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INTRODUCTION: Despite a government-subsidized vaccination program, healthcare personnel (HCP) influenza vaccination uptake remains low in Peru. Using three years of cross-sectional surveys and an additional five years of prior vaccination history of HCP in Peru, we explored HCP knowledge, attitudes, and practices (KAP) of influenza illness and its impact on vaccination frequency. METHODS: In 2016, the Estudio Vacuna de Influenza Peru (VIP) cohort was initiated in Lima, Peru, which collected information about HCP KAP and influenza vaccination history from 2011─2018. HCP were classified by their 8-year influenza vaccination history as never (0 years), infrequently (1─4 years), or frequently (5─8 years) vaccinated. Logistic regression models were used to describe KAP associated with frequent compared to infrequent influenza vaccination, adjusted for each HCP's healthcare workplace, age, sex, preexisting medical conditions, occupation, and length of time providing direct patient care. RESULTS: From 2016─2018, 5131 HCP were recruited and 3120 fully enrolled in VIP; 2782 consistently reported influenza vaccination status and became our analytic sample. From 2011─2018, 14.3% of HCP never, 61.4% infrequently, and 24.4% frequently received influenza vaccines. Compared to HCP who were infrequently vaccinated, frequently vaccinated HCP were more likely to believe they were susceptible to influenza (adjusted odds ratio [aOR]:1.49, 95% confidence interval [CI]:1.22─1.82), perceived vaccination to be effective (aOR:1.92, 95%CI:1.59─2.32), were knowledgeable about influenza and vaccination (aOR:1.37, 95%CI:1.06─1.77), and believed vaccination had emotional benefits like reduced regret or anger if they became ill with influenza (aOR:1.96, 95%CI:1.60─2.42). HCP who reported vaccination barriers like not having time or a convenient place to receive vaccines had reduced odds of frequent vaccination (aOR:0.74, 95%CI:0.61─0.89) compared to those without reported barriers. CONCLUSION: Few HCP frequently received influenza vaccines during an eight-year period. To increase HCP influenza vaccination in middle-income settings like Peru, campaigns could strengthen influenza risk perception, vaccine knowledge, and accessibility.

Social Networking Sites (SNS) have entailed a revolution for society. They have given a say to everyone regardless of their status and this has been translated into loads of data. The task of profiling users constitutes a way to learn from this data in order to show users only the content that is relevant to them. Several recommendation system techniques have been used to address this problem, being mainly based on what the user explicitly says about themselves, on what the user publishes in the SNS and on the similarities between users. However, in social media context, it is also possible to use relations between users. Considering basic relations like follower or followee to extract information from them may result in noise, since they do not imply that users share interest or even ideas. In this work, we present a fuzzy framework to enrich user profiles with complex properties in order to have an even better representation of them. We use basic relations defined by SNSs to complete the information available in user profiles with topics of interest and ideas towards them and to define deep relations that will enable new ways of analysis. We use these deep relations to create clusters of similar users that, ultimately, will allow the expansion of properties from known users to the rest of the cluster. We tested our proposal with a dataset of Tweets in Spanish related to a political event. Our experiments prove the potential that this approach has for a lot of applications in microblogging context.

This work presents FuzzyFeatureRank, a new feature reduction method inspired on PageRank to reduce the dimensionality of the feature space in supervised classification problems. More precisely, as it relies on a weighted directed graph, it is ultimately inspired on TextRank, a PageRank based method that adds weights to the edges to express the strength of the connections between nodes. The method is based on dividing each original feature used to describe the data into a set of fuzzy predicates and then ranking all of them by their ability to differentiate among classes in the light of the training set. In order to do that, both the information gained by each predicate and their redundancy with other already selected predicates are taken into account. The fuzzy predicates with the best scores can then be used as a reduced input to construct fuzzy classifiers that consider only the preselected predicates to build the antecedents of the fuzzy rules. The novelty of the proposal relies on being an approach halfway between feature selection and feature extraction approaches, being able to improve the discrimination ability of the original features but preserving the interpretability of the new features in the sense that they are fuzzy expressions. The experimental results support the suitability of the proposal.

