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Background: Telework satisfaction is a Public Health concern, intensified by the COVID-19 pandemic, and its determinant factors may be related with the negative health effects of teleworking. However, there is still little research exploring this issue. This study aimed to characterize telework during the first wave of the COVID-19 epidemic in Portugal and to identify the major predictors of telework satisfaction. Methods: This is a cross-sectional study aimed at all teleworkers working in Portugal, during the first wave of the COVID-19 epidemic. Data were collected through a Google Forms platform online questionnaire distributed by a snowball method on social networks. Descriptive statistics included crude and relative frequency data. The associations between sociodemographic characteristics, self-perceived health, organization of working time, concentration at work, work-life balance, work disconnection, working conditions, and organizational demands (flexibility and organizational trust based on E-work Life Scale) with telework satisfaction were estimated through logistic regression. Results: This study included 1004 participants. Teleworkers satisfaction levels were high (69%). Better concentration at work (OR = 1.54; 95%CI 1.01–2.34); the satisfaction with the balance between work life and extra work when teleworking (OR = 1.79; 95%CI 1.17–2.74); and higher work flexibility (OR = 2.26; 95%CI 1.46–3.49) were good predictors of greater levels of satisfaction with telework. However, its major predictors were the company’s trust in teleworkers (OR = 4.50; 95%CI 2.89–7.02) and feeling good in the workspace at home (OR = 3.72; 95%CI 1.46–9.49). Conclusions: Our findings point that work environment and organizational culture play a crucial role in affecting telework satisfaction. More studies are needed to monitor telework satisfaction and its effects on physical and mental health, so that Public and Occupational Health (and Safety) can be able to identify and implement the best interventions that allow promoting individual health and foster a healthy work environment for teleworkers. publishersversion published

Modern personal assistants require to access unstructured information in order to successfully fulfill user requests. In this paper, we have studied the use of two machine learning components to design personal assistants: intent classification, to understand the user request, and answer sentence selection, to carry out question answering from unstructured text. The evaluation results derived on five different real-world datasets, associated with different companies, show high accuracy for both tasks. This suggests that modern QA and dialog technology is effective for real-world tasks.

On behalf of the Program Committee, a very warm welcome to the Seventh Italian Conference on Computational Linguistics (CLiC-it 2020). This edition of the conference is held in Bologna and organised by the University of Bologna. The CLiC-it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after six years of activity, has clearly established itself as the premier national forum for research and development in the fields of Computational Linguistics and Natural Language Processing, where leading researchers and practitioners from academia and industry meet to share their research results, experiences, and challenges.

Introduction: Health professionals who provide clinical care are exposed to patients potentially infected by the severe acute respiratory syndrome coronavirus 2 SARS-CoV-2), namely physicians and nurses; consequently, these professionals face higher risks of infection. Objectives: This study aimed to describe the prevalence of coronavirus disease 19 (COVID-19) cases among health professionals and the frequencies of risk factors and psychosocial risk. Methods: This is a cross-sectional study targeted at health professionals working in Portugal during the current COVID-19 pandemic. Data were obtained through a self-administered questionnaire available online at the websites of medical and nursing boards, among other sources. We performed a univariate analysis, calculating absolute and relative frequencies, and a bivariate analysis with a Pearson's chi-squared test. Results: We studied 4,212 health professionals, of which 36.7% (n = 1,514) worked in areas dedicated to the treatment of sick or suspected COVID-19 patients. Of these, 2.11% tested positive for SARS-CoV-2. Among all participants, 76.7% and 79.1% presented moderate to severe levels of fatigue and anxiety, respectively. Fatigue levels were significantly higher in professionals working in areas dedicated to the treatment of patients with COVID-19 (80.5% p = 0.01), but this difference was not observed regarding anxiety (79.5% p = 0.681). Conclusions: The percentage of health professionals who tested positive for SARS-CoV-2 was 2.11%. The reported high levels of fatigue and anxiety should determine a better protection of the health and safety of those who provide health care in the current pandemic. publishersversion published

