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Due to the rapid growth of knowledge graphs (KG) as representational learning methods in recent years, question-answering approaches have received increasing attention from academia and industry. Question-answering systems use knowledge graphs to organize, navigate, search and connect knowledge entities. Managing such systems requires a thorough understanding of the underlying graph-oriented structures and, at the same time, an appropriate query language, such as SPARQL, to access relevant data. Natural language interfaces are needed to enable non-technical users to query ever more complex data. The paper proposes a question-answering approach to support end users in querying graph-oriented knowledge bases. The system pipeline is composed of two main modules: one is dedicated to translating a natural language query submitted by the user into a triple of the form , while the second module implements knowledge graph embedding (KGE) models, exploiting the previous module triple and retrieving the answer to the question. Our framework delivers a fast OpenIE-based knowledge extraction system and a graph-based answer prediction model for question-answering tasks. The system was designed by leveraging existing tools to accomplish a simple prototype for fast experimentation, especially across different knowledge domains, with the added benefit of reducing development time and costs. The experimental results confirm the effectiveness of the proposed system, which provides promising performance, as assessed at the module level. In particular, in some cases, the system outperforms the literature. Finally, a use case example shows the KG generated by user questions in a graphical interface provided by an ad-hoc designed web application.

Despite the existence of an effective live-attenuated vaccine, measles can appear in vaccinated individuals. We investigated breakthrough measles cases identified during our surveillance activities within the measles/rubella surveillance network (MoRoNet) in Milan and surrounding areas (Northern Italy). Between 2017 and 2021, we confirmed measles virus (genotypes B3 or D8) infections in 653 patients and 51 of these (7.8%) were vaccinees. Among vaccinated individuals whose serum was available, a secondary failure was evidenced in 69.4% (25/36) of cases while 11 patients (30.6%) were non-responders. Non-responders were more frequently hospitalized and had significantly lower Ct values in both respiratory and urine samples. Median age and time since the last immunization were similar in the two groups. Importantly, we identified onward transmissions from vaccine failure cases. Vaccinees were involved in 20 outbreaks, in 10 of them they were able to transmit the virus, and in 8 of them, they were the index case. Comparing viral hemagglutinin sequences from vaccinated and non-vaccinated subjects did not show a specific mutation pattern. These results suggest that vaccination failure was likely due to the poor immune response of single individuals and highlights the importance of identifying breakthrough cases and characterizing their clinical and virologic profiles.

This work is devoted to the analysis of the background seismic noise acquired at the volcanoes (Campi Flegrei caldera, Ischia island, and Vesuvius) belonging to the Neapolitan volcanic district (Italy), and at the Colima volcano (Mexico). Continuous seismic acquisition is a complex mixture of volcanic transients and persistent volcanic and/or hydrothermal tremor, anthropogenic/ambient noise, oceanic loading, and meteo-marine contributions. The analysis of the background noise in a stationary volcanic phase could facilitate the identification of relevant waveforms often masked by microseisms and ambient noise. To address this issue, our approach proposes a machine learning (ML) modeling to recognize the “fingerprint” of a specific volcano by analyzing the background seismic noise from the continuous seismic acquisition. Specifically, two ML models, namely multi-layer perceptrons and convolutional neural network were trained to recognize one volcano from another based on the acquisition noise. Experimental results demonstrate the effectiveness of the two models in recognizing the noisy background signal, with promising performance in terms of accuracy, precision, recall, and F1 score. These results suggest that persistent volcanic signals share the same source information, as well as transient events, revealing a common generation mechanism but in different regimes. Moreover, assessing the dynamic state of a volcano through its background noise and promptly identifying any anomalies, which may indicate a change in its dynamics, can be a practical tool for real-time monitoring.

This study aimed at assessing the frequency and the distribution of influenza virus types/subtypes in 172 laboratory-confirmed influenza-positive patients admitted to intensive care units (ICUs) during the 2017–2018 season in the Lombardy region (Northern Italy), and to investigate the presence of molecular pathogenicity markers. A total of 102/172 (59.3%) patients had influenza A infections (83 A/H1N1pdm09, 2 H3N2 and 17 were untyped), while the remaining 70/172 (40.7%) patients had influenza B infections. The 222G/N mutation in the hemagglutinin gene was identified in 33.3% (3/9) of A/H1N1pdm09 strains detected in the lower respiratory tract (LRT) samples and was also associated with more severe infections, whereas no peculiar mutations were observed for influenza B strains. A single-point evolution was observed in site 222 of A/H1N1pdm09 viruses, which might advantage viral evolution by favouring virus binding and replication in the lungs. Data from 17 paired upper respiratory tract (URT) and LRT samples showed that viral load in LRT samples was mostly higher than that detected in URT samples. Of note, influenza viruses were undetectable in 35% of paired URT samples. In conclusion, LRT samples appear to provide more accurate clinical information than URT samples, thus ensuring correct diagnosis and appropriate treatment of patients with severe respiratory infections requiring ICU admission.

