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The heavy metals transfer from the soil, where they accumulate, to the edible parts of the plants, and then, their entrance in the food chain can represent a source of concern for human health. Among heavy metals, arsenic is one of the most widespread in the soil of Lazio (central region of Italy), where the phytoavailable geogenic arsenic enters the food chain, with a dangerous exposition of the local population. In the first part of this work, plants of radish (Raphanus sativus L.) and lettuce (Lactuca sativa L.) were grown in protected culture in the experimental farm of CREA-AA, where they were daily treated with different concentrations of sodium arsenate dibasic heptahydrate in order to investigate differences in their arsenic accumulation capacities. In order to confirm the results achieved, in the second part of this study, the arsenic concentration was determined in commercial products obtained from contaminated areas of Lazio, and the potential exposition risk for human health through consumption of these widely consumed vegetables was estimated. The highest arsenic concentrations were found in the samples of lettuce. To evaluate the potential health risk from consumption of L. sativa and R. sativus, the estimated daily intake (EDI) for adults, adolescents, and elderly was calculated, finding that HRI (health risk index) index value for arsenic was low (< 1) in the case of chronic consumptions for all samples of radishes, and for the lettuces grown in the area of Viterbo. On the contrary, the lettuces obtained from Tuscania and Tarquinia presented very high concentrations of arsenic and a worrying HRI value. In order to reduce the risk of As toxicity in the people through consumption of the vegetables, the irrigation water should contain less than 0.1 mg As L−1. For this reason, the authors tested the application of red mud (RM) to remove As from groundwater before using it for the irrigation of radish and lettuce in greenhouse production. [ABSTRACT FROM AUTHOR]

Coal plants can be a major source of mutagenic pollutants. In this study we used the common land snail Helix aspersa, to detect the mutagenic effect of pollution from a coal plant in central Italy applying the micronucleus test (MN) on snail’s haemocytes and evaluating trace elements concentration (As Cd, Pb, Hg, and Zn) in soil and snails. Snails from a biological farm were exposed for 13 days in five locations at different distances from the plant. Wild snails collected in the same locations were also analysed. MN frequency in exposed snails was significantly higher in four locations within 10 km from to the plant, with respect to the control and the farthest location. Comparing the MN frequency between farmed and wild snails, a significantly higher frequency emerged for the exposed snails in all locations except the farthest, likely indicating adaptation or selection of the wild organisms due to chronic exposure to pollutants. In natural snails significantly higher MN frequencies with near the plant emerged as well. Trace elements analysis showed significant correlations between MN frequencies and both Zn and As concentrations in soil, for both exposed and wild snails, and Zn and Pb concentrations in exposed snails. Our results were consistent with those previously obtained when evaluating primary DNA damage in natural snails from the same area and show that the snails near the plant were affected by a permanent cytogenetic damage. Moreover, they confirm the suitability of snails for biomonitoring the presence of pollutants with mutagenic effect. [ABSTRACT FROM AUTHOR]

In this paper, we focus on online reviews and employ artificial intelligence tools, taken from the cognitive computing field, to help understand the relationships between the textual part of the review and the assigned numerical score. We move from the intuitions that (1) a set of textual reviews expressing different sentiments may feature the same score (and vice-versa), and (2) detecting and analyzing the mismatches between the review content and the actual score may benefit both service providers and consumers, by highlighting specific factors of satisfaction (and dissatisfaction) in texts. To prove the intuitions, we adopt sentiment analysis techniques and we concentrate on hotel reviews, to find polarity mismatches therein. In particular, we first train a text classifier with a set of annotated hotel reviews, taken from the Booking website. Then, we analyze a large dataset, with around 160k hotel reviews collected from TripAdvisor, with the aim of detecting a polarity mismatch, indicating if the textual content of the review is in line, or not, with the associated score. Using well-established artificial intelligence techniques and analyzing in depth the reviews featuring a mismatch between the text polarity and the score, we find that-on a scale of five stars-those reviews ranked with middle scores include a mixture of positive and negative aspects. The approach proposed here, beside acting as a polarity detector, provides an effective selection of reviews-on an initial very large dataset-that may allow both consumers and providers to focus directly on the review subset featuring a text/score disagreement,which conveniently convey to the user a summary of positive and negative features of the review target. [ABSTRACT FROM AUTHOR]

Fraglets represent an execution model for communication protocols that resembles the chemical reactions in living organisms. The strong connection between their way of transforming and reacting and formal rewriting systems makes a fraglet program amenable to automatic verification. Grounded on past work, this paper investigates feasibility of adopting fraglets as model for specifying security protocols and analysing their properties. In particular, we give concrete sample analyses over a secure RFID protocol, showing evolution of the protocol run as chemical dynamics and simulating an adversary trying to circumvent the intended steps. The results of our analysis confirm the effectiveness of the cryptofraglets framework for the model and analysis of security properties and eventually show its potential to identify and uncover protocol flaws. [ABSTRACT FROM AUTHOR]