Background: In the last years, several diseases have been associated with alterations in microRNA (miRNA) expression, and miRNA profiling has proved to be useful to detect or evaluate disease progression. Moreover, there has been attempts to identify differentially expressed (DE) miRNA after exposure to toxicants that could be used as biomarkers of effect. Since human exposure to nanofibres, such as the MWCNT-7, has considerably increased due to their incorporation in several consumer products, this work explores the use of DE miRNA for monitoring human exposure to MWCNT-7 and their effects. Methods: We analyzed DE miRNAs in A549 alveolar cells following 24 hours exposure to an occupationally relevant dose of MWCNT-7. Total RNA was used to generate cDNA libraries for sequencing in MiSeq (Illumina). Differential expression analysis of miRNAs between non-exposed and exposed cells was performed using DESeq2 and EdgeR Exact Test. Results: Several under- and over-expressed miRNAs were sequenced in A549 cells after 24 h exposure to MWCNT-7. After filtering them according to the criteria adopted, a set of 17 DE miRNAs was identified. Some of these miRNAs have been reported as regulators of pathways related to cell growth, proliferation, differentiation and survival. Conclusions: Although more studies are needed, it is likely that the miRNA profile here identified may provide a novel biomarker of human exposure to MWCNT-7 by inhalation. Funded by FCT/MCTES, Project ToxApp4NanoCELFI (PTDC/SAU-PUB/32587/2017) through national funds (PIDDAC), ToxOmics (UID/BIM/00009/2013), and GenomePT project (POCI-01-0145-FEDER-022184). info:eu-repo/semantics/publishedVersion

info:eu-repo/semantics/publishedVersion

Background and Aims Multi-walled carbon nanotubes (MWCNT) are widely used engineered nanomaterials in industrial and biomedical applications, but their inhalation may induce pulmonary adverse effects. Moreover, the MWCNT-7 (Mitsui-7) has been classified as possibly carcinogenic to humans. Nevertheless, MWCNT-7 molecular modes of action are poorly understood and there are no biomarkers of exposure or effect for occupational monitoring. Recently, several pulmonary diseases, including lung cancer, have been associated with alterations in microRNA expression. These changes are potentially useful to detect or evaluate disease progression and to understand the molecular effects induced by the disease or toxicant. In this study, we describe a set of differentially expressed microRNAs (DE miRNAs) in A549 epithelial alveolar cells, following exposure to an occupationally relevant dose of MWCNT-7 or crocidolite. The functional pathways and ontology terms enriched with genes targeted by DE miRNAs were identified. Moreover, an interaction network of DE miRNAs-target cancer genes was constructed. Foundation for Science and Technology, Centre for Toxicogenomics and Human Health ToxOmics under grant UID/BIM/00009 2013 and by ToxApp 4 NanoCELFI (PTDC/SAU PUB/ 32587 2017), GenomePT project (POCI 01 0145 FEDER 022184 supported by COMPETE 2020 Operational Programme for Competitiveness and Internationalisation ( Lisboa Portugal Regional Operational Programme (Lisboa 2020 Algarve Portugal Regional Operational Programme (CRESC Algarve 2020 under the PORTUGAL 2020 Partnership Agreement) N/A

La collana pubblica gli atti del convegno annuale di Linguistica Computazionale (CLiC-it), che ha lo scopo di costituire un luogo di discussione di riferimento nel campo delle ricerce sulla linguistica computazionale. Gli atti includono interventi sul trattamento automatico della lingua, comprendenti le riflessioni teoriche e metodologiche sul tema, e forniscono un contributo importante per questo campo di ricerca. Le altre tematiche principali sono: la linguistica computazionale, la linguistica, le scienze cognitive, l'apprendimento automatico, l'informatica, la rappresentazione della conoscenza, l'information retrieval e l'umanistica digitale. L'organizzazione del convegno è il risultato dello sforzo dell'Associazione Italiana di Linguistica Computazionale (AILC http://www.ai-lc.it/), rappresentata ogni anno da alcuni dei membri organizzatori, che sono affiliati anche ad altre organizzazioni che operano nell'ambito della linguistica computazionale. This book presents the proceedings of the annual Conference of Computational Linguistics (CLiC-it), which aims to provide a forum for discussion in the field of computational linguistics. The proceedings include interventions on the automatic treatment of language, with theoretical and methodological reflections on the subject, and provide an important contribution to this field of research. The other main topics are: computational linguistics, linguistics, cognitive sciences, automatic learning, computer science, knowledge representation, information retrieval and digital humanities. The organization of the conference is the result of the effort of the Italian Association of Computational Linguistics (AILC http://www.ai-lc.it/), represented every year by some of the organizing members, who are also affiliated to other organizations operating in the field of computational linguistics.

