Fourteen midline facial tissue measurements were taken from 615 tracings of lateral radiographs of subadults aged 8 to 20 years. The data were collected to examine two questions: First, are there differences in facial soft tissue measurements between female and male subadults? Second, do facial tissue thicknesses change as children grow? Results indicate that males exhibit greater tissue thickness measurements than females but only significantly so after age 14. Results further indicate a trend of increased facial tissue thickness as individuals grow; however, correlations are weak and suggest that other unknown factors are involved. Data presented here can be of practical application for facial reproduction in forensic cases.

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Correlation coefficients and SEE results for this sample were: (1) DXA vs 3DS; r = 0.74, SEE = 3.2, (2) MA vs DXA; r = 0.82, SEE = 2.8, and (3) MA vs 3DS; r = 0.96, SEE = 1.0. Lin's concordance analysis, including Bland-Altman limits of agreement (LOA), revealed statistically significant measurement agreement among the three measurement modalities (p < 0.05). The application of 3DS scanning to estimate percent BF from commonly used anthropometric measurements are in close agreement with BF estimates derived from analogous MA measurements and from DXA scanning.

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Introduction Personnel engaged in high-stakes occupations, such as military personnel, law enforcement, and emergency first responders, must sustain performance through a range of environmental stressors. To maximize the effectiveness of military personnel, an a priori understanding of traits can help predict their physical and cognitive performance under stress and adversity. This work developed and assessed a suite of measures that have the potential to predict performance during operational scenarios. These measures were designed to characterize four specific trait–based domains: cognitive, health, physical, and social-emotional. Materials and Methods One hundred and ninety-one active duty U.S. Army soldiers completed interleaved questionnaire–based, seated task–based, and physical task–based measures over a period of 3-5 days. Redundancy analysis, dimensionality reduction, and network analyses revealed several patterns of interest. Results First, unique variable analysis revealed a minimally redundant battery of instruments. Second, principal component analysis showed that metrics tended to cluster together in three to five components within each domain. Finally, analyses of cross-domain associations using network analysis illustrated that cognitive, health, physical, and social-emotional domains showed strong construct solidarity. Conclusions The present battery of metrics presents a fieldable toolkit that may be used to predict operational performance that can be clustered into separate components or used independently. It will aid predictive algorithm development aimed to identify critical predictors of individual military personnel and small-unit performance outcomes.