Peopling of the Americas: A new approach to assessing dental morphological variation in Asian and Native American populations.

OBJECTIVES: Through biodistance analyses, anthropologists have used dental morphology to elucidate how people moved into and throughout the Americas. Here, we apply a method that focuses on individuals rather than sample frequencies through the application rASUDAS2, based on a naïve Bayes' algorithm. MATERIALS AND METHODS: Using the database of C.G. Turner II, we calculated the probability that an individual could be assigned to one of seven biogeographic groups (American Arctic, North & South America, East Asia, Southeast Asia & Polynesia, Australo-Melanesia, Western Eurasia, & Sub-Saharan Africa) through rASUDAS2. The frequency of classifications for each biogeographic group was determined for 1418 individuals from six regions across Asia and the Americas. RESULTS: Southeast Asians show mixed assignments but rarely to American Arctic or "American Indian." East Asians are assigned to East Asia half the time while 30% are assigned as Native American. People from the American Arctic and North & South America are assigned to Arctic America or non-Arctic America 75%-80% of the time, with 10%-15% classified as East Asian. DISCUSSION: All Native American groups have a similar degree of morphological affinity to East Asia, as 10%-15% are classified as East Asian. East Asians are classified as Native American in 30% of cases. Individuals in the Western Hemisphere are decreasingly classified as Arctic the farther south they are located. Equivalent levels of classification as East Asian across all Native American groups suggests one divergence between East Asians and the population ancestral to all Native Americans. Non-arctic Native American groups are derived from the Arctic population, which represents the Native American founder group.

DXAGE 2.0 - adult age at death estimation using bone loss in the proximal femur and the second metacarpal.

The accurate age at death assessment of unidentified adult skeletal individuals is a critical research task in forensic anthropology, being a key feature for the determination of biological profiles of individual skeletal remains. We have previously shown that the age-related decrease of bone mineral density (BMD) in the proximal femur could be used to assess age at death in women (Navega et al., J Forensic Sci 63:497-503, 2018). The present study aims to generate models for age estimation in both sexes through bone densitometry of the femur and radiogrammetry of the second metacarpal. The training sample comprised 224 adults (120 females, 104 males) from the "Coimbra Identified Skeletal Collection," and different models were generated through least squares regression and general regression neural networks (GRNN). The models were operationalized in a user-friendly online interface at https://osteomics.com/DXAGE2/ . The mean absolute difference between the known and estimated age at death ranges from 9.39 to 13.18 years among women and from 10.33 to 15.76 among men with the least squares regression models. For the GRNN models, the mean absolute difference between documented and projected age ranges from 8.44 to 12.58 years in women and from 10.56 to 16.18 years in men. DXAGE 2.0 enables age estimation in incomplete and/or fragmentary skeletal remains, using alternative skeletal regions, with reliable results.

Adult Skeletal Age-at-Death Estimation through Deep Random Neural Networks: A New Method and Its Computational Analysis.

Age-at-death assessment is a crucial step in the identification process of skeletal human remains. Nonetheless, in adult individuals this task is particularly difficult to achieve with reasonable accuracy due to high variability in the senescence processes. To improve the accuracy of age-at-estimation, in this work we propose a new method based on a multifactorial macroscopic analysis and deep random neural network models. A sample of 500 identified skeletons was used to establish a reference dataset (age-at-death: 19-101 years old, 250 males and 250 females). A total of 64 skeletal traits are covered in the proposed macroscopic technique. Age-at-death estimation is tackled from a function approximation perspective and a regression approach is used to infer both point and prediction interval estimates. Based on cross-validation and computational experiments, our results demonstrate that age estimation from skeletal remains can be accurately (~6 years mean absolute error) inferred across the entire adult age span and informative estimates and prediction intervals can be obtained for the elderly population. A novel software tool, DRNNAGE, was made available to the community.

New data about the 21st Century Identified Skeletal Collection (University of Coimbra, Portugal).

