The interactive effects of alcohol and temazepam on P300 and reaction time

The present research investigated the separate and interactive effects of the minor tranquilliser, temazepam, and a low dose of alcohol on the amplitude and latency of P300 and on reaction time. Twenty-four participants completed four drug treatments in a repeated measures design. The four drug treatments, organised as a fully repeated 2 x 2 design, included a placebo condition, an alcohol only condition, a temazepam only condition, and an alcohol and temazepam combined condition. Event-related potentials were recorded from midline sites Fz, Cz, and Pz within an oddball paradigm. The results indicated that temazepam, with or without the presence of alcohol, reduced P300 amplitude. Alcohol, on the other hand, with or without the presence of temazepam, affected processing speed and stimulus evaluation as indexed by reaction time and P300 latency. At the low dose levels used in this experiment alcohol and temazepam appear not to interact, which suggests that they affect different aspects of processing in the central nervous system. (C) 2003 Elsevier Inc. All rights reserved.

Interactive effects of MC1R and OCA2 on melanoma risk phenotypes

The relationships between MC1R gene variants and red hair, skin reflectance, degree of freckling and nevus count were investigated in 2331 adolescent twins, their sibs and parents in 645 twin families. Penetrance of each MC1R variant allele was consistent with an allelic model where effects were multiplicative for red hair but additive for skin reflectance. Of nine MC1R variant alleles assayed, four common alleles were strongly associated with red hair and fair skin (Asp84Glu, Arg151Cys, Arg160Trp and Asp294His), with a further three alleles having low penetrance (Val60Leu, Val92Met and Arg163Gln). These variants were separately combined for the purposes of this analysis and designated as strong 'R' (OR=63.3; 95% CI 31.9-139.6) and weak 'r ' (OR=5.1; 95% CI 2.5-11.3) red hair alleles. Red-haired individuals are predominantly seen in the R/R and R/r groups with 67.1 and 10.8%, respectively. To assess the interaction of the brown eye color gene OCA2 on the phenotypic effects of variant MC1R alleles we included eye color as a covariate, and also genotyped two OCA2 SNPs (Arg305Trp and Arg419Gln), which were confirmed as modifying eye color. MC1R genotype effects on constitutive skin color, freckling and mole count were modified by eye color, but not genotype for these two OCA2 SNPs. This is probably due to the association of these OCA2 SNPs with brown/green not blue eye color. Amongst individuals with a R/R genotype (but not R/r), those who also had brown eyes had a mole count twice that of those with blue eyes. This suggests that other OCA2 polymorphisms influence mole count and remain to be described.

Incremental learning for interactive e-mail filtering

In this article, we propose a framework, namely, Prediction-Learning-Distillation (PLD) for interactive document classification and distilling misclassified documents. Whenever a user points out misclassified documents, the PLD learns from the mistakes and identifies the same mistakes from all other classified documents. The PLD then enforces this learning for future classifications. If the classifier fails to accept relevant documents or reject irrelevant documents on certain categories, then PLD will assign those documents as new positive/negative training instances. The classifier can then strengthen its weakness by learning from these new training instances. Our experiments’ results have demonstrated that the proposed algorithm can learn from user-identified misclassified documents, and then distil the rest successfully.

Interactive email filtering: Learning from misclassified examples

Learning from mistakes has proven to be an effective way of learning in the interactive document classifications. In this paper we propose an approach to effectively learning from mistakes in the email filtering process. Our system has employed both SVM and Winnow machine learning algorithms to learn from misclassified email documents and refine the email filtering process accordingly. Our experiments have shown that the training of an email filter becomes much effective and faster