Tissue engineering (TE) is an emerging field for the regeneration of damaged tissues. The combination of hydrogels with stem cells and growth factors (GFs) has become a promising approach to promote cartilage regeneration. In this study, carrageenan-based hydrogels were used to encapsulate both cells and transforming growth factor-b1 (TGF-b1). The ATDC5 cell line was encapsulated to determine the cytotoxicity and the influence of polymer concentration on cell viability and proliferation. Human adipose-derived stem cells (hASCs) were encapsulated with TGF-b1 in the hydrogel networks to enhance the chondrogenic differentiation of hASCs. Specific cartilage extracellular matrix molecules expression by hASCs were observed after 14 days of cultures of the constructs under different conditions. The k-carrageenan was found to be a suitable biomaterial for cell and GF encapsulation. The incorporation of TGF-b1 within the carrageenan-based hydrogel enhanced the cartilage differentiation of hASCs. These findings indicate that this new system for cartilage TE is very promising for injectable thermoresponsive formulation applications.

The general aim of the third CLEF Multilingual Question Answering Track was to set up a common and replicable evaluation framework to test both monolingual and cross-language Question Answering (QA) systems that process queries and documents in several European languages. Nine target languages and ten source languages were exploited to enact 8 monolingual and 73 cross-language tasks. Twenty-four groups participated in the exercise.Overall results showed a general increase in performance in comparison to last year. The best performing monolingual system irrespective of target language answered 64.5% of the questions correctly (in the monolingual Portuguese task), while the average of the best performances for each target language was 42.6%. The cross-language step instead entailed a considerable drop in performance. In addition to accuracy, the organisers also measured the relation between the correctness of an answer and a system's stated confidence in it, showing that the best systems did not always provide the most reliable confidence score.

The fifth QA campaign at CLEF [1], having its first edition in 2003, offered not only a main task but an Answer Validation Exercise (AVE) [2], which continued last year's pilot, and a new pilot: the Question Answering on Speech Transcripts (QAST) [3, 15]. The main task was characterized by the focus on cross-linguality, while covering as many European languages as possible. As novelty, some QA pairs were grouped in clusters. Every cluster was characterized by a topic (not given to participants). The questions from a cluster possibly contain co-references between one of them and the others. Finally, the need for searching answers in web formats was satisfied by introducing Wikipedia 1 as document corpus. The results and the analyses reported by the participants suggest that the introduction of Wikipedia and the topic related questions led to a drop in systems' performance.

The general aim of the third CLEF Multilingual Question Answering Track was to set up a common and replicable evaluation framework to test both monolingual and cross-language Question Answering (QA) systems that process queries and documents in several European languages. Nine target languages and ten source languages were exploited to enact 8 monolingual and 73 cross-language tasks. Twenty-four groups participated in the exercise.Overall results showed a general increase in performance in comparison to last year. The best performing monolingual system irrespective of target language answered 64.5% of the questions correctly (in the monolingual Portuguese task), while the average of the best performances for each target language was 42.6%. The cross-language step instead entailed a considerable drop in performance. In addition to accuracy, the organisers also measured the relation between the correctness of an answer and a system's stated confidence in it, showing that the best systems did not always provide the most reliable confidence score.

In this paper we present a thorough evaluation of a corpus resource for Portuguese, CETEMPúblico, a 180million word newspaper corpus free for R&D in Portuguese processing. We provide information that should be useful to those using the resource, and to considerable improvement for later versions. In addition, we think that the procedures presented can be of interest for the larger NLP community, since corpus evaluation and description is unfortunately not a common exercise.,QWURGXFWLRQCETEMPúblico is a large corpus of European Portuguese newspaper language, available at no cost to the community dealing with the processing of Portuguese. 1 It was created in the framework of the Computational Processing of Portuguese project, a government funded initiative to foster language engineering of the Portuguese language. 2 Evaluating this resource, we have two main goals in mind: To contribute to improve its usefulness; and to suggest ways of going about as far as corpus evaluation is concerned in general (noting that most corpora projects are simply described and not evaluated).