Estimates of absolute crown strength and bite force in the lower postcanine dentition of Gigantopithecus blacki.

Gigantopithecus blacki is hypothesized to have been capable of processing mechanically challenging foods, which likely required this species to have high dental resistance to fracture and/or large bite force. To test this hypothesis, we used two recently developed approaches to estimate absolute crown strength and bite force of the lower postcanine dentition. Sixteen Gigantopithecus mandibular permanent cheek teeth were scanned by micro-computed tomography. From virtual mesial cross-sections, we measured average enamel thickness and bi-cervical diameter to estimate absolute crown strength, and cuspal enamel thickness and dentine horn angle to estimate bite force. We compared G. blacki with a sample of extant great apes (Pan, Pongo, and Gorilla) and australopiths (Australopithecus anamensis, Australopithecus afarensis, Australopithecus africanus, Paranthropus robustus, and Paranthropus boisei). We also evaluated statistical differences in absolute crown strength and bite force between the premolars and molars for G. blacki. Results reveal that molar crown strength is absolutely greater, and molar bite force absolutely higher, in G. blacki than all other taxa except P. boisei, suggesting that G. blacki molars have exceptionally high resistance to fracture and the ability to generate exceptionally high bite force. In addition, G. blacki premolars have comparable absolute crown strength and larger bite force capabilities compared with its molars, implying possible functional specializations in premolars. The dental specialization of G. blacki could thus represent an adaptation to further facilitate the processing of mechanically challenging foods. While it is currently not possible to determine which types of foods were actually consumed by G. blacki through this study, direct evidence (e.g. dental chipping and microwear) left by the foods eaten by G. blacki could potentially lead to greater insights into its dietary ecology.

A deep-learning-based workflow to assess taxonomic affinity of hominid teeth with a test on discriminating Pongo and Homo upper molars.

OBJECTIVES: Convolutional neural network (CNN) is a state-of-art deep learning (DL) method with superior performance in image classification. Here, a CNN-based workflow is proposed to discriminate hominid teeth. Our hope is that this method could help confirm otherwise questionable records of Homo from Pleistocene deposits where there is a standing risk of mis-attributing molars of Pongo to Homo. METHODS AND MATERIALS: A two-step workflow was designed. The first step is converting the enamel-dentine junction (EDJ) into EDJ card, that is, a two-dimensional image conversion of the three-dimensional EDJ surface. In this step, researchers must carefully orient the teeth according to the cervical plane. The second step is training the CNN learner with labeled EDJ cards. A sample consisting of 53 fossil Pongo and 53 Homo (modern human and Neanderthal) was adopted to generate EDJ cards, which were then separated into training set (n = 84) and validation set (n = 22). To assess the feasibility of this workflow, a Pongo-Homo classifier was trained from the aforementioned EDJ card set, and then the classifier was used to predict the taxonomic affinities of six samples (test set) from von Koenigswald's Chinese Apothecary collection. RESULTS: Results show that EDJ cards in validation set are classified accurately by the CNN learner. More importantly, taxonomic predictions for six specimens in test set match well with the diagnosis results deduced from multiple lines of evidence, implying the great potential of CNN method. DISCUSSION: This workflow paves a way for future studies using CNN to address taxonomic complexity (e.g., distinguishing Pongo and Homo teeth from the Pleistocene of Asia). Further improvements include visual interpretation and extending the applicability to moderately worn teeth.

A robust alternative to assessing three-dimensional relative enamel thickness for the use in taxonomic assessment.

OBJECTIVE: Three-dimensional relative enamel thickness (3DRET) is important for assessing hypotheses about taxonomy, phylogeny, and dietary reconstruction for primates. However, its weaknesses have not been thoroughly investigated. Here, we analyze its weaknesses and propose an index aiming at better taxonomic discrimination. MATERIALS AND METHODS: The dimensionless 3D index, ratio of enamel-thickness to dentine-thickness (3DRED), which is defined as the cubic root of the ratio of 3D average enamel thickness (3DAET) to 3D average dentine thickness (3DADT), is proposed here. To compare 3DRET and 3DRED and their sensitivity to voxel size, a fossil orangutan molar was scanned 14 times with different resolutions ranging from 10 to 50 µm. Enamel thickness analysis was carried out for each resultant digital model. In addition, enamel thickness measurements of 179 mandibular permanent molars (eight genera) were analyzed, followed by investigating the relationship between 3DRET and 3DAET and between 3DRED and 3DAET. RESULTS: Regarding sensitivity, 3DRED is more robust than 3DRET. In addition, 3DRET is correlated with 3DAET by linear curve with regression coefficients approximating or larger than 0.8 in most cases, while 3DRED shows less correlation with 3DAET. Furthermore, there are clear separations between different taxa in the bivariate plot of 3DRED against 3DAET, indicative of the taxonomic value of 3DRED. CONCLUSION: Under certain conditions, 3DRED promises to be a robust and reliable alternative to 3DRET in taxonomic study.