Orientation Patch Count (OPC) is a method of research used by biologists and paleontologists to analyze the complexity of an animal’s feeding surface while inferring their diets; diet and tooth complexity have evolved in concert with one another, which is why this method has been used on reptilian and mammalian (denticular) species. However, it has not been extensively tested on edentulous (toothless) clades. Therefore, my research examines the OPC of an edentulous species - specifically the endangered Madagascar big-headed turtle (Erymnochelys madagascariensis) using  three CT-scanned specimens. Three primary programs were used in order to analyze the quantitative morphometricsof the species: Slicer for processing and editing CT scans from the University of Washington’s Friday Harbor Lab, MeshLab for editing 3D models, and RStudio for data analysis. This research contributes to a broader study on turtle species led by paleontologist Brenlee Shipps, who will apply these findings to extinct beaked clades, specifically dicynodonts.

Rotated orientation patch count (OPCr) is a measurement used to quantify the complexity of a 3D surface. OPCr has previously been used to analyze tooth complexity, showing a correlation between complexity and diet in lizards, crocodilians, and mammals. We applied this technique to toothless taxa, with the goal of determining if there is a correlation between the complexity of the occlusal surface of a given species of turtle and its diet category. OPCr is determined by analyzing a 3D mesh of the occlusal surface of turtle specimens, with meshes based on both photogrammetry and CT scans of turtle skulls. Photogrammetry and CT scans are fundamentally different. Photogrammetry is a 3D mesh created from a series of surface images of an object, where the lighting and shadows cast on the object potentially distort its complexity. CT scans are not subject to these errors, and are typically more consistent provided the scan is made properly. However, there is little research analyzing the impact of different scanning techniques on the surface complexity of the resulting mesh. This project is therefore a comparison of photogrammetry and CT scans: do models made from these different methods produce significantly different OPCr scores? Nineteen specimens previously digitized using photogrammetry have been CT scanned. I created 3D models from the CT scan data and analyzed their surfaces using OPCr. I then compared the OPCr values produced by the CT scan models to the photogrammetry counterpart of each specimen. We hypothesize that statistical analyses will show no significant difference between the two methods of digitizing specimens.

Previous studies suggest that tooth morphology (shape, size, and other features of teeth) strongly correlates with an organism’s dietary patterns, and analyzing dentition is common practice in the field of Biology. Orientation patch count rotated (OPCr), a technique used in establishing dentition-diet correlations, has recently been demonstrated as applicable to turtle triturating surfaces to understand their dietary adaptations. The aim of this study is to add to an ongoing project characterizing the relationship between diet and the cutting/grinding surface in the jaw (triturating surface) in edentulous (toothless) organisms using techniques used in traditional dental topographic analysis. Turtles are a diverse group of edentulous organisms with beaks of keratin to process their food — making them ideal for this study. Specimens of the omnivorous Forest-Hinge Back Tortoise (Kinixys erosa) were micro-computed tomographically (CT) scanned. We reconstructed the CT scans into photogrammetric 3D models using Slicer software. Then, we isolated the triturating surface using MeshLab software. Finally, we read the triturating surface into the R package molaR — resulting in OPCr values that estimate the complexity of their specimen’s triturating surface. Ideally, the OPCr values showcase extreme high triturating surface complexity, as previous research suggests tortoises (Testudinidae) have highly complex triturating surfaces compared with other clades of turtles. Our research hopes to contribute to a new technique for analyzing extinct beaked or edentulous taxa.

