A comprehensive survey of Retzius periodicities in fossil hominins and great apes.

Recent studies have provided great insight into hominin life history evolution by utilizing incremental lines found in dental tissues to reconstruct and compare the growth records of extant and extinct humans versus other ape taxa. Among the hominins, studies that have examined Retzius periodicity (RP) variation have come to contradictory conclusions in some instances. To clarify RP variation among hominins and better place this variation in its broader evolutionary context, we conduct the most comprehensive analysis of published RP values for hominins and great apes to date. We gathered all available data from the literature on RP data from extant humans, great apes, and fossil hominins and assessed their variation using parametric and nonparametric analyses of variance. We also performed phylogenetic generalized least-squares regressions of RP data for these taxa as well as a larger set of hominoids for which RP data have been published against data for body mass, encephalization, and mean semicircular canal radius (a proxy for metabolic rate). Our results show that modern humans have a mean RP significantly differing from that of other hominins. Pongo also is significantly different from nearly all other taxa in all analyses. Our results also demonstrate that RP variation among hominins scales with respect to body mass, encephalization, and semicircular canal radius similarly to other hominids but that modern humans and Pongo stand out in this regard. Operating within the hypothesis that RP reflects autonomic biorhythms that regulate multiple life history variables, our results reinforce the idea that Homo sapiens has evolved a life history distinct from other hominins, even from other members of Homo, and suggest that many of these life history differences may be driven by hypothalamic output from the brain.

Discrete Wavelet Packet Transform Based Discriminant Analysis for Whole Genome Sequences.

In recent years, alignment-free methods have been widely applied in comparing genome sequences, as these methods compute efficiently and provide desirable phylogenetic analysis results. These methods have been successfully combined with hierarchical clustering methods for finding phylogenetic trees. However, it may not be suitable to apply these alignment-free methods directly to existing statistical classification methods, because an appropriate statistical classification theory for integrating with the alignment-free representation methods is still lacking. In this article, we propose a discriminant analysis method which uses the discrete wavelet packet transform to classify whole genome sequences. The proposed alignment-free representation statistics of features follow a joint normal distribution asymptotically. The data analysis results indicate that the proposed method provides satisfactory classification results in real time.