Similar Associations of Tooth Microwear and Morphology Indicate Similar Diet across Marsupial and Placental Mammals

Low-magnification microwear techniques have been used effectively to infer diets within many unrelated mammalian orders, but the extent to which patterns are comparable among such different groups, including long extinct mammal lineages, is unknown. Microwear patterns between ecologically equivalent placental and marsupial mammals are found to be statistically indistinguishable, indicating that microwear can be used to infer diet across the mammals. Microwear data were compared to body size and molar shearing crest length in order to develop a system to distinguish the diet of mammals. Insectivores and carnivores were difficult to distinguish from herbivores using microwear alone, but combining microwear data with body size estimates and tooth morphology provides robust dietary inferences. This approach is a powerful tool for dietary assessment of fossils from extinct lineages and from museum specimens of living species where field study would be difficult owing to the animal’s behavior, habitat, or conservation status

The Functional Morphology of the Primate Zygomatic Arch in Relation to Diet

abstract: Craniofacial morphology in primates can vary on the basis of their diet because foods are often disparate in the amount and duration of force required to break them down. Therefore diet has the potential to exercise considerable selective pressure on the morphology of the masticatory system. The zygomatic arch is a known site of relatively high masticatory strain and yet the relationship between arch form and load type is relatively unknown in primates. While the relative position and robusticity of the arch is considered a key indicator of craniofacial adaptations to a mechanically challenging diet, and central to efforts to infer diet in past species, the relationships between morphology and diet type in this feature are not well established. This study tested hypotheses using two diet categorizations: total consumption percent and food material properties (FMPs). The first hypothesis that cortical bone area (CA) and section moduli (bone strength) are positively correlated with masticatory loading tests whether CA and moduli measures were greatest anteriorly and decreased posteriorly along the arch. The results found these measures adhered to this predicted pattern in the majority of taxa. The second hypothesis examines sutural complexity in the zygomaticotemporal suture as a function of dietary loading differences by calculating fractal dimensions as indices of complexity. No predictable pattern was found linking sutural complexity and diet in this primate sample, though hard object consumers possessed the most complex sutures. Lastly, cross-sectional geometric properties were measured to investigate whether bending and torsional resistance and cross-sectional shape are related to differences in masticatory loading. The highest measures of mechanical resistance tracked with areas of greatest strain in the majority of taxa. Cross-sectional shape differences do appear to reflect dietary differences. FMPs were not correlated with cross-sectional variables, however pairwise comparisons suggest taxa that ingest foods of greater stiffness experience relatively larger measures of bending and torsional resistance. The current study reveals that internal and external morphological factors vary across the arch and in conjunction with diet in primates. These findings underscore the importance of incorporating these mechanical differences in models of zygomatic arch mechanical behavior and primate craniofacial biomechanics.Dissertation/ThesisAppendix AAppendix BAppendix DDoctoral Dissertation Anthropology 201

Dental Microwear Variation In Teleoceras Fossiger (Rhinocerotidae) From The Miocene (Hemphillian) Of Kansas, With Consideration Of Masticatory Processes And Enamel Microstructure

Dental microwear analysis is the study of microscopic features on the surfaces of teeth, and is used to reconstruct and analyze diet in extinct and extant animals. Microwear analysis on ungulates is typically conducted on the paracone or protoconid of the second molar, as these cusps are usually the first point of contact between upper and lower teeth during the chewing stroke. However, the exact method of mastication varies in different groups of ungulates, and the influence of mastication on the location and production of microwear features has been studied very little. Additionally, the role of highly specialized enamel microstructure in the production of microwear features has not been examined in many groups of animals. The goal of this project is to analyze central tendency of microwear features among cusps and between chewing facets in order to determine if a single cusp or facet type is more reliable for interpretation than other cusps or facet types in the North American Miocene rhinoceros, Teleoceras fossiger. This is accomplished through the testing of three main hypotheses. First, it is predicted that cusps that collide more frequently with other cusps will have higher numbers of microwear features than cusps that interact less frequently. Second, it is predicted that Phase 1 chewing facets will have more pits than Phase 2 facets, and Phase 2 facets will have more scratches than Phase 1 facets. Third, it is predicted that cusps constructed of normal, soft enamel will have a higher total number of features than cusps constructed of highly resistant enamel. The lower second molars of 11 T. fossiger specimens were selected for analysis, as numerous complete dentaries were available for study. A total of 31 cusps from the 11 teeth were cleaned, prepared, and sampled in order to capture potential variation produced during the chewing stroke. Cusps were identified as Phase 1 or Phase 2 chewing facets, with each Phase associated with either normal enamel or enamel with specialized, resistant Hunter-Schreger Bands. Using low magnification microwear techniques, pits and scratches were identified and counted on all cusps and facets using 0.4 mm2 areas, and the data were analyzed in R 3.1.1. When testing the first hypothesis, eleven paired t-tests and one Wilcoxon paired sample test resulted in a single significant comparison between the hypoconid and the protoconid, with the hypoconid having significantly higher numbers of scratches than the protoconid. When testing the second hypothesis, a paired t-test and a Wilcoxon paired sample test comparing the number of scratches and pits between Phase types did not produce significant values. Finally, when testing the third hypothesis, a paired t-test comparing the total number of features between Phase types indicated no significant differences. Comparison of the characteristics of the hypoconid to other cusps indicates that mastication and enamel microstructure work in combination to preferentially produce more scratches on the hypoconid than on other cusps in T. fossiger, partially supporting the first hypothesis and the third hypothesis. Consequently, it is recommended that the hypoconid is not used for dietary analysis due to its higher variability in the number of scratches, which will affect the results of dietary reconstruction studies on T. fossiger