After the publication of the first article in 2014, 21st Century Identified Skeletal Collection, housed in the Laboratory of Forensic Anthropology, Department of Life Sciences at the University of Coimbra, Portugal, has been growing. Currently, the collection is composed of 302 complete adult skeletons of both sexes, which means that in 5 years it has doubled. The collection consists mostly of elderly individuals, with only 12.25% of the individuals aged less than 61 years old. All individuals are Portuguese nationals who died between 1982 and 2012. Ninety individuals exhibit prostheses, other medical devices and signs of surgical procedures. Moreover, a sub-collection of experimentally burned skeletons is under development, and currently includes 56 individuals (18.54% of the collection). The 21st Century Identified Skeletal Collection constitutes a fundamental tool for forensic anthropology research, including the development and validation studies of methods that focus on elderly individuals, as can be ascertained by the numerous scientific publications and academic scholarship that have been produced in previous years.

Sub-adult aging method selection (SAMS): A decisional tool for selecting and evaluating sub-adult age estimation methods based on standardized methodological parameters.

OBJECTIVES: A decisional tool was developed to select sub-adult age estimation methods referenced in a centralized database. Through a freely accessible webpage interface, this tool allows users to evaluate how much the sampling and statistical protocols of these referenced methods comply with methodological recommendations published for building and applying methods in forensic anthropology. MATERIALS AND METHODS: 261 publications on sub-adult age estimation were collected. Three search parameters describing the anatomical element(s) and the indicators used to obtain age estimates are chosen by the user to filter the database and present the publications that best correspond to the user's selection. A simple algorithm was created to score age estimation methods according to their relevance and validity. "Relevance" and "Validity" parameters indicate how much a publication complies with user queries and published methodological recommendations, respectively; "Score" is a combination of "Relevance" and "Validity". The closer these parameters are to 1, the better the method complies with the user's choice and standardized protocols. RESULTS: The publications resulting from the user's query appear as search results alphabetically. They are characterized by their "Relevance", "Validity" and "Score" values and descriptors relating to their methodology, sampling and statistical protocols. The reference of the publications and an URL to access them online are also provided. CONCLUSIONS: SAMS is a decisional tool based on a centralized database for selecting, accessing and evaluating sub-adult age estimation methods based on published methodological recommendations. Protocol validity can be easily and fully accessed to provide the necessary information for method evaluation. The database will be gradually updated and implemented as new sub-adult age estimation methods are made available online.

Application and validation of Diagnose Sexuelle Probabiliste V2 tool in a miscegenated population.

The hip bone (os coxae) is the skeletal element that presents the greatest level of sexual dimorphism. Therefore, methods involving the analysis of the os coxae provide the most accurate sex estimation, and DSP2 (Diagnose Sexuelle Probabiliste v.2) is one of the most accurate tools used in this identification. The goal of this study is to apply and validate DSP2 in the identification of 103 os coxae (53 male and 50 female) belonging to a Brazilian-identified skeletal collection. Differences between sexes were statistically significant for all measurements, except for the acetabulo-symphyseal and spino-auricular lengths. From the 103 os coxae analyzed, there was a 9.43% error in male individuals and a 14% error in females. The results revealed that DSP2 can be applied to Brazilian-mixed populations with a good index of accuracy, although at a lower accuracy than other population samples. This study also clearly demonstrates that metric variation of the os coxae is extremely useful in sex estimation and reinforces the notion that pelvic sexual dimorphism is not population-specific.

A test and analysis of Calce (2012) method for skeletal age-at-death estimation using the acetabulum in a modern skeletal sample.

