Age Estimation Research Articles

Applications of artificial intelligence in age estimation: a review

Age estimation is a legally significant issue, particularly in underdeveloped and developing countries, due to factors such as inadequate civil registration systems and irregular migration. While various techniques are employed for age estimation using traditional methods, it is known that factors including age, gender, chronic illness, race, and geographical region can result in discrepancies between skeletal age and chronological age. This complicates the process of achieving accurate age estimation. This review aims to discuss recent research on artificial intelligence applications in light of current literature. Artificial intelligence and machine learning have enabled machines to acquire human-like capabilities in thinking, learning, problem solving, and decision making, leading to significant progress in achieving faster and accurate results. In the field of forensic medicine, methods such as linear discriminant analysis, K-Nearest Neighbors, support vector machines, random forests, and artificial neural networks have been employed to classify data and conduct studies on age estimation. Research has focused on five regions: the carpal bones, ossification centers, the middle phalanx of the hand, the third metacarpal, and the radius and ulna. Additionally, facial angles and width obtained through tomographic examinations, as well as measurements of the calcaneus and cuboid bones, panoramic dental radiographs, volumetric analysis of teeth and pulp using Cone Beam Computed Tomography, and analysis of bloodstains on microRNAs, have been analyzed for their distribution across different age. The results demonstrate that artificial intelligence applications can be utilized in age estimation with a high accuracy rate (85-95%). Age estimation using artificial intelligence enhances data-driven decision-making processes, improves the quality of services, and contributes to societal benefit. Therefore, we believe that incorporating artificial intelligence applications alongside traditional methods in age estimation will yield more meaningful outcomes.

The Hungarian fossil record of the Pliocene pig Sus arvernensis (Suidae, Mammalia)

Sus arvernensis is a Pliocene species that occupies a key position in the evolution of suids (Suidae, Artiodactyla, Mammalia) in Eurasia, and besides, it is considered important for biochronological correlations and paleoecological inferences. However, our knowledge on S. arvernensis is largely based on fossil remains from southwestern Europe. Here, we present a revision of the Hungarian fossil record of S. arvernensis. Up to now, the species was known from only two localities of Hungary, Gödöllő (central Hungary) and Süttő (northwest Hungary), and the latter occurrence has even been questioned. After the comparison with other relevant samples of S. arvernensis, of the Early Pleistocene S. strozzii, and of the extant wild boar S. scrofa (motivated by previous attributions and the chronology of the localities), the presence of S. arvernensis from Gödöllő and Süttő is confirmed, and more material of the species is described from Beremend (southern Hungary) and Kisláng (western Hungary). Collectively, the results of the revision carried out herein reveal a relatively widespread distribution of S. arvernensis in Hungary, hence providing an important link from the eastern to western European fossil record of the species. The specimens from Gödöllő and Süttő are slightly larger than the other material of S. arvernensis from France and Italy included in the biometric comparison, although the paucity of the material precludes to evaluate whether these differences are significant and to relate them to a chronological and/or geographical context. The occurrence of S. arvernensis in the Hungarian localities considered in this work is a biochronological indication of an age older than at least 2.6 Ma, since the species is not recorded after the Pliocene–Pleistocene transition. This in agreement with the age estimates available so far for some of the localities or provides new insights. At Süttő, in particular, the identification of S. arvernensis reinforces the view that travertine deposition started already in the Pliocene.

Prognostic assessment based on the trajectory of artificial intelligence-enabled electrocardiograms for age estimation