Paleoecology of Equus africanus from the Late Pleistocene site of Jebel Gharbi; SJ-00-56 (Libya): insights from its dietary adaptations

African wild ass is rara species in archaeological record of north Africa. Jebel Gharbi is one of the few sites representing this species in the archaeological record. This study used menswear and microwear data to analyse paleo ecology of the site. Mesowear and microwear analyses use data from worn tooth surfaces as proxies for feeding ecology. Both dental mesowear and microwear analysis use data from the “damaged” wear surface of a tooth as a proxy for feeding ecology in extant and extinct vertebrates

Feature Selection Analysis of Chewing Activity Based on Contactless Food Intake Detection

This paper presents the feature selection methods for chewing activity detection. Chewing detection typically used for food intake monitoring applications. The work aims to analyze the effect of implementing optimum feature selection that can improve the accuracy of the chewing detection.  The raw chewing data is collected using a proximity sensor. Pre-process procedures are implemented on the data using normalization and bandpass filters. The searching of a suitable combination of bandpass filter parameters such as lower cut-off frequency (Fc1) and steepness targeted for best accuracy was also included. The Fc1 was 0,5Hz, 1.0Hz and 1.2H, while the steepness varied from 0.75 to 0.9 with an interval of 0.5. By using the bandpass filter with the value of [1Hz, 5Hz] with a steepness of 0.8, the system’s accuracy improves by 1.2% compared to the previous work, which uses [0.5Hz, 5Hz] with a steepness of 0.85. The accuracy of using all 40 extracted features is 98.5%. Two feature selection methods based on feature domain and feature ranking are analyzed. The features domain gives an accuracy of 95.8% using 10 features of the time domain, while the combination of time domain and frequency domain gives an accuracy of 98% with 13 features. Three feature ranking methods were used in this paper: minimum redundancy maximum relevance (MRMR), t-Test, and receiver operating characteristic (ROC). The analysis of the feature ranking method has the accuracy of 98.2%, 85.8%, and 98% for MRMR, t-Test, and ROC with 10 features, respectively. While the accuracy of using 20 features is 98.3%, 97.9%, and 98.3% for MRMR, t-Test, and ROC, respectively. It can be concluded that the feature selection method helps to reduce the number of features while giving a good accuracy

Structure, mechanical properties and acoustic emissions of starch extrudates

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Dietary Monitoring Through Sensing Mastication Dynamics

