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AbstractIntroductionIrritable Bowel Syndrome (IBS) is a functional and multifactorial gastrointestinal disorder characterized by pain, abdominal distention and motility changes, currently diagnosed based on the Rome IV criteria. The efficacy of classic pharmacological, psychological and dietary treatments for this condition are generally low. The Fermentable Oligosaccharides, Disaccharides, Monosaccharides and Polyols (FODMAP) are short chain carbohydrates poorly absorbed at the intestinal level, fermentable by the microbiota and presumably involved in IBS-associated symptomatology.AimsTo evaluate the efficacy, feasibility and acceptability of a FODMAP-restricted feeding approach in the relief of symptomatology and in the improvement of the quality of life of patients with IBS, determining the reintroduction of the FODMAP food subgroup(s) involved in the symptom exacerbation.Materials and MethodsAfter assessing the existence of initial emotional disorders through the Hospital Anxiety and Depression Scale (HADS) and eating habits, through dietary history, patients diagnosed with IBS were put on a FODMAP-restricted diet for 6 weeks. During this period, the weekly evolution of symptom frequency was assessed. At the end, tests were undertaken to discover the global evolution of the symptoms through the Irritable Bowel Syndrome - Global Assessment Scale (IBS-GAI), the severity of symptomatology through the Irritable Bowel Syndrome - Severity Scoring System (IBS-SSS) and quality of life through Irritable Bowel Syndrome - Quality of Life (IBS-QoL).Subsequently, participants tested their tolerance to various FODMAP subtypes with weekly and isolated reintroduction of these in their diet.ResultsIn the 36 participants, with a mean age of 38.8 years, there was a reduction in the total consumption of FODMAP from 22.1 g to 2.1 g (p < 0.001). A moderate or substantial improvement in the IBS-GAI was observed in 88.9%. An average reduction of 235 points in the IBS-SSS (p < 0.001) and a mean increase of 28.7 in the IBS-QoL (p < 0.001) was achieved. The initial anxiety and depression levels were not associated with IBS-SSS and presented an inverse association with the IBS-QoL. There was significant improvement in all symptomatology during the 1st week of total FODMAP restriction, except for constipation with an amelioration observed at the 6st week. There was a frequency of intolerance ranging from 30.8% for fructans to 80.8% for lactose with the reintroduction of the FODMAP subtypes.ConclusionA FODMAP-restricted diet, implemented over a period of 6 weeks, is effective in reducing the severity and frequency of GI symptoms and improving the quality of life of portuguese patients with IBS.

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Users tend to search over the Internet to get the most updated news when an event occurs. Search engines should then be capable of effectively retrieving relevant documents for event-related queries. As the previous studies have shown, different retrieval models are needed for different types of events. Therefore, the first step for improving effectiveness is identifying the event-related queries and determining their types. In this paper, we propose a novel model based on deep neural networks to classify event-related queries into four categories: periodic, aperiodic, one-time-only, and non-event. The proposed model combines recurrent neural networks (by feeding two LSTM layers with query frequencies) and visual recognition models (by transforming time-series data from a 1D signal to a 2D image - later passed to a CNN model) for effective query type estimation. Worth noting is that our method uses only the time-series data of query frequencies, without the need to resort to any external sources such as contextual data, which makes it language and domain-independent with regards to the query issued. For evaluation, we build upon the previous datasets on event-related queries to create a new dataset that fits the purpose of our experiments. The obtained results show that our proposed model can achieve an F1-score of 0.87.

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Event extraction (EE) is one of the core information extraction tasks, whose purpose is to automatically identify and extract information about incidents and their actors from texts. This may be beneficial to several domains such as knowledge base construction, question answering and summarization tasks, to name a few. The problem of extracting event information from texts is longstanding and usually relies on elaborately designed lexical and syntactic features, which, however, take a large amount of human effort and lack generalization. More recently, deep neural network approaches have been adopted as a means to learn underlying features automatically. However, existing networks do not make full use of syntactic features, which play a fundamental role in capturing very long-range dependencies. Also, most approaches extract each argument of an event separately without considering associations between arguments which ultimately leads to low efficiency, especially in sentences with multiple events. To address the above-referred problems, we propose a novel joint event extraction framework that aims to extract multiple event triggers and arguments simultaneously by introducing shortest dependency path in the dependency graph. We do this by eliminating irrelevant words in the sentence, thus capturing long-range dependencies. Also, an attention-based graph convolutional network is proposed, to carry syntactically related information along the shortest paths between argument candidates that captures and aggregates the latent associations between arguments; a problem that has been overlooked by most of the literature. Our results show a substantial improvement over state-of-the-art methods on two datasets, namely ACE 2005 and TAC KBP 2015.

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