Hormone therapy (HT) in the menopause is still a tricky question among healthcare providers, women and mass media. Informing women about hormone replacement therapy was a Consensus Conference (CC) organized in 2008: the project Know the Menopause has been launched to shift out the results to women and healthcare providers and to assess the impact of the cc's statement. And Findings: The project, aimed at women aged 45-60 years, was developed in four Italian Regions: Lombardy, Tuscany, Lazio, Sicily, each with one Local Health Unit (LHU) as \"intervention\" and one as \"control\". Activities performed were: survey on the press; training courses for health professionals; educational materials for target populations; survey aimed at women, general practitioners (GPs), and gynaecologists; data analysis on HT drugs' prescription. Local activities were: training courses; public meetings; dissemination on mass media. About 3,700 health professionals were contacted and 1,800 participated in the project. About 146,500 printed leaflets on menopause were distributed to facilitate the dialogue among women and health care professionals. Training courses and educational cascade-process activities: participation ranged 25- 72% of GPs, 17-71% of gynaecologists, 14-78% of pharmacists, 34-85% of midwives. 1,281 women interviewed. More than 90% believed menopause was a normal phase in life. More than half did not receive information about menopause and therapies. HT prescription analysis: prevalence fell from 6% to 4% in five years. No differences in time trends before-after the intervention. Major limitations are: organizational difficulties met by LHU, too short time for some local activities. A huge amount of information was spread through health professionals and women. The issue of menopause was also used to discuss women's wellbeing. This project offered an opportunity to launch a multidisciplinary, multimodal approach to menopause looking not only at pharmacological aspects, but also at quality of life and information.

Diabetic patients are at higher risk of developing infectious diseases and severe complications, compared to the general population. Almost no data is available in the literature on influenza immunization in people with type 1 diabetes mellitus (T1DM). As part of a broader project on immunization in diabetic patients, we conducted a cross-sectional study to: (i) report on seasonal influenza coverage rates in T1DM patients, (ii) explore knowledge, attitudes, and practices (KAPs) towards seasonal influenza in this population, and (iii) identify factors associated with vaccine uptake, including the role of family doctors and diabetologists. A survey was administered to 251 T1DM patients attending the Diabetes Clinic at San Raffaele Research Hospital in Milan, Italy and individual-level coverage data were retrieved from immunization registries. Self-reported seasonal influenza immunization coverage was 36%, which decreased to 21.7% when considering regional immunization registries, far below coverage target of 75%. More than a third (36.2%) of T1DM patients were classified as pro-vaccine, 30.7% as hesitant, 17.9% as uninformed, and 15.1% as anti-vaccine. Diabetologists resulted to be the most trusted source of information on vaccines’ benefits and risks (85.3%) and should be more actively involved in preventive interventions. Our study highlights the importance of developing tailored vaccination campaigns for people with diabetes, including hospital-based programs involving diabetes specialists.

Precision agriculture is evolving toward a contemporary approach that involves multiple sensing techniques to monitor and enhance crop quality while minimizing losses and waste of no longer considered inexhaustible resources, such as soil and water supplies. To understand crop status, it is necessary to integrate data from heterogeneous sensors and employ advanced sensing devices that can assess crop and water status. This study presents a smart monitoring approach in agriculture, involving sensors that can be both stationary (such as soil moisture sensors) and mobile (such as sensor-equipped unmanned aerial vehicles). These sensors collect information from visual maps of crop production and water conditions, to comprehensively understand the crop area and spot any potential vegetation problems. A modular fuzzy control scheme has been designed to interpret spectral indices and vegetative parameters and, by applying fuzzy rules, return status maps about vegetation status. The rules are applied incrementally per a hierarchical design to correlate lower-level data (e.g., temperature, vegetation indices) with higher-level data (e.g., vapor pressure deficit) to robustly determine the vegetation status and the main parameters that have led to it. A case study was conducted, involving the collection of satellite images from artichoke crops in Salerno, Italy, to demonstrate the potential of incremental design and information integration in crop health monitoring. Subsequently, tests were conducted on vineyard regions of interest in Teano, Italy, to assess the efficacy of the framework in the assessment of plant status and water stress. Indeed, comparing the outcomes of our maps with those of cutting-edge machine learning (ML) semantic segmentation has indeed revealed a promising level of accuracy. Specifically, classification performance was compared to the output of conventional ML methods, demonstrating that our approach is consistent and achieves an accuracy of over 90% throughout various seasons of the year.

Background and aims. Parents’ empowerment is advocated to promote and preserve an informed and autonomous decision regarding their children’ immunization. The scope of this study is to develop and evaluate the psychometric properties of an instrument to measure parents’ psychological empowerment in their children’s vaccination decision and propose a context-specific definition of this construct. Materials and Methods. Grounding in previous qualitative data, we generated an initial pool of items which was later content and face validated by a panel of experts. A pretest allowed us to reduce the initial pool to 9 items. Convergent and discriminant validity measures included the General Self-Efficacy Scale, a Psychological Empowerment Scale, and the Control Preference Scale. Vaccination-related outcomes such as attitude and intention were also included. Results. Principal Component Analysis revealed a 2-factor structure, with each factor composed of 2 items. The first factor concerns the perceived influence of one’s personal and family experience with vaccination, while the second factor represents the desire not to ask other parents about their experience with vaccination and their lack of interest in other parents’ vaccination opinion. Conclusions. In light of its association with positive immunization- related outcomes, public health efforts should be directed to reinforce parents’ empowerment.

This work describes a system for supporting the user in the discovery of semantic web services, taking into account personal requirements and preference. Goal is to model an ad-hoc service request by selecting conceptual terms rather than using strict syntax formats. Through a concept-based navigation mechanism indeed, the user discovers conceptual terminology associated to the web resources and uses it to generate an appropriate service request which syntactical matches the names of input/output specifications. The approach exploits the fuzzy formal concept analysis for modeling concepts and relative relationships elicited from web resources. After the request formulation and submission, the system returns the list of semantic web services that match the user query.