UNASSIGNED: Age estimation for sporting activities, legal age determination and migration purpose is oftentimes essential, which makes the need for a scientifically proven method of age estimation an important aspect of medical practice. Forensic odontology is an essential aspect of forensic practice and may be a veritable tool in age estimation.
UNASSIGNED: The objective of this study was to evaluate the usefulness of orthopantomograms (OPG) in age estimation among selected Nigerians.
UNASSIGNED: A retrospective assessment of 202 OPGs was done to review the visibility of the periodontal ligament of third molars with completed root formation according to the methods described by Olze et al (2010). Individual ages were then calculated by deducting date of exposure from the date of birth recorded in years. Mean age with standard deviation were calculated for each group.
UNASSIGNED: Assessing the minimum and maximum age at which each of the stages of the radiographic visibility of the periodontal ligament as seen on the OPGs revealed that there was a gradual increase in the age at which the stage increment occurred, with a few exceptions. Comparison of the radiographic visibility of the periodontal ligament revealed that males tend to achieve early periodontal visibility stages compared to females, but the reverse was observed in later stages. Age 17 is more than likely attained in any person found within stages 1 to 3 as the minimum age found in these stages were above 17 years.
UNASSIGNED: With a few exceptions, the assessment of periodontal ligament visibility on OPGs can adequately predict the age of individuals. Thus, the use of this method may need to be combined with other methods in accurate determination of dental age in instances of forensic age estimation.

OBJECTIVE: Age estimation plays a critical role in personal identification, especially when determining compliance with the age of consent for adolescents. The age of consent refers to the minimum age at which an individual is legally considered capable of providing informed consent for sexual activities. The purpose of this study is to determine whether adolescents meet the age of 14 or 18 by using dental development combined with machine learning.
METHODS: This study combines dental assessment and machine learning techniques to predict whether adolescents have reached the consent age of 14 or 18. Factors such as the staging of the third molar, the third molar index, and the visibility of the periodontal ligament of the second molar are evaluated.
RESULTS: Differences in performance metrics indicate that the posterior probabilities achieved by machine learning exceed 93% for the age of 14 and slightly lower for the age of 18.
CONCLUSIONS: This study provides valuable insights for forensic identification for adolescents in personal identification, emphasizing the potential to improve the accuracy of age determination within this population by combining traditional methods with machine learning. It underscores the importance of protecting and respecting the dignity of all individuals involved.

OBJECTIVE: We aimed to establish a model combining MRI volume measurements from the 1st, 2nd and 3rd molars for age prediction in sub-adults and compare the age prediction performance of different combinations of all three molars, internally in the study cohort.
METHODS: We examined 99 volunteers using a 1.5 T MR scanner with a customized high-resolution single T2 sequence. Segmentation was performed using SliceOmatic (Tomovision©). Age prediction was based on the tooth tissue ratio (high signal soft tissue + low signal soft tissue)/total. The model included three correlation parameters to account for statistical dependence between the molars. Age prediction performance of different combinations of teeth for the three molars was assessed using interquartile range (IQR).
RESULTS: We included data from the 1st molars from 87 participants (F/M 59/28), 2nd molars from 93 (F/M 60/33) and 3rd molars from 67 (F/M 45/22). The age range was 14-24 years with a median age of 18 years. The model with the best age prediction performance (smallest IQR) was 46-47-18 (lower right 1st and 2nd and upper right 3rd molar) in males. The estimated correlation between the different molars was 0.620 (46 vs. 47), 0.430 (46 vs. 18), and 0.598 (47 vs. 18). IQR was the smallest in tooth combinations including a 3rd molar.
CONCLUSIONS: We have established a model for combining tissue volume measurements from the 1st, 2nd and 3rd molars for age prediction in sub-adults. The prediction performance was mostly driven by the 3rd molars. All combinations involving the 3rd molar performed well.

