Multiple micronutrients and docosahexaenoic acid supplementation during pregnancy : A randomized controlled study

Maternal dietary intake during pregnancy needs to meet increased nutritional demands to maintain metabolism and to support fetal development. Docosahexaenoic acid (DHA) is essential for fetal neuro-/visual development and in immunomodulation, accumulating rapidly within the developing brain and central nervous system. Levels available to the fetus are governed by the maternal diet. In this multicenter, parallel, randomized controlled trial, we evaluated once-daily supplementation with multiple micronutrients and DHA (i.e., multiple micronutrient supplementation, MMS) on maternal biomarkers and infant anthropometric parameters during the second and third trimesters of pregnancy compared with no supplementation. Primary efficacy endpoint: change in maternal red blood cell (RBC) DHA (wt% total fatty acids) during the study. Secondary variables: other biomarkers of fatty acid and oxidative status, vitamin D, and infant anthropometric parameters at delivery. Supplementation significantly increased RBC DHA levels, the omega-3 index, and vitamin D levels. Subscapular skinfold thickness was significantly greater with MMS in infants. Safety outcomes were comparable between groups. This first randomized controlled trial of supplementation with multiple micronutrients and DHA in pregnant women indicated that MMS significantly improved maternal DHA and vitamin D status in an industrialized setting\u2014an important finding considering the essential roles of DHA and vitamin D

Proceedings of the Fifth Italian Conference on Computational Linguistics CLiC-it 2018

On behalf of the Program Committee, a very warm welcome to the Fifth Italian Conference on Computational Linguistics (CLiC-­‐it 2018). This edition of the conference is held in Torino. The conference is locally organised by the University of Torino and hosted into its prestigious main lecture hall “Cavallerizza Reale”. The CLiC-­‐it conference series is an initiative of the Italian Association for Computational Linguistics (AILC) which, after five 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

Sentiment Polarity Classification at EVALITA:Lessons Learned and Open Challenges

Sentiment analysis in social media is a popular task attracting the interest of the research community, also in recent evaluation campaigns of natural language processing tasks in several languages. We report on our experience in the organization of SENTIment POLarity Classification Task (SENTIPOLC), a shared task on sentiment classification of Italian tweets, proposed for the first time in 2014 within the Evalita evaluation campaign. We present the datasets - which include an enriched annotation scheme for dealing with the impact of figurative language on polarity - the evaluation methodology, and discuss the approaches and results of participating systems. We also offer a reflection on the open challenges of state-of-the-art systems for sentiment analysis of microblogging in Italian, as they emerge from a qualitative analysis of misclassified tweets. Finally, we provide an evaluation of the resources we have created, and share the lessons learned by running this task for two consecutive editions.</p

Diventare Ninagawa Mika

Tra le fotografe e registe nipponiche più note a livello internazionale, Ninagawa Mika rappresenta oggi anche un'icona pop del Sol Levante, grazie alla scelta di un cromatismo particolare e alle soluzioni ampiamente ispirate ai media di tutte le sue opere. Il volume rappresenta la traduzione della sua autobiografia, grazie alla quale ripercorriamo le tappe più significative degli sperimentalismi visuali del suo mondo

Sentiment Polarity Classification at EVALITA: Lessons Learned and Open Challenges

Sentiment analysis in social media is a popular task attracting the interest of the research community, also in recent evaluation campaigns of natural language processing tasks in sev- eral languages. We report on our experience in the organization of SENTIPOLC (SENTIment POLarity Classification Task), a shared task on sentiment classification of Italian tweets, proposed for the first time in 2014 within the Evalita evaluation campaign. We present the datasets – which include an enriched annotation scheme for dealing with the impact of figurative language on polarity – the evaluation methodology, and discuss the approaches and results of participating systems. We also offer a reflection on the open challenges of state-of-the-art systems for sentiment analysis of microblogging in Italian, as they emerge from a qualitative analysis of misclassified tweets. Finally, we provide an evaluation of the resources we have created, and share the lessons learned by running this task for two consecutive editions

Sentiment Polarity Classification at EVALITA: Lessons Learned and Open Challenges

Sentiment analysis in social media is a popular task attracting the interest of the research community, also in recent evaluation campaigns of natural language processing tasks in several languages. We report on our experience in the organization of SENTIment POLarity Classification Task (SENTIPOLC), a shared task on sentiment classification of Italian tweets, proposed for the first time in 2014 within the Evalita evaluation campaign. We present the datasets - which include an enriched annotation scheme for dealing with the impact of figurative language on polarity - the evaluation methodology, and discuss the approaches and results of participating systems. We also offer a reflection on the open challenges of state-of-the-art systems for sentiment analysis of microblogging in Italian, as they emerge from a qualitative analysis of misclassified tweets. Finally, we provide an evaluation of the resources we have created, and share the lessons learned by running this task for two consecutive editions

Successful treatment of chest NUT-carcinoma in a paediatric patient with a novel NUTM1 rearrangement: case report and review of literature

NUT carcinoma (NC) is an exceedingly rare and poorly differentiated carcinoma characterized by BDR4:NUTM1 gene translocation. Typically, the tumour affects young adults, and no standardized recommendations for therapeutic management are available since the clinical course is mostly dismal. We report the successful multimodal treatment in a 13-year-old boy affected by a primary chest NC with a novel NUTM1 rearrangement, that remains in complete continuous remission at 18 months from diagnosis