In forensic anthropology, the age-at-death of an adult individual is one of the most complex parameters of the biological profile to estimate. The present study aims to evaluate the reliability of the Calce (2012) method for the estimation of age-at-death through acetabulum changes in a sample of Portuguese origin. This method consists of the global analysis of acetabular age-related morphology with focus on three specific traits, namely the acetabular groove, the osteophyte development of the acetabular rim, and the apex growth. This method was tested in 120 individuals sampled from the Twenty-first Century Identified Skeletal Collection (University of Coimbra, Portugal). The test sample is composed of 60 males and 60 females, aged between 25 and 99 years, with well-preserved os coxae. The results showed that only 60% of the individuals were correctly attributed to the age group defined by the technique. The comparison with previous studies in other populations shows significant inter-population differences in the relationship between the acetabulum variables used by Calce and age-at-death. The obtained results advise caution in the use of the Calce (2012) method to estimate the age-at-death of unidentified skeletons.

Age estimation of adult human remains from hip bones using advanced methods.

The assessment of age-at-death is an important and challenging part of investigations of human skeletal remains. The main objective of the present study was to apply different mathematical approaches in order to reach more accurate and reliable results in age estimation. A multi-ethnic dataset (n=941) of evaluated age-related changes on the pubic symphysis and the auricular surface of the hip bone was used. Two research groups examined nine different mathematical approaches. The best results were reached by Multi-linear regression, followed by the Collapsed regression model, with MAE values of 9.7 and 9.9 years, respectively, and with RMSE values of 12.1 and 12.2, respectively. The mean accuracy of decision tree models ranged between 30.7% and 72.3%, with the model using only the PUSx indicator performing the best. Moreover, our results indicate that the limiting factor of age estimation can be the visual evaluation of age-related changes. Further research is required to objectify the proposed methods for estimating age.

DXAGE: A New Method for Age at Death Estimation Based on Femoral Bone Mineral Density and Artificial Neural Networks.

Age at death estimation in adult skeletons is hampered, among others, by the unremarkable correlation of bone estimators with chronological age, implementation of inappropriate statistical techniques, observer error, and skeletal incompleteness or destruction. Therefore, it is beneficial to consider alternative methods to assess age at death in adult skeletons. The decrease in bone mineral density with age was explored to generate a method to assess age at death in human remains. A connectionist computational approach, artificial neural networks, was employed to model femur densitometry data gathered in 100 female individuals from the Coimbra Identified Skeletal Collection. Bone mineral density declines consistently with age and the method performs appropriately, with mean absolute differences between known and predicted age ranging from 9.19 to 13.49 years. The proposed method-DXAGE-was implemented online to streamline age estimation. This preliminary study highlights the value of densitometry to assess age at death in human remains.

Luminol chemiluminescence: contribution to postmortem interval determination of skeletonized remains in Portuguese forensic context.

Postmortem interval (PMI) determination is one of the main challenges of forensic anthropology, and there are several referenced methods that consider intrinsic and extrinsic factors to the skeletonized remains. Therefore, there is an important need to develop a precise, economic, easy and reproducible technique, which will operate as a presumptive test. The chemical reaction that occurs in the presence of luminol, chemiluminescence, is used as a work tool to evaluate the PMI. This is given by luminol high affinity for haemoglobin, which is secured in bone tissue by its high mineral content, after the blood supply ceases postmortem. We evaluated a luminol-based technique, in which we measured the chemiluminescence of skeletonized remains as an indicator of the PMI. We assessed the usage of this technique for PMI estimation and for deciding whether remains have forensic interest, particularly in the Portuguese medicolegal context. Additionally, we evaluate the reproducibility of this technique. The test sample consists of 50 skeletonized individuals of both sexes, adults, between 20 and 98 years, with a known time since death and without bone pathologies. The results of Kappa statistic, with values between 0.79 and 0.97, showed high interobserver and intraobserver agreement when applying the luminol test as a presumption test. We concluded that this technique alone is not precise and of limited value to determine forensic relevance in the Portuguese context. However, it can be used as a presumptive test, reducing time and costs in criminal investigation.

Study on the criteria for assessing skull-face correspondence in craniofacial superimposition.

