Prioritizing a high posterior probability threshold leading to low error rate over high classification accuracy: the validity of MorphoPASSE software for cranial morphological sex estimation in a contemporary population.

An increasing number of software tools can be used in forensic anthropology to estimate a biological profile, but further studies in other populations are required for more robust validation. The present study aimed to evaluate the validity of MorphoPASSE software for sex estimation from sexually dimorphic cranial traits recorded on 3D CT models (n = 180) from three populations samples (Czech, French, and Egyptian). Two independent observers performed scoring of 4 cranial traits (2 of them bilateral) in each population sample of 30 males and 30 females. The accuracy of sex estimation using traditional posterior probability threshold (pp = 0.5) ranged from 85.6% to 88.3% and overall classification error from 14.4% to 11.7% for both observers, and corresponds to the previously published values of the method. The MorphoPASSE method is also affected by the subjectivity of the observers, as both observers show agreement in sex assignment in 83.9% of cases, regardless of the accuracy of the estimates. Applying a higher posterior probability threshold (pp 0.95) provided classification accuracy of 97.9% and 93.3% of individuals (for observer A and B respectively), minimizing the risk of error to 2.1% and 6.7%, respectively. However, sex estimation can only be applied to 54% and 66% of individuals, respectively. Our results demonstrate the validity of the MorphoPASSE software for cranial sex estimation outside the reference population. However, the achieved classification success is accompanied by a high risk of errors, the reduction of which is only possible by increasing the posterior probability threshold.

Prescription eyeglasses as a forensic physical evidence: Prediction of age based on refractive error measures using machine learning algorithm.

Refractive errors (RE) are commonly reported visual impairment problems worldwide. Previous clinical studies demonstrated age-related changes in human eyes. We hypothesized that the binocular RE metrics including sphere and cylinder power, axis orientation, and interpupillary distance (IPD) can be used for forensic age estimation of an unknown individual. RE data of both eyes were collected from the clinical optometric exams and prescription glasses of 2027 Egyptian individuals aged between 2 to 93 years. The differences between age groups as well as sides, and sexual dimorphism were explored. Two modeling methods were compared: multiple and stepwise linear regression (LR) versus machine learning Regression Forest (RFM). Data were apportioned into training and test datasets with a ratio of 80/20. The results showed significant differences among the age groups in each eye for all variables. Stepwise LR improved the results over models based on the one-sided lens due to selection of IPD in addition to the left and right axis, and left sphere as independent variables. For the RFM, the left axis and IPD were the most important features. RFM outperformed LR in terms of accuracy and root mean squared error (RMSE). The estimated age within ±10 years showed 81.4% accuracy rate and RMSE = 8.9 years versus 38.5% accuracy rate and RMSE = 17.99 years using RFM and stepwise LR, respectively, in the test set. The current study upholds the significance of the age-related changes of refractive error in formulating alternative forensic age estimation models when standard methods are unavailable.

TestDimorph: An R package for analysis of interpopulation sexual dimorphism differences using summary statistics.

OBJECTIVE: The degree of sexual dimorphism in certain traits between males and females differ from one sample to another. Although trait differences by sex are often reported in bioanthropological research, few studies test for statistical significance or make raw data available. TestDimorph is the first R package dedicated to testing and comparing the degree of sexual dimorphism among different samples by leveraging summary statistics. MATERIALS AND METHODS: We provide two approaches of analysis of inter-sample differences in degree of sexual dimorphism: univariate and multivariate for two or more samples. The methods follow upon publications primarily from the AJBA. Within-sex size variability between samples is compared using one-way ANOVA followed by control for multiple pairwise comparisons. In addition, we compute the overlapping area between the density functions of two normal distributions from the mixture intersection index or the non-overlapping area using the dissimilarity index as well as Hedges' g with inferential support using the 95% confidence interval. Finally, we use a multivariate analysis of differences in patterning of sexual dimorphism between samples. RESULTS: We demonstrate various results from applying TestDimorph functions to data supplied with the package. DISCUSSION: The package has many features including functionality for working with summary statistics, simulating data from summary statistics, and the extraction of summary statistics from raw data, so that the entire analysis can be performed through the package.

Purkait's triangle revisited: role in sex and ancestry estimation.

