Using metadata for content indexing within an OER network

This paper outlines the ICT solution for a metadata portal indexing open educational resources within a network of institutions. The network is aimed at blending academic and entrepreneurial knowledge,by enabling higher education institutions to publish various academic learning resources e.g. video lectures, course planning materials, or thematic content, whereasenterprises can present different forms of expert knowledge, such as case studies, expert presentations on specific topics, demonstrations of software implementation in practice and the like. As these resources need to bediscoverable, accessible and shared by potential learners across the learning environment, it is very important that they are well described and tagged in a standard way in machine readable form by metadata. Only then can they be successfully used and reused, especially when a large amount of these resources is reached, which makes it hard for the user to locate efficiently those of interest. The metadata set adopted in our approach relies on two standards: Dublin Core and Learning Object Metadata. The aim of metadata and the corresponding metadata portal described in this paper is to provide structured access to information on open educational resources within the network

Creating an environment for free education and technology-enhanced learning

The purpose of this paper is to present a project aimed at making knowledge publically available through opene ducational resources (OER). The focus is on open online courses which will be created by educational institutions and best practice examples offered by leading companies, with the purpose to support life-long education and enhancement of academic education with practical knowledge. The goal is to create diverse high quality educational materials in electronic format, which will be publically available. The educational material will follow basic pedagogical-didactic principles, in order to best meet the needs of the potential learners. In accordance with that a review of didactic principles that can contribute to producing OER content of excellence is given. The choice of a convenient platform, as well as the application of appropriate information technologies enable content representation in a suitable, innovative and meaningful way

Illness perception in tuberculosis by implementation of the Brief Illness Perception Questionnaire : a TBNET study

How patients relate to the experience of their illness has a direct impact over their behavior. We aimed to assess illness perception in patients with pulmonary tuberculosis (TB) by means of the Brief Illness Perception Questionnaire (BIPQ) in correlation with patients’ demographic features and clinical TB score. Our observational questionnaire based study included series of consecutive TB patients enrolled in several countries from October 2008 to January 2011 with 167 valid questionnaires analyzed. Each BIPQ item assessed one dimension of illness perceptions like the consequences, timeline, personal control, treatment control, identity, coherence, emotional representation and concern. An open question referred to the main causes of TB in each patient’s opinion. The over-all BIPQ score (36.25 ± 11.054) was in concordance with the clinical TB score (p ≤ 0.001). TB patients believed in the treatment (the highest item-related score for treatment control) but were unsure about the illness identity. Illness understanding and the clinical TB score were negatively correlated (p < 0.01). Only 25% of the participants stated bacteria or TB contact as the first ranked cause of the illness. For routine clinical practice implementation of the BIPQ is convenient for obtaining fast and easy assessment of illness perception with potential utility in intervention design. This time saving effective personalized approach may improve communication with TB patients and contribute to better behavioral strategies in disease control

A crowdsourced analysis to identify ab initio molecular signatures predictive of susceptibility to viral infection

The response to respiratory viruses varies substantially between individuals, and there are currently no known molecular predictors from the early stages of infection. Here we conduct a community-based analysis to determine whether pre- or early post-exposure molecular factors could predict physiologic responses to viral exposure. Using peripheral blood gene expression profiles collected from healthy subjects prior to exposure to one of four respiratory viruses (H1N1, H3N2, Rhinovirus, and RSV), as well as up to 24 h following exposure, we find that it is possible to construct models predictive of symptomatic response using profiles even prior to viral exposure. Analysis of predictive gene features reveal little overlap among models; however, in aggregate, these genes are enriched for common pathways. Heme metabolism, the most significantly enriched pathway, is associated with a higher risk of developing symptoms following viral exposure. This study demonstrates that pre-exposure molecular predictors can be identified and improves our understanding of the mechanisms of response to respiratory viruses

Cardiopoietic cell therapy for advanced ischemic heart failure: results at 39 weeks of the prospective, randomized, double blind, sham-controlled CHART-1 clinical trial

Cardiopoietic cells, produced through cardiogenic conditioning of patients' mesenchymal stem cells, have shown preliminary efficacy. The Congestive Heart Failure Cardiopoietic Regenerative Therapy (CHART-1) trial aimed to validate cardiopoiesis-based biotherapy in a larger heart failure cohort

Sparse Learning of the Disease Severity Score for High-Dimensional Data

Learning disease severity scores automatically from collected measurements may aid in the quality of both healthcare and scientific understanding. Some steps in that direction have been taken and machine learning algorithms for extracting scoring functions from data have been proposed. Given the rapid increase in both quantity and diversity of data measured and stored, the large amount of information is becoming one of the challenges for learning algorithms. In this work, we investigated the direction of the problem where the dimensionality of measured variables is large. Learning the severity score in such cases brings the issue of which of measured features are relevant. We have proposed a novel approach by combining desirable properties of existing formulations, which compares favorably to alternatives in accuracy and especially in the robustness of the learned scoring function. The proposed formulation has a nonsmooth penalty that induces sparsity. This problem is solved by addressing a dual formulation which is smooth and allows an efficient optimization. The proposed approach might be used as an effective and reliable tool for both scoring function learning and biomarker discovery, as demonstrated by identifying a stable set of genes related to influenza symptoms’ severity, which are enriched in immune-related processes