Abstract Background Artificial Intelligence (AI) estimation of age using data from the 12-lead electrocardiogram (ECG) has demonstrated its utility as a biomarker in predicting the future risk of cardiac diseases and mortality. However, expected long-term outcomes based on temporal changes in the AI-ECG age gap remain unclear. Purpose This study aims to utilize the difference between ages predicted by AI-ECG and actual ages longitudinally for follow-up and to assess whether there are significant clinical differences in mortality among specific groups. Methods Among patients at a hospital who had not been used for the AI-ECG age prediction model (121,702 individuals, 522,261 ECGs), 6,685 individuals with 31,858 ECGs who had an index ECG and the subsequent ECG in the fifth year, with at least one intervening ECG were included for analysis. The absolute age gap, defined as the absolute difference between ECG-predicted and chronological age, was limited to ±10 for analysis. Latent class trajectory modeling was then utilized to classify groups based on the AI-ECG age gap. Results Data-driven analysis revealed four distinct trajectory groups based on age gap trajectories: Consistently Young ECG (27.2%), Reversed ECG Aging (35.3%), Accelerated ECG Aging (8.0%), and Consistently Old ECG (29.5%). Notably, the Accelerated ECG Aging group exhibited a significantly higher cumulative incidence of all-cause death (adjusted hazard ratio (HR) 1.66 (95% confidence interval (CI) 1.30-2.12, p-value <0.001) and non-cardiac death (adjusted HR 1.83 (95% CI 1.38-2.43, p-value <0.001) compared to the Consistently Young ECG group. Conclusion Among 4 distinct trajectories based on the changes in the AI-ECG age gap, Accelerated ECG Aging was associated with a heightened risk of all-cause mortality. Additional in-depth analysis of factors associated with Accelerated ECG Aging is warranted.

Assessing Blue Catfish population dynamics across varying introduction timelines in Georgia

AbstractObjectiveUnderstanding the dynamic rate functions of introduced fish populations is essential in guiding management strategies. Blue Catfish Ictalurus furcatus population dynamics have been well studied; however, there is a gap in knowledge from populations that have been introduced into novel systems.MethodsBlue Catfish were collected from the Altamaha, Ocmulgee, Oconee, and Satilla rivers in Georgia. Age estimates were obtained through otolith analysis. Multimodel growth analysis was used to describe growth patterns, and early life growth was modeled with linear regression. Instantaneous mortality of each population was estimated and compared by using a generalized linear model approach.ResultThe four introduced Blue Catfish populations exhibited rapid growth and low mortality rates during establishment; however, we found that populations with earlier introduction histories generally had slower growth rates and lower mortality. The Satilla River population is comparatively the most recently introduced population (first discovered in 2011) that exhibited the fastest growth and highest mortality.ConclusionOur results suggest that Blue Catfish in our study systems are in an intermediate stage of invasion and have not yet reached an equilibrium state. As density continues to increase in these river systems, we expect a decline in growth rates, delayed sexual maturation, and a greater observed maximum age. However, additional monitoring is needed to identify how population dynamics and reproductive characteristics vary over time.

Assessing the utility of computer vision for age determination of Gulf Menhaden

AbstractObjectiveIn this work, we assess the potential of computer vision techniques for age estimation of Gulf Menhaden Brevoortia patronus scales. Scales are the primary structure used for the age determination of Gulf Menhaden, and the ageing process can be labor intensive. Gulf Menhaden is the second‐largest fishery by weight in the United States, with average annual landings from 2018 to 2022 of 449,540 metric tons, and is assessed with age‐structured models that require information about the age structure of the catch and the stock.MethodsWe used convolutional neural networks and deep neural networks to classify the age from images of Gulf Menhaden scales from three different sets of images of scales. The first set of data consists of images of scales from fish at ages 0 and 1 year. The second set of data consists of images of scales from fish at ages 0–4 years. The last set of data consists of images of scales from fish of ages 0, 1, and 2 years and includes only images of scales for which there is agreement by readers of age estimates derived from analyzing sagittal otoliths and scales from the same individual.ResultThe classification of ages was best when using a convolutional neural network model on the first data set. The poorest classification was for the model using deep neural networks with the second data set.ConclusionAlthough we show that computer vision has promise for age determination from fish scale samples, our results indicate that considerable work must be done for wide adoption of the approach. With the continuous enhancements of computer vision models, improvements in the quality of scale images, and the accumulation of larger sets of scale images that can be used to train machine learning models, we believe that using computer vision can serve to reduce processing time and increase the accuracy of age estimates.

Charting the growth through intelligence: A SWOC analysis on AI-assisted radiologic bone age estimation.

