Abstract
Introduction: Self-report of dietary intake could be biased by social desirability thus affecting risk estimates in epidemiological studies. The objective of this study was to assess the effect of social desirability on dietary intake estimated from a food frequency questionnaire (FFQ). Methods: A convenience sample of 483 Portuguese university students was recruited. Subjects were invited to complete a two-part self-administered questionnaire: the first part included the Marlowe-Crowne Social Desirability Scale (M-CSDS), a physical activity questionnaire and self-reported height and weight; the second part, included a semi-quantitative FFQ validated for Portuguese adults, that should be returned after fulfilment. All subjects completed the first part of the questionnaire and 40.4% returned the FFQ fairly completed. Results: In multiple regression analysis, after adjustment for energy and confounders, social desirability produced a significant positive effect in the estimates of dietary fibre, vitamin C, vitamin E, magnesium and potassium, in both genders. In multiple regression, after adjustment for energy and confounders, social desirability had a significant positive effect in the estimates of vegetable consumption, for both genders, and a negative effect in white bread and beer, for women. Conclusion: Social desirability affected nutritional and food intake estimated from a food frequency questionnaire.

Abstract
We address the problem of predicting the stability of secondary structure motifs of proteins given their linear sequence of residues. Our study is restricted to the prediction of helix structures. We have applied an Inductive Logic Programming (ILP) system to automatically synthesise the predictive rules. ILP systems are well known for being able to induce comprehensible models for data. Furthermore, the models components are definitions provided by a domain expert which makes the model more likely to be helpful in the understanding of the underlying process that produced the data. Our methodology has two stages. First, the system induces a model (set of rules) using just structural information and groupings of the residues to avoid biases by the domain expert. In the second stage, the residues properties are used to make the induced rules Chemically/Biologically appealing. We claim that this methodology is also valuable for general Structure-Activity Relationship (SAR) problems.

Abstract
In this paper we study a new technique we call post-bagging, which consists in resampling parts of a classification model rather then the data. We do this with a particular kind of model: large sets of classification association rules, and in combination with ordinary best rule and weighted voting approaches. We empirically evaluate the effects of the technique in terms of classification accuracy. We also discuss the predictive power of different metrics used for association rule mining, such as confidence, lift, conviction and chi(2). We conclude that, for the described experimental conditions, post-bagging improves classification results and that the best metric is conviction.

Abstract
The cosmetic result is an important endpoint for breast cancer conservative treatment (BCCT), but the verification of this outcome remains without a standard. Objective assessment methods are preferred to overcome the drawbacks of subjective evaluation. In this paper a novel algorithm is proposed, based on support vector machines, for the classification of ordinal categorical data. This classifier is then applied as a new methodology for the objective assessment of the aesthetic result of BCCT. Based on the new classifier, a semi-objective score for quantification of the aesthetic results of BCCT was developed, allowing the discrimination of patients into four classes.

Abstract
Computer generated sounds for music applications have many facets, of which timbre design is of groundbreaking significance. Timbre is a remarkable and rather complex phenomenon that has puzzled researchers for a long time. Actually, the nature of musical signals is not fully understood yet. In this paper, we present a sound synthesis method using an artificial immune network for data clustering, denoted aiNet. Sounds produced by the method are referred to as immunological sounds. Basically, antibody-sounds are generated to recognize a fixed and predefined set of antigen-sounds, thus producing timbral variants with the desired characteristics. The aiNet algorithm provides maintenance of diversity and an adaptive number of resultant antibody-sounds (memory cells), so that the intended aesthetical result is properly achieved by avoiding the formal definition of the timbral attributes. The initial set of antibody-sounds may be randomly generated vectors, sinusoidal waves with random frequency, or a set of loaded waveforms. To evaluate the obtained results we propose an affinity measure based on the average spectral distance from the memory cells to the antigen-sounds. With the validation of the affinity criterion, the experimental procedure is outlined, and the results are depicted and analyzed.

Abstract