MIRACLE at GeoCLEF Query Parsing 2007: Extraction and Classification of Geographical Information

This paper describes the participation of MIRACLE research consortium at the Query Parsing task of GeoCLEF 2007. Our system is composed of three main modules. First, the Named Geo-entity Identifier, whose objective is to perform the geo-entity identification and tagging, i.e., to extract the “where” component of the geographical query, should there be any. This module is based on a gazetteer built up from the Geonames geographical database and carries out a sequential process in three steps that consist on geo-entity recognition, geo-entity selection and query tagging. Then, the Query Analyzer parses this tagged query to identify the “what” and “geo-relation” components by means of a rule-based grammar. Finally, a two-level multiclassifier first decides whether the query is indeed a geographical query and, should it be positive, then determines the query type according to the type of information that the user is supposed to be looking for: map, yellow page or information. According to a strict evaluation criterion where a match should have all fields correct, our system reaches a precision value of 42.8% and a recall of 56.6% and our submission is ranked 1st out of 6 participants in the task. A detailed evaluation of the confusion matrixes reveal that some extra effort must be invested in “user-oriented” disambiguation techniques to improve the first level binary classifier for detecting geographical queries, as it is a key component to eliminate many false-positives

Report of MIRACLE team for the Ad-Hoc track in CLEF 2006

This paper presents the 2006 MIRACLE’s team approach to the AdHoc Information Retrieval track. The experiments for this campaign keep on testing our IR approach. First, a baseline set of runs is obtained, including standard components: stemming, transforming, filtering, entities detection and extracting, and others. Then, a extended set of runs is obtained using several types of combinations of these baseline runs. The improvements introduced for this campaign have been a few ones: we have used an entity recognition and indexing prototype tool into our tokenizing scheme, and we have run more combining experiments for the robust multilingual case than in previous campaigns. However, no significative improvements have been achieved. For the this campaign, runs were submitted for the following languages and tracks: - Monolingual: Bulgarian, French, Hungarian, and Portuguese. - Bilingual: English to Bulgarian, French, Hungarian, and Portuguese; Spanish to French and Portuguese; and French to Portuguese. - Robust monolingual: German, English, Spanish, French, Italian, and Dutch. - Robust bilingual: English to German, Italian to Spanish, and French to Dutch. - Robust multilingual: English to robust monolingual languages. We still need to work harder to improve some aspects of our processing scheme, being the most important, to our knowledge, the entities recognition and normalization

Miracle’s 2005 Approach to Cross-lingual Information Retrieval

This paper presents the 2005 Miracle’s team approach to Bilingual and Multilingual Information Retrieval. In the multilingual track, we have concentrated our work on the merging process of the results of monolingual runs to get the multilingual overall result, relying on available translations. In the bilingual and multilingual tracks, we have used available translation resources, and in some cases we have using a combining approach

Miracle’s 2005 Approach to Monolingual Information Retrieval

This paper presents the 2005 Miracle’s team approach to Monolingual Information Retrieval. The goal for the experiments in this year was twofold: continue testing the effect of combination approaches on information retrieval tasks, and improving our basic processing and indexing tools, adapting them to new languages with strange encoding schemes. The starting point was a set of basic components: stemming, transforming, filtering, proper nouns extracting, paragraph extracting, and pseudo-relevance feedback. Some of these basic components were used in different combinations and order of application for document indexing and for query processing. Second order combinations were also tested, by averaging or selective combination of the documents retrieved by different approaches for a particular query

The Modulation of Mucosal Antiviral Immunity by Immunobiotics: Could They Offer Any Benefit in the SARS-CoV-2 Pandemic?