Bone age estimation (BAE) is based on skeletal maturity and degenerative process of the skeleton. The clinical importance of BAE is in understanding the pediatric and growth-related disorders; whereas medicolegally it is important in determining criminal responsibility and establishing identification. Artificial Intelligence (AI) has been used in the field of the field of medicine and specifically in diagnostics using medical images. AI can greatly benefit the BAE techniques by decreasing the intra observer and inter observer variability as well as by reducing the analytical time. The AI techniques rely on object identification, feature extraction and segregation. Bone age assessment is the classical example where the concepts of AI such as object recognition and segregation can be used effectively. The paper describes various AI based algorithms developed for the purpose of radiologic BAE and the performances of the models. In the current paper we have also carried out qualitative analysis using Strength, Weakness, Opportunities and Challenges (SWOC) to examine critical factors that contribute to the application of AI in BAE. To best of our knowledge, the SWOC analysis is being carried out for the first time to assess the applicability of AI in BAE. Based on the SWOC analysis we have provided strategies for successful implementation of AI in BAE in forensic and medicolegal context.

Profile of Medico-legal Age Estimation Cases in a Tertiary Hospital in Kathmandu

Introduction: Medico-legal age estimation is the estimation of chronological age of an individual which is based upon analysis of physical development of the body, development of secondary sexual traits, tooth eruption, appearance of ossification centers of bones, and the degree of epiphyseal union. Chronological age of the subject whether a minor (less than 18 years) or an adult determines the fate of the case in the court of law. Objective: The present study revealed varied profiles of the perpetrator and the victim who came for medico-legal age estimation. Methods: This is a retrospective, descriptive cross-sectional study conducted in the Department of Forensic Medicine, Maharajgunj Medical Campus, Institute of Medicine (IoM). The data of medico-legal age estimation of 5 years period was analyzed using the statistical software SPSS 20. Results: Total of 313 individuals were studied. Majority of the individuals were within the age group of 16-17 years. Males (70.3 %) outnumbered females (29.7 %). Theft (31.9 %) was common among subjects, followed by sexual assault (18.2 %) and majority of subjects (59.7%) came from Province 3. Sixty percent of the subject’s alleged age, matched the estimated ages. Conclusion: This study shows the status of children in crime which demands attention of concerned authority. Males were significantly involved in theft and females were victims of sexual assault. Forty percent of the alleged age given by the subject differed from the estimated age, to change the fate of the case. These results could greatly benefit the judiciary system of Nepal.

Imaging of Osteomyelitis

In this paper, an analysis of the demographic attributes, presenting symptoms, and infective agents, as well as the areas affected by skull osteomyelitis, of fifty male and fifty female patients is offered. A rough age estimate of around 61 years was found among the patients, with average symptom durations of about 6.8 months or longer. A significant percentage of diabetes (62%) and cranial nerve involvement (62%) were observed. The dominant symptom was headaches, representing 78% of cases, followed by cranial nerve palsy (62%) and hearing loss (48%). S. aureus was found to be the second most common causative agent, following only P. aeruginosa, which was the most common agent (54%). It became clear that there was a relevant infection in the temporomandibular joint and the retropharyngeal joint upon examining regional cases of the illness. In spite of therapy, only 38 percent of patients exhibited a cure, while 46 percent demonstrated improvement and 16 percent experienced a worsening of their condition.

An age-group ranking model for facial age estimation

Age prediction has become an important Computer Vision task. Although this task requires the age of an individual to be predicted from a given face, research has shown that it is more intuitive and easier for humans to decide which of two individuals is older than to decide how old an individual is. This work follows this intuition to aid the age prediction of a face by exploiting the age information available from other faces. It goes further to explore the statistical relationships between facial features within age groups to compute age-group ranks for a given face. The resulting age-group rank is low-dimensional and age-discriminatory, thus improving age prediction accuracy when fed into an age predictor. Experiments on publicly available facial ageing datasets (FGnet, PAL, and Adience) reveal the effectiveness of the proposed age-group ranking model when used with traditional Machine learning algorithms as well as Deep Learning algorithms. Cross-dataset validation, a method of training and testing on entirely different datasets, was also employed to further investigate the effectiveness of this method.