In this paper, we describe our experience on using current methods developed for Community Question Answering (cQA) for a commercial application focused on an Italian help desk. Our approach is based on (i) a search engine to retrieve previously answered question candidates and (ii) kernel methods applied to advanced linguistic structures to rerank the most promising candidates. We show that methods developed for cQA work well also when applied to data generated in customer service scenarios, where the user seeks for explanation about products and a database of previously answered questions is available. The experiments with our system demonstrate its suitability for an industrial scenario. In questo articolo, descriviamo la nostra esperienza nell’usare i metodi attualmente disponibili per il Community Question Answering (cQA) in un’applicazione commerciale riguardante il servizio clienti in lingua italiana. Il nostro approccio si basa su (i) un motore di ricerca per recuperare le domande candidate precedentemente risposte e (ii) metodi kernel applicati a strutture linguistiche avanzate per riordinare i candidati più promettenti. Mostriamo che i metodi sviluppati per il cQA funzionano bene anche quando applicati ai dati generati nell’ambito dell’assistenza clienti, dove l’utente cerca informazioni riguardo a dei prodotti e una base di dati di domande precedentemente risposte è disponibile. Gli esperimenti sul nostro sistema dimostrano l’appropriatezza del suo utilizzo in uno scenario industriale.

The annual conference CLIC–it (''Italian Conference on Computational Linguistics'') is an initiative of the ''Italian Association of Computational Linguistics'' (AILC – www.ai-lc.it) which is intended to meet the need for a national and international forum for the promotion and dissemination of high-level original research in the field of Computational Linguistics (CL), with particular emphasis on Italian. The volume gathers the Proceedings of the ''Third Italian Conference on Computational Linguistics'' (CLiC–it 2016), held in Naples on 5-6 December 2016. The CLiC–it 2016 papers cover a wide range of topics in the area of computational linguistics and natural language (both written and spoken) processing, by targeting state–of–art theoretical results, experimental methodologies, technologies and application perspectives, and by addressing challenges, open issues and new perspectives related to current and novel trends of the discipline.

This paper aims at filling the gap between the accuracy of Italian and English constituency parsing: firstly, we adapt the Bllip parser, i.e., the most accurate constituency parser for English, also known as Charniak parser, for Italian and trained it on the Turin University Treebank (TUT). Secondly, we design a parse reranker based on Support Vector Machines using tree kernels, where the latter can effectively generalize syntactic patterns, requiring little training data for training the model. We show that our approach outperforms the state of the art achieved by the Berkeley parser, improving it from 84.54 to 86.81 in labeled F1. Questo paper mira a colmare il gap di accuratezza tra il constituency parsing dell’Italiano e quello Inglese: come primo miglioramento, abbiamo adattato il parser a costituenti per l’Inglese, Bllip, anche noto come Charniak parser, per l’Italiano e lo abbiamo addestrato sul Turin University Treebank. In seguito, abbiamo progettato un reranker basato sulle Macchine a Vettori di Supporto che usano kernel arborei, i quali possono efficacemente generalizzare pattern sintattici, richiedendo pochi dati di training per addestrare il modello. Il nostro approccio supera lo stato dell’arte ottenuto con il Berkeley parser, migliorando la labeled F1 da 84.54 a 86.81.