Viral respiratory infections are of major importance because of their capacity to cause of a high degree of morbidity and mortality in high-risk populations, and to rapidly spread between countries. Perhaps the best example of this global threat is the infectious disease caused by the new SARS-CoV-2 virus, which has infected more than 4 million people worldwide, causing the death of 287,000 persons according to the WHO's situation report on May 13, 2020. The availability of therapeutic tools that would be used massively to prevent or mitigate the detrimental effects of emerging respiratory viruses on human health is therefore mandatory. In this regard, research from the last decade has reported the impact of the intestinal microbiota on the respiratory immunity. It was conclusively demonstrated how the variations in the intestinal microbiota affect the responses of respiratory epithelial cells and antigen presenting cells against respiratory virus attack. Moreover, the selection of specific microbial strains (immunobiotics) with the ability to modulate immunity in distal mucosal sites made possible the generation of nutritional interventions to strengthen respiratory antiviral defenses. In this article, the most important characteristics of the limited information available regarding the immune response against SARS-CoV-2 virus are revised briefly. In addition, this review summarizes the knowledge on the cellular and molecular mechanisms involved in the improvement of respiratory antiviral defenses by beneficial immunobiotic microorganisms such as Lactobacillus rhamnosus CRL1505. The ability of beneficial microorganisms to enhance type I interferons and antiviral factors in the respiratory tract, stimulate Th1 response and antibodies production, and regulate inflammation and coagulation activation during the course of viral infections reducing tissue damage and preserving lung functionally, clearly indicate the potential of immunobiotics to favorably influence the immune response against SARS-CoV-2 virus.Fil: Villena, Julio Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Centro de Referencia para Lactobacilos; Argentina. Tohoku University; JapónFil: Kitazawa, Haruki. Tohoku University

Are the immunomodulatory properties of Lactobacillus rhamnosus CRL1505 peptidoglycan common for all Lactobacilli during respiratory infection in malnourished mice?

Previously, we reported that Lactobacillus rhamnosus CRL1505 peptidoglycan (PG05) improves the innate immune response in immunocompromised-malnourished mice after Streptococcus pneumoniae infection. This study extends those previous findings by demonstrating that the dietary recovery of malnourished mice with nasal administration of PG05 improves not only the innate immune response but the respiratory and systemic adaptive humoral response as well. PG05 enhanced the Th2 response, the recovery of B cells, and the concentration and opsonophagocytic activity of anti-pneumococcal antibodies. In addition, by performing comparative studies with the peptidoglycans from lactobacilli of the same species (L. rhamnosus CRL534) or with similar immunomodulatory properties (L. plantarum CRL1506), we demonstrated here that PG05 has unique immunomodulatory properties that cannot be extended to peptidoglycans from other probiotic strains. However, the knowledge of the molecular characteristics of PG05 is indispensable to understand immunomodulatory abilities of L. rhamnosus CRL1505.Fil: Kolling, Yanina Noralí. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Centro de Referencia para Lactobacilos; ArgentinaFil: Salva, Maria Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Centro de Referencia para Lactobacilos; ArgentinaFil: Villena, Julio Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Centro de Referencia para Lactobacilos; ArgentinaFil: Alvarez, Gladis Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucumán. Centro de Referencia para Lactobacilos; Argentin

Revisión entre pares como instrumento de aprendizaje

Proyecto de Innovación Docente en las asignaturas de Organización de Contenidos Audiovisuales y e Inteligencia en Redes de ComunicacionesEste artículo describe la experiencia de innovación docente llevada a cabo este último curso basada en la aplicación de la metodología de revisión entre iguales como instrumento para el aprendizaje, desarrollada en las asignaturas Organización de Contenidos Audiovisuales (Ingeniería Técnica de Telecomunicación, especialidad Sonido e Imagen) durante el curso 2003-04 e Inteligencia en Redes de Comunicaciones (Ingeniería de Telecomunicación) durante el curso 2004-05. Los experimentos realizados muestran la utilidad de la metodología de revisión entre pares como instrumento para el aprendizaje, percibido de forma subjetiva por los alumnos participantes y demostrado empíricamente en un experimento de control mediante preguntas test, descrito en este documento.