Dental age estimation in adults using the Lamendin criteria: Validation of a bayesian model in the Brazilian population

Age estimation provides information for biological profile, which is used as an important data during the process of to aid in the identification of bodies of deceased persons. Lamendin et al. proposed a simplified dental age estimation technique for adults based on the analysis of periodontal recession height, root height, and root translucency height. These characteristics were used to build a multi-population database that served in the development of a Bayesian model, known as the Forensic International Dental Database (FIDB). However, before this research, this model did not incorporate dental information from the Brazilian population. The aim of this article was to validate the application of the FIDB Bayesian statistical model for estimating dental age in samples from the Brazilian population and compare it to Lamendin's technique. A cross-sectional study was conducted using 230 dental elements extracted for therapeutic reasons and stored in a biobank. Two independent and calibrated examiners collected the data, considering the randomization of dental samples. Intra- and inter-examiner reliability analysis included the re-evaluation of 20 % (n = 40) of the sample. For statistical analyses, the data were organized in Microsoft© Excel© 365 version 2204 (Microsoft Corp., Redmond, WA, USA) spreadsheets, and R version 4.0.2 (R Foundation, Vienna, Austria) and IBM® SPSS® version 23.0 (SPSS Inc., IBM, Chicago, IL, USA) software were used. After applying inclusion and exclusion criteria, the final sample comprised 200 intact dental elements from 84 male and 116 female individuals. According to the Kolmogorov-Smirnov test, the data did not show a normal distribution, and the Intraclass Correlation Coefficient indicated that intra- and inter-examiner agreements were adequate. Through the Mann-Whitney U test, statistically significant differences between sexes were not observed. In terms of overall accuracy, the Bayesian model and the original Lamendin´s technique showed a mean absolute error of 9.39 years and 9.95, respectively. Regarding bias, it was generally 0.15 years for the Bayesian model and 5.62 years for the original Lamendin´s technique. The Wilcoxon classification test revealed no statistically significant difference between chronological and estimated ages, but only for the Bayesian model. It can be concluded that the developed Bayesian statistical model was able to estimate the age of Brazilian adults satisfactorily, and overall, the error rates were acceptable.

Urinary Neutrophil Gelatinase-Associated Lipocalin Values in Preterm Neonates: A Systematic Review and Meta-analysis.

Acute kidney injury (AKI) is common in hospitalized preterm neonates. Urinary neutrophil gelatinase-associated lipocalin (uNGAL) is a promising noninvasive AKI biomarker. However, normal values of uNGAL in preterm neonates without AKI are poorly characterized. The objective of this study was to evaluate the current literature to determine normal uNGAL values for preterm neonates without AKI. Systematic review and meta-analysis of all articles published before November 2021 evaluating uNGAL values in preterm neonates without AKI. Of 1,607 studies evaluated for eligibility, 11 were included in the final meta-analysis (210 males, 202 females). uNGAL values were higher in the preterm neonates <29 weeks and ranged between 20.7 and 782.65 ng/mL. Meta mean estimates of gestational age (GA), birthweight, and neutrophil gelatinase-associated lipocalin were 29.4 weeks (95% confidence interval [CI]: 28.8-30.0), 1,241 g (95% CI: 1,111-1,372), and 148.9 ng/mL (95% CI: 48-231), respectively. In limited studies, a wide range of uNGAL values in preterm neonates without AKI are reported. Future studies should identify normal uNGAL values in preterm neonates using larger cohorts by GA and birthweight. · Urinary NGAL is a promising noninvasive biomarker of neonatal AKI.. · A wide range of uNGAL is reported in preterm neonates but the baseline values are not well defined.. · Urine NGAL values are higher in extremely preterm compared with preterm neonates..

