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Impaired emotional abilities (higher scores of alexithymia and lower levels of emotional awareness) were found in patients with skin-restricted lupus, warranting examination of the relationship between these abilities and the evolution of skin-restricted lupus, using longitudinal data. A total of 75 consecutive outpatients with skin-restricted lupus were recruited and assessed by a dermatologist and a psychiatrist every 6 months over a period of 2.5 years. Alexithymia and emotional awareness were evaluated with the French versions of the Toronto Alexithymia Scale (TAS-20) and the Levels of Emotional Awareness Scale (LEAS). During follow-up, good stability of the LEAS scores was observed, whereas TAS-20 scores varied; those variations were positively associated both with lupus duration and current psychiatric and personality disorders, but not with lupus remission. Such findings regarding 2 complementary aspects of emotional functioning are of direct interest for the management of patients with skin-restricted lupus.

Healthcare processes are inherently complex as each patient is unique and medical staff deviate from protocols, often for valid reasons. Event logs collected by modern process-aware (healthcare) information systems provide a wealth of data and can be used to analyze the adherence to these protocols. Process mining is a young research area combining data science (machine learning, data mining, etc.) and business process management. Its main contributions have been techniques for process discovery (the automatic learning of process models from event data) and conformance checking (aligning observed and modeled behavior). However, existing techniques face challenging issues discovering high-quality process models in a healthcare setting. In this chapter we introduce the key concepts of process mining such as event logs, process models, and process discovery. We then show the application of two recent process mining techniques on a public event data set from the healthcare domain to demonstrate how some of the common pitfalls can be overcome. A first presented technique projects data statistics on a process model, allowing the analysis of correlations between patient characteristics and the executed activities (e.g., type of treatment). A second technique analyzes process performance without a specific need for a process model by considering contextual information. It correlates process characteristics with process performance.

The advent of digital medical data has brought an exponential increase in information available for each patient, allowing for novel knowledge generation methods to emerge. Tapping into this data brings clinical research and clinical practice closer together, as data generated in ordinary clinical practice can be used towards rapid-learning healthcare systems, continuously improving and personalizing healthcare. In this context, the recent use of Data Science technologies for healthcare is providing mutual benefits to both patients and medical professionals, improving prevention and treatment for several kinds of diseases. However, the adoption and usage of Data Science solutions for healthcare still require social capacity, knowledge and higher acceptance. The goal of this chapter is to provide an overview of needs, opportunities, recommendations and challenges of using (Big) Data Science technologies in the healthcare sector. This contribution is based on a recent whitepaper (http://www.bdva.eu/sites/default/files/Big%20Data%20Technologies%20in%20Healthcare.pdf) provided by the Big Data Value Association (BDVA) (http://www.bdva.eu/), the private counterpart to the EC to implement the BDV PPP (Big Data Value PPP) programme, which focuses on the challenges and impact that (Big) Data Science may have on the entire healthcare chain. © Springer Nature Switzerland AG 2019.

In Italy, the restrictive approach for treated wastewater reuse in agriculture has led to some difficulties in promoting this practice. In order to assess the health risk associated with the use of wastewater in agriculture, an experiment was conducted in an open field near the constructed wetland (CW) system of San Michele di Ganzaria (Eastern Sicily), during the irrigation seasons 2004-2009. In particular the impact on tomato crops of drip and sub-drip irrigation with treated municipal wastewater, aswell as effects of wastewater reuse on the irrigation system, main production features, hydrological soil behaviour, and microbial soil and products contamination were investigated. Notwithstanding the fact that globally CW effluents did not match microbiological standards for wastewater reuse of Italian legislation, the median infection risk (function of the recommended tolerable additional disease burden of 10-6 DALY (disability-adjusted life year) loss per person per year) suggested by the 2006 World Health Organization Guidelines for rotavirus, Campylobacter and Cryptosporidium for lettuce irrigation under unrestricted irrigation scenario was achieved. © 2013 IWA Publishing.

This paper presents MERGILO, a method for reconciling knowledge extracted from multiple natural language sources, and for delivering it as a knowledge graph. The underlying problem is relevant in many application scenarios requiring the creation and dynamic evolution of a knowledge base, e.g. automatic news summarization, human-robot dialoguing, etc. After providing a formal definition of the problem, we propose our holistic approach to handle natural language input - typically independent texts as in news from different sources - and we output a knowledge graph representing their reconciled knowledge. MERGILO is evaluated on its ability to identify corresponding entities and events across documents against a manually annotated corpus of news, showing promising results. (C) 2016 Elsevier B.V. All rights reserved.