Objectives This study examined the effects of sacubitril/valsartan on N-terminal pro–B-type natriuretic peptide (NT-proBNP) levels and determined patient characteristics associated with favorable NT-proBNP reduction response. Background NT-proBNP levels reflect cardiac wall stress and predict event risk in patients with acute decompensated heart failure (ADHF). Methods Post-hoc analysis of the TRANSITION (Comparison of Pre- and Post-discharge Initiation of Sacubitril/Valsartan Therapy in HFrEF Patients After an Acute Decompensation Event) study, including stabilized ADHF patients with reduced ejection fraction, randomized to open-label sacubitril/valsartan initiation in-hospital (pre-discharge) versus post-discharge. NT-proBNP was measured at randomization (baseline), discharge, and 4 and 10 weeks post-randomization. A favorable NT-proBNP response was defined as reduction to ≤1,000 pg/ml or >30% from baseline. Results In patients receiving sacubitril/valsartan in-hospital, NT-proBNP was reduced by 28% at discharge, with 46% of patients obtaining favorable NT-proBNP reduction response compared with a 4% reduction and 18% favorable response rate in patients initiated post-discharge (p Conclusions In-hospital initiation of sacubitril/valsartan produced rapid reductions in NT-proBNP, statistically significant at discharge. A favorable NT-proBNP response over time was associated with a better prognosis and predicted by higher starting dose and predisposing clinical profile. (Comparison of Pre- and Post-discharge Initiation of LCZ696 Therapy in HFrEF Patients After an Acute Decompensation Event [TRANSITION]; NCT02661217)

This study aimed to assess and compare the influence of alcohol intake and cannabis smoking on different visual functions. A total of 64 young and healthy volunteers took part in the study. All undertook several randomised experimental sessions in which different visual functions, namely distance stereopsis, retinal straylight, visual discrimination capacity, and contrast sensitivity, were tested. Cannabis smokers (N = 30) took a baseline session and a session after smoking a cannabis cigarette, whereas alcohol users (N = 34) underwent a baseline session, a session after a low alcohol intake (Alcohol 1), and a session after a moderate to high alcohol intake (Alcohol 2). All visual functions were impaired by cannabis and alcohol use, particularly for the Cannabis and Alcohol 2 groups. The deterioration of all visual variables was higher for the Alcohol 2 than for the Alcohol 1 and Cannabis groups, except for retinal straylight, the deterioration of which was equal for the Cannabis group, and distant stereopsis, which was more impaired for the Cannabis group. The Alcohol 2 group experienced the most impairing conditions, although very similar to the cannabis group, and that factors other than the experimental conditions, such as sex and age, also influenced these visual changes. Alcohol and cannabis use clearly impair vision. The deterioration caused by cannabis is similar to, but slightly lower than, that produced by a moderate to high alcohol intake, with the experimental conditions, sex and age all having an impact on the variability of visual deterioration.

Two symmetrical diacylguanidines, triophamine (1) and limaciamine (2), have been found to occur in three Polyceridae nudibranchs from Canary Islands. These compounds were previously reported from taxonomically related species collected from distinct geographical areas. Due to the peculiar occurrence of 1 and 2 in Polyceridae nudibranchs and, in particular, exclusively in members of Polycerinae and Triophinae subfamilies, it should be suggested that these diacylguanidines are distinctive chemical markers of this group of nudibranchs.

Lysosomes fulfill multiple functions involved in cellular homeostasis and their dysfunction leads to substrates accumulation causing neurodegenerative diseases. The transcription factor EB (TFEB) has emerged as a master regulator of lysosomal biogenesis, exocytosis and autophagy, promoting the clearance of substrates stored in cells. c-Abl is a tyrosine kinase that participates in cellular signaling in physiological and pathophysiological conditions. In this study, we explored the connection between c-Abl and TFEB. Here we show that under pharmacological and genetic c-Abl inhibition, TFEB translocates into the nucleus promoting the expression of its target genes independently of its well-known regulator, mTORC1. Active c-Abl induces TFEB phosphorylation on tyrosine and the inhibition of this kinase promotes lysosomal biogenesis, autophagy and exocytosis. c-Abl inhibition in pharmacological Niemann Pick type-C (NPC) models, a neurodegenerative disease characterized by cholesterol accumulation in lysosomes, promotes a cholesterol-lowering effect in a TFEB dependent manner. In genetic NPC models, c-Abl inhibition promotes cellular clearance through TFEB activation. We propose the modulation of this novel c-Abl/TFEB axis, using the FDA-approved drugs Imatinib or Nilotinib, as a therapeutic strategy to treat different neurodegenerative storage disorders.