Accurate age estimation is vital for clinical and forensic purposes. With the rapid advancement of artificial intelligence(AI) technologies, traditional methods relying on tooth development, while reliable, can be enhanced by leveraging deep learning, particularly neural networks. This study evaluated the efficiency of an AI model by applying the entire panoramic image for age estimation. The outcome performances were analyzed through supervised learning (SL) models.
Total of 27,877 dental panorama images from 5 to 90 years of age were classified by 2 types of grouping. In type 1 they were classified by each age and in type 2, applying heuristic grouping, the age over 20 years were classified by every 5 years. Wide ResNet (WRN) and DenseNet (DN) were used for supervised learning. In addition, the analysis with ± 3 years of deviation in both types were performed.
For the DN model, while the type 1 grouping achieved an accuracy of 0.1016 and F1 score of 0.058, the type 2 achieved an accuracy of 0.3146 and F1 score of 0.2027. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.281, 0.7323 respectively; and the F1 score were 0.1768, 0.6583 respectively. For the WRN model, while the type 1 grouping achieved an accuracy of 0.1041 and F1 score of 0.0599, the type 2 achieved an accuracy of 0.3182 and F1 score of 0.2071. Incorporating ± 3years of deviation, the accuracy of type 1 and 2 were 0.2716, 0.7323 respectively; and the F1 score were 0.1709, 0.6437 respectively.
The application of entire panorama image data for supervised with classification by heuristics grouping with ± 3years of deviation for supervised learning models and demonstrated satisfactory outcome for the age estimation.

The American burying beetle, Nicrophorus americanus Olivier, is a federally protected insect that once occupied most of eastern North America. Adult beetles feature distinct, recognizable markings on the pronotum and elytra, and color changes with age have been observed. Among the challenges faced by research scientists and conservation practitioners is the ability to determine beetle age in the field between and including teneral (young) and senescent (old) adult stages. Using 20 (10 male and 10 female) captive-bred beetles, we characterized the change in greyscale and red, green, and blue (RGB) color channels over the lifespan of each beetle for field-aging applications. Individual beetles were photographed at set intervals from eclosion to death, and color data were extracted using open-source ImageJ Version 1.54f software. A series of linear mixed-effects models determined that red color showed the steepest decrease among all color channels in the pronotum and elytral markings, with a more significant decrease in the pronotum. The change in greyscale between the pronotum and elytral markings was visibly different, with more rapid darkening in the pronotum. The resulting pronotum color chart was tested under field conditions in Oklahoma, aging 299 adult N. americanus, and six age categories (day range) were discernable by eye: teneral (0-15), late teneral (15-31), early mature (31-45), mature (45-59), early senescent (59-76), and senescent (76-90). The ability to more precisely estimate age will improve population structure estimates, laboratory breeding programs, and potential reintroduction efforts.

N6-methyladenine (6mA) in the DNA is a conserved epigenetic mark with various cellular, physiological and developmental functions. Although the presence of 6mA was discovered a few years ago in the nuclear genome of distantly related animal taxa and just recently in mammalian mitochondrial DNA (mtDNA), accumulating evidence at present seriously questions the presence of N6-adenine methylation in these genetic systems, attributing it to methodological errors. In this paper, we present a reliable, PCR-based method to determine accurately the relative 6mA levels in the mtDNA of Caenorhabditis elegans, Drosophila melanogaster and dogs, and show that these levels gradually increase with age. Furthermore, daf-2(-)-mutant worms, which are defective for insulin/IGF-1 (insulin-like growth factor) signaling and live twice as long as the wild type, display a half rate at which 6mA progressively accumulates in the mtDNA as compared to normal values. Together, these results suggest a fundamental role for mtDNA N6-adenine methylation in aging and reveal an efficient diagnostic technique to determine age using DNA.

The dataset comprises a comprehensive systematic review and meta-analysis exploring the utility of biological clocks as age estimation markers in the context of animal ecology. The systematic review adhered to PRISMA guidelines and employed optimized Boolean search strings to retrieve relevant studies from Scopus and Dimensions databases. A total of 78 methylation studies and 108 telomere studies were included after rigorous screening. Effect sizes were computed, and statistical transformations were applied when necessary, ensuring compatibility for meta-analysis. Data from these studies were meticulously collected, encompassing statistical measures, study attributes, and additional biological information. The dataset comprises several folders, carefully organized to facilitate access and understanding. It contains raw and processed data used in the systematic review and meta-analysis, including Boolean search strings, database search results, citation network analysis data, PRISMA statements, extracted study data, and input data for meta-analysis. Each folder\'s contents are described in detail, ensuring clarity and reusability. This dataset aggregates primary research studies spanning diverse ecosystems and taxa, providing a valuable resource for researchers, biodiversity managers and policymakers. This dataset offers a wealth of information and analysis potential for researchers studying age estimation markers in animal ecology, serving as a robust foundation for future investigations and reviews in this evolving field.

