Morphometric analysis of Macaca arctoides and M-thibetana in relation to other macaque species

As a first step in reviewing the classification of the two stump-tailed macaque species, Macaca arctoides and M. thibetana, as compared with other species of the genus Macaca, 72 linear dental and cranial variables of 11 macaque species were examined by morphometric analyses. The results indicate that the two stump-tailed species are the largest of the macaques and although rather similar overall, they exhibit significant differences in the pattern of variation in most of the five skull regions as shown by Principal Components and Canonical Variate Analyses. Euclidean Distances based on Canonical Variate scores indicate that the females of M. arctoides and M. thibetana are more widely separated than eight other pairs of macaque species, and that the separations of the respective males are greater than those of three other pairs of species. These findings are consistent with FOODEN's classification of the stump-tailed macaques as two separate species (FOODEN, 1976; FOODEN et al., 1985). The present results suggest, as other researchers have proposed on the basis of external features, biochemistry and genetics, that the two stump-tailed macaque species and M. assamensis are closely related. The results also tentatively imply associations with M. fuscata and M. sylvanus but these require further study. The findings have implications for the assessment of the various Chinese Pleistocene macaque fossils.

The relation between translation speed and protein secondary structure

Based on the statistical analysis of 119 human and 92 E. coli proteins it was found that for both human and E. coli, the mRNA sequences consisting of tri-codon and tetra-codon with high translation speed preferably code for alpha helices more than for coils. For beta strand, the preference/ avoidance oscillates with the translation speed. Moreover, the non-homogeneous usages of tri-codon and tetra-codon with different translation speeds in a given secondary structure have also been found. These results cannot be simply explained by the effect of stochastic fluctuation.

On the relation of slow feature analysis and Laplacian eigenmaps

The past decade has seen a rise of interest in Laplacian eigenmaps (LEMs) for nonlinear dimensionality reduction. LEMs have been used in spectral clustering, in semisupervised learning, and for providing efficient state representations for reinforcement learning. Here, we show that LEMs are closely related to slow feature analysis (SFA), a biologically inspired, unsupervised learning algorithm originally designed for learning invariant visual representations. We show that SFA can be interpreted as a function approximation of LEMs, where the topological neighborhoods required for LEMs are implicitly defined by the temporal structure of the data. Based on this relation, we propose a generalization of SFA to arbitrary neighborhood relations and demonstrate its applicability for spectral clustering. Finally, we review previous work with the goal of providing a unifying view on SFA and LEMs. © 2011 Massachusetts Institute of Technology.