Salmonella Typhimurium is an intracellular pathogen that is capable of generating systemic fever in a murine model. Over the course of the infection, Salmonella faces different kinds of stressors, including harmful reactive oxygen species (ROS). Various defence mechanisms enable Salmonella to successfully complete the infective process in the presence of such stressors. The transcriptional factor SlyA is involved in the oxidative stress response and invasion of murine macrophages. We evaluated the role of SlyA in response to H2O2 and NaOCl and found an increase of slyA expression upon exposure to these toxics. However, the SlyA target genes and the molecular mechanisms by which they influence the infective process are unknown. We hypothesised that SlyA regulates the expression of genes required for ROS resistance, metabolism, or virulence under oxidative stress conditions. Transcriptional profiling in wild type and ΔslyA strains confirmed that SlyA regulates the expression of several genes involved in virulence [sopD (STM14_3550), sopE2 (STM14_2244), hilA (STM14_3475)] and central metabolism [kgtP (STM14_3252), fruK (STM14_2722), glpA (STM14_2819)] in response to H2O2 and NaOCl. These findings were corroborated by functional assay and transcriptional fusion assays using GFP. DNA-protein interaction assays showed that SlyA regulates these genes through direct interaction with their promoter regions.

Recently, the Spanish olive sector has undergone deep changes regarding agronomic practices. Olive grove cultivation tends to move from traditional low-density to new high-density cropping systems. Irrigation has produced a major change in the olive grove sector and the integrated production plays an important role due to the application of rational farming techniques. The aim of this study was to compare the environmental impacts of a high diversity of olive growing systems existing in Spain, including the integrated production systems. The Life Cycle Assessment methodology has been used to calculate and evaluate potential environmental impacts associated to the olives production phase from the extraction of the raw materials to the oil mill gate. Eight traditional systems, three intensive systems and one super-intensive system were selected to be compared. The agricultural practices were grouped in different stages each one including processes and flows: irrigation, soil management, pruning, fertilizers, pesticides and harvesting. Impact categories such as climate change, acidification, freshwater eutrophication, freshwater ecotoxicity, land use and water resource depletion were selected. The fertilizers stage was clearly the highest contributor in all impact categories, specifically, in the climate change and acidification categories in the intensive not irrigated conventional system. Thus, optimization of fertilization should be the first priority to optimize olive growing. The systems that showed the largest environmental impact for most categories were the intensive irrigated integrated system and the super-intensive irrigated integrated system, despite having higher productivity compared to the rest. The organic systems presented the lowest impacts but should improve their productivity. Integrated production was the best olive fruit production system from an overall environmental and productive point of view, especially the traditional mechanized systems.

In both statistical non-inferiority (NI) and superiority (S) tests, the critical region must be a Barnard convex set for two main reasons. One, being computational in nature, based on the fact that calculating test sizes is a computationally intensive problem due to the presence of a nuisance parameter. However, this calculation is considerably reduced when the critical region is a Barnard convex set. The other reason is that in order for the NI/S statistical tests to make sense, its critical regions must be Barnard convex sets. While it is indeed possible for NI/S tests’ critical regions to not be Barnard convex sets, for the reasons stated above, it is desirable that they are. Therefore, it is important to generate, from a given NI/S test, a test which guarantees that the critical regions are Barnard convex sets. We propose a method by which, from a given NI/S test, we construct another NI/S test, ensuring that the critical regions corresponding to the modified test are Barnard convex sets, we illustrate this through examples. This work is theoretical because the type of developments refers to the general framework of NI/S testing for two independent binomial proportions and it is applied because statistical tests that do not ensure that their critical regions are Barnard convex sets may appear in practice, particularly in the clinical trials area.

