A New Approach of Juvenile Age Estimation using Measurements of the Ilium and Multivariate Adaptive Regression Splines (MARS) Models for Better Age Prediction

Mixed cumulative probit: a multivariate generalization of transition analysis that accommodates variation in the shape, spread and structure of data

Biological data are frequently nonlinear, heteroscedastic and conditionally dependent, and often researchers deal with missing data. To account for characteristics common in biological data in one algorithm, we developed the mixed cumulative probit (MCP), a novel latent trait model that is a formal generalization of the cumulative probit model usually used in transition analysis. Specifically, the MCP accommodates heteroscedasticity, mixtures of ordinal and continuous variables, missing values, conditional dependence and alternative specifications of the mean response and noise response. Cross-validation selects the best model parameters (mean response and the noise response for simple models, as well as conditional dependence for multivariate models), and the Kullback-Leibler divergence evaluates information gain during posterior inference to quantify mis-specified models (conditionally dependent versus conditionally independent). Two continuous and four ordinal skeletal and dental variables collected from 1296 individuals (aged birth to 22 years) from the Subadult Virtual Anthropology Database are used to introduce and demonstrate the algorithm. In addition to describing the features of the MCP, we provide material to help fit novel datasets using the MCP. The flexible, general formulation with model selection provides a process to robustly identify the modelling assumptions that are best suited for the data at hand.

Machine learning models for streamflow regionalization in a tropical watershed

This study aims to assess different machine learning approaches for streamflow regionalization in a tropical watershed, analyzing their advantages and limitations, and to point the benefits of using them for water resources management. The algorithms applied were: Random Forest, Earth and linear model. The response variables were the three types of minimum streamflow (Q_7.10, Q₉₅ and Q₉₀), besides the long-term average streamflow (Q_mld). The database involved 76 environmental covariates related to morphometry, topography, climate, land use and cover, and surface conditions. The elimination of covariates was performed using two processes: Pearson's correlation analysis and importance analysis by Recursive Feature Elimination (RFE). To validate the models, the following statistical metrics were used: Nash-Sutcliffe coefficient (NSE), percent bias (PBIAS), Willmott's index of agreement (d), coefficient of determination (R²), root mean square error (RMSE), mean absolute error (MAE) and relative error (RE). The linear model was unsatisfactory for all response variables. The results show that nonlinear models performed well, and their covariate of greatest predictive importance was flow equivalent to the precipitated volume, considering the subtraction of an abstraction factor of 750 mm (Peq750). Generally, the Random Forest and Earth models showed similar performances and great ability to predict the minimum streamflow and long-term average streamflow assessed, constituting powerful and promising alternatives for the streamflow regionalization in support to the management and integrated planning of water resources at the level of river basins.

Standardizing ordinal subadult age indicators: Testing for observer agreement and consistency across modalities

Skeletal and dental data for subadult analyses obtained from dry bones or various types of medical images, such as computed tomography (CT) scans or conventional radiographs/x-rays, should be consistent and repeatable to ensure method applicability across modalities and support combining study samples. The present study evaluates observer agreement of epiphyseal fusion and dental development stages obtained on CT scans of a U.S. sample and the consistency of epiphyseal fusion stages between CT scans and projected scan radiographs/scout images (U.S. CT sample), and between dry bones and conventional x-rays (Colombian osteological sample). Results show that both intra- and interobserver agreements of scores on CT scans were high (intra: mean Cohen's kappa=0.757-0.939, inter: mean Cohen's kappa=0.773-0.836). Agreements were lower for dental data (intra: mean Cohen's kappa=0.757, inter: mean Cohen's kappa=0.773-0.0.820) compared to epiphyseal fusion data (intra: mean Cohen's kappa=0.939, inter: mean Cohen's kappa=0.807-0.836). Consistency of epiphyseal fusion stages was higher between dry bones and conventional x-rays than between CT scans and scout images (mean Cohen's kappa=0.708-0.824 and 0.726-0.738, respectively). Differences rarely surpassed a one-stage value between observers or modalities. The complexity of some ossification patterns and superimposition had a greater negative impact on agreement and consistency rates than observer experience. Results suggest ordinal subadult skeletal data can be collected and combined across modalities.

Contributions of the pars lateralis, pars basilaris and femur to age estimations of the immature skeleton within a South African forensic setting

Dental development and eruption sequences have prevailed as the gold standard in age estimations of previously unidentified immature individuals within a legal context. However, in the absence of the dentition, skeletal assessments have served as a frequently applied alternative. While various cranial and postcranial skeletal elements have been used in estimating age of the immature skeleton, little is known about the anthropometric value of the pars basilaris, pars lateralis and femur as skeletal age estimation tools. Thus, this study aimed to assess if these bones of the immature human skeleton were useful elements in estimating the age of prenatal and postnatal individuals. These bones were excised from the remains of 74 unclaimed human immature individuals and evaluated using traditional anthropometric methods. The study sample was sourced from the Johannesburg Forensic Pathology Services (JFPS) and the Johannesburg Forensic Paediatric Collection (JFPC), University of the Witwatersrand and subdivided into an early prenatal (younger than 30 gestational weeks); late prenatal (30 to 40 gestational weeks) and postnatal (birth to 7.5 months) age ranges. Statistically significant differences (p ≤ 0.05) were found when assessing the maximum length, sagittal length, maximum width and distal width of the bones across each of the age ranges (30 gestational weeks to 7 postnatal months). The cranial and postcranial skeletal elements investigated in this study provide a valuable contribution to skeletal ageing in African individuals.

Estimation of age in forensic anthropology: historical perspective and recent methodological advances

Estimation of age represents a central focus of forensic anthropological analysis of human skeletal remains and of the living. Advances registered in recent research include the topics of taphonomic impact, new anatomical areas of interest, histology, population variation, the dental pulp chamber, technology, mathematical approaches, biochemical analysis and techniques specifically targeting the living. This article reviews the historical development of age estimation methods and considers likely future directions.