Consumption and portion sizes of tree nuts, peanuts and seeds in the European Prospective Investigation into Cancer and Nutrition (EPIC) cohorts from 10 European countries

Tree nuts, peanuts and seeds are nutrient dense foods whose intake has been shown to be associated with reduced risk of some chronic diseases. They are regularly consumed in European diets either as whole, in spreads or from hidden sources (e.g. commercial products). However, little is known about their intake profiles or differences in consumption between European countries or geographic regions. The objective of this study was to analyse the population mean intake and average portion sizes in subjects reporting intake of nuts and seeds consumed as whole, derived from hidden sources or from spreads. Data was obtained from standardised 24-hour dietary recalls collected from 36 994 subjects in 10 different countries that are part of the European Prospective Investigation into Cancer and Nutrition (EPIC). Overall, for nuts and seeds consumed as whole, the percentage of subjects reporting intake on the day of the recall was: tree nuts = 4. 4%, peanuts = 2.3 % and seeds = 1.3 %. The data show a clear northern (Sweden: mean intake = 0.15 g/d, average portion size = 15.1 g/d) to southern (Spain: mean intake = 2.99 g/d, average portion size = 34.7 g/d) European gradient of whole tree nut intake. The three most popular tree nuts were walnuts, almonds and hazelnuts, respectively. In general, tree nuts were more widely consumed than peanuts or seeds. In subjects reporting intake, men consumed a significantly higher average portion size of tree nuts (28.5 v. 23.1 g/d, P<0.01) and peanuts (46.1 v. 35.1 g/d, P<0.01) per day than women. These data may be useful in devising research initiatives and health policy strategies based on the intake of this food group.

Effective diagnosis of Alzheimer's disease by means of large margin-based methodology.

BACKGROUND Functional brain images such as Single-Photon Emission Computed Tomography (SPECT) and Positron Emission Tomography (PET) have been widely used to guide the clinicians in the Alzheimer's Disease (AD) diagnosis. However, the subjectivity involved in their evaluation has favoured the development of Computer Aided Diagnosis (CAD) Systems. METHODS It is proposed a novel combination of feature extraction techniques to improve the diagnosis of AD. Firstly, Regions of Interest (ROIs) are selected by means of a t-test carried out on 3D Normalised Mean Square Error (NMSE) features restricted to be located within a predefined brain activation mask. In order to address the small sample-size problem, the dimension of the feature space was further reduced by: Large Margin Nearest Neighbours using a rectangular matrix (LMNN-RECT), Principal Component Analysis (PCA) or Partial Least Squares (PLS) (the two latter also analysed with a LMNN transformation). Regarding the classifiers, kernel Support Vector Machines (SVMs) and LMNN using Euclidean, Mahalanobis and Energy-based metrics were compared. RESULTS Several experiments were conducted in order to evaluate the proposed LMNN-based feature extraction algorithms and its benefits as: i) linear transformation of the PLS or PCA reduced data, ii) feature reduction technique, and iii) classifier (with Euclidean, Mahalanobis or Energy-based methodology). The system was evaluated by means of k-fold cross-validation yielding accuracy, sensitivity and specificity values of 92.78%, 91.07% and 95.12% (for SPECT) and 90.67%, 88% and 93.33% (for PET), respectively, when a NMSE-PLS-LMNN feature extraction method was used in combination with a SVM classifier, thus outperforming recently reported baseline methods. CONCLUSIONS All the proposed methods turned out to be a valid solution for the presented problem. One of the advances is the robustness of the LMNN algorithm that not only provides higher separation rate between the classes but it also makes (in combination with NMSE and PLS) this rate variation more stable. In addition, their generalization ability is another advance since several experiments were performed on two image modalities (SPECT and PET).