Historical capture records of bluefin tuna (Thunnus thynnus; BFT hereafter) from the Gibraltar Strait and Western Mediterranean show pronounced short- and long-term fluctuations. Some of these fluctuations are believed to be associated with biological and ecological process, as well as distinct climate factors. For the period of study (1700–1936) of this work, we found a long-term increasing trend in the BFT captures and in the climate variables. After applying a statistical time series analysis of relevant climate variables and long-term tuna capture records, it is highlighted the role played by sea-surface temperature (SST) on bluefin population variations. The most relevant result of this study is the strong correlation found between the total solar irradiance (TSI) — an external component of the climate system — and bluefin captures. The solar irradiance could have affected storminess during the period under study, mainly during the time interval 1700–1810. We suggest physico-biological mechanisms that explain the BFT catch fluctuations in two consecutive time intervals. In the first period, from 1700 to 1810, this mechanism could be high storm and wind activity, which would have made the BFT fisheries activities more difficult by reducing their efficacy. In contrast, during the interval from 1810 to 1907, the effects of wind and storms could be on spawning behaviour and larval ecology, and hence on year class strength, rather than on fish or fisherman's behaviour. These findings open up a range of new lines of enquiry that are relevant for both, fisheries and climate change research.

We model electricity demand of a city in a complex way including appliances and users.We propose a scalable real time method for managing the demand.We implement this method using a multi-objective criteria optimisation algorithm.A solution for managing loads is found within the time requirements.Demand can be reduced up to 20% by controlling three types of appliances. In recent years, power grids have been evolving to decentralised production and control. Direct Load Control (DLC) methods are oriented to manage loads on the demand side. A DLC method based on Multi-Objective Particle Swarm Optimisation (MOPSO) algorithm is described. This method sets up the operation of the appliances when a power restriction must be accomplished. Since the method must operate in real-time, the calculations are distributed. The operation of appliances is obtained by dividing the power restriction among neighbourhoods and calculating multiple local optimisations. The method has been experimentally evaluated through simulations.

Template-based approaches have proven to be one of the most efficient and robustest ways of addressing Question Answering problems. Templates embody the expert's knowledge on the domain and his/her ability to understand and answer questions, but designing these templates may become a complex task since it is usually carried out manually. Although these methods are not automatic, companies may prefer to undertake this solution in order to offer a better service. In this article, we propose a semiautomatic method to reduce the problem of creating templates to that of validate, and possibly modify, a list of proposed templates. In this way, a better trade-off between reliability-the system is still monitored by an expert-and cost is achieved. In addition, updating templates after domain changes becomes easier, human mistakes are reduced, and portability is increased. Our proposal is based on inferring regular expressions that induce the language conveyed by a set of previously collected query reformulations. The main contribution of this work consists of the definition of a suitable optimisation measure that effectively reflects some important aspects of the problem and the theoretical soundness that supports it.

FAQ (Frequency Asked Questions) lists have attracted increasing attention for companies and organizations. There is thus a need for high-precision and fast methods able to manage large FAQ collections. In this context, we present a FAQ retrieval system as part of a FAQ exploiting project. Following the growing trend towards Web 2.0, we aim to provide users with mechanisms to navigate through the domain of knowledge and to facilitate both learning and searching, beyond classic FAQ retrieval algorithms. To this purpose, our system involves two different modules: an efficient and precise FAQ retrieval module and, a tag cloud generation module designed to help users to complete the comprehension of the retrieved information. Empirical results evidence the validity of our approach with respect to a number of state-of-the-art algorithms in terms of the most popular metrics in the field.

In this paper, we present a new key term extraction system able to handle with the particularities of ''support documents''. Our system takes advantages of frequency-based and thesaurus-based approaches to recognize two different classes of key terms. On the one hand, it identifies multi-domain key terms of the collection using Wikipedia as knowledge resource. On the other hand, the system extracts specific key terms highly related with the context of a support document. We use the frequency in language as a criterion to detect and rank such terms. To prove the validity of our system we have designed a set of experiment using a Frequently Asked Questions (FAQ) collection of documents. Since our approach is generic, minor modifications should be undertaken to adapt the system to other kind of support documents. The empirical results evidence the validity of our approach.