Testing the phenomenological interacting dark energy model with gamma-ray bursts and type Ia supernovae

Abstract In this paper, we utilize recent observational data from gamma-ray bursts (GRBs) and Pantheon+ supernovae Ia (SNe Ia) sample to explore the interacting dark energy (IDE) model in a phenomenological scenario. Results from GRBs alone, SNe Ia and GRBs+SNe Ia indicate that the energy is transferred from dark energy to dark matter and the coincidence problem is alleviated. The value of $H_0$ from GRBs+SNe Ia in the IDE scenario shows agreement with the SH0ES measurement. Considering the age estimate of the quasar APM 08279+5255 at $z = 3.91$, we find that the phenomenological IDE scenario can predict a cosmic age greater than that of the $\Lambda$CDM model, thus the cosmic age problem can be alleviated.

A review of artificial intelligence-based brain age estimation and its applications for related diseases.

The study of brain age has emerged over the past decade, aiming to estimate a person's age based on brain imaging scans. Ideally, predicted brain age should match chronological age in healthy individuals. However, brain structure and function change in the presence of brain-related diseases. Consequently, brain age also changes in affected individuals, making the brain age gap (BAG)-the difference between brain age and chronological age-a potential biomarker for brain health, early screening, and identifying age-related cognitive decline and disorders. With the recent successes of artificial intelligence in healthcare, it is essential to track the latest advancements and highlight promising directions. This review paper presents recent machine learning techniques used in brain age estimation (BAE) studies. Typically, BAE models involve developing a machine learning regression model to capture age-related variations in brain structure from imaging scans of healthy individuals and automatically predict brain age for new subjects. The process also involves estimating BAG as a measure of brain health. While we discuss recent clinical applications of BAE methods, we also review studies of biological age that can be integrated into BAE research. Finally, we point out the current limitations of BAE's studies.

The true age of multi-planet host star HD 110067. Or: Don't be fooled by K0V-type stars

HD 110067 is a near (d = 32.22 pc, V = 8.43 mag) K0V star in Coma Ber that was recently discovered to host a six-planet\ system in stable resonances. The star has a very old age of 8+/-4 Gyrs. However, by the nature of the respective evolution tracks (i.e. with masses of 0.78...0.85 odot $), which run parallel to the zero-age main sequence for approx 8 Gyrs, such age estimates are impossible unless the mass and luminosity are independently known to very high precision. We demonstrate this point using physical parameters derived from two different spectroscopic methods. As an alternative age indicator, we looked at the emission in Ca II H K using TIGRE/HEROS spectra (Guanajuato, Mexico) and 78 archive TNG/HARPS-N spectra from 2021 to 2024. Surprisingly, HD 110067 has a high and persistent activity level of S$_ MWO =0.32$. From the estimated empirical and Rossby number of 0.4, and with the parameterised spin-down timescale, we derive an activity age of approx 2.5($ Similarly, a possible rotation period of 20 days, consistent with TESS photometric variations and our vsin(i), suggests $Ro=0.32$ and an age of just 1.7 Gyrs. Such a relatively young activity age is indeed consistent with a very small lithium signature (the equivalent width of the 6707.8 AA doublet is 1.1pm 0.2m AA ) and implies that HD110067 can be directly compared to its virtual twin sigma Draconis, which has an even weaker lithium presence and an activity cycle around &lt;S&gt;$_ MWO $ approx 0.22.

Dentin thickness model: A novel method for dental age estimation using panoramic radiography

The study aims to evaluate the reliability of the novel dentin thickness model based on root parameters when applied to panoramic radiography images of mandibular first molar in the southern Turkish subpopulation. A total of 946 panoramic images were analyzed for the study. The samples were allocated into four age groups: Group 1(between 10 and 18), Group 2(between 19 and 30), Group 3(between 31 and 50), Group 4(between 51 and 78). The dentin thickness model measurements consisted of root dentin thickness of the mesial aspect of the distal root and distal canal width. Regression, intraclass correlation coefficient, and Durbin-Watson analyses were used for statistics. A medium correlation was detected in the dentin thickness model (R = 0.398). Root dentin thickness and canal width had a significant effect (p < 0.05), but gender did not (p > 0.05). Group 3 showed the highest accuracy (p < 0.001). Group 4 presented the lowest correlation with chronological age compared to other age groups (p < 0.001). The dentin thickness model showed a promising result for the dental age estimation with a medium correlation. The dentin thickness model included root measurements of the mesial aspect of the mandibular first molar without any crown parameters in the southern Turkish subpopulation. Gender did not affect the dentin thickness model.

