These effects are associated with a further decrease in AKT and NFκB signaling, two key pathways involved in melanoma cell survival. Importantly, the enhancing cell killing effects of fucoidan can be recapitulated by inhibiting ERBB3 by either a specific shRNA or a novel, selective ERBB3 neutralizing antibody, reiterating the key roles played by this receptor in melanoma. We therefore propose the use of lapatinib or specific ERBB inhibitors, in combination with fucoidan as a new treatment of melanoma that potentiates the effects of the inhibitors while protecting from their potential side effects

An in vitro study of the interaction of Sea-Nine with rat lever mitochondria

The interactions of the antifouling compound Sea-Ninetwith rat liver mitochondria have been studied. The results indicate that low doses of this compound inhibit adenosine 59-triphosphate (ATP) synthesis. Further investigations indicate that ATP synthesis inhibition should be due to an interaction of Sea-Nine with the succinic dehydrogenase in the mitochondrial respiratory chain