S Margetts | PubMed Health

PubMed2001

Use of 'chalk' in rock climbing: sine qua non or myth?

Magnesium carbonate, or 'chalk', is used by rock climbers to dry their hands to increase the coefficient of friction, thereby improving the grip of the holds. To date, no scientific research supports this practice; indeed, some evidence suggests that magnesium carbonate could decrease the coefficient of friction. Fifteen participants were asked to apply a force with the tip of their fingers to hold a flattened rock (normal force), while a tangential force pulled the rock away. The coefficient of friction--that is, the ratio between the tangential force (pulling the rock) and the normal force (applied by the participants)--was calculated. Coating (chalk vs no chalk), dampness (water vs no water) and rock (sandstone, granite and slate) were manipulated. The results showed that chalk decreased the coefficient of friction. Sandstone was found to be less slippery than granite and slate. Finally, water had no significant effect on the coefficient of friction. The counter-intuitive effect of chalk appears to be caused by two independent factors. First, magnesium carbonate dries the skin, decreasing its compliance and hence reducing the coefficient of friction. Secondly, magnesium carbonate creates a slippery granular layer. We conclude that, to improve the coefficient of friction in rock climbing, an effort should be made to remove all particles of chalk; alternative methods for drying the fingers are preferable.

Adult↗

PubMed1998

Feature selection for genetic sequence classification.

MOTIVATION: Most of the existing methods for genetic sequence classification are based on a computer search for homologies in nucleotide or amino acid sequences. The standard sequence alignment programs scale very poorly as the number of sequences increases or the degree of sequence identity is <30%. Some new computationally inexpensive methods based on nucleotide or amino acid compositional analysis have been proposed, but prediction results are still unsatisfactory and depend on the features chosen to represent the sequences. RESULTS: In this paper, a feature selection method based on the Gamma (or near-neighbour) test is proposed. If there is a continuous or smooth map from feature space to the classification target values, the Gamma test gives an estimate for the mean-squared error of the classification, despite the fact that one has no a priori knowledge of the smooth mapping. We can search a large space of possible feature combinations for a combination which gives a smallest estimated mean-squared error using a genetic algorithm. The method was used for feature selection and classification of the large subunits of rRNA according to RDP (Ribosomal Database Project) phylogenetic classes. The sequences were represented by dinucleotide frequency distribution. The nearest-neighbour criterion has been used to estimate the predictive accuracy of the classification based on the selected features. For examples discussed, we found that the classification according to the first nearest neighbour is correct for 80% of the test samples. If we consider the set of the 10 nearest neighbours, then 94% of the test samples are classified correctly. AVAILABILITY: The principal novel component of this method is the Gamma test and this can be downloaded compiled for Unix Sun 4, Windows 95 and MS-DOS from http://www.cs.cf.ac.uk/ec/ CONTACT: s.margetts@cs.cf.ac.uk

Algorithms↗