Mathematical cognition: individual differences in resource allocation

Individuals scoring higher in tests of general cognitive abilities tend to perform better on novel and familiar mathematical tasks. It has been scarcely investigated how this superior mathematical performance relates to the amount of cognitive resources that is invested to solve a given task. In this study we propose that, on novel tasks, individuals with high cognitive abilities outperform less able individuals, because they allocate a higher amount of resources. On familiar tasks, however, individuals with higher abilities profit from more efficient processes compared to individuals of lower cognitive abilities. We tested this hypothesis by administering to 11th graders a geometric analogy task not practiced at school and an algebraic transformation task comprising operations that are routinely required during mathematical courses. General cognitive abilities were measured with Ravens Advanced Progressive matrices (fluid intelligence), the d2 (focused attention) and KAI-N (working memory capacity). Resource allocation was measured by assessing pupil diameter during the problem-solving process. Performance on both the analogy and the algebra task was correlated with general cognitive abilities, especially fluid intelligence. In line with our assumptions, a positive correlation between fluid intelligence and resource allocation was observed in the novel geometric analogy task, whereas the correlation was not significant in the more familiar algebra task.     

Searching for in silico predicted metabolites and designer modifications of (cortico)steroids in urine by high‐resolution liquid chromatography/time‐of‐flight mass spectrometry

Glucocorticosteroids are a restricted class of substances and appear on the ‘in‐competition’ prohibited list of the World Anti‐Doping Agency (WADA). Analysis of glucocorticosteroids is complicated since they show significant phase 1 and 2 metabolism in the human body and are excreted into urine in concentrations in the µg/L range. Full scan, high‐resolution time‐of‐flight mass spectrometry analysis generates information on all ionisable components in urine, including known and unknown metabolites of steroids and even designer modifications of anabolic steroids. However, evaluation of the data obtained can be difficult and time‐consuming because of the need to differentiate between endogenous components and compounds of interest. MetaboLynx?, a spectral and chromatographic search program, was modified for the determination of in silico predicted metabolites of glucocorticosteroids and designer modifications of anabolic steroids in human urine. Spiked urine samples were successfully screened for known components in a targeted approach and for unknown species in a non‐targeted approach using data filtering to limit potential false‐positives. A simplified combined approach of targeted and untargeted screening was used for the detection of metabolites and designer modifications of existing compounds. This approach proved successful and showed its strength in the detection of tetrahydrogestrinone (THG), a designer modification of gestrinone. THG was positively detected in a spiked urine sample and correctly identified as a twofold hydrogenation of gestrinone. The developed screening method can easily be adapted to specific needs and it is envisaged that a similar approach would be amendable to the discovery of metabolites or designer modifications of other compounds of interest. Copyright © 2009 John Wiley & Sons, Ltd.     

Ultrasonic absorption studies in some solutions of carboxylic-acids in dioxan

Ultrasonic absorption studies are carried out in carboxylic acid-dioxan mixtures for various concentrations at 30±0.1°C. The measured absorption is much higher than the classical absorption. The excess absorption initially increases with increase of carboxylic acid concentration, attains a maximum and then decreases with further increase in concentration. The related parameters such as volume viscosity, relaxation time and free energy difference between the two states (ΔF) have also been calculated. The results can be explained satisfactorily using Hall’s two-state model.