Previous studies have found a correlation between the complexity of an animal’s teeth and its diet. However, not all vertebrates have teeth, such as turtles, which is problematic because dental topographical analysis has not been completed on toothless—or, edentulous—animals. Regardless of whether a species has teeth, we can use the measurement OPCr (orientation patch count rotated) to quantify the complexity of a surface, and subsequently use that value to analyze species’ diet. OPCr calculates the number of separately oriented patches on a 3D surface. A higher OPCr value indicates a more complex topography. To obtain OPCr values, we edited CT scans of the turtle species Malaclemys terrapin in Slicer and MeshLab, then analyzed the resulting model using the R package molaR. From this, we obtained OPCr values. However, R struggles to analyze meshes at a higher resolution, so we use various downsampling filters in MeshLab to make the models usable in R. One such filter is Quadratic Edge Collapse Decimation (QECD). The algorithm behind QECD is QSLIM, which reduces the complexity of polygonal meshes by eliminating edges based on error metrics from quadratic formulations, but still preserves the original shape as much as possible. Currently, we downsample all meshes to just 10,000 faces before reading them into R. My role in this project is to determine whether we can reliably use higher resolution scans by altering the number of faces to be slightly higher at 15,000 and slightly lower at 5,000, then examining the impact of these resolutions on OPCr values. So far, our analysis shows that importing a higher resolution mesh tends to give higher OPCr values, and a lower resolution gives a lower OPCr value.

Diet is one of the most significant contributors to an organism’s morphology, as without morphological features to acquire food the organism will cease to live. Previous studies have quantified these morphological features in toothed taxa using Rotated Orientation Patch Count (OPCr) but not in edentulous taxa. Previously, we obtained OPCr from several turtle species using photogrammetry, created 3D models with Slicer, edited them down to just the triturating surface in MeshLab, and ran statistical analysis in R. Specifically, I worked on the unique, endangered turtle species Carettochelys insculpta (n=6) using CT scans obtained from MorphoSource to add to our photogrammetry data. However, the OPCr values obtained from these meshes discarded more surface area and were significantly lower than the meshes made from photogrammetry. To increase the surface area counted in the OPCr and potentially get results more comparable to the photogrammetry meshes we experimented with decreasing the percentage of patches discarded during analysis in R from 1% to 0.1% and tried smoothing the meshes in Slicer using a factors of 0.3, 0.5, and 0.7. A simple T-test was used to determine significant differences. To increase the number of available specimens and compare turtle species with different diets – durophagous and omnivorous respectively – Malaclemys terrapin specimens (n=5) were used in addition to the Carettochelys insculpta specimens. We expect to find increased surface area and higher OPCr values when increasing the percentage of patches discarded from 1% to 0.1%. We also expect that smoothing will increase the amount of surface area counted at both 1% and 0.1%. As a result of this study, we hope to create a better method for processing CT scans for morphological analysis of the triturating surfaces of turtles, and to develop a methodology for determining diet in any edentulous organism.

Previous studies have shown that the diet of an organism can provide valuable insight into a variety of characteristics including habitat, behavior, and ecological role. Analyzing dentition is one method used to determine an organism’s diet, but this becomes complicated for edentulous taxa. In this study, we investigated the dietary ecology of Caretta caretta, or the loggerhead sea turtle, through the 3D morphometrics of several CT-scanned skull specimens. We are particularly interested in studying a notable feature on the occlusal surface: the accessory triturating ridge. This structure functions as a way to process food and thus provides important insight into what kinds of nutritional sources Caretta caretta may be drawing from. To analyze and interpret the morphology of the ridge, we took a series of computed tomography (CT) scans and processed them into 3D models using Slicer. We then isolated the occlusal surface in MeshLab and used R to assess variations in morphology. This results in a rotated orientation patch count (OPCr), which we can use to analyze the complexity of the occlusal surface. This acts as a topographic map, with a higher OPCr value likely indicating an omnivorous or herbivorous diet, and a lower OPCr value predicting a carnivorous diet. Because Caretta caretta are known to be omnivorous, we expect to see a higher OPCr value, suggesting that their occlusal surface is more complex than that of other turtles. Analysis of this species contributes to our project's overarching goal of applying morphological analyses to edentulous species and can offer insights into conservation efforts for this ecologically vulnerable turtle.