Respiratory antiviral immunity and immunobiotics: Beneficial effects on inflammation-coagulation interaction during influenza virus infection

Influenza virus (IFV) is a major respiratory pathogen of global importance, and the cause of a high degree of morbidity and mortality, especially in high-risk populations such as infants, elderly, and immunocompromised hosts. Given its high capacity to change antigenically, acquired immunity is often not effective to limit IFV infection and therefore vaccination must be constantly redesigned to achieve effective protection. Improvement of respiratory and systemic innate immune mechanisms has been proposed to reduce the incidence and severity of IFV disease. In the last decade, several research works have demonstrated that microbes with the capacity to modulate the mucosal immune system (immunobiotics) are a potential alternative to beneficially modulate the outcome of IFV infection. This review provides an update of the current status on the modulation of respiratory immunity by orally and nasally administered immunobiotics, and their beneficial impact on IFV clearance and inflammatory-mediated lung tissue damage. In particular, we describe the research of our group that investigated the influence of immunobiotics on inflammation-coagulation interactions during IFV infection. Studies have clearly demonstrated that hostile inflammation is accompanied by dysfunctional coagulation in respiratory IFV disease, and our investigations have proved that some immunobiotic strains are able to reduce viral disease severity through their capacity to modulate the immune-coagulative responses in the respiratory tract.Fil: Zelaya, María Hortensia del Rosario. Grupo de Investigación de Inmunobioticos; Argentina. Universidad Nacional de Tucumán. Facultad de Bioquímica, Química y Farmacia. Instituto de Bioquímica Aplicada; ArgentinaFil: Alvarez, Gladis Susana. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Centro de Referencia Para Lactobacilos; Argentina. Grupo de Investigación de Inmunobioticos; Argentina. Universidad Nacional de Tucumán. Facultad de Bioquímica, Química y Farmacia. Instituto de Bioquímica Aplicada; ArgentinaFil: Kitazawa, Haruki. Tohoku University; JapónFil: Villena, Julio Cesar. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - Tucuman. Centro de Referencia Para Lactobacilos; Argentina. Grupo de Investigación de Inmunobioticos; Argentin

DAEDALUS at PAN 2014: Guessing tweet author's gender and age

This paper describes our participation at PAN 2014 author profiling task. Our idea was to define, develop and evaluate a simple machine learning classifier able to guess the gender and the age of a given user based on his/her texts, which could become part of the solution portfolio of the company. We were interested in finding not the best possible classifier that achieves the highest accuracy, but to find the optimum balance between performance and throughput using the most simple strategy and less dependent of external systems. Results show that our software using Naive Bayes Multinomial with a term vector model representation of the text is ranked quite well among the rest of participants in terms of accuracy

MIRACLE at ImageCLEFanot 2007: Machine Learning Experiments on Medical Image Annotation

This paper describes the participation of MIRACLE research consortium at the ImageCLEF Medical Image Annotation task of ImageCLEF 2007. Our areas of expertise do not include image analysis, thus we approach this task as a machine-learning problem, regardless of the domain. FIRE is used as a black-box algorithm to extract different groups of image features that are later used for training different classifiers in order to predict the IRMA code. Three types of classifiers are built. The first type is a single classifier that predicts the complete IRMA code. The second type is a two level classifier composed of four classifiers that individually predict each axis of the IRMA code. The third type is similar to the second one but predicts a combined pair of axes. The main idea behind the definition of our experiments is to evaluate whether an axis-by-axis prediction is better than a prediction by pairs of axes or the complete code, or vice versa. We submitted 30 experiments to be evaluated and results are disappointing compared to other groups. However, the main conclusion that can be drawn from the experiments is that, irrespective of the selected image features, the axis-by-axis prediction achieves more accurate results not only than the prediction of a combined pair of axes but also, in turn, than the prediction of the complete IRMA code. In addition, data normalization seems to improve the predictions and vector-based features are preferred over histogram-based ones