Having a reliable and feasible method to estimate whether an individual has reached 16 years of age would greatly benefit forensic analysis. The study of age using dental information has matured recently. In addition, machine learning (ML) is gradually being applied for dental age estimation.
The purpose of this study was to evaluate the development of the third molar using the Demirjian method (Demirjian3M), measure the development index of the third molar (I3M) using the method by Cameriere, and assess the periodontal ligament development of the second molar (PL2M). This study aimed to predict whether Chinese adolescents have reached the age of criminal responsibility (16 years) by combining the above measurements with ML techniques.
A total of 665 Chinese adolescents aged between 12 and 20 years were recruited for this study. The development of the second and third molars was evaluated by taking orthopantomographs. ML algorithms, including random forests (RF), decision trees (DT), support vector machines (SVM), K-nearest neighbours (KNN), Bernoulli Naive Bayes (BNB), and logistic regression (LR), were used for training and testing to determine the dental age. This is the first study to combine ML with an evaluation of periodontal ligament and tooth development to predict whether individuals are over 16 years of age.
The study showed that SVM had the highest Bayesian posterior probability at 0.917 and a Youden index of 0.752. This finding provides an important reference for forensic identification, and the combination of traditional methods and ML is expected to improve the accuracy of age determination for this population, which is of substantial significance for criminal litigation.

This study proposes a rapid and non-destructive technique for determining the age of Potentilla griffithii individuals in the field by observing the sequence of leaf scars. Based on two- to three-year-old P. griffithii seedlings, planted in a common garden in the western Sichuan Plateau, China, the study found that the rates of basal leaf production were consistent, with leaves growing from March to April and falling off from October to December, leaving behind basal leaf scars. Thus, the age of individuals in situ could be determined by counting the leaf scars. Through this method, we determined the age structure and growth strategy of P. griffithii populations in two typical habitats in the western Sichuan Plateau. In open land habitats, the age structure of P. griffithii populations was relatively younger compared to understory habitats. In open land, P. griffithii tends to allocate more photosynthate terminal organs (leaves and fine roots) to absorbing more resources, as well as to its reproductive organs (flower stems and aggregate fruits), to expand the population. The P. griffithii population in the understory habitat is in its middle-age stage and concentrates more photosynthate in the coarse root part (e.g., the high coarse root mass fraction (FRMF)) to support the plant. Additionally, we found a significant correlation between P. griffithii plant age and various traits in open land habitats. Therefore, we conclude that plant age can be used as a good predictor of plant growth condition in open land. These results allow for predicting ecological processes, based on the ages and traits of P. griffithii plants, providing a theoretical basis to support the large-scale breeding of P. griffithii.

An articulated pelvic region and additional isolated material of Varanops brevirostris, which are indistinguishable from those of the generotype from the Cacops bonebed, demonstrate the presence of this large varanopid at the Richards Spur locality. The articulated specimen includes lumbar, sacral, and anterior caudal vertebrae, partial pelvis, femur, and proximal part of tibia, confirming the autapomorphies previously suggested for this species. These include the presence of distinct blade-like shapes of the neural spines in the sacral region, the presence of deeply excavated pubis, and the presence of a distinct transverse ridge on the ventral surface of the femur distal to the intertrochanteric fossa. It has also been found that the transverse ridges and grooves become larger during ontogeny since the juvenile specimen did not exhibit a well-developed ridge. Histological analysis of isolated limb bones and neutron computed tomography (nCT) of the articulated specimen indicate that the latter likely belonged to an adult individual. This is in contrast to the other varanopid at Richards Spur, the significantly smaller, more gracile predator Mesenosaurus efremovi, which also shows the presence of growth lines and the external fundamental system with an estimated minimum age of fourteen.