Climate emotions, thoughts, and plans among US adolescents and young adults: a cross-sectional descriptive survey and analysis by political party identification and self-reported exposure to severe weather events

Climate change has adverse effects on youth mental health and wellbeing, but limited large-scale data exist globally or in the USA. Understanding the patterns and consequences of climate-related distress among US youth can inform necessary responses at the individual, community, and policy level. A cross-sectional descriptive online survey was done of US youth aged 16-25 years from all 50 states and Washington, DC, between July 20 and Nov 7, 2023, via the Cint digital survey marketplace. The survey assessed: climate-related emotions and thoughts, including indicators of mental health; relational aspects of climate-related emotions; beliefs about who or what has responsibility for causing and responding to climate change; desired and planned actions in response to climate change; and emotions and thoughts about the US Government response to climate change. Respondents were asked whether they had been affected by various severe weather events linked to climate change and for their political party identification. Sample percentages were weighted according to 2022 US census age, sex, and race estimates. To test the effects of political party identification and self-reported exposure to severe weather events on climate-related thoughts and beliefs we used linear and logistic regression models, which included terms for political party identification, the number of self-reported severe weather event types in respondents' area of residence in the past year, and demographic control variables. We evaluated survey responses from 15 793 individuals (weighted proportions: 80·5% aged 18-25 years and 19·5% aged 16-17 years; 48·8% female and 51·2% male). Overall, 85·0% of respondents endorsed being at least moderately worried, and 57·9% very or extremely worried, about climate change and its impacts on people and the planet. 42·8% indicated an impact of climate change on self-reported mental health, and 38·3% indicated that their feelings about climate change negatively affect their daily life. Respondents reported negative thoughts about the future due to climate change and actions planned in response, including being likely to vote for political candidates who support aggressive climate policy (72·8%). In regression models, self-reported exposure to more types of severe weather events was significantly associated with stronger endorsement of climate-related distress and desire and plans for action. Political party identification as Democrat or as Independent or Other (vs Republican) was also significantly associated with stronger endorsement of distress and desire and plans for action, although a majority of self-identified Republicans reported at least moderate distress. For all survey outcomes assessed in the models, the effect of experiencing more types of severe weather events did not significantly differ by political party identification. Climate change is causing widespread distress among US youth and affecting their beliefs and plans for the future. These effects may intensify, across the political spectrum, as exposure to climate-related severe weather events increases. Avaaz Foundation.

Towards the construction of regional marine radiocarbon calibration curves: an unsupervised machine learning approach

Abstract. Radiocarbon may serve as a powerful dating tool in palaeoceanography, but its accuracy is limited by the need to calibrate radiocarbon dates to calendar ages. A key problem is that marine radiocarbon dates must be corrected for past offsets from either the contemporary atmosphere (i.e. “reservoir age” offsets) or a modelled estimate of the global average surface ocean (i.e. delta-R offsets). This presents a challenge because the spatial distribution of reservoir ages and delta-R offsets can vary significantly, particularly over periods of major marine hydrographic and/or carbon cycle change such as the last deglaciation. Modern reservoir age and delta-R estimates therefore have limited applicability. While forward modelling of past R-age variability has been proposed as a means of resolving this problem, this requires accurate a priori knowledge of past global radiocarbon budget closure (i.e. production, and cycling), which we currently lack. In this context, the construction of empirical regional marine calibration curves could provide a way forward. However, the spatial reach of such calibrations and their robustness subject to (uncertain) temporal changes in climate and ocean circulation would need to be tested. Here, we use unsupervised machine learning techniques to define distinct regions of the surface ocean that exhibit coherent behaviour in terms of their radiocarbon age offsets from the contemporary atmosphere (R ages), regardless of the causes of R-age variability. We apply multiple algorithms (k-means, k-medoids, and hierarchical clustering) to outputs from two different numerical models spanning a range of climate states, forcings, and timescales of adjustment. Comparisons between the cluster assignments across model runs confirm some robust regional patterns that likely stem from constraints imposed by large-scale ocean and atmospheric physics. At the coarsest scale, regions of coherent R-age variability correspond to the major ocean basins. By further dividing basin-scale shape-based clusters into amplitude-based subclusters, we recover regional associations, such as increased high-latitude R ages, or the propagation of R-age anomalies from regions of deep mixing in the Southern Ocean to upwelling sites in the eastern equatorial Pacific, which cohere with known modern oceanographic processes. We show that the medoids (i.e. the most representative locations) for these regional sub-clusters provide significantly better approximations of simulated local R-age variability than constant offsets from the global surface average. This remains true when cluster assignments obtained from one model simulation are applied to simulated R-age time series from another. Further, model-based clusters are found to be broadly consistent with existing reservoir age reconstructions that span the last ∼30 kyr. We therefore propose that machine learning provides a promising approach to the problem of defining regions for which empirical marine radiocarbon calibration curves may eventually be generated.