Craniofacial superimposition has the potential to be used as an identification method when other traditional biological techniques are not applicable due to insufficient quality or absence of ante-mortem and post-mortem data. Despite having been used in many countries as a method of inclusion and exclusion for over a century it lacks standards. Thus, the purpose of this research is to provide forensic practitioners with standard criteria for analysing skull-face relationships. Thirty-seven experts from 16 different institutions participated in this study, which consisted of evaluating 65 criteria for assessing skull-face anatomical consistency on a sample of 24 different skull-face superimpositions. An unbiased statistical analysis established the most objective and discriminative criteria. Results did not show strong associations, however, important insights to address lack of standards were provided. In addition, a novel methodology for understanding and standardizing identification methods based on the observation of morphological patterns has been proposed.

A method for sex estimation using the proximal femur.

The assessment of sex is crucial to the establishment of a biological profile of an unidentified skeletal individual. The best methods currently available for the sexual diagnosis of human skeletal remains generally rely on the presence of well-preserved pelvic bones, which is not always the case. Postcranial elements, including the femur, have been used to accurately estimate sex in skeletal remains from forensic and bioarcheological settings. In this study, we present an approach to estimate sex using two measurements (femoral neck width [FNW] and femoral neck axis length [FNAL]) of the proximal femur. FNW and FNAL were obtained in a training sample (114 females and 138 males) from the Luís Lopes Collection (National History Museum of Lisbon). Logistic regression and the C4.5 algorithm were used to develop models to predict sex in unknown individuals. Proposed cross-validated models correctly predicted sex in 82.5-85.7% of the cases. The models were also evaluated in a test sample (96 females and 96 males) from the Coimbra Identified Skeletal Collection (University of Coimbra), resulting in a sex allocation accuracy of 80.1-86.2%. This study supports the relative value of the proximal femur to estimate sex in skeletal remains, especially when other exceedingly dimorphic skeletal elements are not accessible for analysis.

One for all and all for one: Linear regression from the mass of individual bones to assess human skeletal mass completeness.

OBJECTIVES: Complete and accurate human skeletal inventory is seldom possible in archaeological and forensic cases involving severe fragmentation. In such cases, skeletal mass comparisons with published references may be used as an alternative to assess skeletal completeness but they are too general for a case-by-case routine analysis. The objective is to solve this issue by creating linear regression equations to estimate the total mass of a skeleton based on the mass of individual bones. MATERIALS AND METHODS: Total adult skeletal mass and individual mass of the clavicle, humerus, femur, patella, carpal, metacarpal, tarsal, and metatarsal bones were recorded in a sample of 60 skeletons from the 21st century identified skeletal collection (University of Coimbra). The sample included 32 females and 28 males with ages ranging from 31 to 96 years (mean = 76.4; sd = 14.8). Skeletal mass linear regression equations were calculated based on this sample. RESULTS: The mass of individual bones was successfully used to predict the approximate total mass of the adult skeleton. The femur, humerus, and second metacarpal were the best predictors of total skeletal mass with root mean squared errors ranging from 292.9 to 346.1 g. DISCUSSION: Linear regression was relatively successful at estimating adult skeletal mass. The non-normal distribution of the sample in terms of mass may have reduced the predictive power of the equations. These results have clear impact for bioanthropology, especially forensic anthropology, since this method may provide better estimates of the completeness of the skeleton or the minimum number of individuals. Am J Phys Anthropol 160:427-432, 2016. © 2016 Wiley Periodicals, Inc.

Sex estimation from the tarsal bones in a Portuguese sample: a machine learning approach.