Unhealthy dietary habits (such as eating disorders, eating too fast, excessive energy intake, and chewing side preference) are major causes of some chronic diseases, including obesity, heart disease, digestive system disease, and diabetes. Dietary monitoring is necessary and important for patients to change their unhealthy diet and eating habits. However, the existing monitoring methods are either intrusive or not accurate enough. In this dissertation, we present our efforts to use wearable motion sensors to sense mastication dynamics for continuous dietary monitoring. First, we study how to detect a subject\u27s eating activity and count the number of chews. We observe that during eating the mastication muscles contract and hence bulge to some degree. This bulge of the mastication muscles has the same frequency as chewing. These observations motivate us to detect eating activity and count chews through attaching a triaxial accelerometer on the temporalis. The proposed method does not record any private personal information (audio, video, etc.). Because the accelerometer is embedded into a headband, this method is comparatively less intrusive for the user\u27s daily living than previously-used methods. Experiments are conducted and the results are promising. For eating activity detection, the average accuracy and F-score of five classifiers are 94.4% and 87.2%, respectively, in 10-fold cross-validation test using only 5 seconds of acceleration data. For chew counts, the average error rate of four users is 12.2%. Second, we study how to recognize different food types. We observe that each type of food has its own intrinsic properties, such as hardness, elasticity, fracturability, adhesiveness, and size, which result in different mastication dynamics. Accordingly, we propose to use wearable motion sensors to sense mastication dynamics and infer food types. We specifically define six mastication dynamics parameters to represent these food properties. They are chewing speed, the number of chews, chewing time, chewing force, chewing cycle duration, and skull vibration. We embed motion sensors in a headband worn over the temporalis muscles to sense mastication dynamics accurately and less intrusively than other methods. In addition, we extract 37 hand-crafted features from each chewing sequence to explicitly characterize the mastication dynamics using motion sensor data. A real-world evaluation dataset of 11 food categories (20 types of food in total) is collected from 15 human subjects. The average recognition accuracy reaches 74.3%. The highest recognition accuracy for a single subject is up to 86.7%. Third, we study how to detect chewing sides. We observe that the temporalis muscle bulge and skull vibration of the chewing side are different from those of the non-chewing side. This observation motivates us to deploy motion sensors on the left and right temporalis muscles to detect chewing sides. We utilize a heuristic-rules based method to exclude non-chewing data and segment each chew accurately. Then, the relative difference series of the left and right sensors are calculated to characterize the difference of muscle bulge and skull vibration between the chewing side and the non-chewing side. To accurately detect chewing sides, we train a two-class classifier using long short-term memory (LSTM), an artificial recurrent neural network that is especially suitable for temporal data with unequal input lengths. A real-world evaluation dataset of eight food types is collected from eight human subjects. The average detection accuracy reaches 84.8%. The highest detection accuracy for a single subject is up to 97.4%

Quantitative Analysis of Occlusal Microwear in Australopithecus and Paranthropus

Quantitative analysis of microwear features preserved on the occlusal surfaces of the M2s of southern African specimens of Australopithecus and Paranthropus (the so-called \u27gracile\u27 and \u27robust\u27 australopithecines) reveals that there is no striking relationship in either taxon between occlusal facet inclination and the incidence of wear features. Within each taxon, Phase I and Phase II facets tend to differ in a similar manner in the total number of wear features, the percentage frequency of pitting, and in the orientation of wear scratches. Nevertheless, Paranthropus molars tend to display significantly greater numbers of microwear features on both Phase I and II facets than do Australopithecus homologues, and Paranthropus molars also evince significantly higher proportions of occlusal pitting on these surfaces. Paranthropus and Australopithecus crowns also differ significantly in the degree by which the occlusal wear scratches vary in their orientation. On each facet, Australopithecus tooth scratches display a greater degree of directional similarity. The differences between the Phase I and Phase II facets of Australopithecus and Paranthropus M2s suggest that the dietary items involved in the production of these observed patterns differed also. The diets of these Plio-Pleistocene hominids appear to have been qualitatively dissimilar

DETECTION OF HEALTH-RELATED BEHAVIOURS USING HEAD-MOUNTED DEVICES

The detection of health-related behaviors is the basis of many mobile-sensing applications for healthcare and can trigger other inquiries or interventions. Wearable sensors have been widely used for mobile sensing due to their ever-decreasing cost, ease of deployment, and ability to provide continuous monitoring. In this dissertation, we develop a generalizable approach to sensing eating-related behavior. First, we developed Auracle, a wearable earpiece that can automatically detect eating episodes. Using an off-the-shelf contact microphone placed behind the ear, Auracle captures the sound of a person chewing as it passes through the head. This audio data is then processed by a custom circuit board. We collected data with 14 participants for 32 hours in free-living conditions and achieved accuracy exceeding 92.8% and F1 score exceeding77.5% for eating detection with 1-minute resolution. Second, we adapted Auracle for measuring children’s eating behavior, and improved the accuracy and robustness of the eating-activity detection algorithms. We used this improved prototype in a laboratory study with a sample of 10 children for 60 total sessions and collected 22.3 hours of data in both meal and snack scenarios. Overall, we achieved 95.5% accuracy and 95.7% F1 score for eating detection with 1-minute resolution. Third, we developed a computer-vision approach for eating detection in free-living scenarios. Using a miniature head-mounted camera, we collected data with 10 participants for about 55 hours. The camera was fixed under the brim of a cap, pointing to the mouth of the wearer and continuously recording video (but not audio) throughout their normal daily activity. We evaluated performance for eating detection using four different Convolutional Neural Network (CNN) models. The best model achieved 90.9% accuracy and 78.7%F1 score for eating detection with 1-minute resolution. Finally, we validated the feasibility of deploying the 3D CNN model in wearable or mobile platforms when considering computation, memory, and power constraints