The age of the early stage formation of Ag-Bi-Cu-Ni-Co the Khovu-Aksyn deposit (Republic of Tyva): U-Pb (ID-TIMS) geochronological studies of garnet

Mineralogical and U-Pb (ID-TIMS) geochronological studies of garnets from three types of scarn associations of the Khovu-Aksyn Ag–Bi–Cu–Ni–Co deposit (Republic of Tyva) were carried out. The U-Pb garnet age estimate (404±2 million years) is close to the Early Devonian stage of basitic and alkaline-basitic magmatism identified within the Tuva trough of the Altay-Sayan folded region. The age of the scarn formation and the associated with it early stage of mineralization of the Khovu-Aksynskoye Ag–Bi–Cu–Ni–Co deposit has been established for the first time.

Attenuated total reflection Fourier transform infrared spectroscopy (ATR-FTIR) analysis of human nails: Implications for age determination in forensics.

A person's age estimation from biological evidence is a crucial aspect of forensic investigations, aiding in victim identification and criminal profiling. In this study, we present a novel approach of utilizing Attenuated Total Reflection Fourier Transform Infrared (ATR FT-IR) spectroscopy to predict the age of donors based on nail samples. A diverse dataset comprising nails from donors spanning different age groups was analyzed using ATR FT-IR, with subsequent multivariate analysis techniques used for age prediction. The developed partial least squares regression (PLS-R) model demonstrated promising accuracy in age estimation, with a root mean square error of prediction (RMSEP) equal to 11.1 during external validation. Additionally, a partial least squares discriminant analysis (PLS-DA) classification model achieved high accuracy of 88% in classifying donors into younger and older age groups during external validation. This proof-of-concept study highlights the potential of ATR FT-IR spectroscopy as a non-destructive and efficient tool for age estimation in forensic investigations, offering a new approach to forensic analysis with practical implications.

Bones at Home

Remote teaching during the COVID-19 pandemic led to a range of pedagogical challenges for anthropology laboratory courses. In biological anthropology courses such as Human Osteology, hands-on experience is essential to achieving learning outcomes, including basic bone and feature (i.e., landmark) identification, identification from fragmentary remains, and age and sex estimation. To address the need for training that includes object-based, tactile (haptic) learning in fields such as biological anthropology and archaeology, all Human Osteology students at Mount Royal University and the University of Manitoba took home plastic model skeletons. The purpose of this study was to evaluate how well remotely educated undergraduates (REUs) met human osteology learning objectives when supported by plastic model skeletons at home. We present the results of a survey designed to test core osteological skills obtained by REUs in comparison with undergraduates educated with in-person laboratory components (IPUs) and experts in the field (zero to four and five or more years of experience). REU scores did not differ significantly from those of IPU or Junior Experts with less than five years of experience. Students performed well in bone identification but were limited in their ability to apply common sex and age estimation methods and to identify incomplete elements. Our findings reinforce the importance of haptic learning and years of experience in human osteological learning. They support the use of take-home models as valuable resources in both remote and in-person undergraduate teaching. This work is a step toward more inclusive universal instructional design that can be applied across various anthropology laboratory courses.