Sex estimation is extremely important in the analysis of human remains as many of the subsequent biological parameters are sex specific (e.g., age at death, stature, and ancestry). When dealing with incomplete or fragmented remains, metric analysis of the tarsal bones of the feet has proven valuable. In this study, the utility of 18 width, length, and height tarsal measurements were assessed for sex-related variation in a Portuguese sample. A total of 300 males and females from the Coimbra Identified Skeletal Collection were used to develop sex prediction models based on statistical and machine learning algorithm such as discriminant function analysis, logistic regression, classification trees, and artificial neural networks. All models were evaluated using 10-fold cross-validation and an independent test sample composed of 60 males and females from the Identified Skeletal Collection of the 21st Century. Results showed that tarsal bone sex-related variation can be easily captured with a high degree of repeatability. A simple tree-based multivariate algorithm involving measurements from the calcaneus, talus, first and third cuneiforms, and cuboid resulted in 88.3% correct sex estimation both on training and independent test sets. Traditional statistical classifiers such as the discriminant function analysis were outperformed by machine learning techniques. Results obtained show that machine learning algorithm are an important tool the forensic practitioners should consider when developing new standards for sex estimation.

AncesTrees: ancestry estimation with randomized decision trees.

In forensic anthropology, ancestry estimation is essential in establishing the individual biological profile. The aim of this study is to present a new program--AncesTrees--developed for assessing ancestry based on metric analysis. AncesTrees relies on a machine learning ensemble algorithm, random forest, to classify the human skull. In the ensemble learning paradigm, several models are generated and co-jointly used to arrive at the final decision. The random forest algorithm creates ensembles of decision trees classifiers, a non-linear and non-parametric classification technique. The database used in AncesTrees is composed by 23 craniometric variables from 1,734 individuals, representative of six major ancestral groups and selected from the Howells' craniometric series. The program was tested in 128 adult crania from the following collections: the African slaves' skeletal collection of Valle da Gafaria; the Medical School Skull Collection and the Identified Skeletal Collection of 21st Century, both curated at the University of Coimbra. The first step of the test analysis was to perform ancestry estimation including all the ancestral groups of the database. The second stage of our test analysis was to conduct ancestry estimation including only the European and the African ancestral groups. In the first test analysis, 75% of the individuals of African ancestry and 79.2% of the individuals of European ancestry were correctly identified. The model involving only African and European ancestral groups had a better performance: 93.8% of all individuals were correctly classified. The obtained results show that AncesTrees can be a valuable tool in forensic anthropology.

Sex estimation using the second cervical vertebra: a morphometric analysis in a documented Portuguese skeletal sample.

Biological sex estimation is one of the main parameters required in the construction of a biological profile of an unknown deceased person. In corpses in an advanced state of decomposition, skeletonized or severely mutilated, bone analysis may provide the only way to access biological sex. Although the hip bones are the most dimorphic and useful bones for sex estimation, they are often badly preserved and/or fragmented or may not even be present in some cases. For that reason, it is necessary to develop sex estimation methods based on bones less dimorphic. In this study, 13 dimensions of the second cervical vertebra were measured in order to quantify sex-related variation and to generate a simple predictive model based on logistic regression analysis. For logistic regression fitting, 190 individuals from the Coimbra Identified Skeletal Collection were used as a training sample. The resulting model was also evaluated in an independent test sample composed of 47 individuals from the Identified Skeletal Collection of the 21st Century (University of Coimbra). The developed logistic regression model correctly estimated known sex in 86.7 to 89.7 % of the cases. The second cervical vertebra demonstrated to be a useful alternative for sex estimation when other skeletal elements are not available or suitable for analysis. This method seems promising but more reliability studies are required for a more robust validation.

A new forensic collection housed at the University of Coimbra, Portugal: The 21st century identified skeletal collection.

The purpose of this study is to characterize and contextualize the new collection of identified skeletons housed in the Department of Life Sciences at the University of Coimbra, Portugal. The 21st Century Identified Skeletal Collection, which is still being enlarged, is currently composed of 159 complete adult skeletons (age at death range: 29-99 years) of both sexes. The skeletons consist almost exclusively of Portuguese nationals who died between 1995 and 2008. The state of preservation is good and more detailed antemortem information is presently being collected. This collection constitutes a fundamental tool for forensic anthropology research, including development and validation studies of skeletal aging and sexing methods that target elderly adults. Moreover, this collection can also be used in conjunction with the other reference collections housed in the University of Coimbra to investigate secular trends in skeletal development and aging, among others.