Objective: To assess the relationship between habitual fish intake and fatty acid levels in serum as well as in the LDL fractions of serum phospholipids and cholesteryl esters. Design: Cross-sectional study. Setting: Cohort of Gipuzkoa (Basque Country, northern Spain) included in the European Prospective Investigation into Cancer and Nutrition (EPIC) project. Subjects: Random sample of 120 healthy volunteers of both sexes aged 35 -65 y, divided into various consumption groups according to daily fish intake. Methods: Data on habitual intake over the previous year was collected by trained interviewers by means of a computerized questionnaire based on the diet history method. Fasting venous blood samples were drawn and fatty acids were measured by gas-liquid chromatography. Results: Lean fish accounted for 78% of all fish consumption in the highest consumption group ( > 115 g=day) and for 60% in the lowest ( < 31 g=day). The mean concentrations of o-3 polyunsaturated fatty acids (PUFA), eicosapentaenoic acid (EPA, C20:5, o-3), and docosahexaenoic acid (DHA, C22:6, o-3) in serum and in the LDL fractions of serum phospholipids and cholesteryl esters increased significantly from the lowest to the highest fish consumption categories. Fish intake showed a statistically significant relationship with o-3 PUFA, EPA and DHA in serum and in the LDL fractions of serum phospholipids and cholesteryl esters both in the simple linear regression analysis and in a multiple regression model adjusted by age, body mass index (BMI) and vegetable intake. Conclusions: Habitual fish intake is reflected in the content of EPA and DHA in serum and in the LDL phospholipid and cholesteryl esters fractions. The concentrations of very-long-chain o-3 fatty acids are useful biomarkers for dietary fish intake, mainly lean fish. Sponsorship: Europe Against Cancer Programme of the European Union (agreement SOC 97 200302 05F02); 'Fondo de Investigaciones Sanitarias', Spanish Ministry of Health (FIS grant 99=0024-05); Government of the Basque Country; and 'Fundación Científica de la Asociación Española contra el Cáncer'. Descriptors: fish intake; fish oil; n-3 fatty acids; biological markers; eicosapentaenoic acid; docosahexaenoic acid

This study is the first southern European validated population-derived model for predicting 5-year fatal/non-fatal CHD and CVD risk in patients with newly diagnosed type 2 diabetes.

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In the problem of one-class classification (OCC) one of the classes, the target class, has to be distinguished from all other possible objects, considered as nontargets. In many biomedical problems this situation arises, for example, in diagnosis, image based tumor recognition or analysis of electrocardiogram data. In this paper an approach to OCC based on a typicality test is experimentally compared with reference state-of-the-art OCC techniques—Gaussian, mixture of Gaussians, naive Parzen, Parzen, and support vector data description—using biomedical data sets. We evaluate the ability of the procedures using twelve experimental data sets with not necessarily continuous data. As there are few benchmark data sets for one-class classification, all data sets considered in the evaluation have multiple classes. Each class in turn is considered as the target class and the units in the other classes are considered as new units to be classified. The results of the comparison show the good performance of the typicality approach, which is available for high dimensional data; it is worth mentioning that it can be used for any kind of data (continuous, discrete, or nominal), whereas state-of-the-art approaches application is not straightforward when nominal variables are present.

This paper presents the process required to implement a data driven Predictive Maintenance (PdM) not only in the machine decision making, but also in data acquisition and processing. A short review of the different approaches and techniques in maintenance is given. The main contribution of this paper is a solution for the predictive maintenance problem in a real machining process. Several steps are needed to reach the solution, which are carefully explained. The obtained results show that the Preventive Maintenance (PM), which was carried out in a real machining process, could be changed into a PdM approach. A decision making application was developed to provide a visual analysis of the Remaining Useful Life (RUL) of the machining tool. This work is a proof of concept of the methodology presented in one process, but replicable for most of the process for serial productions of pieces.

This paper presents a solution to two problems that arise in the classification of data such as types of tumor, samples of gene expression profiles or general biomedical data. First, to estimate the real number of clusters in a data set and second to decide whether a new unit belongs to one of these previously identified clusters or it is an outlier or atypical unit. We propose a new statistic which allows us to solve these problems. As our approach is based on a measure of distance or dissimilarity between any pair of units, it can be applied to any kind of multivariate data (continuous, binary or multi-attribute data) and it has applications in many biomedical fields. We validated the approach in simulated examples and applied it to the diagnosis of dermal diseases and to the analysis of lymphatic cancer data, showing the good performance of our approach.

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