Case-Based Reasoning (CBR) has proven to be a very useful technique to solve problems in Closed-Domains Question Answering such as FAQ retrieval. Instead of trying to uderstand the question this method consists of retrieving the most similar case (Question/Answer pairs) among all cases by analogy. Keyword comparison criterion or statistical approaches are often used to implement similarity measure. However, those methods present the following disadvantages. On the one side, choosing keywords is an expert-knowledge domain-dependant task that is often performed manually. Furthermore, keyword comparison criterion does not guarantee the total differentiation among cases. On the other side, statistical approaches do not perform with enough information in sentence-level problems and are not interpretable. In order to alleviate these deficiencies we present a new method called the Minimal Differentiator Expressions (MDE) algorithm. This algorithm automatically obtains a set of linguistic patterns (expressions) used to retrieve the most relevant case to the user question. Those patterns present the following advantages: they are composed by the simplest sets of words which permit differentiation among cases and they are easily interpretable.

To facilitate access to information, companies usually try to anticipate and answer most typical customer's questions by creating Frequently Asked Questions (FAQs) lists. In this scenario, FAQ retrieval is the area of study concerned with recovering the most relevant Question/Answer pairs contained in FAQ compilations. Despite the amount of effort that has been devoted to investigate FAQ retrieval methods, how to create an maintain high quality FAQs has received less attention. In this article, we propose an entire framework to use, create and maintain intelligent FAQs. Usage mining techniques have been developed to take advantage of usage information in order to provide FAQ managers with meaningful information to improve their FAQs. Usage mining techniques include weaknesses detection and knowledge gaps discovery. In this way, the management of the FAQ is no longer directed only by expert knowledge but also by users requirements.

Highlights? Able to deal with the tendency of many users to express their views in non-standard language. ? Detects the targets of users' opinions in multi-domain scenarios. ? Linguistic modularized knowledge model with low-cost adaptability. ? Hierarchical lexicon specifically designed to analyze news comments. Thanks to the technological revolution that has accompanied the Web 2.0, users are able to interact intensively on the Internet, as reflected in social networks, blogs, forums, etc. In these scenarios, users can speak freely on any relevant topic. However, the high volume of user-generated content makes a manual analysis of this discourse unviable. Consequently, automatic analysis techniques are needed to extract the opinions expressed in users' comments, given that these opinions are an implicit barometer of unquestionable interest for a wide variety of companies, agencies, and organisms. We thus propose a lexicon-based Comments-oriented News Sentiment Analyzer (LCN-SA), which is able to deal with the following: (a) the tendency of many users to express their views in non-standard language; (b) the detection of the target of users' opinions in a multi-domain scenario; (c) the design of a linguistic modularized knowledge model with low-cost adaptability. The system proposed consists of an automatic Focus Detection Module and a Sentiment Analysis Module capable of assessing user opinions of topics in news items. These modules use a taxonomy-lexicon specifically designed for news analysis. Experiments show that the results obtained thus far are extremely promising.

Highlights? Intelligent surveillance system to detect, in real time, danger due to unprotected children in traffic zones. ? The system uses annotated video as input. ? The objects tracking developed is based on the classification of objects and 3D positioning. ? We propose a fuzzy control model to perform the object behavior analysis and the danger detection. ? The system is highly scalable, flexible and portable to any environment. Currently, an important demand for security exists in many fields. In the area of traffic safety, children are vulnerable elements because they may create a dangerous situation easily. Therefore, in this paper, we propose an intelligent surveillance system to detect, in real time, the danger due to the existence of unprotected children in traffic zones. We analyze the behavior of objects from video content analysis. The developed system is based on fuzzy rules for describing semantically the studied risk. The fuzzy logic provides a gradual danger detection. Besides, the model can be adjusted through membership functions to fuzzy concepts. The system is characterized by being highly scalable and flexible. Moreover, it is portable to any environment. We also highlight in our proposal the high-level tracking module developed, which is based on the classification of objects and 3D positioning. This tracking method is robust to errors that 2D tracking can present. The results obtained in the experimental stage show a high system performance.

