Abstract
Introduction: The study of eating behaviour should consider the presence of potential sources of bias, including social desirability. This is particularly relevant among students of Nutrition Sciences, since they have a higher risk of eating disorders. Objective: To analyse the effect of social desirability in the assessment of eating behaviour dimensions among nutrition students. Methods: In this cross-sectional study, we analysed data from 149 students of Nutrition Sciences. Participants completed a questionnaire assessing social desirability and eating behaviour dimensions (emotional, external and binge eating, flexible and rigid control, and eating self-efficacy). Results: Among males, social desirability had a negative association with binge eating, while among women it had a negative association with emotional, external and binge eating and a positive association with eating self-efficacy. In both subsamples, social desirability showed no significant association with any of the two types of dietary restraint (rigid and flexible control). Discussion: Overall, the association between social desirability and eating behaviour dimensions among students of Nutrition Sciences occurs in the same direction as found in students from other areas. However, alongside these similarities, there is a stronger association between social desirability and binge eating among male students of Nutrition Sciences. We hypothesize that this may be related with the different knowledge of students from different areas, and the way they perceive and face the treatment of eating disorders. Conclusion: Our study shows that social desirability should be considered while assessing eating behaviour among nutrition students, particularly when studying external eating, binge eating and eating self-efficacy. Moreover, when tailoring interventions to reduce the possible effects of eating behaviour on nutritionists and dieticians' practice, we should consider the influence of social desirability.

Abstract
Objectives: Fluid overload (FO) is frequently present in peritoneal dialysis (PD) patients and is associated with markers of malnutrition, inflammation, and atherosclerosis/calcification (MIAC) syndrome. We examined the relationships in stable PD patients between phase angle (PhA) and the spectrum of uremic vasculopathy including vascular calcification and arterial stiffness and between PhA and changes in serum fetuin-A levels. Methods: Sixty-one stable adult PD patients were evaluated in a cross-sectional study (ST1). Phase angle was measured by multifrequency bioimpedance analysis (InbodyS10, Biospace, Korea) at 50 kHz. Augmentation index (AI), a surrogate marker of arterial stiffness, was assessed by digital pulse amplitude tonometry (Endo PAT, Itamar Medical, Caesarea, Israel). Vascular calcification was assessed by simplified calcification score (SCS). Serum fetuin-A levels were measured by ELISA (Thermo scientific; Waltham, MA, USA). Serum albumin was used as a nutritional marker, and serum C-reactive protein (CRP) was used as an inflammatory marker. The same assessments were carried out longitudinally (ST2) in the first 33 patients who completed 1 year of evaluation in ST1. Results: In ST1, patients with PhA < 6(omicron) had higher CRP levels, AI, and SCS and lower serum albumin and fetuin-A levels, in comparison with patients with PhA >= 6(omicron). In addition, PhA was a predictor of both AI (beta = -0.351, p = 0.023) and SCS >= 3 (EXP (B) = 0.243, p = 0.005). In ST2, the increase of PhA over time was associated with decreases in both AI (r = -0.378, p = 0.042) and CRP levels (r= -0.426, p = 0.021), as well as with the increase in serum fetuin-A levels (r = 0.411, p = 0.030). Conclusions: Phase angle predicts both arterial stiffness and vascular calcification in stable PD patients.

Abstract
Background/Aims: Personalised nutrition has potential to revolutionise dietary health promotion if accepted by the general public. We studied trust and preferences regarding personalised nutrition services, how they influence intention to adopt these services, and cultural and social differences therein. Methods: A total of 9,381 participants were quota-sampled to be representative of each of 9 EU countries (Germany, Greece, Ireland, Poland, Portugal, Spain, the Netherlands, the UK, and Norway) and surveyed by a questionnaire assessing their intention to adopt personalised nutrition, trust in service regulators and information sources, and preferences for service providers and information channels. Results: Trust and preferences significantly predicted intention to adopt personalised nutrition. Higher trust in the local department of health care was associated with lower intention to adopt personalised nutrition. General practitioners were the most trusted of service regulators, except in Portugal, where consumer organisations and universities were most trusted. In all countries, family doctors were the most trusted information providers. Trust in the National Health Service as service regulator and information source showed high variability across countries. Despite its highest variability across countries, personal meeting was the preferred communication channel, except in Spain, where an automated internet service was preferred. General practitioners were the preferred service providers, except in Poland, where dietitians and nutritionists were preferred. The preference for dietitians and nutritionists as service providers highly varied across countries. Conclusion: These results may assist in informing local initiatives to encourage acceptance and adoption of country-specific tailored personalised nutrition services, therefore benefiting individual and public health. (C) 2017 S. Karger AG, Basel

Abstract
Time has strong influence on web search. The temporal intent of the searcher adds an important dimension to the relevance judgments of web queries. However, lack of understanding their temporal requirements increases the ambiguity of the queries, turning retrieval effectiveness improvements into a complex task. In this paper, we propose an approach to classify web queries into four different categories considering their temporal ambiguity. For each query, we develop features from its search volumes and related queries using Google trends and its related top Wikipedia pages. Our experiment results show that these features can determine temporal ambiguity of a given query with high accuracy. We have demonstrated that a Multilayer Perceptron Networks can achieve better results in classifying temporal class of queries in comparison to other classifiers.

Abstract
Recently, many historical texts have become digitized and made accessible for search and browsing. Professionals who work with collections of such texts often need to verify the correctness of documents' key metadata-their creation dates. In this paper, we demonstrate an interactive system for estimating the age of documents. It may be useful not only for tagging a large number of undated documents, but also for verifying already known timestamps. In order to infer probable dates, we rely on a large scale lexical corpora, Google Books Ngrams. Besides estimating the document creation year, the system also outputs evidences to support age detection and reasoning process and allows testing different hypotheses about document's age.

Abstract
Many user information needs are strongly influenced by time. Some of these intents are expressed by users in queries issued indistinctively over time. Others follow a seasonal pattern. Examples of the latter are the queries "Golden Globe Award", "September 11th" or "Halloween", which refer to seasonal events that occur or have occurred at a specific occasion and for which, people often search in a planned and cyclic manner. Understanding this seasonal behavior, may help search engines to provide better ranking approaches and to respond with temporally relevant results leading into user's satisfaction. Detecting the diverse types of seasonal queries is therefore a key step for any search engine looking to present accurate results. In this paper, we categorize web search queries by their seasonality into 4 different categories: Non-Seasonal (NS, e.g., "Secure passwords"), Seasonal-related to ongoing events (SOE, "Golden Globe Award"), Seasonal-related to historical events (SHE, e.g., "September 11th") and Seasonal-related to special days and traditions (SSD, e.g., "Halloween"). To classify a given query we extract both time series (using the document publish date) and content features from its relevant documents. A Random Forest classifier is then used to classify web queries by their seasonality. Our experimental results show that they can be categorized with high accuracy. © 2017 Copyright held by the owner/author(s).