Lepidosauria is a clade of reptiles including Rhyncocephalia and Squamata, constituting much of the diversity of living reptiles. Squamates include lizards and snakes, and are the most species-rich group of lepidosaurs. Rhyncocephalians were more diverse and widespread in the Mesozoic Era, but today are represented by a single living species, the tuatara of New Zealand. Lepidosaurs first evolve in the Triassic Period, making their fossil record from this interval critical to understanding the evolutionary origins this group. New lepidosaur fossil material from Petrified Forest National Park has been recovered from screenwashing sediment from the Kaye Quarry, a fossil bearing locality within the Sonsela Member of the Upper Triassic Chinle Formation. Three mandibles of unknown taxonomic affinity from the Kaye Quarry have been selected for anatomical description and phylogenetic analysis. All three mandibles display labiolingually compressed, recurved teeth, along the majority of the dentary. Two dentaries display a larger conical tooth, protruding dorsally from the anterior end of the mandible. Other mandibles recovered from the Chinle Formation display similar dental anatomy, indicating these specimens belong to the clade Rhynchocephalia. There are currently no lepidosaur fossils known from the Sonsela Member of the Chinle Formation. Sectioning and computed tomography (CT) scanning will be used to create detailed three-dimensional images of the mandibles for the basis of anatomical description and phylogenetic analysis. CT scanning hosts the potential for internal morphology including tooth implantation and neurovasculature.

Rheumatoid arthritis (RA) is a chronic autoimmune disease that causes joint damage, frailty, and potential disability. Its progression is unpredictable, making it difficult to manage in clinical settings. A major challenge in treatment is the lack of reliable clinical indicators or biomarkers to track disease activity and predict long-term outcomes like frailty and joint damage. Growth differentiation factor-15 (GDF-15) has shown promise as a biomarker in other diseases, but its role in RA remains unclear. This study explores whether GDF-15 can predict disease progression, frailty, and joint damage in RA patients. To understand the role of GDF-15 in RA, we measured its levels in both RA patients and healthy individuals using ELISA, which detects specific proteins. We explored how GDF-15 levels are related to disease activity, inflammation, and joint damage. In a group of patients followed for 8 years, we investigated whether GDF-15 levels at diagnosis could predict how the disease might progress. We used various statistical tests to analyze the data. The Mann-Whitney U-test helped compare GDF-15 levels between RA patients and healthy controls, Spearman’s correlation showed the relationship between GDF-15 levels and disease activity, and logistic regression allowed us to evaluate whether GDF-15 levels at diagnosis could predict future RA development. Through this study, we (i) analyzed how GDF-15 levels are linked to disease activity and inflammation in RA, (ii) explored whether measuring GDF-15 levels early on could predict disease progression and (iii) assessed whether GDF-15 could help identify patients at higher risk of developing severe joint damage or other complications. Ultimately, this research could help rheumatologists better understand and predict how RA will progress in patients, leading to more personalized and effective treatments.

The triturating surface of a beaked animal is the part of the beak that contacts food. Previous work has been conducted on determining a value for the complexity of beaked turtles’ triturating surface by creating a 3D mesh of it. We analyzed these meshes using  the R package molaR which then determined an OPCr (orientation patch count rotated) number that could be compared to the known diet of the turtle. My role in this study is  to examine the effect that manipulation of thresholding the skull has on the OPCr output using five different skulls from the species Malaclemys terrapin, which are known to be durophagous. Thresholding is conducted in the first half of mesh construction, when the CT scan is run through Slicer. At this step, we input both a higher and lower threshold value, as well as a standard value. A higher threshold value will lead to higher density material being excluded from the data set. The skull that is constructed in Slicer is then put into MeshLab to be further trimmed into only the triturating surface, and then it is run through molaR. We suspect that a higher threshold value will lead to a higher OPCr value than a lower thresholding value would. The implication of these results will determine what effect thresholding has on the scan, and estimate what value will be most optimal for preserving the integrity of the scan.