Highlights? Virtual assistant framework that helps users find useful information in a website. ? It engages users in conversation using natural language, as real assistants. ? Users can omit some words if they are implicit in the conversation (context). ? It answers people's questions and offer related information of the highest interest. ? It is multilingual and can be integrated into every existing website. Since its beginning in 1969, the Internet has grown rapidly, especially over the past few years. Companies and organizations store more and more information about themselves on the Internet. Sometimes, that information is not well organized. Other times, the huge volume of available data makes useful information difficult to be found. The result is that users have to waste their time looking for what they want to know using the traditional menu-driven navigation and keyword search that websites provide. This is a critical issue because it decreases users interest about companies. In order to avoid this problem, in this paper we propose a framework for designing virtual assistants, which are, considering first results, an ideal alternative to help users find, not only the information that they are looking for, but also some related information which could be of the highest interest.

Recently, interest about security in public and private spaces has increased in favour of social welfare. Surveillance systems are increasingly needed to provide security for citizens and infrastructures. Currently there are many buildings that are equipped with cameras, sensors or microphones. However, it is difficult to find tools that integrate the information from these sources in a homogeneous system. On the other hand, the intruder detection is increasingly demanded in the corporate, commercial or private sector. For these reasons, we propose a multi-sensor intelligent system that uses information from several sources analysis (video, audio and other sensors) to identify dangerous or interest intrusions. So, we have designed a generic ontology that allows to integrate in a homogeneous way all the input heterogeneous knowledge. To perform the intrusion analysis, we propose a rule-based model, which process all the information obtained from the monitored environment. This model is easily customizable and adjustable, since the rules that define an intrusion in a semantic way can be configured depending on the scenario and circumstances. The system generates an alarm whenever an intrusion is detected. Besides, this alarm is also notified via mobile devices. So, the system reports in real time according to device capabilities, generating a context-sensitive notification.

The present paper reports on the results obtained from a teachers' opinion survey on the use of ICT tools to support of attendance-based teaching. In order to carry out this study, it was necessary to design a questionnaire to collect data among all in-service teachers with access to the university virtual campus. The findings show that respondents keep an open mind on incorporating ICT tools into their daily practices, on that point, this research has significantly contributed to the educational institution by providing accurate information about ICT use. The survey has also encouraged the university in its work on the integration of ICT in every area where it is possible, with the aim objectives of increasing access to learning, providing equal educational opportunities for all and ensuring lifelong learning, which are the mainstays of the Bologna Declaration.

Collision among moving objects in space is one of the most common risks in daily life. In this context, we have developed an abstract model that allows to detect the presence of risk of future collisions among objects from the video content analysis. Our proposal carries out several stages. First, a camera calibration process calculates the real location of object in scene. Then, we estimate the object speed and their future trajectory in order to predict possible collisions. All the information of the objects is described in an ontology. Using the properties of objects (such as location, speed, trajectories), we have defined a fuzzy rule that permits to identify whether an object is in danger because another could hit him. The use of fuzzy logic results in two points: the collision detection is gradual and the model can be adjusted through membership functions to fuzzy concepts. Furthermore, the proposed model is easily adaptable to any situation and can be applied on various fields. With the aim of testing our proposal, we have focused on pedestrian accidents, a case of special interest since a lot of pedestrians die or are injured in traffic accidents daily. We have developed an application based on our model that is able to predict, in real time, the traffic accidents where a vehicle could run over a pedestrian. The obtained results in the experimental stage show a high performance of the system.

Short-term fluctuations in the octopus catches off the Canary Islands have been attributed to changes in the sea surface temperature (SST) and the North Atlantic Oscillation (NAO) pattern. These results have been found using stationarity assumptions. However, the behavior of environmental systems is not always linear, and environmental time series do not always satisfy the statistical property of stationarity. Wavelet spectral analysis is a methodology which can deal with non-linear, non-stationary and noisy time series. Cross wavelet analysis (wavelet coherence) is applied to investigate the environmental effects (SST, NAO) on octopus abundance fluctuations measured as capture per unit of effort (CPUE) from 1989 to 2007 in the waters of the Canary Islands. A slightly positive correlation exists between NAO and CPUE at lags and leads of a few months. Additionally, a good relationship between SST and CPUE exists on the same seasonal scale, but there is a relatively weak relationship between SST anomalies and the NAO. When the analysis is extended to the interannual scale, the wavelet coherence identifies a statistically significant relationship between CPUE and NAO, but this does not happen when the wavelet coherence between CPUE and SST is computed. These results suggest that fluctuations in octopus catches could be the result of SST fluctuations but in synergy with other unknown environmental variables which are also affected by the NAO pattern.

