Identification of sex from the skeleton is an important demographic assessment in medicolegal investigations. Studies have demonstrated that populations differ from each other in size and proportions and that these differences can affect the metric assessment of sex. It is therefore vital to determine if population differences are great enough to necessitate group-specific standards. To date, there have been no attempts to create standards of assessment for modern Thais. Therefore the purpose of this research is to establish standards from which to determine sex from the femur using a new skeletal collection housed at the Chiang Mai University Department of Anatomy. The sample is composed of 104 individuals (70 males, 34 females). Six standard osteometric dimensions including maximum length, maximum head diameter, midshaft circumference, midshaft anterior-posterior and transverse diameters, and bicondylar breadth were measured and analyzed by stepwise discriminant function statistics. To understand population differences, formulas derived from Chinese, South African whites and American whites and blacks using the same method and variables were tested on the Thai sample. Results indicated that maximum head diameter and bicondylar breadth are the optimal combination for sex diagnosis and yielded 94.2% accuracy. Direct analysis using predetermined single or multiple variables also revealed bicondylar breadth as the best single dimension (93.3%). In cross-tests on the Thais, the Chinese formula gave the most favorable outcome with unsatisfactory results for all other groups. The present research confirms that sexual dimorphism is better reflected in breadth dimensions than in bone length. Comparisons showed that Thais are very different metrically from whites and blacks, and although they most resemble the Chinese, these two groups are not identical. These findings underscore the need for population-specific formulas for identification of sex from the skeleton.

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Protein-peptide interactions mediate many of the connections in intracellular signaling networks. A generalized computational framework for atomically precise modeling of protein-peptide specificity may allow for predicting molecular interactions, anticipating the effects of drugs and genetic mutations, and redesigning molecules for new interactions. We have developed an extensible, general algorithm for structure-based prediction of protein-peptide specificity as part of the Rosetta molecular modeling package. The algorithm is not restricted to any one peptide-binding domain family and, at minimum, does not require an experimentally characterized structure of the target protein nor any information about sequence specificity; although known structural data can be incorporated when available to improve performance. We demonstrate substantial success in specificity prediction across a diverse set of peptide-binding proteins, and show how performance is affected when incorporating varying degrees of input structural data. We also illustrate how structure-based approaches can provide atomic-level insight into mechanisms of peptide recognition and can predict the effects of point mutations on peptide specificity. Shortcomings and artifacts of our benchmark predictions are explained and limits on the generality of the method are explored. This work provides a promising foundation upon which further development of completely generalized, de novo prediction of peptide specificity may progress.

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