Heterozygosity for a point mutation in an invariant splice donor site of dihydropyrimidine dehydrogenase and severe 5-fluorouracil related toxicity

Grayscal

The influence of learner characteristics on degree and type of participation in a CSCL environment

Computer-Supported Collaborative Learning (CSCL) is often presented as a promising learning method. However, it is also facing some new challenges. Apart from answering the question of whether or not working with CSCL generates satisfying learning outcomes, it is important to determine whether or not all participants profit from collaboration, with the computer as a means of communication. This paper describes the implementation and effects of an experimental program in 5 classes with a total of 120 students in elementary education who, in groups of four, engaged in Knowledge Forum discussion tasks on the subject of healthy eating. The study explores whether or not differences occur in the participation of students who differ in gender, sociocultural background and ability, and whether or not computer skills, computer attitudes, comprehensive reading scores and popularity with classmates are related to student participation. Students’ participation in this CSCL environment appears to be dependent on a number of learner characteristics. Girls contribute more words to the discussions than boys do and are more dependent on their computer skills in this production. Students who are good at comprehensive reading also contribute more words. Popularity among classmates appears to influence the degree of participation further. We also found indications that students with immigrant parents write fewer contributions than those whose parents are not immigrants

Profound variation in dihydropyrimidine dehydrogenase activity in human blood cells: major implications for the detection of partly deficient patients

Dihydropyrimidine dehydrogenase (DPD) is responsible for the breakdown of the widely used antineoplastic agent 5-fluorouracil (5FU), thereby limiting the efficacy of the therapy. To identify patients suffering from a complete or partial DPD deficiency, the activity of DPD is usually determined in peripheral blood mononuclear cells (PBM cells). In this study, we demonstrated that the highest activity of DPD was found in monocytes followed by that of lymphocytes, granulocytes and platelets, whereas no significant activity of DPD could be detected in erythrocytes. The activity of DPD in PBM cells proved to be intermediate compared with the DPD activity observed in monocytes and lymphocytes. The mean percentage of monocytes in the PBM cells obtained from cancer patients proved to be significantly higher than that observed in PBM cells obtained from healthy volunteers. Moreover, a profound positive correlation was observed between the DPD activity of PBM cells and the percentage of monocytes, thus introducing a large inter- and intrapatient variability in the activity of DPD and hindering the detection of patients with a partial DPD deficiency. © 1999 Cancer Research Campaig

Uncertainty quantification in graph-based classification of high dimensional data

Classification of high dimensional data finds wide-ranging applications. In many of these applications equipping the resulting classification with a measure of uncertainty may be as important as the classification itself. In this paper we introduce, develop algorithms for, and investigate the properties of, a variety of Bayesian models for the task of binary classification; via the posterior distribution on the classification labels, these methods automatically give measures of uncertainty. The methods are all based around the graph formulation of semi-supervised learning. We provide a unified framework which brings together a variety of methods which have been introduced in different communities within the mathematical sciences. We study probit classification in the graph-based setting, generalize the level-set method for Bayesian inverse problems to the classification setting, and generalize the Ginzburg-Landau optimization-based classifier to a Bayesian setting; we also show that the probit and level set approaches are natural relaxations of the harmonic function approach introduced in [Zhu et al 2003]. We introduce efficient numerical methods, suited to large data-sets, for both MCMC-based sampling as well as gradient-based MAP estimation. Through numerical experiments we study classification accuracy and uncertainty quantification for our models; these experiments showcase a suite of datasets commonly used to evaluate graph-based semi-supervised learning algorithms.Comment: 33 pages, 14 figure