Resumen Introduccion El cancer diferenciado de tiroides es la neoplasia endocrina mas frecuente. El tratamiento con radioyodo es una pieza importante del tratamiento. Objetivo Analizar los efectos de esta terapia sobre la funcion ovarica y reproductiva en las pacientes tratadas con I 131 . Material y metodo Se realizo un analisis retrospectivo de los datos de 202 mujeres afectas de cancer diferenciado de tiroides y tratadas con I 131 en nuestro servicio entre 1985 – 2008. Se recogieron datos acerca de la edad de la menopausia de las pacientes y de sus madres y hermanas, historia menstrual, fertilidad y patologia neonatal. Resultados 34 mujeres alcanzaron la edad de menopausia durante su seguimiento, con una edad media de 49,94±3,45 anos mientras que en madres y hermanas fue de 49,20±5,37 y 48,73 ± 3,74 anos, respectivamente. Tres de estas pacientes presentaron oligoamenorrea transitoria. Ninguna tuvo infertilidad ni alteraciones en la descendencia. Conclusiones En nuestra serie no se hallo un adelanto significativo de la edad de la menopausia respecto a sus familiares de primer grado ni una incidencia significativa de patologia neonatal, infertilidad y/o alteraciones menstruales.

The effect of temperature on the common octopus life cycle has been well studied. However, how other climatic patterns affect them is poorly understood. The present work emphasises the importance of the temperature on common octopus catches by the small-scale trap fishery off the Canary Islands, and also highlights the effect of the North Atlantic Oscillation (NAO) pattern. As well as an inverse and significant correlation between octopus abundance (measured as CPUE) and Sea Surface Temperature (SST), a direct relationship between abundance and NAO, off the Canary Archipelago, is reported. Using a linear model (lm) with a stepwise procedure, SST is found to be the most important and significant variable in autumn, accounting for 34.21%. Meanwhile, the NAO became more important in spring with 28.64% and a 31.13% of the explained variance in autumn.

This paper presents an innovatory system for controlling the emotional state of an embodied conversational agent. Unlike other existing models, our system is fast adaptable to different application domains and it is highly interpretable. This will provide specialists with a tool to easily test their hypotheses about the most suitable emotional attitude for an agent in a specific domain. It also provides emotional stability and dynamic and automatic behavior orientation, which will result in the design of more believable conversational agents that behave as humans do.

Se resenan las caracteristicas de 5 casos clinicos de encefalopatia de Hashimoto con una presentacion clinica variable y pruebas morfologicas normales, en las que la tomografia computarizada de emision monofotonica (SPECT) cerebral contribuyo decisivamente al diagnostico, asi como la evolucion de las imagenes cerebrales mediante SPECT y su relacion con la clinica y los parametros inmunologicos en el caso de mas larga evolucion al que hemos asistido.

In this paper, we have introduced a new method in which every training point learns what is happening in its neighborhood. So, a hyperplane is learned and associated to each point. With this hyperplane we can define the bands distance, a distance measure that bring closer or move away points depending on its classes. We have used this new distance in classification tasks and have performed tests over 68 datasets: 18 well-known UCI-Repository datasets, one private dataset, and 49 ad hoc synthetic datasets. We have used 10-fold cross-validation and, in order to compare the results of the classifiers, we have considered the mean accuracy and have also performed a paired two-tailored t-Student's test with a significance level of 95%. The results are encouraging and confirm the good behavior of the new proposed classification method. The bands distance has obtained the best overall results with 1-NN and k-NN classifiers when compared with other distances. Finally, we extract conclusions and outline some lines of future work.

In this work we will explore the theoretical connections existing between fuzzy rule-based systems (FRBS) applied on univariate time series and two statistical reference tools, the autoregressive (AR) models and the smooth transition autoregressive (STAR) model. We will show that a TSK fuzzy rule happens to be a localised AR model and that a STAR model can hence be interpreted as a restricted FRBS. Several consequences derive from this fact, and we will explore some of them, including a statistical inference-based procedure to incrementally build FRBS or the linguistic interpretation of STAR models. Finally an econometric example is given to illustrate the core idea.