This paper introduces an open, interoperable, and cloud-computing-based citizen engagement platform for the management of administrative processes of public administrations, which also increases the engagement of citizens. The citizen engagement platform is the outcome of a 3-year Italian national project called PRISMA (Interoperable cloud platforms for smart government). The aim of the project is to constitute a new model of digital ecosystem that can support and enable new methods of interaction among public administrations, citizens, companies, and other stakeholders surrounding cities. The platform has been defined by the media as a flexible (enable the addition of any kind of application or service) and open (enable access to open services) Italian "cloud" that allows public administrations to access to a vast knowledge base represented as linked open data to be reused by a stakeholder community with the aim of developing new applications ("Cloud Apps") tailored to the specific needs of citizens. The platform has been used by Catania and Syracuse municipalities, two of the main cities of southern Italy, located in the Sicilian region. The fully adoption of the platform is rapidly spreading around the whole region (local developers have already used available application programming interfaces (APIs) to create additional services for citizens and administrations) to such an extent that other provinces of Sicily and Italy in general expressed their interest for its usage. The platform is available online and, as mentioned above, is open source and provides APIs for full exploitation., Comment: 23 pages, 7 figures, journal paper

Sentilo is an unsupervised, domain-independent system that performs sentiment analysis by hybridizing natural language processing techniques and semantic Web technologies. Given a sentence expressing an opinion, Sentilo recognizes its holder, detects the topics and subtopics that it targets, links them to relevant situations and events referred to by it and evaluates the sentiment expressed on each topic/subtopic. Sentilo relies on a novel lexical resource, which enables a proper propagation of sentiment scores from topics to subtopics, and on a formal model expressing the semantics of opinion sentences. Sentilo provides its output as a RDF graph, and whenever possible it resolves holders' and topics' identity on Linked Data. © 2014 Springer Science+Business Media New York.

Satellite remote sensed data on canopy biophysical properties, ground data and agro-meteorological information were combined to estimate evapotranspiration (ET) fluxes of orange orchards using a modified Penman-Monteith equation. The study was carried out during the irrigation season 2004 in an irrigation district, cover for about 95% with orange groves, of 1550 ha located in eastern Sicily (Italy). The spatial pattern in ET-fluxes have been analysed using IKONOS high-resolution satellite and hyper-spectral ground data acquired and processed for the study-area. The remote estimates of ET-fluxes varied between 1.3 and 5.7 mm/day, with a daily average value of about 4.2 mm, showing a good agreement with crop ET values determined as residual of soil water balance of selected ground control sites. Crop coefficient estimates ranged between 0.22 and 1.08 showing positive correlations with percentages of ground cover (Cg) increasing from 30 to 80% ground shading and with LAI values. By comparing ET estimates with water volumes supplied in each sub-district of the study-area, the performance indicator "IP" was evaluated, allowing to rank the conditions of un-fulfilment of crop water requirements by public and private water distribution systems. Generally, out of 29 sub-districts, 14 had "IP" values less than 50%, revealing a sub-optimal water supply for the study-area. © 2005 Elsevier B.V. All rights reserved.

In Mediterranean countries, water shortage is becoming a problem of high concern affecting the local economy, mostly based on agriculture. The problem is not only the scarcity of water in terms of average per capita, but the high cost to make water available at the right place, at the right time with the required quality. In these cases, an integrated approach for water resources management including wastewater is required. The management should also include treated wastewater (TWW) reclamation and reuse, especially for agricultural irrigation. In Italy, TWW reuse is regulated by a quite restrictive approach (Ministry Decree, M.D. 185/03), especially for some chemical compounds and microbiological parameters. The aim of the paper is the evaluation of TWW reuse potential in Sicily. A Geographic Information System (GIS) was built at regional level to quantify and locate the available TWW volumes. In particular, the characteristics of wastewater treatment plants (WWTPs) were integrated, through the GIS, with data on irrigation district areas. Moreover, in order to evaluate the Italian approach for reuse practice in agriculture, the water quality of different TWW effluents was analysed on the basis of both the Italian standards and the WHO guidelines. © IWA Publishing 2012.