The effects of diet and beauty products on the uptake and storage of 14C in hair and nails: ramifications for the application of bomb pulse dating to forensic anthropological casework.

Radiocarbon dating is a useful tool in the examination of unknown human remains. Recent studies have shown that the analysis of hair and nail samples can provide a highly accurate estimation of the year of death (YOD). However, little research has examined factors that may influence the uptake and storage of 14C in these tissues, such as diet, or the use of beauty products. This study measured the level of 14C in human hair and nail samples collected from living individuals to determine whether diet, and the use of hair dye or nail polish, has a significant impact on the estimation of YOD. The results of this study showed that diet did not appear to impact the radiocarbon content in human hair and nail, and thus should not be considered a limitation when analysing samples obtained from unidentified human remains. The use of nail polish, and in the majority of cases, hair dye, did not significantly impact the 14C concentration in nails and hair. While the results of this study are preliminary, they suggest that in most cases, both hair and nail can be successfully analysed using radiocarbon dating to estimate an individual's YOD. However, best practice should involve the analysis of multiple tissue types, to minimise any error that may be introduced as a result of the decedent's use of beauty products.

The importance of bone sample selection when using radiocarbon analysis in cases of unidentified human remains.

The radiocarbon (14C) analysis of skeletonised human remains can provide vital information regarding the time since death. This study analysed the 14C content in both cortical and trabecular bone from the femur and rib of 18 human remains donated to the Australian Facility for Taphonomic Experimental Research. To determine the most appropriate bone to sample for 14C analysis when estimating the time since death, differences in turnover time between the bone types, and the difference between environmental 14C during the year of death (YOD) and the level of 14C found within the bone (i.e., the lag time) were examined. Overall, the average lag time for all donors was 25 years, with the cortical bone from femur samples providing the largest lag time (39 years) and the trabecular bone from rib samples containing the lowest level of 14C (most modern), with an average lag time of 13 years. The results of this study suggest that in cases of unidentified human remains, the analysis of trabecular bone from a rib may be preferred sample, as this bone contains the most recent 14C, and can provide an estimate of both the time since death, and a broad approximation of YOD.

Examining the use of different sample types following decomposition to estimate year of death using bomb pulse dating.

When human remains are discovered, confirming the identification of the decedent is the first part of the forensic medical investigation. In cases where the remains are skeletonised or badly decomposed, differential preservation often increases the difficulty of this task. Bomb pulse dating, which directly compares levels of 14 C within human tissues to atmospheric levels, can provide an estimate of the year of death, which may assist in the identification process. This study measured the 14 C content in samples of hair, nail and puparia collected from donors at the Australian Facility for Taphonomic Experimental Research (AFTER). The radiocarbon results demonstrated that the nail samples provided the most accurate year of death estimation, with 91% correctly predicting YOD, closely followed by hair, with a 79% correct prediction rate, with both hair and nails having a lag time of 0-1 years. This is consistent with the time taken for atmospheric CO 2 to enter the food chain, and be taken in by humans. Puparia was found to have the highest levels of 14 C, and was the least consistent with the actual YOD (46% correct). However, predicted YOD ranges were still within 4 years of the actual YOD. Based on the results of this study, hair, nail and puparia should be considered as useful samples to obtain accurate estimates for YOD using bomb pulse dating.

Why so GLUMM? Detecting depression clusters through graphing lifestyle-environs using machine-learning methods (GLUMM).

BACKGROUND: Key lifestyle-environ risk factors are operative for depression, but it is unclear how risk factors cluster. Machine-learning (ML) algorithms exist that learn, extract, identify and map underlying patterns to identify groupings of depressed individuals without constraints. The aim of this research was to use a large epidemiological study to identify and characterise depression clusters through "Graphing lifestyle-environs using machine-learning methods" (GLUMM). METHODS: Two ML algorithms were implemented: unsupervised Self-organised mapping (SOM) to create GLUMM clusters and a supervised boosted regression algorithm to describe clusters. Ninety-six "lifestyle-environ" variables were used from the National health and nutrition examination study (2009-2010). Multivariate logistic regression validated clusters and controlled for possible sociodemographic confounders. RESULTS: The SOM identified two GLUMM cluster solutions. These solutions contained one dominant depressed cluster (GLUMM5-1, GLUMM7-1). Equal proportions of members in each cluster rated as highly depressed (17%). Alcohol consumption and demographics validated clusters. Boosted regression identified GLUMM5-1 as more informative than GLUMM7-1. Members were more likely to: have problems sleeping; unhealthy eating; ≤2 years in their home; an old home; perceive themselves underweight; exposed to work fumes; experienced sex at ≤14 years; not perform moderate recreational activities. A positive relationship between GLUMM5-1 (OR: 7.50, P<0.001) and GLUMM7-1 (OR: 7.88, P<0.001) with depression was found, with significant interactions with those married/living with partner (P=0.001). CONCLUSION: Using ML based GLUMM to form ordered depressive clusters from multitudinous lifestyle-environ variables enabled a deeper exploration of the heterogeneous data to uncover better understandings into relationships between the complex mental health factors.