Identification of unknown remains recovered from marine and terrestrial locations is a significant humanitarian problem. This investigation proposes a simple method applicable to fragmentary femora for a more refined level of ancestry and/or sex estimation. To that end, we re-examined Purkait's triangle which involves three inter-landmark distances between the traction epiphyses and the articular rim of femoral head. A large sample (n = 584) from geographically diverse (Egyptian, Indian and Greek) populations was compiled. Additionally, shape (n = 3) and trigonometrically derived variables and ratios (n = 9 variables) were employed to detect any geographically-clustered morphological differences between these populations. Random forest modelling (RFM) and linear discriminant function analysis (LDA) were employed to create classification models in instances where sex was known or unknown. The sample was apportioned into training and test sets with a ratio 70/30. The classification accuracies were evaluated by means of k fold cross-validation procedure. In sex estimation, RFM showed similar performance to LDA. However, RFM outperformed LDA in ancestry estimation. Ancestry estimation was satisfactory in the Indian and Egyptian samples albeit the Greek sample was problematic. The Greek samples presented greater morphological overlap with the Indian sample due to high within-group variation. Test samples were accurately assigned to their ancestral category when sex was known. Generally, higher classification accuracies in the validation sample were obtained in the sex-specific model of females than in males. Using RFM and the linear variables, the overall accuracy reached 83% which is distributed as 95%, 71% and 86% for the Egyptian, Indian and Greek females, respectively; whereas in males, the overall accuracy is 72% and is distributed as 58%, 87% and 50% for the Egyptian, Indian and Greek males, respectively. Classification accuracies were also calculated per group in the test data using the 12 derived variables. For the females, the accuracies using the medians model was comparable to the linear model whereas in males the angles model outperformed the linear model for each group but with similar overall accuracy. The classification rates of male specific ancestry were 82%, 78% and 56% for the Egyptian, Indian and Greek males, respectively. In conclusion, Purkait's triangle has potential utility in ancestry and sex estimation albeit it is not possible to separate all groups successfully with the same efficiency. Intrapopulation variation may impact the accuracy of assigned group membership in forensic contexts. Key pointsPurkait's method is a possible ancestry group indicator applicable to fragmentary femora.Random forest model surpassed linear discriminant function analysis in multi-group ancestry classification.Ancestry is more accurately assessed in females than males.The intertrochanteric distance is the most important feature in discrimination of sex whereas in ancestry it was the head to lesser trochanter distance.Sex differences override ancestry due to the tendency of misclassification into same sex but different group rather than the opposite sex of the same ancestry.

Efficiency of the Adjusted Binary Classification (ABC) Approach in Osteometric Sex Estimation: A Comparative Study of Different Linear Machine Learning Algorithms and Training Sample Sizes.

The adjusted binary classification (ABC) approach was proposed to assure that the binary classification model reaches a particular accuracy level. The present study evaluated the ABC for osteometric sex classification using multiple machine learning (ML) techniques: linear discriminant analysis (LDA), boosted generalized linear model (GLMB), support vector machine (SVM), and logistic regression (LR). We used 13 femoral measurements of 300 individuals from a modern Turkish population sample and split data into two sets: training (n = 240) and testing (n = 60). Then, the five best-performing measurements were selected for training univariate models, while pools of these variables were used for the multivariable models. ML classifier type did not affect the performance of unadjusted models. The accuracy of univariate models was 82−87%, while that of multivariate models was 89−90%. After applying ABC to the crossvalidation set, the accuracy and the positive and negative predictive values for uni- and multivariate models were ≥95%. Sex could be estimated for 28−75% of individuals using univariate models but with an obvious sexing bias, likely caused by different degrees of sexual dimorphism and between-group overlap. However, using multivariate models, we minimized the bias and properly classified 81−87% of individuals. A similar performance was also noted in the testing sample (except for FEB), with accuracies of 96−100%, and a proportion of classified individuals between 30% and 82% in univariate models, and between 90% and 91% in multivariate models. When considering different training sample sizes, we demonstrated that LR was the most sensitive with limited sample sizes (n < 150), while GLMB was the most stable classifier.

A cautionary note on altered pace of aging in the COVID-19 era.

