Abstract
Teaching/educating patients with end stage renal disease (ESRD) and identifying their self-care behaviors for vascular network preservation are very important. However, the self-care behaviors regularly performed by patients are still unknown. We compared self-care behaviors for vascular network preservation performed by patients who are/are not followed-up by the nephrologist. The study design was a prospective, observational and comparative study. Inclusion criteria were as follows: ESRD patients (at stages 4 or 5); at least 18 years old; in pre-dialysis with at least a 6-month follow-up period by the nephrologist or who started dialysis in emergency and were not followed-up by the nephrologist; with no memory problems; and medically stable. Primary outcome was the frequency of self-care behaviors for vascular network preservation. Secondary outcome was the comparison between self-care behaviors by ESRD patients who were/were not followed-up by the nephrologist. The study involved 145 patients, 64.1% were female, the mean age was 69.5 years and the self-care behaviors mean score was 36.8% (with a SD of 39.8%). The number of patients followed-up and not followed-up by the nephrologist was 109 (group 1) and 36 (group 2), respectively. Social characteristics were similar in the two groups (P > 0.05). The mean self-care behaviors were 29.4% and 59.2% in groups 1 and 2, respectively (P = 0.000). Patients performed self-care behaviors for vascular network preservation with a relatively low frequency (the mean score was 36.8% only). Patients not followed by the nephrologist performed self-care behaviors more often than those who were followed (59.2% vs. 29.4% respectively, P = 0.000).

Abstract
The size of a farm is one of the factors that influence its productivity, in an ambiguous relationship that is often discussed in the industrial economy. In Portugal, the Demarcated Douro Region (DDR) is characterized by very small farms. Usually, this trend is considered a limitating factor in the profitability of the wine farms. In order to assess the correctness of this sentence, the variation of wine productivity per land size, from 2010 to 2016, was studied in the DDR, considering its three distinctive areas: Baixo Corgo, Cima Corgo and Douro Superior. The farms were categorized in nine different size ranges; as these variables outnumber the available seven observations, the Generalized Maximum Entropy (GME) estimator was used, since it suits the need to solve an ill-conditioned problem. GME was applied with the MATLAB (MATrix LABoratory) software along with the Bootstrap technique. According to the simulations, larger farms (with an area greater than 20 ha) on Douro Superior and Cima Corgo reveal higher marginal productivity given the current state of the region. On the other hand, Baixo Corgo's results suggest that medium-sized farms (with area ranges between 2 and 5 ha) display higher marginal increments to the region wine productivity. © 2018 UniCeSV, University of Florence

Abstract
Understanding the role of the climate on the wine production is one of the major concerns of this sector since the environment usually determines the output of this industry. There are only a few previous studies that attempted to compile these environmental effects as an index, usually considering the temperature and the precipitation as their core variables. The present study suggests a new climate index which is based on descriptive statistics. Our index tries to mimic the target region characteristics and avoid the past studies premise of imposing previously conceived restrictions such as a fixed optimal climate. We then used yearly production and daily temperature data (1950-2016) from the Portuguese Minho wine region to test our proposed index and compare it with Ribéreau-Gayon and Peynaud (RGP, Ribéreau-Gayon et al., 2003) and Growing Degree-Days (GDD, Winkler et al., 1974) indexes. Our results showed that the newly proposed index may outperform the explanatory power of the other indexes and, in addition, may output interesting and unknown characteristics such as the different ideal temperatures regarding the studied region. © The Authors, published by EDP Sciences, 2018.

Abstract
The burst of social networks and the possibility of being continuously connected has provided a fast way for information diffusion. More specifically, real-time posting allowed news and events to be reported quicker through social networks than traditional news media. However, the massive data that is daily available makes newsworthy information a needle in a haystack. Therefore, our goal is to build models that can detect journalistic relevance automatically in social networks. In order to do it, it is essential to establish a ground truth with a large number of entries that can provide a suitable basis for the learning algorithms due to the difficulty inherent to the ambiguity and wide scope associated with the concept of relevance. In this paper, we propose and compare two different methodologies to annotate posts regarding their relevance: automatic and human annotation. Preliminary results show that supervised models trained with the automatic annotation methodology tend to perform better than using human annotation in a test dataset labeled by experts.

Abstract
Sentiment lexicons are an essential component on most state-of-the-art sentiment analysis methods. However, the terms included are usually restricted to verbs and adjectives because they (1) usually have similar meanings among different domains and (2) are the main indicators of subjectivity in the text. This can lead to a problem in the classification of short informal texts since sometimes the absence of these types of parts of speech does not mean an absence of sentiment. Therefore, our hypothesis states that knowledge of terms regarding certain events and respective sentiment (public opinion) can improve the task of sentiment analysis. Consequently, to complement traditional sentiment dictionaries, we present a system for lexicon expansion that extracts the most relevant terms from news and assesses their positive or negative score through Twitter. Preliminary results on a labelled dataset show that our complementary lexicons increase the performance of three state-of-the-art sentiment systems, therefore proving the effectiveness of our approach.

Abstract
Misinformation propagation on social media has been significantly growing, reaching a major exposition in the 2016 United States Presidential Election. Since then, the scientific community and major tech companies have been working on the problem to avoid the propagation of misinformation. For this matter, research has been focused on three major sub-fields: the identification of fake news through the analysis of unreliable posts, the propagation patterns of posts in social media, and the detection of bots and spammers. However, few works have tried to identify the characteristics of a post that shares unreliable content and the associated behaviour of its account. This work presents four main contributions for this problem. First, we provide a methodology to build a large knowledge database with tweets who disseminate misinformation links. Then, we answer research questions on the data with the goal of bridging these problems to similar problem explored in the literature. Next, we focus on accounts which are constantly propagating misinformation links. Finally, based on the analysis conducted, we develop a model to detect social media accounts that spread unreliable content. Using Decision Trees, we achieved 96% in the F1-score metric, which provides reliability on our approach. Copyright 2018 by SCITEPRESS – Science and Technology Publications, Lda. All rights reserved