Coronavirus disease 2019 (COVID-19) is highly age-dependent due to hi-jacking the molecular control of the immune cells by the severe acute respiratory syndrome-corona virus 2 (SARS-CoV-2) leading to aberrant DNA methylation (DNAm) pattern of blood in comparison to normal individuals. These epigenetic modifications have been linked to perturbations to the epigenetic clock, development of long COVID-19 syndrome, and all-cause mortality risk. I reviewed the effects of COVID-19 on different molecular age markers such as the DNAm, telomere length (TL), and signal joint T-cell receptor excision circle (sjTREC). Integrating the accumulated clinical research data, COVID-19 and novel medical management may alter the pace of aging in adult individuals (<60 years). As such, COVID-19 might be a confounder in epigenetic age estimation similar to life style diversities, pathogens and pathologies which may influence the interpretation of DNAm data. Similarly, the SARS-CoV-2 affects T-lymphocyte function with possible influence on sjTREC levels. In contrast, TL measurements performed years before the SARS-CoV-2 pandemic proved that short TL predisposes to severe COVID- 19 independently from chronological age. However, the persistence of COVID-19 epigenetic scars and the durability of the immune response after vaccination and their effect on the ongoing pace of aging are still unknown. In the light of these data, the heterogeneous nature of the samples in these studies mandates a systematic evaluation of the currrent methods. SARS-CoV-2 may modify the reliability of the age estimation models in real casework because blood is the most common biological sample encountered in forensic contexts.

Role of Near-Infrared Spectroscopy in Monitoring the Clinical Course of Asphyxiated Neonates Treated with Hypothermia.

OBJECTIVE: Hypoxic-ischemic encephalopathy (HIE) affects millions of newborns annually, especially in low-resource settings. Real-time monitoring of hypoxic-ischemic brain damage is urgently needed for assessment of severity and management of neonates with birth asphyxia. Aim of the work is monitoring of near-infrared spectroscopy (NIRS)-measured cerebral regional oxygen saturation (cRSO2) and cerebral fractional tissue oxygen extraction (FTOE) in neonates after birth asphyxia in relation to their clinical course. STUDY DESIGN: Forty asphyxiated-term and near-term neonates with mild to severe HIE admitted at neonatal intensive care unit of Alexandria University Maternity Hospital from March to October 2019, received therapeutic hypothermia (TH) and had continuous NIRS monitoring of cRSO2 for 72 hours. Infants were categorized into HIE with seizing and nonseizing groups, and abnormal and normal magnetic resonance imaging (MRI) groups. RESULTS: Clinical seizures (CS) occurred in 15 (37.5%) of HIE neonates and 13.3% of them died (n = 2). In the current study, significantly higher cRSO2 and lower FTOE values were found in the seizing infants as compared with nonseizing group (p < 0.001). NIRS-measured day 2-cRSO2 and day 1-FTOE were associated with CS in newborns with HIE and day 1-cRSO2 and FTOE were associated with abnormal MRI at 1 month of age. cRSO2 values were found to correlate positively with initial Thompson score especially in days 1 and 2. Further, neonates with CS were more likely to have MRI abnormalities at follow-up. CONCLUSION: NIRS measures may highlight differences between asphyxiated neonates who develop CS or later MRI abnormalities and those who do not. KEY POINTS: · Day 1 FTOE is the early and sensitive predictor for both clinical seizures and abnormal MRI.. · Cerebral oxygenation metrics help in selecting patients in urgent need of an early MRI scan.. · Cerebral oxygenation metrics can be used hand in hand with clinical assessment using Thompson score at admission to select patient candidate for therapeutic hypothermia..

Tailored logistic regression models for sex estimation of unknown individuals using the published population data of the humeral epiphyses.

This paper introduces a far-flung approach to formulate population independent models based on the humeral epiphyses as a supplementary tool for biological sex estimation of unknown partial remains. Resources for this study include the published summary statistics of 7 modern populations inhabited the continents of Africa, Asia, Europe, and South America. The regenerated humeral metric data (n = 1490) via truncation approach were modeled using logistic regression. Three fitted models were evaluated for applicability across populations on an independent test sample (n = 430). The experiment was assessed graphically and quantitatively using histogram of posterior probabilities and the classification table. The predictive power of the models was evaluated at the conventional (0.5) and high (0.95) posterior probability thresholds. It was found that the vertical humeral head model is insufficient for sex estimation especially in the European females due to different levels of interpopulation size variability. Interestingly, the distal biepicondylar breadth model showed overall better performance achieving the highest total and sex specific accuracies. Findings indicated that together, the epiphyseal measurements are capable of discriminating sex with overall accuracy of 90.2% which is raised up to 98.8% with 95% confidence of accurate estimates in more than 50% of the test sample. While evidences have been presented pointing to the biological and statistical meaningfulness of the humeral epiphyses model, the analysis allowed pinpointing the utility of the distal biepicondylar breadth model in sex diagnosis in transpopulation application settings. Additionally, few variables are needed to reach satisfactory sex prediction in a diverse sample.